spark-instrumented-optimizer/sql
Dongjoon Hyun 42c0b175ce [SPARK-33338][SQL] GROUP BY using literal map should not fail
### What changes were proposed in this pull request?

This PR aims to fix `semanticEquals` works correctly on `GetMapValue` expressions having literal maps with `ArrayBasedMapData` and `GenericArrayData`.

### Why are the changes needed?

This is a regression from Apache Spark 1.6.x.
```scala
scala> sc.version
res1: String = 1.6.3

scala> sqlContext.sql("SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k]").show
+---+
|_c0|
+---+
| v1|
+---+
```

Apache Spark 2.x ~ 3.0.1 raise`RuntimeException` for the following queries.
```sql
CREATE TABLE t USING ORC AS SELECT map('k1', 'v1') m, 'k1' k
SELECT map('k1', 'v1')[k] FROM t GROUP BY 1
SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k]
SELECT map('k1', 'v1')[k] a FROM t GROUP BY a
```

**BEFORE**
```scala
Caused by: java.lang.RuntimeException: Couldn't find k#3 in [keys: [k1], values: [v1][k#3]#6]
	at scala.sys.package$.error(package.scala:27)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:85)
	at org.apache.spark.sql.catalyst.expressions.BindReferences$$anonfun$bindReference$1$$anonfun$applyOrElse$1.apply(BoundAttribute.scala:79)
	at org.apache.spark.sql.catalyst.errors.package$.attachTree(package.scala:52)
```

**AFTER**
```sql
spark-sql> SELECT map('k1', 'v1')[k] FROM t GROUP BY 1;
v1
Time taken: 1.278 seconds, Fetched 1 row(s)
spark-sql> SELECT map('k1', 'v1')[k] FROM t GROUP BY map('k1', 'v1')[k];
v1
Time taken: 0.313 seconds, Fetched 1 row(s)
spark-sql> SELECT map('k1', 'v1')[k] a FROM t GROUP BY a;
v1
Time taken: 0.265 seconds, Fetched 1 row(s)
```

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

No.

### How was this patch tested?

Pass the CIs with the newly added test case.

Closes #30246 from dongjoon-hyun/SPARK-33338.

Authored-by: Dongjoon Hyun <dhyun@apple.com>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2020-11-04 08:35:10 -08:00
..
catalyst [SPARK-33338][SQL] GROUP BY using literal map should not fail 2020-11-04 08:35:10 -08:00
core [SPARK-33338][SQL] GROUP BY using literal map should not fail 2020-11-04 08:35:10 -08:00
hive [SPARK-33214][TEST][HIVE] Stop HiveExternalCatalogVersionsSuite from using a hard-coded location to store localized Spark binaries 2020-11-04 06:51:54 +00:00
hive-thriftserver [SPARK-33193][SQL][TEST] Hive ThriftServer JDBC Database MetaData API Behavior Auditing 2020-10-23 13:34:33 -07:00
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

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.