[ https://issues.apache.org/jira/browse/SPARK-16632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Dongjoon Hyun updated SPARK-16632: ---------------------------------- Fix Version/s: 2.0.1 > Vectorized parquet reader fails to read certain fields from Hive tables > ----------------------------------------------------------------------- > > Key: SPARK-16632 > URL: https://issues.apache.org/jira/browse/SPARK-16632 > Project: Spark > Issue Type: Bug > Components: SQL > Affects Versions: 2.0.0 > Environment: Hive 1.1 (CDH) > Reporter: Marcelo Vanzin > Assignee: Marcelo Vanzin > Fix For: 2.0.1, 2.1.0 > > > The vectorized parquet reader fails to read certain tables created by Hive. > When the tables have type "tinyint" or "smallint", Catalyst converts those to > "ByteType" and "ShortType" respectively. But when Hive writes those tables in > parquet format, the parquet schema in the files contains "int32" fields. > To reproduce, run these commands in the hive shell (or beeline): > {code} > create table abyte (value tinyint) stored as parquet; > create table ashort (value smallint) stored as parquet; > insert into abyte values (1); > insert into ashort values (1); > {code} > Then query them with Spark 2.0: > {code} > spark.sql("select * from abyte").show(); > spark.sql("select * from ashort").show(); > {code} > You'll see this exception (for the byte case): > {noformat} > 16/07/13 12:24:23 ERROR datasources.InsertIntoHadoopFsRelationCommand: > Aborting job. > org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in > stage 0.0 failed 4 times, most recent failure: Lost task 0.3 in stage 0.0 > (TID 3, scm-centos71-iqalat-2.gce.cloudera.com): > org.apache.spark.SparkException: Task failed while writing rows > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:261) > at > org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143) > at > org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelationCommand$$anonfun$run$1$$anonfun$apply$mcV$sp$1.apply(InsertIntoHadoopFsRelationCommand.scala:143) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70) > at org.apache.spark.scheduler.Task.run(Task.scala:85) > at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274) > at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615) > at java.lang.Thread.run(Thread.java:745) > Caused by: java.lang.NullPointerException > at > org.apache.spark.sql.execution.vectorized.OnHeapColumnVector.getByte(OnHeapColumnVector.java:159) > at > org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown > Source) > at > org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) > at > org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370) > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply$mcV$sp(WriterContainer.scala:253) > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252) > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer$$anonfun$writeRows$1.apply(WriterContainer.scala:252) > at > org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1325) > at > org.apache.spark.sql.execution.datasources.DefaultWriterContainer.writeRows(WriterContainer.scala:258) > ... 8 more > {noformat} > This works when you point Spark directly at the files (instead of using the > metastore data), or when you disable the vectorized parquet reader. > The root cause seems to be that Hive creates these tables with a > not-so-complete schema: > {noformat} > $ parquet-tools schema /tmp/byte.parquet > message hive_schema { > optional int32 value; > } > {noformat} > There's no indication that the field is a 32-bit field used to store 8-bit > values. When the ParquetReadSupport code tries to consolidate both schemas, > it just chooses whatever is in the parquet file for primitive types (see > ParquetReadSupport.clipParquetType); the vectorized reader uses the catalyst > schema, which comes from the Hive metastore, and says it's a byte field, so > when it tries to read the data, the byte data stored in "OnHeapColumnVector" > is null. > I have tested a small change to {{ParquetReadSupport.clipParquetType}} that > fixes this particular issue, but I haven't run any other tests, so I'll do > that while I wait for others to chime in and maybe tell me that's not the > right place to fix this. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org