Spark：从2.1.0升级到2.2.1时，数据框动作确实很慢

时间：2018-11-20 20:03:40

标签： java scala apache-spark

我刚刚将spark 2.1.0升级到spark 2.2.1。是否有人在dataframe.filter(…).collect()上看到过慢的行为？..特别是在collect上进行过filter操作。 dataframe.collect看起来还不错。但是，dataframe.filter(…).collect()永远存在。它仅包含2条记录。并进行单元测试。当我回到spark 2.1.0时，它恢复到正常速度

我查看了线程转储，找不到明显的原因。我已尽力确保我正在使用的所有库也都在使用Spark 2.2.1。任何建议将不胜感激。

似乎卡在了这个堆栈跟踪中

scala.collection.mutable.FlatHashTable$class.addEntry(FlatHashTable.scala:151)
scala.collection.mutable.HashSet.addEntry(HashSet.scala:40)
scala.collection.mutable.FlatHashTable$class.addElem(FlatHashTable.scala:142)
scala.collection.mutable.HashSet.addElem(HashSet.scala:40)
scala.collection.mutable.HashSet.$plus$eq(HashSet.scala:59)
scala.collection.mutable.HashSet.$plus$eq(HashSet.scala:40)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
scala.collection.generic.Growable$$anonfun$$plus$plus$eq$1.apply(Growable.scala:59)
scala.collection.mutable.HashSet.foreach(HashSet.scala:78)
scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
scala.collection.mutable.AbstractSet.$plus$plus$eq(Set.scala:46)
scala.collection.mutable.HashSet.clone(HashSet.scala:83)
scala.collection.mutable.HashSet.clone(HashSet.scala:40)
org.apache.spark.sql.catalyst.expressions.ExpressionSet.$plus(ExpressionSet.scala:65)
org.apache.spark.sql.catalyst.expressions.ExpressionSet.$plus(ExpressionSet.scala:50)
scala.collection.SetLike$$anonfun$$plus$plus$1.apply(SetLike.scala:141)
scala.collection.SetLike$$anonfun$$plus$plus$1.apply(SetLike.scala:141)
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
scala.collection.TraversableOnce$$anonfun$foldLeft$1.apply(TraversableOnce.scala:157)
scala.collection.immutable.HashSet$HashSet1.foreach(HashSet.scala:316)
scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
scala.collection.immutable.HashSet$HashTrieSet.foreach(HashSet.scala:972)
scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
scala.collection.AbstractTraversable.foldLeft(Traversable.scala:104)
scala.collection.TraversableOnce$class.$div$colon(TraversableOnce.scala:151)
scala.collection.AbstractTraversable.$div$colon(Traversable.scala:104)
scala.collection.SetLike$class.$plus$plus(SetLike.scala:141)
org.apache.spark.sql.catalyst.expressions.ExpressionSet.$plus$plus(ExpressionSet.scala:50)
org.apache.spark.sql.catalyst.plans.logical.UnaryNode$$anonfun$getAliasedConstraints$1.apply(LogicalPlan.scala:323)
org.apache.spark.sql.catalyst.plans.logical.UnaryNode$$anonfun$getAliasedConstraints$1.apply(LogicalPlan.scala:320)
scala.collection.immutable.List.foreach(List.scala:392)
org.apache.spark.sql.catalyst.plans.logical.UnaryNode.getAliasedConstraints(LogicalPlan.scala:320)
org.apache.spark.sql.catalyst.plans.logical.Project.validConstraints(basicLogicalOperators.scala:65)
org.apache.spark.sql.catalyst.plans.QueryPlan.constraints$lzycompute(QueryPlan.scala:188) => holding Monitor(org.apache.spark.sql.catalyst.plans.logical.Aggregate@1129881457})
org.apache.spark.sql.catalyst.plans.QueryPlan.constraints(QueryPlan.scala:188)
org.apache.spark.sql.catalyst.plans.logical.Aggregate.validConstraints(basicLogicalOperators.scala:555)
org.apache.spark.sql.catalyst.plans.QueryPlan.constraints$lzycompute(QueryPlan.scala:188) => holding Monitor(org.apache.spark.sql.catalyst.plans.logical.Aggregate@1129881457})
org.apache.spark.sql.catalyst.plans.QueryPlan.constraints(QueryPlan.scala:188)
org.apache.spark.sql.catalyst.plans.QueryPlan.getConstraints(QueryPlan.scala:196)
org.apache.spark.sql.catalyst.optimizer.PruneFilters$$anonfun$apply$16$$anonfun$25.apply(Optimizer.scala:717)
org.apache.spark.sql.catalyst.optimizer.PruneFilters$$anonfun$apply$16$$anonfun$25.apply(Optimizer.scala:716)
scala.collection.TraversableLike$$anonfun$partition$1.apply(TraversableLike.scala:314)
scala.collection.TraversableLike$$anonfun$partition$1.apply(TraversableLike.scala:314)
scala.collection.immutable.List.foreach(List.scala:392)
scala.collection.TraversableLike$class.partition(TraversableLike.scala:314)
scala.collection.AbstractTraversable.partition(Traversable.scala:104)
org.apache.spark.sql.catalyst.optimizer.PruneFilters$$anonfun$apply$16.applyOrElse(Optimizer.scala:716)
org.apache.spark.sql.catalyst.optimizer.PruneFilters$$anonfun$apply$16.applyOrElse(Optimizer.scala:705)
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)
org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)
org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:256)
org.apache.spark.sql.catalyst.optimizer.PruneFilters.apply(Optimizer.scala:705)
org.apache.spark.sql.catalyst.optimizer.PruneFilters.apply(Optimizer.scala:704)
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:85)
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:82)
scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)
scala.collection.immutable.List.foldLeft(List.scala:84)
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:82)
org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:74)
scala.collection.immutable.List.foreach(List.scala:392)
org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:74)
org.apache.spark.sql.execution.QueryExecution.optimizedPlan$lzycompute(QueryExecution.scala:78) => holding Monitor(org.apache.spark.sql.execution.QueryExecution@1193326176})
org.apache.spark.sql.execution.QueryExecution.optimizedPlan(QueryExecution.scala:78)
org.apache.spark.sql.execution.QueryExecution.sparkPlan$lzycompute(QueryExecution.scala:84) => holding Monitor(org.apache.spark.sql.execution.QueryExecution@1193326176})
org.apache.spark.sql.execution.QueryExecution.sparkPlan(QueryExecution.scala:80)
org.apache.spark.sql.execution.QueryExecution.executedPlan$lzycompute(QueryExecution.scala:89) => holding Monitor(org.apache.spark.sql.execution.QueryExecution@1193326176})
org.apache.spark.sql.execution.QueryExecution.executedPlan(QueryExecution.scala:89)
org.apache.spark.sql.Dataset.withAction(Dataset.scala:2837)
org.apache.spark.sql.Dataset.collect(Dataset.scala:2387)

1 个答案:

答案 0 :(得分：1)

解决了。因此，问题在于此属性spark.sql.constraintPropagation.enabled。在Spark 2.2.1中，默认值为true。 stacktrace指示其停留在某些查询计划生成中。我在this blog

中找到了答案

简短答案：将所述属性设置为false。 spark.conf.set(SQLConf.CONSTRAINT_PROPAGATION_ENABLED.key, false)

相关问题
最新问题