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Posted to issues@spark.apache.org by "Cheng Lian (JIRA)" <ji...@apache.org> on 2016/07/20 05:07:20 UTC
[jira] [Resolved] (SPARK-16632) Vectorized parquet reader fails to
read certain fields from Hive tables
[ https://issues.apache.org/jira/browse/SPARK-16632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Cheng Lian resolved SPARK-16632.
--------------------------------
Resolution: Fixed
Fix Version/s: 2.1.0
Issue resolved by pull request 14272
[https://github.com/apache/spark/pull/14272]
> 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
> Fix For: 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.
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