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From Takeshi Yamamuro <linguin....@gmail.com>
Subject Re: FAILED_TO_UNCOMPRESS Error - Spark 1.3.1
Date Mon, 30 May 2016 12:22:48 GMT
Hi,

This is a known issue.
You need to check a related JIRA ticket:
https://issues.apache.org/jira/browse/SPARK-4105

// maropu

On Mon, May 30, 2016 at 7:51 PM, Prashant Singh Thakur <
prashant.thakur@impetus.co.in> wrote:

> Hi,
>
>
>
> We are trying to use Spark Data Frames for our use case where we are
> getting this exception.
>
> The parameters used are listed below. Kindly suggest if we are missing
> something.
>
> Version used is Spark 1.3.1
>
> Jira is still showing this issue as Open
> https://issues.apache.org/jira/browse/SPARK-4105
>
> Kindly suggest if there is workaround .
>
>
>
> Exception :
>
> Caused by: org.apache.spark.SparkException: Job aborted due to stage
> failure: Task 88 in stage 40.0 failed 4 times, most recent failure: Lost
> task 88.3 in stage 40.0 : java.io.IOException: FAILED_TO_UNCOMPRESS(5)
>
>               at
> org.xerial.snappy.SnappyNative.throw_error(SnappyNative.java:78)
>
>               at org.xerial.snappy.SnappyNative.rawUncompress(Native
> Method)
>
>               at org.xerial.snappy.Snappy.rawUncompress(Snappy.java:391)
>
>               at org.xerial.snappy.Snappy.uncompress(Snappy.java:427)
>
>               at
> org.xerial.snappy.SnappyInputStream.readFully(SnappyInputStream.java:127)
>
>               at
> org.xerial.snappy.SnappyInputStream.readHeader(SnappyInputStream.java:88)
>
>               at
> org.xerial.snappy.SnappyInputStream.<init>(SnappyInputStream.java:58)
>
>               at
> org.apache.spark.io.SnappyCompressionCodec.compressedInputStream(CompressionCodec.scala:160)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$7.apply(TorrentBroadcast.scala:213)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$7.apply(TorrentBroadcast.scala:213)
>
>               at scala.Option.map(Option.scala:145)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast$.unBlockifyObject(TorrentBroadcast.scala:213)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast$$anonfun$readBroadcastBlock$1.apply(TorrentBroadcast.scala:177)
>
>               at
> org.apache.spark.util.Utils$.tryOrIOException(Utils.scala:1153)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast.readBroadcastBlock(TorrentBroadcast.scala:164)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast._value$lzycompute(TorrentBroadcast.scala:64)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast._value(TorrentBroadcast.scala:64)
>
>               at
> org.apache.spark.broadcast.TorrentBroadcast.getValue(TorrentBroadcast.scala:87)
>
>               at
> org.apache.spark.broadcast.Broadcast.value(Broadcast.scala:70)
>
>               at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:61)
>
>               at
> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:41)
>
>               at org.apache.spark.scheduler.Task.run(Task.scala:64)
>
>               at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:203)
>
>               at
> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
>
>               at
> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
>
>               at java.lang.Thread.run(Thread.java:745)
>
>
>
> Parameters Changed :
>
> spark.akka.frameSize=50
>
> spark.shuffle.memoryFraction=0.4
>
> spark.storage.memoryFraction=0.5
>
> spark.worker.timeout=120000
>
> spark.storage.blockManagerSlaveTimeoutMs=120000
>
> spark.akka.heartbeat.pauses=6000
>
> spark.akka.heartbeat.interval=1000
>
> spark.ui.port=21000
>
> spark.port.maxRetries=50
>
> spark.executor.memory=10G
>
> spark.executor.instances=100
>
> spark.driver.memory=8G
>
> spark.executor.cores=2
>
> spark.shuffle.compress=true
>
> spark.io.compression.codec=snappy
>
> spark.broadcast.compress=true
>
> spark.rdd.compress=true
>
> spark.worker.cleanup.enabled=true
>
> spark.worker.cleanup.interval=600
>
> spark.worker.cleanup.appDataTtl=600
>
> spark.shuffle.consolidateFiles=true
>
> spark.yarn.preserve.staging.files=false
>
> spark.yarn.driver.memoryOverhead=1024
>
> spark.yarn.executor.memoryOverhead=1024
>
>
>
> Best Regards,
>
> Prashant Singh Thakur
>
> Mobile: +91-9740266522
>
>
>
> ------------------------------
>
>
>
>
>
>
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-- 
---
Takeshi Yamamuro

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