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https://issues.apache.org/jira/browse/SPARK-24018?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16537551#comment-16537551
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Patrick Clay edited comment on SPARK-24018 at 7/9/18 8:49 PM:
--------------------------------------------------------------
I believe we are both partially correct in that a fix (with Spark 2.3.0) does
require snappy-java-1.1.2, and it was caused by SPARK-18646. The native library
loader of Snappy 1.0.4 [uses a self-described
hack|https://github.com/xerial/snappy-java/blob/snappy-java-1.0.4/src/main/java/org/xerial/snappy/SnappyLoader.java#L175]
to inject the loader onto the root class loader. The hack was [later
removed|https://github.com/xerial/snappy-java/commit/06f007a08#diff-a1c8fc77f8]
in 1.1.2, which allows the non-inheriting class loader to pick it up.
I believed this only affects spark-shell, because neither pyspark (the REPL and
spark-submit) nor
{code:java}
./bin/spark-submit --class org.apache.spark.examples.sql.SQLDataSourceExample
examples/jars/spark-examples_2.11-2.3.0.jar{code}
have this issue. What repro did you have without spark-shell?
I don't believe this is related to Parquet versioning because this also throws:
{code:java}
scala> import org.xerial.snappy.Snappy
import org.xerial.snappy.Snappy
scala> sc.parallelize(Seq("foo")).map(Snappy.compress).collect
2018-07-09 13:44:14 ERROR Executor:91 - Exception in task 11.0 in stage 0.0
(TID 11)
java.lang.UnsatisfiedLinkError:
org.xerial.snappy.SnappyNative.maxCompressedLength(I)I
...{code}
In answer to your last question I did not pass any arguments to spark-shell.
All I did to repro was
{code:java}
export SPARK_DIST_CLASSPATH=$(~/Downloads/hadoop-2.8.3/bin/hadoop classpath)
~/Downloads/spark-2.3.0-bin-without-hadoop/bin/spark-shell{code}
was (Author: pclay):
I believe we are both partially correct in that a fix (with Spark 2.3.0) does
require snappy-java-1.1.2, and it was caused by SPARK-18646. The native library
loader of Snappy 1.0.4 [uses a self-described
hack|https://github.com/xerial/snappy-java/blob/snappy-java-1.0.4/src/main/java/org/xerial/snappy/SnappyLoader.java#L175]
to inject the loader onto the root class loader. The hack was [later
removed|https://github.com/xerial/snappy-java/commit/06f007a08#diff-a1c8fc77f8]
in 1.1.2, which allows the non-inheriting class loader to pick it up.
I believed this only affects spark-shell, because neither pyspark (the REPL and
spark-submit) nor
{code:java}
./bin/spark-submit --class org.apache.spark.examples.sql.SQLDataSourceExample
examples/jars/spark-examples_2.11-2.3.0.jar{code}
have this issue. What repro did you have without spark-shell?
I don't believe this is related to Parquet versioning because this also throws:
{code:java}
scala> import org.xerial.snappy.Snappy
import org.xerial.snappy.Snappy
scala> sc.parallelize(Seq("foo")).map(Snappy.compress).collect
2018-07-09 13:44:14 ERROR Executor:91 - Exception in task 11.0 in stage 0.0
(TID 11)
java.lang.UnsatisfiedLinkError:
org.xerial.snappy.SnappyNative.maxCompressedLength(I)I
...{code}
In answer to your last question I did not pass any arguments to spark-shell.
All I did to repro was
{code:java}
export SPARK_DIST_CLASSPATH=$(~/Downloads/hadoop-2.8.3/bin/hadoop classpath)
~/Downloads/spark-2.3.0-bin-without-hadoop/bin/spark-shell{code}
> 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.
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