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From Josh Wills <jwi...@cloudera.com>
Subject Re: Multiple Writes in a single pipeline.
Date Fri, 22 Aug 2014 15:12:04 GMT
That is super-interesting; let me try to replicate it in a test.

J


On Fri, Aug 22, 2014 at 7:26 AM, Danny Morgan <unluckyboy@hotmail.com>
wrote:

> This issue looks similar to
> https://issues.apache.org/jira/browse/CRUNCH-67
>
> It turns out even if I get rid of the reduce phase and do just this:
>
>
>  PTable<String, String> lines = this.read(mySource);
>  PCollection<Log> parsed = lines.parallelDo("initial-
> parsing", new myParser(), Avros.specifics(Log.class));
>
>  PTable<Visit, Pair<Long, Long>> visits = parsed.parallelDo("visits-parsing",
> new VisitsExtractor(),
>           Avros.tableOf(Avros.specifics(Visit.class),
> Avros.pairs(Avros.longs(), Avros.longs())));
>
> visits.write(To.avroFile(outputPath+"/visits"), WriteMode.OVERWRITE);
> parsed.write(To.avroFile(outputPath+"/raw"), WriteMode.OVERWRITE);
> this.done();
>
> The plan shows I should be writing to two different targets in a single
> map phase however only "/raw" as data written out to it and "/visits" just
> contains a _SUCCESS file and no data.
>
> Might this be an issue writing out to two different Avro types in the same
> phase?
>
> Thanks Again,
>
> Danny
>
>
> ------------------------------
> From: unluckyboy@hotmail.com
> To: user@crunch.apache.org
> Subject: RE: Multiple Writes in a single pipeline.
> Date: Fri, 22 Aug 2014 02:02:20 +0000
>
>
> Hi Josh,
>
>
> ------------------------------
> From: jwills@cloudera.com
> Date: Thu, 21 Aug 2014 17:40:25 -0700
> Subject: Re: Multiple Writes in a single pipeline.
> To: user@crunch.apache.org
>
> The two different executions you have are doing different things, however.
> In the first one, Crunch is running a single MapReduce job where the /raw
> directory is written as a mapper side-output, and the /visits directory is
> being written out on the reduce side (or at least, should be-- is there any
> evidence of a failure in the job in the logs? Are bytes being written out
> from the reducer?)
>
> No evidence of any failures in the logs, the single mapper and reducers
> both succeed. The mapper definitely writes to HDFS the reducer does not,
> here are the relevant counters from the reducer:
>
> FILE: Number of bytes read
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/FILE_BYTES_READ>
> 6 FILE: Number of bytes written
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/FILE_BYTES_WRITTEN>
> 91811 FILE: Number of large read operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/FILE_LARGE_READ_OPS>
> 0FILE: Number of read operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/FILE_READ_OPS>
> 0 FILE: Number of write operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/FILE_WRITE_OPS>
> 0HDFS: Number of bytes read
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/HDFS_BYTES_READ>
> 6205 HDFS: Number of bytes written
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/HDFS_BYTES_WRITTEN>
> 0 HDFS: Number of large read operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/HDFS_LARGE_READ_OPS>
> 0 HDFS: Number of read operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/HDFS_READ_OPS>
> 4HDFS: Number of write operations
> <http://ec2-54-166-194-165.compute-1.amazonaws.com:19888/jobhistory/singletaskcounter/task_1408490264848_0012_r_000000/org.apache.hadoop.mapreduce.FileSystemCounter/HDFS_WRITE_OPS>
> 2
> I couldn't find anything related on the crunch jira.
>
> For this problem, I think it would be more efficient to write the parsed
> -> /raw output first, call run(), then do the agg -> /visits output
> followed by done(), which would mean that you would only need to parse the
> raw input once, instead of twice.
>
> Would the first option be more efficient if it worked?
>
> A helpful trick for seeing how the Crunch planner is mapping your logic
> into MapReduce jobs is to look at the plan dot file via one of the
> following mechanisms:
>
> 1) Instead of calling Pipeline.run(), call Pipeline.runAsync() and then
> call the getPlanDotFile() method on the returned PipelineExecution object.
> You can print the dot file to a file and use a dot file viewer to look at
> how the DoFns are broken up into MR jobs and map/reduce phases.
> 2) Call MRPipeline.plan() directly, which returns a MRExecutor object that
> also implements PipelineExecution. (The difference being that calling
> MRPipeline.plan will not start the jobs running, whereas calling runAsync
> will.)
>
> I ran the two different version through dot and you're right they are two
> complete different executions, pretty cool!
>
> Thanks!
>
>


-- 
Director of Data Science
Cloudera <http://www.cloudera.com>
Twitter: @josh_wills <http://twitter.com/josh_wills>

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