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hyukjinkwon e0b6383218 [SPARK-23731][SQL] Make FileSourceScanExec canonicalizable after being (de)serialized ## What changes were proposed in this pull request? ### What's problem? In some cases, sub scalar query could throw a NPE, which is caused in execution side. ``` java.lang.NullPointerException at org.apache.spark.sql.execution.FileSourceScanExec.<init>(DataSourceScanExec.scala:169) at org.apache.spark.sql.execution.FileSourceScanExec.doCanonicalize(DataSourceScanExec.scala:526) at org.apache.spark.sql.execution.FileSourceScanExec.doCanonicalize(DataSourceScanExec.scala:159) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:211) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:210) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$3.apply(QueryPlan.scala:225) at org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$3.apply(QueryPlan.scala:225) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234) at scala.collection.immutable.List.foreach(List.scala:392) at scala.collection.TraversableLike$class.map(TraversableLike.scala:234) at scala.collection.immutable.List.map(List.scala:296) at org.apache.spark.sql.catalyst.plans.QueryPlan.doCanonicalize(QueryPlan.scala:225) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:211) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:210) at org.apache.spark.sql.catalyst.plans.QueryPlan.sameResult(QueryPlan.scala:258) at org.apache.spark.sql.execution.ScalarSubquery.semanticEquals(subquery.scala:58) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$Expr.equals(EquivalentExpressions.scala:36) at scala.collection.mutable.HashTable$class.elemEquals(HashTable.scala:364) at scala.collection.mutable.HashMap.elemEquals(HashMap.scala:40) at scala.collection.mutable.HashTable$class.scala$collection$mutable$HashTable$$findEntry0(HashTable.scala:139) at scala.collection.mutable.HashTable$class.findEntry(HashTable.scala:135) at scala.collection.mutable.HashMap.findEntry(HashMap.scala:40) at scala.collection.mutable.HashMap.get(HashMap.scala:70) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExpr(EquivalentExpressions.scala:56) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExprTree(EquivalentExpressions.scala:97) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$$anonfun$addExprTree$1.apply(EquivalentExpressions.scala:98) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$$anonfun$addExprTree$1.apply(EquivalentExpressions.scala:98) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExprTree(EquivalentExpressions.scala:98) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:1102) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:1102) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.subexpressionElimination(CodeGenerator.scala:1102) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.generateExpressions(CodeGenerator.scala:1154) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.createCode(GenerateUnsafeProjection.scala:270) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:319) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.generate(GenerateUnsafeProjection.scala:308) at org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:181) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:71) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:70) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:818) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:818) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:367) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) ``` ### How does this happen? Here looks what happen now: 1. Sub scalar query was made (for instance `SELECT (SELECT id FROM foo)`). 2. Try to extract some common expressions (via `CodeGenerator.subexpressionElimination`) so that it can generates some common codes and can be reused. 3. During this, seems it extracts some expressions that can be reused (via `EquivalentExpressions.addExprTree`) `b2deef64f6/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/codegen/CodeGenerator.scala (L1102)` 4. During this, if the hash (`EquivalentExpressions.Expr.hashCode`) happened to be the same at `EquivalentExpressions.addExpr` anyhow, `EquivalentExpressions.Expr.equals` is called to identify object in the same hash, which eventually calls `semanticEquals` in `ScalarSubquery` `087879a77a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala (L54)` `087879a77a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala (L36)` 5. `ScalarSubquery`'s `semanticEquals` needs `SubqueryExec`'s `sameResult` `77a2fc5b52/sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala (L58)` 6. `SubqueryExec`'s `sameResult` requires a canonicalized plan which calls `FileSourceScanExec`'s `doCanonicalize` `e008ad1752/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/plans/QueryPlan.scala (L258)` 7. In `FileSourceScanExec`'s `doCanonicalize`, `FileSourceScanExec`'s `relation` is required but seems `transient` so it becomes `null`. `e76b0124fb/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala (L527)` `e76b0124fb/sql/core/src/main/scala/org/apache/spark/sql/execution/DataSourceScanExec.scala (L160)` 8. NPE is thrown. \1. driver side \2., 3., 4., 5., 6., 7., 8. executor side Note that most of cases, it looks fine because we will usually call: `087879a77a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala (L40)` which make a canonicalized plan via: `b045315e5d/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/Expression.scala (L192)` `77a2fc5b52/sql/core/src/main/scala/org/apache/spark/sql/execution/subquery.scala (L52)` ### How to reproduce? This looks what happened now. I can reproduce this by a bit of messy way: ```diff diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala index 8d06804ce1e..d25fc9a7ba9 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/expressions/EquivalentExpressions.scala -37,7 +37,9 class EquivalentExpressions { case _ => false } - override def hashCode: Int = e.semanticHash() + override def hashCode: Int = { + 1 + } } ``` ```scala spark.range(1).write.mode("overwrite").parquet("/tmp/foo") spark.read.parquet("/tmp/foo").createOrReplaceTempView("foo") spark.conf.set("spark.sql.codegen.wholeStage", false) sql("SELECT (SELECT id FROM foo) == (SELECT id FROM foo)").collect() ``` ### How does this PR fix? - Make all variables that access to `FileSourceScanExec`'s `relation` as `lazy val` so that we avoid NPE. This is a temporary fix. - Allow `makeCopy` in `SparkPlan` without Spark session too. This looks still able to be accessed within executor side. For instance: ``` at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:70) at org.apache.spark.sql.execution.SparkPlan.makeCopy(SparkPlan.scala:47) at org.apache.spark.sql.catalyst.trees.TreeNode.withNewChildren(TreeNode.scala:233) at org.apache.spark.sql.catalyst.plans.QueryPlan.doCanonicalize(QueryPlan.scala:243) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized$lzycompute(QueryPlan.scala:211) at org.apache.spark.sql.catalyst.plans.QueryPlan.canonicalized(QueryPlan.scala:210) at org.apache.spark.sql.catalyst.plans.QueryPlan.sameResult(QueryPlan.scala:258) at org.apache.spark.sql.execution.ScalarSubquery.semanticEquals(subquery.scala:58) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$Expr.equals(EquivalentExpressions.scala:36) at scala.collection.mutable.HashTable$class.elemEquals(HashTable.scala:364) at scala.collection.mutable.HashMap.elemEquals(HashMap.scala:40) at scala.collection.mutable.HashTable$class.scala$collection$mutable$HashTable$$findEntry0(HashTable.scala:139) at scala.collection.mutable.HashTable$class.findEntry(HashTable.scala:135) at scala.collection.mutable.HashMap.findEntry(HashMap.scala:40) at scala.collection.mutable.HashMap.get(HashMap.scala:70) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExpr(EquivalentExpressions.scala:54) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExprTree(EquivalentExpressions.scala:95) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$$anonfun$addExprTree$1.apply(EquivalentExpressions.scala:96) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions$$anonfun$addExprTree$1.apply(EquivalentExpressions.scala:96) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.expressions.EquivalentExpressions.addExprTree(EquivalentExpressions.scala:96) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:1102) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext$$anonfun$subexpressionElimination$1.apply(CodeGenerator.scala:1102) at scala.collection.immutable.List.foreach(List.scala:392) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.subexpressionElimination(CodeGenerator.scala:1102) at org.apache.spark.sql.catalyst.expressions.codegen.CodegenContext.generateExpressions(CodeGenerator.scala:1154) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.createCode(GenerateUnsafeProjection.scala:270) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.create(GenerateUnsafeProjection.scala:319) at org.apache.spark.sql.catalyst.expressions.codegen.GenerateUnsafeProjection$.generate(GenerateUnsafeProjection.scala:308) at org.apache.spark.sql.catalyst.expressions.UnsafeProjection$.create(Projection.scala:181) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:71) at org.apache.spark.sql.execution.ProjectExec$$anonfun$9.apply(basicPhysicalOperators.scala:70) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:818) at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsWithIndexInternal$1$$anonfun$apply$24.apply(RDD.scala:818) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38) at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:324) at org.apache.spark.rdd.RDD.iterator(RDD.scala:288) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at org.apache.spark.scheduler.Task.run(Task.scala:109) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:367) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748) ``` This PR takes over https://github.com/apache/spark/pull/20856. ## How was this patch tested? Manually tested and unit test was added. Closes #20856 Author: hyukjinkwon <gurwls223@apache.org> Closes #21815 from HyukjinKwon/SPARK-23731.		2018-07-20 20:59:48 +08:00
..
catalyst	[SPARK-24871][SQL] Refactor Concat and MapConcat to avoid creating concatenator object for each row.	2018-07-20 20:08:42 +08:00
core	[SPARK-23731][SQL] Make FileSourceScanExec canonicalizable after being (de)serialized	2018-07-20 20:59:48 +08:00
hive	[SPARK-24268][SQL] Use datatype.catalogString in error messages	2018-07-19 23:29:29 -07:00
hive-thriftserver	[SPARK-24553][WEB-UI] http 302 fixes for href redirect	2018-06-27 15:36:59 -07:00
create-docs.sh	[MINOR][DOCS] Minor doc fixes related with doc build and uses script dir in SQL doc gen script	2017-08-26 13:56:24 +09:00
gen-sql-markdown.py	[SPARK-21485][FOLLOWUP][SQL][DOCS] Describes examples and arguments separately, and note/since in SQL built-in function documentation	2017-08-05 10:10:56 -07:00
mkdocs.yml	[SPARK-21485][SQL][DOCS] Spark SQL documentation generation for built-in functions	2017-07-26 09:38:51 -07:00
README.md	[MINOR][DOC] Fix some typos and grammar issues	2018-04-06 13:37:08 +08:00

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.