accumulo-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Krishmin Rai <kr...@missionfoc.us>
Subject Re: Accumulo Map Reduce is not distributed
Date Mon, 05 Nov 2012 17:14:38 GMT
Duane,
  I've run into a similar issue before: jobs were always being run locally and not being submitted
to the job tracker. The fix in our case was to make sure that we explicitly added the mapred-site.xml
file to the configuration object before creating the job. Something like:

conf.addResource(new Path(<path_to_mapred-site.xml>));

-Krishmin


On Nov 5, 2012, at 11:54 AM, Cornish, Duane C. wrote:

> Billie,
>  
> Thanks for the advice.  I have had those variables set correctly in accumulo-env.sh.
 I’ve been using this cloud for a couple months with no problems (I was not running map
reduce jobs on it though).  I also just checked and re-exported those environment variables
right before I run my Accumulo MR job.  I tried outputting the environment variables from
within my job class and they resolve correctly. 
>  
> Does it matter that I am using Accumulo version 1.4.1 and hadoop 1.0.3?  I know that
Accumulo 1.4.1 was tested with hadoop 0.20.2. 
>  
> Any further guidance would be greatly appreciated.
>  
> Duane
>  
> From: Billie Rinaldi [mailto:billie@apache.org] 
> Sent: Monday, November 05, 2012 10:04 AM
> To: user@accumulo.apache.org
> Subject: Re: Accumulo Map Reduce is not distributed
>  
> On Mon, Nov 5, 2012 at 6:46 AM, Cornish, Duane C. <Duane.Cornish@jhuapl.edu> wrote:
> Billie,
>  
> I think I just started to come to that same conclusion (I’m relatively new to cloud
computing).  It appears that it is running in local mode.  My console output says “mapred.LocalJobRunner”
and the job never appears on my Hadoop Job page.  How do I fix this problem?  I also found
that the “JobTracker” link on my Accumulo Overview page points to  http://0.0.0.0:50030/
 instead of the actual computer name. 
> 
> First check your accumulo-env.sh in the Accumulo conf directory.  For the lines that
look like the following, change the "/path/to/X" locations to the actual Java, Hadoop, and
Zookeeper directories.
> 
> test -z "$JAVA_HOME"             && export JAVA_HOME=/path/to/java
> test -z "$HADOOP_HOME"           && export HADOOP_HOME=/path/to/hadoop
> test -z "$ZOOKEEPER_HOME"        && export ZOOKEEPER_HOME=/path/to/zookeeper
> 
> You may also need to make sure that the command you use to run the MR job has JAVA_HOME,
HADOOP_HOME, ZOOKEEPER_HOME, and ACCUMULO_HOME environment variables, which can be done by
using export commands like the ones above.
> 
> Billie
> 
>  
>  
> Duane
>  
> From: Billie Rinaldi [mailto:billie@apache.org] 
> Sent: Monday, November 05, 2012 9:41 AM
> 
> To: user@accumulo.apache.org
> Subject: Re: Accumulo Map Reduce is not distributed
>  
> On Mon, Nov 5, 2012 at 6:13 AM, John Vines <vines@apache.org> wrote:
> 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.
> 
> 
> Also make sure your MR job isn't running in local mode.  Sometimes that happens if your
job can't find the Hadoop configuration directory.
> 
> Billie
> 
>  
> 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
>  
>  
>  


Mime
View raw message