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From John Vines <vi...@apache.org>
Subject RE: Accumulo Map Reduce is not distributed
Date Mon, 05 Nov 2012 14:13:39 GMT
So it sounds like the job was correctly set to 4 mappers and your issue is
in your MapReduce configuration. I would check the jobtracker page and
verify the number of map slots, as well as how they're running, as print
statements are not the most accurate in the framework.

Sent from my phone, pardon the typos and brevity.
On Nov 5, 2012 8:59 AM, "Cornish, Duane C." <Duane.Cornish@jhuapl.edu>
wrote:

> Hi William,****
>
> ** **
>
> Thanks for helping me out and sorry I didn’t get back to you sooner, I was
> away for the weekend.  I am only callying ToolRunner.run once.****
>
> ** **
>
> *public* *static* *void* ExtractFeaturesFromNewImages() *throws*Exception{
> ****
>
>               String[] parameters = *new* String[1];****
>
>               parameters[0] = "foo";****
>
>               *InitializeFeatureExtractor*();****
>
>               ToolRunner.*run*(CachedConfiguration.*getInstance*(), *new*Accumulo_FE_MR_Job(),
parameters);
> ****
>
>        }****
>
> ** **
>
> Another indicator that I’m only calling it once is that before I was
> pre-splitting the table, I was just getting one larger map-reduce job with
> only 1 mapper.  Based on my print statements, the job was running in
> sequence (which I guess makes sense because the table only existed on one
> node in my cluster.  Then after pre-splitting my table, I was getting one
> job that had 4 mappers.  Each was running one after the other.  I hadn’t
> changed any code (other than adding in the splits).  So, I’m only calling
> ToolRunner.run once.  Furthermore, my run function in my job class is
> provided below:****
>
> ** **
>
>        @Override****
>
>        *public* *int* run(String[] arg0) *throws* Exception {        ****
>
>               runOneTable();****
>
>               *return* 0;****
>
>        }****
>
> ** **
>
> Thanks,****
>
> Duane****
>
> *From:* William Slacum [mailto:wilhelm.von.cloud@accumulo.net]
> *Sent:* Friday, November 02, 2012 8:48 PM
> *To:* user@accumulo.apache.org
> *Subject:* Re: Accumulo Map Reduce is not distributed****
>
> ** **
>
> What about the main method that calls ToolRunner.run? If you have 4 jobs
> being created, then you're calling run(String[]) or runOneTable() 4 times.
> ****
>
> On Fri, Nov 2, 2012 at 5:21 PM, Cornish, Duane C. <
> Duane.Cornish@jhuapl.edu> wrote:****
>
> Thanks for the prompt response John!****
>
> When I say that I’m pre-splitting my table, I mean I am using the
> tableOperations().addSplits(table,splits) command.  I have verified that
> this is correctly splitting my table into 4 tablets and it is being
> distributed across my cloud before I start my map reduce job.****
>
>  ****
>
> Now, I only kick off the job once, but it appears that 4 separate jobs run
> (one after the other).  The first one reaches 100% in its map phase (and
> based on my output only handled ¼ of the data), then the next job starts at
> 0% and reaches 100%, and so on.  So I think I’m “only running one mapper
> at a time in an MR job that has 4 mappers total.”.  I have 2 mapper slots
> per node.  My hadoop is set up so that one machine is the namenode and the
> other 3 are datanodes.  This gives me 6 slots total.  (This is not
> congruent to my accumulo where the master is also a slave – giving 4 total
> slaves).  ****
>
>  ****
>
> My map reduce job is not a chain job, so all 4 tablets should be able to
> run at the same time.****
>
>  ****
>
> Here is my job class code below:****
>
>  ****
>
> *import* org.apache.accumulo.core.security.Authorizations;****
>
> *import* org.apache.accumulo.core.client.mapreduce.AccumuloOutputFormat;**
> **
>
> *import* org.apache.accumulo.core.client.mapreduce.AccumuloRowInputFormat;
> ****
>
> *import* org.apache.hadoop.conf.Configured;****
>
> *import* org.apache.hadoop.io.DoubleWritable;****
>
> *import* org.apache.hadoop.io.Text;****
>
> *import* org.apache.hadoop.mapreduce.Job;****
>
> *import* org.apache.hadoop.util.Tool;****
>
> *import* org.apache.log4j.Level;****
>
>  ****
>
>  ****
>
> *public* *class* Accumulo_FE_MR_Job *extends* Configured *implements*Tool{
> ****
>
>        ****
>
>        *private* *void* runOneTable() *throws* Exception {****
>
>         System.*out*.println("Running Map Reduce Feature Extraction Job");
> ****
>
>  ****
>
>         Job job  = *new* Job(getConf(), getClass().getName());****
>
>  ****
>
>         job.setJarByClass(getClass());****
>
>         job.setJobName("MRFE");****
>
>  ****
>
>         job.setInputFormatClass(AccumuloRowInputFormat.*class*);****
>
>         AccumuloRowInputFormat.*setZooKeeperInstance*
> (job.getConfiguration(),****
>
>                 HMaxConstants.*INSTANCE*,****
>
>                 HMaxConstants.*ZOO_SERVERS*);****
>
>  ****
>
>         AccumuloRowInputFormat.*setInputInfo*(job.getConfiguration(),****
>
>                      HMaxConstants.*USER*, ****
>
>                 HMaxConstants.*PASSWORD*.getBytes(), ****
>
>                 HMaxConstants.*FEATLESS_IMG_TABLE*,****
>
>                 *new* Authorizations());****
>
>         ****
>
>         AccumuloRowInputFormat.*setLogLevel*(job.getConfiguration(),
> Level.*FATAL*);****
>
>  ****
>
>         job.setMapperClass(AccumuloFEMapper.*class*);****
>
>         job.setMapOutputKeyClass(Text.*class*);****
>
>         job.setMapOutputValueClass(DoubleWritable.*class*);****
>
>  ****
>
>         job.setNumReduceTasks(4);****
>
>         job.setReducerClass(AccumuloFEReducer.*class*);****
>
>         job.setOutputKeyClass(Text.*class*);****
>
>         job.setOutputValueClass(Text.*class*);****
>
>  ****
>
>         job.setOutputFormatClass(AccumuloOutputFormat.*class*);****
>
>         AccumuloOutputFormat.*setZooKeeperInstance*
> (job.getConfiguration(),****
>
>                      HMaxConstants.*INSTANCE*,****
>
>                      HMaxConstants.*ZOO_SERVERS*);****
>
>         AccumuloOutputFormat.*setOutputInfo*(job.getConfiguration(),****
>
>                      HMaxConstants.*USER*,****
>
>                      HMaxConstants.*PASSWORD*.getBytes(),****
>
>                 *true*,****
>
>                 HMaxConstants.*ALL_IMG_TABLE*);****
>
>  ****
>
>         AccumuloOutputFormat.*setLogLevel*(job.getConfiguration(), Level.*
> FATAL*);****
>
>  ****
>
>         job.waitForCompletion(*true*);****
>
>         *if* (job.isSuccessful()) {****
>
>             System.*err*.println("Job Successful");****
>
>         } *else* {****
>
>             System.*err*.println("Job Unsuccessful");****
>
>         }****
>
>      }****
>
>        ****
>
>        @Override****
>
>        *public* *int* run(String[] arg0) *throws* Exception {****
>
>               runOneTable();****
>
>               *return* 0;****
>
>        }****
>
> }****
>
>  ****
>
> Thanks,****
>
> Duane****
>
>  ****
>
> *From:* John Vines [mailto:vines@apache.org]
> *Sent:* Friday, November 02, 2012 5:04 PM
> *To:* user@accumulo.apache.org
> *Subject:* Re: Accumulo Map Reduce is not distributed****
>
>  ****
>
> This sounds like an issue with how your MR environment is configured
> and/or how you're kicking off your mapreduce.
>
> Accumulo's input formats with automatically set the number of mappers to
> the number of tablets you have, so you should have seen your job go from 1
> mapper to 4. What you describe is you now do 4 MR jobs instead of just one,
> is that correct? Because that doesn't make a lot of sense, unless by
> presplitting your table you meant you now have 4 different support tables.
> Or do you mean that you're only running one mapper at a time in an MR job
> that has 4 mappers total?
>
> I believe it's somewhere in your kickoff that things may be a bit
> misconstrued. Just so I'm clear, how many mapper slots do you have per
> node, is your job a chain MR job, and do you mind sharing your code which
> sets up and kicks off your MR job so I have an idea of what could be
> kicking off 4 jobs.
>
> John****
>
>  ****
>
> On Fri, Nov 2, 2012 at 4:53 PM, Cornish, Duane C. <
> Duane.Cornish@jhuapl.edu> wrote:****
>
> Hello,****
>
>  ****
>
> I apologize if this discuss should be directed to a hadoop map reduce
> forum, however, I have some concern that my problem may be with my use of
> accumulo.  ****
>
>  ****
>
> I have a map reduce job that I want to run over data in a table.  I have
> an index table and a support table which contains a subset of the data in
> the index table.  I would like to map reduce over the support table on my
> small 4 node cluster.  ****
>
>  ****
>
> I have written a map reduce job that uses the AccumuloRowInputFormat class
> and sets the support table as its input table.****
>
>  ****
>
> In my mapper, I read in a row of the support table, and make a call to a
> static function which pulls information out of the index table.  Next, I
> use the data pulled back from the function call as input to a call to an
> external .so file that is stored on the name node.  I then make another
> static function call to ingest the new data back into the index table.  (I
> know I could emit this in the reduce step, but what I’m ingesting is
> formatted in a somewhat complex java object and I already had a static
> function that ingested it the way I needed it.)  My reduce step is
> completely empty.****
>
>  ****
>
> I output print statements from my mapper to see my progress.  The problem
> that I’m getting is that my entire job appears to run in sequence not in
> parallel.  I am running it from the accumulo master on the 4 node system.
> ****
>
>  ****
>
> I realized that my support table is very small and was not being split
> across any tables.  I am now presplitting this table across all 4 nodes.
> Now, when I run the map reduce job it appears that 4 separate map reduce
> jobs run one after each other.  The first map reduce job runs, gets to
> 100%, then the next map reduce job runs, etc.  The job is only called once,
> why are there 4 jobs running?  Why won’t these jobs run in parallel?****
>
>  ****
>
> Is there any way to set the number of tasks that can run?  This is
> possible from the hadoop command line, is it possible from the java API?
> Also, could my problem stem from the fact that during my mapper I am making
> static function calls to another class in my java project, accessing my
> accumulo index table, or making a call to an exteral .so library?  I could
> restructure the job to avoid making static function calls and I could write
> directly to the Accumulo table from my map reduce job if that would fix my
> problem.  I can’t avoid making the external .so library call.  Any help
> would be greatly appreciated.  ****
>
>  ****
>
> Thanks,****
>
> Duane****
>
>  ****
>
> ** **
>

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