Details
-
Bug
-
Status: Resolved
-
Minor
-
Resolution: Invalid
-
2.4.5
-
None
-
None
Description
After installing pyspark (pip install pyspark) on both macOS and Ubuntu (a clean Docker image with default-jre), Spark fails to write text-based files (CSV and JSON) with snappy compression. It can snappy compress parquet and orc, gzipping CSVs also works.
This is a clean PySpark installation, snappy jars are in place
$ ls -1 /usr/local/lib/python3.7/site-packages/pyspark/jars/ | grep snappy
{{snappy-0.2.jar
}}snappy-java-1.1.7.3.jar
Repro 1 (Scala):
{{$ spark-shell}}
spark.sql("select 1").write.option("compression", "snappy").mode("overwrite").parquet("tmp/foo")
{{spark.sql("select 1").write.option("compression", "snappy").mode("overwrite").csv("tmp/foo")}}
The first (parquet) will work, the second one won't.
Repro 2 (PySpark):
from pyspark.sql import SparkSession
if _name_ == '_main_':spark
SparkSession.builder.appName('snappy_testing').getOrCreate()
spark.sql('select 1').write.option('compression', 'snappy').mode('overwrite').parquet('tmp/works_fine')
spark.sql('select 1').write.option('compression', 'gzip').mode('overwrite').csv('tmp/also_works')
spark.sql('select 1').write.option('compression', 'snappy').mode('overwrite').csv('tmp/snappy_not_found')
In either case I get the following traceback
java.lang.RuntimeException: native snappy library not available: this version of libhadoop was built without snappy support.java.lang.RuntimeException: native snappy library not available: this version of libhadoop was built without snappy support. at org.apache.hadoop.io.compress.SnappyCodec.checkNativeCodeLoaded(SnappyCodec.java:65) at org.apache.hadoop.io.compress.SnappyCodec.getCompressorType(SnappyCodec.java:134) at org.apache.hadoop.io.compress.CodecPool.getCompressor(CodecPool.java:150) at org.apache.hadoop.io.compress.CompressionCodec$Util.createOutputStreamWithCodecPool(CompressionCodec.java:131) at org.apache.hadoop.io.compress.SnappyCodec.createOutputStream(SnappyCodec.java:100) at org.apache.spark.sql.execution.datasources.CodecStreams$$anonfun$createOutputStream$1.apply(CodecStreams.scala:84) at org.apache.spark.sql.execution.datasources.CodecStreams$$anonfun$createOutputStream$1.apply(CodecStreams.scala:84) at scala.Option.map(Option.scala:146) at org.apache.spark.sql.execution.datasources.CodecStreams$.createOutputStream(CodecStreams.scala:84) at org.apache.spark.sql.execution.datasources.CodecStreams$.createOutputStreamWriter(CodecStreams.scala:92) at org.apache.spark.sql.execution.datasources.csv.CsvOutputWriter.<init>(CSVFileFormat.scala:177) at org.apache.spark.sql.execution.datasources.csv.CSVFileFormat$$anon$1.newInstance(CSVFileFormat.scala:85) at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.newOutputWriter(FileFormatDataWriter.scala:120) at org.apache.spark.sql.execution.datasources.SingleDirectoryDataWriter.<init>(FileFormatDataWriter.scala:108) at org.apache.spark.sql.execution.datasources.FileFormatWriter$.org$apache$spark$sql$execution$datasources$FileFormatWriter$$executeTask(FileFormatWriter.scala:236) at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:170) at org.apache.spark.sql.execution.datasources.FileFormatWriter$$anonfun$write$1.apply(FileFormatWriter.scala:169) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90) at org.apache.spark.scheduler.Task.run(Task.scala:123) at org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:408) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:414) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624) at java.lang.Thread.run(Thread.java:748)