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https://issues.apache.org/jira/browse/SPARK-19109?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16328330#comment-16328330
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Dongjoon Hyun edited comment on SPARK-19109 at 1/17/18 6:27 AM:
----------------------------------------------------------------
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
based on ORC 1.4.1. This issue is fixed in Apache Spark default configuration.
{code}
public static final int PROTOBUF_MESSAGE_MAX_LIMIT = 1024 << 20; // 1GB
{code}
was (Author: dongjoon):
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
based on ORC 1.4.1. 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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