apache / apache/parquet-java

`integer overflow` in CapacityByteArrayOutputStream

オープン
#3,261 コメント 6 件 リアクション 0 件 担当者 0 名 GitHub で見る
Type: bug
主要言語
Java
スター
3.1k
フォーク
1.6k
平均マージ
3日 12時間
マージ済み PR(30日)
33

説明

### Describe the bug, including details regarding any error messages, version, and platform.

The following exception was thrown when we read a column of `ARRAY` in Spark 3.5.0 and Parquet 1.15.2

```
Caused by: java.lang.ArithmeticException: integer overflow
at java.base/java.lang.Math.addExact(Math.java:883)
at org.apache.parquet.bytes.CapacityByteArrayOutputStream.addSlab(CapacityByteArrayOutputStream.java:198)
at org.apache.parquet.bytes.CapacityByteArrayOutputStream.write(CapacityByteArrayOutputStream.java:220)
at org.apache.parquet.bytes.LittleEndianDataOutputStream.write(LittleEndianDataOutputStream.java:76)
at java.base/java.io.OutputStream.write(OutputStream.java:127)
at org.apache.parquet.io.api.Binary$ByteArrayBackedBinary.writeTo(Binary.java:319)
at org.apache.parquet.column.values.plain.PlainValuesWriter.writeBytes(PlainValuesWriter.java:55)
at org.apache.parquet.column.values.fallback.FallbackValuesWriter.writeBytes(FallbackValuesWriter.java:178)
at org.apache.parquet.column.impl.ColumnWriterBase.write(ColumnWriterBase.java:196)
at org.apache.parquet.io.MessageColumnIO$MessageColumnIORecordConsumer.addBinary(MessageColumnIO.java:473)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeWriter$9(ParquetWriteSupport.scala:212)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeWriter$9$adapted(ParquetWriteSupport.scala:210)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$5(ParquetWriteSupport.scala:354)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$4(ParquetWriteSupport.scala:354)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeGroup(ParquetWriteSupport.scala:484)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$3(ParquetWriteSupport.scala:352)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$2(ParquetWriteSupport.scala:347)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeGroup(ParquetWriteSupport.scala:484)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$1(ParquetWriteSupport.scala:346)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$makeArrayWriter$1$adapted(ParquetWriteSupport.scala:342)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$writeFields$1(ParquetWriteSupport.scala:168)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeField(ParquetWriteSupport.scala:490)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.writeFields(ParquetWriteSupport.scala:168)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.$anonfun$write$1(ParquetWriteSupport.scala:158)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.consumeMessage(ParquetWriteSupport.scala:478)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.write(ParquetWriteSupport.scala:158)
at org.apache.spark.sql.execution.datasources.parquet.ParquetWriteSupport.write(ParquetWriteSupport.scala:54)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.write(InternalParquetRecordWriter.java:152)
at org.apache.parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:240)
at org.apache.parquet.hadoop.ParquetRecordWriter.write(ParquetRecordWriter.java:41)
at org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.write(ParquetOutputWriter.scala:39)
```

The issue can be worked around by increasing `spark.sql.shuffle.partitions` to divide data into smaller partitions.
Can it be solved at parquet side?

### Component(s)

Core

コントリビューションガイド

このリポジトリのコントリビューションガイドは索引されていません

調査の方向性

CapacityByteArrayOutputStream.javaから始め、特に198行目のaddSlabと220行目のwriteを確認してから、Spark 3.5.0とParquet 1.15.2を使って報告されたARRAYの書き込みシナリオを再現します。容量計算がどのようにMath.addExactに到達するかを追跡し、大規模な書き込みを壊すことなく整数オーバーフローによる失敗が防止されることを確認します。

索引モデルが issue の本文から書いたものです。

評価

技術スタック
java, spark
領域
data
issue の種類
バグ
難易度
3/5
見積もり時間
1〜2日
活発さ
静か
明瞭さ
おおむね明確
初心者へのやさしさ
55/100

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