Actually, this is the more relevant JIRA (which is resolved): https://issues.apache.org/jira/browse/SPARK-3595
6352 is about saveAsParquetFile, which is not in use here. Here is a DirectOutputCommitter implementation: https://gist.github.com/aarondav/c513916e72101bbe14ec and it can be configured in Spark with: sparkConf.set("spark.hadoop.mapred.output.committer.class", classOf[DirectOutputCommitter].getName) On Tue, Mar 17, 2015 at 8:05 AM, Imran Rashid <iras...@cloudera.com> wrote: > I'm not super familiar w/ S3, but I think the issue is that you want to > use a different output committers with "object" stores, that don't have a > simple move operation. There have been a few other threads on S3 & > outputcommitters. I think the most relevant for you is most probably this > open JIRA: > > https://issues.apache.org/jira/browse/SPARK-6352 > > On Fri, Mar 13, 2015 at 5:51 PM, Shuai Zheng <szheng.c...@gmail.com> > wrote: > >> Hi All, >> >> >> >> I try to run a sorting on a r3.2xlarge instance on AWS. I just try to run >> it as a single node cluster for test. The data I use to sort is around 4GB >> and sit on S3, output will also on S3. >> >> >> >> I just connect spark-shell to the local cluster and run the code in the >> script (because I just want a benchmark now). >> >> >> >> My job is as simple as: >> >> val parquetFile = >> sqlContext.parquetFile("s3n://...,s3n://...,s3n://...,s3n://...,s3n://...,s3n://...,s3n://...,") >> >> parquetFile.registerTempTable("Test") >> >> val sortedResult = sqlContext.sql("SELECT * FROM Test order by time").map >> { row => { row.mkString("\t") } } >> >> sortedResult.saveAsTextFile("s3n://myplace,"); >> >> >> >> The job takes around 6 mins to finish the sort when I am monitoring the >> process. After I notice the process stop at: >> >> >> >> 15/03/13 22:38:27 INFO DAGScheduler: Job 2 finished: saveAsTextFile at >> <console>:31, took 581.304992 s >> >> >> >> At that time, the spark actually just write all the data to the >> _temporary folder first, after all sub-tasks finished, it will try to move >> all the ready result from _temporary folder to the final location. This >> process might be quick locally (because it will just be a cut/paste), but >> it looks like very slow on my S3, it takes a few second to move one file >> (usually there will be 200 partitions). And then it raise exceptions after >> it move might be 40-50 files. >> >> >> >> org.apache.http.NoHttpResponseException: The target server failed to >> respond >> >> at >> org.apache.http.impl.conn.DefaultResponseParser.parseHead(DefaultResponseParser.java:101) >> >> at >> org.apache.http.impl.io.AbstractMessageParser.parse(AbstractMessageParser.java:252) >> >> at >> org.apache.http.impl.AbstractHttpClientConnection.receiveResponseHeader(AbstractHttpClientConnection.java:281) >> >> at >> org.apache.http.impl.conn.DefaultClientConnection.receiveResponseHeader(DefaultClientConnection.java:247) >> >> at >> org.apache.http.impl.conn.AbstractClientConnAdapter.receiveResponseHeader(AbstractClientConnAdapter.java:219) >> >> >> >> >> >> I try several times, but never get the full job finished. I am not sure >> anything wrong here, but I use something very basic and I can see the job >> has finished and all result on the S3 under temporary folder, but then it >> raise the exception and fail. >> >> >> >> Any special setting I should do here when deal with S3? >> >> >> >> I don’t know what is the issue here, I never see MapReduce has similar >> issue. So it could not be S3’s problem. >> >> >> >> Regards, >> >> >> >> Shuai >> > >
