b14bfc3f8e
## What changes were proposed in this pull request?
The sameResult() method does not work when the logical plan contains subquery expressions.
**Before the fix**
```SQL
scala> val ds = spark.sql("select * from s1 where s1.c1 in (select s2.c1 from s2 where s1.c1 = s2.c1)")
ds: org.apache.spark.sql.DataFrame = [c1: int]
scala> ds.cache
res13: ds.type = [c1: int]
scala> spark.sql("select * from s1 where s1.c1 in (select s2.c1 from s2 where s1.c1 = s2.c1)").explain(true)
== Analyzed Logical Plan ==
c1: int
Project [c1#86]
+- Filter c1#86 IN (list#78 [c1#86])
: +- Project [c1#87]
: +- Filter (outer(c1#86) = c1#87)
: +- SubqueryAlias s2
: +- Relation[c1#87] parquet
+- SubqueryAlias s1
+- Relation[c1#86] parquet
== Optimized Logical Plan ==
Join LeftSemi, ((c1#86 = c1#87) && (c1#86 = c1#87))
:- Relation[c1#86] parquet
+- Relation[c1#87] parquet
```
**Plan after fix**
```SQL
== Analyzed Logical Plan ==
c1: int
Project [c1#22]
+- Filter c1#22 IN (list#14 [c1#22])
: +- Project [c1#23]
: +- Filter (outer(c1#22) = c1#23)
: +- SubqueryAlias s2
: +- Relation[c1#23] parquet
+- SubqueryAlias s1
+- Relation[c1#22] parquet
== Optimized Logical Plan ==
InMemoryRelation [c1#22], true, 10000, StorageLevel(disk, memory, deserialized, 1 replicas)
+- *BroadcastHashJoin [c1#1, c1#1], [c1#2, c1#2], LeftSemi, BuildRight
:- *FileScan parquet default.s1[c1#1] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/dbiswal/mygit/apache/spark/bin/spark-warehouse/s1], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c1:int>
+- BroadcastExchange HashedRelationBroadcastMode(List((shiftleft(cast(input[0, int, true] as bigint), 32) | (cast(input[0, int, true] as bigint) & 4294967295))))
+- *FileScan parquet default.s2[c1#2] Batched: true, Format: Parquet, Location: InMemoryFileIndex[file:/Users/dbiswal/mygit/apache/spark/bin/spark-warehouse/s2], PartitionFilters: [], PushedFilters: [], ReadSchema: struct<c1:int>
```
## How was this patch tested?
New tests are added to CachedTableSuite.
Author: Dilip Biswal <dbiswal@us.ibm.com>
Closes #17330 from dilipbiswal/subquery_cache_final.
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| catalyst | ||
| core | ||
| hive | ||
| hive-thriftserver | ||
| 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 an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allows 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.