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Posted to issues@spark.apache.org by "Dongjoon Hyun (JIRA)" <ji...@apache.org> on 2018/01/17 06:28:01 UTC
[jira] [Commented] (SPARK-19109) ORC metadata section can sometimes
exceed protobuf message size limit
[ https://issues.apache.org/jira/browse/SPARK-19109?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16328330#comment-16328330 ]
Dongjoon Hyun commented on SPARK-19109:
---------------------------------------
HIVE-11592 is fixed in Hive 1.3.0 and ORC 1.4.1 library has the patch. Since SPARK-20682 / SPARK-20728 / SPARK-22279, we are using native ORC implementation. This issue is fixed by default configuration.
{code}
public static final int PROTOBUF_MESSAGE_MAX_LIMIT = 1024 << 20; // 1GB
{code}
> ORC metadata section can sometimes exceed protobuf message size limit
> ---------------------------------------------------------------------
>
> Key: SPARK-19109
> URL: https://issues.apache.org/jira/browse/SPARK-19109
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.3, 2.0.2, 2.1.0, 2.2.0
> Reporter: Nic Eggert
> Priority: Major
> Attachments: InsertPic_.png
>
>
> Basically, Spark inherits HIVE-11592 from its Hive dependency. From that issue:
> If there are too many small stripes and with many columns, the overhead for storing metadata (column stats) can exceed the default protobuf message size of 64MB. Reading such files will throw the following exception
> {code}
> Exception in thread "main" com.google.protobuf.InvalidProtocolBufferException: Protocol message was too large. May be malicious. Use CodedInputStream.setSizeLimit() to increase the size limit.
> at com.google.protobuf.InvalidProtocolBufferException.sizeLimitExceeded(InvalidProtocolBufferException.java:110)
> at com.google.protobuf.CodedInputStream.refillBuffer(CodedInputStream.java:755)
> at com.google.protobuf.CodedInputStream.readRawBytes(CodedInputStream.java:811)
> at com.google.protobuf.CodedInputStream.readBytes(CodedInputStream.java:329)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StringStatistics.<init>(OrcProto.java:1331)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StringStatistics.<init>(OrcProto.java:1281)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StringStatistics$1.parsePartialFrom(OrcProto.java:1374)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StringStatistics$1.parsePartialFrom(OrcProto.java:1369)
> at com.google.protobuf.CodedInputStream.readMessage(CodedInputStream.java:309)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$ColumnStatistics.<init>(OrcProto.java:4887)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$ColumnStatistics.<init>(OrcProto.java:4803)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$ColumnStatistics$1.parsePartialFrom(OrcProto.java:4990)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$ColumnStatistics$1.parsePartialFrom(OrcProto.java:4985)
> at com.google.protobuf.CodedInputStream.readMessage(CodedInputStream.java:309)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StripeStatistics.<init>(OrcProto.java:12925)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StripeStatistics.<init>(OrcProto.java:12872)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StripeStatistics$1.parsePartialFrom(OrcProto.java:12961)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$StripeStatistics$1.parsePartialFrom(OrcProto.java:12956)
> at com.google.protobuf.CodedInputStream.readMessage(CodedInputStream.java:309)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$Metadata.<init>(OrcProto.java:13599)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$Metadata.<init>(OrcProto.java:13546)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$Metadata$1.parsePartialFrom(OrcProto.java:13635)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$Metadata$1.parsePartialFrom(OrcProto.java:13630)
> at com.google.protobuf.AbstractParser.parsePartialFrom(AbstractParser.java:200)
> at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:217)
> at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:223)
> at com.google.protobuf.AbstractParser.parseFrom(AbstractParser.java:49)
> at org.apache.hadoop.hive.ql.io.orc.OrcProto$Metadata.parseFrom(OrcProto.java:13746)
> at org.apache.hadoop.hive.ql.io.orc.ReaderImpl$MetaInfoObjExtractor.<init>(ReaderImpl.java:468)
> at org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:314)
> at org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
> at org.apache.hadoop.hive.ql.io.orc.FileDump.main(FileDump.java:67)
> 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.hadoop.util.RunJar.run(RunJar.java:221)
> at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
> {code}
> This is fixed in Hive 1.3, so it should be fairly straightforward to pick up the patch.
> As a side note: Spark's management of its Hive fork/dependency seems incredibly arcane to me. Surely there's a better way than publishing to central from developers' personal repos.
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