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Posted to issues@spark.apache.org by "Gengliang Wang (Jira)" <ji...@apache.org> on 2019/09/17 15:47:00 UTC
[jira] [Commented] (SPARK-29122) Propagate all the SQL conf to
executors in SQLQueryTestSuite
[ https://issues.apache.org/jira/browse/SPARK-29122?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16931582#comment-16931582 ]
Gengliang Wang commented on SPARK-29122:
----------------------------------------
[~maropu] Could you help on this one? I met it in the PR https://github.com/apache/spark/pull/25804 .
I think we should propagate all the SQL conf to executors to improve the test coverage
> Propagate all the SQL conf to executors in SQLQueryTestSuite
> ------------------------------------------------------------
>
> Key: SPARK-29122
> URL: https://issues.apache.org/jira/browse/SPARK-29122
> Project: Spark
> Issue Type: Improvement
> Components: SQL, Tests
> Affects Versions: 3.0.0
> Reporter: Gengliang Wang
> Priority: Major
>
> Currently, the execution of SQLQueryTestSuite execute the SQL query as following:
> {code:java}
> val answer = hiveResultString(df.queryExecution.executedPlan).map(replaceNotIncludedMsg)
> {code}
> We should propagate all the SQL conf to executors:
> {code:java}
> val answer =
> SQLExecution.withNewExecutionId(session, df.queryExecution, Some(sql)) {
> hiveResultString(df.queryExecution.executedPlan).map(replaceNotIncludedMsg)
> }
> {code}
> However, there is exception after changes when running "pgSQL/numeric.sql":
> {code:java}
> CREATE TABLE num_data (id int, val decimal(38,10)) USING parquet;
> ....
> select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4): QueryOutput(select sum(udf(CAST(null AS Decimal(38,0)))) from range(1,4),struct<>,java.lang.IllegalArgumentException
> [info] requirement failed: MutableProjection cannot use UnsafeRow for output data types: decimal(38,0)) (SQLQueryTestSuite.scala:380)
> {code}
> To reproduce, run with sbt::
> {code:java}
> testOnly org.apache.spark.sql.SQLQueryTestSuite -- -z "pgSQL/numeric.sql"
> {code}
> The back trace:
> {code:java}
> at scala.Predef$.require(Predef.scala:281)
> at org.apache.spark.sql.catalyst.expressions.InterpretedMutableProjection.target(InterpretedMutableProjection.scala:56)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.createNewAggregationBuffer(TungstenAggregationIterator.scala:129)
> at org.apache.spark.sql.execution.aggregate.TungstenAggregationIterator.<init>(TungstenAggregationIterator.scala:156)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.$anonfun$doExecute$2(HashAggregateExec.scala:136)
> at org.apache.spark.sql.execution.aggregate.HashAggregateExec.$anonfun$doExecute$2$adapted(HashAggregateExec.scala:111)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2(RDD.scala:867)
> at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2$adapted(RDD.scala:867)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:327)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:291)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:327)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:291)
> at org.apache.spark.shuffle.ShuffleWriteProcessor.write(ShuffleWriteProcessor.scala:59)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:94)
> at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:52)
> at org.apache.spark.scheduler.Task.run(Task.scala:127)
> at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:449)
> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:452)
> at java.util.concurrent.ThreadPoolExecuDD.compute(MapPartitionsRDD.scala:52)tor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:748)
> {code}
> I tried the queries with spark-sql, it doesn't reproduce. I think it is the problem of the test suite.
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