[
https://issues.apache.org/jira/browse/SPARK-24018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16537815#comment-16537815
]
Jean-Francis Roy commented on SPARK-24018:
------------------------------------------
Oh, indeed you are right! I was mistaken when I thought that I had the error
using spark-submit, I just verified again and it works, thanks for the
explanation!
> Spark-without-hadoop package fails to create or read parquet files with
> snappy compression
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-24018
> URL: https://issues.apache.org/jira/browse/SPARK-24018
> Project: Spark
> Issue Type: Bug
> Components: Deploy
> Affects Versions: 2.3.0
> Reporter: Jean-Francis Roy
> Priority: Minor
>
> On a brand-new installation of Spark 2.3.0 with a user-provided hadoop-2.8.3,
> Spark fails to read or write dataframes in parquet format with snappy
> compression.
> This is due to an incompatibility between the snappy-java version that is
> required by parquet (parquet is provided in Spark jars but snappy isn't) and
> the version that is available from hadoop-2.8.3.
>
> Steps to reproduce:
> * Download and extract hadoop-2.8.3
> * Download and extract spark-2.3.0-without-hadoop
> * export JAVA_HOME, HADOOP_HOME, SPARK_HOME, PATH
> * Following instructions from
> [https://spark.apache.org/docs/latest/hadoop-provided.html], set
> SPARK_DIST_CLASSPATH=$(hadoop classpath) in spark-env.sh
> * Start a spark-shell, enter the following:
>
> {code:java}
> import spark.implicits._
> val df = List(1, 2, 3, 4).toDF
> df.write
> .format("parquet")
> .option("compression", "snappy")
> .mode("overwrite")
> .save("test.parquet")
> {code}
>
>
> This fails with the following:
> {noformat}
> java.lang.UnsatisfiedLinkError:
> org.xerial.snappy.SnappyNative.maxCompressedLength(I)I
> at org.xerial.snappy.SnappyNative.maxCompressedLength(Native Method)
> at org.xerial.snappy.Snappy.maxCompressedLength(Snappy.java:316)
> at
> org.apache.parquet.hadoop.codec.SnappyCompressor.compress(SnappyCompressor.java:67)
> at
> org.apache.hadoop.io.compress.CompressorStream.compress(CompressorStream.java:81)
> at
> org.apache.hadoop.io.compress.CompressorStream.finish(CompressorStream.java:92)
> at
> org.apache.parquet.hadoop.CodecFactory$BytesCompressor.compress(CodecFactory.java:112)
> at
> org.apache.parquet.hadoop.ColumnChunkPageWriteStore$ColumnChunkPageWriter.writePage(ColumnChunkPageWriteStore.java:93)
> at
> org.apache.parquet.column.impl.ColumnWriterV1.writePage(ColumnWriterV1.java:150)
> at
> org.apache.parquet.column.impl.ColumnWriterV1.flush(ColumnWriterV1.java:238)
> at
> org.apache.parquet.column.impl.ColumnWriteStoreV1.flush(ColumnWriteStoreV1.java:121)
> at
> org.apache.parquet.hadoop.InternalParquetRecordWriter.flushRowGroupToStore(InternalParquetRecordWriter.java:167)
> at
> org.apache.parquet.hadoop.InternalParquetRecordWriter.close(InternalParquetRecordWriter.java:109)
> at
> org.apache.parquet.hadoop.ParquetRecordWriter.close(ParquetRecordWriter.java:163)
> at
> org.apache.spark.sql.execution.datasources.parquet.ParquetOutputWriter.close(ParquetOutputWriter.scala:42)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.releaseResources(FileFormatWriter.scala:405)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$SingleDirectoryWriteTask.execute(FileFormatWriter.scala:396)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:269)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask$3.apply(FileFormatWriter.scala:267)
> at
> org.apache.spark.util.Utils$.tryWithSafeFinallyAndFailureCallbacks(Utils.scala:1411)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:272)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:197)
> at
> org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:196)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87) at
> org.apache.spark.scheduler.Task.run(Task.scala:109)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:345)
> at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
> at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
> at java.lang.Thread.run(Thread.java:748){noformat}
>
> Downloading snappy-java-1.1.2.6.jar and placing it in Sparks's jar folder
> solves the issue.
--
This message was sent by Atlassian JIRA
(v7.6.3#76005)
---------------------------------------------------------------------
To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org
For additional commands, e-mail: issues-h...@spark.apache.org
