If you use fileStream, there's an option to filter out files. In your case you can easily create a filter to remove _temporary files. In that case, you will have to move your codes inside foreachRDD of the dstream since the application will become a streaming app.
Thanks Best Regards On Sat, Mar 14, 2015 at 4:26 AM, Shuai Zheng <szheng.c...@gmail.com> wrote: > And one thing forget to mention, even I have this exception and the result > is not well format in my target folder (part of them are there, rest are > under different folder structure of _tempoary folder). In the webUI of > spark-shell, it is still be marked as successful step. I think this is a > bug? > > > > Regards, > > > > Shuai > > > > *From:* Shuai Zheng [mailto:szheng.c...@gmail.com] > *Sent:* Friday, March 13, 2015 6:51 PM > *To:* user@spark.apache.org > *Subject:* Spark will process _temporary folder on S3 is very slow and > always cause failure > > > > 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 >
