Top -N Query performance issue and high CPU usage

On Sat, 31 Jan 2026, 13:30 yudhi s, <learnerdatabase99@gmail.com> wrote:

Hello Experts,
We have a "Select" query which is using three to five main transaction
tables (txn_tbl, txn_status, txn_decision, txn_sale, ath) holding ~2million
rows in each of them(which is going to increase to have ~50-100million in
future) and others(6-7) tables out of which some are master and some other
small tables.

When we are running this query , and it's taking ~2-3seconds , however
when we hit this query from 10-15 session at same time its causing CPU
spike up to ~50-60% for the DB instance and this is increasing and touching
90% when we are increasing the hits further to 40-50 times concurrently.

This query is going to be called in the first page of an UI screen and is
supposed to show the latest 1000 rows based on a certain transaction date.
This query is supposed to allow thousands of users to hit this same query
at the first landing page at the same time. Its postgres version 17. The
instance has 2-VCPU and 16GB RAM.

I have the following questions.

1)Why is this query causing a high cpu spike ,if there is any way in
postgres to understand what part/line of the query is contributing to the
high cpu time?
2)How can we tune this query to further reduce response time and mainly
CPU consumption ? Is any additional index or anything will make this plan
better further?
3) Is there any guidance or best practices exists , to create/design top
N-queries for such UI scenarios where performance is an important factor?
4)And based on the CPU core and memory , is there any calculation by using
which , we can say that this machine can support a maximum N number of
concurrent queries of such type beyond which we need more cpu cores
machines?

Below is the query and its current plan:-
https://gist.github.com/databasetech0073/6688701431dc4bf4eaab8d345c1dc65f

Regards
Yudhi

Plan says it's using temp files for sorting....I would suggest you increase
work_mem for this to avoid temp.fike creation...Although not the answer to
all your problems, it would be a good start .

On 1/31/26 05:30, yudhi s wrote:

Hello Experts,

This query is going to be called in the first page of an UI screen and
is supposed to show the latest 1000 rows based on a certain transaction
date. This query is supposed to allow thousands of users to hit this
same query at the first landing page at the same time. Its postgres
version 17.  The instance has 2-VCPU and 16GB RAM.

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB RAM
and is insufficient resources for what you want to do.

2) You will need to provide the schema definitions for the tables involved.

4)And based on the CPU core and memory , is there any calculation by
using which , we can say that this machine can support a maximum N
number of concurrent queries of such type beyond which we need more cpu
cores machines?

You already have the beginnings of a chart:

1 session 2-3 secs

10-15 sessions 50-60% usage

40-50 sessions 90% usage

Regards
Yudhi

--
Adrian Klaver
adrian.klaver@aklaver.com

Plan says it's using temp files for sorting....I would suggest you
increase work_mem for this to avoid temp.fike creation...Although not the
answer to all your problems, it would be a good start .

Even setting work_mem to 64MB remove all the "temp read" and showig all
memory reads, but still we are seeing similar cpu spike when executing this
query from multiple sessions and also the response time is staying same.

Thank you.

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB RAM
and is insufficient resources for what you want to do.

Can you please explain a bit in detail, how much minimum VCPU and RAM will
be enough resources to suffice this requirement? and you normally do that
calculation?

2) You will need to provide the schema definitions for the tables involved.

Do you mean table DDL or just the index definitions on the tables should

help?

Also i was trying to understand , by just looking into the "explain
analyze" output, is there any way we can tie the specific step in the plan
, which is the major contributor of the cpu resources? Such that we can
then try to fix that part rather than looking throughout the query as its
big query?

And if any suggestion to improve the TOP-N queries where the base table may
have many rows in it.

On Sat, Jan 31, 2026 at 2:47 PM yudhi s <learnerdatabase99@gmail.com> wrote:

Thank you.

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB RAM
and is insufficient resources for what you want to do.

Can you please explain a bit in detail, how much minimum VCPU and RAM will
be enough resources to suffice this requirement? and you normally do that
calculation?

2) You will need to provide the schema definitions for the tables
involved.

Do you mean table DDL or just the index definitions on the tables should

help?

Also i was trying to understand , by just looking into the "explain
analyze" output, is there any way we can tie the specific step in the plan
, which is the major contributor of the cpu resources? Such that we can
then try to fix that part rather than looking throughout the query as its
big query?

It looks like 71% (748ms of a total 1056ms) of elapsed time is taken by the
c_1.tran_date external sort on line 150.

That, obviously, is what you should work on.

1. You say you increased work_mem. From what, to what?
2. But that it did not reduce execution time. Please post the EXPLAIN from
after increasing work_mem.
3. Did you remember to run SELECT pg_reload_conf(); after increasing
work_mem?
4. Is there an index on APP_schema.txn_tbl.tran_date?

And if any suggestion to improve the TOP-N queries where the base table may

have many rows in it.

--
Death to <Redacted>, and butter sauce.
Don't boil me, I'm still alive.
<Redacted> lobster!

On Sat, Jan 31, 2026 at 10:05 PM Ron Johnson <ronljohnsonjr@gmail.com>
wrote:

On Sat, Jan 31, 2026 at 2:47 PM yudhi s <learnerdatabase99@gmail.com>
wrote:

Thank you.

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB RAM
and is insufficient resources for what you want to do.

Can you please explain a bit in detail, how much minimum VCPU and RAM
will be enough resources to suffice this requirement? and you normally do
that calculation?

2) You will need to provide the schema definitions for the tables
involved.

Do you mean table DDL or just the index definitions on the tables should

help?

Also i was trying to understand , by just looking into the "explain
analyze" output, is there any way we can tie the specific step in the plan
, which is the major contributor of the cpu resources? Such that we can
then try to fix that part rather than looking throughout the query as its
big query?

It looks like 71% (748ms of a total 1056ms) of elapsed time is taken by
the c_1.tran_date external sort on line 150.

That, obviously, is what you should work on.

1. You say you increased work_mem. From what, to what?
2. But that it did not reduce execution time. Please post the EXPLAIN
from after increasing work_mem.
3. Did you remember to run SELECT pg_reload_conf(); after increasing
work_mem?
4. Is there an index on APP_schema.txn_tbl.tran_date?

And if any suggestion to improve the TOP-N queries where the base table

may have many rows in it.

The DBtune Free Edition <http://app.dbtune.com&gt; can help you find the
correct adjustments for work_mem and other server parameters
<https://dbtune.com/blog/dbtunes-multi-dimensional-performance-tuning-space&gt;.
It's designed to help optimize your PostgreSQL runtime for your current
hardware setup.

On 2026-02-01 01:16:56 +0530, yudhi s wrote:

Thank you. 

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB RAM
and is insufficient resources for what you want to do.

Can you please explain a bit in detail, how much minimum VCPU and RAM will be
enough resources to suffice this requirement? and you normally do that
calculation?

You wrote:

| This query is supposed to allow thousands of users to hit this same
| query at the first landing page at the same time.

If you meant that literally, you would need thousands of cores to handle
those thousands of simultaneous queries and enough RAM for thousands of
sessions, each performing a rather complex query. So possibly hundreds
of maybe even thousands of gigabytes, not 16.

However, maybe you didn't mean that. There are relatively few
applications where thousands of users log in within a second. Maybe you
just meant that there would be thousands of users logged in in total. If
so, how many simultaneus queries do you really expect?

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

hjp

--
_ | Peter J. Holzer | Story must make more sense than reality.
|_|_) | |
| | | hjp@hjp.at | -- Charles Stross, "Creative writing
__/ | http://www.hjp.at/ | challenge!"

On Sun, Feb 1, 2026 at 4:47 PM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:
[snip]

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

That's what I was thinking, too: app server background process continually
runs that query in a loop, feeding the results to a shared cache; the end
user connections then read the latest version of the cached results.

--
Death to <Redacted>, and butter sauce.
Don't boil me, I'm still alive.
<Redacted> lobster!

On 1/31/26 11:46, yudhi s wrote:

Thank you.

1) Without even looking at the plan I'm going to say 2-VCPU and 16GB
RAM
and is insufficient resources for what you want to do.

Can you please explain a bit in detail, how much minimum VCPU and RAM
will be enough resources to suffice this requirement? and you normally
do that calculation?

Don't know what the minimum requirements are. It would depend on many
variables 1) The plan being chosen, which in turn depends on the schema
information as well as the data turnover. 2) What the VCPU is actually
emulating. 3) The efficiency of of the virtual machines/containers with
regard to accessing memory and storage. 4) The service limits of the
virtualization. 5) What the storage system and how performant it is.

In other words this is something you will need to test and derive your
own formula for.

2) You will need to provide the schema definitions for the tables
involved.

Do you mean table DDL or just the index definitions on the tables should
help?

Basically what you get in psql when you do \d some_table.

Also i was trying to understand , by just looking into the "explain
analyze" output, is there any way we can tie the specific step in the
plan , which is the major contributor of the cpu resources? Such that we
can then try to fix that part rather than looking throughout the query
as its big query?

And if any suggestion to improve the TOP-N queries where the base table
may have many rows in it.

--
Adrian Klaver
adrian.klaver@aklaver.com

On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

However, maybe you didn't mean that. There are relatively few
applications where thousands of users log in within a second. Maybe you
just meant that there would be thousands of users logged in in total. If
so, how many simultaneus queries do you really expect?

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

Thank you so much. I need to get back on the exact number of such queries
which can hit the database. However, as 1000 of users will be there, so the
possibility of all logging into the system on the same page at same time
needs to be found out. Will double check on this.

However, when you said caching :- The results on the base tables are going
to be ~30-50 million. This landing page has filters on it so it may be of
30+ different combinations based on the user's choice. So do you suggest ,
we will populate the base data in a materialized view(named like "landing
page data") which we can refresh (maybe once in ~5 minutes behind the
scenes) and then that can be queried in the landing page directly. And we
can have suitable indexes created on the materialized view based on the
dynamic filter criteria?

On Mon, 2 Feb, 2026, 11:21 am Rob Sargent, <robjsargent@gmail.com> wrote:

Thank you so much. I need to get back on the exact number of such

queries which can hit the database. However, as 1000 of users will be
there, so the possibility of all logging into the system on the same page
at same time needs to be found out. Will double check on this.

However, when you said caching :- The results on the base tables are

going to be ~30-50 million. This landing page has filters on it so it may
be of 30+ different

I know I read OP’s earlier descriptions to suggest that each login saw the
same data. I was wrong and I suspect the suggestion to cache goes out the
window.

The need for more resources now comes centre stage, right beside query
tuning. You won’t get much help here on the latter problem without more DDL
on the tables involved. Help on the hardware is just money - though most
desktops these days are more powerful than that vert described up-thread

Won't , the materialized view having a minimum Delta refresh frequency(5-10

Hi

Would it do any good to restrict the transaction date for the limit to something like "current timestamp - 1 day/hour/month". How about partitioning?

My two dimes

Thiemo

On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

There was no index on column tran_date , I created one and it's making
the query finish in ~200ms, a lot faster than in the past. Below is the
portion of the query and its plan which actually consumes most of the
resource and time post the new index creation.

https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492

1) Now the part which takes time is the "nested loop" join on the
"ent_id" column. Can we do anything to make it much better/faster?

2) Also another question I had was, with this new index the table scan of
txn_tbl is now fully eliminated by the "Index Scan Backward" even i have
other columns from that table projected in the query, so how its getting
all those column values without visiting table but just that index scan
backward operation?

On Mon, Feb 2, 2026 at 6:39 AM yudhi s <learnerdatabase99@gmail.com> wrote:

On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

There was no index on column tran_date , I created one and it's making
the query finish in ~200ms, a lot faster than in the past. Below is the
portion of the query and its plan which actually consumes most of the
resource and time post the new index creation.

https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492

1) Now the part which takes time is the "nested loop" join on the
"ent_id" column. Can we do anything to make it much better/faster?

2) Also another question I had was, with this new index the table scan of
txn_tbl is now fully eliminated by the "Index Scan Backward" even i have
other columns from that table projected in the query, so how its getting
all those column values without visiting table but just that index scan
backward operation?

Reading through EXPLAIN output isn't always a mystery.

Search for "actual time" and you'll find row 53, which is the "deepest"
(most nested) row with the highest actual time.

That tells you where the time is now spent, and what it's doing.

--
Death to <Redacted>, and butter sauce.
Don't boil me, I'm still alive.
<Redacted> lobster!

On Mon, Feb 2, 2026 at 7:04 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:

On Mon, Feb 2, 2026 at 6:39 AM yudhi s <learnerdatabase99@gmail.com>
wrote:

On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

There was no index on column tran_date , I created one and it's making
the query finish in ~200ms, a lot faster than in the past. Below is the
portion of the query and its plan which actually consumes most of the
resource and time post the new index creation.

https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492

1) Now the part which takes time is the "nested loop" join on the
"ent_id" column. Can we do anything to make it much better/faster?

2) Also another question I had was, with this new index the table scan
of txn_tbl is now fully eliminated by the "Index Scan Backward" even i have
other columns from that table projected in the query, so how its getting
all those column values without visiting table but just that index scan
backward operation?

Reading through EXPLAIN output isn't always a mystery.

Search for "actual time" and you'll find row 53, which is the "deepest"
(most nested) row with the highest actual time.

That tells you where the time is now spent, and what it's doing.

My apologies if i misunderstand the plan, But If I see, it's spending
~140ms(140ms-6ms) i.e. almost all the time now, in performing the below
nested loop join. So my question was , is there any possibility to reduce
the resource consumption or response time further here? Hope my
understanding is correct here.

-> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual time=
*6.009..147.695* rows=1049 loops=1)
Join Filter: ((df.ent_id)::numeric = m.ent_id)
Rows Removed by Join Filter: 513436
Buffers: shared hit=1939

On Mon, Feb 2, 2026 at 8:53 AM yudhi s <learnerdatabase99@gmail.com> wrote:

On Mon, Feb 2, 2026 at 7:04 PM Ron Johnson <ronljohnsonjr@gmail.com>
wrote:

On Mon, Feb 2, 2026 at 6:39 AM yudhi s <learnerdatabase99@gmail.com>
wrote:

On Mon, Feb 2, 2026 at 3:17 AM Peter J. Holzer <hjp-pgsql@hjp.at> wrote:

If you do have that many simultaneous accesses to the landing page, and
you can't speed up the query significantly (I take it you've seen the
suggestion to check whether there's an index on
APP_schema.txn_tbl.tran_date), then maybe you don't need to perform it
for every user? I don't know what the query is supposed to do, but
unless the "ent_id" is really a user id, it doesn't seem to be specific
to the user. So maybe you can cache the result for a minute or an hour
and show the same result to everybody who logs in during that time.

There was no index on column tran_date , I created one and it's
making the query finish in ~200ms, a lot faster than in the past. Below is
the portion of the query and its plan which actually consumes most of the
resource and time post the new index creation.

https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492

1) Now the part which takes time is the "nested loop" join on the
"ent_id" column. Can we do anything to make it much better/faster?

2) Also another question I had was, with this new index the table scan
of txn_tbl is now fully eliminated by the "Index Scan Backward" even i have
other columns from that table projected in the query, so how its getting
all those column values without visiting table but just that index scan
backward operation?

Reading through EXPLAIN output isn't always a mystery.

Search for "actual time" and you'll find row 53, which is the "deepest"
(most nested) row with the highest actual time.

That tells you where the time is now spent, and what it's doing.

My apologies if i misunderstand the plan, But If I see, it's spending
~140ms(140ms-6ms) i.e. almost all the time now, in performing the below
nested loop join. So my question was , is there any possibility to reduce
the resource consumption or response time further here? Hope my
understanding is correct here.

-> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual time=
*6.009..147.695* rows=1049 loops=1)
Join Filter: ((df.ent_id)::numeric = m.ent_id)
Rows Removed by Join Filter: 513436
Buffers: shared hit=1939

I don't see m.ent_id in the actual query. Did you only paste a portion of
the query?

Also, casting in a JOIN typically brutalizes the ability to use an index.

--
Death to <Redacted>, and butter sauce.
Don't boil me, I'm still alive.
<Redacted> lobster!

On Mon, Feb 2, 2026 at 8:57 PM Ron Johnson <ronljohnsonjr@gmail.com> wrote:

My apologies if i misunderstand the plan, But If I see, it's spending
~140ms(140ms-6ms) i.e. almost all the time now, in performing the below
nested loop join. So my question was , is there any possibility to reduce
the resource consumption or response time further here? Hope my
understanding is correct here.

-> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual
time=*6.009..147.695* rows=1049 loops=1)
Join Filter: ((df.ent_id)::numeric = m.ent_id)
Rows Removed by Join Filter: 513436
Buffers: shared hit=1939

I don't see m.ent_id in the actual query. Did you only paste a portion
of the query?

Also, casting in a JOIN typically brutalizes the ability to use an index.

Thank you.

Actually i tried executing the first two CTE where the query was spending
most of the time and teh alias has changed. Also here i have changed the
real table names before putting it here, hope that is fine.
However , i verified the data type of the ent_id column in "ent" its "int8"
and in table "txn_tbl" is "numeric 12", so do you mean to say this
difference in the data type is causing this high response time during the
nested loop join? My understanding was it will be internally castable
without additional burden. Also, even i tried creating an index on the
"(df.ent_id)::numeric"
its still reulting into same plan and response time.

On Mon, Feb 2, 2026 at 1:39 PM yudhi s <learnerdatabase99@gmail.com> wrote:

On Mon, Feb 2, 2026 at 8:57 PM Ron Johnson <ronljohnsonjr@gmail.com>
wrote:

My apologies if i misunderstand the plan, But If I see, it's spending
~140ms(140ms-6ms) i.e. almost all the time now, in performing the below
nested loop join. So my question was , is there any possibility to reduce
the resource consumption or response time further here? Hope my
understanding is correct here.

-> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual
time=*6.009..147.695* rows=1049 loops=1)
Join Filter: ((df.ent_id)::numeric = m.ent_id)
Rows Removed by Join Filter: 513436
Buffers: shared hit=1939

I don't see m.ent_id in the actual query. Did you only paste a portion
of the query?

Also, casting in a JOIN typically brutalizes the ability to use an index.

Thank you.

Actually i tried executing the first two CTE where the query was spending
most of the time and teh alias has changed.

We need to see everything, not just what you think is relevant.

Also here i have changed the real table names before putting it here, hope
that is fine.
However , i verified the data type of the ent_id column in "ent" its
"int8" and in table "txn_tbl" is "numeric 12", so do you mean to say this
difference in the data type is causing this high response time during the
nested loop join? My understanding was it will be internally castable
without additional burden. Also, even i tried creating an index on the "(df.ent_id)::numeric"
its still reulting into same plan and response time.

If you'd shown the "\d" table definitions like Adrian asked two days ago,
we'd know what indexes are on each table, and not have to beg you to
dispense dribs and drabs of information.

--
Death to <Redacted>, and butter sauce.
Don't boil me, I'm still alive.
<Redacted> lobster!

On Tue, Feb 3, 2026 at 1:01 AM Ron Johnson <ronljohnsonjr@gmail.com> wrote:

On Mon, Feb 2, 2026 at 1:39 PM yudhi s <learnerdatabase99@gmail.com>
wrote:

On Mon, Feb 2, 2026 at 8:57 PM Ron Johnson <ronljohnsonjr@gmail.com>
wrote:

My apologies if i misunderstand the plan, But If I see, it's spending
~140ms(140ms-6ms) i.e. almost all the time now, in performing the below
nested loop join. So my question was , is there any possibility to reduce
the resource consumption or response time further here? Hope my
understanding is correct here.

-> Nested Loop (cost=266.53..1548099.38 rows=411215 width=20) (actual
time=*6.009..147.695* rows=1049 loops=1)
Join Filter: ((df.ent_id)::numeric = m.ent_id)
Rows Removed by Join Filter: 513436
Buffers: shared hit=1939

I don't see m.ent_id in the actual query. Did you only paste a portion
of the query?

Also, casting in a JOIN typically brutalizes the ability to use an index.

Thank you.

Actually i tried executing the first two CTE where the query was spending
most of the time and teh alias has changed.

We need to see everything, not just what you think is relevant.

Also here i have changed the real table names before putting it here,
hope that is fine.
However , i verified the data type of the ent_id column in "ent" its
"int8" and in table "txn_tbl" is "numeric 12", so do you mean to say this
difference in the data type is causing this high response time during the
nested loop join? My understanding was it will be internally castable
without additional burden. Also, even i tried creating an index on the "(df.ent_id)::numeric"
its still reulting into same plan and response time.

If you'd shown the "\d" table definitions like Adrian asked two days ago,
we'd know what indexes are on each table, and not have to beg you to
dispense dribs and drabs of information.

I am unable to run "\d" from the dbeaver sql worksheet. However, I have
fetched the DDL for the three tables and their selected columns, used in
the smaller version of the query and its plan , which I recently updated.

https://gist.github.com/databasetech0073/e4290b085f8f974e315fb41bdc47a1f3

https://gist.github.com/databasetech0073/344df46c328e02b98961fab0cd221492

Regards
Yudhi