diff --git a/doc/src/sgml/ddl.sgml b/doc/src/sgml/ddl.sgml
index 13a1001761..45f369806a 100644
--- a/doc/src/sgml/ddl.sgml
+++ b/doc/src/sgml/ddl.sgml
@@ -2833,8 +2833,9 @@ VALUES ('Albany', NULL, NULL, 'NY');
     </listitem>
    </itemizedlist>
 
-   These deficiencies will probably be fixed in some future release,
-   but in the meantime considerable care is needed in deciding whether
+   Some functionality not implemented for inheritance hierarchies is
+   implemented for declarative partitioning.
+   Considerable care is needed in deciding whether partitioning with legacy
    inheritance is useful for your application.
   </para>
 
@@ -3927,6 +3928,83 @@ EXPLAIN SELECT count(*) FROM measurement WHERE logdate &gt;= DATE '2008-01-01';
    </itemizedlist>
    </para>
   </sect2>
+  
+  <sect2 id="ddl-partitioning-declarative-best-practices">
+   <title>Declarative Partitioning Best Practices</title>
+
+   <para>
+    The choice of how to partition a table should be made carefully as the
+    performance of query planning and execution can be negatively affected by
+    poorly made design decisions.
+   </para>
+
+   <para>
+    One of the most critical design decisions will be the column or columns
+    by which you partition your data.  Often the best choice will be to
+    partition by the column or set of columns which most commonly appear in
+    <literal>WHERE</literal> clauses of queries being executed on the
+    partitioned table.  <literal>WHERE</literal> clause items that match and
+    are compatible with the partition key can be used to prune away unneeded
+    partitions.  Removal of unwanted data is also a factor to consider when
+    planning your partitioning strategy as an entire partition can be removed
+    fairly quickly.  However, if data that you want to keep exists in that
+    partition then that means having to resort to using
+    <command>DELETE</command> instead of removing the partition.
+   </para>
+
+   <para>
+    Choosing the target number of partitions by which the table should be
+    divided into is also a critical decision to make.  Not having enough
+    partitions may mean that indexes remain too large and that data locality
+    remains poor which could result in poor cache hit ratios.  However,
+    dividing the table into too many partitions can also cause issues.
+    Too many partitions can mean slower query planning times and higher memory
+    consumption during both query planning and execution.  It's also important
+    to consider what changes may occur in the future when choosing how to
+    partition your table.  For example, if you choose to have one partition
+    per customer and you currently have a small number of large customers,
+    what will the implications be if in several years you obtain a large
+    number of small customers.  In this case, it may be better to choose to
+    partition by <literal>RANGE</literal> and choose a reasonable number of
+    partitions rather than trying to partition by <literal>LIST</literal> and
+    hoping that the number of customers does not increase significantly over
+    time.
+   </para>
+
+   <para>
+    Sub-partitioning can be useful to further divide partitions that are
+    expected to become larger than other partitions, although excessive
+    sub-partitioning can easily lead to large numbers of partitions and can
+    cause the problems mentioned in the preceding paragraph.
+   </para>
+
+   <para>
+    It is also important to consider the overhead of partitioning during
+    query planning and execution.  The query planner is generally able to
+    handle partition hierarchies up a few hundred partitions.  With larger
+    numbers of partitions planning times become slower and memory consumption
+    becomes higher.  This is particularly true for the
+    <command>UPDATE</command> and <command>DELETE</command> commands.  It also
+    may be undesirable to have a large number of partitions as each partition
+    requires metadata about the partition to be stored in each session that
+    touches it.  If each session touches a large number of partitions over a
+    period of time then the memory consumption for this may become
+    significant.
+   </para>
+
+   <para>
+    With data warehouse type workloads it can make sense to use a larger
+    number of partitions than with an OLTP type workload.  Generally, in data
+    warehouses, query planning time is less of a concern as the majority of
+    processing time is spent during query execution.  With either of these two
+    types of workload it is important to make the right decisions early as
+    re-partitioning large quantities of data can be painfully slow.
+    Simulations of the intended workload are often beneficial for optimizing
+    the partitioning strategy.  Never assume that more partitions are better
+    than fewer partitions and vice-versa.
+   </para>
+  </sect2>
+
  </sect1>
 
  <sect1 id="ddl-foreign-data">