[
https://issues.apache.org/jira/browse/SPARK-19109?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Nic Eggert updated SPARK-19109:
-------------------------------
Description:
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$MetaInfoObjExtractor.<init>(ReaderImpl.java:468)
at
org.apache.hadoop.hive.ql.io.orc.ReaderImpl.<init>(ReaderImpl.java:314)
at
org.apache.hadoop.hive.ql.io.orc.OrcFile.createReader(OrcFile.java:228)
at org.apache.hadoop.hive.ql.io.orc.FileDump.main(FileDump.java:67)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606)
at org.apache.hadoop.util.RunJar.run(RunJar.java:221)
at org.apache.hadoop.util.RunJar.main(RunJar.java:136)
{code}
This is fixed in Hive 1.3, so it should be fairly straightforward to pick up
the patch.
As a side note: Spark's management of its Hive fork/dependency seems incredibly
arcane to me. Surely there's a better way than publishing to central from
developers' personal repos.
was:
Basically, Spark inherits HIVE-11592 from its Hive dependency. From that issue:
If there are too many small stripes and with many columns, the overhead for
storing metadata (column stats) can exceed the default protobuf message size of
64MB. Reading such files will throw the following exception
{code}
Exception in thread "main" com.google.protobuf.InvalidProtocolBufferException:
Protocol message was too large. May be malicious. Use
CodedInputStream.setSizeLimit() to increase the size limit.
at
com.google.protobuf.InvalidProtocolBufferException.sizeLimitExceeded(InvalidProtocolBufferException.java:110)
at
com.google.protobuf.CodedInputStream.refillBuffer(CodedInputStream.java:755)
at
com.google.protobuf.CodedInputStream.readRawBytes(CodedInputStream.java:811)
at
com.google.protobuf.CodedInputStream.readBytes(CodedInputStream.java:329)
at
org.apache.hadoop.hive.ql.io.orc.OrcProto$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.
> 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
>
> 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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