Hi Jerry, Do you have speculation enabled? A write which produces one million files / output partitions might be using tons of driver memory via the OutputCommitCoordinator's bookkeeping data structures.
On Sun, Oct 25, 2015 at 5:50 PM, Jerry Lam <chiling...@gmail.com> wrote: > Hi spark guys, > > I think I hit the same issue SPARK-8890 > https://issues.apache.org/jira/browse/SPARK-8890. It is marked as > resolved. However it is not. I have over a million output directories for 1 > single column in partitionBy. Not sure if this is a regression issue? Do I > need to set some parameters to make it more memory efficient? > > Best Regards, > > Jerry > > > > > On Sun, Oct 25, 2015 at 8:39 PM, Jerry Lam <chiling...@gmail.com> wrote: > >> Hi guys, >> >> After waiting for a day, it actually causes OOM on the spark driver. I >> configure the driver to have 6GB. Note that I didn't call refresh myself. >> The method was called when saving the dataframe in parquet format. Also I'm >> using partitionBy() on the DataFrameWriter to generate over 1 million >> files. Not sure why it OOM the driver after the job is marked _SUCCESS in >> the output folder. >> >> Best Regards, >> >> Jerry >> >> >> On Sat, Oct 24, 2015 at 9:35 PM, Jerry Lam <chiling...@gmail.com> wrote: >> >>> Hi Spark users and developers, >>> >>> Does anyone encounter any issue when a spark SQL job produces a lot of >>> files (over 1 millions), the job hangs on the refresh method? I'm using >>> spark 1.5.1. Below is the stack trace. I saw the parquet files are produced >>> but the driver is doing something very intensively (it uses all the cpus). >>> Does it mean Spark SQL cannot be used to produce over 1 million files in a >>> single job? >>> >>> Thread 528: (state = BLOCKED) >>> - java.util.Arrays.copyOf(char[], int) @bci=1, line=2367 (Compiled >>> frame) >>> - java.lang.AbstractStringBuilder.expandCapacity(int) @bci=43, line=130 >>> (Compiled frame) >>> - java.lang.AbstractStringBuilder.ensureCapacityInternal(int) @bci=12, >>> line=114 (Compiled frame) >>> - java.lang.AbstractStringBuilder.append(java.lang.String) @bci=19, >>> line=415 (Compiled frame) >>> - java.lang.StringBuilder.append(java.lang.String) @bci=2, line=132 >>> (Compiled frame) >>> - org.apache.hadoop.fs.Path.toString() @bci=128, line=384 (Compiled >>> frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation$FileStatusCache$$anonfun$listLeafFiles$1.apply(org.apache.hadoop.fs.FileStatus) >>> @bci=4, line=447 (Compiled frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation$FileStatusCache$$anonfun$listLeafFiles$1.apply(java.lang.Object) >>> @bci=5, line=447 (Compiled frame) >>> - >>> scala.collection.TraversableLike$$anonfun$map$1.apply(java.lang.Object) >>> @bci=9, line=244 (Compiled frame) >>> - >>> scala.collection.TraversableLike$$anonfun$map$1.apply(java.lang.Object) >>> @bci=2, line=244 (Compiled frame) >>> - >>> scala.collection.IndexedSeqOptimized$class.foreach(scala.collection.IndexedSeqOptimized, >>> scala.Function1) @bci=22, line=33 (Compiled frame) >>> - scala.collection.mutable.ArrayOps$ofRef.foreach(scala.Function1) >>> @bci=2, line=108 (Compiled frame) >>> - >>> scala.collection.TraversableLike$class.map(scala.collection.TraversableLike, >>> scala.Function1, scala.collection.generic.CanBuildFrom) @bci=17, line=244 >>> (Compiled frame) >>> - scala.collection.mutable.ArrayOps$ofRef.map(scala.Function1, >>> scala.collection.generic.CanBuildFrom) @bci=3, line=108 (Interpreted frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation$FileStatusCache.listLeafFiles(java.lang.String[]) >>> @bci=279, line=447 (Interpreted frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation$FileStatusCache.refresh() >>> @bci=8, line=453 (Interpreted frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation.org$apache$spark$sql$sources$HadoopFsRelation$$fileStatusCache$lzycompute() >>> @bci=26, line=465 (Interpreted frame) >>> - >>> org.apache.spark.sql.sources.HadoopFsRelation.org$apache$spark$sql$sources$HadoopFsRelation$$fileStatusCache() >>> @bci=12, line=463 (Interpreted frame) >>> - org.apache.spark.sql.sources.HadoopFsRelation.refresh() @bci=1, >>> line=540 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.parquet.ParquetRelation.refresh() >>> @bci=1, line=204 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp() >>> @bci=392, line=152 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply() >>> @bci=1, line=108 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply() >>> @bci=1, line=108 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(org.apache.spark.sql.SQLContext, >>> org.apache.spark.sql.SQLContext$QueryExecution, scala.Function0) @bci=96, >>> line=56 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(org.apache.spark.sql.SQLContext) >>> @bci=718, line=108 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute() >>> @bci=20, line=57 (Interpreted frame) >>> - org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult() >>> @bci=15, line=57 (Interpreted frame) >>> - org.apache.spark.sql.execution.ExecutedCommand.doExecute() @bci=12, >>> line=69 (Interpreted frame) >>> - org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply() >>> @bci=11, line=140 (Interpreted frame) >>> - org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply() >>> @bci=1, line=138 (Interpreted frame) >>> - >>> org.apache.spark.rdd.RDDOperationScope$.withScope(org.apache.spark.SparkContext, >>> java.lang.String, boolean, boolean, scala.Function0) @bci=131, line=147 >>> (Interpreted frame) >>> - org.apache.spark.sql.execution.SparkPlan.execute() @bci=189, line=138 >>> (Interpreted frame) >>> - org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute() >>> @bci=21, line=933 (Interpreted frame) >>> - org.apache.spark.sql.SQLContext$QueryExecution.toRdd() @bci=13, >>> line=933 (Interpreted frame) >>> - >>> org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(org.apache.spark.sql.SQLContext, >>> java.lang.String, java.lang.String[], org.apache.spark.sql.SaveMode, >>> scala.collection.immutable.Map, org.apache.spark.sql.DataFrame) @bci=293, >>> line=197 (Interpreted frame) >>> - org.apache.spark.sql.DataFrameWriter.save() @bci=64, line=146 >>> (Interpreted frame) >>> - org.apache.spark.sql.DataFrameWriter.save(java.lang.String) @bci=24, >>> line=137 (Interpreted frame) >>> - org.apache.spark.sql.DataFrameWriter.parquet(java.lang.String) >>> @bci=8, line=304 (Interpreted frame) >>> >>> Best Regards, >>> >>> Jerry >>> >>> >>> >> >
