c5981976f1
## What changes were proposed in this pull request? DataFrameRangeSuite.test("Cancelling stage in a query with Range.") stays sometimes in an infinite loop and times out the build. There were multiple issues with the test: 1. The first valid stageId is zero when the test started alone and not in a suite and the following code waits until timeout: ``` eventually(timeout(10.seconds), interval(1.millis)) { assert(DataFrameRangeSuite.stageToKill > 0) } ``` 2. The `DataFrameRangeSuite.stageToKill` was overwritten by the task's thread after the reset which ended up in canceling the same stage 2 times. This caused the infinite wait. This PR solves this mentioned flakyness by removing the shared `DataFrameRangeSuite.stageToKill` and using `onTaskStart` where stage ID is provided. In order to make sure cancelStage called for all stages `waitUntilEmpty` is called on `ListenerBus`. In [PR20888](https://github.com/apache/spark/pull/20888) this tried to get solved by: * Stopping the executor thread with `wait` * Wait for all `cancelStage` called * Kill the executor thread by setting `SparkContext.SPARK_JOB_INTERRUPT_ON_CANCEL` but the thread killing left the shared `SparkContext` sometimes in a state where further jobs can't be submitted. As a result DataFrameRangeSuite.test("Cancelling stage in a query with Range.") test passed properly but the next test inside the suite was hanging. ## How was this patch tested? Existing unit test executed 10k times. Author: Gabor Somogyi <gabor.g.somogyi@gmail.com> Closes #21214 from gaborgsomogyi/SPARK-23775_1. |
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catalyst | ||
core | ||
hive | ||
hive-thriftserver | ||
create-docs.sh | ||
gen-sql-markdown.py | ||
mkdocs.yml | ||
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 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
.