sunchao commented on code in PR #46745:
URL: https://github.com/apache/spark/pull/46745#discussion_r1635336429
##########
docs/sql-performance-tuning.md:
##########
@@ -428,3 +428,118 @@ You can control the details of how AQE works by providing
your own cost evaluato
<td>3.2.0</td>
</tr>
</table>
+
+## Storage Partition Join
+
+Storage Partition Join (SPJ) is an optimization technique in Spark SQL that
makes use the existing storage layout to avoid the shuffle phase.
+
+This is a generalization of the concept of Bucket Joins, which is only
applicable for
[bucketed](sql-data-sources-load-save-functions.html#bucketing-sorting-and-partitioning)
tables, to tables partitioned by functions registered in FunctionCatalog.
Storage Partition Joins are currently supported for compatible V2 DataSources.
+
+The following SQL properties enable Storage Partition Join.
Review Comment:
nit: perhaps `The following SQL properties enable Storage Partition Join and
various optimizations of it`?
##########
docs/sql-performance-tuning.md:
##########
@@ -428,3 +428,118 @@ You can control the details of how AQE works by providing
your own cost evaluato
<td>3.2.0</td>
</tr>
</table>
+
+## Storage Partition Join
+
+Storage Partition Join (SPJ) is an optimization technique in Spark SQL that
makes use the existing storage layout to avoid the shuffle phase.
+
+This is a generalization of the concept of Bucket Joins, which is only
applicable for
[bucketed](sql-data-sources-load-save-functions.html#bucketing-sorting-and-partitioning)
tables, to tables partitioned by functions registered in FunctionCatalog.
Storage Partition Joins are currently supported for compatible V2 DataSources.
Review Comment:
nit: this `bucketed` link doesn't work
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