You are viewing a plain text version of this content. The canonical link for it is here.
Posted to issues@spark.apache.org by "liupengcheng (JIRA)" <ji...@apache.org> on 2017/09/01 01:03:00 UTC
[jira] [Commented] (SPARK-21885) HiveMetastoreCatalog.InferIfNeeded
too slow when caseSensitiveInference enabled
[ https://issues.apache.org/jira/browse/SPARK-21885?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16149880#comment-16149880 ]
liupengcheng commented on SPARK-21885:
--------------------------------------
[~srowen] Fixed! thanks! anybody check this problem?
> HiveMetastoreCatalog.InferIfNeeded too slow when caseSensitiveInference enabled
> -------------------------------------------------------------------------------
>
> Key: SPARK-21885
> URL: https://issues.apache.org/jira/browse/SPARK-21885
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.1.0, 2.2.0, 2.3.0
> Environment: `spark.sql.hive.caseSensitiveInferenceMode` set to INFER_ONLY
> Reporter: liupengcheng
> Labels: slow, sql
>
> Currently, SparkSQL infer schema is too slow, almost take 2 minutes.
> I digged into the code, and finally findout the reason:
> 1. In the analysis process of LogicalPlan spark will try to infer table schema if `spark.sql.hive.caseSensitiveInferenceMode` set to INFER_ONLY, and it will list all the leaf files of the rootPaths(just tableLocation), and then call `getFileBlockLocations` to turn `FileStatus` into `LocatedFileStatus`. This `getFileBlockLocations` for so manny leaf files will take a long time, and it seems that the locations info is never used.
> 2. When infer a parquet schema, if there is only one file, it will still launch a spark job to merge schema. I think it's expensive.
> Time costly stack is as follow:
> {code:java}
> at org.apache.hadoop.ipc.Client.call(Client.java:1403)
> at org.apache.hadoop.ipc.ProtobufRpcEngine$Invoker.invoke(ProtobufRpcEngine.java:232)
> at com.sun.proxy.$Proxy16.getBlockLocations(Unknown Source)
> at org.apache.hadoop.hdfs.protocolPB.ClientNamenodeProtocolTranslatorPB.getBlockLocations(ClientNamenodeProtocolTranslatorPB.java:259)
> at sun.reflect.GeneratedMethodAccessor15.invoke(Unknown Source)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:187)
> at org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:102)
> at com.sun.proxy.$Proxy17.getBlockLocations(Unknown Source)
> at org.apache.hadoop.hdfs.DFSClient.callGetBlockLocations(DFSClient.java:1234)
> at org.apache.hadoop.hdfs.DFSClient.getLocatedBlocks(DFSClient.java:1224)
> at org.apache.hadoop.hdfs.DFSClient.getBlockLocations(DFSClient.java:1274)
> at org.apache.hadoop.hdfs.DistributedFileSystem$1.doCall(DistributedFileSystem.java:221)
> at org.apache.hadoop.hdfs.DistributedFileSystem$1.doCall(DistributedFileSystem.java:217)
> at org.apache.hadoop.fs.FileSystemLinkResolver.resolve(FileSystemLinkResolver.java:81)
> at org.apache.hadoop.hdfs.DistributedFileSystem.getFileBlockLocations(DistributedFileSystem.java:217)
> at org.apache.hadoop.hdfs.DistributedFileSystem.getFileBlockLocations(DistributedFileSystem.java:209)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$listLeafFiles$3.apply(PartitioningAwareFileIndex.scala:427)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$listLeafFiles$3.apply(PartitioningAwareFileIndex.scala:410)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:186)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:186)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$.org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$listLeafFiles(PartitioningAwareFileIndex.scala:410)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$bulkListLeafFiles$1.apply(PartitioningAwareFileIndex.scala:302)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$$anonfun$org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$bulkListLeafFiles$1.apply(PartitioningAwareFileIndex.scala:301)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
> at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
> at scala.collection.AbstractTraversable.map(Traversable.scala:104)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex$.org$apache$spark$sql$execution$datasources$PartitioningAwareFileIndex$$bulkListLeafFiles(PartitioningAwareFileIndex.scala:301)
> at org.apache.spark.sql.execution.datasources.PartitioningAwareFileIndex.listLeafFiles(PartitioningAwareFileIndex.scala:253)
> at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.refresh0(InMemoryFileIndex.scala:74)
> at org.apache.spark.sql.execution.datasources.InMemoryFileIndex.<init>(InMemoryFileIndex.scala:50)
> at org.apache.spark.sql.execution.datasources.PrunedInMemoryFileIndex.<init>(CatalogFileIndex.scala:108)
> at org.apache.spark.sql.execution.datasources.CatalogFileIndex.filterPartitions(CatalogFileIndex.scala:76)
> at org.apache.spark.sql.execution.datasources.CatalogFileIndex.listFiles(CatalogFileIndex.scala:54)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog.org$apache$spark$sql$hive$HiveMetastoreCatalog$$inferIfNeeded(HiveMetastoreCatalog.scala:312)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$5.apply(HiveMetastoreCatalog.scala:243)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog$$anonfun$5.apply(HiveMetastoreCatalog.scala:229)
> at scala.Option.getOrElse(Option.scala:121)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog.org$apache$spark$sql$hive$HiveMetastoreCatalog$$convertToLogicalRelation(HiveMetastoreCatalog.scala:229)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog$ParquetConversions$.org$apache$spark$sql$hive$HiveMetastoreCatalog$ParquetConversions$$convertToParquetRelation(HiveMetastoreCatalog.scala:357)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog$ParquetConversions$$anonfun$apply$1.applyOrElse(HiveMetastoreCatalog.scala:374)
> at org.apache.spark.sql.hive.HiveMetastoreCatalog$ParquetConversions$$anonfun$apply$1.applyOrElse(HiveMetastoreCatalog.scala:365)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:310)
> at org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:309)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformChildren(TreeNode.scala:329)
> at org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:307)
> at org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$5.apply(TreeNode.scala:331)
> at org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:188)
> {code}
--
This message was sent by Atlassian JIRA
(v6.4.14#64029)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org
For additional commands, e-mail: issues-help@spark.apache.org