spark-instrumented-optimizer/sql
Dongjoon Hyun 5c4d8f9538 [SPARK-34696][SQL][TESTS] Fix CodegenInterpretedPlanTest to generate correct test cases
### What changes were proposed in this pull request?

SPARK-23596 added `CodegenInterpretedPlanTest` at Apache Spark 2.4.0 in a wrong way because `withSQLConf` depends on the execution time `SQLConf.get` instead of `test` function declaration time. So, the following code executes the test twice without controlling the `CodegenObjectFactoryMode`. This PR aims to fix it correct and introduce a new function `testFallback`.

```scala
trait CodegenInterpretedPlanTest extends PlanTest {

   override protected def test(
       testName: String,
       testTags: Tag*)(testFun: => Any)(implicit pos: source.Position): Unit = {
     val codegenMode = CodegenObjectFactoryMode.CODEGEN_ONLY.toString
     val interpretedMode = CodegenObjectFactoryMode.NO_CODEGEN.toString

     withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> codegenMode) {
       super.test(testName + " (codegen path)", testTags: _*)(testFun)(pos)
     }
     withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> interpretedMode) {
       super.test(testName + " (interpreted path)", testTags: _*)(testFun)(pos)
     }
   }
 }
```

### Why are the changes needed?

1. We need to use like the following.
```scala
super.test(testName + " (codegen path)", testTags: _*)(
   withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> codegenMode) { testFun })(pos)
super.test(testName + " (interpreted path)", testTags: _*)(
   withSQLConf(SQLConf.CODEGEN_FACTORY_MODE.key -> interpretedMode) { testFun })(pos)
```

2. After we fix this behavior with the above code, several test cases including SPARK-34596 and SPARK-34607 fail because they didn't work at both `CODEGEN` and `INTERPRETED` mode. Those test cases only work at `FALLBACK` mode. So, inevitably, we need to introduce `testFallback`.

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

No.

### How was this patch tested?

Pass the CIs.

Closes #31766 from dongjoon-hyun/SPARK-34596-SPARK-34607.

Lead-authored-by: Dongjoon Hyun <dhyun@apple.com>
Co-authored-by: Dongjoon Hyun <dongjoon@apache.org>
Signed-off-by: Dongjoon Hyun <dhyun@apple.com>
2021-03-10 23:41:49 -08:00
..
catalyst [SPARK-34696][SQL][TESTS] Fix CodegenInterpretedPlanTest to generate correct test cases 2021-03-10 23:41:49 -08:00
core [MINOR][SQL] Remove unnecessary extend from BroadcastHashJoinExec 2021-03-10 23:38:53 -08:00
hive [SPARK-34603][SQL] Support ADD ARCHIVE and LIST ARCHIVES command 2021-03-09 21:28:35 +09:00
hive-thriftserver [SPARK-34373][SQL] HiveThriftServer2 startWithContext may hang with a race issue 2021-02-21 17:37:12 +09:00
create-docs.sh [SPARK-34010][SQL][DODCS] Use python3 instead of python in SQL documentation build 2021-01-05 19:48:10 +09:00
gen-sql-api-docs.py [SPARK-34022][DOCS][FOLLOW-UP] Fix typo in SQL built-in function docs 2021-01-06 09:28:22 -08:00
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.