Shawn Smith created SPARK-37222: ----------------------------------- Summary: Max iterations reached in Operator Optimization w/left_anti or left_semi join and nested structures Key: SPARK-37222 URL: https://issues.apache.org/jira/browse/SPARK-37222 Project: Spark Issue Type: Bug Components: Optimizer Affects Versions: 3.2.0, 3.1.2 Environment: I've reproduced the error on Spark 3.1.2, 3.2.0, and with the current branch-3.2 HEAD (git commit 966c90c0b5) as of November 5, 2021.
The problem does not occur with Spark 3.0.1. Reporter: Shawn Smith The query optimizer never reaches a fixed point when optimizing the query below. This manifests as a warning: > WARN: Max iterations (100) reached for batch Operator Optimization before > Inferring Filters, please set 'spark.sql.optimizer.maxIterations' to a larger > value. But the suggested fix won't help. The actual problem is that the optimizer fails to make progress on each iteration and gets stuck in a loop. In practice, Spark logs a warning but continues on and appears to execute the query successfully, albeit perhaps sub-optimally. To reproduce, paste the following into the Spark shell. With Spark 3.1.2 and 3.2.0 but not 3.0.1 it will throw an exception: {noformat} case class Nested(b: Boolean, n: Long) case class Table(id: String, nested: Nested) case class Identifier(id: String) locally { System.setProperty("spark.testing", "true") // Fail instead of logging a warning val df = List.empty[Table].toDS.cache() val ids = List.empty[Identifier].toDS.cache() df.join(ids, Seq("id"), "left_anti") // also fails with "left_semi" .select('id, 'nested("n")) .explain() } {noformat} Looking at the query plan as the optimizer iterates in {{RuleExecutor.execute()}}, here's an example of the plan after 49 iterations: {noformat} Project [id#2, _gen_alias_108#108L AS nested.n#28L] +- Join LeftAnti, (id#2 = id#18) :- Project [id#2, nested#3.n AS _gen_alias_108#108L] : +- InMemoryRelation [id#2, nested#3], StorageLevel(disk, memory, deserialized, 1 replicas) : +- LocalTableScan <empty>, [id#2, nested#3] +- InMemoryRelation [id#18], StorageLevel(disk, memory, deserialized, 1 replicas) +- LocalTableScan <empty>, [id#18] {noformat} And here's the plan after one more iteration. You can see that all that has changed is new aliases for the column in the nested column: "{{_gen_alias_108#108L}}" to "{{_gen_alias_109#109L}}". {noformat} Project [id#2, _gen_alias_109#109L AS nested.n#28L] +- Join LeftAnti, (id#2 = id#18) :- Project [id#2, nested#3.n AS _gen_alias_109#109L] : +- InMemoryRelation [id#2, nested#3], StorageLevel(disk, memory, deserialized, 1 replicas) : +- LocalTableScan <empty>, [id#2, nested#3] +- InMemoryRelation [id#18], StorageLevel(disk, memory, deserialized, 1 replicas) +- LocalTableScan <empty>, [id#18] {noformat} The optimizer continues looping and tweaking the alias until it hits the max iteration count and bails out. Here's an example that includes a stack trace: {noformat} $ bin/spark-shell Welcome to ____ __ / __/__ ___ _____/ /__ _\ \/ _ \/ _ `/ __/ '_/ /___/ .__/\_,_/_/ /_/\_\ version 3.2.0 /_/ Using Scala version 2.12.15 (OpenJDK 64-Bit Server VM, Java 11.0.12) Type in expressions to have them evaluated. Type :help for more information. scala> :paste // Entering paste mode (ctrl-D to finish) case class Nested(b: Boolean, n: Long) case class Table(id: String, nested: Nested) case class Identifier(id: String) locally { System.setProperty("spark.testing", "true") // Fail instead of logging a warning val df = List.empty[Table].toDS.cache() val ids = List.empty[Identifier].toDS.cache() df.join(ids, Seq("id"), "left_anti") // also fails with "left_semi" .select('id, 'nested("n")) .explain() } // Exiting paste mode, now interpreting. java.lang.RuntimeException: Max iterations (100) reached for batch Operator Optimization before Inferring Filters, please set 'spark.sql.optimizer.maxIterations' to a larger value. at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1(RuleExecutor.scala:246) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$execute$1$adapted(RuleExecutor.scala:200) at scala.collection.immutable.List.foreach(List.scala:431) at org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:200) at org.apache.spark.sql.catalyst.rules.RuleExecutor.$anonfun$executeAndTrack$1(RuleExecutor.scala:179) at org.apache.spark.sql.catalyst.QueryPlanningTracker$.withTracker(QueryPlanningTracker.scala:88) at org.apache.spark.sql.catalyst.rules.RuleExecutor.executeAndTrack(RuleExecutor.scala:179) at org.apache.spark.sql.execution.QueryExecution.$anonfun$optimizedPlan$1(QueryExecution.scala:138) at org.apache.spark.sql.catalyst.QueryPlanningTracker.measurePhase(QueryPlanningTracker.scala:111) at org.apache.spark.sql.execution.QueryExecution.$anonfun$executePhase$1(QueryExecution.scala:196) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775) at org.apache.spark.sql.execution.QueryExecution.executePhase(QueryExecution.scala:196) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:134) at org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:130) at org.apache.spark.sql.execution.QueryExecution.assertOptimized(QueryExecution.scala:148) at org.apache.spark.sql.execution.QueryExecution.$anonfun$executedPlan$1(QueryExecution.scala:166) at org.apache.spark.sql.execution.QueryExecution.withCteMap(QueryExecution.scala:73) at org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:163) at org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:163) at org.apache.spark.sql.execution.QueryExecution.$anonfun$simpleString$2(QueryExecution.scala:220) at org.apache.spark.sql.catalyst.plans.QueryPlan$.append(QueryPlan.scala:600) at org.apache.spark.sql.execution.QueryExecution.simpleString(QueryExecution.scala:220) at org.apache.spark.sql.execution.QueryExecution.org$apache$spark$sql$execution$QueryExecution$$explainString(QueryExecution.scala:247) at org.apache.spark.sql.execution.QueryExecution.explainString(QueryExecution.scala:228) at org.apache.spark.sql.Dataset.$anonfun$explain$1(Dataset.scala:543) at scala.runtime.java8.JFunction0$mcV$sp.apply(JFunction0$mcV$sp.java:23) at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:775) at org.apache.spark.sql.Dataset.explain(Dataset.scala:543) at org.apache.spark.sql.Dataset.explain(Dataset.scala:567) ... 47 elided {noformat} -- This message was sent by Atlassian Jira (v8.3.4#803005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org