Out of memory error when writing out spark datafra

2019-06-22 13:49发布

站内文章 / Java
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问题:

I'm trying to query data from a database, do some transformations on it and save the new data in parquet format on hdfs.

Since the database query returns a large number of rows, I'm getting the data in batches and running the above process on every incoming batch.

UPDATE 2: The batch processing logic is:

import scala.collection.JavaConverters._

import org.apache.spark.SparkContext
import org.apache.spark.sql.SQLContext
import org.apache.spark.sql.Row
import org.apache.spark.sql.types.{StructType, StructField, StringType}

class Batch(rows: List[String], 
            sqlContext: SQLContext) {

      // The actual schema has around 60 fields
      val schema = Array("name", "age", "address").map(field =>
                       StructField(field, StringType, true)
                   )

      val transformedRows = rows.map(rows => {

              // transformation logic (returns Array[Array[String]] type)

          }).map(row => Row.fromSeq(row.toSeq))

      val dataframe = sqlContext.createDataFrame(transformedRows.asJava, schema)

}

val sparkConf = new sparkConf().setAppName("Spark App")
val sparkContext = new SparkContext(sparkConf)
val sqlContext = new SQLContext(sparkContext)

// Code to query database
// queryResponse is essentially an iterator that fetches the next batch on calling queryResponse.next

var batch_num = 0

while (queryResponse.hasNext) {
    val batch = queryResponse.next

    val batchToSave = new Batch(
                          batch.toList.map(_.getDocument.toString),
                          sqlContext)

    batchToSave.dataframe.write.parquet(batch_num + "_Parquet")

    batch_num += 1

}

My Spark version in 1.6.1 and the spark-submit is:

spark-submit target/scala-2.10/Spark\ Application-assembly-1.0.jar

The problem is that after a certain number of batches, I get a java.lang.OutOfMemoryError error.

The entire stacktrace is:

Exception in thread "main" java.lang.OutOfMemoryError: Java heap space
    at java.util.Arrays.copyOfRange(Arrays.java:2694)
    at java.lang.String.<init>(String.java:203)
    at java.lang.StringBuilder.toString(StringBuilder.java:405)
    at scala.StringContext.standardInterpolator(StringContext.scala:125)
    at scala.StringContext.s(StringContext.scala:90)
    at org.apache.spark.sql.execution.QueryExecution.toString(QueryExecution.scala:70)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:52)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
    at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:256)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:139)
    at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:334)
    at app.Application$.main(App.scala:156)
    at app.Application.main(App.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)

I tried coalescing the data into a single partition but that didn't make any difference.

dataframe.coalesce(1).write.parquet(batch_num + "_Parquet")

Any help would be appreciated.

UPDATE 1

Not doing a coalesce transform on the RDD still gives an error but the stacktrace is as follows. Seems to be an issue with Parquet.

Driver stacktrace:
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1431)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1419)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1418)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1418)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:799)
    at scala.Option.foreach(Option.scala:236)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:799)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1640)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1599)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1588)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:620)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1855)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1868)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:1945)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:58)
    at org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:56)
    at org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:70)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:55)
    at org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:256)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:148)
    at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:139)
    at org.apache.spark.sql.DataFrameWriter.parquet(DataFrameWriter.scala:334)
    at app.Application$.main(App.scala:156)
    at app.Application.main(App.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:606)
    at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:731)
    at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:181)
    at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:206)
    at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:121)
    at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
Caused by: java.lang.OutOfMemoryError: Java heap space
    at org.apache.parquet.column.values.dictionary.IntList.initSlab(IntList.java:90)
    at org.apache.parquet.column.values.dictionary.IntList.<init>(IntList.java:86)
    at org.apache.parquet.column.values.dictionary.DictionaryValuesWriter.<init>(DictionaryValuesWriter.java:93)
    at org.apache.parquet.column.values.dictionary.DictionaryValuesWriter$PlainBinaryDictionaryValuesWriter.<init>(DictionaryValuesWriter.java:229)
    at org.apache.parquet.column.ParquetProperties.dictionaryWriter(ParquetProperties.java:131)
    at org.apache.parquet.column.ParquetProperties.dictWriterWithFallBack(ParquetProperties.java:178)
    at org.apache.parquet.column.ParquetProperties.getValuesWriter(ParquetProperties.java:203)
    at org.apache.parquet.column.impl.ColumnWriterV1.<init>(ColumnWriterV1.java:83)
    at org.apache.parquet.column.impl.ColumnWriteStoreV1.newMemColumn(ColumnWriteStoreV1.java:68)
    at org.apache.parquet.column.impl.ColumnWriteStoreV1.getColumnWriter(ColumnWriteStoreV1.java:56)
    at org.apache.parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.<init>(MessageColumnIO.java:183)
    at org.apache.parquet.io.MessageColumnIO.getRecordWriter(MessageColumnIO.java:375)
    at org.apache.parquet.hadoop.InternalParquetRecordWriter.initStore(InternalParquetRecordWriter.java:109)
    at org.apache.parquet.hadoop.InternalParquetRecordWriter.<init>(InternalParquetRecordWriter.java:99)
    at org.apache.parquet.hadoop.ParquetRecordWriter.<init>(ParquetRecordWriter.java:100)
    at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:303)
    at org.apache.parquet.hadoop.ParquetOutputFormat.getRecordWriter(ParquetOutputFormat.java:262)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.<init>(ParquetRelation.scala:94)
    at org.apache.spark.sql.execution.datasources.parquet.ParquetRelation$$anon$3.newInstance(ParquetRelation.scala:286)
    at org.apache.spark.sql.execution.datasources.BaseWriterContainer.newOutputWriter(WriterContainer.scala:129)
    at org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:255)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
    at org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1$$anonfun$apply$mcV$sp$3.apply(InsertIntoHadoopFsRelation.scala:150)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
    at org.apache.spark.scheduler.Task.run(Task.scala:89)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
    at java.lang.Thread.run(Thread.java:745)

回答1:

Had the same problem today as well. Turned out my execution plan was rather complex and toString generated 150 MB of information which combined with String interpolation of Scala lead to the driver running out of memory.

You can try to increase the driver memory (I had to double it from 8 GB to 16 GB).



标签： java scala apache-spark parquet
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