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Yuming Wang 54348dbd21 [SPARK-32767][SQL] Bucket join should work if spark.sql.shuffle.partitions larger than bucket number 
### What changes were proposed in this pull request?

Bucket join should work if `spark.sql.shuffle.partitions` larger than bucket number, such as:
```scala
spark.range(1000).write.bucketBy(432, "id").saveAsTable("t1")
spark.range(1000).write.bucketBy(34, "id").saveAsTable("t2")
sql("set spark.sql.shuffle.partitions=600")
sql("set spark.sql.autoBroadcastJoinThreshold=-1")
sql("select * from t1 join t2 on t1.id = t2.id").explain()
```

Before this pr:
```
== Physical Plan ==
*(5) SortMergeJoin [id#26L], [id#27L], Inner
:- *(2) Sort [id#26L ASC NULLS FIRST], false, 0
:  +- Exchange hashpartitioning(id#26L, 600), true
:     +- *(1) Filter isnotnull(id#26L)
:        +- *(1) ColumnarToRow
:           +- FileScan parquet default.t1[id#26L] Batched: true, DataFilters: [isnotnull(id#26L)], Format: Parquet, PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 432 out of 432
+- *(4) Sort [id#27L ASC NULLS FIRST], false, 0
   +- Exchange hashpartitioning(id#27L, 600), true
      +- *(3) Filter isnotnull(id#27L)
         +- *(3) ColumnarToRow
            +- FileScan parquet default.t2[id#27L] Batched: true, DataFilters: [isnotnull(id#27L)], Format: Parquet, PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 34 out of 34
```

After this pr:
```
== Physical Plan ==
*(4) SortMergeJoin [id#26L], [id#27L], Inner
:- *(1) Sort [id#26L ASC NULLS FIRST], false, 0
:  +- *(1) Filter isnotnull(id#26L)
:     +- *(1) ColumnarToRow
:        +- FileScan parquet default.t1[id#26L] Batched: true, DataFilters: [isnotnull(id#26L)], Format: Parquet, PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 432 out of 432
+- *(3) Sort [id#27L ASC NULLS FIRST], false, 0
   +- Exchange hashpartitioning(id#27L, 432), true
      +- *(2) Filter isnotnull(id#27L)
         +- *(2) ColumnarToRow
            +- FileScan parquet default.t2[id#27L] Batched: true, DataFilters: [isnotnull(id#27L)], Format: Parquet, PartitionFilters: [], PushedFilters: [IsNotNull(id)], ReadSchema: struct<id:bigint>, SelectedBucketsCount: 34 out of 34
```

### Why are the changes needed?

Spark 2.4 support this.

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Unit test.

Closes #29612 from wangyum/SPARK-32767.

Authored-by: Yuming Wang <yumwang@ebay.com>
Signed-off-by: Wenchen Fan <wenchen@databricks.com>
2020-09-02 04:16:20 +00:00
..
catalyst [SPARK-32754][SQL][TEST] Unify to assertEqualJoinPlans for join reorder suites 2020-09-01 09:08:35 -07:00
core [SPARK-32767][SQL] Bucket join should work if spark.sql.shuffle.partitions larger than bucket number 2020-09-02 04:16:20 +00:00
hive [SPARK-32592][SQL] Make DataFrameReader.table take the specified options 2020-08-31 13:21:15 +00:00
hive-thriftserver [SPARK-32733][SQL] Add extended information - arguments/examples/since/notes of expressions to the remarks field of GetFunctionsOperation 2020-08-31 11:03:01 +09:00
create-docs.sh [SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc 2020-04-27 17:08:52 +09:00
gen-sql-api-docs.py [SPARK-31474][SQL][FOLLOWUP] Replace _FUNC_ placeholder with functionname in the note field of expression info 2020-04-23 13:33:04 +09:00
gen-sql-config-docs.py [SPARK-31550][SQL][DOCS] Set nondeterministic configurations with general meanings in sql configuration doc 2020-04-27 17:08:52 +09:00
gen-sql-functions-docs.py [SPARK-31562][SQL] Update ExpressionDescription for substring, current_date, and current_timestamp 2020-04-26 11:46:52 -07:00
mkdocs.yml [SPARK-30731] Update deprecated Mkdocs option 2020-02-19 17:28:58 +09:00
README.md [SPARK-30510][SQL][DOCS] Publicly document Spark SQL configuration options 2020-02-09 19:20:47 +09:00

README.md

Spark SQL

This module provides support for executing relational queries expressed in either SQL or the DataFrame/Dataset API.

Spark SQL is broken up into four subprojects:

  • Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.
  • Execution (sql/core) - A query planner / execution engine for translating Catalyst's logical query plans into Spark RDDs. This component also includes a new public interface, SQLContext, that allows users to execute SQL or LINQ statements against existing RDDs and Parquet files.
  • Hive Support (sql/hive) - Includes extensions that allow users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
  • HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.

Running ./sql/create-docs.sh generates SQL documentation for built-in functions under sql/site, and SQL configuration documentation that gets included as part of configuration.md in the main docs directory.