After some investigation, I found out that there's lots of temp files under
/tmp/hadoop-root/s3/
But this is strange since in both conf files,
~/ephemeral-hdfs/conf/core-site.xml and ~/spark/conf/core-site.xml, the
setting `hadoop.tmp.dir` is set to `/mnt/ephemeral-hdfs/`. Why spark jobs
still write temp files to /tmp/hadoop-root ?
2014-05-06 18:05 GMT+02:00 Han JU <ju.han.fe...@gmail.com>:
> Hi,
>
> I've a `no space left on device` exception when pulling some 22GB data
> from s3 block storage to the ephemeral HDFS. The cluster is on EC2 using
> spark-ec2 script with 4 m1.large.
>
> The code is basically:
> val in = sc.textFile("s3://...")
> in.saveAsTextFile("hdfs://...")
>
> Spark creates 750 input partitions based on the input splits, when it
> begins throwing this exception, there's no space left on the root file
> system on some worker machine:
>
> Filesystem 1K-blocks Used Available Use% Mounted on
> /dev/xvda1 8256952 8256952 0 100% /
> tmpfs 3816808 0 3816808 0% /dev/shm
> /dev/xvdb 433455904 29840684 381596916 8% /mnt
> /dev/xvdf 433455904 29437000 382000600 8% /mnt2
>
> Before the job begins, only 35% is used.
>
> Filesystem 1K-blocks Used Available Use% Mounted on
> /dev/xvda1 8256952 2832256 5340840 35% /
> tmpfs 3816808 0 3816808 0% /dev/shm
> /dev/xvdb 433455904 29857768 381579832 8% /mnt
> /dev/xvdf 433455904 29470104 381967496 8% /mnt2
>
>
> Some suggestions on this problem? Does Spark caches/stores some data
> before writing to HDFS?
>
>
> Full stacktrace:
> ---------------------
> java.io.IOException: No space left on device
> at java.io.FileOutputStream.writeBytes(Native Method)
> at java.io.FileOutputStream.write(FileOutputStream.java:345)
> at java.io.BufferedOutputStream.write(BufferedOutputStream.java:122)
> at
> org.apache.hadoop.fs.s3.Jets3tFileSystemStore.retrieveBlock(Jets3tFileSystemStore.java:210)
> at sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at
> org.apache.hadoop.io.retry.RetryInvocationHandler.invokeMethod(RetryInvocationHandler.java:82)
> at
> org.apache.hadoop.io.retry.RetryInvocationHandler.invoke(RetryInvocationHandler.java:59)
> at com.sun.proxy.$Proxy8.retrieveBlock(Unknown Source)
> at
> org.apache.hadoop.fs.s3.S3InputStream.blockSeekTo(S3InputStream.java:160)
> at org.apache.hadoop.fs.s3.S3InputStream.read(S3InputStream.java:119)
> at java.io.DataInputStream.read(DataInputStream.java:100)
> at org.apache.hadoop.util.LineReader.readLine(LineReader.java:134)
> at
> org.apache.hadoop.mapred.LineRecordReader.<init>(LineRecordReader.java:92)
> at
> org.apache.hadoop.mapred.TextInputFormat.getRecordReader(TextInputFormat.java:51)
> at org.apache.spark.rdd.HadoopRDD$$anon$1.<init>(HadoopRDD.scala:156)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:149)
> at org.apache.spark.rdd.HadoopRDD.compute(HadoopRDD.scala:64)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.rdd.MappedRDD.compute(MappedRDD.scala:31)
> at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:241)
> at org.apache.spark.rdd.RDD.iterator(RDD.scala:232)
> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:109)
> at org.apache.spark.scheduler.Task.run(Task.scala:53)
> at
> org.apache.spark.executor.Executor$TaskRunner$$anonfun$run$1.apply$mcV$sp(Executor.scala:213)
> at
> org.apache.spark.deploy.SparkHadoopUtil.runAsUser(SparkHadoopUtil.scala:49)
> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:178)
> 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:744)
>
>
> --
> *JU Han*
>
> Data Engineer @ Botify.com
>
> +33 0619608888
>
--
*JU Han*
Data Engineer @ Botify.com
+33 0619608888
