Adjusting hash join memory limit to handle batch explosion
Started by Tomas Vondraabout 1 year ago28 messages
Hi,
I've been once again reminded of the batch explosion issue in hashjoin,
due to how it enforces the memory limit. This resurfaces every now and
then, when a used gets strange OOM issues - see for example these
threads from ~2019 for an example, and even some patches: [1]/messages/by-id/20190504003414.bulcbnge3rhwhcsh@development [2]/messages/by-id/20230228190643.1e368315@karst [3]/messages/by-id/bc138e9f-c89e-9147-5395-61d51a757b3b@gusw.net
Let me restart the discussion, resubmit some of the older patches, and
present a plan for what to do about this ...
Just to remind the basic details, a brief summary - the hashjoin does
not account for the spill files when enforcing the memory limit. The
hash table gets full, it decides to double the number of batches which
cuts the hash table size in half. But with enough batches the doubling
can actually make the situation much worse - the new batches simply use
more memory than was saved.
This can happen for various reasons. A simple example is that we under
estimate the size of the input relation, so the hash needs to be built
on many more tuples. This is bad, but usually not disastrous.
It's much worse when there's a batch that is not "divisible", i.e.
adding more batches does not split it roughly in half. This can happen
due to hash collisions (in the part that determines the batch),
duplicate values that didn't make it into MCV (and thus the skew
optimization does not kick in).
This is fairly rare, but when it happens it can easily lead to batch
explosion, i.e. rapidly increasing the number of batches. We add
batches, but the batch does not split, so we promptly hit the limit
again, triggering another increase. It often stops only when we exhaust
the 32-bit hash space, ending with 100s of thousands of batches.
Attached is a SQL script that reproduces something like this. It builds
a table with values with hashes that have 0s in the upper bits. And then
the hash join just spirals into a batch explosion.
Note: The script is a bit dumb and needs a lot of temp space (~50GB)
when generating the values with duplicate hashes.
In 2019 I shared a bunch of patches [4]/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development improving this, but then I got
distracted and the discussion stalled because there were proposals to
fix this by introducing a special hash join "mode" to address these
issues [5]/messages/by-id/CAAKRu_YsWm7gc_b2nBGWFPE6wuhdOLfc1LBZ786DUzaCPUDXCA@mail.gmail.com, but we never got past a prototype and there's a lot of
outstanding questions.
So I decided to revisit the three patches from 2019. Attached are
rebased and cleaned up versions. A couple comments on each one:
1) v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patch
I believe this is the way to go, for now. The basic idea is to keep the
overall behavior, but "relax" the memory limit as the number of batches
increases to minimize the total memory use.
This may seem a bit weird, but as the number of batches grows there's no
way to not violate the limit. And the current code simply ignores this
and allocates arbitrary amounts of memory.
2) v20241231-single-spill-0001-Hash-join-with-a-single-spill.patch
The basic idea is that we keep only a small "slice" of batches in
memory, and data for later batches are spilled into a single file. This
means that even if the number of batches increases, the memory use does
not change. Which means the memory limit is enforced very strictly.
The problem is this performs *terribly* because it shuffles data many
times, always just to the next slice. So if we happen to have 128
batches in memory and the number explodes to ~128k batches, we end up
reading/writing each tuple ~500x.
3) v20241231-multi-spill-0001-Hash-join-with-a-multiple-spil.patch
This is an improvement of the "single spill", except that we keep one
spill file per slice, which greatly reduces the amount of temporary
traffic. The trouble is this means we can no longer enforce the memory
limit that strictly, because the number of files does grow with the
number of batches, although not 1:1. But with a slice of 128 batches we
get only 1 file per 128 batches, which is a nice reduction.
This means that ultimately it's either (1) or (3), and the more I've
been looking into this the more I prefer (1), for a couple reasons:
* It's much simpler (it doesn't really change anything on the basic
behavior, doesn't introduce any new files or anything like that.
* There doesn't seem to be major difference in total memory consumption
between the two approaches. Both allow allocating more memory.
* It actually helps with the "indivisible batch" case - it relaxes the
limit, so there's a chance the batch eventually fits and we stop adding
more and more batches. With spill files that's not the case - we still
keep the original limit, and we end up with the batch explosion (but
then we handle it much more efficiently).
Unless there are some objections, my plan is to get (1) cleaned up and
try to get it in for 18, possibly in the January CF. It's not a
particularly complex patch, and it already passes check-world (it only
affected three plans in join_hash, and those make sense I think).
One thing I'm not sure about yet is whether this needs to tweak the
hashjoin costing to also consider the files when deciding how many
batches to use. Maybe it should?
regards
[1]: /messages/by-id/20190504003414.bulcbnge3rhwhcsh@development
/messages/by-id/20190504003414.bulcbnge3rhwhcsh@development
[2]: /messages/by-id/20230228190643.1e368315@karst
[3]: /messages/by-id/bc138e9f-c89e-9147-5395-61d51a757b3b@gusw.net
/messages/by-id/bc138e9f-c89e-9147-5395-61d51a757b3b@gusw.net
[4]: /messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development
/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development
[5]: /messages/by-id/CAAKRu_YsWm7gc_b2nBGWFPE6wuhdOLfc1LBZ786DUzaCPUDXCA@mail.gmail.com
/messages/by-id/CAAKRu_YsWm7gc_b2nBGWFPE6wuhdOLfc1LBZ786DUzaCPUDXCA@mail.gmail.com
--
Tomas Vondra
Attachments:
v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patchtext/x-patch; charset=UTF-8; name=v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patchDownload
From edcaac0705193acea33c4423bf0d59128219a46f Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Tue, 31 Dec 2024 17:09:40 +0100
Subject: [PATCH v20241231-adjust-limit] Account for batch files in hash join
spilling
Hash joins try to limit the amount of memory used by the Hash node by
only keeping a single batch in memory and spilling future batches to
disk. Unfortunately, the implementation does not account for the files
used for spilling, which can lead to issues with many batches. Each file
keeps a BLCKSZ buffer in memory, and we need 2*nbatches of those files,
so with many batches this may use substatial amounts of memory.
The hash join code however assumes adding batches is virtually free (in
terms of memory needed), ignoring this issue. It increases the number of
batches, possibly keeping the current batch within the limit, but ends
up using much more memory for the files.
This can be particularly painful with adversary data sets, with a batch
that can't be split. This may happen due to hash collisions (overlaps in
the part used to calculate "batch"), or a value with many duplicities
that however didn't make it to the MCV list (and thus the skew table
can't help). In these cases the hash can get into a cycle of increasing
the number of batches, often reaching 256k or 512k batches before
exhausting available hash space (32-bits).
If this happens, there's not much point in enforcing the original memory
limit. That's simply not feasible - especially in the case of a single
batch exceeding the allowed space.
Instead, the best we can do is relaxing the limit, and focusing on using
as little total memory as possible. By allowing the batches to be
larger, we reduce the number of batch files. The adjustment formula is
based on the observation that doubling the number of batches doubles the
amount of memory needed for the files, while cutting the batch size in
half. This defines the "break even" point for the next batch increase.
---
src/backend/executor/nodeHash.c | 153 +++++++++++++++++++++---
src/test/regress/expected/join_hash.out | 4 +-
src/test/regress/sql/join_hash.sql | 4 +-
3 files changed, 142 insertions(+), 19 deletions(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 3e22d50e3a4..680d2897738 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -54,6 +54,9 @@ static void ExecHashSkewTableInsert(HashJoinTable hashtable,
uint32 hashvalue,
int bucketNumber);
static void ExecHashRemoveNextSkewBucket(HashJoinTable hashtable);
+static void ExecHashUpdateSpacePeak(HashJoinTable hashtable);
+static bool ExecHashExceededMemoryLimit(HashJoinTable hashtable);
+static void ExecHashAdjustMemoryLimit(HashJoinTable hashtable);
static void *dense_alloc(HashJoinTable hashtable, Size size);
static HashJoinTuple ExecParallelHashTupleAlloc(HashJoinTable hashtable,
@@ -199,10 +202,8 @@ MultiExecPrivateHash(HashState *node)
if (hashtable->nbuckets != hashtable->nbuckets_optimal)
ExecHashIncreaseNumBuckets(hashtable);
- /* Account for the buckets in spaceUsed (reported in EXPLAIN ANALYZE) */
- hashtable->spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+ /* update info about peak memory usage */
+ ExecHashUpdateSpacePeak(hashtable);
hashtable->partialTuples = hashtable->totalTuples;
}
@@ -1036,6 +1037,9 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
hashtable, nfreed, ninmemory, hashtable->spaceUsed);
#endif
+ /* adjust the memory limit for the new nbatches etc. */
+ ExecHashAdjustMemoryLimit(hashtable);
+
/*
* If we dumped out either all or none of the tuples in the table, disable
* further expansion of nbatch. This situation implies that we have
@@ -1673,11 +1677,12 @@ ExecHashTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
- if (hashtable->spaceUsed +
- hashtable->nbuckets_optimal * sizeof(HashJoinTuple)
- > hashtable->spaceAllowed)
+
+ /* update info about peak memory usage */
+ ExecHashUpdateSpacePeak(hashtable);
+
+ /* Should we add more batches, to enforce memory limit? */
+ if (ExecHashExceededMemoryLimit(hashtable))
ExecHashIncreaseNumBatches(hashtable);
}
else
@@ -1843,6 +1848,120 @@ ExecHashGetBucketAndBatch(HashJoinTable hashtable,
}
}
+/*
+ * ExecHashUpdateSpacePeak
+ * Update information about peak memory usage.
+ *
+ * This considers tuples added to the hash table, buckets of the hash table
+ * itself, and also the bufferer batch files on both the inner and outer side.
+ * Each file has a BLCKSZ buffer, so with enough batches this may actually
+ * represent most of the memory used by the hash join node.
+ */
+static void
+ExecHashUpdateSpacePeak(HashJoinTable hashtable)
+{
+ Size spaceUsed = hashtable->spaceUsed;
+
+ /* buckets of the hash table */
+ spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
+
+ /* buffered batch files (inner + outer), each has a BLCKSZ buffer */
+ spaceUsed += hashtable->nbatch * sizeof(PGAlignedBlock) * 2;
+
+ /* if we exceeded the current peak, remember the new one */
+ if (spaceUsed > hashtable->spacePeak)
+ hashtable->spacePeak = spaceUsed;
+}
+
+/*
+ * ExecHashMemoryLimitExceeded
+ * Check if the amount of memory used exceeds spaceAllowed.
+ *
+ * Check if the total amount of space used by the hash join exceeds the
+ * current value of spaceAllowed, and we should try to increase the number
+ * of batches.
+ *
+ * We need to consider both the data added to the hash and the hashtable
+ * itself (i.e. buckets), but also the files used for future batches.
+ * Each batch needs a file for inner/outer side, so we need (2*nbatch)
+ * files in total, and each BufFile has a BLCKSZ buffer. If we ignored
+ * the files and simply doubled the number of batches, we could easily
+ * increase the total amount of memory because while we expect to cut the
+ * batch size in half to, doubling the number of batches also doubles the
+ * amount of memory allocated by BufFile.
+ *
+ * That means doubling the number of batches is pointless when
+ *
+ * (spaceUsed / 2) < 2 * (nbatches * sizeof(BufFile))
+ *
+ * because it would result in allocating more memory than it saves.
+ *
+ * This is a temporary decision - we can't stop adding batches entirely,
+ * just until the hash table grows enough to make it a win again.
+ */
+static bool
+ExecHashExceededMemoryLimit(HashJoinTable hashtable)
+{
+ return (hashtable->spaceUsed +
+ hashtable->nbuckets_optimal * sizeof(HashJoinTuple) +
+ hashtable->nbatch * sizeof(PGAlignedBlock) * 2
+ > hashtable->spaceAllowed);
+}
+
+/*
+ * ExecHashAdjustMemoryLimit
+ * Adjust the memory limit after increasing the number of batches.
+ *
+ * We can't keep the same spaceAllowed value, because as we keep adding
+ * batches we're guaranteed to exceed the older values simply thanks to
+ * the BufFile allocations.
+ *
+ * Instead, we consider the "break even" threshold for the current number
+ * of batches, add a bit of slack (so that we don't get into a cycle of
+ * incrementing number of batches), and calculate the new limit from that.
+ *
+ * For well estimated cases this should do nothing, as the batches are
+ * expected to account only for a small fraction of work_mem. But if we
+ * significantly underestimate the number of batches, or if one batch
+ * happens to be very large, this will relax the limit a bit.
+ *
+ * This means we won't enforce the work_mem limit strictly - but without
+ * adjusting the limit that wouldn't be the case either, we'd just use
+ * a lot of memory for the BufFiles without accounting for that. This
+ * way we do our best to minimize the amount of memory used.
+ */
+static void
+ExecHashAdjustMemoryLimit(HashJoinTable hashtable)
+{
+ Size newSpaceAllowed;
+
+ /*
+ * The next time increasing the number of batches "breaks even" is when
+ *
+ * (spaceUsed / 2) == (2 * nbatches * BLCKSZ)
+ *
+ * which means
+ *
+ * spaceUsed == (4 * nbatches * BLCKSZ)
+ *
+ * However, this is a "break even" threshold, when we shrink the hash
+ * table just enough to compensate the new batches, and we'd hit the
+ * new threshold almost immediately again. In practice we want to free
+ * more memory to allow new data before having to increase the number
+ * of batches again. So we allow 25% more space.
+ */
+ newSpaceAllowed
+ = 1.25 * (4 * hashtable->nbatch * sizeof(PGAlignedBlock));
+
+ /* but also account for the buckets, and the current batch files */
+ newSpaceAllowed += hashtable->nbuckets_optimal * sizeof(HashJoinTuple);
+ newSpaceAllowed += (2 * hashtable->nbatch * sizeof(PGAlignedBlock));
+
+ /* shouldn't go down, but use Max() to make sure */
+ hashtable->spaceAllowed = Max(hashtable->spaceAllowed,
+ newSpaceAllowed);
+}
+
/*
* ExecScanHashBucket
* scan a hash bucket for matches to the current outer tuple
@@ -2349,8 +2468,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
+ mcvsToUse * sizeof(int);
hashtable->spaceUsedSkew += nbuckets * sizeof(HashSkewBucket *)
+ mcvsToUse * sizeof(int);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak memory usage */
+ ExecHashUpdateSpacePeak(hashtable);
/*
* Create a skew bucket for each MCV hash value.
@@ -2399,8 +2519,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
hashtable->nSkewBuckets++;
hashtable->spaceUsed += SKEW_BUCKET_OVERHEAD;
hashtable->spaceUsedSkew += SKEW_BUCKET_OVERHEAD;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak memory usage */
+ ExecHashUpdateSpacePeak(hashtable);
}
free_attstatsslot(&sslot);
@@ -2489,8 +2610,10 @@ ExecHashSkewTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
hashtable->spaceUsedSkew += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak memory usage */
+ ExecHashUpdateSpacePeak(hashtable);
+
while (hashtable->spaceUsedSkew > hashtable->spaceAllowedSkew)
ExecHashRemoveNextSkewBucket(hashtable);
diff --git a/src/test/regress/expected/join_hash.out b/src/test/regress/expected/join_hash.out
index 4fc34a0e72a..8d54822eb8c 100644
--- a/src/test/regress/expected/join_hash.out
+++ b/src/test/regress/expected/join_hash.out
@@ -198,7 +198,7 @@ rollback to settings;
-- non-parallel
savepoint settings;
set local max_parallel_workers_per_gather = 0;
-set local work_mem = '128kB';
+set local work_mem = '512kB';
set local hash_mem_multiplier = 1.0;
explain (costs off)
select count(*) from simple r join simple s using (id);
@@ -232,7 +232,7 @@ rollback to settings;
-- parallel with parallel-oblivious hash join
savepoint settings;
set local max_parallel_workers_per_gather = 2;
-set local work_mem = '128kB';
+set local work_mem = '512kB';
set local hash_mem_multiplier = 1.0;
set local enable_parallel_hash = off;
explain (costs off)
diff --git a/src/test/regress/sql/join_hash.sql b/src/test/regress/sql/join_hash.sql
index 6b0688ab0a6..ca8758900aa 100644
--- a/src/test/regress/sql/join_hash.sql
+++ b/src/test/regress/sql/join_hash.sql
@@ -145,7 +145,7 @@ rollback to settings;
-- non-parallel
savepoint settings;
set local max_parallel_workers_per_gather = 0;
-set local work_mem = '128kB';
+set local work_mem = '512kB';
set local hash_mem_multiplier = 1.0;
explain (costs off)
select count(*) from simple r join simple s using (id);
@@ -160,7 +160,7 @@ rollback to settings;
-- parallel with parallel-oblivious hash join
savepoint settings;
set local max_parallel_workers_per_gather = 2;
-set local work_mem = '128kB';
+set local work_mem = '512kB';
set local hash_mem_multiplier = 1.0;
set local enable_parallel_hash = off;
explain (costs off)
--
2.47.1
v20241231-multi-spill-0001-Hash-join-with-a-multiple-spil.patchtext/x-patch; charset=UTF-8; name=v20241231-multi-spill-0001-Hash-join-with-a-multiple-spil.patchDownload
From 030fca4e56132dd516b8aba23ed36c47311b33f5 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Tue, 31 Dec 2024 17:08:17 +0100
Subject: [PATCH v20241231-multi-spill] Hash join with a multiple spill files
We can't use arbitrary number of batches in the hash join, because that
can use substantial memory use, ignored by the memory limit. Instead,
decide how many batches we can keep in memory, and open files only for
this "slice" of batches. For future batches we keep on spill file per
slice.
Then use these spill files after "switching" to the first batch in each
of those non-in-memory slices.
NB: Compared to the "single-spill" patch, this is less strict, as we
need one spill file per slice, and the number of slices may grow during
execution. It's better than the current behavior, because even with
modest work_mem values we can fit 32-128 batches into memory, thus
thus reducing the number of files to 1/32x - 1/128x. But with many
batches (as with batch explosion) this can still use substantial
amounts of memory (certainly more than work_mem).
Ultimately, it behaves similarly to the "adjustment" patch, but with
more complexity. And it can't handle the "oversized batch" really well,
because the limit is not adjusted.
---
src/backend/commands/explain.c | 6 +-
src/backend/executor/nodeHash.c | 256 +++++++++++++++++++++++---
src/backend/executor/nodeHashjoin.c | 75 +++++---
src/backend/optimizer/path/costsize.c | 2 +
src/include/executor/hashjoin.h | 4 +
src/include/executor/nodeHash.h | 8 +
src/include/nodes/execnodes.h | 1 +
7 files changed, 300 insertions(+), 52 deletions(-)
diff --git a/src/backend/commands/explain.c b/src/backend/commands/explain.c
index a201ed30824..aa3fbeda3dd 100644
--- a/src/backend/commands/explain.c
+++ b/src/backend/commands/explain.c
@@ -3466,19 +3466,23 @@ show_hash_info(HashState *hashstate, ExplainState *es)
hinstrument.nbatch, es);
ExplainPropertyInteger("Original Hash Batches", NULL,
hinstrument.nbatch_original, es);
+ ExplainPropertyInteger("In-Memory Hash Batches", NULL,
+ hinstrument.nbatch_original, es);
ExplainPropertyUInteger("Peak Memory Usage", "kB",
spacePeakKb, es);
}
else if (hinstrument.nbatch_original != hinstrument.nbatch ||
+ hinstrument.nbatch_inmemory != hinstrument.nbatch ||
hinstrument.nbuckets_original != hinstrument.nbuckets)
{
ExplainIndentText(es);
appendStringInfo(es->str,
- "Buckets: %d (originally %d) Batches: %d (originally %d) Memory Usage: " UINT64_FORMAT "kB\n",
+ "Buckets: %d (originally %d) Batches: %d (originally %d, in-memory %d) Memory Usage: " UINT64_FORMAT "kB\n",
hinstrument.nbuckets,
hinstrument.nbuckets_original,
hinstrument.nbatch,
hinstrument.nbatch_original,
+ hinstrument.nbatch_inmemory,
spacePeakKb);
}
else
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 3e22d50e3a4..e9d482577ac 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -80,6 +80,7 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+static void ExecHashUpdateSpacePeak(HashJoinTable hashtable);
/* ----------------------------------------------------------------
* ExecHash
@@ -199,10 +200,8 @@ MultiExecPrivateHash(HashState *node)
if (hashtable->nbuckets != hashtable->nbuckets_optimal)
ExecHashIncreaseNumBuckets(hashtable);
- /* Account for the buckets in spaceUsed (reported in EXPLAIN ANALYZE) */
- hashtable->spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
hashtable->partialTuples = hashtable->totalTuples;
}
@@ -451,6 +450,7 @@ ExecHashTableCreate(HashState *state)
size_t space_allowed;
int nbuckets;
int nbatch;
+ int nbatch_inmemory;
double rows;
int num_skew_mcvs;
int log2_nbuckets;
@@ -477,7 +477,8 @@ ExecHashTableCreate(HashState *state)
state->parallel_state != NULL ?
state->parallel_state->nparticipants - 1 : 0,
&space_allowed,
- &nbuckets, &nbatch, &num_skew_mcvs);
+ &nbuckets, &nbatch, &nbatch_inmemory,
+ &num_skew_mcvs);
/* nbuckets must be a power of 2 */
log2_nbuckets = my_log2(nbuckets);
@@ -503,6 +504,7 @@ ExecHashTableCreate(HashState *state)
hashtable->nSkewBuckets = 0;
hashtable->skewBucketNums = NULL;
hashtable->nbatch = nbatch;
+ hashtable->nbatch_inmemory = nbatch_inmemory;
hashtable->curbatch = 0;
hashtable->nbatch_original = nbatch;
hashtable->nbatch_outstart = nbatch;
@@ -512,6 +514,8 @@ ExecHashTableCreate(HashState *state)
hashtable->skewTuples = 0;
hashtable->innerBatchFile = NULL;
hashtable->outerBatchFile = NULL;
+ hashtable->innerOverflowFiles = NULL;
+ hashtable->outerOverflowFiles = NULL;
hashtable->spaceUsed = 0;
hashtable->spacePeak = 0;
hashtable->spaceAllowed = space_allowed;
@@ -552,6 +556,7 @@ ExecHashTableCreate(HashState *state)
if (nbatch > 1 && hashtable->parallel_state == NULL)
{
MemoryContext oldctx;
+ int cnt = Min(nbatch, nbatch_inmemory);
/*
* allocate and initialize the file arrays in hashCxt (not needed for
@@ -559,8 +564,19 @@ ExecHashTableCreate(HashState *state)
*/
oldctx = MemoryContextSwitchTo(hashtable->spillCxt);
- hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch);
- hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch);
+ hashtable->innerBatchFile = palloc0_array(BufFile *, cnt);
+ hashtable->outerBatchFile = palloc0_array(BufFile *, cnt);
+
+ /* also allocate files for overflow batches */
+ if (nbatch > nbatch_inmemory)
+ {
+ int nslices = (nbatch / nbatch_inmemory);
+
+ Assert(nslices % 2 == 0);
+
+ hashtable->innerOverflowFiles = palloc0_array(BufFile *, nslices + 1);
+ hashtable->outerOverflowFiles = palloc0_array(BufFile *, nslices + 1);
+ }
MemoryContextSwitchTo(oldctx);
@@ -661,6 +677,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t *space_allowed,
int *numbuckets,
int *numbatches,
+ int *numbatches_inmemory,
int *num_skew_mcvs)
{
int tupsize;
@@ -669,6 +686,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t bucket_bytes;
size_t max_pointers;
int nbatch = 1;
+ int nbatch_inmemory = 1;
int nbuckets;
double dbuckets;
@@ -811,6 +829,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
space_allowed,
numbuckets,
numbatches,
+ numbatches_inmemory,
num_skew_mcvs);
return;
}
@@ -848,11 +867,24 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * See how many batches we can fit into memory (driven mostly by size
+ * of BufFile, with PGAlignedBlock being the largest part of that).
+ * We need one BufFile for inner and outer side, so we count it twice
+ * for each batch, and we stop once we exceed (work_mem/2).
+ */
+ while ((nbatch_inmemory * 2) * sizeof(PGAlignedBlock) * 2
+ <= (work_mem * 1024L / 2))
+ nbatch_inmemory *= 2;
+
+ // nbatch_inmemory = nbatch;
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
*numbuckets = nbuckets;
*numbatches = nbatch;
+ *numbatches_inmemory = nbatch_inmemory;
}
@@ -874,13 +906,27 @@ ExecHashTableDestroy(HashJoinTable hashtable)
*/
if (hashtable->innerBatchFile != NULL)
{
- for (i = 1; i < hashtable->nbatch; i++)
+ int n = Min(hashtable->nbatch, hashtable->nbatch_inmemory);
+
+ for (i = 1; i < n; i++)
{
if (hashtable->innerBatchFile[i])
BufFileClose(hashtable->innerBatchFile[i]);
if (hashtable->outerBatchFile[i])
BufFileClose(hashtable->outerBatchFile[i]);
}
+
+ /* number of batch slices */
+ n = (hashtable->nbatch / hashtable->nbatch_inmemory) + 1;
+
+ for (i = 1; i < n; i++)
+ {
+ if (hashtable->innerOverflowFiles[i])
+ BufFileClose(hashtable->innerOverflowFiles[i]);
+
+ if (hashtable->outerOverflowFiles[i])
+ BufFileClose(hashtable->outerOverflowFiles[i]);
+ }
}
/* Release working memory (batchCxt is a child, so it goes away too) */
@@ -923,11 +969,14 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (hashtable->innerBatchFile == NULL)
{
+ /* XXX nbatch=1, no need to deal with nbatch_inmemory here */
+ int nbatch_tmp = Min(nbatch, hashtable->nbatch_inmemory);
+
MemoryContext oldcxt = MemoryContextSwitchTo(hashtable->spillCxt);
/* we had no file arrays before */
- hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch);
- hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch);
+ hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch_tmp);
+ hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch_tmp);
MemoryContextSwitchTo(oldcxt);
@@ -936,9 +985,35 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
}
else
{
+ int nbatch_tmp = Min(nbatch, hashtable->nbatch_inmemory);
+ int oldnbatch_tmp = Min(oldnbatch, hashtable->nbatch_inmemory);
+
/* enlarge arrays and zero out added entries */
- hashtable->innerBatchFile = repalloc0_array(hashtable->innerBatchFile, BufFile *, oldnbatch, nbatch);
- hashtable->outerBatchFile = repalloc0_array(hashtable->outerBatchFile, BufFile *, oldnbatch, nbatch);
+ hashtable->innerBatchFile = repalloc0_array(hashtable->innerBatchFile, BufFile *, oldnbatch_tmp, nbatch_tmp);
+ hashtable->outerBatchFile = repalloc0_array(hashtable->outerBatchFile, BufFile *, oldnbatch_tmp, nbatch_tmp);
+
+ if (nbatch > hashtable->nbatch_inmemory)
+ {
+ int nslices = (nbatch / hashtable->nbatch_inmemory);
+ int oldnslices = (oldnbatch / hashtable->nbatch_inmemory);
+
+ Assert(nslices > 1);
+ Assert(nslices % 2 == 0);
+ Assert((oldnslices == 1) || (oldnslices % 2 == 0));
+ Assert(oldnslices <= nslices);
+
+ if (hashtable->innerOverflowFiles == NULL)
+ {
+ hashtable->innerOverflowFiles = palloc0_array(BufFile *, nslices + 1);
+ hashtable->outerOverflowFiles = palloc0_array(BufFile *, nslices + 1);
+ }
+ else
+ {
+ hashtable->innerOverflowFiles = repalloc0_array(hashtable->innerOverflowFiles, BufFile *, oldnslices + 1, nslices + 1);
+ hashtable->outerOverflowFiles = repalloc0_array(hashtable->outerOverflowFiles, BufFile *, oldnslices + 1, nslices + 1);
+ }
+ }
+
}
hashtable->nbatch = nbatch;
@@ -1008,11 +1083,18 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
}
else
{
+ BufFile **batchFile;
+
/* dump it out */
Assert(batchno > curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ hashtable->innerOverflowFiles);
+
ExecHashJoinSaveTuple(HJTUPLE_MINTUPLE(hashTuple),
hashTuple->hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
hashtable->spaceUsed -= hashTupleSize;
@@ -1673,22 +1755,33 @@ ExecHashTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
+
+ /* Consider increasing number of batches if we filled work_mem. */
if (hashtable->spaceUsed +
- hashtable->nbuckets_optimal * sizeof(HashJoinTuple)
+ hashtable->nbuckets_optimal * sizeof(HashJoinTuple) +
+ Min(hashtable->nbatch, hashtable->nbatch_inmemory) * sizeof(PGAlignedBlock) * 2 /* inner + outer */
> hashtable->spaceAllowed)
ExecHashIncreaseNumBatches(hashtable);
}
else
{
+ BufFile **batchFile;
+
/*
* put the tuple into a temp file for later batches
*/
Assert(batchno > hashtable->curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ hashtable->innerOverflowFiles);
+
ExecHashJoinSaveTuple(tuple,
hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
}
@@ -1843,6 +1936,108 @@ ExecHashGetBucketAndBatch(HashJoinTable hashtable,
}
}
+int
+ExecHashGetBatchIndex(HashJoinTable hashtable, int batchno)
+{
+ int slice,
+ curslice;
+
+ if (hashtable->nbatch < hashtable->nbatch_inmemory)
+ return batchno;
+
+ slice = batchno / hashtable->nbatch_inmemory;
+ curslice = hashtable->curbatch / hashtable->nbatch_inmemory;
+
+ /* slices can't go backwards */
+ Assert(slice >= curslice);
+
+ /* overflow slice */
+ if (slice > curslice)
+ return -1;
+
+ /* current slice, compute index in the current array */
+ return (batchno % hashtable->nbatch_inmemory);
+}
+
+BufFile **
+ExecHashGetBatchFile(HashJoinTable hashtable, int batchno,
+ BufFile **batchFiles, BufFile **overflowFiles)
+{
+ int idx = ExecHashGetBatchIndex(hashtable, batchno);
+
+ /* get the right overflow file */
+ if (idx == -1)
+ {
+ int slice = (batchno / hashtable->nbatch_inmemory);
+
+ return &overflowFiles[slice];
+ }
+
+ /* batch file in the current slice */
+ return &batchFiles[idx];
+}
+
+void
+ExecHashSwitchToNextBatchSlice(HashJoinTable hashtable)
+{
+ int slice = (hashtable->curbatch / hashtable->nbatch_inmemory);
+
+ memset(hashtable->innerBatchFile, 0,
+ hashtable->nbatch_inmemory * sizeof(BufFile *));
+
+ hashtable->innerBatchFile[0] = hashtable->innerOverflowFiles[slice];
+ hashtable->innerOverflowFiles[slice] = NULL;
+
+ memset(hashtable->outerBatchFile, 0,
+ hashtable->nbatch_inmemory * sizeof(BufFile *));
+
+ hashtable->outerBatchFile[0] = hashtable->outerOverflowFiles[slice];
+ hashtable->outerOverflowFiles[slice] = NULL;
+}
+
+int
+ExecHashSwitchToNextBatch(HashJoinTable hashtable)
+{
+ int batchidx;
+
+ hashtable->curbatch++;
+
+ /* see if we skipped to the next batch slice */
+ batchidx = ExecHashGetBatchIndex(hashtable, hashtable->curbatch);
+
+ /* Can't be -1, current batch is in the current slice by definition. */
+ Assert(batchidx >= 0 && batchidx < hashtable->nbatch_inmemory);
+
+ /*
+ * If we skipped to the next slice of batches, reset the array of files
+ * and use the overflow file as the first batch.
+ */
+ if (batchidx == 0)
+ ExecHashSwitchToNextBatchSlice(hashtable);
+
+ return hashtable->curbatch;
+}
+
+static void
+ExecHashUpdateSpacePeak(HashJoinTable hashtable)
+{
+ Size spaceUsed = hashtable->spaceUsed;
+
+ /* Account for the buckets in spaceUsed (reported in EXPLAIN ANALYZE) */
+ spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
+
+ /* Account for memory used for batch files (inner + outer) */
+ spaceUsed += Min(hashtable->nbatch, hashtable->nbatch_inmemory) *
+ sizeof(PGAlignedBlock) * 2;
+
+ /* Account for slice files (inner + outer) */
+ spaceUsed += (hashtable->nbatch / hashtable->nbatch_inmemory) *
+ sizeof(PGAlignedBlock) * 2;
+
+ if (spaceUsed > hashtable->spacePeak)
+ hashtable->spacePeak = spaceUsed;
+}
+
/*
* ExecScanHashBucket
* scan a hash bucket for matches to the current outer tuple
@@ -2349,8 +2544,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
+ mcvsToUse * sizeof(int);
hashtable->spaceUsedSkew += nbuckets * sizeof(HashSkewBucket *)
+ mcvsToUse * sizeof(int);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
/*
* Create a skew bucket for each MCV hash value.
@@ -2399,8 +2595,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
hashtable->nSkewBuckets++;
hashtable->spaceUsed += SKEW_BUCKET_OVERHEAD;
hashtable->spaceUsedSkew += SKEW_BUCKET_OVERHEAD;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
}
free_attstatsslot(&sslot);
@@ -2489,8 +2686,10 @@ ExecHashSkewTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
hashtable->spaceUsedSkew += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
+
while (hashtable->spaceUsedSkew > hashtable->spaceAllowedSkew)
ExecHashRemoveNextSkewBucket(hashtable);
@@ -2569,10 +2768,17 @@ ExecHashRemoveNextSkewBucket(HashJoinTable hashtable)
}
else
{
+ BufFile **batchFile;
+
/* Put the tuple into a temp file for later batches */
Assert(batchno > hashtable->curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ hashtable->innerOverflowFiles);
+
ExecHashJoinSaveTuple(tuple, hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
pfree(hashTuple);
hashtable->spaceUsed -= tupleSize;
@@ -2750,6 +2956,8 @@ ExecHashAccumInstrumentation(HashInstrumentation *instrument,
hashtable->nbatch);
instrument->nbatch_original = Max(instrument->nbatch_original,
hashtable->nbatch_original);
+ instrument->nbatch_inmemory = Min(hashtable->nbatch,
+ hashtable->nbatch_inmemory);
instrument->space_peak = Max(instrument->space_peak,
hashtable->spacePeak);
}
diff --git a/src/backend/executor/nodeHashjoin.c b/src/backend/executor/nodeHashjoin.c
index ea0045bc0f3..580b5da93bd 100644
--- a/src/backend/executor/nodeHashjoin.c
+++ b/src/backend/executor/nodeHashjoin.c
@@ -481,10 +481,15 @@ ExecHashJoinImpl(PlanState *pstate, bool parallel)
if (batchno != hashtable->curbatch &&
node->hj_CurSkewBucketNo == INVALID_SKEW_BUCKET_NO)
{
+ BufFile **batchFile;
bool shouldFree;
MinimalTuple mintuple = ExecFetchSlotMinimalTuple(outerTupleSlot,
&shouldFree);
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->outerBatchFile,
+ hashtable->outerOverflowFiles);
+
/*
* Need to postpone this outer tuple to a later batch.
* Save it in the corresponding outer-batch file.
@@ -492,7 +497,7 @@ ExecHashJoinImpl(PlanState *pstate, bool parallel)
Assert(parallel_state == NULL);
Assert(batchno > hashtable->curbatch);
ExecHashJoinSaveTuple(mintuple, hashvalue,
- &hashtable->outerBatchFile[batchno],
+ batchFile,
hashtable);
if (shouldFree)
@@ -1030,17 +1035,19 @@ ExecHashJoinOuterGetTuple(PlanState *outerNode,
}
else if (curbatch < hashtable->nbatch)
{
- BufFile *file = hashtable->outerBatchFile[curbatch];
+ BufFile **file = ExecHashGetBatchFile(hashtable, curbatch,
+ hashtable->outerBatchFile,
+ hashtable->outerOverflowFiles);
/*
* In outer-join cases, we could get here even though the batch file
* is empty.
*/
- if (file == NULL)
+ if (*file == NULL)
return NULL;
slot = ExecHashJoinGetSavedTuple(hjstate,
- file,
+ *file,
hashvalue,
hjstate->hj_OuterTupleSlot);
if (!TupIsNull(slot))
@@ -1135,9 +1142,18 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
BufFile *innerFile;
TupleTableSlot *slot;
uint32 hashvalue;
+ int batchidx;
+ int curbatch_old;
nbatch = hashtable->nbatch;
curbatch = hashtable->curbatch;
+ curbatch_old = curbatch;
+
+ /* index of the old batch */
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
+
+ /* has to be in the current slice of batches */
+ Assert(batchidx >= 0 && batchidx < hashtable->nbatch_inmemory);
if (curbatch > 0)
{
@@ -1145,9 +1161,9 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* We no longer need the previous outer batch file; close it right
* away to free disk space.
*/
- if (hashtable->outerBatchFile[curbatch])
- BufFileClose(hashtable->outerBatchFile[curbatch]);
- hashtable->outerBatchFile[curbatch] = NULL;
+ if (hashtable->outerBatchFile[batchidx])
+ BufFileClose(hashtable->outerBatchFile[batchidx]);
+ hashtable->outerBatchFile[batchidx] = NULL;
}
else /* we just finished the first batch */
{
@@ -1182,45 +1198,50 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* scan, we have to rescan outer batches in case they contain tuples that
* need to be reassigned.
*/
- curbatch++;
+ curbatch = ExecHashSwitchToNextBatch(hashtable);
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
+
while (curbatch < nbatch &&
- (hashtable->outerBatchFile[curbatch] == NULL ||
- hashtable->innerBatchFile[curbatch] == NULL))
+ (hashtable->outerBatchFile[batchidx] == NULL ||
+ hashtable->innerBatchFile[batchidx] == NULL))
{
- if (hashtable->outerBatchFile[curbatch] &&
+ if (hashtable->outerBatchFile[batchidx] &&
HJ_FILL_OUTER(hjstate))
break; /* must process due to rule 1 */
- if (hashtable->innerBatchFile[curbatch] &&
+ if (hashtable->innerBatchFile[batchidx] &&
HJ_FILL_INNER(hjstate))
break; /* must process due to rule 1 */
- if (hashtable->innerBatchFile[curbatch] &&
+ if (hashtable->innerBatchFile[batchidx] &&
nbatch != hashtable->nbatch_original)
break; /* must process due to rule 2 */
- if (hashtable->outerBatchFile[curbatch] &&
+ if (hashtable->outerBatchFile[batchidx] &&
nbatch != hashtable->nbatch_outstart)
break; /* must process due to rule 3 */
/* We can ignore this batch. */
/* Release associated temp files right away. */
- if (hashtable->innerBatchFile[curbatch])
- BufFileClose(hashtable->innerBatchFile[curbatch]);
- hashtable->innerBatchFile[curbatch] = NULL;
- if (hashtable->outerBatchFile[curbatch])
- BufFileClose(hashtable->outerBatchFile[curbatch]);
- hashtable->outerBatchFile[curbatch] = NULL;
- curbatch++;
+ if (hashtable->innerBatchFile[batchidx])
+ BufFileClose(hashtable->innerBatchFile[batchidx]);
+ hashtable->innerBatchFile[batchidx] = NULL;
+ if (hashtable->outerBatchFile[batchidx])
+ BufFileClose(hashtable->outerBatchFile[batchidx]);
+ hashtable->outerBatchFile[batchidx] = NULL;
+
+ curbatch = ExecHashSwitchToNextBatch(hashtable);
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
}
if (curbatch >= nbatch)
+ {
+ hashtable->curbatch = curbatch_old;
return false; /* no more batches */
-
- hashtable->curbatch = curbatch;
+ }
/*
* Reload the hash table with the new inner batch (which could be empty)
*/
ExecHashTableReset(hashtable);
- innerFile = hashtable->innerBatchFile[curbatch];
+ innerFile = hashtable->innerBatchFile[batchidx];
if (innerFile != NULL)
{
@@ -1246,15 +1267,15 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* needed
*/
BufFileClose(innerFile);
- hashtable->innerBatchFile[curbatch] = NULL;
+ hashtable->innerBatchFile[batchidx] = NULL;
}
/*
* Rewind outer batch file (if present), so that we can start reading it.
*/
- if (hashtable->outerBatchFile[curbatch] != NULL)
+ if (hashtable->outerBatchFile[batchidx] != NULL)
{
- if (BufFileSeek(hashtable->outerBatchFile[curbatch], 0, 0, SEEK_SET))
+ if (BufFileSeek(hashtable->outerBatchFile[batchidx], 0, 0, SEEK_SET))
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not rewind hash-join temporary file")));
diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c
index c36687aa4df..6f40600d10b 100644
--- a/src/backend/optimizer/path/costsize.c
+++ b/src/backend/optimizer/path/costsize.c
@@ -4173,6 +4173,7 @@ initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
int num_hashclauses = list_length(hashclauses);
int numbuckets;
int numbatches;
+ int numbatches_inmemory;
int num_skew_mcvs;
size_t space_allowed; /* unused */
@@ -4227,6 +4228,7 @@ initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
&space_allowed,
&numbuckets,
&numbatches,
+ &numbatches_inmemory,
&num_skew_mcvs);
/*
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 2d8ed8688cd..76c983dbd4d 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -320,6 +320,7 @@ typedef struct HashJoinTableData
int *skewBucketNums; /* array indexes of active skew buckets */
int nbatch; /* number of batches */
+ int nbatch_inmemory; /* max number of in-memory batches */
int curbatch; /* current batch #; 0 during 1st pass */
int nbatch_original; /* nbatch when we started inner scan */
@@ -331,6 +332,9 @@ typedef struct HashJoinTableData
double partialTuples; /* # tuples obtained from inner plan by me */
double skewTuples; /* # tuples inserted into skew tuples */
+ BufFile **innerOverflowFiles; /* temp file for inner overflow batches */
+ BufFile **outerOverflowFiles; /* temp file for outer overflow batches */
+
/*
* These arrays are allocated for the life of the hash join, but only if
* nbatch > 1. A file is opened only when we first write a tuple into it
diff --git a/src/include/executor/nodeHash.h b/src/include/executor/nodeHash.h
index e4eb7bc6359..ef1d2bec15a 100644
--- a/src/include/executor/nodeHash.h
+++ b/src/include/executor/nodeHash.h
@@ -16,6 +16,7 @@
#include "access/parallel.h"
#include "nodes/execnodes.h"
+#include "storage/buffile.h"
struct SharedHashJoinBatch;
@@ -46,6 +47,12 @@ extern void ExecHashGetBucketAndBatch(HashJoinTable hashtable,
uint32 hashvalue,
int *bucketno,
int *batchno);
+extern int ExecHashGetBatchIndex(HashJoinTable hashtable, int batchno);
+extern BufFile **ExecHashGetBatchFile(HashJoinTable hashtable, int batchno,
+ BufFile **batchFiles,
+ BufFile **overflowFiles);
+extern void ExecHashSwitchToNextBatchSlice(HashJoinTable hashtable);
+extern int ExecHashSwitchToNextBatch(HashJoinTable hashtable);
extern bool ExecScanHashBucket(HashJoinState *hjstate, ExprContext *econtext);
extern bool ExecParallelScanHashBucket(HashJoinState *hjstate, ExprContext *econtext);
extern void ExecPrepHashTableForUnmatched(HashJoinState *hjstate);
@@ -62,6 +69,7 @@ extern void ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t *space_allowed,
int *numbuckets,
int *numbatches,
+ int *numbatches_inmemory,
int *num_skew_mcvs);
extern int ExecHashGetSkewBucket(HashJoinTable hashtable, uint32 hashvalue);
extern void ExecHashEstimate(HashState *node, ParallelContext *pcxt);
diff --git a/src/include/nodes/execnodes.h b/src/include/nodes/execnodes.h
index 1590b643920..eb8eaea89ab 100644
--- a/src/include/nodes/execnodes.h
+++ b/src/include/nodes/execnodes.h
@@ -2755,6 +2755,7 @@ typedef struct HashInstrumentation
int nbuckets_original; /* planned number of buckets */
int nbatch; /* number of batches at end of execution */
int nbatch_original; /* planned number of batches */
+ int nbatch_inmemory; /* number of batches kept in memory */
Size space_peak; /* peak memory usage in bytes */
} HashInstrumentation;
--
2.47.1
v20241231-single-spill-0001-Hash-join-with-a-single-spill.patchtext/x-patch; charset=UTF-8; name=v20241231-single-spill-0001-Hash-join-with-a-single-spill.patchDownload
From 55142837eaf439652fd91e165f3d4c3607ea9987 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Tue, 31 Dec 2024 17:34:59 +0100
Subject: [PATCH v20241231-single-spill] Hash join with a single spill file
We can't use arbitrary number of batches in the hash join, because that
can use substantial memory use, ignored by the memory limit. Instead,
decide how many batches we can keep in memory, and open files only for
those batches, and one "overflow" file for all future batches.
Then use this spill file after "switching" to the first batch that is
not in memory.
NB: This allows enforcing the memory limit very strictly, but it's very
inefficient and causes a lot of temporary file traffic.
---
src/backend/commands/explain.c | 6 +-
src/backend/executor/nodeHash.c | 206 +++++++++++++++++++++++---
src/backend/executor/nodeHashjoin.c | 76 ++++++----
src/backend/optimizer/path/costsize.c | 2 +
src/include/executor/hashjoin.h | 4 +
src/include/executor/nodeHash.h | 7 +
src/include/nodes/execnodes.h | 1 +
7 files changed, 250 insertions(+), 52 deletions(-)
diff --git a/src/backend/commands/explain.c b/src/backend/commands/explain.c
index a201ed30824..aa3fbeda3dd 100644
--- a/src/backend/commands/explain.c
+++ b/src/backend/commands/explain.c
@@ -3466,19 +3466,23 @@ show_hash_info(HashState *hashstate, ExplainState *es)
hinstrument.nbatch, es);
ExplainPropertyInteger("Original Hash Batches", NULL,
hinstrument.nbatch_original, es);
+ ExplainPropertyInteger("In-Memory Hash Batches", NULL,
+ hinstrument.nbatch_original, es);
ExplainPropertyUInteger("Peak Memory Usage", "kB",
spacePeakKb, es);
}
else if (hinstrument.nbatch_original != hinstrument.nbatch ||
+ hinstrument.nbatch_inmemory != hinstrument.nbatch ||
hinstrument.nbuckets_original != hinstrument.nbuckets)
{
ExplainIndentText(es);
appendStringInfo(es->str,
- "Buckets: %d (originally %d) Batches: %d (originally %d) Memory Usage: " UINT64_FORMAT "kB\n",
+ "Buckets: %d (originally %d) Batches: %d (originally %d, in-memory %d) Memory Usage: " UINT64_FORMAT "kB\n",
hinstrument.nbuckets,
hinstrument.nbuckets_original,
hinstrument.nbatch,
hinstrument.nbatch_original,
+ hinstrument.nbatch_inmemory,
spacePeakKb);
}
else
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 3e22d50e3a4..58062a8b256 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -80,6 +80,7 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+static void ExecHashUpdateSpacePeak(HashJoinTable hashtable);
/* ----------------------------------------------------------------
* ExecHash
@@ -199,10 +200,8 @@ MultiExecPrivateHash(HashState *node)
if (hashtable->nbuckets != hashtable->nbuckets_optimal)
ExecHashIncreaseNumBuckets(hashtable);
- /* Account for the buckets in spaceUsed (reported in EXPLAIN ANALYZE) */
- hashtable->spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
hashtable->partialTuples = hashtable->totalTuples;
}
@@ -451,6 +450,7 @@ ExecHashTableCreate(HashState *state)
size_t space_allowed;
int nbuckets;
int nbatch;
+ int nbatch_inmemory;
double rows;
int num_skew_mcvs;
int log2_nbuckets;
@@ -477,7 +477,8 @@ ExecHashTableCreate(HashState *state)
state->parallel_state != NULL ?
state->parallel_state->nparticipants - 1 : 0,
&space_allowed,
- &nbuckets, &nbatch, &num_skew_mcvs);
+ &nbuckets, &nbatch, &nbatch_inmemory,
+ &num_skew_mcvs);
/* nbuckets must be a power of 2 */
log2_nbuckets = my_log2(nbuckets);
@@ -503,6 +504,7 @@ ExecHashTableCreate(HashState *state)
hashtable->nSkewBuckets = 0;
hashtable->skewBucketNums = NULL;
hashtable->nbatch = nbatch;
+ hashtable->nbatch_inmemory = nbatch_inmemory;
hashtable->curbatch = 0;
hashtable->nbatch_original = nbatch;
hashtable->nbatch_outstart = nbatch;
@@ -512,6 +514,8 @@ ExecHashTableCreate(HashState *state)
hashtable->skewTuples = 0;
hashtable->innerBatchFile = NULL;
hashtable->outerBatchFile = NULL;
+ hashtable->innerOverflowFile = NULL;
+ hashtable->outerOverflowFile = NULL;
hashtable->spaceUsed = 0;
hashtable->spacePeak = 0;
hashtable->spaceAllowed = space_allowed;
@@ -553,14 +557,16 @@ ExecHashTableCreate(HashState *state)
{
MemoryContext oldctx;
+ int cnt = Min(nbatch, nbatch_inmemory);
+
/*
* allocate and initialize the file arrays in hashCxt (not needed for
* parallel case which uses shared tuplestores instead of raw files)
*/
oldctx = MemoryContextSwitchTo(hashtable->spillCxt);
- hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch);
- hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch);
+ hashtable->innerBatchFile = palloc0_array(BufFile *, cnt);
+ hashtable->outerBatchFile = palloc0_array(BufFile *, cnt);
MemoryContextSwitchTo(oldctx);
@@ -661,6 +667,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t *space_allowed,
int *numbuckets,
int *numbatches,
+ int *numbatches_inmemory,
int *num_skew_mcvs)
{
int tupsize;
@@ -669,6 +676,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t bucket_bytes;
size_t max_pointers;
int nbatch = 1;
+ int nbatch_inmemory = 1;
int nbuckets;
double dbuckets;
@@ -811,6 +819,7 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
space_allowed,
numbuckets,
numbatches,
+ numbatches_inmemory,
num_skew_mcvs);
return;
}
@@ -848,11 +857,22 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * See how many batches we can fit into memory (driven mostly by size
+ * of BufFile, with PGAlignedBlock being the largest part of that).
+ * We need one BufFile for inner and outer side, so we count it twice
+ * for each batch, and we stop once we exceed (work_mem/2).
+ */
+ while ((nbatch_inmemory * 2) * sizeof(PGAlignedBlock) * 2
+ <= (work_mem * 1024L / 2))
+ nbatch_inmemory *= 2;
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
*numbuckets = nbuckets;
*numbatches = nbatch;
+ *numbatches_inmemory = nbatch_inmemory;
}
@@ -874,13 +894,21 @@ ExecHashTableDestroy(HashJoinTable hashtable)
*/
if (hashtable->innerBatchFile != NULL)
{
- for (i = 1; i < hashtable->nbatch; i++)
+ int nbatch = Min(hashtable->nbatch, hashtable->nbatch_inmemory);
+
+ for (i = 1; i < nbatch; i++)
{
if (hashtable->innerBatchFile[i])
BufFileClose(hashtable->innerBatchFile[i]);
if (hashtable->outerBatchFile[i])
BufFileClose(hashtable->outerBatchFile[i]);
}
+
+ if (hashtable->innerOverflowFile)
+ BufFileClose(hashtable->innerOverflowFile);
+
+ if (hashtable->outerOverflowFile)
+ BufFileClose(hashtable->outerOverflowFile);
}
/* Release working memory (batchCxt is a child, so it goes away too) */
@@ -923,11 +951,13 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (hashtable->innerBatchFile == NULL)
{
+ int nbatch_tmp = Min(nbatch, hashtable->nbatch_inmemory);
+
MemoryContext oldcxt = MemoryContextSwitchTo(hashtable->spillCxt);
/* we had no file arrays before */
- hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch);
- hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch);
+ hashtable->innerBatchFile = palloc0_array(BufFile *, nbatch_tmp);
+ hashtable->outerBatchFile = palloc0_array(BufFile *, nbatch_tmp);
MemoryContextSwitchTo(oldcxt);
@@ -936,9 +966,12 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
}
else
{
+ int nbatch_tmp = Min(nbatch, hashtable->nbatch_inmemory);
+ int oldnbatch_tmp = Min(oldnbatch, hashtable->nbatch_inmemory);
+
/* enlarge arrays and zero out added entries */
- hashtable->innerBatchFile = repalloc0_array(hashtable->innerBatchFile, BufFile *, oldnbatch, nbatch);
- hashtable->outerBatchFile = repalloc0_array(hashtable->outerBatchFile, BufFile *, oldnbatch, nbatch);
+ hashtable->innerBatchFile = repalloc0_array(hashtable->innerBatchFile, BufFile *, oldnbatch_tmp, nbatch_tmp);
+ hashtable->outerBatchFile = repalloc0_array(hashtable->outerBatchFile, BufFile *, oldnbatch_tmp, nbatch_tmp);
}
hashtable->nbatch = nbatch;
@@ -1008,11 +1041,18 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
}
else
{
+ BufFile **batchFile;
+
/* dump it out */
Assert(batchno > curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ &hashtable->innerOverflowFile);
+
ExecHashJoinSaveTuple(HJTUPLE_MINTUPLE(hashTuple),
hashTuple->hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
hashtable->spaceUsed -= hashTupleSize;
@@ -1673,22 +1713,33 @@ ExecHashTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
+
+ /* Consider increasing number of batches if we filled work_mem. */
if (hashtable->spaceUsed +
- hashtable->nbuckets_optimal * sizeof(HashJoinTuple)
+ hashtable->nbuckets_optimal * sizeof(HashJoinTuple) +
+ Min(hashtable->nbatch, hashtable->nbatch_inmemory) * sizeof(PGAlignedBlock) * 2 /* inner + outer */
> hashtable->spaceAllowed)
ExecHashIncreaseNumBatches(hashtable);
}
else
{
+ BufFile **batchFile;
+
/*
* put the tuple into a temp file for later batches
*/
Assert(batchno > hashtable->curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ &hashtable->innerOverflowFile);
+
ExecHashJoinSaveTuple(tuple,
hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
}
@@ -1843,6 +1894,100 @@ ExecHashGetBucketAndBatch(HashJoinTable hashtable,
}
}
+int
+ExecHashGetBatchIndex(HashJoinTable hashtable, int batchno)
+{
+ int slice,
+ curslice;
+
+ if (hashtable->nbatch <= hashtable->nbatch_inmemory)
+ return batchno;
+
+ slice = batchno / hashtable->nbatch_inmemory;
+ curslice = hashtable->curbatch / hashtable->nbatch_inmemory;
+
+ /* slices can't go backwards */
+ Assert(slice >= curslice);
+
+ /* overflow slice */
+ if (slice > curslice)
+ return -1;
+
+ /* current slice, compute index in the current array */
+ return (batchno % hashtable->nbatch_inmemory);
+}
+
+BufFile **
+ExecHashGetBatchFile(HashJoinTable hashtable, int batchno,
+ BufFile **batchFiles, BufFile **overflowFile)
+{
+ int idx = ExecHashGetBatchIndex(hashtable, batchno);
+
+ if (idx == -1)
+ return overflowFile;
+
+ return &batchFiles[idx];
+}
+
+void
+ExecHashSwitchToNextBatchSlice(HashJoinTable hashtable)
+{
+ memset(hashtable->innerBatchFile, 0,
+ hashtable->nbatch_inmemory * sizeof(BufFile *));
+
+ hashtable->innerBatchFile[0] = hashtable->innerOverflowFile;
+ hashtable->innerOverflowFile = NULL;
+
+ memset(hashtable->outerBatchFile, 0,
+ hashtable->nbatch_inmemory * sizeof(BufFile *));
+
+ hashtable->outerBatchFile[0] = hashtable->outerOverflowFile;
+ hashtable->outerOverflowFile = NULL;
+}
+
+int
+ExecHashSwitchToNextBatch(HashJoinTable hashtable)
+{
+ int batchidx;
+
+ hashtable->curbatch++;
+
+ /* see if we skipped to the next batch slice */
+ batchidx = ExecHashGetBatchIndex(hashtable, hashtable->curbatch);
+
+ /* Can't be -1, current batch is in the current slice by definition. */
+ Assert(batchidx >= 0 && batchidx < hashtable->nbatch_inmemory);
+
+ /*
+ * If we skipped to the next slice of batches, reset the array of files
+ * and use the overflow file as the first batch.
+ */
+ if (batchidx == 0)
+ ExecHashSwitchToNextBatchSlice(hashtable);
+
+ return hashtable->curbatch;
+}
+
+static void
+ExecHashUpdateSpacePeak(HashJoinTable hashtable)
+{
+ Size spaceUsed = hashtable->spaceUsed;
+
+ /* Account for the buckets in spaceUsed (reported in EXPLAIN ANALYZE) */
+ spaceUsed += hashtable->nbuckets * sizeof(HashJoinTuple);
+
+ /* Account for memory used for batch files (inner + outer) */
+ spaceUsed += Min(hashtable->nbatch, hashtable->nbatch_inmemory) *
+ sizeof(PGAlignedBlock) * 2;
+
+ /* Account for slice files (inner + outer) */
+ spaceUsed += (hashtable->nbatch / hashtable->nbatch_inmemory) *
+ sizeof(PGAlignedBlock) * 2;
+
+ if (spaceUsed > hashtable->spacePeak)
+ hashtable->spacePeak = spaceUsed;
+}
+
/*
* ExecScanHashBucket
* scan a hash bucket for matches to the current outer tuple
@@ -2349,8 +2494,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
+ mcvsToUse * sizeof(int);
hashtable->spaceUsedSkew += nbuckets * sizeof(HashSkewBucket *)
+ mcvsToUse * sizeof(int);
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
/*
* Create a skew bucket for each MCV hash value.
@@ -2399,8 +2545,9 @@ ExecHashBuildSkewHash(HashState *hashstate, HashJoinTable hashtable,
hashtable->nSkewBuckets++;
hashtable->spaceUsed += SKEW_BUCKET_OVERHEAD;
hashtable->spaceUsedSkew += SKEW_BUCKET_OVERHEAD;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
}
free_attstatsslot(&sslot);
@@ -2489,8 +2636,10 @@ ExecHashSkewTableInsert(HashJoinTable hashtable,
/* Account for space used, and back off if we've used too much */
hashtable->spaceUsed += hashTupleSize;
hashtable->spaceUsedSkew += hashTupleSize;
- if (hashtable->spaceUsed > hashtable->spacePeak)
- hashtable->spacePeak = hashtable->spaceUsed;
+
+ /* refresh info about peak used memory */
+ ExecHashUpdateSpacePeak(hashtable);
+
while (hashtable->spaceUsedSkew > hashtable->spaceAllowedSkew)
ExecHashRemoveNextSkewBucket(hashtable);
@@ -2569,10 +2718,17 @@ ExecHashRemoveNextSkewBucket(HashJoinTable hashtable)
}
else
{
+ BufFile **batchFile;
+
/* Put the tuple into a temp file for later batches */
Assert(batchno > hashtable->curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->innerBatchFile,
+ &hashtable->innerOverflowFile);
+
ExecHashJoinSaveTuple(tuple, hashvalue,
- &hashtable->innerBatchFile[batchno],
+ batchFile,
hashtable);
pfree(hashTuple);
hashtable->spaceUsed -= tupleSize;
@@ -2750,6 +2906,8 @@ ExecHashAccumInstrumentation(HashInstrumentation *instrument,
hashtable->nbatch);
instrument->nbatch_original = Max(instrument->nbatch_original,
hashtable->nbatch_original);
+ instrument->nbatch_inmemory = Min(hashtable->nbatch,
+ hashtable->nbatch_inmemory);
instrument->space_peak = Max(instrument->space_peak,
hashtable->spacePeak);
}
diff --git a/src/backend/executor/nodeHashjoin.c b/src/backend/executor/nodeHashjoin.c
index ea0045bc0f3..2f5d94653e8 100644
--- a/src/backend/executor/nodeHashjoin.c
+++ b/src/backend/executor/nodeHashjoin.c
@@ -481,6 +481,7 @@ ExecHashJoinImpl(PlanState *pstate, bool parallel)
if (batchno != hashtable->curbatch &&
node->hj_CurSkewBucketNo == INVALID_SKEW_BUCKET_NO)
{
+ BufFile **batchFile;
bool shouldFree;
MinimalTuple mintuple = ExecFetchSlotMinimalTuple(outerTupleSlot,
&shouldFree);
@@ -491,8 +492,13 @@ ExecHashJoinImpl(PlanState *pstate, bool parallel)
*/
Assert(parallel_state == NULL);
Assert(batchno > hashtable->curbatch);
+
+ batchFile = ExecHashGetBatchFile(hashtable, batchno,
+ hashtable->outerBatchFile,
+ &hashtable->outerOverflowFile);
+
ExecHashJoinSaveTuple(mintuple, hashvalue,
- &hashtable->outerBatchFile[batchno],
+ batchFile,
hashtable);
if (shouldFree)
@@ -1030,17 +1036,19 @@ ExecHashJoinOuterGetTuple(PlanState *outerNode,
}
else if (curbatch < hashtable->nbatch)
{
- BufFile *file = hashtable->outerBatchFile[curbatch];
+ BufFile **file = ExecHashGetBatchFile(hashtable, curbatch,
+ hashtable->outerBatchFile,
+ &hashtable->outerOverflowFile);
/*
* In outer-join cases, we could get here even though the batch file
* is empty.
*/
- if (file == NULL)
+ if (*file == NULL)
return NULL;
slot = ExecHashJoinGetSavedTuple(hjstate,
- file,
+ *file,
hashvalue,
hjstate->hj_OuterTupleSlot);
if (!TupIsNull(slot))
@@ -1135,9 +1143,18 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
BufFile *innerFile;
TupleTableSlot *slot;
uint32 hashvalue;
+ int batchidx;
+ int curbatch_old;
nbatch = hashtable->nbatch;
curbatch = hashtable->curbatch;
+ curbatch_old = curbatch;
+
+ /* index of the old batch */
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
+
+ /* has to be in the current slice of batches */
+ Assert(batchidx >= 0 && batchidx < hashtable->nbatch_inmemory);
if (curbatch > 0)
{
@@ -1145,9 +1162,9 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* We no longer need the previous outer batch file; close it right
* away to free disk space.
*/
- if (hashtable->outerBatchFile[curbatch])
- BufFileClose(hashtable->outerBatchFile[curbatch]);
- hashtable->outerBatchFile[curbatch] = NULL;
+ if (hashtable->outerBatchFile[batchidx])
+ BufFileClose(hashtable->outerBatchFile[batchidx]);
+ hashtable->outerBatchFile[batchidx] = NULL;
}
else /* we just finished the first batch */
{
@@ -1182,45 +1199,50 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* scan, we have to rescan outer batches in case they contain tuples that
* need to be reassigned.
*/
- curbatch++;
+ curbatch = ExecHashSwitchToNextBatch(hashtable);
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
+
while (curbatch < nbatch &&
- (hashtable->outerBatchFile[curbatch] == NULL ||
- hashtable->innerBatchFile[curbatch] == NULL))
+ (hashtable->outerBatchFile[batchidx] == NULL ||
+ hashtable->innerBatchFile[batchidx] == NULL))
{
- if (hashtable->outerBatchFile[curbatch] &&
+ if (hashtable->outerBatchFile[batchidx] &&
HJ_FILL_OUTER(hjstate))
break; /* must process due to rule 1 */
- if (hashtable->innerBatchFile[curbatch] &&
+ if (hashtable->innerBatchFile[batchidx] &&
HJ_FILL_INNER(hjstate))
break; /* must process due to rule 1 */
- if (hashtable->innerBatchFile[curbatch] &&
+ if (hashtable->innerBatchFile[batchidx] &&
nbatch != hashtable->nbatch_original)
break; /* must process due to rule 2 */
- if (hashtable->outerBatchFile[curbatch] &&
+ if (hashtable->outerBatchFile[batchidx] &&
nbatch != hashtable->nbatch_outstart)
break; /* must process due to rule 3 */
/* We can ignore this batch. */
/* Release associated temp files right away. */
- if (hashtable->innerBatchFile[curbatch])
- BufFileClose(hashtable->innerBatchFile[curbatch]);
- hashtable->innerBatchFile[curbatch] = NULL;
- if (hashtable->outerBatchFile[curbatch])
- BufFileClose(hashtable->outerBatchFile[curbatch]);
- hashtable->outerBatchFile[curbatch] = NULL;
- curbatch++;
+ if (hashtable->innerBatchFile[batchidx])
+ BufFileClose(hashtable->innerBatchFile[batchidx]);
+ hashtable->innerBatchFile[batchidx] = NULL;
+ if (hashtable->outerBatchFile[batchidx])
+ BufFileClose(hashtable->outerBatchFile[batchidx]);
+ hashtable->outerBatchFile[batchidx] = NULL;
+
+ curbatch = ExecHashSwitchToNextBatch(hashtable);
+ batchidx = ExecHashGetBatchIndex(hashtable, curbatch);
}
if (curbatch >= nbatch)
+ {
+ hashtable->curbatch = curbatch_old;
return false; /* no more batches */
-
- hashtable->curbatch = curbatch;
+ }
/*
* Reload the hash table with the new inner batch (which could be empty)
*/
ExecHashTableReset(hashtable);
- innerFile = hashtable->innerBatchFile[curbatch];
+ innerFile = hashtable->innerBatchFile[batchidx];
if (innerFile != NULL)
{
@@ -1246,15 +1268,15 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
* needed
*/
BufFileClose(innerFile);
- hashtable->innerBatchFile[curbatch] = NULL;
+ hashtable->innerBatchFile[batchidx] = NULL;
}
/*
* Rewind outer batch file (if present), so that we can start reading it.
*/
- if (hashtable->outerBatchFile[curbatch] != NULL)
+ if (hashtable->outerBatchFile[batchidx] != NULL)
{
- if (BufFileSeek(hashtable->outerBatchFile[curbatch], 0, 0, SEEK_SET))
+ if (BufFileSeek(hashtable->outerBatchFile[batchidx], 0, 0, SEEK_SET))
ereport(ERROR,
(errcode_for_file_access(),
errmsg("could not rewind hash-join temporary file")));
diff --git a/src/backend/optimizer/path/costsize.c b/src/backend/optimizer/path/costsize.c
index c36687aa4df..6f40600d10b 100644
--- a/src/backend/optimizer/path/costsize.c
+++ b/src/backend/optimizer/path/costsize.c
@@ -4173,6 +4173,7 @@ initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
int num_hashclauses = list_length(hashclauses);
int numbuckets;
int numbatches;
+ int numbatches_inmemory;
int num_skew_mcvs;
size_t space_allowed; /* unused */
@@ -4227,6 +4228,7 @@ initial_cost_hashjoin(PlannerInfo *root, JoinCostWorkspace *workspace,
&space_allowed,
&numbuckets,
&numbatches,
+ &numbatches_inmemory,
&num_skew_mcvs);
/*
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 2d8ed8688cd..c612ae98117 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -320,6 +320,7 @@ typedef struct HashJoinTableData
int *skewBucketNums; /* array indexes of active skew buckets */
int nbatch; /* number of batches */
+ int nbatch_inmemory; /* max number of in-memory batches */
int curbatch; /* current batch #; 0 during 1st pass */
int nbatch_original; /* nbatch when we started inner scan */
@@ -331,6 +332,9 @@ typedef struct HashJoinTableData
double partialTuples; /* # tuples obtained from inner plan by me */
double skewTuples; /* # tuples inserted into skew tuples */
+ BufFile *innerOverflowFile; /* temp file for overflow batch batch */
+ BufFile *outerOverflowFile; /* temp file for overflow batch batch */
+
/*
* These arrays are allocated for the life of the hash join, but only if
* nbatch > 1. A file is opened only when we first write a tuple into it
diff --git a/src/include/executor/nodeHash.h b/src/include/executor/nodeHash.h
index e4eb7bc6359..fc82ea43c3b 100644
--- a/src/include/executor/nodeHash.h
+++ b/src/include/executor/nodeHash.h
@@ -16,6 +16,7 @@
#include "access/parallel.h"
#include "nodes/execnodes.h"
+#include "storage/buffile.h"
struct SharedHashJoinBatch;
@@ -46,6 +47,11 @@ extern void ExecHashGetBucketAndBatch(HashJoinTable hashtable,
uint32 hashvalue,
int *bucketno,
int *batchno);
+extern int ExecHashGetBatchIndex(HashJoinTable hashtable, int batchno);
+extern BufFile **ExecHashGetBatchFile(HashJoinTable hashtable, int batchno,
+ BufFile **files, BufFile **overflow);
+extern void ExecHashSwitchToNextBatchSlice(HashJoinTable hashtable);
+extern int ExecHashSwitchToNextBatch(HashJoinTable hashtable);
extern bool ExecScanHashBucket(HashJoinState *hjstate, ExprContext *econtext);
extern bool ExecParallelScanHashBucket(HashJoinState *hjstate, ExprContext *econtext);
extern void ExecPrepHashTableForUnmatched(HashJoinState *hjstate);
@@ -62,6 +68,7 @@ extern void ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
size_t *space_allowed,
int *numbuckets,
int *numbatches,
+ int *numbatches_inmemory,
int *num_skew_mcvs);
extern int ExecHashGetSkewBucket(HashJoinTable hashtable, uint32 hashvalue);
extern void ExecHashEstimate(HashState *node, ParallelContext *pcxt);
diff --git a/src/include/nodes/execnodes.h b/src/include/nodes/execnodes.h
index 1590b643920..eb8eaea89ab 100644
--- a/src/include/nodes/execnodes.h
+++ b/src/include/nodes/execnodes.h
@@ -2755,6 +2755,7 @@ typedef struct HashInstrumentation
int nbuckets_original; /* planned number of buckets */
int nbatch; /* number of batches at end of execution */
int nbatch_original; /* planned number of batches */
+ int nbatch_inmemory; /* number of batches kept in memory */
Size space_peak; /* peak memory usage in bytes */
} HashInstrumentation;
--
2.47.1
Re: Adjusting hash join memory limit to handle batch explosion
Hi,
I kept thinking about this, thinking about alternative approaches, and
also about how hashjoin limits memory in general.
First, I want to discuss one thing I tried, but I think it does not
really work. The annoying part about the "memory rebalance" patch is
that it relaxes the memory limit. However, the memory limit is a lie,
because we enforce it by adding batches, and that unfortunately is not
free - each batch is a BufFile with BLCKSZ buffer, so while we may
succeed in keeping the hash table in work_mem, we may end up with the
batches using gigabytes of memory - which is not reported in EXPLAIN,
but it's still allocated. This does happen even without the hash
explosion, the hash explosion is just an extreme version.
There's no way to work around this ... as long as we use BufFiles. What
if we used plain File(s), without the buffering? Then the per-batch cost
would be considerably lower. Of course, this would be acceptable only if
not having the buffering has acceptable impact on performance. That
seemed unlikely, but I decided to give it a try - see a PoC of this in
the attached files-poc-patches.tgz (patch 0001).
Unfortunately, the impact does not seem acceptable - it does enforce the
limit, but the lack of buffering does make a huge difference, making it
~2x slower depending on the query.
I experimented a bit with a cross-file buffer, much smaller than the sum
of BufFile buffers, but still allowing combining writes into larger
chunks. Imagine you have a buffer large enough for (nbatch * 2) tuples,
then we may expect writing two tuples at once into each batch file.
The 0002 patch in the PoC series tries to do this, but it does not
really help, and in some cases it's doing even worse than 0001, because
the cost of maintaining the shared buffer increases with the number of
batches. I'm sure some of this is my fault, and it could be improved and
optimized quite a bit.
I decided not to do that, because this experiment made me realize that:
a) The buffer would need to grow with the number of batches, to have any
chance of combining the writes. If we want to combine K tuples into a
single write (on average), we'd need the buffer to keep (nbatches * K)
tuples, and once the nbatches gets large (because who cares about
BufFile allocations with a handful of batches), that may be a lot of
memory. Say we get to 32k batches (which is not that hard), and we want
to keep 16 tuples, each 128B, that's ~64MB. Not a huge amount, and much
less than the 512MB we'd need for batches. But still, it means we're not
really enforcing the memory limit - which was the point of using files
without buffering.
b) It does enforce the limit on the hash table itself, though. And that
is actually not great, because it means it can't possibly help with the
batch explosion, caused by a single "indivisible" batch.
c) It's pretty invasive.
So I still think adjusting the memory as we're adding batches seems like
a better approach. The question is where to do the adjustments, based on
what logic ...
I think the general idea and formula explained in [1]/messages/by-id/7bed6c08-72a0-4ab9-a79c-e01fcdd0940f@vondra.me is right, but
while working on the PoC patch I started to think about how to formalize
this. And I ended up creating two tables that I think visualize is
pretty nicely.
Imagine a table (in the spreadsheet sense), with work_mem values in rows
and nbatch values in columns. And the cell is "total memory" used to
execute such hash join, i.e.
work_mem + (2 * nbatches * 8K)
(Yes, there's a multiplier for the hash table size, but I use work_mem
for simplicity.) This is what the two attached PDF files show,
highlighting two interesting patterns, so let's talk about that.
1) hash-memory-model-1.pdf
Imagine you're executing a hash join - you're in a particular cell of
the table. And we've reached the current memory limit, i.e. we've filled
the hash table, and need to do something. The only solution is to
"expand" the expected "total hash size" (nbatch * hash_table_size),
which we do by simply doubling the number of batches. And often that's
the right thing to do.
For example, let's say we're running with work_mem=4MB and nbatch=16,
we've filled the hash table and are using 4336kB of memory (a little bit
more than work_mem). If we double the number of batches, we may use up
to 4352kB of memory in the next cycle. And that's fine.
But hey, there's another way to double the "total hash size" - allowing
the in-memory hash table to be twice as large. In the above case, that
would be wrong, because doubling work_mem would use up to 8432kB.
So in this case it's clearly right to double the number of batches,
because that minimizes the total memory used in the next step.
However, consider for example the cell with work_mem=4MB, nbatch=8192.
We're using 135MB of memory, and need to decide what to do. Doubling the
batches means we'll use up to 266MB. But doubling work_mem increases the
memory use only to 139MB.
This is what the green/red in the table means. Green means "better to
double nbatch" while red is "better to double work_mem". And clearly,
the table is split into two regions, separated by the diagonal.
The diagonal is the "optimal" path - if you start in any cell, the
red/green decisions will get you to the diagonal, and then along it.
The patch [1]/messages/by-id/7bed6c08-72a0-4ab9-a79c-e01fcdd0940f@vondra.me aims to do this, but I think this visual explanation is
much clearer than anything in that patch.
2) hash-memory-model-2.pdf
I've also asked if maybe the patch should do something about the choice
of initial nbatch value, which gets me to the second PDF.
Imagine we know the total amount of table in the Hash node is 1GB. There
are different ways to split that into batches. If we have enough memory,
we could do hash join without batching. With work_mem=1MB we'll need to
split this into 1024 batches, or we might do work_mem=2MB with only 512
batches. And so on - we're moving along the anti-diagonal.
The point is that while this changes the work_mem, this can have pretty
non-intuitive impact on total memory use. For example, with wm=1MB we
actually use 17MB of memory, while with wm=2MB we use only 10MB.
But each anti-diagonal has a minimum - the value on the diagonal. I
believe this is the "optimal starting cell" for the hash join. If we
don't pick this, the rules explained in (1) will eventually get us to
the diagonal anyway.
A different visualization is in the attached SVG, which is a surface
plot / heat map of the total memory use. It shows that there really is a
"valley" of minimal values on the diagonal, and that the growth for
doubling batches is much steeper than for doubling work_mem.
Attached is an "adjust-size" patch implementing this. In the end it has
pretty much the same effect as the patch in [1]/messages/by-id/7bed6c08-72a0-4ab9-a79c-e01fcdd0940f@vondra.me, except that it's much
simpler - everything important happens in just two simple blocks, one in
ExecChooseHashTableSize(), the other in ExecHashIncreaseNumBatches().
There's a bit of complexity, because if we allow growing the size of the
in-memory hash table, we probably need to allow increasing number of
buckets. But that's not possible with how we split the hashvalue now, so
the patch adjusts that by reversing the hashvalue bits when calculating
the batch. I'm not sure if this is the best way to do this, there might
well be a better solution.
I admit all of this seemed a bit weird / wrong initially, because it
feels like giving up the memory limit. But the simple truth is that
memory limit is pretty much just a lie - the fact that we only show the
hash table size in EXPLAIN does not mean we're not using gigabytes more
memory, we're just not making it clear. So I'd argue this actually does
a better job in limiting memory usage.
When thinking about reasons why doubling the work_mem might not be the
right thing, I can think of one case - CPU caches. IIRC it may be much
faster to do the lookups if the hash is small enough to fit into L3, and
and doubling this might work against the goal, although I'm not sure how
bad the impact may be. In the batch explosion case it surely doesn't
matter - the cost of spilling/loading many files is much higher. But for
regular (well estimated) cases it might have negative impact.
This is why the patch only adjusts the initial parameters in the "red"
area, not in the green. Maybe it should be a bit more conservative and
only kick in when nbatch value above some threshold.
I'd appreciate opinions and alternative ideas about this.
I'm also attaching the data + SQL script I use to trigger the batch
explosion with up to 2M batches.
regards
[1]: /messages/by-id/7bed6c08-72a0-4ab9-a79c-e01fcdd0940f@vondra.me
/messages/by-id/7bed6c08-72a0-4ab9-a79c-e01fcdd0940f@vondra.me
--
Tomas Vondra
Attachments:
vadjust-size-0001-hashjoin-sizing-balance.patchtext/x-patch; charset=UTF-8; name=vadjust-size-0001-hashjoin-sizing-balance.patchDownload
From 20d56eb519d5785160b333bbbd1bb9d909529a5c Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:24:23 +0100
Subject: [PATCH vadjust-size 1/2] hashjoin sizing balance
---
src/backend/executor/nodeHash.c | 113 +++++++++++++++++++++++++++++++-
1 file changed, 111 insertions(+), 2 deletions(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..271ffeb2228 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -848,6 +848,37 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * Optimize the total amount of memory consumed by the hash, including
+ * the buffer files. It may be more efficient to use fewer large batches.
+ *
+ * Try to move on the anti-diagonal and see if we'd consume less memory.
+ * If yes, do that, and also adjust the bucket counts. We only move in
+ * the direction of fewer batches, because that part of the "matrix" has
+ * the most issues with memory consumption.
+ *
+ * We might try this for the other direction too, but only as long as
+ * the memory consumption is more than the work_mem value. It can only
+ * be less than 2x worse than the ideal memory allowance, and after one
+ * step we'll get on the "boundary" I think.
+ */
+ while (nbatch > 0)
+ {
+ /* how much memory would we use with half the batches? */
+ size_t space = hash_table_bytes * 2 + (nbatch * BLCKSZ);
+ size_t current = hash_table_bytes + (2 * nbatch * BLCKSZ);
+
+ /* we're already optimal */
+ if (current < space)
+ break;
+
+ /* better to use fewer larger batches */
+ *space_allowed = *space_allowed * 2;
+
+ nbatch /= 2;
+ nbuckets *= 2;
+ }
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
@@ -943,6 +974,29 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
hashtable->nbatch = nbatch;
+ /*
+ * Consider adjusting the allowed batch size, depending on the number of
+ * batches, to minimize the overall amount of memory consumed - for both
+ * the hash table and batch files.
+ *
+ * XXX If we increase the batch size, we should also adjust the optimal
+ * number of buckets. But we can't do that, because right now we split
+ * the hash into batch/bucket like this [...|batch|bucket] so adding
+ * bits to bucket changes the batch in a way that breaks the spilling
+ * (the batchno could move "backwards, but we assume that can't happen).
+ */
+ {
+ size_t optimalSpace = (nbatch * 2 * BLCKSZ);
+
+ while (hashtable->spaceAllowed < optimalSpace)
+ {
+ hashtable->spaceAllowed *= 2;
+
+ hashtable->nbuckets_optimal *= 2;
+ hashtable->log2_nbuckets_optimal++;
+ }
+ }
+
/*
* Scan through the existing hash table entries and dump out any that are
* no longer of the current batch.
@@ -1793,6 +1847,62 @@ ExecParallelHashTableInsertCurrentBatch(HashJoinTable hashtable,
heap_free_minimal_tuple(tuple);
}
+static inline unsigned char
+reverse_byte(unsigned char x)
+{
+ static const unsigned char table[] = {
+ 0x00, 0x80, 0x40, 0xc0, 0x20, 0xa0, 0x60, 0xe0,
+ 0x10, 0x90, 0x50, 0xd0, 0x30, 0xb0, 0x70, 0xf0,
+ 0x08, 0x88, 0x48, 0xc8, 0x28, 0xa8, 0x68, 0xe8,
+ 0x18, 0x98, 0x58, 0xd8, 0x38, 0xb8, 0x78, 0xf8,
+ 0x04, 0x84, 0x44, 0xc4, 0x24, 0xa4, 0x64, 0xe4,
+ 0x14, 0x94, 0x54, 0xd4, 0x34, 0xb4, 0x74, 0xf4,
+ 0x0c, 0x8c, 0x4c, 0xcc, 0x2c, 0xac, 0x6c, 0xec,
+ 0x1c, 0x9c, 0x5c, 0xdc, 0x3c, 0xbc, 0x7c, 0xfc,
+ 0x02, 0x82, 0x42, 0xc2, 0x22, 0xa2, 0x62, 0xe2,
+ 0x12, 0x92, 0x52, 0xd2, 0x32, 0xb2, 0x72, 0xf2,
+ 0x0a, 0x8a, 0x4a, 0xca, 0x2a, 0xaa, 0x6a, 0xea,
+ 0x1a, 0x9a, 0x5a, 0xda, 0x3a, 0xba, 0x7a, 0xfa,
+ 0x06, 0x86, 0x46, 0xc6, 0x26, 0xa6, 0x66, 0xe6,
+ 0x16, 0x96, 0x56, 0xd6, 0x36, 0xb6, 0x76, 0xf6,
+ 0x0e, 0x8e, 0x4e, 0xce, 0x2e, 0xae, 0x6e, 0xee,
+ 0x1e, 0x9e, 0x5e, 0xde, 0x3e, 0xbe, 0x7e, 0xfe,
+ 0x01, 0x81, 0x41, 0xc1, 0x21, 0xa1, 0x61, 0xe1,
+ 0x11, 0x91, 0x51, 0xd1, 0x31, 0xb1, 0x71, 0xf1,
+ 0x09, 0x89, 0x49, 0xc9, 0x29, 0xa9, 0x69, 0xe9,
+ 0x19, 0x99, 0x59, 0xd9, 0x39, 0xb9, 0x79, 0xf9,
+ 0x05, 0x85, 0x45, 0xc5, 0x25, 0xa5, 0x65, 0xe5,
+ 0x15, 0x95, 0x55, 0xd5, 0x35, 0xb5, 0x75, 0xf5,
+ 0x0d, 0x8d, 0x4d, 0xcd, 0x2d, 0xad, 0x6d, 0xed,
+ 0x1d, 0x9d, 0x5d, 0xdd, 0x3d, 0xbd, 0x7d, 0xfd,
+ 0x03, 0x83, 0x43, 0xc3, 0x23, 0xa3, 0x63, 0xe3,
+ 0x13, 0x93, 0x53, 0xd3, 0x33, 0xb3, 0x73, 0xf3,
+ 0x0b, 0x8b, 0x4b, 0xcb, 0x2b, 0xab, 0x6b, 0xeb,
+ 0x1b, 0x9b, 0x5b, 0xdb, 0x3b, 0xbb, 0x7b, 0xfb,
+ 0x07, 0x87, 0x47, 0xc7, 0x27, 0xa7, 0x67, 0xe7,
+ 0x17, 0x97, 0x57, 0xd7, 0x37, 0xb7, 0x77, 0xf7,
+ 0x0f, 0x8f, 0x4f, 0xcf, 0x2f, 0xaf, 0x6f, 0xef,
+ 0x1f, 0x9f, 0x5f, 0xdf, 0x3f, 0xbf, 0x7f, 0xff,
+ };
+
+ return table[x];
+}
+
+static inline uint32
+reverse_uint32(uint32 x)
+{
+ uint32 ret;
+
+ unsigned char *src = (unsigned char *) &x;
+ unsigned char *dst = (unsigned char *) &ret;
+
+ dst[0] = reverse_byte(src[3]);
+ dst[1] = reverse_byte(src[2]);
+ dst[2] = reverse_byte(src[1]);
+ dst[3] = reverse_byte(src[0]);
+
+ return ret;
+}
/*
* ExecHashGetBucketAndBatch
@@ -1833,8 +1943,7 @@ ExecHashGetBucketAndBatch(HashJoinTable hashtable,
if (nbatch > 1)
{
*bucketno = hashvalue & (nbuckets - 1);
- *batchno = pg_rotate_right32(hashvalue,
- hashtable->log2_nbuckets) & (nbatch - 1);
+ *batchno = reverse_uint32(hashvalue) & (nbatch - 1);
}
else
{
--
2.47.1
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Re: Adjusting hash join memory limit to handle batch explosion
Hi Tomas,
Thanks for working on this. I haven't studied this problem recently,
but here are some ideas that occur to me:
1. Try to reduce the per-batch overhead.
2. Stop increasing the number of batches when the per-batch overhead
exceeds a small percentage of work_mem (10%? 5%? 1%?).
If you've reached a point where the per-batch overhead is using up
=10% of your work_mem, then at the next doubling it's going to be
using >=20%, which is pretty insane, and the next doubling after that
is going to be >=40%, which is really silly. For 1MB of work_mem and
what I gather from your remarks is 16kB/batch, we exceed the 10%
threshold at 16 batches. Somebody might claim that capping the number
of batches to 16 is insane, but those 16 batches are using 256kB of
memory and we're supposed to finish the entire operation using <= 1MB
of memory, it really isn't. We pretty obviously are not going to be
able to stay within 1MB no matter what we do.
I think your proposal might be a more refined version of this, where
instead of just completely ceasing to create new batches, you try to
balance creating new batches with overrunning work_mem to get the best
outcome possible overall. Maybe that's a good approach, although
perhaps it is more complicated than we need? At any rate, I found the
vadjust-size patch to be quite hard to understand. I think you if you
want to go that route it would need more comments and to have the
existing ones rewritten so that they are understandable without
needing to scour this email thread (e.g. "Try to move on the
anti-diagonal and see if we'd consume less memory" doesn't seem like
something most people are going to understand without a lot of
context).
...Robert
Re: Adjusting hash join memory limit to handle batch explosion
On 1/6/25 16:42, Robert Haas wrote:
Hi Tomas,
Thanks for working on this. I haven't studied this problem recently,
but here are some ideas that occur to me:1. Try to reduce the per-batch overhead.
Yeah. The "use files without buffering" approach may be seen as an
extreme version of this, but it didn't perform well enough. The "shared"
buffering was an attempt to have a buffer that doesn't need to scale
linearly with the number of batches, but that has issues too (I'm sure
some of that is due to my faults in the PoC patch).
I wonder if maybe a better solution would be to allow BufFiles with
smaller buffers, not just hard-coded 8kB. OTOH I'm not sure how much
that helps, before the buffering stops being effective as the buffer
gets smaller. I mean, we only have 8kB buffer, so if we cut the buffer
in half for every nbatch doubling, we'd be down to 1B after 13 rounds
(but the buffer is useless once it gets too small to hold multiple
tuples, it's only like 5 cycles).
Maybe it'd still work well enough if we only did that for large nbatch
values, and ensured the buffer can't get too small (say, less than 1kB).
But that only gives 3 doubling cycles - i.e. instead of 8GB of memory
we'd only use 1GB. That's an improvement, but also not very different
from what the "balancing" achieves, except that it's way more invasive
and complex.
2. Stop increasing the number of batches when the per-batch overhead
exceeds a small percentage of work_mem (10%? 5%? 1%?).If you've reached a point where the per-batch overhead is using up
=10% of your work_mem, then at the next doubling it's going to be
using >=20%, which is pretty insane, and the next doubling after that
is going to be >=40%, which is really silly. For 1MB of work_mem and
what I gather from your remarks is 16kB/batch, we exceed the 10%
threshold at 16 batches. Somebody might claim that capping the number
of batches to 16 is insane, but those 16 batches are using 256kB of
memory and we're supposed to finish the entire operation using <= 1MB
of memory, it really isn't. We pretty obviously are not going to be
able to stay within 1MB no matter what we do.
Agreed.
I think your proposal might be a more refined version of this, where
instead of just completely ceasing to create new batches, you try to
balance creating new batches with overrunning work_mem to get the best
outcome possible overall. Maybe that's a good approach, although
perhaps it is more complicated than we need? At any rate, I found the
vadjust-size patch to be quite hard to understand. I think you if you
want to go that route it would need more comments and to have the
existing ones rewritten so that they are understandable without
needing to scour this email thread (e.g. "Try to move on the
anti-diagonal and see if we'd consume less memory" doesn't seem like
something most people are going to understand without a lot of
context).
Yes, the proposal does essentially this. And you're certainly right some
of the comments are hard to understand without reading some of the
thread, so that would need to improve.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Mon, Jan 6, 2025 at 11:51 AM Tomas Vondra <tomas@vondra.me> wrote:
I wonder if maybe a better solution would be to allow BufFiles with
smaller buffers, not just hard-coded 8kB. OTOH I'm not sure how much
that helps, before the buffering stops being effective as the buffer
gets smaller. I mean, we only have 8kB buffer, so if we cut the buffer
in half for every nbatch doubling, we'd be down to 1B after 13 rounds
(but the buffer is useless once it gets too small to hold multiple
tuples, it's only like 5 cycles).
I was more thinking about whether we need to keep all of those buffers
around all the time. If the number of batches doesn't increase, then
after we finish moving things into batches they should never need to
be moved into a different batch. If it does, then things are
different, but for example if we initially plan on 64 batches and then
later decide we need 256 batches, we should really only need 3 buffers
at a time, except for the initial work during batch 0. (In this
example, a tuple that is initially assigned to batch 1 might need to
be moved to batch 65, 129, or 193, but it can't need to go anywhere
else.)
But I don't quite know how we could avoid needing all the buffers at
once during batch 0. That said, it's questionable whether it really
make sense to have an initial number of batches that is very large.
Does partitioning the input data into 64k batches really make sense,
or would it be more efficient to partition it 256 ways initially and
then do a second pass over each of those to split them up another 256
ways? It's a lot more I/O, but trying to split 64k ways at once is
presumably going to thrash the File table as well as do a lot of
completely random physical I/O, so maybe it's worth considering.
Another thought is that, if we really do want to partition 64k ways
all at once, maybe 16kb set aside for each batch is not the right
approach. 64k batches * 16kB/buffer = 1GB, but if we have 1GB of
memory available for partitioning, wouldn't it make sense to read a
gigabyte of tuples, sort them by batch #, and then open each file that
needs to get at least 1 tuple, write all the tuples into that file,
and close it? This seems more scalable than what we do today, because
it doesn't require a certain amount of memory per batch. The
performance might not be great if you're using a really small amount
of memory for a really large number of batches, but it might still be
better than the current algorithm, which could easily leave a lot of
that memory idling a lot of the time.
Said another way, I think the current algorithm is optimized for small
numbers of batches. Repeatedly filling and flushing a 16kB buffer
makes sense if the number of buffers isn't that big so that flushes
are regular and a buffer is typically going to spend a lot of its time
approximately half full. But when the number of batches becomes large,
buffers will start to be flushed less and less often, especially if
there is skew in the data but to some degree even if there isn't. Any
buffer that sits there for "a long time" -- whatever that means
exactly -- without getting flushed is not a good use of memory.
I'm just spitballing here. Don't confuse anything in this email with a
demand for you to do something different than you are.
--
Robert Haas
EDB: http://www.enterprisedb.com
Re: Adjusting hash join memory limit to handle batch explosion
On 1/6/25 19:50, Robert Haas wrote:
On Mon, Jan 6, 2025 at 11:51 AM Tomas Vondra <tomas@vondra.me> wrote:
I wonder if maybe a better solution would be to allow BufFiles with
smaller buffers, not just hard-coded 8kB. OTOH I'm not sure how much
that helps, before the buffering stops being effective as the buffer
gets smaller. I mean, we only have 8kB buffer, so if we cut the buffer
in half for every nbatch doubling, we'd be down to 1B after 13 rounds
(but the buffer is useless once it gets too small to hold multiple
tuples, it's only like 5 cycles).I was more thinking about whether we need to keep all of those buffers
around all the time. If the number of batches doesn't increase, then
after we finish moving things into batches they should never need to
be moved into a different batch. If it does, then things are
different, but for example if we initially plan on 64 batches and then
later decide we need 256 batches, we should really only need 3 buffers
at a time, except for the initial work during batch 0. (In this
example, a tuple that is initially assigned to batch 1 might need to
be moved to batch 65, 129, or 193, but it can't need to go anywhere
else.)
Right.
But I don't quite know how we could avoid needing all the buffers at
once during batch 0. That said, it's questionable whether it really
make sense to have an initial number of batches that is very large.
Does partitioning the input data into 64k batches really make sense,
or would it be more efficient to partition it 256 ways initially and
then do a second pass over each of those to split them up another 256
ways? It's a lot more I/O, but trying to split 64k ways at once is
presumably going to thrash the File table as well as do a lot of
completely random physical I/O, so maybe it's worth considering.
True, but as soon as you limit the number of batches you could generate,
it's that more or less the same as not enforcing the limit on the amount
of memory consumed by the hash table? Because you have to keep the
tuples that belong to the current batch in memory ...
I suppose you could do this recursively, i.e. split to 256 batches, and
once you can keep the current batch in memory, spill it to disk too. And
then read it from file, and split it into 256 more batches. I think we'd
need to remember the minimum nbatch value for each batch (when it was
created), and then go through all the stages up to current nbatch. But
it could work, I guess.
The thing is - I don't think increasing the work_mem is bad - in fact,
it's exactly the thing that may stop the batch explosion when there are
hash collisions / overlaps. That's what the test script does to trigger
the explosion, although I admit it's an artificial / adversary case. But
similar stuff can happen in PROD, and we blindly increase nbatch when it
can't possibly help, stopping only after running out of hash bits.
Another thought is that, if we really do want to partition 64k ways
all at once, maybe 16kb set aside for each batch is not the right
approach. 64k batches * 16kB/buffer = 1GB, but if we have 1GB of
memory available for partitioning, wouldn't it make sense to read a
gigabyte of tuples, sort them by batch #, and then open each file that
needs to get at least 1 tuple, write all the tuples into that file,
and close it? This seems more scalable than what we do today, because
it doesn't require a certain amount of memory per batch. The
performance might not be great if you're using a really small amount
of memory for a really large number of batches, but it might still be
better than the current algorithm, which could easily leave a lot of
that memory idling a lot of the time.
This is pretty much the idea behind the 0002 patch in the "raw files"
PoC patch, although I tried to use a much smaller batch. Maybe with 1GB
(and better coding than in my PoC patch) it would work better.
Still, if we have 1GB for a buffer, maybe it'd be better to use some of
that for a larger hash table, and not need that many batches ...
Said another way, I think the current algorithm is optimized for small
numbers of batches. Repeatedly filling and flushing a 16kB buffer
makes sense if the number of buffers isn't that big so that flushes
are regular and a buffer is typically going to spend a lot of its time
approximately half full. But when the number of batches becomes large,
buffers will start to be flushed less and less often, especially if
there is skew in the data but to some degree even if there isn't. Any
buffer that sits there for "a long time" -- whatever that means
exactly -- without getting flushed is not a good use of memory.
Right. FWIW I suspect we had similar discussions for the hashagg.
I'm just spitballing here. Don't confuse anything in this email with a
demand for you to do something different than you are.
No, thanks. It's good to have these discussions and be forced to think
about it from a different angle.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Tue, Dec 31, 2024 at 6:07 PM Tomas Vondra <tomas@vondra.me> wrote:
So I decided to revisit the three patches from 2019. Attached are
rebased and cleaned up versions. A couple comments on each one:1) v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patch
I believe this is the way to go, for now. The basic idea is to keep the
overall behavior, but "relax" the memory limit as the number of batches
increases to minimize the total memory use.This may seem a bit weird, but as the number of batches grows there's no
way to not violate the limit. And the current code simply ignores this
and allocates arbitrary amounts of memory.
I'm just catching up on this thread and haven't read all the mails
yet. I started with looking at the patches in the first email and got
a bit confused.
In this patch (v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patch),
I see that you've started accounting for the spill files in
hashtable->spaceUsed -- in the same way that is done for the tuples in
the hashtable. I know the other memory contexts (hashCxt and batchCxt)
in hashtable aren't appropriate for figuring out spaceUsed, but I was
wondering if the hashtable->spillCxt accurately reflects how much
memory is being used for these spill files at one time? Perhaps it
doesn't make sense to use this, but when we added it in 8c4040edf45, I
thought we might one day be able to use it for determining peak space
usage. Or perhaps you are imagining it only be used for observability?
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 1/9/25 17:17, Melanie Plageman wrote:
On Tue, Dec 31, 2024 at 6:07 PM Tomas Vondra <tomas@vondra.me> wrote:
So I decided to revisit the three patches from 2019. Attached are
rebased and cleaned up versions. A couple comments on each one:1) v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patch
I believe this is the way to go, for now. The basic idea is to keep the
overall behavior, but "relax" the memory limit as the number of batches
increases to minimize the total memory use.This may seem a bit weird, but as the number of batches grows there's no
way to not violate the limit. And the current code simply ignores this
and allocates arbitrary amounts of memory.I'm just catching up on this thread and haven't read all the mails
yet. I started with looking at the patches in the first email and got
a bit confused.In this patch (v20241231-adjust-limit-0001-Account-for-batch-files-in-ha.patch),
I see that you've started accounting for the spill files in
hashtable->spaceUsed -- in the same way that is done for the tuples in
the hashtable. I know the other memory contexts (hashCxt and batchCxt)
in hashtable aren't appropriate for figuring out spaceUsed, but I was
wondering if the hashtable->spillCxt accurately reflects how much
memory is being used for these spill files at one time? Perhaps it
doesn't make sense to use this, but when we added it in 8c4040edf45, I
thought we might one day be able to use it for determining peak space
usage. Or perhaps you are imagining it only be used for observability?
Good question. Yes, the patch from 12/31 does look at all the memory,
including the batch files. I thought about using the spillCtx too, but I
don't think it it would work because the context tracks *current* memory
usage, and we're interested in how much memory would be used *after*
doubling the number of batches.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Tue, Dec 31, 2024 at 6:07 PM Tomas Vondra <tomas@vondra.me> wrote:
This means that ultimately it's either (1) or (3), and the more I've
been looking into this the more I prefer (1), for a couple reasons:* It's much simpler (it doesn't really change anything on the basic
behavior, doesn't introduce any new files or anything like that.* There doesn't seem to be major difference in total memory consumption
between the two approaches. Both allow allocating more memory.* It actually helps with the "indivisible batch" case - it relaxes the
limit, so there's a chance the batch eventually fits and we stop adding
more and more batches. With spill files that's not the case - we still
keep the original limit, and we end up with the batch explosion (but
then we handle it much more efficiently).
Okay, I've read all the patches proposed in this mail and most of the
downthread ideas, and I want to cast my vote for option 1.
I find the design of 3 too complicated for what it buys us.
The slices make it harder to understand how the system works. The
current 1-1 relationship in master between batches and spill files is
easier to reason about. With the slices, I'm imagining trying to
understand why we, for example, have to move tuples from batch 4 just
because batch 5 got too big for the hashtable.
I think something like this might be worth it if it solved the problem
entirely, but if it is just a somewhat better coping mechanism, I
don't think it is worth it.
I was excited about your raw file experiment. As Robert and you point
out -- we may need a file per batch, but for most of the hash join's
execution we don't need to keep buffers for each batch around.
However, given that the experiment didn't yield great results and we
haven't come up with an alternative solution with sufficiently few
flaws, I'm still in favor of 1.
The part of 1 I struggled to understand is the math in
ExecHashExceededMemoryLimit(). I think the other email you sent with
the charts and diagonals is about choosing the optimal hashtable size
and number of batches (when to stop growing the number of batches and
just increase the size of the hashtable). So, I'll dive into that.
One thing I'm not sure about yet is whether this needs to tweak the
hashjoin costing to also consider the files when deciding how many
batches to use. Maybe it should?
I think it definitely should. The ExecChooseHashTableSize()
calculations look similar to what we use to calculate spaceAllowed, so
it makes sense that we would consider buffile sizes if we are counting
those in spaceUsed now.
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On Sun, Jan 5, 2025 at 10:00 PM Tomas Vondra <tomas@vondra.me> wrote:
I think the general idea and formula explained in [1] is right, but
while working on the PoC patch I started to think about how to formalize
this. And I ended up creating two tables that I think visualize is
pretty nicely.Imagine a table (in the spreadsheet sense), with work_mem values in rows
and nbatch values in columns. And the cell is "total memory" used to
execute such hash join, i.e.work_mem + (2 * nbatches * 8K)
(Yes, there's a multiplier for the hash table size, but I use work_mem
for simplicity.) This is what the two attached PDF files show,
highlighting two interesting patterns, so let's talk about that.1) hash-memory-model-1.pdf
Imagine you're executing a hash join - you're in a particular cell of
the table. And we've reached the current memory limit, i.e. we've filled
the hash table, and need to do something. The only solution is to
"expand" the expected "total hash size" (nbatch * hash_table_size),
which we do by simply doubling the number of batches. And often that's
the right thing to do.For example, let's say we're running with work_mem=4MB and nbatch=16,
we've filled the hash table and are using 4336kB of memory (a little bit
more than work_mem). If we double the number of batches, we may use up
to 4352kB of memory in the next cycle. And that's fine.
If you double the number of batches, isn't that an additional 32 files
with 8kB each -- so 256kB more memory (not 16 kB)?
But hey, there's another way to double the "total hash size" - allowing
the in-memory hash table to be twice as large. In the above case, that
would be wrong, because doubling work_mem would use up to 8432kB.So in this case it's clearly right to double the number of batches,
because that minimizes the total memory used in the next step.However, consider for example the cell with work_mem=4MB, nbatch=8192.
We're using 135MB of memory, and need to decide what to do. Doubling the
batches means we'll use up to 266MB. But doubling work_mem increases the
memory use only to 139MB.
Right, it makes sense to use this as the basis for deciding whether or
not to increase nbatches.
This is what the green/red in the table means. Green means "better to
double nbatch" while red is "better to double work_mem". And clearly,
the table is split into two regions, separated by the diagonal.The diagonal is the "optimal" path - if you start in any cell, the
red/green decisions will get you to the diagonal, and then along it.The patch [1] aims to do this, but I think this visual explanation is
much clearer than anything in that patch.
Yes, the visual is great, thanks!
2) hash-memory-model-2.pdf
I've also asked if maybe the patch should do something about the choice
of initial nbatch value, which gets me to the second PDF.Imagine we know the total amount of table in the Hash node is 1GB. There
are different ways to split that into batches. If we have enough memory,
we could do hash join without batching. With work_mem=1MB we'll need to
split this into 1024 batches, or we might do work_mem=2MB with only 512
batches. And so on - we're moving along the anti-diagonal.The point is that while this changes the work_mem, this can have pretty
non-intuitive impact on total memory use. For example, with wm=1MB we
actually use 17MB of memory, while with wm=2MB we use only 10MB.But each anti-diagonal has a minimum - the value on the diagonal. I
believe this is the "optimal starting cell" for the hash join. If we
don't pick this, the rules explained in (1) will eventually get us to
the diagonal anyway.
Makes sense.
Attached is an "adjust-size" patch implementing this. In the end it has
pretty much the same effect as the patch in [1], except that it's much
simpler - everything important happens in just two simple blocks, one in
ExecChooseHashTableSize(), the other in ExecHashIncreaseNumBatches().
It's interesting -- since the new patch no longer needs to count
buffile overhead in spaceUsed, spacePeak won't include that overhead.
And ultimately EXPLAIN uses the spacePeak, right?
There's a bit of complexity, because if we allow growing the size of the
in-memory hash table, we probably need to allow increasing number of
buckets. But that's not possible with how we split the hashvalue now, so
the patch adjusts that by reversing the hashvalue bits when calculating
the batch. I'm not sure if this is the best way to do this, there might
well be a better solution.
This part is pretty unpleasant looking (reverse_byte array in the
code). I'll try and think of different ideas. However, I wonder what
other kinds of effects allowing increasing the number of buckets
during execution might have?
I admit all of this seemed a bit weird / wrong initially, because it
feels like giving up the memory limit. But the simple truth is that
memory limit is pretty much just a lie - the fact that we only show the
hash table size in EXPLAIN does not mean we're not using gigabytes more
memory, we're just not making it clear. So I'd argue this actually does
a better job in limiting memory usage.
I guess people can multiply the number of batches * 8kB to get that
extra memory overhead. Maybe we should consider putting that in
EXPLAIN output?
When thinking about reasons why doubling the work_mem might not be the
right thing, I can think of one case - CPU caches. IIRC it may be much
faster to do the lookups if the hash is small enough to fit into L3, and
and doubling this might work against the goal, although I'm not sure how
bad the impact may be. In the batch explosion case it surely doesn't
matter - the cost of spilling/loading many files is much higher. But for
regular (well estimated) cases it might have negative impact.This is why the patch only adjusts the initial parameters in the "red"
area, not in the green. Maybe it should be a bit more conservative and
only kick in when nbatch value above some threshold.
Wait isn't that the opposite of what you are saying? That is, if we
want to keep the hashtable fitting in L3, wouldn't we want to allow
increasing the number of batches even if it uses more memory? That is
the green area. I see your patch does the red -- increase hashtable
size and decrease nbatches if it is better. But that seems
inconsistent with your point about making the hashtable fit in L3.
I'd appreciate opinions and alternative ideas about this.
I really like the overall idea about being principled in the number of
batches vs hashtable size. I think the question about increasing the
number of buckets and how to do it (during execution) is important to
figure out a good way of doing.
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 1/9/25 23:18, Melanie Plageman wrote:
On Sun, Jan 5, 2025 at 10:00 PM Tomas Vondra <tomas@vondra.me> wrote:
I think the general idea and formula explained in [1] is right, but
while working on the PoC patch I started to think about how to formalize
this. And I ended up creating two tables that I think visualize is
pretty nicely.Imagine a table (in the spreadsheet sense), with work_mem values in rows
and nbatch values in columns. And the cell is "total memory" used to
execute such hash join, i.e.work_mem + (2 * nbatches * 8K)
(Yes, there's a multiplier for the hash table size, but I use work_mem
for simplicity.) This is what the two attached PDF files show,
highlighting two interesting patterns, so let's talk about that.1) hash-memory-model-1.pdf
Imagine you're executing a hash join - you're in a particular cell of
the table. And we've reached the current memory limit, i.e. we've filled
the hash table, and need to do something. The only solution is to
"expand" the expected "total hash size" (nbatch * hash_table_size),
which we do by simply doubling the number of batches. And often that's
the right thing to do.For example, let's say we're running with work_mem=4MB and nbatch=16,
we've filled the hash table and are using 4336kB of memory (a little bit
more than work_mem). If we double the number of batches, we may use up
to 4352kB of memory in the next cycle. And that's fine.If you double the number of batches, isn't that an additional 32 files
with 8kB each -- so 256kB more memory (not 16 kB)?
Right, I think that's a typo in my message, not sure where I got the
4352kB. The table has a correct value 4592kB.
But hey, there's another way to double the "total hash size" - allowing
the in-memory hash table to be twice as large. In the above case, that
would be wrong, because doubling work_mem would use up to 8432kB.So in this case it's clearly right to double the number of batches,
because that minimizes the total memory used in the next step.However, consider for example the cell with work_mem=4MB, nbatch=8192.
We're using 135MB of memory, and need to decide what to do. Doubling the
batches means we'll use up to 266MB. But doubling work_mem increases the
memory use only to 139MB.Right, it makes sense to use this as the basis for deciding whether or
not to increase nbatches.This is what the green/red in the table means. Green means "better to
double nbatch" while red is "better to double work_mem". And clearly,
the table is split into two regions, separated by the diagonal.The diagonal is the "optimal" path - if you start in any cell, the
red/green decisions will get you to the diagonal, and then along it.The patch [1] aims to do this, but I think this visual explanation is
much clearer than anything in that patch.Yes, the visual is great, thanks!
Glad you find it useful too.
2) hash-memory-model-2.pdf
I've also asked if maybe the patch should do something about the choice
of initial nbatch value, which gets me to the second PDF.Imagine we know the total amount of table in the Hash node is 1GB. There
are different ways to split that into batches. If we have enough memory,
we could do hash join without batching. With work_mem=1MB we'll need to
split this into 1024 batches, or we might do work_mem=2MB with only 512
batches. And so on - we're moving along the anti-diagonal.The point is that while this changes the work_mem, this can have pretty
non-intuitive impact on total memory use. For example, with wm=1MB we
actually use 17MB of memory, while with wm=2MB we use only 10MB.But each anti-diagonal has a minimum - the value on the diagonal. I
believe this is the "optimal starting cell" for the hash join. If we
don't pick this, the rules explained in (1) will eventually get us to
the diagonal anyway.Makes sense.
Attached is an "adjust-size" patch implementing this. In the end it has
pretty much the same effect as the patch in [1], except that it's much
simpler - everything important happens in just two simple blocks, one in
ExecChooseHashTableSize(), the other in ExecHashIncreaseNumBatches().It's interesting -- since the new patch no longer needs to count
buffile overhead in spaceUsed, spacePeak won't include that overhead.
And ultimately EXPLAIN uses the spacePeak, right?
Right. I think this is a good point - I think it was actually helpful
that the initial patch make this extra memory visible in EXPLAIN. But
without the changes to spacePeak that's no longer the case, so maybe we
should add a separate field or something like that ...
There's a bit of complexity, because if we allow growing the size of the
in-memory hash table, we probably need to allow increasing number of
buckets. But that's not possible with how we split the hashvalue now, so
the patch adjusts that by reversing the hashvalue bits when calculating
the batch. I'm not sure if this is the best way to do this, there might
well be a better solution.This part is pretty unpleasant looking (reverse_byte array in the
code). I'll try and think of different ideas. However, I wonder what
other kinds of effects allowing increasing the number of buckets
during execution might have?
Agreed. It's simply the simplest approach to make the hashing work, I
haven't even tried to measure the overhead. I was looking for some
built-in function to reverse bits etc. but found nothing.
I admit all of this seemed a bit weird / wrong initially, because it
feels like giving up the memory limit. But the simple truth is that
memory limit is pretty much just a lie - the fact that we only show the
hash table size in EXPLAIN does not mean we're not using gigabytes more
memory, we're just not making it clear. So I'd argue this actually does
a better job in limiting memory usage.I guess people can multiply the number of batches * 8kB to get that
extra memory overhead. Maybe we should consider putting that in
EXPLAIN output?
Exactly what I suggested above (to add that to EXPLAIN).
Expecting people to realize the batches are backed by batch files and
multiply the number by 8kB didn't quite work, I think. People don't
realize each file has a 8kB buffer, and experienced users/hackers are
surprised by how much memory it quietly consumes.
When thinking about reasons why doubling the work_mem might not be the
right thing, I can think of one case - CPU caches. IIRC it may be much
faster to do the lookups if the hash is small enough to fit into L3, and
and doubling this might work against the goal, although I'm not sure how
bad the impact may be. In the batch explosion case it surely doesn't
matter - the cost of spilling/loading many files is much higher. But for
regular (well estimated) cases it might have negative impact.This is why the patch only adjusts the initial parameters in the "red"
area, not in the green. Maybe it should be a bit more conservative and
only kick in when nbatch value above some threshold.Wait isn't that the opposite of what you are saying? That is, if we
want to keep the hashtable fitting in L3, wouldn't we want to allow
increasing the number of batches even if it uses more memory? That is
the green area. I see your patch does the red -- increase hashtable
size and decrease nbatches if it is better. But that seems
inconsistent with your point about making the hashtable fit in L3.
You're right. The "red" area means that we double work_mem, so the hash
table would probably exceed the L3. I don't recall what exactly was my
reasoning, maybe I just didn't think it through.
But I think the L3 benefit likely disappears once we exceed some number
of batches, because batching is fairly expensive. (I haven't measured
this yes, but I find it likely.) That'd mean having some threshold (e.g.
1024 batches), and only apply this new balancing when we exceed it,
would be reasonable.
I'd appreciate opinions and alternative ideas about this.
I really like the overall idea about being principled in the number of
batches vs hashtable size. I think the question about increasing the
number of buckets and how to do it (during execution) is important to
figure out a good way of doing.
Agreed.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On 1/9/25 21:42, Melanie Plageman wrote:
On Tue, Dec 31, 2024 at 6:07 PM Tomas Vondra <tomas@vondra.me> wrote:
This means that ultimately it's either (1) or (3), and the more I've
been looking into this the more I prefer (1), for a couple reasons:* It's much simpler (it doesn't really change anything on the basic
behavior, doesn't introduce any new files or anything like that.* There doesn't seem to be major difference in total memory consumption
between the two approaches. Both allow allocating more memory.* It actually helps with the "indivisible batch" case - it relaxes the
limit, so there's a chance the batch eventually fits and we stop adding
more and more batches. With spill files that's not the case - we still
keep the original limit, and we end up with the batch explosion (but
then we handle it much more efficiently).Okay, I've read all the patches proposed in this mail and most of the
downthread ideas, and I want to cast my vote for option 1.
I find the design of 3 too complicated for what it buys us.The slices make it harder to understand how the system works. The
current 1-1 relationship in master between batches and spill files is
easier to reason about. With the slices, I'm imagining trying to
understand why we, for example, have to move tuples from batch 4 just
because batch 5 got too big for the hashtable.I think something like this might be worth it if it solved the problem
entirely, but if it is just a somewhat better coping mechanism, I
don't think it is worth it.I was excited about your raw file experiment. As Robert and you point
out -- we may need a file per batch, but for most of the hash join's
execution we don't need to keep buffers for each batch around.
However, given that the experiment didn't yield great results and we
haven't come up with an alternative solution with sufficiently few
flaws, I'm still in favor of 1.
But I think those were two distinct proposals.
My experiment with raw files keeps adding batches just like the current
code (so it might quickly explode to 1M batches) and then keep feeding
data to 1M files at the same time. This doesn't work, the buffering
clearly helps a lot, and it'd affect all hashjoins, even those with
fewer batches.
Robert's idea kept using buffered files, but limited how many we can
fill at any phase. Say we'd use a limit of 1024 batches, but we actually
need 1M batches. Then we'd do the build in two phases - we'd generate
1024 batches, and then we'd split each of those batches into 1024
smaller batches. The trick (as I understand it) is those batches can't
overlap, so we'd not need more than 1024 batches, which greatly limits
the memory consumption. We could even use a lower limit, derived from
work_mem or something like that.
Of course, this is a more complex change than the "balancing" patch. But
maybe not that much, not sure. For me the main disadvantage is it
doesn't really help with the batch explosion for skewed data sets (or
data with many hash collisions). It can easily happen we blindly
increase nbatch until we use all the bits, and then break the work_mem
limit anyway.
But maybe there's a way to address that - the growthEnabled=false safety
is an unreliable solution, because it requires the whole batch to fall
to either of the new batches. A single tuple breaks that.
What if we instead compared the two new batches, and instead looked at
how far the split is from 1/2? And if it's very far from 1/2, we'd
either increase work_mem (a bit like the balancing), or disable nbatch
increases (maybe just temporarily).
The part of 1 I struggled to understand is the math in
ExecHashExceededMemoryLimit(). I think the other email you sent with
the charts and diagonals is about choosing the optimal hashtable size
and number of batches (when to stop growing the number of batches and
just increase the size of the hashtable). So, I'll dive into that.
That math is a bit unclear even to me, that patch was written before I
took the time to work out the formulas and visualizations. It works and
does about the right decisions, but with less rigor. So maybe don't
waste too much time trying to understand it.
One thing I'm not sure about yet is whether this needs to tweak the
hashjoin costing to also consider the files when deciding how many
batches to use. Maybe it should?I think it definitely should. The ExecChooseHashTableSize()
calculations look similar to what we use to calculate spaceAllowed, so
it makes sense that we would consider buffile sizes if we are counting
those in spaceUsed now.
Yeah. I think the flaw is we may not actually know the number of batches
during planning. In the batch explosion example we start with very few
batches, that only happens during execution.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Thu, Jan 9, 2025 at 6:59 PM Tomas Vondra <tomas@vondra.me> wrote:
On 1/9/25 21:42, Melanie Plageman wrote:
I was excited about your raw file experiment. As Robert and you point
out -- we may need a file per batch, but for most of the hash join's
execution we don't need to keep buffers for each batch around.
However, given that the experiment didn't yield great results and we
haven't come up with an alternative solution with sufficiently few
flaws, I'm still in favor of 1.But I think those were two distinct proposals.
My experiment with raw files keeps adding batches just like the current
code (so it might quickly explode to 1M batches) and then keep feeding
data to 1M files at the same time. This doesn't work, the buffering
clearly helps a lot, and it'd affect all hashjoins, even those with
fewer batches.
I see.
Robert's idea kept using buffered files, but limited how many we can
fill at any phase. Say we'd use a limit of 1024 batches, but we actually
need 1M batches. Then we'd do the build in two phases - we'd generate
1024 batches, and then we'd split each of those batches into 1024
smaller batches. The trick (as I understand it) is those batches can't
overlap, so we'd not need more than 1024 batches, which greatly limits
the memory consumption. We could even use a lower limit, derived from
work_mem or something like that.
I think this is because we get the batch based on
*batchno = pg_rotate_right32(hashvalue, hashtable->log2_nbuckets) &
(nbatch - 1);
And tuples can only spill forward. I think Robert's example is if we
plan for 64 batches and eventually increase to 256 batches, a tuple
assigned to batch 1 could go to 65, 129, or 193 but no other batch --
meaning we would only need 3 files open when processing batch 1. But I
think we would need to do more explicit file flushing and closing and
opening, right? Which maybe doesn't matter when compared to the
overhead of so many more buffers.
Of course, this is a more complex change than the "balancing" patch. But
maybe not that much, not sure. For me the main disadvantage is it
doesn't really help with the batch explosion for skewed data sets (or
data with many hash collisions). It can easily happen we blindly
increase nbatch until we use all the bits, and then break the work_mem
limit anyway.But maybe there's a way to address that - the growthEnabled=false safety
is an unreliable solution, because it requires the whole batch to fall
to either of the new batches. A single tuple breaks that.What if we instead compared the two new batches, and instead looked at
how far the split is from 1/2? And if it's very far from 1/2, we'd
either increase work_mem (a bit like the balancing), or disable nbatch
increases (maybe just temporarily).
Meaning like have some threshold for the number of tuples over the
limit we are? Right now, we decide to increase batches when we
encounter that one tuple that puts us over the limit. So, I could see
it making sense to decide with more foresight. Or we could even keep
track of the amount over the limit we are and increase the number of
batches once we hit that threshold.
This kind of seems like it would circle back to your algorithm for
deciding on the right tradeoff between hashtable size and number of
batches, though.
You could do something like this _and_ do something like close the
files that can't be the target of tuples from the current batch --
which would allow you to tolerate many more batch increases before
doubling the hashtable size is worth it. But it seems like the
algorithm to adapt the hashtable size based on the optimal tradeoff
between hashtable size and number of batches could be done first and
the patch to close files could be done later.
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 1/10/25 15:54, Melanie Plageman wrote:
On Thu, Jan 9, 2025 at 6:59 PM Tomas Vondra <tomas@vondra.me> wrote:
On 1/9/25 21:42, Melanie Plageman wrote:
I was excited about your raw file experiment. As Robert and you point
out -- we may need a file per batch, but for most of the hash join's
execution we don't need to keep buffers for each batch around.
However, given that the experiment didn't yield great results and we
haven't come up with an alternative solution with sufficiently few
flaws, I'm still in favor of 1.But I think those were two distinct proposals.
My experiment with raw files keeps adding batches just like the current
code (so it might quickly explode to 1M batches) and then keep feeding
data to 1M files at the same time. This doesn't work, the buffering
clearly helps a lot, and it'd affect all hashjoins, even those with
fewer batches.I see.
Robert's idea kept using buffered files, but limited how many we can
fill at any phase. Say we'd use a limit of 1024 batches, but we actually
need 1M batches. Then we'd do the build in two phases - we'd generate
1024 batches, and then we'd split each of those batches into 1024
smaller batches. The trick (as I understand it) is those batches can't
overlap, so we'd not need more than 1024 batches, which greatly limits
the memory consumption. We could even use a lower limit, derived from
work_mem or something like that.I think this is because we get the batch based on
*batchno = pg_rotate_right32(hashvalue, hashtable->log2_nbuckets) &
(nbatch - 1);And tuples can only spill forward. I think Robert's example is if we
plan for 64 batches and eventually increase to 256 batches, a tuple
assigned to batch 1 could go to 65, 129, or 193 but no other batch --
meaning we would only need 3 files open when processing batch 1.
Yes, I think that's why we only need 3 more files when splitting a
batch. The way I explain it is that going from 64 -> 256 adds 2 more
bits to the "batchno" part of the batch, and one of the patterns means
"current batch", so 3 new files.
This does remind me the "one spill file per slice" patch in [1]/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development,
although it approaches it from a different angle. My patch defined the
"slice" as batches we can keep in work_mem, while Robert proposed to
decide how many batches we can open (first level of batching), and then
maybe do that recursively if needed. That seems like a fundamentally
more sound approach (indeed, my patch can create too many slices).
[1]: /messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development
/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@development
But I think we would need to do more explicit file flushing and
closing and opening, right? Which maybe doesn't matter when compared
to the overhead of so many more buffers.
Would it be all that much flushing and closing? Yes, we'd need to flush
and release the buffers (which I don't think BufFiles can do right now,
but let's ignore that for now). But I'd hope the batches are fairly
large (because that's why expect to generate them), so one more write
should not make a lot of difference on top of the actual bach split.
Chances are it's far cheaper than the extra memory pressure due to
keeping all batches in memory ...
I wonder if it might be a problem that those future batches are "far
apart". I mean, we're splitting batch 1, and the tuple may go to batches
+64, +128 and +192 batches ahead. If we allow larger "jumps" (e.g. from
256 to 64k batches), it'd be even more visible.
For the case of batch explosion I don't think it matters too much - it
will still explode into absurd number of batches, that doesn't change.
But that's fine, the point is to not cause OOM. Improving this case
would require increasing the work_mem limit (either directly or by
stopping the growth).
For regular cases I think the idea is the limit would be high enough to
not really hit this too often. I mean, how many real-world queries use
more than ~1024 batches? I don't think that's very common.
Of course, this is a more complex change than the "balancing" patch. But
maybe not that much, not sure. For me the main disadvantage is it
doesn't really help with the batch explosion for skewed data sets (or
data with many hash collisions). It can easily happen we blindly
increase nbatch until we use all the bits, and then break the work_mem
limit anyway.But maybe there's a way to address that - the growthEnabled=false safety
is an unreliable solution, because it requires the whole batch to fall
to either of the new batches. A single tuple breaks that.What if we instead compared the two new batches, and instead looked at
how far the split is from 1/2? And if it's very far from 1/2, we'd
either increase work_mem (a bit like the balancing), or disable nbatch
increases (maybe just temporarily).Meaning like have some threshold for the number of tuples over the
limit we are? Right now, we decide to increase batches when we
encounter that one tuple that puts us over the limit. So, I could see
it making sense to decide with more foresight. Or we could even keep
track of the amount over the limit we are and increase the number of
batches once we hit that threshold.
Not sure I understand. I meant that we disable nbatch growth like this:
if (nfreed == 0 || nfreed == ninmemory)
{
hashtable->growEnabled = false;
}
which means that it only takes a single tuple that makes it to the other
batch to keep growing. But if 99.9999% tuples went to one of the
batches, increasing nbatch seems pretty futile.
But it goes in the opposite direction too. Imagine a uniform data set
with plenty of distinct values, but correlated / sorted, and each value
having more rows that can fit into a single batch. We'll immediately
disable growth, which is ... not great.
These are somewhat separate / independent issues, but I thin having a
concept of "retrying the nbatch growth after a while" would help.
This kind of seems like it would circle back to your algorithm for
deciding on the right tradeoff between hashtable size and number of
batches, though.
Yes, it's about the same general idea, just expressed in a slightly
different way (the growing the work_mem part).
You could do something like this _and_ do something like close the
files that can't be the target of tuples from the current batch --
which would allow you to tolerate many more batch increases before
doubling the hashtable size is worth it. But it seems like the
algorithm to adapt the hashtable size based on the optimal tradeoff
between hashtable size and number of batches could be done first and
the patch to close files could be done later.
Right. I don't think Robert's idea is a a complete answer, because it
does not consider the tradeoff that maybe increasing work_mem would be
better. OTOH maybe that's not something the hashjoin should worry about.
The goal is not to optimize the work_mem value, but make sure we don't
use significantly more memory ...
If hashjoin starts to optimize this, why shouldn't the other places
using work_mem do something similar?
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Fri, Jan 10, 2025 at 11:18 AM Tomas Vondra <tomas@vondra.me> wrote:
On 1/10/25 15:54, Melanie Plageman wrote:
On Thu, Jan 9, 2025 at 6:59 PM Tomas Vondra <tomas@vondra.me> wrote:
I think this is because we get the batch based on*batchno = pg_rotate_right32(hashvalue, hashtable->log2_nbuckets) &
(nbatch - 1);And tuples can only spill forward. I think Robert's example is if we
plan for 64 batches and eventually increase to 256 batches, a tuple
assigned to batch 1 could go to 65, 129, or 193 but no other batch --
meaning we would only need 3 files open when processing batch 1.Yes, I think that's why we only need 3 more files when splitting a
batch. The way I explain it is that going from 64 -> 256 adds 2 more
bits to the "batchno" part of the batch, and one of the patterns means
"current batch", so 3 new files.This does remind me the "one spill file per slice" patch in [1],
although it approaches it from a different angle. My patch defined the
"slice" as batches we can keep in work_mem, while Robert proposed to
decide how many batches we can open (first level of batching), and then
maybe do that recursively if needed. That seems like a fundamentally
more sound approach (indeed, my patch can create too many slices).[1]
/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@developmentBut I think we would need to do more explicit file flushing and
closing and opening, right? Which maybe doesn't matter when compared
to the overhead of so many more buffers.Would it be all that much flushing and closing? Yes, we'd need to flush
and release the buffers (which I don't think BufFiles can do right now,
but let's ignore that for now). But I'd hope the batches are fairly
large (because that's why expect to generate them), so one more write
should not make a lot of difference on top of the actual bach split.
Chances are it's far cheaper than the extra memory pressure due to
keeping all batches in memory ...I wonder if it might be a problem that those future batches are "far
apart". I mean, we're splitting batch 1, and the tuple may go to batches
+64, +128 and +192 batches ahead. If we allow larger "jumps" (e.g. from
256 to 64k batches), it'd be even more visible.
I don't follow. Why would it be a problem if tuples have to go to
batches that are far away in number?
Of course, this is a more complex change than the "balancing" patch. But
maybe not that much, not sure. For me the main disadvantage is it
doesn't really help with the batch explosion for skewed data sets (or
data with many hash collisions). It can easily happen we blindly
increase nbatch until we use all the bits, and then break the work_mem
limit anyway.But maybe there's a way to address that - the growthEnabled=false safety
is an unreliable solution, because it requires the whole batch to fall
to either of the new batches. A single tuple breaks that.What if we instead compared the two new batches, and instead looked at
how far the split is from 1/2? And if it's very far from 1/2, we'd
either increase work_mem (a bit like the balancing), or disable nbatch
increases (maybe just temporarily).Meaning like have some threshold for the number of tuples over the
limit we are? Right now, we decide to increase batches when we
encounter that one tuple that puts us over the limit. So, I could see
it making sense to decide with more foresight. Or we could even keep
track of the amount over the limit we are and increase the number of
batches once we hit that threshold.Not sure I understand. I meant that we disable nbatch growth like this:
if (nfreed == 0 || nfreed == ninmemory)
{
hashtable->growEnabled = false;
}
Ah, right. I was thinking of the wrong thing.
which means that it only takes a single tuple that makes it to the other
batch to keep growing. But if 99.9999% tuples went to one of the
batches, increasing nbatch seems pretty futile.
Right. Yes, that is unfortunate. You could do a percentage threshold.
Or if we knew how big the biggest batch is, we could decide whether or
not to disable growth based on the size the hashtable would be for
that batch vs the overhead of another doubling of nbatches.
But it goes in the opposite direction too. Imagine a uniform data set
with plenty of distinct values, but correlated / sorted, and each value
having more rows that can fit into a single batch. We'll immediately
disable growth, which is ... not great.These are somewhat separate / independent issues, but I thin having a
concept of "retrying the nbatch growth after a while" would help.
Yes, I think retrying nbatch growth later makes sense in this case. Or
when doubling nbatches wouldn't help split one rogue batch but would
help other big batches.
You could do something like this _and_ do something like close the
files that can't be the target of tuples from the current batch --
which would allow you to tolerate many more batch increases before
doubling the hashtable size is worth it. But it seems like the
algorithm to adapt the hashtable size based on the optimal tradeoff
between hashtable size and number of batches could be done first and
the patch to close files could be done later.Right. I don't think Robert's idea is a a complete answer, because it
does not consider the tradeoff that maybe increasing work_mem would be
better. OTOH maybe that's not something the hashjoin should worry about.
The goal is not to optimize the work_mem value, but make sure we don't
use significantly more memory ...
Well it's also not a complete solution because it doesn't solve the
hash collision/batch explosion case.
If hashjoin starts to optimize this, why shouldn't the other places
using work_mem do something similar?
Yes, I suppose other spilling operators (like hashagg) that use
buffered files may consider doing this. But I don't think that is a
reason not to use this particular strategy to "fix" this hash join
batch explosion issue.
You could make the argument that because it is the buffers and not the
actual number of batches that is the problem, that we should fix it by
closing the files that aren't being used while processing a batch.
But I really like how small and isolated your sizing balance patch is.
And I actually think that the fact that it could be used to optimize
this tradeoff (work_mem/file buffers) in other places is good. Anyway,
my point was just that we could do both -- likely in any order.
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 1/11/25 00:09, Melanie Plageman wrote:
On Fri, Jan 10, 2025 at 11:18 AM Tomas Vondra <tomas@vondra.me> wrote:
On 1/10/25 15:54, Melanie Plageman wrote:
On Thu, Jan 9, 2025 at 6:59 PM Tomas Vondra <tomas@vondra.me> wrote:
I think this is because we get the batch based on*batchno = pg_rotate_right32(hashvalue, hashtable->log2_nbuckets) &
(nbatch - 1);And tuples can only spill forward. I think Robert's example is if we
plan for 64 batches and eventually increase to 256 batches, a tuple
assigned to batch 1 could go to 65, 129, or 193 but no other batch --
meaning we would only need 3 files open when processing batch 1.Yes, I think that's why we only need 3 more files when splitting a
batch. The way I explain it is that going from 64 -> 256 adds 2 more
bits to the "batchno" part of the batch, and one of the patterns means
"current batch", so 3 new files.This does remind me the "one spill file per slice" patch in [1],
although it approaches it from a different angle. My patch defined the
"slice" as batches we can keep in work_mem, while Robert proposed to
decide how many batches we can open (first level of batching), and then
maybe do that recursively if needed. That seems like a fundamentally
more sound approach (indeed, my patch can create too many slices).[1]
/messages/by-id/20190428141901.5dsbge2ka3rxmpk6@developmentBut I think we would need to do more explicit file flushing and
closing and opening, right? Which maybe doesn't matter when compared
to the overhead of so many more buffers.Would it be all that much flushing and closing? Yes, we'd need to flush
and release the buffers (which I don't think BufFiles can do right now,
but let's ignore that for now). But I'd hope the batches are fairly
large (because that's why expect to generate them), so one more write
should not make a lot of difference on top of the actual bach split.
Chances are it's far cheaper than the extra memory pressure due to
keeping all batches in memory ...I wonder if it might be a problem that those future batches are "far
apart". I mean, we're splitting batch 1, and the tuple may go to batches
+64, +128 and +192 batches ahead. If we allow larger "jumps" (e.g. from
256 to 64k batches), it'd be even more visible.I don't follow. Why would it be a problem if tuples have to go to
batches that are far away in number?
I think you're right it's not a problem, I was just thinking aloud (or
whatever you do in an e-mail).
Of course, this is a more complex change than the "balancing" patch. But
maybe not that much, not sure. For me the main disadvantage is it
doesn't really help with the batch explosion for skewed data sets (or
data with many hash collisions). It can easily happen we blindly
increase nbatch until we use all the bits, and then break the work_mem
limit anyway.But maybe there's a way to address that - the growthEnabled=false safety
is an unreliable solution, because it requires the whole batch to fall
to either of the new batches. A single tuple breaks that.What if we instead compared the two new batches, and instead looked at
how far the split is from 1/2? And if it's very far from 1/2, we'd
either increase work_mem (a bit like the balancing), or disable nbatch
increases (maybe just temporarily).Meaning like have some threshold for the number of tuples over the
limit we are? Right now, we decide to increase batches when we
encounter that one tuple that puts us over the limit. So, I could see
it making sense to decide with more foresight. Or we could even keep
track of the amount over the limit we are and increase the number of
batches once we hit that threshold.Not sure I understand. I meant that we disable nbatch growth like this:
if (nfreed == 0 || nfreed == ninmemory)
{
hashtable->growEnabled = false;
}Ah, right. I was thinking of the wrong thing.
which means that it only takes a single tuple that makes it to the other
batch to keep growing. But if 99.9999% tuples went to one of the
batches, increasing nbatch seems pretty futile.Right. Yes, that is unfortunate. You could do a percentage threshold.
Or if we knew how big the biggest batch is, we could decide whether or
not to disable growth based on the size the hashtable would be for
that batch vs the overhead of another doubling of nbatches.
I was thinking it might be possible to express this as a formula similar
to the "balancing". I mean, something that says "just double as you
wish" when the current doubling split the batch 50:50, but delays the
next doubling if the batch gets split 99:1 (with some continuous
transition between those two extremes).
Or maybe this could also drive increasing the memory limit. Yes, there's
a chance that the next doubling will split it more evenly, but I think
it's much more likely there really are hash collisions of some sort.
But it goes in the opposite direction too. Imagine a uniform data set
with plenty of distinct values, but correlated / sorted, and each value
having more rows that can fit into a single batch. We'll immediately
disable growth, which is ... not great.These are somewhat separate / independent issues, but I thin having a
concept of "retrying the nbatch growth after a while" would help.Yes, I think retrying nbatch growth later makes sense in this case. Or
when doubling nbatches wouldn't help split one rogue batch but would
help other big batches.
Exactly. Giving up the growth entirely seems a bit premature. I don't
think there's a principled formula to determine when to retry, but it
might be enough to try after the hash table doubles in size. That's
pretty much the "let's increase work_mem a bit" I mentioned above.
You could do something like this _and_ do something like close the
files that can't be the target of tuples from the current batch --
which would allow you to tolerate many more batch increases before
doubling the hashtable size is worth it. But it seems like the
algorithm to adapt the hashtable size based on the optimal tradeoff
between hashtable size and number of batches could be done first and
the patch to close files could be done later.Right. I don't think Robert's idea is a a complete answer, because it
does not consider the tradeoff that maybe increasing work_mem would be
better. OTOH maybe that's not something the hashjoin should worry about.
The goal is not to optimize the work_mem value, but make sure we don't
use significantly more memory ...Well it's also not a complete solution because it doesn't solve the
hash collision/batch explosion case.
I think it does, in a way. It doesn't prevent the batch explosion, of
course, it still ends with millions of batches. But it's not keeping all
the BufFiles open at the same time, so it does not use the insane
amounts of memory. And it's slower than the balancing, of course.
If hashjoin starts to optimize this, why shouldn't the other places
using work_mem do something similar?Yes, I suppose other spilling operators (like hashagg) that use
buffered files may consider doing this. But I don't think that is a
reason not to use this particular strategy to "fix" this hash join
batch explosion issue.You could make the argument that because it is the buffers and not the
actual number of batches that is the problem, that we should fix it by
closing the files that aren't being used while processing a batch.
But I really like how small and isolated your sizing balance patch is.
And I actually think that the fact that it could be used to optimize
this tradeoff (work_mem/file buffers) in other places is good. Anyway,
my point was just that we could do both -- likely in any order.
Right. The way I'm looking at this is the balancing patch is a strict
improvement over the current state. Robert's proposal is more principled
in that it actually tries to enforce the promised memory limit.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On 1/10/25 15:54, Melanie Plageman wrote:
On Thu, Jan 9, 2025 at 6:59 PM Tomas Vondra <tomas@vondra.me> wrote:
...
Robert's idea kept using buffered files, but limited how many we can
fill at any phase. Say we'd use a limit of 1024 batches, but we actually
need 1M batches. Then we'd do the build in two phases - we'd generate
1024 batches, and then we'd split each of those batches into 1024
smaller batches. The trick (as I understand it) is those batches can't
overlap, so we'd not need more than 1024 batches, which greatly limits
the memory consumption. We could even use a lower limit, derived from
work_mem or something like that.I think this is because we get the batch based on
*batchno = pg_rotate_right32(hashvalue, hashtable->log2_nbuckets) &
(nbatch - 1);And tuples can only spill forward. I think Robert's example is if we
plan for 64 batches and eventually increase to 256 batches, a tuple
assigned to batch 1 could go to 65, 129, or 193 but no other batch --
meaning we would only need 3 files open when processing batch 1. But I
think we would need to do more explicit file flushing and closing and
opening, right? Which maybe doesn't matter when compared to the
overhead of so many more buffers.
I had a quiet evening yesterday, so I decided to take a stab at this and
see how hard would it be, and how bad would the impact be. Attached is
an experimental patch, doing the *bare* minimum for a simple query:
1) It defines a limit of 128 batches (a bit low, but also 1MB). In
practice we'd use something like 256 - 1024, probably. Doesn't matter.
2) Ensures the initial pass over data in MultiExecPrivateHash does not
use more than 128 batches, switches to "tooManyBatches=true" if that
happens (and dumps the batch to file ExecHashDumpBatchToFile, even if
it's batchno=0). And later it calls ExecHashHandleTooManyBatches() to
increase the nbatch further.
3) Does something similar for the outer relation - if there are too many
batches, we do ExecHashJoinRepartitionBatches() which first partitions
into 128 batches. This only does a single pass in the WIP, though.
Should be recursive or something.
4) Extends the BufFile API with BufFileHasBuffer/BufFileFreeBuffer so
that the code can free the buffers. It also means the buffer needs to be
allocated separately, not embedded in BufFile struct. (I'm a bit
surprised it works without having to re-read the buffer after freeing
it, but that's probably thanks to how hashjoin uses the files).
Anyway, this seems to work, and a simple experiment looks like this:
--------------------------------------------------------------------
create table t (a int, b text);
insert into t select i, md5(i::text)
from generate_series(1,100000) s(i);
insert into t select i, md5(i::text)
from generate_series(1,100000) s(i);
insert into t select i, md5(i::text)
from generate_series(1,100000) s(i);
insert into t select i, md5(i::text)
from generate_series(1,100000) s(i);
vacuum analyze;
set work_mem='128kB';
explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
--------------------------------------------------------------------
This is just enough to need 256 batches, i.e. "one doubling" over the
128 batch limit.
On master I get this:
QUERY PLAN
-----------------------------------------------------------------------
Hash Join (cost=15459.00..51555.40 rows=1638740 width=74) (actual
time=80.065..337.192 rows=1600000 loops=1)
Hash Cond: (t1.a = t2.a)
Buffers: shared hit=6668, temp read=5806 written=5806
-> Seq Scan on t t1 (cost=0.00..7334.00 rows=400000 width=37)
(actual time=0.008..19.867 rows=400000 loops=1)
Buffers: shared hit=3334
-> Hash (cost=7334.00..7334.00 rows=400000 width=37) (actual
time=76.824..76.824 rows=400000 loops=1)
Buckets: 4096 Batches: 256 Memory Usage: 132kB
Buffers: shared hit=3334, temp written=2648
-> Seq Scan on t t2 (cost=0.00..7334.00 rows=400000 width=37)
(actual time=0.001..20.855 rows=400000 loops=1)
Buffers: shared hit=3334
Planning Time: 0.100 ms
Execution Time: 385.779 ms
(12 rows)
while with the patch we get this:
QUERY PLAN
-----------------------------------------------------------------------
Hash Join (cost=15459.00..51555.40 rows=1638740 width=74) (actual
time=93.346..325.604 rows=1600000 loops=1)
Hash Cond: (t1.a = t2.a)
Buffers: shared hit=6668, temp read=8606 written=8606
-> Seq Scan on t t1 (cost=0.00..7334.00 rows=400000 width=37)
(actual time=0.006..14.416 rows=400000 loops=1)
Buffers: shared hit=3334
-> Hash (cost=7334.00..7334.00 rows=400000 width=37) (actual
time=48.481..48.482 rows=400000 loops=1)
Buckets: 4096 (originally 4096) Batches: 256 (originally 128)
Memory Usage: 132kB
Buffers: shared hit=3334, temp read=23 written=2860
-> Seq Scan on t t2 (cost=0.00..7334.00 rows=400000 width=37)
(actual time=0.001..14.754 rows=400000 loops=1)
Buffers: shared hit=3334
Planning Time: 0.061 ms
Execution Time: 374.229 ms
(12 rows)
So for this particular query there doesn't seem to be a particularly
massive hit. With more batches that's unfortunately not the case, but
that seems to be mostly due to looping over all batches when freeing
buffers. Looping over 1M batches (which we do for every batch) is
expensive, but that could be improved somehow - we only have a couple
files open (say 1024), so we could keep them in a list or something. And
I think we don't need to free the batches this often anyway, we might
not even opened any future batches.
I'm sure there's plenty of issues with the patch - e.g. it may not not
handle nbatch increases later (after batch 0), I ignored skew buckets,
and stuff like that ...
But it does seem like a workable idea ...
regards
--
Tomas Vondra
Attachments:
hashjoin-file-limit.patchtext/x-patch; charset=UTF-8; name=hashjoin-file-limit.patchDownload
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..6cd0a3328bb 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -79,7 +79,8 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
size_t size);
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
-
+static void ExecHashDumpBatchToFile(HashJoinTable hashtable);
+static void ExecHashHandleTooManyBatches(HashJoinTable hashtable, TupleTableSlot *slot);
/* ----------------------------------------------------------------
* ExecHash
@@ -195,6 +196,13 @@ MultiExecPrivateHash(HashState *node)
}
}
+ /*
+ * If we had to disable adding more batches while building the hash, now
+ * is a good time to do the next phase - release all file buffers and split
+ * each batch into new batches.
+ */
+ ExecHashHandleTooManyBatches(hashtable, node->ps.ps_ResultTupleSlot);
+
/* resize the hash table if needed (NTUP_PER_BUCKET exceeded) */
if (hashtable->nbuckets != hashtable->nbuckets_optimal)
ExecHashIncreaseNumBuckets(hashtable);
@@ -502,10 +510,17 @@ ExecHashTableCreate(HashState *state)
hashtable->skewBucketLen = 0;
hashtable->nSkewBuckets = 0;
hashtable->skewBucketNums = NULL;
- hashtable->nbatch = nbatch;
hashtable->curbatch = 0;
- hashtable->nbatch_original = nbatch;
- hashtable->nbatch_outstart = nbatch;
+
+ /* limit the number of batches we can populate at once */
+ hashtable->nbatch_maximum = HASHJOIN_BATCHES_PER_PHASE;
+ hashtable->nbatch = Min(nbatch, hashtable->nbatch_maximum);
+ hashtable->curbatch = 0;
+ hashtable->nbatch_original = Min(nbatch, hashtable->nbatch_maximum);
+ hashtable->nbatch_outstart = Min(nbatch, hashtable->nbatch_maximum);
+
+ /* assume we haven't hit the batch count limit */
+ hashtable->tooManyBatches = (nbatch > HASHJOIN_BATCHES_PER_PHASE);
hashtable->growEnabled = true;
hashtable->totalTuples = 0;
hashtable->partialTuples = 0;
@@ -874,7 +889,8 @@ ExecHashTableDestroy(HashJoinTable hashtable)
*/
if (hashtable->innerBatchFile != NULL)
{
- for (i = 1; i < hashtable->nbatch; i++)
+ /* XXX We might have allocated a file for batch 0, so release it. */
+ for (i = 0; i < hashtable->nbatch; i++)
{
if (hashtable->innerBatchFile[i])
BufFileClose(hashtable->innerBatchFile[i]);
@@ -905,6 +921,12 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
long nfreed;
HashMemoryChunk oldchunks;
+ /*
+ * We should never get here after hitting too many batches, and switching
+ * to spilling data for all batches.
+ */
+ Assert(!hashtable->tooManyBatches);
+
/* do nothing if we've decided to shut off growth */
if (!hashtable->growEnabled)
return;
@@ -1054,6 +1076,166 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
}
}
+/*
+ * ExecHashDumpBatchToFile
+ * Dump the current hash table into a file for the current batch.
+ *
+ * Once we hit the number of batches we're populating, we stop doubling the
+ * number of batches. But we also can't keep the current batch in memory, so
+ * instead we dump the data to file and will read it later after we increase
+ * the number of batches (at which point we won't need to keep the current
+ * batches).
+ *
+ * XXX This is virtually the same thing we do in ExecHashIncreaseNumBatches.
+ */
+static void
+ExecHashDumpBatchToFile(HashJoinTable hashtable)
+{
+ HashMemoryChunk oldchunks;
+ int curbatch = hashtable->curbatch;
+
+ memset(hashtable->buckets.unshared, 0,
+ sizeof(HashJoinTuple) * hashtable->nbuckets);
+ oldchunks = hashtable->chunks;
+ hashtable->chunks = NULL;
+
+ /* so, let's scan through the old chunks, and all tuples in each chunk */
+ while (oldchunks != NULL)
+ {
+ HashMemoryChunk nextchunk = oldchunks->next.unshared;
+
+ /* position within the buffer (up to oldchunks->used) */
+ size_t idx = 0;
+
+ /* process all tuples stored in this chunk (and then free it) */
+ while (idx < oldchunks->used)
+ {
+ HashJoinTuple hashTuple = (HashJoinTuple) (HASH_CHUNK_DATA(oldchunks) + idx);
+ MinimalTuple tuple = HJTUPLE_MINTUPLE(hashTuple);
+ int hashTupleSize = (HJTUPLE_OVERHEAD + tuple->t_len);
+ int bucketno;
+ int batchno;
+
+ ExecHashGetBucketAndBatch(hashtable, hashTuple->hashvalue,
+ &bucketno, &batchno);
+
+ /* everything belongs to current batch, we're not adding any */
+ Assert(batchno == curbatch);
+
+ /* dump it out */
+ ExecHashJoinSaveTuple(HJTUPLE_MINTUPLE(hashTuple),
+ hashTuple->hashvalue,
+ &hashtable->innerBatchFile[batchno],
+ hashtable);
+
+ hashtable->spaceUsed -= hashTupleSize;
+
+ /* next tuple in this chunk */
+ idx += MAXALIGN(hashTupleSize);
+
+ /* allow this loop to be cancellable */
+ CHECK_FOR_INTERRUPTS();
+ }
+
+ /* we're done with this chunk - free it and proceed to the next one */
+ pfree(oldchunks);
+ oldchunks = nextchunk;
+ }
+}
+
+/*
+ * ExecHashHandleTooManyBatches
+ * Handle the case when we hit the number of batches while building the
+ * hash table.
+ *
+ * We can hit the situation again (e.g. we need 1M batches, but the limit is
+ * 128, so we go 128 -> 16384 -> 1M).
+ */
+static void
+ExecHashHandleTooManyBatches(HashJoinTable hashtable, TupleTableSlot *slot)
+{
+ if (!hashtable->tooManyBatches)
+ return;
+
+ /* didn't run into the issue, nothing to do */
+ while (hashtable->tooManyBatches)
+ {
+ int nbatch;
+ int oldnbatch;
+
+ BufFile *file = hashtable->innerBatchFile[hashtable->curbatch];
+
+ Assert(file != NULL);
+
+ /* XXX stupid way to increase number of batches */
+ oldnbatch = hashtable->nbatch;
+ nbatch = Max(hashtable->nbatch, hashtable->nbatch * 2);
+
+ hashtable->nbatch = nbatch;
+ hashtable->tooManyBatches = false;
+
+ /* enlarge arrays and zero out added entries */
+ hashtable->innerBatchFile = repalloc0_array(hashtable->innerBatchFile, BufFile *, oldnbatch, nbatch);
+ hashtable->outerBatchFile = repalloc0_array(hashtable->outerBatchFile, BufFile *, oldnbatch, nbatch);
+
+ /* set to NULL, so that we create a new / empty file for this batch */
+ hashtable->innerBatchFile[hashtable->curbatch] = NULL;
+
+ /* FIXME close/flush all current files (release the buffer) */
+ for (int i = 0; i < oldnbatch; i++)
+ {
+ if (hashtable->innerBatchFile[i] != NULL)
+ BufFileFreeBuffer(hashtable->innerBatchFile[i]);
+ }
+
+ BufFileSeek(file, 0, 0, SEEK_SET);
+
+ /* read the current tuples from the batch file, decide what
+ * to do with them */
+ while (true)
+ {
+ uint32 hashvalue;
+ uint32 header[2];
+ size_t nread;
+ MinimalTuple tuple;
+
+ /*
+ * We check for interrupts here because this is typically taken as an
+ * alternative code path to an ExecProcNode() call, which would include
+ * such a check.
+ */
+ CHECK_FOR_INTERRUPTS();
+
+ /*
+ * Since both the hash value and the MinimalTuple length word are uint32,
+ * we can read them both in one BufFileRead() call without any type
+ * cheating.
+ */
+ nread = BufFileReadMaybeEOF(file, header, sizeof(header), true);
+ if (nread == 0) /* end of file */
+ {
+ ExecClearTuple(slot);
+ break;
+ }
+
+ hashvalue = header[0];
+ tuple = (MinimalTuple) palloc(header[1]);
+ tuple->t_len = header[1];
+
+ BufFileReadExact(file,
+ (char *) tuple + sizeof(uint32),
+ header[1] - sizeof(uint32));
+
+ ExecForceStoreMinimalTuple(tuple, slot, true);
+
+ ExecHashTableInsert(hashtable, slot, hashvalue);
+ }
+
+ /* close the old batch file */
+ BufFileClose(file);
+ }
+}
+
/*
* ExecParallelHashIncreaseNumBatches
* Every participant attached to grow_batches_barrier must run this
@@ -1625,8 +1807,11 @@ ExecHashTableInsert(HashJoinTable hashtable,
/*
* decide whether to put the tuple in the hash table or a temp file
+ *
+ * XXX If we are already in "too many batches" situation, just write the
+ * data directly to the temp file, even for current batch.
*/
- if (batchno == hashtable->curbatch)
+ if (batchno == hashtable->curbatch && !hashtable->tooManyBatches)
{
/*
* put the tuple in hash table
@@ -1678,14 +1863,29 @@ ExecHashTableInsert(HashJoinTable hashtable,
if (hashtable->spaceUsed +
hashtable->nbuckets_optimal * sizeof(HashJoinTuple)
> hashtable->spaceAllowed)
- ExecHashIncreaseNumBatches(hashtable);
+ {
+ /*
+ * If we already hit the maximum for this phase, we can't increase
+ * the number of batches further, so we stop and flush everything
+ * into the temp file.
+ */
+ if (hashtable->nbatch == hashtable->nbatch_maximum)
+ {
+ /* write everything from this batch to the temp file */
+ ExecHashDumpBatchToFile(hashtable);
+ hashtable->tooManyBatches = true;
+ }
+ else
+ ExecHashIncreaseNumBatches(hashtable);
+ }
}
else
{
/*
* put the tuple into a temp file for later batches
*/
- Assert(batchno > hashtable->curbatch);
+ Assert(batchno > hashtable->curbatch ||
+ (hashtable->tooManyBatches && batchno == hashtable->curbatch));
ExecHashJoinSaveTuple(tuple,
hashvalue,
&hashtable->innerBatchFile[batchno],
@@ -2494,6 +2694,7 @@ ExecHashSkewTableInsert(HashJoinTable hashtable,
while (hashtable->spaceUsedSkew > hashtable->spaceAllowedSkew)
ExecHashRemoveNextSkewBucket(hashtable);
+ /* XXX maybe should check nbatch_maximum? */
/* Check we are not over the total spaceAllowed, either */
if (hashtable->spaceUsed > hashtable->spaceAllowed)
ExecHashIncreaseNumBatches(hashtable);
diff --git a/src/backend/executor/nodeHashjoin.c b/src/backend/executor/nodeHashjoin.c
index 5661ad76830..a7514ca5137 100644
--- a/src/backend/executor/nodeHashjoin.c
+++ b/src/backend/executor/nodeHashjoin.c
@@ -963,6 +963,107 @@ ExecEndHashJoin(HashJoinState *node)
ExecEndNode(innerPlanState(node));
}
+/*
+ * ExecHashJoinRepartitionBatches
+ * Build the hashes for the outer side, recursively to not need more than
+ * HASHJOIN_BATCHES_PER_PHASE batches.
+ *
+ * XXX There's no recursion right now, we just do the first phase and leave
+ * the rest to the regular re-batching. This won't be enough if we need more
+ * than a single repartition cycle.
+ *
+ * XXX Does this need to worry about the skew buckets? Probably yes.
+ */
+static void
+ExecHashJoinRepartitionBatches(PlanState *outerNode,
+ HashJoinState *hjstate)
+{
+ TupleTableSlot *slot;
+ HashJoinTable hashtable = hjstate->hj_HashTable;
+ int nbatch = hashtable->nbatch;
+ int64 ntuples = 0;
+
+ /* fake the value so that we calculate the first phase batch */
+ hashtable->nbatch = HASHJOIN_BATCHES_PER_PHASE;
+
+ while (true)
+ {
+ /*
+ * Check to see if first outer tuple was already fetched by
+ * ExecHashJoin() and not used yet.
+ */
+ slot = hjstate->hj_FirstOuterTupleSlot;
+ if (!TupIsNull(slot))
+ hjstate->hj_FirstOuterTupleSlot = NULL;
+ else
+ slot = ExecProcNode(outerNode);
+
+ while (!TupIsNull(slot))
+ {
+ uint32 hashvalue;
+ bool isnull;
+
+ /*
+ * We have to compute the tuple's hash value.
+ */
+ ExprContext *econtext = hjstate->js.ps.ps_ExprContext;
+
+ econtext->ecxt_outertuple = slot;
+
+ ResetExprContext(econtext);
+
+ hashvalue = DatumGetUInt32(ExecEvalExprSwitchContext(hjstate->hj_OuterHash,
+ econtext,
+ &isnull));
+
+ if (!isnull)
+ {
+ int bucketno;
+ int batchno;
+ bool shouldFree;
+ MinimalTuple mintuple;
+
+ ntuples++;
+
+ /* remember outer relation is not empty for possible rescan */
+ hjstate->hj_OuterNotEmpty = true;
+
+ ExecHashGetBucketAndBatch(hashtable, hashvalue,
+ &bucketno, &batchno);
+
+ /*
+ * The tuple might not belong to the current batch (where
+ * "current batch" includes the skew buckets if any).
+ */
+ mintuple = ExecFetchSlotMinimalTuple(slot, &shouldFree);
+
+ /*
+ * Need to postpone this outer tuple to a later batch.
+ * Save it in the corresponding outer-batch file.
+ */
+ Assert(batchno >= hashtable->curbatch);
+ ExecHashJoinSaveTuple(mintuple, hashvalue,
+ &hashtable->outerBatchFile[batchno],
+ hashtable);
+
+ if (shouldFree)
+ heap_free_minimal_tuple(mintuple);
+ }
+
+ /*
+ * That tuple couldn't match because of a NULL, so discard it and
+ * continue with the next one.
+ */
+ slot = ExecProcNode(outerNode);
+ }
+
+ break;
+ }
+
+ /* restore the correct value */
+ hashtable->nbatch = nbatch;
+}
+
/*
* ExecHashJoinOuterGetTuple
*
@@ -983,8 +1084,36 @@ ExecHashJoinOuterGetTuple(PlanState *outerNode,
HashJoinTable hashtable = hjstate->hj_HashTable;
int curbatch = hashtable->curbatch;
TupleTableSlot *slot;
+ bool repartitioned = false;
- if (curbatch == 0) /* if it is the first pass */
+ /*
+ * If this is the first batch, consider performing the repartitioning. This
+ * will build HASHJOIN_BATCHES_PER_PHASE batches, which then need to be
+ * rebatched using the regular logic.
+ *
+ * This includes the first batch, which means we then need to read the
+ * tuples from the file, just like for every other batch.
+ *
+ * But do that only once, which we detect by (file == NULL).
+ */
+ if ((curbatch == 0) &&
+ (hashtable->nbatch > HASHJOIN_BATCHES_PER_PHASE) &&
+ (hashtable->outerBatchFile[0] == NULL))
+ {
+ ExecHashJoinRepartitionBatches(outerNode, hjstate);
+
+ /* make sure to reset the first batch to start reading */
+ if (hashtable->outerBatchFile[0])
+ BufFileSeek(hashtable->outerBatchFile[0], 0, 0, SEEK_SET);
+ }
+
+ /*
+ * We need to know if this is repartitioned even for later tuples in
+ * the first batch. We detect that by having the batch file.
+ */
+ repartitioned = (curbatch == 0) && (hashtable->outerBatchFile[0] != NULL);
+
+ if (curbatch == 0 && !repartitioned) /* if it is the first pass */
{
/*
* Check to see if first outer tuple was already fetched by
@@ -1120,6 +1249,23 @@ ExecParallelHashJoinOuterGetTuple(PlanState *outerNode,
return NULL;
}
+/* XXX this is very expensive, we have to walk long arrays often */
+static void
+ExecHashJoinFreeBuffers(HashJoinTable hashtable)
+{
+ if ((!hashtable->innerBatchFile) && (!hashtable->outerBatchFile))
+ return;
+
+ for (int i = hashtable->curbatch; i < hashtable->nbatch; i++)
+ {
+ if (hashtable->innerBatchFile[i])
+ BufFileFreeBuffer(hashtable->innerBatchFile[i]);
+
+ if (hashtable->outerBatchFile[i])
+ BufFileFreeBuffer(hashtable->outerBatchFile[i]);
+ }
+}
+
/*
* ExecHashJoinNewBatch
* switch to a new hashjoin batch
@@ -1139,6 +1285,8 @@ ExecHashJoinNewBatch(HashJoinState *hjstate)
nbatch = hashtable->nbatch;
curbatch = hashtable->curbatch;
+ ExecHashJoinFreeBuffers(hashtable);
+
if (curbatch > 0)
{
/*
@@ -1398,6 +1546,20 @@ ExecParallelHashJoinNewBatch(HashJoinState *hjstate)
return false;
}
+static int
+ExecHashCountBuffers(BufFile **files, int nfiles)
+{
+ int cnt = 0;
+
+ for (int i = 0; i < nfiles; i++)
+ {
+ if (files[i] && BufFileHasBuffer(files[i]))
+ cnt++;
+ }
+
+ return cnt;
+}
+
/*
* ExecHashJoinSaveTuple
* save a tuple to a batch file.
@@ -1438,6 +1600,13 @@ ExecHashJoinSaveTuple(MinimalTuple tuple, uint32 hashvalue,
*fileptr = file;
MemoryContextSwitchTo(oldctx);
+
+ if (false)
+ {
+ int ninner = ExecHashCountBuffers(hashtable->innerBatchFile, hashtable->nbatch);
+ int nouter = ExecHashCountBuffers(hashtable->outerBatchFile, hashtable->nbatch);
+ elog(WARNING, "curbatch %d inner %d outer %d", hashtable->curbatch, ninner, nouter);
+ }
}
BufFileWrite(file, &hashvalue, sizeof(uint32));
diff --git a/src/backend/storage/file/buffile.c b/src/backend/storage/file/buffile.c
index 6449f82a72b..e155976cc23 100644
--- a/src/backend/storage/file/buffile.c
+++ b/src/backend/storage/file/buffile.c
@@ -100,7 +100,8 @@ struct BufFile
* XXX Should ideally us PGIOAlignedBlock, but might need a way to avoid
* wasting per-file alignment padding when some users create many files.
*/
- PGAlignedBlock buffer;
+ MemoryContext bufctx;
+ PGAlignedBlock *buffer;
};
static BufFile *makeBufFileCommon(int nfiles);
@@ -128,6 +129,9 @@ makeBufFileCommon(int nfiles)
file->pos = 0;
file->nbytes = 0;
+ file->bufctx = CurrentMemoryContext;
+ file->buffer = NULL;
+
return file;
}
@@ -423,6 +427,33 @@ BufFileClose(BufFile *file)
pfree(file);
}
+static void
+BufFileAllocBuffer(BufFile *file)
+{
+ if (file->buffer != NULL)
+ return;
+
+ file->buffer = MemoryContextAlloc(file->bufctx, sizeof(PGAlignedBlock));
+}
+
+void
+BufFileFreeBuffer(BufFile *file)
+{
+ if (file->buffer == NULL)
+ return;
+
+ BufFileFlush(file);
+
+ pfree(file->buffer);
+ file->buffer = NULL;
+}
+
+bool
+BufFileHasBuffer(BufFile *file)
+{
+ return (file->buffer != NULL);
+}
+
/*
* BufFileLoadBuffer
*
@@ -454,12 +485,14 @@ BufFileLoadBuffer(BufFile *file)
else
INSTR_TIME_SET_ZERO(io_start);
+ BufFileAllocBuffer(file);
+
/*
* Read whatever we can get, up to a full bufferload.
*/
file->nbytes = FileRead(thisfile,
- file->buffer.data,
- sizeof(file->buffer),
+ file->buffer->data,
+ sizeof(file->buffer->data),
file->curOffset,
WAIT_EVENT_BUFFILE_READ);
if (file->nbytes < 0)
@@ -535,7 +568,7 @@ BufFileDumpBuffer(BufFile *file)
INSTR_TIME_SET_ZERO(io_start);
bytestowrite = FileWrite(thisfile,
- file->buffer.data + wpos,
+ file->buffer->data + wpos,
bytestowrite,
file->curOffset,
WAIT_EVENT_BUFFILE_WRITE);
@@ -616,7 +649,7 @@ BufFileReadCommon(BufFile *file, void *ptr, size_t size, bool exact, bool eofOK)
nthistime = size;
Assert(nthistime > 0);
- memcpy(ptr, file->buffer.data + file->pos, nthistime);
+ memcpy(ptr, file->buffer->data + file->pos, nthistime);
file->pos += nthistime;
ptr = (char *) ptr + nthistime;
@@ -679,6 +712,8 @@ BufFileWrite(BufFile *file, const void *ptr, size_t size)
Assert(!file->readOnly);
+ BufFileAllocBuffer(file);
+
while (size > 0)
{
if (file->pos >= BLCKSZ)
@@ -700,7 +735,7 @@ BufFileWrite(BufFile *file, const void *ptr, size_t size)
nthistime = size;
Assert(nthistime > 0);
- memcpy(file->buffer.data + file->pos, ptr, nthistime);
+ memcpy(file->buffer->data + file->pos, ptr, nthistime);
file->dirty = true;
file->pos += nthistime;
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 67ae89c8257..1ba7a6d1b88 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -322,9 +322,11 @@ typedef struct HashJoinTableData
int nbatch; /* number of batches */
int curbatch; /* current batch #; 0 during 1st pass */
+ int nbatch_maximum; /* maximum number of batches to create */
int nbatch_original; /* nbatch when we started inner scan */
int nbatch_outstart; /* nbatch when we started outer scan */
+ bool tooManyBatches; /* did we generate too many batches? */
bool growEnabled; /* flag to shut off nbatch increases */
double totalTuples; /* # tuples obtained from inner plan */
@@ -362,4 +364,7 @@ typedef struct HashJoinTableData
dsa_pointer current_chunk_shared;
} HashJoinTableData;
+
+#define HASHJOIN_BATCHES_PER_PHASE 128
+
#endif /* HASHJOIN_H */
diff --git a/src/include/storage/buffile.h b/src/include/storage/buffile.h
index 6b92668d90f..7dd2f394c0d 100644
--- a/src/include/storage/buffile.h
+++ b/src/include/storage/buffile.h
@@ -55,5 +55,7 @@ extern BufFile *BufFileOpenFileSet(FileSet *fileset, const char *name,
extern void BufFileDeleteFileSet(FileSet *fileset, const char *name,
bool missing_ok);
extern void BufFileTruncateFileSet(BufFile *file, int fileno, off_t offset);
+extern void BufFileFreeBuffer(BufFile *file);
+extern bool BufFileHasBuffer(BufFile *file);
#endif /* BUFFILE_H */
Re: Adjusting hash join memory limit to handle batch explosion
On Sat, Jan 11, 2025 at 7:42 PM Tomas Vondra <tomas@vondra.me> wrote:
I had a quiet evening yesterday, so I decided to take a stab at this and
see how hard would it be, and how bad would the impact be. Attached is
an experimental patch, doing the *bare* minimum for a simple query:1) It defines a limit of 128 batches (a bit low, but also 1MB). In
practice we'd use something like 256 - 1024, probably. Doesn't matter.2) Ensures the initial pass over data in MultiExecPrivateHash does not
use more than 128 batches, switches to "tooManyBatches=true" if that
happens (and dumps the batch to file ExecHashDumpBatchToFile, even if
it's batchno=0). And later it calls ExecHashHandleTooManyBatches() to
increase the nbatch further.3) Does something similar for the outer relation - if there are too many
batches, we do ExecHashJoinRepartitionBatches() which first partitions
into 128 batches. This only does a single pass in the WIP, though.
Should be recursive or something.4) Extends the BufFile API with BufFileHasBuffer/BufFileFreeBuffer so
that the code can free the buffers. It also means the buffer needs to be
allocated separately, not embedded in BufFile struct. (I'm a bit
surprised it works without having to re-read the buffer after freeing
it, but that's probably thanks to how hashjoin uses the files).
I started looking at this. Even though you do explain what it does
above, I still found it a bit hard to follow. Could you walk through
an example (like the one you gave in SQL) but explaining what happens
in the implementation? Basically what you have in 2 and 3 above but
with a specific example.
This is my understanding of what this does:
if we are at the max number of batches when building the hashtable and
we run out of space and need to double nbatches, we
1. dump the data from the current batch that is in the hashtable into a file
2. close and flush are the currently open buffiles, double the number
of batches, and then only open files for the batches we need to store
tuples from the batch we were trying to put in the hashtable when we
hit the limit (now in a temp file)
I also don't understand why ExecHashJoinRepartitionBatches() is needed
-- I think it has something to do with needing a certain number of
buffers open while processing batch 0, but what does this have to do
with the outer side of the join?
Another random question: why doesn't ExecHashHandleTooManyBatches()
free the outer batch files?
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 1/13/25 17:32, Melanie Plageman wrote:
On Sat, Jan 11, 2025 at 7:42 PM Tomas Vondra <tomas@vondra.me> wrote:
I had a quiet evening yesterday, so I decided to take a stab at this and
see how hard would it be, and how bad would the impact be. Attached is
an experimental patch, doing the *bare* minimum for a simple query:1) It defines a limit of 128 batches (a bit low, but also 1MB). In
practice we'd use something like 256 - 1024, probably. Doesn't matter.2) Ensures the initial pass over data in MultiExecPrivateHash does not
use more than 128 batches, switches to "tooManyBatches=true" if that
happens (and dumps the batch to file ExecHashDumpBatchToFile, even if
it's batchno=0). And later it calls ExecHashHandleTooManyBatches() to
increase the nbatch further.3) Does something similar for the outer relation - if there are too many
batches, we do ExecHashJoinRepartitionBatches() which first partitions
into 128 batches. This only does a single pass in the WIP, though.
Should be recursive or something.4) Extends the BufFile API with BufFileHasBuffer/BufFileFreeBuffer so
that the code can free the buffers. It also means the buffer needs to be
allocated separately, not embedded in BufFile struct. (I'm a bit
surprised it works without having to re-read the buffer after freeing
it, but that's probably thanks to how hashjoin uses the files).I started looking at this. Even though you do explain what it does
above, I still found it a bit hard to follow. Could you walk through
an example (like the one you gave in SQL) but explaining what happens
in the implementation? Basically what you have in 2 and 3 above but
with a specific example.
OK, I'll try ... see the end of this message.
This is my understanding of what this does:
if we are at the max number of batches when building the hashtable and
we run out of space and need to double nbatches, we
1. dump the data from the current batch that is in the hashtable into a file
2. close and flush are the currently open buffiles, double the number
of batches, and then only open files for the batches we need to store
tuples from the batch we were trying to put in the hashtable when we
hit the limit (now in a temp file)
Roughly, but the second step needs to happen only after we finish the
first pass over the inner relation. I'll try to explain this as part of
the example.
I also don't understand why ExecHashJoinRepartitionBatches() is needed
-- I think it has something to do with needing a certain number of
buffers open while processing batch 0, but what does this have to do
with the outer side of the join?
No, this is about building batches on the outer side. We've built the
hash table, and we may have ended with a very high nbatch. We can't
build all of them right away (would need too many buffiles), so we do
that in multiple phases, to not cross the limit.
Another random question: why doesn't ExecHashHandleTooManyBatches()
free the outer batch files?
Because it was tailored for the example when all batch splits happen for
batch 0, before we even start processing the outer side. In practice it
probably should free the files.
Let's do the example - as I mentioned, I only tried doing this for the
case where all the batch increases happen for batch 0, before we start
building the outer batches. I'm 99% sure the patch will need to modify a
couple more places to handle batch increases in later stages.
Assume we don't want to use more than 128 batches, but that we're
running a query that needs 256 batches. The patch will do this:
1) ExecHashTableCreate will set nbatch_maximum=128 as the limit for the
current pass over inner relation, and it'll cap the other nbatch fields
accordingly. If we already know we'll need more batches, we set
tooManyBatches=true to remember this.
But let's we start with nbatch=64, nbatch_maximum=128 (and thus also
with tooManyBatches=false).
2) We start loading data into the hash table, until exceed the memory
limit for the first time. We double the number to 128, move some of the
data from the hash table to the new batch, and continue.
3) We hit the memory limit again, but this time we've hit
(nbatch == nbatch_maximum)
so we can't double the number of batches. But we also can't continue
adding data to the in-memory hash table, so we set tooManyBatches=true
and we start spilling even the current batch to a file.
4) We finish the first pass over the inner relation with
nbatch = 128
nbatch_maximum = 128
tooManyBatches = true
so we need to do something. We run ExecHashHandleTooManyBatches() starts
increasing the nbatches until the current batch fits into work_mem. We
have nbatch=128, and the query needs nbatch=256, so we only do one loop.
Note: Right now it simply doubles the number of batches in each loop.
But it could be faster and do up to 128 in one step.
128 -> 16k -> 1M
The later batches will already do all the increases in a single step,
that needs an improvement too.
4) After ExecHashHandleTooManyBatches completed, we have the inner side
of the batch mostly "done". We have nbatch=256.
5) We start building batches on the outer side, but we also don't want
to build all the batches at once - we want to build 128 and only then go
to 256 (or further). This is what ExecHashJoinRepartitionBatches does.
If we have too many batches for one pass, we build 128 batches in the
first pass. And then we just read the batch files, doing further splits.
Right now this just does a single pass and thus splits the relation into
128 batches, and then just continues as before. That's enough for 256
batches, because 256 is a single step past 128.
But it really should be recursive / do multiple passes, to handle more
cases with more than 16k batches (although with higher limit it would be
less of an issue).
5) It does free the file buffers in various places. Chances are some of
those places are unnecessary, and it should be done in some more places.
As I said, I don't claim this to handle all cases, especially with
splits in later batches.
Does this make it clearer?
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
Hi,
Here's a somewhat cleaned up version of the original patch series (with
memory balancing) from [1]/messages/by-id/9e01b538-fb62-4386-b703-548818911702@vondra.me.
1) v20250125-0001-Balance-memory-usage-with-hashjoin-batch-e.patch
------------------------------------------------------------------
The 0001 patch does exactly the same thing as
vadjust-size-0001-hashjoin-sizing-balance.patch, except that it moves
the code a bit and (hopefully) does a better job at explaining the logic
in the comments.
I'm fairly happy with how simple and non-invasive this is, and how well
it deals the issue. Sure, limiting the number of batch files (and then
splitting them recursively" later) seems possible and perhaps more
"correct" (in the sense that it better enforces the memory limit). But
it's far more invasive, impacts everyone (not just the rare case of
batch explosion), and doesn't help with "indivisible" batches (we still
end up with the batch explosion).
I don't have capacity/interest to continue working on this (limiting the
number of spill files) in the near term, and even if I had I don't think
it'd be doable for PG18, considering there's just one commitfest.
My plan is to get something like 0001 into PG18. It's strictly better
than what we have now, that's for sure, and I think is good enough for
the rare cases of batch explosion.
The one open question I have is what to do about the hashing, and how we
calculate bucket/batch. With the current scheme we can't increase the
number of buckets above nbuckets_optimal, which is sized for the largest
hash_table we can fit into (work_mem * hash_mem_multiplier). But the
patch is based on the idea that at some point it's better to grow the
hash table beyond that limit.
So either we need to change how we split hash, or just accept that if
the hash table grows too much, we may get longer chains. Which I guess
might be still better than having too many batch files.
The patch uses the lookup table algorithm to reverse bits in the hash
from here:
https://graphics.stanford.edu/~seander/bithacks.html#BitReverseTable
And then it takes nbatch from the reversed value, i.e from the beginning
of the hash (while nbuckets is taken from the end as before). I tried
the other algorithms, but they all seemed slower. Another option would
be to start calculating two separate hashes, or a 64-bit hash (and split
it into two). Not sure.
2) v20250125-0002-Postpone-hashtable-growth-instead-of-disab.patch
------------------------------------------------------------------
0002 is an experimental patch to handle another failure I speculated
about, namely "premature disabling of nbatch growth". The theory was
that if the inner relation is correlated, we may disable nbatch growth
prematurely, because the first nbatch increase happens to not split the
batch at all (because of the correlation).
It turned out to be harder to trigger, because it assumes we actually
start with too few batches - i.e. that we significantly underestimate
the inner relation. I'm sure that can easily happen, but the impact
seems to be less severe than for the batch explosion.
There's a SQL script with an example triggering this in 0003.
The patch simply stops using the "true/false" flag, and instead just
doubles the spaceAllowed threshold (a bit like 0001), effectively
postponing next round of nbatch doubling.
This made me realize we already have the issue with nbuckets sizing - if
we disable nbatch growth (be it forever or just temporarily), we
essentially allow the hash table to exceed the expected size. And thus
the nbuckets may be too low. So we'd already need to increase the number
of buckets, it's not just a matter of the 0001 patch.
3) v20250125-0003-hashjoin-patch-tests.patch
--------------------------------------------
This has some files illustrating the memory usage etc. as explained in
the original message [1]/messages/by-id/9e01b538-fb62-4386-b703-548818911702@vondra.me. But it also has three scripts to reproduce the
issues.
- patch/batch-explosion.sql - batch explosion
- patch/disabled-growth.sql - disabled growth / correlated data
- patch/disabled-growth2.sql - disabled growth / hash pattern
To use these scripts, copy the hash-collisions.data to /tmp (the scripts
copy the data into a table). And then to \i of the script.
Each of the patches has a GUC to enable the behavior
- enable_hashjoin_adjust
- enable_hashjoin_growth
and by default it's "false" i.e. disabled. So if you want to see the new
behavior, you need to explicitly set it to 'true' before the script.
These GUCs are meant only for easier development and would be removed
from the final commit.
regards
[1]: /messages/by-id/9e01b538-fb62-4386-b703-548818911702@vondra.me
/messages/by-id/9e01b538-fb62-4386-b703-548818911702@vondra.me
--
Tomas Vondra
Attachments:
v20250125-0001-Balance-memory-usage-with-hashjoin-batch-e.patchtext/x-patch; charset=UTF-8; name=v20250125-0001-Balance-memory-usage-with-hashjoin-batch-e.patchDownload
From 8c60a1fdec40b8c7b29843e40c8a85abef454461 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:24:23 +0100
Subject: [PATCH v20250125 1/3] Balance memory usage with hashjoin batch
explosion
The basic logic to pick the number of hashjoin batches is concerned only
with the in-memory hash table, adding batches to keep the hash table
within the limit defined by work_mem and hash_mem_multiplier. It ignores
the memory needed by the batch files, but with enough batches this may
be a substantial amount of memory, easily orders of magnitude more than
the hash table.
We've seen reports of hash joins with hundreds of thousands or millions
of batch files, consuming gigabytes of memory, and triggering OOM. These
cases are not too common, but it's clearly possible to hit them.
This patch improves the situation by rebalancing how the memory is
distributed between the hash table and batch files, to minimize the
total memory consumption.
Whenever we need to increase the capacity of the hash node, we can do
that by either doubling the number of batches or doubling the size of
the in-memory hash table. The outcome is the same, allowing the hash
node to handle a relation twice the size. But the memory requirements
may be substantially different, depending on the current hashjoin
parameters (for low nbatch values it's better to add batches, for high
nbatch values it's better to allow a larger hash table).
It may seem a bit strange, as it clearly allows exceeding the memory
limit specified by the GUC parameters. But it has always been like this,
except that the code assumed adding batches is free. The patch just
makes this visible and explicit.
Increasing the hashtable memory limit may also help to prevent the batch
explosion in the first place. Given enough hash collisions or duplicate
hashes it's easy to get a batch that can't be split, resulting in a
cycle of quickly doubling the number of batches. Allowing the hashtable
to get larger may stop this.
---
src/backend/executor/nodeHash.c | 243 +++++++++++++++++++++++++++-
src/backend/utils/misc/guc_tables.c | 11 ++
src/include/executor/hashjoin.h | 2 +
3 files changed, 254 insertions(+), 2 deletions(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..ce3dc6f1fa6 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -80,6 +80,8 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+/* enable adaptive adjustment of hashtable size */
+bool enable_hashjoin_adjust = false;
/* ----------------------------------------------------------------
* ExecHash
@@ -848,6 +850,84 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * Optimize the total amount of memory consumed by the hash node.
+ *
+ * The nbatch calculation above focuses on the size of the in-memory hash
+ * table, ignoring the memory used by batch files. But that can be a lot
+ * of memory - each batch file has a BLCKSZ buffer, and we may need two
+ * files per batch (inner and outer side). So with enough batches this can
+ * be significantly more memory than the hashtable itself, and it grows
+ * quickly as we're adding more batches.
+ *
+ * It might seem cleaner to adjust the calculation above, to consider
+ * memory for both the in-memory hashtable and the batch files, and ensure
+ * it fits into hash_table_bytes. That is, look for a nbatch value so that
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ) <= hash_table_bytes
+ *
+ * But that has a flaw - for sufficiently large inner_rel_bytes value it
+ * may not have a solution (either the hash table is too large or it
+ * requires too many batches). So instead we merely try to minimize the
+ * impact, and use as little memory as possible, instead of strictly
+ * enforcing the memory limit. (But we haven't really enforced it before
+ * either, as we simply ignored the batch files.)
+ *
+ * The basic observation is that given an inner relation of a given size,
+ * we may divide it in arbitrary number of batches, which determines the
+ * memory consumption per the already mentioned formula:
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ)
+ *
+ * That is, we can reduce the number of batches to (nbatch/2), at the
+ * cost of doubling the size of the in-memory hash table. But these two
+ * terms work in opposite ways - size of the in-memory part decreases
+ * with nbatch, while the batch file memory grows very quickly. Initially
+ * the memory usage is dominated by in-memory hash table (for nbatch=0),
+ * then at some point the batch files start to consume more memory.
+ *
+ * If you combine these two, the memory consumption (for a fixed size of
+ * the inner relation) has a u-shape, with a minimum at some nbatch value.
+ * Our goal is to look for this minimum. We do that by calculating memory
+ * usage for (nbatch/2), and accepting it if it's lower than current.
+ *
+ * This means we're only ever reducing nbatch values, we'll never increase
+ * it. We could try moving in both directions, depending on which part of
+ * the curve we're at initially, but that does not seem very useful. Yes,
+ * it might reduce the memory, but in this part the memory is dominated by
+ * the hash table, which we used for the initial nbatch calculation, so
+ * there is little risk of OOM or major memory overruns. The impact on
+ * memory consumption is less significant than when reducing nbatches, and
+ * adding batches can have significant impact. It does not seem worth it.
+ *
+ * While reducing the nbatch value, we increase the nbucket value. With
+ * fewer batches we accept the in-memory hashtable will get larger, but
+ * the earlier calculation sized it for the hash_table_bytes.
+ */
+ while (nbatch > 0)
+ {
+ /* how much memory would we use with half the batches? */
+ size_t space = hash_table_bytes * 2 + (nbatch * BLCKSZ);
+ size_t current = hash_table_bytes + (2 * nbatch * BLCKSZ);
+
+ /* Is the adaptive behavior enabled? */
+ if (!enable_hashjoin_adjust)
+ break;
+
+ /* If the memory usage does not decrease, we have the optimum. */
+ if (current < space)
+ break;
+
+ /*
+ * It's better to use half the batches, so do that and adjust the
+ * nbucket in the opposite direction, and the allowance.
+ */
+ nbatch /= 2;
+ nbuckets *= 2;
+
+ *space_allowed = *space_allowed * 2;
+ }
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
@@ -890,6 +970,99 @@ ExecHashTableDestroy(HashJoinTable hashtable)
pfree(hashtable);
}
+/*
+ * Consider adjusting the allowed hash table size, depending on the number
+ * of batches, to minimize the overall memory usage (for both the hashtable
+ * and batch files).
+ */
+static bool
+ExecHashIncreaseBatchSize(HashJoinTable hashtable)
+{
+ HashMemoryChunk chunk;
+
+ /*
+ * How much memory would doubling nbatch use? Each batch may require
+ * two buffered files (inner/outer), with a BLCKSZ buffer.
+ */
+ size_t batchSpace = (hashtable->nbatch * 2 * BLCKSZ);
+
+ /* Do nothing if the adaptive behavior is disabled. */
+ if (!enable_hashjoin_adjust)
+ return false;
+
+ /*
+ * Compare the new space needed for doubling nbatch and for enlarging the
+ * in-memory hash table. If doubling the hash table needs less memory,
+ * just do that. Otherwise, continue with doubling the nbatch.
+ *
+ * XXX We're comparing the current spaceAllowed/batchSpace values, because
+ * if we double either of those this is the new memory we'll use.
+ */
+ if (hashtable->spaceAllowed > batchSpace)
+ {
+ /* countinue with nbatch doubling */
+ return false;
+ }
+
+ /*
+ * Better to double the size of the in-memory hashtable. We want to increase
+ * the number of buckets too, so we'll rebuild the hash table.
+ */
+ hashtable->spaceAllowed *= 2;
+ hashtable->nbuckets_optimal *= 2;
+ hashtable->log2_nbuckets_optimal++;
+
+ /*
+ * We will scan through the chunks directly, so that we can reset the
+ * buckets now and not have to keep track which tuples in the buckets have
+ * already been processed. We will free the old chunks as we go.
+ *
+ * XXX This is copy from ExecHashIncreaseNumBatches, except that we don't
+ * need to copy/shuffle tuples to other batches, we're only rebuilding
+ * the buckets.
+ */
+ memset(hashtable->buckets.unshared, 0,
+ sizeof(HashJoinTuple) * hashtable->nbuckets);
+ chunk = hashtable->chunks;
+
+ /* so, let's scan through the chunks, and all tuples in each chunk */
+ while (chunk != NULL)
+ {
+ HashMemoryChunk nextchunk = chunk->next.unshared;
+
+ /* position within the buffer (up to oldchunks->used) */
+ size_t idx = 0;
+
+ /* process all tuples stored in this chunk (and then free it) */
+ while (idx < chunk->used)
+ {
+ HashJoinTuple hashTuple = (HashJoinTuple) (HASH_CHUNK_DATA(chunk) + idx);
+ MinimalTuple tuple = HJTUPLE_MINTUPLE(hashTuple);
+ int hashTupleSize = (HJTUPLE_OVERHEAD + tuple->t_len);
+ int bucketno;
+ int batchno;
+
+ ExecHashGetBucketAndBatch(hashtable, hashTuple->hashvalue,
+ &bucketno, &batchno);
+
+ /* and add it back to the appropriate bucket */
+ hashTuple->next.unshared = hashtable->buckets.unshared[bucketno];
+ hashtable->buckets.unshared[bucketno] = hashTuple;
+
+ /* next tuple in this chunk */
+ idx += MAXALIGN(hashTupleSize);
+
+ /* allow this loop to be cancellable */
+ CHECK_FOR_INTERRUPTS();
+ }
+
+ /* we're done with this chunk - proceed to the next one */
+ chunk = nextchunk;
+ }
+
+ return true;
+}
+
/*
* ExecHashIncreaseNumBatches
* increase the original number of batches in order to reduce
@@ -913,6 +1086,10 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (oldnbatch > Min(INT_MAX / 2, MaxAllocSize / (sizeof(void *) * 2)))
return;
+ /* consider increasing size of the in-memory hash table instead */
+ if (ExecHashIncreaseBatchSize(hashtable))
+ return;
+
nbatch = oldnbatch * 2;
Assert(nbatch > 1);
@@ -1793,6 +1970,69 @@ ExecParallelHashTableInsertCurrentBatch(HashJoinTable hashtable,
heap_free_minimal_tuple(tuple);
}
+static inline unsigned char
+reverse_byte(unsigned char x)
+{
+ static const unsigned char table[] = {
+ 0x00, 0x80, 0x40, 0xc0, 0x20, 0xa0, 0x60, 0xe0,
+ 0x10, 0x90, 0x50, 0xd0, 0x30, 0xb0, 0x70, 0xf0,
+ 0x08, 0x88, 0x48, 0xc8, 0x28, 0xa8, 0x68, 0xe8,
+ 0x18, 0x98, 0x58, 0xd8, 0x38, 0xb8, 0x78, 0xf8,
+ 0x04, 0x84, 0x44, 0xc4, 0x24, 0xa4, 0x64, 0xe4,
+ 0x14, 0x94, 0x54, 0xd4, 0x34, 0xb4, 0x74, 0xf4,
+ 0x0c, 0x8c, 0x4c, 0xcc, 0x2c, 0xac, 0x6c, 0xec,
+ 0x1c, 0x9c, 0x5c, 0xdc, 0x3c, 0xbc, 0x7c, 0xfc,
+ 0x02, 0x82, 0x42, 0xc2, 0x22, 0xa2, 0x62, 0xe2,
+ 0x12, 0x92, 0x52, 0xd2, 0x32, 0xb2, 0x72, 0xf2,
+ 0x0a, 0x8a, 0x4a, 0xca, 0x2a, 0xaa, 0x6a, 0xea,
+ 0x1a, 0x9a, 0x5a, 0xda, 0x3a, 0xba, 0x7a, 0xfa,
+ 0x06, 0x86, 0x46, 0xc6, 0x26, 0xa6, 0x66, 0xe6,
+ 0x16, 0x96, 0x56, 0xd6, 0x36, 0xb6, 0x76, 0xf6,
+ 0x0e, 0x8e, 0x4e, 0xce, 0x2e, 0xae, 0x6e, 0xee,
+ 0x1e, 0x9e, 0x5e, 0xde, 0x3e, 0xbe, 0x7e, 0xfe,
+ 0x01, 0x81, 0x41, 0xc1, 0x21, 0xa1, 0x61, 0xe1,
+ 0x11, 0x91, 0x51, 0xd1, 0x31, 0xb1, 0x71, 0xf1,
+ 0x09, 0x89, 0x49, 0xc9, 0x29, 0xa9, 0x69, 0xe9,
+ 0x19, 0x99, 0x59, 0xd9, 0x39, 0xb9, 0x79, 0xf9,
+ 0x05, 0x85, 0x45, 0xc5, 0x25, 0xa5, 0x65, 0xe5,
+ 0x15, 0x95, 0x55, 0xd5, 0x35, 0xb5, 0x75, 0xf5,
+ 0x0d, 0x8d, 0x4d, 0xcd, 0x2d, 0xad, 0x6d, 0xed,
+ 0x1d, 0x9d, 0x5d, 0xdd, 0x3d, 0xbd, 0x7d, 0xfd,
+ 0x03, 0x83, 0x43, 0xc3, 0x23, 0xa3, 0x63, 0xe3,
+ 0x13, 0x93, 0x53, 0xd3, 0x33, 0xb3, 0x73, 0xf3,
+ 0x0b, 0x8b, 0x4b, 0xcb, 0x2b, 0xab, 0x6b, 0xeb,
+ 0x1b, 0x9b, 0x5b, 0xdb, 0x3b, 0xbb, 0x7b, 0xfb,
+ 0x07, 0x87, 0x47, 0xc7, 0x27, 0xa7, 0x67, 0xe7,
+ 0x17, 0x97, 0x57, 0xd7, 0x37, 0xb7, 0x77, 0xf7,
+ 0x0f, 0x8f, 0x4f, 0xcf, 0x2f, 0xaf, 0x6f, 0xef,
+ 0x1f, 0x9f, 0x5f, 0xdf, 0x3f, 0xbf, 0x7f, 0xff,
+ };
+
+ return table[x];
+}
+
+/*
+ * Reverse bits using a lookup table from here:
+ *
+ * https://graphics.stanford.edu/~seander/bithacks.html#BitReverseTable
+ *
+ * FWIW the 4-ops algorithm seems to be a bit faster.
+ */
+static inline uint32
+reverse_uint32(uint32 x)
+{
+ uint32 ret;
+
+ unsigned char *src = (unsigned char *) &x;
+ unsigned char *dst = (unsigned char *) &ret;
+
+ dst[0] = reverse_byte(src[3]);
+ dst[1] = reverse_byte(src[2]);
+ dst[2] = reverse_byte(src[1]);
+ dst[3] = reverse_byte(src[0]);
+
+ return ret;
+}
/*
* ExecHashGetBucketAndBatch
@@ -1833,8 +2073,7 @@ ExecHashGetBucketAndBatch(HashJoinTable hashtable,
if (nbatch > 1)
{
*bucketno = hashvalue & (nbuckets - 1);
- *batchno = pg_rotate_right32(hashvalue,
- hashtable->log2_nbuckets) & (nbatch - 1);
+ *batchno = reverse_uint32(hashvalue) & (nbatch - 1);
}
else
{
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index 38cb9e970d5..874cc3547a2 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -46,6 +46,7 @@
#include "commands/vacuum.h"
#include "common/file_utils.h"
#include "common/scram-common.h"
+#include "executor/hashjoin.h"
#include "jit/jit.h"
#include "libpq/auth.h"
#include "libpq/libpq.h"
@@ -900,6 +901,16 @@ struct config_bool ConfigureNamesBool[] =
true,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_adjust", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables adjusting hashtable size to minimize memory usage."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_adjust,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 67ae89c8257..e16a03a87f8 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -71,6 +71,8 @@
* ----------------------------------------------------------------
*/
+extern PGDLLIMPORT bool enable_hashjoin_adjust;
+
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
/* typedef struct HashJoinTableData *HashJoinTable; */
--
2.47.1
v20250125-0002-Postpone-hashtable-growth-instead-of-disab.patchtext/x-patch; charset=UTF-8; name=v20250125-0002-Postpone-hashtable-growth-instead-of-disab.patchDownload
From 68e2b96406c8c70042ce0f6dc32d5ebb88e52c4e Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Thu, 23 Jan 2025 22:21:28 +0100
Subject: [PATCH v20250125 2/3] Postpone hashtable growth instead of disabling
it
After increasing the number of batches and splitting the current one, we
used to disable further growth if all tuples went into only one of the
two new batches. It's possible to construct cases when this leads to
disabling growth prematurely - maybe we can't split the batch now, but
that doesn't mean we could not split it later.
This generally requires underestimated size of the inner relation, so
that we need to increase the number of batches. And then also hashes
non-random in some way. There may be a "common" prefix, or maybe the
data is just correlated in some way.
So instead of hard-disabling the growth permanently, double the memory
limit so that we retry the split after processing more data. Doubling
the limit is somewhat arbitrary - it's the earliest when we could split
the batch in half even if all the current tuples have duplicate hashes.
But we could pick any other value, to retry sooner/later.
---
src/backend/executor/nodeHash.c | 50 ++++++++++++++++++++++++++++-
src/backend/utils/misc/guc_tables.c | 10 ++++++
src/include/executor/hashjoin.h | 1 +
3 files changed, 60 insertions(+), 1 deletion(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index ce3dc6f1fa6..ba95c5e87e9 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -83,6 +83,9 @@ static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
/* enable adaptive adjustment of hashtable size */
bool enable_hashjoin_adjust = false;
+/* enable soft-disable of nbatch growth */
+bool enable_hashjoin_growth = false;
+
/* ----------------------------------------------------------------
* ExecHash
*
@@ -1220,10 +1223,55 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
* Increasing nbatch will not fix it since there's no way to subdivide the
* group any more finely. We have to just gut it out and hope the server
* has enough RAM.
+ *
+ * XXX This logic for hard-disabling nbatch growth assumes that if we're
+ * unable to split the batch now, we'll be unable to split it forever. That
+ * works well for "nice" random data sets, but it's not difficult to come
+ * up with cases where the assumption does not hold.
+ *
+ * 1) The hashes may share the same "prefix", but the next bit may be
+ * random - and doubling the number of batches again would split the batch
+ * about evenly. This is rather unlikely to happen by chance, of course.
+ *
+ * 2) The dataset may be correlated in some way, i.e. produce values with
+ * one hash value first, before producing other values. This might happen
+ * for data read from index, etc. If we underestimate the amount of data
+ * we will need to add to the hash, this may disable nbatch growth while
+ * still reading the first value, i.e. too soon.
+ *
+ * XXX With the "hard disable" this also handles the case when we run out
+ * of hash bits, because the first doubling after that only has "0" in the
+ * new bit, which means we get (nfreed == ninmemory) and stop adding more
+ * batches. But with the soft disable that's no longer the case, and we'd
+ * try adding more batches after a while. Not sure this a big deal, as the
+ * memory limit would need to be pretty substantial in that case already
+ * (assuming we already doubled the limit multiple times). But maybe t
+ * could be the first time we're hitting this condition? Perhaps the right
+ * solution is to detect running out of hash bits explicitly, and just do
+ * a "hard disable" in this case?
+ *
+ * XXX This should probably also increase the number of buckets etc.
*/
if (nfreed == 0 || nfreed == ninmemory)
{
- hashtable->growEnabled = false;
+ /*
+ * If enable_hashjoin_grow=true, don't disable the growth permanently,
+ * and instead just increase the limit in the hope that we'll be able
+ * to split the batches in the future.
+ *
+ * XXX Not sure if 2.0 is the optimal growth factor, but it seems quite
+ * reasonable because it aligns with the expectation that we'll be able
+ * to split the memory usage in half. It's not perfect, because if we
+ * add a random bit to the hash, we might split earlier than that.
+ */
+ if (enable_hashjoin_growth)
+ {
+ /* XXX Do we need to worry about overlowing spaceAllowed? */
+ hashtable->spaceAllowed *= 2.0;
+ }
+ else
+ hashtable->growEnabled = false;
+
#ifdef HJDEBUG
printf("Hashjoin %p: disabling further increase of nbatch\n",
hashtable);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index 874cc3547a2..7881f4439c9 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -911,6 +911,16 @@ struct config_bool ConfigureNamesBool[] =
false,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_growth", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables growing the hash table memory limit instead of disabling batch increases permanently."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_growth,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index e16a03a87f8..87df7f5013b 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -72,6 +72,7 @@
*/
extern PGDLLIMPORT bool enable_hashjoin_adjust;
+extern PGDLLIMPORT bool enable_hashjoin_growth;
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
--
2.47.1
v20250125-0003-hashjoin-patch-tests.patchtext/x-patch; charset=UTF-8; name=v20250125-0003-hashjoin-patch-tests.patchDownload
From 08a5eea3713923e34ab0f43342e88315dc84e4b8 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:25:34 +0100
Subject: [PATCH v20250125 3/3] hashjoin patch tests
---
patch/batch-explosion.sql | 46 +
patch/disabled-growth.sql | 41 +
patch/disabled-growth2.sql | 44 +
patch/hash-collisions.data | 3819 +++++++++++++++++++++++++++++++++
patch/hash-memory-model-1.pdf | Bin 0 -> 16991 bytes
patch/hash-memory-model-2.pdf | Bin 0 -> 17323 bytes
patch/hash-memory-model.ods | Bin 0 -> 38902 bytes
patch/hash.data | 395 ++++
patch/hash.plot | 16 +
patch/hashjoin.png | Bin 0 -> 258027 bytes
patch/hashjoin.svg | 1083 ++++++++++
11 files changed, 5444 insertions(+)
create mode 100644 patch/batch-explosion.sql
create mode 100644 patch/disabled-growth.sql
create mode 100644 patch/disabled-growth2.sql
create mode 100644 patch/hash-collisions.data
create mode 100644 patch/hash-memory-model-1.pdf
create mode 100644 patch/hash-memory-model-2.pdf
create mode 100644 patch/hash-memory-model.ods
create mode 100644 patch/hash.data
create mode 100644 patch/hash.plot
create mode 100644 patch/hashjoin.png
create mode 100644 patch/hashjoin.svg
diff --git a/patch/batch-explosion.sql b/patch/batch-explosion.sql
new file mode 100644
index 00000000000..ca347379100
--- /dev/null
+++ b/patch/batch-explosion.sql
@@ -0,0 +1,46 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+
+insert into t select i, md5(i::text) from generate_series(1,1000000) s(i);
+
+create index on t (b);
+cluster t USING t_b_idx;
+
+vacuum analyze;
+
+set hash_mem_multiplier = 1.0;
+
+set work_mem = '2MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '1MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '512kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '768kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
diff --git a/patch/disabled-growth.sql b/patch/disabled-growth.sql
new file mode 100644
index 00000000000..88aa0706945
--- /dev/null
+++ b/patch/disabled-growth.sql
@@ -0,0 +1,41 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data' ;
+
+-- make it larger than 1MB
+insert into t select * from t;
+insert into t select * from t;
+insert into t select * from t;
+
+-- add a couple redundant columns
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+
+insert into t select i, md5(i::text), r, r, r, r from (select i, random() as r from generate_series(1,1000000) s(i) where hashint4(i) > 1000000);
+
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '512kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/disabled-growth2.sql b/patch/disabled-growth2.sql
new file mode 100644
index 00000000000..c82f4087461
--- /dev/null
+++ b/patch/disabled-growth2.sql
@@ -0,0 +1,44 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+-- make sure the hash has first 3 bits set to 0
+insert into t select i a, md5(i::text) from generate_series(1,1000000) s(i) where hashint4(i)::bit(32) & 0xE0000000::bit(32) = 0x00000000::bit(32);
+
+insert into t select * from t;
+insert into t select * from t;
+
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '2MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '256kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/hash-collisions.data b/patch/hash-collisions.data
new file mode 100644
index 00000000000..5771aba40db
--- /dev/null
+++ b/patch/hash-collisions.data
@@ -0,0 +1,3819 @@
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diff --git a/patch/hash-memory-model.ods b/patch/hash-memory-model.ods
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diff --git a/patch/hash.data b/patch/hash.data
new file mode 100644
index 00000000000..53a9240b125
--- /dev/null
+++ b/patch/hash.data
@@ -0,0 +1,395 @@
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diff --git a/patch/hash.plot b/patch/hash.plot
new file mode 100644
index 00000000000..76703ce5579
--- /dev/null
+++ b/patch/hash.plot
@@ -0,0 +1,16 @@
+set terminal svg size 1200,800 fname 'Verdana,10'
+set output 'hashjoin.svg'
+
+#set terminal pngcairo size 1600,1000 enhanced font 'Verdana,9'
+#set output 'hashjoin.png'
+
+set nokey
+
+set ticslevel 0
+set pm3d at b
+set xtics offset 0,-0.5 rotate by 45
+
+set xlabel "workmem"
+set ylabel "batches"
+
+splot "hash.data" using 1:2:5:xtic(3):ytic(4) with lines lc rgb "#00bb00"
diff --git a/patch/hashjoin.png b/patch/hashjoin.png
new file mode 100644
index 0000000000000000000000000000000000000000..aef9d118a3e6217f6fd8022e82c11e288674ad76
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+<g fill="none" color="black" stroke="currentColor" stroke-width="1.00" stroke-linecap="butt" stroke-linejoin="miter">
+ <path stroke='black' d='M1068.74,318.51 L1076.24,318.51 M1114.36,273.12 L1106.86,273.12 '/> <g transform="translate(1121.36,276.37)" stroke="none" fill="black" font-family="Verdana" font-size="10.00" text-anchor="start">
+ <text><tspan font-family="Verdana" > 2×10</tspan><tspan font-family="Verdana" font-size="8.0" dy="-5.00px">7</tspan><tspan font-size="10.0" dy="5.00"></tspan></text>
+ </g>
+</g>
+<g fill="none" color="black" stroke="currentColor" stroke-width="1.00" stroke-linecap="butt" stroke-linejoin="miter">
+ <path stroke='black' d='M1068.74,273.12 L1076.24,273.12 M1114.36,227.74 L1106.86,227.74 '/> <g transform="translate(1121.36,230.99)" stroke="none" fill="black" font-family="Verdana" font-size="10.00" text-anchor="start">
+ <text><tspan font-family="Verdana" > 2.5×10</tspan><tspan font-family="Verdana" font-size="8.0" dy="-5.00px">7</tspan><tspan font-size="10.0" dy="5.00"></tspan></text>
+ </g>
+</g>
+<g fill="none" color="black" stroke="currentColor" stroke-width="1.00" stroke-linecap="butt" stroke-linejoin="miter">
+ <path stroke='black' d='M1068.74,227.74 L1076.24,227.74 M1114.36,182.35 L1106.86,182.35 '/> <g transform="translate(1121.36,185.60)" stroke="none" fill="black" font-family="Verdana" font-size="10.00" text-anchor="start">
+ <text><tspan font-family="Verdana" > 3×10</tspan><tspan font-family="Verdana" font-size="8.0" dy="-5.00px">7</tspan><tspan font-size="10.0" dy="5.00"></tspan></text>
+ </g>
+</g>
+<g fill="none" color="black" stroke="currentColor" stroke-width="1.00" stroke-linecap="butt" stroke-linejoin="miter">
+ <path stroke='black' d='M1068.74,182.35 L1076.24,182.35 '/></g>
+</g>
+</svg>
+
--
2.47.1
Re: Adjusting hash join memory limit to handle batch explosion
Hi,
Here's a slightly simplified version of the "balancing" patch. I decided
to stop increasing the nbucket value at runtime, even if the hashtable
grows larger than the memory limit (which is what we used to calculate
the initial nbucket value in ExecChooseHashTableSize).
Without the nbucket changes the patch does not need to change how
ExecHashGetBucketAndBatch() calculates bucketno/batchno, which added
quite a bit of complexity (the patch from a couple days ago simply
inverted bits in the hash when calculating batchno, but I'm sure there
are other ways to do this).
This doesn't mean we can't adjust nbucket at all - we just can't do that
at runtime, after we started building the hash. We still can adjust
nbucket in ExecChooseHashTableSize(), if we know we'll need many batches
at that point.
FWIW this is not a new issue, introduced by this patch. We can already
have issues with nbucket being too low if we disable growth of the hash
table early, in which case it can get almost arbitrarily large. And we
don't resize nbucket in that case either. Granted, we usually disable
growth in cases when there are duplicate values/hashes, so increasing
nbucket would not really do much (the tuples would still go to the same
bucket). But there are ways to disable disable growth early.
The 0002 patch helps with those cases a bit by retrying the nbatch
growth after a while, instead of disabling it forever.
0003 is just a WIP patch with a couple reproducers for the issues.
regards
--
Tomas Vondra
Attachments:
v20250206-0003-hashjoin-patch-tests.patchtext/x-patch; charset=UTF-8; name=v20250206-0003-hashjoin-patch-tests.patchDownload
From 87f9b93a802b07abaaa252bf2ee6be0d65b70b88 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:25:34 +0100
Subject: [PATCH v20250206 3/3] hashjoin patch tests
---
patch/batch-explosion.sql | 46 +
patch/disabled-growth.sql | 41 +
patch/disabled-growth2.sql | 44 +
patch/hash-collisions.data | 3819 +++++++++++++++++++++++++++++++++
patch/hash-memory-model-1.pdf | Bin 0 -> 16991 bytes
patch/hash-memory-model-2.pdf | Bin 0 -> 17323 bytes
patch/hash-memory-model.ods | Bin 0 -> 38902 bytes
patch/hash.data | 395 ++++
patch/hash.plot | 16 +
patch/hashjoin.png | Bin 0 -> 258027 bytes
patch/hashjoin.svg | 1083 ++++++++++
11 files changed, 5444 insertions(+)
create mode 100644 patch/batch-explosion.sql
create mode 100644 patch/disabled-growth.sql
create mode 100644 patch/disabled-growth2.sql
create mode 100644 patch/hash-collisions.data
create mode 100644 patch/hash-memory-model-1.pdf
create mode 100644 patch/hash-memory-model-2.pdf
create mode 100644 patch/hash-memory-model.ods
create mode 100644 patch/hash.data
create mode 100644 patch/hash.plot
create mode 100644 patch/hashjoin.png
create mode 100644 patch/hashjoin.svg
diff --git a/patch/batch-explosion.sql b/patch/batch-explosion.sql
new file mode 100644
index 00000000000..ca347379100
--- /dev/null
+++ b/patch/batch-explosion.sql
@@ -0,0 +1,46 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+
+insert into t select i, md5(i::text) from generate_series(1,1000000) s(i);
+
+create index on t (b);
+cluster t USING t_b_idx;
+
+vacuum analyze;
+
+set hash_mem_multiplier = 1.0;
+
+set work_mem = '2MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '1MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '512kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '768kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
diff --git a/patch/disabled-growth.sql b/patch/disabled-growth.sql
new file mode 100644
index 00000000000..88aa0706945
--- /dev/null
+++ b/patch/disabled-growth.sql
@@ -0,0 +1,41 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data' ;
+
+-- make it larger than 1MB
+insert into t select * from t;
+insert into t select * from t;
+insert into t select * from t;
+
+-- add a couple redundant columns
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+
+insert into t select i, md5(i::text), r, r, r, r from (select i, random() as r from generate_series(1,1000000) s(i) where hashint4(i) > 1000000);
+
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '512kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/disabled-growth2.sql b/patch/disabled-growth2.sql
new file mode 100644
index 00000000000..e162ad135ea
--- /dev/null
+++ b/patch/disabled-growth2.sql
@@ -0,0 +1,44 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+-- make sure the hash has first 3 bits set to 0
+insert into t select i a, md5(i::text) from generate_series(1,1000000) s(i) where hashint4(i)::bit(32) & 0x0000E000::bit(32) = 0x00000000::bit(32);
+
+insert into t select * from t;
+insert into t select * from t;
+
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '2MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '256kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/hash-collisions.data b/patch/hash-collisions.data
new file mode 100644
index 00000000000..5771aba40db
--- /dev/null
+++ b/patch/hash-collisions.data
@@ -0,0 +1,3819 @@
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diff --git a/patch/hash-memory-model.ods b/patch/hash-memory-model.ods
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diff --git a/patch/hash.data b/patch/hash.data
new file mode 100644
index 00000000000..53a9240b125
--- /dev/null
+++ b/patch/hash.data
@@ -0,0 +1,395 @@
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diff --git a/patch/hash.plot b/patch/hash.plot
new file mode 100644
index 00000000000..76703ce5579
--- /dev/null
+++ b/patch/hash.plot
@@ -0,0 +1,16 @@
+set terminal svg size 1200,800 fname 'Verdana,10'
+set output 'hashjoin.svg'
+
+#set terminal pngcairo size 1600,1000 enhanced font 'Verdana,9'
+#set output 'hashjoin.png'
+
+set nokey
+
+set ticslevel 0
+set pm3d at b
+set xtics offset 0,-0.5 rotate by 45
+
+set xlabel "workmem"
+set ylabel "batches"
+
+splot "hash.data" using 1:2:5:xtic(3):ytic(4) with lines lc rgb "#00bb00"
diff --git a/patch/hashjoin.png b/patch/hashjoin.png
new file mode 100644
index 0000000000000000000000000000000000000000..aef9d118a3e6217f6fd8022e82c11e288674ad76
GIT binary patch
literal 258027
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+ <text><tspan font-family="Verdana" > 1×10</tspan><tspan font-family="Verdana" font-size="8.0" dy="-5.00px">7</tspan><tspan font-size="10.0" dy="5.00"></tspan></text>
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+ <text><tspan font-family="Verdana" > 3×10</tspan><tspan font-family="Verdana" font-size="8.0" dy="-5.00px">7</tspan><tspan font-size="10.0" dy="5.00"></tspan></text>
+ </g>
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+ <path stroke='black' d='M1068.74,182.35 L1076.24,182.35 '/></g>
+</g>
+</svg>
+
--
2.47.1
v20250206-0002-Postpone-hashtable-growth-instead-of-disab.patchtext/x-patch; charset=UTF-8; name=v20250206-0002-Postpone-hashtable-growth-instead-of-disab.patchDownload
From edb34c794b8078f4c51bb70be48652e2baf3cfce Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Thu, 23 Jan 2025 22:21:28 +0100
Subject: [PATCH v20250206 2/3] Postpone hashtable growth instead of disabling
it
After increasing the number of batches and splitting the current one, we
used to disable further growth if all tuples went into only one of the
two new batches. It's possible to construct cases when this leads to
disabling growth prematurely - maybe we can't split the batch now, but
that doesn't mean we could not split it later.
This generally requires underestimated size of the inner relation, so
that we need to increase the number of batches. And then also hashes
non-random in some way. There may be a "common" prefix, or maybe the
data is just correlated in some way.
So instead of hard-disabling the growth permanently, double the memory
limit so that we retry the split after processing more data. Doubling
the limit is somewhat arbitrary - it's the earliest when we could split
the batch in half even if all the current tuples have duplicate hashes.
But we could pick any other value, to retry sooner/later.
---
src/backend/executor/nodeHash.c | 50 ++++++++++++++++++++++++++++-
src/backend/utils/misc/guc_tables.c | 10 ++++++
src/include/executor/hashjoin.h | 1 +
3 files changed, 60 insertions(+), 1 deletion(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 8979b635c1f..d34824097f8 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -83,6 +83,9 @@ static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
/* enable adaptive adjustment of hashtable size */
bool enable_hashjoin_adjust = false;
+/* enable soft-disable of nbatch growth */
+bool enable_hashjoin_growth = false;
+
/* ----------------------------------------------------------------
* ExecHash
*
@@ -1182,10 +1185,55 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
* Increasing nbatch will not fix it since there's no way to subdivide the
* group any more finely. We have to just gut it out and hope the server
* has enough RAM.
+ *
+ * XXX This logic for hard-disabling nbatch growth assumes that if we're
+ * unable to split the batch now, we'll be unable to split it forever. That
+ * works well for "nice" random data sets, but it's not difficult to come
+ * up with cases where the assumption does not hold.
+ *
+ * 1) The hashes may share the same "prefix", but the next bit may be
+ * random - and doubling the number of batches again would split the batch
+ * about evenly. This is rather unlikely to happen by chance, of course.
+ *
+ * 2) The dataset may be correlated in some way, i.e. produce values with
+ * one hash value first, before producing other values. This might happen
+ * for data read from index, etc. If we underestimate the amount of data
+ * we will need to add to the hash, this may disable nbatch growth while
+ * still reading the first value, i.e. too soon.
+ *
+ * XXX With the "hard disable" this also handles the case when we run out
+ * of hash bits, because the first doubling after that only has "0" in the
+ * new bit, which means we get (nfreed == ninmemory) and stop adding more
+ * batches. But with the soft disable that's no longer the case, and we'd
+ * try adding more batches after a while. Not sure this a big deal, as the
+ * memory limit would need to be pretty substantial in that case already
+ * (assuming we already doubled the limit multiple times). But maybe t
+ * could be the first time we're hitting this condition? Perhaps the right
+ * solution is to detect running out of hash bits explicitly, and just do
+ * a "hard disable" in this case?
+ *
+ * XXX This should probably also increase the number of buckets etc.
*/
if (nfreed == 0 || nfreed == ninmemory)
{
- hashtable->growEnabled = false;
+ /*
+ * If enable_hashjoin_grow=true, don't disable the growth permanently,
+ * and instead just increase the limit in the hope that we'll be able
+ * to split the batches in the future.
+ *
+ * XXX Not sure if 2.0 is the optimal growth factor, but it seems quite
+ * reasonable because it aligns with the expectation that we'll be able
+ * to split the memory usage in half. It's not perfect, because if we
+ * add a random bit to the hash, we might split earlier than that.
+ */
+ if (enable_hashjoin_growth)
+ {
+ /* XXX Do we need to worry about overlowing spaceAllowed? */
+ hashtable->spaceAllowed *= 2.0;
+ }
+ else
+ hashtable->growEnabled = false;
+
#ifdef HJDEBUG
printf("Hashjoin %p: disabling further increase of nbatch\n",
hashtable);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index b173b1d46c5..9b2404d17a7 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -911,6 +911,16 @@ struct config_bool ConfigureNamesBool[] =
false,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_growth", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables growing the hash table memory limit instead of disabling batch increases permanently."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_growth,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 0eb1da8f66a..af5044802d8 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -72,6 +72,7 @@
*/
extern PGDLLIMPORT bool enable_hashjoin_adjust;
+extern PGDLLIMPORT bool enable_hashjoin_growth;
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
--
2.47.1
v20250206-0001-Balance-memory-usage-with-hashjoin-batch-e.patchtext/x-patch; charset=UTF-8; name=v20250206-0001-Balance-memory-usage-with-hashjoin-batch-e.patchDownload
From 86af4fb9a11b4169152fa1c9863ef94b8639fff7 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:24:23 +0100
Subject: [PATCH v20250206 1/3] Balance memory usage with hashjoin batch
explosion
The basic logic to pick the number of hashjoin batches is concerned only
with the in-memory hash table, adding batches to keep the hash table
within the limit defined by work_mem and hash_mem_multiplier. It ignores
the memory needed by the batch files, but with enough batches this may
be a substantial amount of memory, easily orders of magnitude more than
the hash table.
We've seen reports of hash joins with hundreds of thousands or millions
of batch files, consuming gigabytes of memory, and triggering OOM. These
cases are not too common, but it's clearly possible to hit them.
This patch improves the situation by rebalancing how the memory is
distributed between the hash table and batch files, to minimize the
total memory consumption.
Whenever we need to increase the capacity of the hash node, we can do
that by either doubling the number of batches or doubling the size of
the in-memory hash table. The outcome is the same, allowing the hash
node to handle a relation twice the size. But the memory requirements
may be substantially different, depending on the current hashjoin
parameters (for low nbatch values it's better to add batches, for high
nbatch values it's better to allow a larger hash table).
It may seem a bit strange, as it clearly allows exceeding the memory
limit specified by the GUC parameters. But it has always been like this,
except that the code assumed adding batches is free. The patch just
makes this visible and explicit.
Increasing the hashtable memory limit may also help to prevent the batch
explosion in the first place. Given enough hash collisions or duplicate
hashes it's easy to get a batch that can't be split, resulting in a
cycle of quickly doubling the number of batches. Allowing the hashtable
to get larger may stop this.
---
src/backend/executor/nodeHash.c | 139 ++++++++++++++++++++++++++++
src/backend/utils/misc/guc_tables.c | 11 +++
src/include/executor/hashjoin.h | 2 +
3 files changed, 152 insertions(+)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..8979b635c1f 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -80,6 +80,8 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+/* enable adaptive adjustment of hashtable size */
+bool enable_hashjoin_adjust = false;
/* ----------------------------------------------------------------
* ExecHash
@@ -848,6 +850,105 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * Optimize the total amount of memory consumed by the hash node.
+ *
+ * The nbatch calculation above focuses on the size of the in-memory hash
+ * table, ignoring the memory used by batch files. But that can be a lot
+ * of memory - each batch file has a BLCKSZ buffer, and we may need two
+ * files per batch (inner and outer side). So with enough batches this can
+ * be significantly more memory than the hashtable itself, and it grows
+ * quickly as we're adding more batches.
+ *
+ * It might seem cleaner to adjust the calculation above, to consider
+ * memory for both the in-memory hashtable and the batch files, and ensure
+ * it fits into hash_table_bytes. That is, look for a nbatch value so that
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ) <= hash_table_bytes
+ *
+ * But that has a flaw - for sufficiently large inner_rel_bytes value it
+ * may not have a solution (either the hash table is too large or it
+ * requires too many batches). So instead we merely try to minimize the
+ * impact, and use as little memory as possible, instead of strictly
+ * enforcing the memory limit. (But we haven't really enforced it before
+ * either, as we simply ignored the batch files.)
+ *
+ * The basic observation is that given an inner relation of a given size,
+ * we may divide it in arbitrary number of batches, which determines the
+ * memory consumption per the already mentioned formula:
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ)
+ *
+ * That is, we can reduce the number of batches to (nbatch/2), at the
+ * cost of doubling the size of the in-memory hash table. But these two
+ * terms work in opposite ways - size of the in-memory part decreases
+ * with nbatch, while the batch file memory grows very quickly. Initially
+ * the memory usage is dominated by in-memory hash table (for nbatch=0),
+ * then at some point the batch files start to consume more memory.
+ *
+ * If you combine these two, the memory consumption (for a fixed size of
+ * the inner relation) has a u-shape, with a minimum at some nbatch value.
+ * Our goal is to look for this minimum. We do that by calculating memory
+ * usage for (nbatch/2), and accepting it if it's lower than current.
+ *
+ * This means we're only ever reducing nbatch values, we'll never increase
+ * it (as we're not considering nbatch*2). We could counsider that too,
+ * depending on which part of the [nbatch,work_mem] table we're in. And
+ * for cases with high work_mem values, we would find that adding batches
+ * reduces memory usage. But the hashtable size is what we consider when
+ * calculating the initial nbatch value, and if it's dominating the memory
+ * usage, if means we're not exceeding the expected memory limit (at least
+ * not significantly). There is little risk of OOM or memory overruns. Our
+ * goal is not to minimize the memory usage, but to enforce the limit set
+ * by the user. Minimizing the memory usage would result in spilling many
+ * more batch files, which does not seem great for performance. So we only
+ * ever reduce nbatch, never increase it.
+ *
+ * While growing the hashtable, we also adjust the number of buckets, to
+ * not have more than one tuple per bucket. We can only do this during
+ * the initial sizing - once we start building the hash, we can't add
+ * buckets, due to how ExecHashGetBucketAndBatch() calculates batchno
+ * and bucketno from the hash. Increasing the nbucket value would move
+ * the batchno part in a way that could result in the batchno going
+ * backwards, but that violates the expectation that splitting a batch
+ * moves the tuples only to "future" batches.
+ *
+ * So after the initial sizing (here in ExecChooseHashTableSize), the
+ * number of buckets is effectively fixed. ExecHashGetBucketAndBatch
+ * could calculate batchno/bucketno in a different way, but that's
+ * left as a separate improvement. To some extent this is a preexisting
+ * issue - if we set growEnabled=false, this allows the hashtable to
+ * exceed the memory limit too, and we don't adjust the bucket count.
+ * However, that likely happens due to duplicate values and/or hash
+ * collisions, so it's not clear if increasing the bucket count would
+ * actually spread the tuples through the buckets. It would help with
+ * skewed data sets, when we may disable the growth early, and then
+ * add more tuples with distinct hash values.
+ */
+ while (nbatch > 0)
+ {
+ /* how much memory would we use with half the batches? */
+ size_t space = hash_table_bytes * 2 + (nbatch * BLCKSZ);
+ size_t current = hash_table_bytes + (2 * nbatch * BLCKSZ);
+
+ /* Is the adaptive behavior enabled? */
+ if (!enable_hashjoin_adjust)
+ break;
+
+ /* If the memory usage does not decrease, we have the optimum. */
+ if (current < space)
+ break;
+
+ /*
+ * It's better to use half the batches, so do that and adjust the
+ * nbucket in the opposite direction, and the allowance.
+ */
+ nbatch /= 2;
+ nbuckets *= 2;
+
+ *space_allowed = *space_allowed * 2;
+ }
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
@@ -890,6 +991,40 @@ ExecHashTableDestroy(HashJoinTable hashtable)
pfree(hashtable);
}
+/*
+ * Consider adjusting the allowed hash table size, depending on the number
+ * of batches, to minimize the overall memory usage (for both the hashtable
+ * and batch files).
+ *
+ * XXX Note that while we're adjusting the size of the hash table, we're not
+ * adjusting the (optimal) number of buckets. We can't change that once we
+ * start building the hash, due to how ExecHashGetBucketAndBatch splits the
+ * hash into batchno/bucketno.
+ */
+static bool
+ExecHashIncreaseBatchSize(HashJoinTable hashtable)
+{
+ /*
+ * How much memory would doubling nbatch use? Each batch may require
+ * two buffered files (inner/outer), with a BLCKSZ buffer.
+ */
+ size_t batchSpace = (hashtable->nbatch * 2 * BLCKSZ);
+
+ /* Do nothing if the adaptive behavior is disabled. */
+ if (!enable_hashjoin_adjust)
+ return false;
+
+ /*
+ * Compare the new space needed for doubling nbatch and for enlarging the
+ * in-memory hash table. If doubling the hash table needs less memory,
+ * just do that. Otherwise, continue with doubling the nbatch.
+ *
+ * XXX We're comparing the current spaceAllowed/batchSpace values, because
+ * if we double either of those this is the new memory we'll use.
+ */
+ return (hashtable->spaceAllowed <= batchSpace);
+}
+
/*
* ExecHashIncreaseNumBatches
* increase the original number of batches in order to reduce
@@ -913,6 +1048,10 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (oldnbatch > Min(INT_MAX / 2, MaxAllocSize / (sizeof(void *) * 2)))
return;
+ /* consider increasing size of the in-memory hash table instead */
+ if (ExecHashIncreaseBatchSize(hashtable))
+ return;
+
nbatch = oldnbatch * 2;
Assert(nbatch > 1);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index 71448bb4fdd..b173b1d46c5 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -46,6 +46,7 @@
#include "commands/vacuum.h"
#include "common/file_utils.h"
#include "common/scram-common.h"
+#include "executor/hashjoin.h"
#include "jit/jit.h"
#include "libpq/auth.h"
#include "libpq/libpq.h"
@@ -900,6 +901,16 @@ struct config_bool ConfigureNamesBool[] =
true,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_adjust", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables adjusting hashtable size to minimize memory usage."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_adjust,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index ecff4842fd3..0eb1da8f66a 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -71,6 +71,8 @@
* ----------------------------------------------------------------
*/
+extern PGDLLIMPORT bool enable_hashjoin_adjust;
+
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
/* typedef struct HashJoinTableData *HashJoinTable; */
--
2.47.1
Re: Adjusting hash join memory limit to handle batch explosion
On Thu, Feb 6, 2025 at 1:48 PM Tomas Vondra <tomas@vondra.me> wrote:
Hi,
Here's a slightly simplified version of the "balancing" patch. I decided
to stop increasing the nbucket value at runtime, even if the hashtable
grows larger than the memory limit (which is what we used to calculate
the initial nbucket value in ExecChooseHashTableSize).
I started looking at these.
First question is if the guc enable_hashjoin_adjust is for development
or you mean it for users (and for it to be off by default).
In 0001, in ExecChooseHashTableSize(), I would start the large block
comment with something that indicates this is a continuation of the
calculation above it for getting the required number of batches.
You say:
* Optimize the total amount of memory consumed by the hash node.
* The nbatch calculation above focuses on the size of the in-memory hash
* table, ignoring the memory used by batch files. But that can be a lot
* of memory - each batch file has a BLCKSZ buffer, and we may need two
* files per batch (inner and outer side). So with enough batches this can
* be significantly more memory than the hashtable itself, and it grows
* quickly as we're adding more batches.
I might make it more explicit:
nbatch is calculated above purely on the size of the inner relation
and the bytes available for the hashtable, assuming no per-batch
overhead. Now, recalibrate the number of batches and the size of the
hashtable to optimize the total amount of memory consumed by the
hashnode.
Then go into the rest of your details.
This paragraph, while essential, could probably use a bit of massaging
* This means we're only ever reducing nbatch values, we'll never increase
* it (as we're not considering nbatch*2). We could counsider that too,
* depending on which part of the [nbatch,work_mem] table we're in. And
* for cases with high work_mem values, we would find that adding batches
* reduces memory usage. But the hashtable size is what we consider when
* calculating the initial nbatch value, and if it's dominating the memory
* usage, if means we're not exceeding the expected memory limit (at least
* not significantly). There is little risk of OOM or memory overruns. Our
* goal is not to minimize the memory usage, but to enforce the limit set
* by the user. Minimizing the memory usage would result in spilling many
* more batch files, which does not seem great for performance. So we only
* ever reduce nbatch, never increase it.
The point is that if we aren't exceeding the expected memory limit,
then we won't increase the number of batches to try and save memory
because it will probably hurt performance in other ways. All the other
details are useful, but I found myself a bit lost in them (the way
they are phrased now).
* While growing the hashtable, we also adjust the number of buckets, to
* not have more than one tuple per bucket. We can only do this during
What does "to not have more than one tuple per bucket" mean?
* So after the initial sizing (here in ExecChooseHashTableSize), the
* number of buckets is effectively fixed. ExecHashGetBucketAndBatch
* could calculate batchno/bucketno in a different way, but that's
* left as a separate improvement. To some extent this is a preexisting
* issue - if we set growEnabled=false, this allows the hashtable to
* exceed the memory limit too, and we don't adjust the bucket count.
* However, that likely happens due to duplicate values and/or hash
* collisions, so it's not clear if increasing the bucket count would
* actually spread the tuples through the buckets. It would help with
* skewed data sets, when we may disable the growth early, and then
* add more tuples with distinct hash values.
After "is effectively fixed", I'm not sure how much more of this
detail I would include in this comment. There is already quite a lot
of information. Especially the sentence "ExecHashGetBucketAndBatch
* could calculate batchno/bucketno in a different way, but that's
* left as a separate improvement."
seems like it would need more information to be clear enough to the
reader -- so maybe just omit it.
If nothing else, I would move the discussion about why we don't
increase the number of buckets to a place where we are actually _not_
increasing the number of buckets (ExecHashIncreaseBatchSize()). In
this location, we are increasing nbuckets.
As for ExecHashIncreaseBatchSize()
* XXX We're comparing the current spaceAllowed/batchSpace values, because
* if we double either of those this is the new memory we'll use.
I don't get this. Firstly why is it XXX? Secondly, why are we using
the current spaceAllowed value?
In fact, I don't quite understand how this is actually increasing the
size of the hashtable at all. All it does is cause us to dump out of
ExecHashIncreaseNumBatches() without increasing the number of batches.
Is the reason you don't increase the actual spaceAllowed value because
you don't want it to be larger for other batches that might benefit
from a batch doubling?
- Melanie
Re: Adjusting hash join memory limit to handle batch explosion
On 2/7/25 23:14, Melanie Plageman wrote:
On Thu, Feb 6, 2025 at 1:48 PM Tomas Vondra <tomas@vondra.me> wrote:
Hi,
Here's a slightly simplified version of the "balancing" patch. I decided
to stop increasing the nbucket value at runtime, even if the hashtable
grows larger than the memory limit (which is what we used to calculate
the initial nbucket value in ExecChooseHashTableSize).I started looking at these.
First question is if the guc enable_hashjoin_adjust is for development
or you mean it for users (and for it to be off by default).
No, that's meant for development only, and should be removed before
commit. Sorry for not making that clear in the patches. It's why I
haven't documented these GUCs at all.
In 0001, in ExecChooseHashTableSize(), I would start the large block
comment with something that indicates this is a continuation of the
calculation above it for getting the required number of batches.You say:
* Optimize the total amount of memory consumed by the hash node.
* The nbatch calculation above focuses on the size of the in-memory hash
* table, ignoring the memory used by batch files. But that can be a lot
* of memory - each batch file has a BLCKSZ buffer, and we may need two
* files per batch (inner and outer side). So with enough batches this can
* be significantly more memory than the hashtable itself, and it grows
* quickly as we're adding more batches.I might make it more explicit:
nbatch is calculated above purely on the size of the inner relation
and the bytes available for the hashtable, assuming no per-batch
overhead. Now, recalibrate the number of batches and the size of the
hashtable to optimize the total amount of memory consumed by the
hashnode.
OK, that's a good idea. I'm afraid I've left out important details in
some of the comments due to me "just knowing" them from working on this
for so long, so it's good someone reads through that.
Then go into the rest of your details.
This paragraph, while essential, could probably use a bit of massaging
* This means we're only ever reducing nbatch values, we'll never increase
* it (as we're not considering nbatch*2). We could counsider that too,
* depending on which part of the [nbatch,work_mem] table we're in. And
* for cases with high work_mem values, we would find that adding batches
* reduces memory usage. But the hashtable size is what we consider when
* calculating the initial nbatch value, and if it's dominating the memory
* usage, if means we're not exceeding the expected memory limit (at least
* not significantly). There is little risk of OOM or memory overruns. Our
* goal is not to minimize the memory usage, but to enforce the limit set
* by the user. Minimizing the memory usage would result in spilling many
* more batch files, which does not seem great for performance. So we only
* ever reduce nbatch, never increase it.The point is that if we aren't exceeding the expected memory limit,
then we won't increase the number of batches to try and save memory
because it will probably hurt performance in other ways. All the other
details are useful, but I found myself a bit lost in them (the way
they are phrased now).
Yes, sorry about that. I struggled with explaining this, and I chose to
make it more verbose, hoping it'd explain the goal better. I'll try
rephrasing this. This comment is pretty long anyway, I want to shorten
it a bit. I wonder if there's a good place for a more detailed comment.
* While growing the hashtable, we also adjust the number of buckets, to
* not have more than one tuple per bucket. We can only do this duringWhat does "to not have more than one tuple per bucket" mean?
It means that we aim for load factor 1.0, which means that on average we
have 1 tuple per bucket. There's still chaining, though, so for hash
collisions we get a linked list of tuples.
* So after the initial sizing (here in ExecChooseHashTableSize), the
* number of buckets is effectively fixed. ExecHashGetBucketAndBatch
* could calculate batchno/bucketno in a different way, but that's
* left as a separate improvement. To some extent this is a preexisting
* issue - if we set growEnabled=false, this allows the hashtable to
* exceed the memory limit too, and we don't adjust the bucket count.
* However, that likely happens due to duplicate values and/or hash
* collisions, so it's not clear if increasing the bucket count would
* actually spread the tuples through the buckets. It would help with
* skewed data sets, when we may disable the growth early, and then
* add more tuples with distinct hash values.After "is effectively fixed", I'm not sure how much more of this
detail I would include in this comment. There is already quite a lot
of information. Especially the sentence "ExecHashGetBucketAndBatch
* could calculate batchno/bucketno in a different way, but that's
* left as a separate improvement."
seems like it would need more information to be clear enough to the
reader -- so maybe just omit it.
Yes, this was included more as a comment for reviewers, to explain what
would it take to relax this limitation.
If nothing else, I would move the discussion about why we don't
increase the number of buckets to a place where we are actually _not_
increasing the number of buckets (ExecHashIncreaseBatchSize()). In
this location, we are increasing nbuckets.
Good point.
As for ExecHashIncreaseBatchSize()
* XXX We're comparing the current spaceAllowed/batchSpace values, because
* if we double either of those this is the new memory we'll use.I don't get this. Firstly why is it XXX? Secondly, why are we using
the current spaceAllowed value?
The XXX is there because this was initially a comment for myself during
development, and it's more of an implementation detail. I think we could
remove the XXX if we choose to.
Why are we comparing the "current" values? Well, we're going to double
exactly one of those, right? So we're asking "Doubling which would
increase the memory usage more?" I think the logic might have been a bit
more complex in earlier patch version, but it didn't seem obvious to me
and thus the comment.
In fact, I don't quite understand how this is actually increasing the
size of the hashtable at all. All it does is cause us to dump out of
ExecHashIncreaseNumBatches() without increasing the number of batches.
Is the reason you don't increase the actual spaceAllowed value because
you don't want it to be larger for other batches that might benefit
from a batch doubling?
Darn, you're right. In a last-minute cleanup before submitting the patch
I removed the line that actually doubled the memory allowance. Fixed in
the attached version (I haven't done anything about the comments yet).
Thanks!
--
Tomas Vondra
Attachments:
v20250208-0003-hashjoin-patch-tests.patchtext/x-patch; charset=UTF-8; name=v20250208-0003-hashjoin-patch-tests.patchDownload
From efd46aa54d3b9bbc371c7ae4cbf1b5ff45229b3a Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:25:34 +0100
Subject: [PATCH v20250208 3/3] hashjoin patch tests
---
patch/batch-explosion.sql | 46 +
patch/disabled-growth.sql | 41 +
patch/disabled-growth2.sql | 44 +
patch/hash-collisions.data | 3819 +++++++++++++++++++++++++++++++++
patch/hash-memory-model-1.pdf | Bin 0 -> 16991 bytes
patch/hash-memory-model-2.pdf | Bin 0 -> 17323 bytes
patch/hash-memory-model.ods | Bin 0 -> 38902 bytes
patch/hash.data | 395 ++++
patch/hash.plot | 16 +
patch/hashjoin.png | Bin 0 -> 258027 bytes
patch/hashjoin.svg | 1083 ++++++++++
11 files changed, 5444 insertions(+)
create mode 100644 patch/batch-explosion.sql
create mode 100644 patch/disabled-growth.sql
create mode 100644 patch/disabled-growth2.sql
create mode 100644 patch/hash-collisions.data
create mode 100644 patch/hash-memory-model-1.pdf
create mode 100644 patch/hash-memory-model-2.pdf
create mode 100644 patch/hash-memory-model.ods
create mode 100644 patch/hash.data
create mode 100644 patch/hash.plot
create mode 100644 patch/hashjoin.png
create mode 100644 patch/hashjoin.svg
diff --git a/patch/batch-explosion.sql b/patch/batch-explosion.sql
new file mode 100644
index 00000000000..ca347379100
--- /dev/null
+++ b/patch/batch-explosion.sql
@@ -0,0 +1,46 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+
+insert into t select i, md5(i::text) from generate_series(1,1000000) s(i);
+
+create index on t (b);
+cluster t USING t_b_idx;
+
+vacuum analyze;
+
+set hash_mem_multiplier = 1.0;
+
+set work_mem = '2MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '1MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '512kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '768kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
diff --git a/patch/disabled-growth.sql b/patch/disabled-growth.sql
new file mode 100644
index 00000000000..88aa0706945
--- /dev/null
+++ b/patch/disabled-growth.sql
@@ -0,0 +1,41 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data' ;
+
+-- make it larger than 1MB
+insert into t select * from t;
+insert into t select * from t;
+insert into t select * from t;
+
+-- add a couple redundant columns
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+
+insert into t select i, md5(i::text), r, r, r, r from (select i, random() as r from generate_series(1,1000000) s(i) where hashint4(i) > 1000000);
+
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '512kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/disabled-growth2.sql b/patch/disabled-growth2.sql
new file mode 100644
index 00000000000..e162ad135ea
--- /dev/null
+++ b/patch/disabled-growth2.sql
@@ -0,0 +1,44 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+-- make sure the hash has first 3 bits set to 0
+insert into t select i a, md5(i::text) from generate_series(1,1000000) s(i) where hashint4(i)::bit(32) & 0x0000E000::bit(32) = 0x00000000::bit(32);
+
+insert into t select * from t;
+insert into t select * from t;
+
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '2MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '256kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/hash-collisions.data b/patch/hash-collisions.data
new file mode 100644
index 00000000000..5771aba40db
--- /dev/null
+++ b/patch/hash-collisions.data
@@ -0,0 +1,3819 @@
+1550703 744386c032b9d66f7068b34d02c3cf67
+1784955 ea69a3645dc43af5199926de9fe58075
+2248616 7eb3840bdae3edace248b1f10bf02397
+2793345 598c0d9b8c82502d12c79af81172da97
+3339251 db0878d708523338bdfda175319c092a
+3446589 bcfab619fed1378a6ca6c87fb87040df
+5047642 c6cdd6a849c522cad135c2aea9c4e6e0
+6166259 e03dffc92c981254c2cc154447d50be7
+6447204 7347ec2c9712b09b239eb4e9f4048fce
+1005501778 fb05bf820e5348c115bf1d1fb9803da5
+7532263 2fab47c383bc9ece03cba93cb8f04151
+8708202 3842357e614c4876056285ed1cfe9529
+10022434 e870baec59340d7076c976079d04ac1c
+1008540694 552e88c743f254ab4723498a4ffe62fc
+10587823 6f866563994a869ae7577b2cc5215314
+12080897 8ce2e3b3fb5f9807a55c45ffa3a6de4a
+1010612029 9615f534c427902323a8e2a640831f76
+12404188 ab7285e191efdf22badba248754f6aae
+1010971725 3ebdb624df5f8b4981223e6d91f846bb
+12619333 e345368c0c76d60f679ef57e5a655efd
+1011038408 4c9eb558535765dd3f85865fb4994d1c
+1011830734 37a12fd2552e6411c175fa0d2e6f2cea
+1012380389 c811ad2d88abd5c895a6b5f133db297e
+14127014 69a50a320a5ed7209f0260c632753984
+1012784986 d8cfb67be9b8c4d9d4b69c0bb45629a2
+14271746 88042dea9cdfd2e0c7c976d375fe4fa2
+1012824381 811ffd72c5347cd2a3f990e8c30b75f7
+14821640 9575bc78981f6f3c74467f91116fc82b
+15915697 d09d1edabb25576378ae728a2a291ad7
+16153467 8e3ce320f7757d64cd5fbb695db0ed4c
+1015835612 47073bb31e9b30f38f0da91582b6dcb0
+17464541 f2abf98b5f22471a4e8925c923ee54ee
+18337469 b88113e2779168f7fd75dcc9a841f099
+1017472003 8a412c2481de0b5335ac4660b0a7fff8
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diff --git a/patch/hash-memory-model.ods b/patch/hash-memory-model.ods
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diff --git a/patch/hash.data b/patch/hash.data
new file mode 100644
index 00000000000..53a9240b125
--- /dev/null
+++ b/patch/hash.data
@@ -0,0 +1,395 @@
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diff --git a/patch/hash.plot b/patch/hash.plot
new file mode 100644
index 00000000000..76703ce5579
--- /dev/null
+++ b/patch/hash.plot
@@ -0,0 +1,16 @@
+set terminal svg size 1200,800 fname 'Verdana,10'
+set output 'hashjoin.svg'
+
+#set terminal pngcairo size 1600,1000 enhanced font 'Verdana,9'
+#set output 'hashjoin.png'
+
+set nokey
+
+set ticslevel 0
+set pm3d at b
+set xtics offset 0,-0.5 rotate by 45
+
+set xlabel "workmem"
+set ylabel "batches"
+
+splot "hash.data" using 1:2:5:xtic(3):ytic(4) with lines lc rgb "#00bb00"
diff --git a/patch/hashjoin.png b/patch/hashjoin.png
new file mode 100644
index 0000000000000000000000000000000000000000..aef9d118a3e6217f6fd8022e82c11e288674ad76
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--
2.47.1
v20250208-0002-Postpone-hashtable-growth-instead-of-disab.patchtext/x-patch; charset=UTF-8; name=v20250208-0002-Postpone-hashtable-growth-instead-of-disab.patchDownload
From e8da3259fdb0df183a11573f98b657799512bbd6 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Thu, 23 Jan 2025 22:21:28 +0100
Subject: [PATCH v20250208 2/3] Postpone hashtable growth instead of disabling
it
After increasing the number of batches and splitting the current one, we
used to disable further growth if all tuples went into only one of the
two new batches. It's possible to construct cases when this leads to
disabling growth prematurely - maybe we can't split the batch now, but
that doesn't mean we could not split it later.
This generally requires underestimated size of the inner relation, so
that we need to increase the number of batches. And then also hashes
non-random in some way. There may be a "common" prefix, or maybe the
data is just correlated in some way.
So instead of hard-disabling the growth permanently, double the memory
limit so that we retry the split after processing more data. Doubling
the limit is somewhat arbitrary - it's the earliest when we could split
the batch in half even if all the current tuples have duplicate hashes.
But we could pick any other value, to retry sooner/later.
---
src/backend/executor/nodeHash.c | 50 ++++++++++++++++++++++++++++-
src/backend/utils/misc/guc_tables.c | 10 ++++++
src/include/executor/hashjoin.h | 1 +
3 files changed, 60 insertions(+), 1 deletion(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 580269367c3..40db20bae68 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -83,6 +83,9 @@ static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
/* enable adaptive adjustment of hashtable size */
bool enable_hashjoin_adjust = false;
+/* enable soft-disable of nbatch growth */
+bool enable_hashjoin_growth = false;
+
/* ----------------------------------------------------------------
* ExecHash
*
@@ -1189,10 +1192,55 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
* Increasing nbatch will not fix it since there's no way to subdivide the
* group any more finely. We have to just gut it out and hope the server
* has enough RAM.
+ *
+ * XXX This logic for hard-disabling nbatch growth assumes that if we're
+ * unable to split the batch now, we'll be unable to split it forever. That
+ * works well for "nice" random data sets, but it's not difficult to come
+ * up with cases where the assumption does not hold.
+ *
+ * 1) The hashes may share the same "prefix", but the next bit may be
+ * random - and doubling the number of batches again would split the batch
+ * about evenly. This is rather unlikely to happen by chance, of course.
+ *
+ * 2) The dataset may be correlated in some way, i.e. produce values with
+ * one hash value first, before producing other values. This might happen
+ * for data read from index, etc. If we underestimate the amount of data
+ * we will need to add to the hash, this may disable nbatch growth while
+ * still reading the first value, i.e. too soon.
+ *
+ * XXX With the "hard disable" this also handles the case when we run out
+ * of hash bits, because the first doubling after that only has "0" in the
+ * new bit, which means we get (nfreed == ninmemory) and stop adding more
+ * batches. But with the soft disable that's no longer the case, and we'd
+ * try adding more batches after a while. Not sure this a big deal, as the
+ * memory limit would need to be pretty substantial in that case already
+ * (assuming we already doubled the limit multiple times). But maybe t
+ * could be the first time we're hitting this condition? Perhaps the right
+ * solution is to detect running out of hash bits explicitly, and just do
+ * a "hard disable" in this case?
+ *
+ * XXX This should probably also increase the number of buckets etc.
*/
if (nfreed == 0 || nfreed == ninmemory)
{
- hashtable->growEnabled = false;
+ /*
+ * If enable_hashjoin_grow=true, don't disable the growth permanently,
+ * and instead just increase the limit in the hope that we'll be able
+ * to split the batches in the future.
+ *
+ * XXX Not sure if 2.0 is the optimal growth factor, but it seems quite
+ * reasonable because it aligns with the expectation that we'll be able
+ * to split the memory usage in half. It's not perfect, because if we
+ * add a random bit to the hash, we might split earlier than that.
+ */
+ if (enable_hashjoin_growth)
+ {
+ /* XXX Do we need to worry about overlowing spaceAllowed? */
+ hashtable->spaceAllowed *= 2.0;
+ }
+ else
+ hashtable->growEnabled = false;
+
#ifdef HJDEBUG
printf("Hashjoin %p: disabling further increase of nbatch\n",
hashtable);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index 6ef37c51783..67a4865397c 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -911,6 +911,16 @@ struct config_bool ConfigureNamesBool[] =
false,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_growth", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables growing the hash table memory limit instead of disabling batch increases permanently."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_growth,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index 0eb1da8f66a..af5044802d8 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -72,6 +72,7 @@
*/
extern PGDLLIMPORT bool enable_hashjoin_adjust;
+extern PGDLLIMPORT bool enable_hashjoin_growth;
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
--
2.47.1
v20250208-0001-Balance-memory-usage-with-hashjoin-batch-e.patchtext/x-patch; charset=UTF-8; name=v20250208-0001-Balance-memory-usage-with-hashjoin-batch-e.patchDownload
From f0b907168d75eb007095e74ca43a4c87b333cbd2 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:24:23 +0100
Subject: [PATCH v20250208 1/3] Balance memory usage with hashjoin batch
explosion
The basic logic to pick the number of hashjoin batches is concerned only
with the in-memory hash table, adding batches to keep the hash table
within the limit defined by work_mem and hash_mem_multiplier. It ignores
the memory needed by the batch files, but with enough batches this may
be a substantial amount of memory, easily orders of magnitude more than
the hash table.
We've seen reports of hash joins with hundreds of thousands or millions
of batch files, consuming gigabytes of memory, and triggering OOM. These
cases are not too common, but it's clearly possible to hit them.
This patch improves the situation by rebalancing how the memory is
distributed between the hash table and batch files, to minimize the
total memory consumption.
Whenever we need to increase the capacity of the hash node, we can do
that by either doubling the number of batches or doubling the size of
the in-memory hash table. The outcome is the same, allowing the hash
node to handle a relation twice the size. But the memory requirements
may be substantially different, depending on the current hashjoin
parameters (for low nbatch values it's better to add batches, for high
nbatch values it's better to allow a larger hash table).
It may seem a bit strange, as it clearly allows exceeding the memory
limit specified by the GUC parameters. But it has always been like this,
except that the code assumed adding batches is free. The patch just
makes this visible and explicit.
Increasing the hashtable memory limit may also help to prevent the batch
explosion in the first place. Given enough hash collisions or duplicate
hashes it's easy to get a batch that can't be split, resulting in a
cycle of quickly doubling the number of batches. Allowing the hashtable
to get larger may stop this.
---
src/backend/executor/nodeHash.c | 146 ++++++++++++++++++++++++++++
src/backend/utils/misc/guc_tables.c | 11 +++
src/include/executor/hashjoin.h | 2 +
3 files changed, 159 insertions(+)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..580269367c3 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -80,6 +80,8 @@ static bool ExecParallelHashTuplePrealloc(HashJoinTable hashtable,
static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+/* enable adaptive adjustment of hashtable size */
+bool enable_hashjoin_adjust = false;
/* ----------------------------------------------------------------
* ExecHash
@@ -848,6 +850,105 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * Optimize the total amount of memory consumed by the hash node.
+ *
+ * The nbatch calculation above focuses on the size of the in-memory hash
+ * table, ignoring the memory used by batch files. But that can be a lot
+ * of memory - each batch file has a BLCKSZ buffer, and we may need two
+ * files per batch (inner and outer side). So with enough batches this can
+ * be significantly more memory than the hashtable itself, and it grows
+ * quickly as we're adding more batches.
+ *
+ * It might seem cleaner to adjust the calculation above, to consider
+ * memory for both the in-memory hashtable and the batch files, and ensure
+ * it fits into hash_table_bytes. That is, look for a nbatch value so that
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ) <= hash_table_bytes
+ *
+ * But that has a flaw - for sufficiently large inner_rel_bytes value it
+ * may not have a solution (either the hash table is too large or it
+ * requires too many batches). So instead we merely try to minimize the
+ * impact, and use as little memory as possible, instead of strictly
+ * enforcing the memory limit. (But we haven't really enforced it before
+ * either, as we simply ignored the batch files.)
+ *
+ * The basic observation is that given an inner relation of a given size,
+ * we may divide it in arbitrary number of batches, which determines the
+ * memory consumption per the already mentioned formula:
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ)
+ *
+ * That is, we can reduce the number of batches to (nbatch/2), at the
+ * cost of doubling the size of the in-memory hash table. But these two
+ * terms work in opposite ways - size of the in-memory part decreases
+ * with nbatch, while the batch file memory grows very quickly. Initially
+ * the memory usage is dominated by in-memory hash table (for nbatch=0),
+ * then at some point the batch files start to consume more memory.
+ *
+ * If you combine these two, the memory consumption (for a fixed size of
+ * the inner relation) has a u-shape, with a minimum at some nbatch value.
+ * Our goal is to look for this minimum. We do that by calculating memory
+ * usage for (nbatch/2), and accepting it if it's lower than current.
+ *
+ * This means we're only ever reducing nbatch values, we'll never increase
+ * it (as we're not considering nbatch*2). We could counsider that too,
+ * depending on which part of the [nbatch,work_mem] table we're in. And
+ * for cases with high work_mem values, we would find that adding batches
+ * reduces memory usage. But the hashtable size is what we consider when
+ * calculating the initial nbatch value, and if it's dominating the memory
+ * usage, if means we're not exceeding the expected memory limit (at least
+ * not significantly). There is little risk of OOM or memory overruns. Our
+ * goal is not to minimize the memory usage, but to enforce the limit set
+ * by the user. Minimizing the memory usage would result in spilling many
+ * more batch files, which does not seem great for performance. So we only
+ * ever reduce nbatch, never increase it.
+ *
+ * While growing the hashtable, we also adjust the number of buckets, to
+ * not have more than one tuple per bucket. We can only do this during
+ * the initial sizing - once we start building the hash, we can't add
+ * buckets, due to how ExecHashGetBucketAndBatch() calculates batchno
+ * and bucketno from the hash. Increasing the nbucket value would move
+ * the batchno part in a way that could result in the batchno going
+ * backwards, but that violates the expectation that splitting a batch
+ * moves the tuples only to "future" batches.
+ *
+ * So after the initial sizing (here in ExecChooseHashTableSize), the
+ * number of buckets is effectively fixed. ExecHashGetBucketAndBatch
+ * could calculate batchno/bucketno in a different way, but that's
+ * left as a separate improvement. To some extent this is a preexisting
+ * issue - if we set growEnabled=false, this allows the hashtable to
+ * exceed the memory limit too, and we don't adjust the bucket count.
+ * However, that likely happens due to duplicate values and/or hash
+ * collisions, so it's not clear if increasing the bucket count would
+ * actually spread the tuples through the buckets. It would help with
+ * skewed data sets, when we may disable the growth early, and then
+ * add more tuples with distinct hash values.
+ */
+ while (nbatch > 0)
+ {
+ /* how much memory would we use with half the batches? */
+ size_t space = hash_table_bytes * 2 + (nbatch * BLCKSZ);
+ size_t current = hash_table_bytes + (2 * nbatch * BLCKSZ);
+
+ /* Is the adaptive behavior enabled? */
+ if (!enable_hashjoin_adjust)
+ break;
+
+ /* If the memory usage does not decrease, we have the optimum. */
+ if (current < space)
+ break;
+
+ /*
+ * It's better to use half the batches, so do that and adjust the
+ * nbucket in the opposite direction, and the allowance.
+ */
+ nbatch /= 2;
+ nbuckets *= 2;
+
+ *space_allowed = *space_allowed * 2;
+ }
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
@@ -890,6 +991,47 @@ ExecHashTableDestroy(HashJoinTable hashtable)
pfree(hashtable);
}
+/*
+ * Consider adjusting the allowed hash table size, depending on the number
+ * of batches, to minimize the overall memory usage (for both the hashtable
+ * and batch files).
+ *
+ * Returns true if we chose to increase the batch size (and thus we don't
+ * need to add batches), and false if we should increase nbatch.
+ *
+ * XXX Note that while we're adjusting the size of the hash table, we're not
+ * adjusting the (optimal) number of buckets. We can't change that once we
+ * start building the hash, due to how ExecHashGetBucketAndBatch splits the
+ * hash into batchno/bucketno.
+ */
+static bool
+ExecHashIncreaseBatchSize(HashJoinTable hashtable)
+{
+ /*
+ * How much memory would doubling nbatch use? Each batch may require
+ * two buffered files (inner/outer), with a BLCKSZ buffer.
+ */
+ size_t batchSpace = (hashtable->nbatch * 2 * BLCKSZ);
+
+ /* Do nothing if the adaptive behavior is disabled. */
+ if (!enable_hashjoin_adjust)
+ return false;
+
+ /*
+ * Compare the new space needed for doubling nbatch and for enlarging the
+ * in-memory hash table. If doubling the hash table needs less memory,
+ * just do that. Otherwise, continue with doubling the nbatch.
+ *
+ */
+ if (hashtable->spaceAllowed <= batchSpace)
+ {
+ hashtable->spaceAllowed *= 2;
+ return true;
+ }
+
+ return false;
+}
+
/*
* ExecHashIncreaseNumBatches
* increase the original number of batches in order to reduce
@@ -913,6 +1055,10 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (oldnbatch > Min(INT_MAX / 2, MaxAllocSize / (sizeof(void *) * 2)))
return;
+ /* consider increasing size of the in-memory hash table instead */
+ if (ExecHashIncreaseBatchSize(hashtable))
+ return;
+
nbatch = oldnbatch * 2;
Assert(nbatch > 1);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index ce7534d4d23..6ef37c51783 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -46,6 +46,7 @@
#include "commands/vacuum.h"
#include "common/file_utils.h"
#include "common/scram-common.h"
+#include "executor/hashjoin.h"
#include "jit/jit.h"
#include "libpq/auth.h"
#include "libpq/libpq.h"
@@ -900,6 +901,16 @@ struct config_bool ConfigureNamesBool[] =
true,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_adjust", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables adjusting hashtable size to minimize memory usage."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_adjust,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index ecff4842fd3..0eb1da8f66a 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -71,6 +71,8 @@
* ----------------------------------------------------------------
*/
+extern PGDLLIMPORT bool enable_hashjoin_adjust;
+
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
/* typedef struct HashJoinTableData *HashJoinTable; */
--
2.47.1
Re: Adjusting hash join memory limit to handle batch explosion
Here is an updated patch series, with improved comments etc.
1) v20250218-0001-Reduce-the-impact-of-hashjoin-batch-explos.patch
I believe this bit is ready to go - I've removed the GUC (which was
meant only for development), reworded and cleaned up many of the
comments, etc. It would be good if someone could read through those
again. I plan to get this pushed soon.
2) v20250218-0002-Postpone-hashtable-growth-instead-of-disab.patch
I'm not quite sure about this part yet. While I think this issue can
happen in practice (as demonstrated by reproducers in 0003), I don't
recall any reports - so maybe it's so unlikely it's artificial?
If we assume the issue not purely theoretical, I think the patch is a
reasonable way to handle it. But I'm not sure what to do about running
out of hash bits (if anything).
As we're doubling nbatch values, at some point we run out of bits in the
hash, i.e. we start adding just "0" bits. With the current growEnabled
flag we simply set growEnabled=false, effectively disabling further
nbatch increases. But with "soft" disable we might retry adding more
batches - which won't do anything, but it's a bit pointless. Do you
think the patch needs to handle this case explicitly?
ISTM this issue is largely theoretical, or could be ignored thanks to
the "balancing" patch 0001. Because as we increase the batch size, that
makes the "fast nbatch growth" not an issue. So maybe once 0001 gets in,
the right solution is to get rid of growEnabled entirely?
After all, the patch does almost exactly the same thing as 0001 - it
just increases batch size, to delay the next nbatch doubling.
AFAIK the only piece we'd lose by getting rid of growEnabled is the
handling of using all hash bits.
regards
--
Tomas Vondra
Attachments:
v20250218-0001-Reduce-the-impact-of-hashjoin-batch-explos.patchtext/x-patch; charset=UTF-8; name=v20250218-0001-Reduce-the-impact-of-hashjoin-batch-explos.patchDownload
From 75965b58a13787f7b798533ab75450d18481db95 Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas.vondra@postgresql.org>
Date: Tue, 18 Feb 2025 15:32:11 +0100
Subject: [PATCH v20250218 1/3] Reduce the impact of hashjoin batch explosion
Until now ExecChooseHashTableSize() considered only the size of the
in-memory hash table when picking the nbatch value, and completely
ignored the memory needed for the batch files. Which can be a lot,
because each batch needs two BufFiles (each with a BLCKSZ buffer).
Same for increasing the number of batches during execution.
With enough batches, the batch files may use orders of magnitude more
memory than the in-memory hash table. But the sizing logic is oblivious
to this.
It's also possible to trigger a "batch explosion", e.g. due to duplicate
values or skew in general. We've seen reports of joins with hundreds of
thousands (or even millions) of batches, consuming gigabytes of memory,
triggering OOM errors. These cases are fairly rare, but it's clearly
possible to hit them.
We can't prevent this during planning - we could improve the planning,
but that does nothing for the execution-time batch explosion. But we can
reduce the impact by using as little memory as possible.
This patch improves the memory by rebalancing how the memory is divided
between the hash table and batch files. Sometimes it's better to use
fewer batch files, even if it means the hash table exceeds the limit.
Whenever we need to increase the capacity of the hash node, we can do
that by either doubling the number of batches or doubling the size of
the in-memory hash table. The outcome is the same, allowing the hash
node to handle a relation twice the size. But the memory usage may be
very different - for low nbatch values it's better to add batches, for
high nbatch values it's better to allow a larger hash table.
It might seem like relaxing the memory limit - but that's not really the
case. It has always been like that, except the memory used by batches
was ignored, as if the files were free. This commit improves the
situation by considering this memory when adjusting nbatch values.
Increasing the hashtable memory limit may also help to prevent the batch
explosion in the first place. Given enough hash collisions or duplicate
hashes it's easy to get a batch that can't be split, resulting in a
cycle of quickly doubling the number of batches. Allowing the hashtable
to get larger may stop this, once the batch gets large enough to fit the
skewed data.
---
src/backend/executor/nodeHash.c | 129 ++++++++++++++++++++++++++++++++
1 file changed, 129 insertions(+)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 6f8a379e3b9..4748ee845db 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -848,6 +848,90 @@ ExecChooseHashTableSize(double ntuples, int tupwidth, bool useskew,
nbatch = pg_nextpower2_32(Max(2, minbatch));
}
+ /*
+ * Optimize the total amount of memory consumed by the hash node.
+ *
+ * The nbatch calculation above focuses on the size of the in-memory hash
+ * table, assuming no per-batch overhead. Now adjust the number of batches
+ * and the size of the hash table to minimize total memory consumed by the
+ * hash node.
+ *
+ * Each batch file has a BLCKSZ buffer, and we may need two files per
+ * batch (inner and outer side). So with enough batches this can be
+ * significantly more memory than the hashtable itself.
+ *
+ * The total memory usage may be expressed by this formula:
+ *
+ * (inner_rel_bytes / nbatch) + (2 * nbatch * BLCKSZ) <= hash_table_bytes
+ *
+ * where (inner_rel_bytes / nbatch) is the size of the in-memory hash
+ * table and (2 * nbatch * BLCKSZ) is the amount of memory used by file
+ * buffers. But for sufficiently large values of inner_rel_bytes value
+ * there may not be a nbatch value that would make both parts fit into
+ * hash_table_bytes.
+ *
+ * In this case we can't enforce the memory limit - we're going to exceed
+ * it. We can however minimize the impact and use as little memory as
+ * possible. (We haven't really enforced it before either, as we simply
+ * ignored the batch files.)
+ *
+ * The formula for total memory usage says that given an inner relation of
+ * size inner_rel_bytes, we may divide it into an arbitrary number of of
+ * batches. This determines both the size of the in-memory hash table and
+ * the amount of memory needed for batch files. These two terms work in
+ * opposite ways - when one decreases, the other increases.
+ *
+ * For low nbatch values, the hash table takes most of the memory, but at
+ * some point the batch files start to dominate. If you combine these two
+ * terms, the memory consumption (for a fixed size of the inner relation)
+ * has a u-shape, with a minimum at some nbatch value.
+ *
+ * Our goal is to find this nbatch value, minimizing the memory usage. We
+ * calculate the memory usage with half the batches (i.e. nbatch/2), and
+ * if it's lower than the current memory usage we know it's better to use
+ * fewer batches. We repeat this until reducing the number of batches does
+ * not reduce the memory usage - we found the optimum. We know the optimum
+ * exists, thanks to the u-shape.
+ *
+ * We only want to do this when exceeding the memory limit, not every
+ * time. The goal is not to minimize memory usage in every case, but to
+ * minimize the memory usage when we can't stay within the memory limit.
+ *
+ * For this reason we only consider reducing the number of batches. We
+ * could try the opposite direction too, but that would save memory only
+ * when most of the memory is used by the hash table. And the hash table
+ * was used for the initial sizing, so we shouldn't be exceeding the
+ * memory limit too much. We might save memory by using more batches, but
+ * it would result in spilling more batch files, which does not seem like
+ * a great trade off.
+ *
+ * While growing the hashtable, we also adjust the number of buckets, to
+ * not have more than one tuple per bucket (load factor 1). We can onl do
+ * this during the initial sizing - once we start building the hash,
+ * nbucket is fixed.
+ */
+ while (nbatch > 0)
+ {
+ /* how much memory are we using with current nbatch value */
+ size_t current_space = hash_table_bytes + (2 * nbatch * BLCKSZ);
+
+ /* how much memory would we use with half the batches */
+ size_t new_space = hash_table_bytes * 2 + (nbatch * BLCKSZ);
+
+ /* If the memory usage would not decrease, we found the optimum. */
+ if (current_space < new_space)
+ break;
+
+ /*
+ * It's better to use half the batches, so do that and adjust the
+ * nbucket in the opposite direction, and double the allowance.
+ */
+ nbatch /= 2;
+ nbuckets *= 2;
+
+ *space_allowed = (*space_allowed) * 2;
+ }
+
Assert(nbuckets > 0);
Assert(nbatch > 0);
@@ -890,6 +974,47 @@ ExecHashTableDestroy(HashJoinTable hashtable)
pfree(hashtable);
}
+/*
+ * Consider adjusting the allowed hash table size, depending on the number
+ * of batches, to minimize the overall memory usage (for both the hashtable
+ * and batch files).
+ *
+ * We're adjusting the size of the hash table, not the (optimal) number of
+ * buckets. We can't change that once we start building the hash, due to how
+ * ExecHashGetBucketAndBatch calculates batchno/bucketno from the hash. This
+ * means the load factor may not be optimal, but we're in damage control so
+ * we accept slower lookups. It's still much better than batch explosion.
+ *
+ * Returns true if we chose to increase the batch size (and thus we don't
+ * need to add batches), and false if we should increase nbatch.
+ */
+static bool
+ExecHashIncreaseBatchSize(HashJoinTable hashtable)
+{
+ /*
+ * How much additional memory would doubling nbatch use? Each batch may
+ * require two buffered files (inner/outer), with a BLCKSZ buffer.
+ */
+ size_t batchSpace = (hashtable->nbatch * 2 * BLCKSZ);
+
+ /*
+ * Compare the new space needed for doubling nbatch and for enlarging the
+ * in-memory hash table. If doubling the hash table needs less memory,
+ * just do that. Otherwise, continue with doubling the nbatch.
+ *
+ * We're either doubling spaceAllowed of batchSpace, so which of those
+ * increases the memory usage the least is the same as comparing the
+ * values directly.
+ */
+ if (hashtable->spaceAllowed <= batchSpace)
+ {
+ hashtable->spaceAllowed *= 2;
+ return true;
+ }
+
+ return false;
+}
+
/*
* ExecHashIncreaseNumBatches
* increase the original number of batches in order to reduce
@@ -913,6 +1038,10 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
if (oldnbatch > Min(INT_MAX / 2, MaxAllocSize / (sizeof(void *) * 2)))
return;
+ /* consider increasing size of the in-memory hash table instead */
+ if (ExecHashIncreaseBatchSize(hashtable))
+ return;
+
nbatch = oldnbatch * 2;
Assert(nbatch > 1);
--
2.48.1
v20250218-0002-Postpone-hashtable-growth-instead-of-disab.patchtext/x-patch; charset=UTF-8; name=v20250218-0002-Postpone-hashtable-growth-instead-of-disab.patchDownload
From 961e6e1b7da98eab10753aaf117bcf1102d0ebea Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Tue, 18 Feb 2025 15:10:28 +0100
Subject: [PATCH v20250218 2/3] Postpone hashtable growth instead of disabling
it
After increasing the number of batches and splitting the current one, we
used to disable further growth if all tuples went into only one of the
two new batches. It's possible to construct cases when this leads to
disabling growth prematurely - maybe we can't split the batch now, but
that doesn't mean we could not split it later.
This generally requires underestimated size of the inner relation, so
that we need to increase the number of batches. And then also hashes
non-random in some way. There may be a "common" prefix, or maybe the
data is just correlated in some way.
So instead of hard-disabling the growth permanently, double the memory
limit so that we retry the split after processing more data. Doubling
the limit is somewhat arbitrary - it's the earliest when we could split
the batch in half even if all the current tuples have duplicate hashes.
But we could pick any other value, to retry sooner/later.
---
src/backend/executor/nodeHash.c | 50 ++++++++++++++++++++++++++++-
src/backend/utils/misc/guc_tables.c | 11 +++++++
src/include/executor/hashjoin.h | 2 ++
3 files changed, 62 insertions(+), 1 deletion(-)
diff --git a/src/backend/executor/nodeHash.c b/src/backend/executor/nodeHash.c
index 4748ee845db..df3e945af0e 100644
--- a/src/backend/executor/nodeHash.c
+++ b/src/backend/executor/nodeHash.c
@@ -81,6 +81,9 @@ static void ExecParallelHashMergeCounters(HashJoinTable hashtable);
static void ExecParallelHashCloseBatchAccessors(HashJoinTable hashtable);
+/* enable soft-disable of nbatch growth */
+bool enable_hashjoin_growth = false;
+
/* ----------------------------------------------------------------
* ExecHash
*
@@ -1172,10 +1175,55 @@ ExecHashIncreaseNumBatches(HashJoinTable hashtable)
* Increasing nbatch will not fix it since there's no way to subdivide the
* group any more finely. We have to just gut it out and hope the server
* has enough RAM.
+ *
+ * XXX This logic for hard-disabling nbatch growth assumes that if we're
+ * unable to split the batch now, we'll be unable to split it forever. That
+ * works well for "nice" random data sets, but it's not difficult to come
+ * up with cases where the assumption does not hold.
+ *
+ * 1) The hashes may share the same "prefix", but the next bit may be
+ * random - and doubling the number of batches again would split the batch
+ * about evenly. This is rather unlikely to happen by chance, of course.
+ *
+ * 2) The dataset may be correlated in some way, i.e. produce values with
+ * one hash value first, before producing other values. This might happen
+ * for data read from index, etc. If we underestimate the amount of data
+ * we will need to add to the hash, this may disable nbatch growth while
+ * still reading the first value, i.e. too soon.
+ *
+ * XXX With the "hard disable" this also handles the case when we run out
+ * of hash bits, because the first doubling after that only has "0" in the
+ * new bit, which means we get (nfreed == ninmemory) and stop adding more
+ * batches. But with the soft disable that's no longer the case, and we'd
+ * try adding more batches after a while. Not sure this a big deal, as the
+ * memory limit would need to be pretty substantial in that case already
+ * (assuming we already doubled the limit multiple times). But maybe t
+ * could be the first time we're hitting this condition? Perhaps the right
+ * solution is to detect running out of hash bits explicitly, and just do
+ * a "hard disable" in this case?
+ *
+ * XXX This should probably also increase the number of buckets etc.
*/
if (nfreed == 0 || nfreed == ninmemory)
{
- hashtable->growEnabled = false;
+ /*
+ * If enable_hashjoin_grow=true, don't disable the growth permanently,
+ * and instead just increase the limit in the hope that we'll be able
+ * to split the batches in the future.
+ *
+ * XXX Not sure if 2.0 is the optimal growth factor, but it seems quite
+ * reasonable because it aligns with the expectation that we'll be able
+ * to split the memory usage in half. It's not perfect, because if we
+ * add a random bit to the hash, we might split earlier than that.
+ */
+ if (enable_hashjoin_growth)
+ {
+ /* XXX Do we need to worry about overlowing spaceAllowed? */
+ hashtable->spaceAllowed *= 2.0;
+ }
+ else
+ hashtable->growEnabled = false;
+
#ifdef HJDEBUG
printf("Hashjoin %p: disabling further increase of nbatch\n",
hashtable);
diff --git a/src/backend/utils/misc/guc_tables.c b/src/backend/utils/misc/guc_tables.c
index cce73314609..ac283f61ab1 100644
--- a/src/backend/utils/misc/guc_tables.c
+++ b/src/backend/utils/misc/guc_tables.c
@@ -46,6 +46,7 @@
#include "commands/vacuum.h"
#include "common/file_utils.h"
#include "common/scram-common.h"
+#include "executor/hashjoin.h"
#include "jit/jit.h"
#include "libpq/auth.h"
#include "libpq/libpq.h"
@@ -901,6 +902,16 @@ struct config_bool ConfigureNamesBool[] =
true,
NULL, NULL, NULL
},
+ {
+ {"enable_hashjoin_growth", PGC_USERSET, QUERY_TUNING_METHOD,
+ gettext_noop("Enables growing the hash table memory limit instead of disabling batch increases permanently."),
+ NULL,
+ GUC_EXPLAIN
+ },
+ &enable_hashjoin_growth,
+ false,
+ NULL, NULL, NULL
+ },
{
{"enable_gathermerge", PGC_USERSET, QUERY_TUNING_METHOD,
gettext_noop("Enables the planner's use of gather merge plans."),
diff --git a/src/include/executor/hashjoin.h b/src/include/executor/hashjoin.h
index ecff4842fd3..c73c587b120 100644
--- a/src/include/executor/hashjoin.h
+++ b/src/include/executor/hashjoin.h
@@ -71,6 +71,8 @@
* ----------------------------------------------------------------
*/
+extern PGDLLIMPORT bool enable_hashjoin_growth;
+
/* these are in nodes/execnodes.h: */
/* typedef struct HashJoinTupleData *HashJoinTuple; */
/* typedef struct HashJoinTableData *HashJoinTable; */
--
2.48.1
v20250218-0003-hashjoin-patch-tests.patchtext/x-patch; charset=UTF-8; name=v20250218-0003-hashjoin-patch-tests.patchDownload
From 4b745d822cfc18c315b27445a7a6e029e31073bf Mon Sep 17 00:00:00 2001
From: Tomas Vondra <tomas@vondra.me>
Date: Sun, 5 Jan 2025 21:25:34 +0100
Subject: [PATCH v20250218 3/3] hashjoin patch tests
---
patch/batch-explosion.sql | 46 +
patch/disabled-growth.sql | 41 +
patch/disabled-growth2.sql | 44 +
patch/hash-collisions.data | 3819 +++++++++++++++++++++++++++++++++
patch/hash-memory-model-1.pdf | Bin 0 -> 16991 bytes
patch/hash-memory-model-2.pdf | Bin 0 -> 17323 bytes
patch/hash-memory-model.ods | Bin 0 -> 38902 bytes
patch/hash.data | 395 ++++
patch/hash.plot | 16 +
patch/hashjoin.png | Bin 0 -> 258027 bytes
patch/hashjoin.svg | 1083 ++++++++++
11 files changed, 5444 insertions(+)
create mode 100644 patch/batch-explosion.sql
create mode 100644 patch/disabled-growth.sql
create mode 100644 patch/disabled-growth2.sql
create mode 100644 patch/hash-collisions.data
create mode 100644 patch/hash-memory-model-1.pdf
create mode 100644 patch/hash-memory-model-2.pdf
create mode 100644 patch/hash-memory-model.ods
create mode 100644 patch/hash.data
create mode 100644 patch/hash.plot
create mode 100644 patch/hashjoin.png
create mode 100644 patch/hashjoin.svg
diff --git a/patch/batch-explosion.sql b/patch/batch-explosion.sql
new file mode 100644
index 00000000000..ca347379100
--- /dev/null
+++ b/patch/batch-explosion.sql
@@ -0,0 +1,46 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+copy t from '/tmp/hash-collisions.data';
+
+insert into t select i, md5(i::text) from generate_series(1,1000000) s(i);
+
+create index on t (b);
+cluster t USING t_b_idx;
+
+vacuum analyze;
+
+set hash_mem_multiplier = 1.0;
+
+set work_mem = '2MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '1MB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '512kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
+
+
+set work_mem = '768kB';
+
+explain select * from t t1 join t t2 on (t1.a = t2.a);
+explain analyze select * from t t1 join t t2 on (t1.a = t2.a);
diff --git a/patch/disabled-growth.sql b/patch/disabled-growth.sql
new file mode 100644
index 00000000000..88aa0706945
--- /dev/null
+++ b/patch/disabled-growth.sql
@@ -0,0 +1,41 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+copy t from '/tmp/hash-collisions.data' ;
+
+-- make it larger than 1MB
+insert into t select * from t;
+insert into t select * from t;
+insert into t select * from t;
+
+-- add a couple redundant columns
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+
+insert into t select i, md5(i::text), r, r, r, r from (select i, random() as r from generate_series(1,1000000) s(i) where hashint4(i) > 1000000);
+
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '512kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/disabled-growth2.sql b/patch/disabled-growth2.sql
new file mode 100644
index 00000000000..e162ad135ea
--- /dev/null
+++ b/patch/disabled-growth2.sql
@@ -0,0 +1,44 @@
+drop table if exists t;
+
+create table t (a int, b text);
+
+-- make sure the hash has first 3 bits set to 0
+insert into t select i a, md5(i::text) from generate_series(1,1000000) s(i) where hashint4(i)::bit(32) & 0x0000E000::bit(32) = 0x00000000::bit(32);
+
+insert into t select * from t;
+insert into t select * from t;
+
+alter table t add column r1 double precision, add column r2 double precision, add column r3 double precision, add column r4 double precision;
+update t set r1 = 0, r2 = 0, r3 = 0, r4 = 0;
+
+vacuum full t;
+vacuum analyze t;
+
+set max_parallel_workers_per_gather = 0;
+
+set work_mem = '2MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '1MB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
+
+set work_mem = '256kB';
+
+explain analyze
+select * from t t1 join t t2 on (t1.a = t2.a)
+ where t1.r1 + 0.0 < 1.0
+ and t1.r2 + 0.0 < 1.0
+ and t1.r3 + 0.0 < 1.0
+ and t1.r4 + 0.0 < 1.0;
diff --git a/patch/hash-collisions.data b/patch/hash-collisions.data
new file mode 100644
index 00000000000..5771aba40db
--- /dev/null
+++ b/patch/hash-collisions.data
@@ -0,0 +1,3819 @@
+1550703 744386c032b9d66f7068b34d02c3cf67
+1784955 ea69a3645dc43af5199926de9fe58075
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diff --git a/patch/hash-memory-model-1.pdf b/patch/hash-memory-model-1.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..ab82fe64ede953bab86f37c6d5f4ff9e45fb8bc6
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diff --git a/patch/hash-memory-model-2.pdf b/patch/hash-memory-model-2.pdf
new file mode 100644
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diff --git a/patch/hash-memory-model.ods b/patch/hash-memory-model.ods
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diff --git a/patch/hash.data b/patch/hash.data
new file mode 100644
index 00000000000..53a9240b125
--- /dev/null
+++ b/patch/hash.data
@@ -0,0 +1,395 @@
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diff --git a/patch/hash.plot b/patch/hash.plot
new file mode 100644
index 00000000000..76703ce5579
--- /dev/null
+++ b/patch/hash.plot
@@ -0,0 +1,16 @@
+set terminal svg size 1200,800 fname 'Verdana,10'
+set output 'hashjoin.svg'
+
+#set terminal pngcairo size 1600,1000 enhanced font 'Verdana,9'
+#set output 'hashjoin.png'
+
+set nokey
+
+set ticslevel 0
+set pm3d at b
+set xtics offset 0,-0.5 rotate by 45
+
+set xlabel "workmem"
+set ylabel "batches"
+
+splot "hash.data" using 1:2:5:xtic(3):ytic(4) with lines lc rgb "#00bb00"
diff --git a/patch/hashjoin.png b/patch/hashjoin.png
new file mode 100644
index 0000000000000000000000000000000000000000..aef9d118a3e6217f6fd8022e82c11e288674ad76
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2.48.1
Re: Adjusting hash join memory limit to handle batch explosion
I've pushed the first (and main) part of the patch series, after some
more cleanup and comment polishing.
As explained in my previous message, I'm not sure about 0002. I don't
know if we need to worry about it (no reports AFAICS). And while the
patch works I'm not sure it's the best fix, or whether we need to do
something about exhausting hash bits.
In any case, it's not PG18 material. And it's a separate issue, so I'm
marking this as committed.
Thanks everyone who helped with any of the many old patch versions!
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Wed, Feb 19, 2025 at 12:22 PM Tomas Vondra <tomas@vondra.me> wrote:
I've pushed the first (and main) part of the patch series, after some
more cleanup and comment polishing.
Two comments on your merged patch --
First, it's easier to see what's going on if we overlook the logic to
round to nearest power of two, and solve the optimization problem
algebraically. Let T = the total memory needed to hash all input rows,
and B = the size of per-batch metadata (= 2 * BLKSIZE, which is
typically 16 KB). Then, solving the optimization problem, the minimum
memory usage occurs at n = nbatches = SQRT(T / B) and w = workmem =
SQRT(B * T).
(Here I am using "workmem" for the hash table's "space_allowed.")
The total working memory used, at the minimum, is always 2 * w: twice
the optimal "workmem" ("space_allowed").
This says that the maximum input size that can be (optimally) hashed
with the default 8 MB workmem (= work_mem * hash_mem_multiplier) is 4
GB, and the total working memory used would actually be 16 MB.
Also, to hash 64 GB, or 16x as much, requires a 32 MB workmem, with 64
MB of total working memory used. So "workmem" grows with the SQRT of
T, the total hash memory needed; and total working memory is 2x
"workmem."
Second -- the algebraic solution illustrates the difficulty in
tracking and restricting working memory usage for Hash Joins! Your
patch improves the "hash 64 GB" situation, because it eliminates 96 GB
of per-batch metadata, by reducing n = nbatches from 8192 to 2048, at
a cost of only 24 MB of workmem. Using the default 8 MB workmem,
*actual* total working memory used would be 8 MB + 16 KB * (64 GB / 8
MB) = 136 MB. By increasing workmem to 32 MB, total working memory is
only 64 MB; so we save 72 MB overall. This is a good thing, but--
The "but" is that the customer really should have set their workmem to
64 MB, in the first place; and we should have taken half of that for
the hash table, and left the other half for per-batch metadata.
-- OK, but historically we have pretended that the per-batch metadata
used no memory. So the customer should have set their workmem to 32
MB, with the understanding that PostgreSQL would have actually used 64
MB...
-- OK, but the customer *didn't* set their workmem to 32 MB. (If they
had, we wouldn't need this patch -- but we *do* need this patch, which
means the customer hasn't set their workmem high enough.) Why not?
Well, because if they set it to 32 MB, they'd run OOM!
-- So we are (secretly!) increasing the customer's workmem to 32 MB,
but only for this particular Hash Join. The customer can't increase it
to 32 MB for all Hash Joins, or they'd run OOM. So we increase it just
for this Hash Join, in the hopes that by doing so we'll avoid running
OOM... which is good; but we don't *tell* the customer we've done
this, and we just hope that the customer actually has 64 MB (= 2x
workmem) free (because, if they don't, they'll run OOM anyway).
All of this is to say that this patch illustrates the need for
something like proposal [1]/messages/by-id/CAJVSvF6s1LgXF6KB2Cz68sHzk+v+O_vmwEkaon=H8O9VcOr-tQ@mail.gmail.com, which allows PostgreSQL to set workmem
limits on individual execution nodes, based on the optimizer's memory
estimates. In the above patch, we're blindly making things better,
without knowing whether we've made them good enough. (The customer is
less likely to run OOM using 64 MB instead of 136 MB, but OOM is still
possible since their workmem limit is 8 MB!)
In v.next of my patchset at [1]/messages/by-id/CAJVSvF6s1LgXF6KB2Cz68sHzk+v+O_vmwEkaon=H8O9VcOr-tQ@mail.gmail.com (should be done by end of day today) I
will deal with the case discussed above by:
1. Doubling Plan.workmem_limit whenever we halve nbatches (so we track
the "workmem" needed by the hash table);
2. Displaying Plan.workmem_limit + Hash.nbatches * (2 * BLCKSIZE),
inside EXPLAIN (work_mem on), (so we display to the customer our best
estimate of the effective workmem limit).
Thanks,
James
[1]: /messages/by-id/CAJVSvF6s1LgXF6KB2Cz68sHzk+v+O_vmwEkaon=H8O9VcOr-tQ@mail.gmail.com
Re: Adjusting hash join memory limit to handle batch explosion
On 2/25/25 17:30, James Hunter wrote:
On Wed, Feb 19, 2025 at 12:22 PM Tomas Vondra <tomas@vondra.me> wrote:
I've pushed the first (and main) part of the patch series, after some
more cleanup and comment polishing.Two comments on your merged patch --
First, it's easier to see what's going on if we overlook the logic to
round to nearest power of two, and solve the optimization problem
algebraically. Let T = the total memory needed to hash all input rows,
and B = the size of per-batch metadata (= 2 * BLKSIZE, which is
typically 16 KB). Then, solving the optimization problem, the minimum
memory usage occurs at n = nbatches = SQRT(T / B) and w = workmem =
SQRT(B * T).(Here I am using "workmem" for the hash table's "space_allowed.")
The total working memory used, at the minimum, is always 2 * w: twice
the optimal "workmem" ("space_allowed").This says that the maximum input size that can be (optimally) hashed
with the default 8 MB workmem (= work_mem * hash_mem_multiplier) is 4
GB, and the total working memory used would actually be 16 MB.Also, to hash 64 GB, or 16x as much, requires a 32 MB workmem, with 64
MB of total working memory used. So "workmem" grows with the SQRT of
T, the total hash memory needed; and total working memory is 2x
"workmem."
Yes, this is a nice way to explain the issue, and how we solve it. It's
probably better than the comment in my commit, I guess.
Second -- the algebraic solution illustrates the difficulty in
tracking and restricting working memory usage for Hash Joins! Your
patch improves the "hash 64 GB" situation, because it eliminates 96 GB
of per-batch metadata, by reducing n = nbatches from 8192 to 2048, at
a cost of only 24 MB of workmem. Using the default 8 MB workmem,
*actual* total working memory used would be 8 MB + 16 KB * (64 GB / 8
MB) = 136 MB. By increasing workmem to 32 MB, total working memory is
only 64 MB; so we save 72 MB overall. This is a good thing, but--
Agreed.
The "but" is that the customer really should have set their workmem to
64 MB, in the first place; and we should have taken half of that for
the hash table, and left the other half for per-batch metadata.-- OK, but historically we have pretended that the per-batch metadata
used no memory. So the customer should have set their workmem to 32
MB, with the understanding that PostgreSQL would have actually used 64
MB...
Sure, we could have considered the per-batch metadata during planning.
And if we find we can't run the hash join, we'd "disable" (penalize) it
in some way. No argument there.
But that assumes we correctly estimate the number of batches during
planning, and it's easy to get that wrong. E.g. the nbatch explosion
cases are a good example.
And that's what my patch was aiming to improve. It does not matter how
the user sets the work_mem GUC, really.
-- OK, but the customer *didn't* set their workmem to 32 MB. (If they
had, we wouldn't need this patch -- but we *do* need this patch, which
means the customer hasn't set their workmem high enough.) Why not?
Well, because if they set it to 32 MB, they'd run OOM!
Not sure I follow the reasoning here :-( If the query completed with a
lower work_mem value, it should complete with work_mem = 32MB, because
that reduces the amount of memory needed. But yes, it's possible they
hit OOM in both cases, it's an attempt to reduce the impact.
-- So we are (secretly!) increasing the customer's workmem to 32 MB,
but only for this particular Hash Join. The customer can't increase it
to 32 MB for all Hash Joins, or they'd run OOM. So we increase it just
for this Hash Join, in the hopes that by doing so we'll avoid running
OOM... which is good; but we don't *tell* the customer we've done
this, and we just hope that the customer actually has 64 MB (= 2x
workmem) free (because, if they don't, they'll run OOM anyway).
Right. This is meant to be a best-effort mitigation for rare cases.
Maybe we should track/report it somehow, though. I mean, if 1% of hash
joins need this, you're probably fine. If 99% hash joins hit it, you
probably really need a higher work_mem value because the hashed relation
is just too large.
But you have a point - maybe we should track/report this somewhere.
First step would be to make the total memory usage better visible in
explain (it's not obvious it does not include the per-batch metadata).
All of this is to say that this patch illustrates the need for
something like proposal [1], which allows PostgreSQL to set workmem
limits on individual execution nodes, based on the optimizer's memory
estimates. In the above patch, we're blindly making things better,
without knowing whether we've made them good enough. (The customer is
less likely to run OOM using 64 MB instead of 136 MB, but OOM is still
possible since their workmem limit is 8 MB!)In v.next of my patchset at [1] (should be done by end of day today) I
will deal with the case discussed above by:1. Doubling Plan.workmem_limit whenever we halve nbatches (so we track
the "workmem" needed by the hash table);
2. Displaying Plan.workmem_limit + Hash.nbatches * (2 * BLCKSIZE),
inside EXPLAIN (work_mem on), (so we display to the customer our best
estimate of the effective workmem limit).Thanks,
James[1] /messages/by-id/CAJVSvF6s1LgXF6KB2Cz68sHzk+v+O_vmwEkaon=H8O9VcOr-tQ@mail.gmail.com
I'm not opposed to doing something like this, but I'm not quite sure how
could it help the cases I meant to address with my patch, where we plan
with low nbatch value, and then it explodes as execution time.
regards
--
Tomas Vondra
Re: Adjusting hash join memory limit to handle batch explosion
On Tue, Feb 25, 2025 at 9:39 AM Tomas Vondra <tomas@vondra.me> wrote:
On 2/25/25 17:30, James Hunter wrote:
On Wed, Feb 19, 2025 at 12:22 PM Tomas Vondra <tomas@vondra.me> wrote:
-- OK, but the customer *didn't* set their workmem to 32 MB. (If they
had, we wouldn't need this patch -- but we *do* need this patch, which
means the customer hasn't set their workmem high enough.) Why not?
Well, because if they set it to 32 MB, they'd run OOM!Not sure I follow the reasoning here :-( If the query completed with a
lower work_mem value, it should complete with work_mem = 32MB, because
that reduces the amount of memory needed. But yes, it's possible they
hit OOM in both cases, it's an attempt to reduce the impact.
Yes, your patch is a Pareto improvement, because it means we use less
working memory than we would otherwise.
-- So we are (secretly!) increasing the customer's workmem to 32 MB,
but only for this particular Hash Join. The customer can't increase it
to 32 MB for all Hash Joins, or they'd run OOM. So we increase it just
for this Hash Join, in the hopes that by doing so we'll avoid running
OOM... which is good; but we don't *tell* the customer we've done
this, and we just hope that the customer actually has 64 MB (= 2x
workmem) free (because, if they don't, they'll run OOM anyway).Right. This is meant to be a best-effort mitigation for rare cases.
Maybe we should track/report it somehow, though. I mean, if 1% of hash
joins need this, you're probably fine. If 99% hash joins hit it, you
probably really need a higher work_mem value because the hashed relation
is just too large.But you have a point - maybe we should track/report this somewhere.
First step would be to make the total memory usage better visible in
explain (it's not obvious it does not include the per-batch metadata).
Right -- my point is that mitigation is good, but tracking /
visibility is also necessary.
All of this is to say that this patch illustrates the need for
something like proposal [1], which allows PostgreSQL to set workmem
limits on individual execution nodes, based on the optimizer's memory
estimates. In the above patch, we're blindly making things better,
without knowing whether we've made them good enough. (The customer is
less likely to run OOM using 64 MB instead of 136 MB, but OOM is still
possible since their workmem limit is 8 MB!)In v.next of my patchset at [1] (should be done by end of day today) I
will deal with the case discussed above by:
...
I'm not opposed to doing something like this, but I'm not quite sure how
could it help the cases I meant to address with my patch, where we plan
with low nbatch value, and then it explodes as execution time.
Your patch addresses two cases: (1) where we plan with high nbatch
value; and (2) where we plan with low nbatch value, and then it
explodes at execution time.
Case (2) can be solved only by taking some action at runtime, and that
action is always best effort (because if we really don't have enough
extra memory free, we have to run OOM). Once a query has started
running, we have fewer options.
Case (1) can be solved in various ways, because it occurs before we
started running the query. For example, we can:
1. Delay starting execution of the query until enough memory becomes
available; or
2. Take memory away from other execution nodes to give to this Hash Join.
But these case (1) solutions require access to the actual
working-memory calculation. That's all I'm saying -- by tracking our
"best-effort" decision, we make it possible to address case (1).
(Your patch solves case (2), as well as it can be solved, by giving
memory to this Hash Join at runtime, and hoping that no one else was
using it. It's hard to improve on that, because PG execution nodes
don't, in general, have the ability to give up memory after they've
started running. But we can do better for case (1), if other
components can basically see the results of your formula.)
Thanks,
James