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https://issues.apache.org/jira/browse/PIG-1483?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Richard Ding updated PIG-1483:
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Attachment: PIG-1483.patch
This is the initial patch with a few caveats:
# Each mapper processes only one job history file. This loader will create as
many map tasks as the number of files to process.
# It uses _org.apache.hadoop.mapred.DefaultJobHistoryParser_ to parse the job
history files. This parser isn't production ready.
> [piggybank] Add HadoopJobHistoryLoader to the piggybank
> -------------------------------------------------------
>
> Key: PIG-1483
> URL: https://issues.apache.org/jira/browse/PIG-1483
> Project: Pig
> Issue Type: New Feature
> Reporter: Richard Ding
> Assignee: Richard Ding
> Fix For: 0.8.0
>
> Attachments: PIG-1483.patch
>
>
> PIG-1333 added many script-related entries to the MR job xml file and thus
> it's now possible to use Pig for querying Hadoop job history/xml files to get
> script-level usage statistics. What we need is a Pig loader that can parse
> these files and generate corresponding data objects.
> The goal of this jira is to create a HadoopJobHistoryLoader in piggybank.
> Here is an example that shows the intended usage:
> *Find all the jobs grouped by script and user:*
> {code}
> a = load '/mapred/history/_logs/history/' using HadoopJobHistoryLoader() as
> (j:map[], m:map[], r:map[]);
> b = foreach a generate (Chararray) j#'PIG_SCRIPT_ID' as id, (Chararray)
> j#'USER' as user, (Chararray) j#'JOBID' as job;
> c = filter b by not (id is null);
> d = group c by (id, user);
> e = foreach d generate flatten(group), c.job;
> dump e;
> {code}
> A couple more examples:
> *Find scripts that use only the default parallelism:*
> {code}
> a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map[],
> m:map[], r:map[]);
> b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME'
> as script_name, (Long) r#'NUMBER_REDUCES' as reduces;
> c = group b by (id, user, script_name) parallel 10;
> d = foreach c generate group.user, group.script_name, MAX(b.reduces) as
> max_reduces;
> e = filter d by max_reduces == 1;
> dump e;
> {code}
> *Find the running time of each script (in seconds):*
> {code}
> a = load '/mapred/history/done' using HadoopJobHistoryLoader() as (j:map[],
> m:map[], r:map[]);
> b = foreach a generate j#'PIG_SCRIPT_ID' as id, j#'USER' as user, j#'JOBNAME'
> as script_name, (Long) j#'SUBMIT_TIME' as start, (Long) j#'FINISH_TIME' as
> end;
> c = group b by (id, user, script_name)
> d = foreach c generate group.user, group.script_name, (MAX(b.end) -
> MIN(b.start)/1000;
> dump d;
> {code}
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