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https://issues.apache.org/jira/browse/SPARK-50992?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18019385#comment-18019385
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Raju Ansari edited comment on SPARK-50992 at 9/10/25 4:06 PM:
--------------------------------------------------------------
I had a similar issue while executing a count query when queryExectuion tries
the plan explainString at `queryExecution.explainString(planDescriptionMode)`.
This is happening mainly in TreeNode.argString which doesn't honor the
configuration `
spark.sql.maxPlanStringLength`
Curious to know if there's any workaround to not print the plan at all in some
situations or truncate it in TreeNode.argString method itself.
{code:java}
java.lang.OutOfMemoryError at
java.lang.AbstractStringBuilder.hugeCapacity(AbstractStringBuilder.java:161) at
java.lang.AbstractStringBuilder.newCapacity(AbstractStringBuilder.java:155) at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:125)
at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:448) at
java.lang.StringBuilder.append(StringBuilder.java:141) at
scala.collection.mutable.StringBuilder.append(StringBuilder.scala:203) at
scala.collection.immutable.Stream.addString(Stream.scala:691) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:377) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:376) at
scala.collection.immutable.Stream.mkString(Stream.scala:760) at
org.apache.spark.sql.catalyst.util.package$.truncatedString(package.scala:179)
at
org.apache.spark.sql.catalyst.expressions.Expression.toString(Expression.scala:307)
at java.lang.String.valueOf(String.java:2994) at
java.lang.StringBuilder.append(StringBuilder.java:136) at
org.apache.spark.sql.catalyst.expressions.If.toString(conditionalExpressions.scala:105)
at java.lang.String.valueOf(String.java:2994) at
java.lang.StringBuilder.append(StringBuilder.java:136) at
org.apache.spark.sql.catalyst.expressions.Alias.toString(namedExpressions.scala:213)
at org.apache.spark.sql.catalyst.trees.TreeNode.formatArg(TreeNode.scala:918)
at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$formatArg$1(TreeNode.scala:911)
at scala.collection.immutable.List.map(List.scala:297) at
org.apache.spark.sql.catalyst.trees.TreeNode.formatArg(TreeNode.scala:911) at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$argString$1(TreeNode.scala:931)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:486) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:492) at
scala.collection.Iterator.foreach(Iterator.scala:943) at
scala.collection.Iterator.foreach$(Iterator.scala:943) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1431) at
scala.collection.TraversableOnce.addString(TraversableOnce.scala:424) at
scala.collection.TraversableOnce.addString$(TraversableOnce.scala:407) at
scala.collection.AbstractIterator.addString(Iterator.scala:1431) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:377) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:376) at
scala.collection.AbstractIterator.mkString(Iterator.scala:1431) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:379) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:379) at
scala.collection.AbstractIterator.mkString(Iterator.scala:1431) at
org.apache.spark.sql.catalyst.trees.TreeNode.argString(TreeNode.scala:949){code}
was (Author: JIRAUSER310936):
I had a similar issue while executing a count query when queryExectuion tries
the plan explainString at `queryExecution.explainString(planDescriptionMode)`
{code:java}
java.lang.OutOfMemoryError at
java.lang.AbstractStringBuilder.hugeCapacity(AbstractStringBuilder.java:161) at
java.lang.AbstractStringBuilder.newCapacity(AbstractStringBuilder.java:155) at
java.lang.AbstractStringBuilder.ensureCapacityInternal(AbstractStringBuilder.java:125)
at java.lang.AbstractStringBuilder.append(AbstractStringBuilder.java:448) at
java.lang.StringBuilder.append(StringBuilder.java:141) at
scala.collection.mutable.StringBuilder.append(StringBuilder.scala:203) at
scala.collection.immutable.Stream.addString(Stream.scala:691) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:377) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:376) at
scala.collection.immutable.Stream.mkString(Stream.scala:760) at
org.apache.spark.sql.catalyst.util.package$.truncatedString(package.scala:179)
at
org.apache.spark.sql.catalyst.expressions.Expression.toString(Expression.scala:307)
at java.lang.String.valueOf(String.java:2994) at
java.lang.StringBuilder.append(StringBuilder.java:136) at
org.apache.spark.sql.catalyst.expressions.If.toString(conditionalExpressions.scala:105)
at java.lang.String.valueOf(String.java:2994) at
java.lang.StringBuilder.append(StringBuilder.java:136) at
org.apache.spark.sql.catalyst.expressions.Alias.toString(namedExpressions.scala:213)
at org.apache.spark.sql.catalyst.trees.TreeNode.formatArg(TreeNode.scala:918)
at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$formatArg$1(TreeNode.scala:911)
at scala.collection.immutable.List.map(List.scala:297) at
org.apache.spark.sql.catalyst.trees.TreeNode.formatArg(TreeNode.scala:911) at
org.apache.spark.sql.catalyst.trees.TreeNode.$anonfun$argString$1(TreeNode.scala:931)
at scala.collection.Iterator$$anon$11.nextCur(Iterator.scala:486) at
scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:492) at
scala.collection.Iterator.foreach(Iterator.scala:943) at
scala.collection.Iterator.foreach$(Iterator.scala:943) at
scala.collection.AbstractIterator.foreach(Iterator.scala:1431) at
scala.collection.TraversableOnce.addString(TraversableOnce.scala:424) at
scala.collection.TraversableOnce.addString$(TraversableOnce.scala:407) at
scala.collection.AbstractIterator.addString(Iterator.scala:1431) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:377) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:376) at
scala.collection.AbstractIterator.mkString(Iterator.scala:1431) at
scala.collection.TraversableOnce.mkString(TraversableOnce.scala:379) at
scala.collection.TraversableOnce.mkString$(TraversableOnce.scala:379) at
scala.collection.AbstractIterator.mkString(Iterator.scala:1431) at
org.apache.spark.sql.catalyst.trees.TreeNode.argString(TreeNode.scala:949){code}
> OOMs and performance issues with AQE in large plans
> ---------------------------------------------------
>
> Key: SPARK-50992
> URL: https://issues.apache.org/jira/browse/SPARK-50992
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.5.3, 3.5.4, 4.0.0
> Reporter: Ángel Álvarez Pascua
> Priority: Major
> Labels: pull-request-available
> Attachments: Main.scala
>
>
> When AQE is enabled, Spark triggers update events to the internal listener
> bus whenever a plan changes. These events include a plain-text description of
> the plan, which is computationally expensive to generate for large plans.
> {*}Key Issues:{*}{*}{{*}}
> *1. High Cost of Plan String Calculation:*
> * Generating the string description for large physical plans is a costly
> operation.
> * This impacts performance, particularly in complex workflows with frequent
> plan updates (e.g. persisting DataFrames).
> *2. Out-of-Memory (OOM) Errors:*
> * Events are stored in the listener bus as {{SQLExecutionUIData}} objects
> and retained until a threshold is reached.
> * This retention behavior can lead to memory exhaustion when processing
> large plans, causing OOM errors.
>
> *Current Workarounds Are Ineffective:*
> * *Reducing Retained Executions* ({{{}spark.sql.ui.retainedExecutions{}}}):
> Even when set to {{1}} or {{{}0{}}}, events are still created, requiring plan
> string calculations.
> * *Limiting Plan String Length* ({{{}spark.sql.maxPlanStringLength{}}}):
> Reducing the maximum string length (e.g., to {{{}1,000,000{}}}) may mitigate
> OOMs but does not eliminate the overhead of string generation.
> * *Available Explain Modes:* All existing explain modes are verbose and
> computationally expensive, failing to resolve these issues.
>
> *Proposed Solution:*
> Introduce a new explain mode, {*}{{off}}{*}, which suppresses the generation
> of plan string descriptions.
> * When this mode is enabled, Spark skips the calculation of plan
> descriptions altogether.
> * This resolves OOM errors and restores performance parity with non-AQE
> execution.
>
> *Impact of Proposed Solution:*
> * Eliminates OOMs in large plans with AQE enabled.
> * Reduces the performance overhead associated with plan string generation.
> * Ensures Spark scales better in environments with large, complex plans.
>
> *Reproducibility:*
> A test reproducing the issue has been attached.
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