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From David Ortiz <dor...@videologygroup.com>
Subject RE: Crunch Planner Hint to Not Combine Tasks
Date Wed, 02 Dec 2015 15:00:13 GMT
Easiest way to get more mappers is to take the DoFn that is blowing up, override the configure
method, and adjust the split sizes.

For example:

@Override
public void configure(Configuration conf) {
   conf.set("crunch.combine.file.size", "67108864");
   conf.set("mapreduce.input.fileinputformat.split.maxsize", "67108864");
   conf.set("mapreduce.input.fileinputformat.split.minsize", "67108864");
}



From: Jeff Quinn [mailto:jeff@nuna.com]
Sent: Wednesday, December 02, 2015 1:23 AM
To: Robinson, Landon - Landon <landon.t.robinson@lowes.com>
Cc: user@crunch.apache.org
Subject: Re: Crunch Planner Hint to Not Combine Tasks

Hey Landon,

Taking a look at the original code you posted, the issue is that you are not making any #write
calls before you call #run. As a result the call to #run doesn't actually produce any MR jobs;
the planner thinks there is not any work to do because there is no sink defined for the pipeline
at the point. I would revise this code to add:

records_with_intervals.write(To.sequenceFile(new Path("/my/special/temp/path")), WriteMode.CHECKPOINT)

before

pipeline.run()

And so on for the rest of the times you call #run. As you have pointed out in your follow
up, calling PCollection#cache basically accomplishes the same thing (and maybe even is the
preferred way?).

Anyway, as for the data spills, I see now I did not fully appreciate your problem. Indeed
scaleFactor will only ever affect the number of reducers for the job, so all of this separation
of DoFns will never really help you in that department. To adjust the number of mappers I
would look at the parameters Ron Hashimshony provided earlier, which will force the creation
of more map tasks. Crunch will take care of the reducers for you because you are setting scaleFactor
correctly. I think perhaps you do not even need to bother with splitting up your DoFns :).


On Tue, Dec 1, 2015 at 2:11 PM, Robinson, Landon - Landon <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
wrote:
On a side note, I was able to use a tip from the User Guide to force the Crunch planner not
to combine jobs, using pcollection.cache() (you can also use .materialize()) between DoFn
calls (similar to the earlier suggested approach).

However, I’m still having an issue with my job not leveraging enough nodes/mappers for the
job that grows exponentially. Again, I have a DoFn where the data grows to a size 46 times
larger as it passes through.
I’ve tried using scaleFactor of 46.0f, but it doesn’t do much more than assign more reducer
tasks.

The issue happens that all the data spills about 1/3 the way through the job to the linux
node disk, because the space is filled up. I need the job to leverage more mappers on more
nodes. Is there a way?
Thank you immensely!
---------------------------------------------------------------------------
Landon Robinson
Big Data/Hadoop Engineer
---------------------------------------------------------------------------

From: <Robinson>, LCI <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
Reply-To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Date: Tuesday, December 1, 2015 at 2:33 PM
To: Jeff Quinn <jeff@nuna.com<mailto:jeff@nuna.com>>

Cc: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Subject: Re: Crunch Planner Hint to Not Combine Tasks

More code, as requested! Hope this sheds some light on what I may be doing wrong…
Thanks Jeff and Everett (and others) for all the help!

CODE

logger.info<http://logger.info>("Generating Map-Reduce Pipeline...");
Pipeline pipeline = new MRPipeline(MyClass.class, "Crunch Pipeline", crunchConf);

logger.info<http://logger.info>("Establishing OrcFile Target for Later Output...");
OrcFileTarget target = new OrcFileTarget(new Path(outputPath));
//</editor-fold>

// =======================================================================
// * INGEST DATA
// * Ingest a text file, and for every row in it, create a Java object.
// =======================================================================

//<editor-fold desc="== Ingestion, Object Creation ==">
logger.info<http://logger.info>("Reading file (" + inputPath + ") into PCollection...");
PCollection<String> my_data = pipeline.readTextFile(inputPath);
logger.info<http://logger.info>("# of lines ingested from (" + inputPath + "): " + my_data.length().getValue());

logger.info<http://logger.info>("Converting Data to 'RecordStageOne' objects...");
PCollection<RecordStageOne> records = my_data.parallelDo(DoFn_CreateJavaRecords(), Avros.records(RecordStageOne.class));
logger.info<http://logger.info>("Records created: " + records.length().getValue() +
" out of " + my_data.length().getValue() + " rows in file.");

//</editor-fold>

// =======================================================================
// * APPLY INTERVAL CALCULATIONS TO CURRENT RECORD SET
// * For every record, Calculate and Apply Affected Intervals
// =======================================================================

//<editor-fold desc="== Apply Intervals ==">
logger.info<http://logger.info>("Determining and Applying Working Intervals on data...");
PCollection<RecordStageTwo> records_with_intervals = records.parallelDo(DoFn_ApplyIntervals(time_intervals),
        Avros.records(RecordStageTwo.class));

logger.info<http://logger.info>("Records successfully processed in Apply Intervals step:
" + records_with_intervals.length().getValue() + "/" + records.length().getValue());

pipeline.run();
//</editor-fold>

//It’s at this point that Crunch Planner combines the rest of the parallelDo calls into
a single job, and I think overwhelms the single worker the job runs on..

// =======================================================================
// * CALCULATE CONTRIBUTION RESULTS (THE 3 COUNTS)
// * Report every record that matches a primary key and count its values.
// =======================================================================

//<editor-fold desc="== Calculate Count Contributions ==">
logger.info<http://logger.info>("Calculating contributions to clock in count, employee
count, and hours worked...");
PTable<String, Tuple3<Integer, Integer, Integer>> calculatedSet = records_with_intervals.parallelDo(DoFn_CalculateResults(time_intervals),
        tableOf(strings(), triples(ints(), ints(), ints())));


pipeline.run();

//</editor-fold>

// =======================================================================
// * GROUP AND AGGREGATE
// * Group records by their key and sum their values (the 3 counts)
// =======================================================================

//<editor-fold desc="== Group and Aggregate ==">
logger.info<http://logger.info>("Grouping Records By Key and Aggregating Their New Values...");
PTable<String, Tuple3<Integer, Integer, Integer>> reducer = calculatedSet
        .groupByKey()
        .combineValues(Aggregators.tripAggregator(
                Aggregators.SUM_INTS(),
                Aggregators.SUM_INTS(),
                Aggregators.SUM_INTS()));


pipeline.run();

//</editor-fold>

// =======================================================================
// * PRODUCE FINAL RECORDS IN ORC FORMAT
// * Convert final records to ORC format.
// =======================================================================

//<editor-fold desc="== Convert Data to ORC Records ==">
logger.info<http://logger.info>("Producing Final ORC-Format Records...");
PCollection<TupleN> finalDataOrc = reducer.parallelDo(DoFn_ProduceFinalRecords(), Orcs.tuples(
        Writables.ints(),
        Writables.ints(),
        Writables.strings(),
        Writables.strings(),
        Writables.strings(),
        Writables.ints(),
        Writables.ints(),
        Writables.ints(),
        Writables.ints(),
        Writables.strings()
));

//</editor-fold>

// =======================================================================
// * WRITE TO ORC FILE AND EXECUTE PIPELINE
// * Report results in final .orc file.
// =======================================================================

//<editor-fold desc="== ORC File Output, Pipeline Closure, and Exit ==">
logger.info<http://logger.info>("Writing ORC file(s) to " + outputPath);
pipeline.write(finalDataOrc, target, Target.WriteMode.OVERWRITE);

PipelineResult result = pipeline.done();
logger.info<http://logger.info>("Pipeline ending.");

END OF CODE

The issue still: Crunch combines all the jobs halfway through the code, even with calls to
pipeline.run() and setting max jobs to 1.
Help/Guidance appreciated!
---------------------------------------------------------------------------
Landon Robinson
Big Data/Hadoop Engineer
---------------------------------------------------------------------------

From: Jeff Quinn <jeff@nuna.com<mailto:jeff@nuna.com>>
Date: Tuesday, November 24, 2015 at 7:59 PM
To: LCI <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
Cc: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Subject: Re: Crunch Planner Hint to Not Combine Tasks

For the MRExecutor, each materialized PCollection is backed by a Path, if something deletes
that Path you are left with this error.

So it looks like the crunch temporary directory was cleaned up by the time you called #write.
Can you post more code? Is there a call to Pipeline#done somewhere before this (that is the
only call that cleans up crunch tmp as far as a I know)

On Tue, Nov 24, 2015 at 4:54 PM, Robinson, Landon - Landon <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
wrote:
Jeff/Everett,
Thanks so much! Though I am slightly confused... I implemented what I believe you were going
for, after a Dofn is processed I used the write command everett mentioned followed by a call
to pipeline.run, but received this issue:

2015-11-24 19:52:31,346 ERROR [main] org.apache.crunch.materialize.MaterializableIterable:
Could not materialize: SeqFile(/tmp/crunch-245883570/p1)
java.io.IOException: No files found to materialize at: /tmp/crunch-245883570/p1

Any ideas?
---------------------------------------------------------------------------
Landon Robinson
Big Data/Hadoop Engineer
---------------------------------------------------------------------------

From: Jeff Quinn <jeff@nuna.com<mailto:jeff@nuna.com>>
Reply-To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Date: Tuesday, November 24, 2015 at 1:36 PM
To: LCI <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
Cc: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>

Subject: Re: Crunch Planner Hint to Not Combine Tasks

Hey Landon,

Happy to help!

As Everett said you can even skip the "Ingest that file into a new Pcollection" step, as long
as you write to a SequenceFile target, this will be implicit.

As for #run vs #done, what we do is call #run in between each segment of DoFns and then #done
once finally at the end. #run and #done do basically the same thing (#done calls #run), except
#done cleans up the crunch temporary directories after calling #run.




On Tue, Nov 24, 2015 at 10:00 AM, Robinson, Landon - Landon <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
wrote:
Apologies, pipeline.execute should be pipeline.done
---------------------------------------------------------------------------
[cid:image001.png@01D12CE8.3A06BD10]
Landon Robinson
Big Data/Hadoop Engineer
Lowe’s Companies Inc. | IT Business Intelligence
---------------------------------------------------------------------------

From: <Robinson>, LCI <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
Reply-To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Date: Tuesday, November 24, 2015 at 12:55 PM
To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>,
Jeff Quinn <jeff@nuna.com<mailto:jeff@nuna.com>>

Subject: Re: Crunch Planner Hint to Not Combine Tasks

Jeff,

Thanks for that awesome set of tips. Building on your solution, here’s some information
about ours:
Our program:

  *   Starts the MR pipeline
  *   Does some DoFn ParallelDos
  *   Calls pipeline.execute
We never leveraged pipeline.run(). Going with your suggestion, would my course of action be:

  *   Start the MR pipeline
  *   Do some of the dofn parallelDos
  *   Call pipeline.write
  *   Call pipeline.run
  *   Ingest that file into a new Pcollection
  *   Call another (or more) Dofns
  *   Call pipeline.execute
Does that seem in line with your recommendation, Jeff? Let me know if my logic needs adjusting…
- Landon
---------------------------------------------------------------------------
Landon Robinson
Big Data/Hadoop Engineer
---------------------------------------------------------------------------

From: Jeff Quinn <jeff@nuna.com<mailto:jeff@nuna.com>>
Reply-To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Date: Tuesday, November 24, 2015 at 12:00 PM
To: Apache Crunch Mailing List <user@crunch.apache.org<mailto:user@crunch.apache.org>>
Subject: Re: Crunch Planner Hint to Not Combine Tasks

Hey Landon,

Our team has dealt with this exact problem before. Our solution was to call PCollection#write
after each DoFn or string of DoFns then call Pipeline#run, which will do an iteration of crunch
planning / job submission, then use that same PCollection object for the next round of DoFns,
call #write, and then #call run again, etc.

#parallelDo -> #write -> #run -> repeat

The importance of calling #write is that it the crunch planner will not actually do any work
unless it has seen that there is at least one materialization of the data. This technique
allows you to guarantee your DoFns are segmented into different MR jobs, as DoFns cannot be
combined if they are already completed.

Hope this helps,

Jeff

On Tue, Nov 24, 2015 at 6:14 AM, Ron Hashimshony <ron.hashimshony@myheritage.com<mailto:ron.hashimshony@myheritage.com>>
wrote:
Try set mapreduce.input.fileinputformat.split.minsize & mapreduce.input.fileinputformat.split.maxsize
to a lower number from the default (usually 64 MB).
If you know of a specific DoFn in which this is required, better put it there in its configure
function.

On Tue, Nov 24, 2015 at 3:28 PM Robinson, Landon - Landon <landon.t.robinson@lowes.com<mailto:landon.t.robinson@lowes.com>>
wrote:
Hi all,

I have a Crunch job that tries to combine the last four tasks of my program into one M/R job.
That’s normally not a problem, but my data starts small and grows exponentially in the most
major of those DoFn tasks, resulting in spills to disk (local, not HDFS).

I’ve already:

  *   Implemented scaleFactor on the DoFn where the data will emit back more records than
it consumed, which is 40.0f
  *   Set io.sort.mb parameter to cluster setting, which is 1792
  *   Implemented map-side compression with snappy
Data set I’m ingesting is from a previous map-reduce job, which comes out to 19 files of
10mb size (which in Crunch comes to 2 splits).
Help?
---------------------------------------------------------------------------
Landon Robinson
Big Data/Hadoop Engineer
---------------------------------------------------------------------------
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