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Posted to issues@spark.apache.org by "Hyukjin Kwon (JIRA)" <ji...@apache.org> on 2019/05/21 04:11:06 UTC
[jira] [Resolved] (SPARK-19299) Nulls in non nullable columns
causes data corruption in parquet
[ https://issues.apache.org/jira/browse/SPARK-19299?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Hyukjin Kwon resolved SPARK-19299.
----------------------------------
Resolution: Incomplete
> Nulls in non nullable columns causes data corruption in parquet
> ---------------------------------------------------------------
>
> Key: SPARK-19299
> URL: https://issues.apache.org/jira/browse/SPARK-19299
> Project: Spark
> Issue Type: Bug
> Components: PySpark, Spark Core
> Affects Versions: 1.6.0, 2.0.0, 2.0.1, 2.0.2, 2.1.0
> Reporter: Franklyn Dsouza
> Priority: Major
> Labels: bulk-closed
>
> The problem we're seeing is that if a null occurs in a non-nullable field and is written down to parquet the resulting file gets corrupted and can not be read back correctly.
> One way that this can occur is if a long value in python overflows the sql LongType, this results in a null value inside the dataframe.
> We're also seeing that the behaviour is different depending on whether or not the vectorized reader is enabled.
> Here's an example in PySpark
> {code}
> from datetime import datetime
> from pyspark.sql import types
> data = [
> (1, 6),
> (2, 7),
> (3, 2 ** 64), # value overflows sql LongType
> (4, 8),
> (5, 9)
> ]
> schema = types.StructType([
> types.StructField("index", types.LongType(), False),
> types.StructField("long", types.LongType(), False),
> ])
> df = sc.sql.createDataFrame(data, schema)
> df.collect()
> df.write.parquet("corrupt_parquet")
> df_parquet = sqlCtx.read.parquet("corrupt_parquet/*.parquet")
> df_parquet.collect()
> {code}
> with the vectorized reader enabled this causes
> {code}
> In [2]: df.collect()
> Out[2]:
> [Row(index=1, long=6),
> Row(index=2, long=7),
> Row(index=3, long=None),
> Row(index=4, long=8),
> Row(index=5, long=9)]
> In [3]: df_parquet.collect()
> Out[3]:
> [Row(index=1, long=6),
> Row(index=2, long=7),
> Row(index=3, long=8),
> Row(index=4, long=9),
> Row(index=5, long=5)]
> {code}
> as you can see reading the data back from disk causes data to get shifted up and between columns.
> with the vectorized reader disabled we are completely unable to read the file.
> {code}
> Py4JJavaError: An error occurred while calling o143.collectToPython.
> : org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 3.0 failed 1 times, most recent failure: Lost task 0.0 in stage 3.0 (TID 3, localhost): org.apache.parquet.io.ParquetDecodingException: Can not read value at 4 in block 0 in file file:/Users/franklyndsouza/dev/starscream/corrupt/part-r-00000-4fa5aee8-2138-4e0c-b6d8-22a418d90fd3.snappy.parquet
> at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:228)
> at org.apache.parquet.hadoop.ParquetRecordReader.nextKeyValue(ParquetRecordReader.java:201)
> at org.apache.spark.sql.execution.datasources.RecordReaderIterator.hasNext(RecordReaderIterator.scala:39)
> at org.apache.spark.sql.execution.datasources.FileScanRDD$$anon$1.hasNext(FileScanRDD.scala:91)
> at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
> at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
> at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:246)
> at org.apache.spark.sql.execution.SparkPlan$$anonfun$4.apply(SparkPlan.scala:240)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$24.apply(RDD.scala:803)
> at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
> at org.apache.spark.scheduler.Task.run(Task.scala:86)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
> at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
> at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
> at java.lang.Thread.run(Thread.java:745)
> Caused by: org.apache.parquet.io.ParquetDecodingException: Can't read value in column [long] INT64 at value 5 out of 5, 5 out of 5 in currentPage. repetition level: 0, definition level: 0
> at org.apache.parquet.column.impl.ColumnReaderImpl.readValue(ColumnReaderImpl.java:462)
> at org.apache.parquet.column.impl.ColumnReaderImpl.writeCurrentValueToConverter(ColumnReaderImpl.java:364)
> at org.apache.parquet.io.RecordReaderImplementation.read(RecordReaderImplementation.java:405)
> at org.apache.parquet.hadoop.InternalParquetRecordReader.nextKeyValue(InternalParquetRecordReader.java:209)
> ... 19 more
> Caused by: org.apache.parquet.io.ParquetDecodingException: could not read long
> at org.apache.parquet.column.values.plain.PlainValuesReader$LongPlainValuesReader.readLong(PlainValuesReader.java:131)
> at org.apache.parquet.column.impl.ColumnReaderImpl$2$4.read(ColumnReaderImpl.java:258)
> at org.apache.parquet.column.impl.ColumnReaderImpl.readValue(ColumnReaderImpl.java:458)
> ... 22 more
> Caused by: java.io.EOFException
> at org.apache.parquet.bytes.LittleEndianDataInputStream.readFully(LittleEndianDataInputStream.java:90)
> at org.apache.parquet.bytes.LittleEndianDataInputStream.readLong(LittleEndianDataInputStream.java:377)
> at org.apache.parquet.column.values.plain.PlainValuesReader$LongPlainValuesReader.readLong(PlainValuesReader.java:129)
> {code}
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