<pre><code> -help : Gets this help message and exits
-analysis_topup : A special initialization mode when (1) pipeline_create_commands are switched off and (2) only newly defined analyses are added to the database
This mode is only useful in the process of putting together a new pipeline.
-job_topup : Another special initialization mode when only jobs are created - no other structural changes to the pipeline are acted upon.
-hive_force_init : If set to 1, forces the (re)creation of the hive database even if a previous version of it is present in the server.</code></pre>
<pre><code> -hive_force_init : If set to 1, forces the (re)creation of the hive database even if a previous version of it is present in the server.</code></pre>
<pre><code> This script is used for offline examination of resources used by a Hive pipeline running on LSF
(the script is [Pp]latform-dependent).
<pre><code> This script obtains resource usage data for your pipeline from the Meadow and stores it in 'worker_resource_usage' table.
Your Meadow class/plugin has to support offline examination of resources in order for this script to work.
Based on the command-line parameters 'start_date' and 'end_date', or on the start time of the first
worker and end time of the last worker (as recorded in pipeline DB), it pulls the relevant data out
of LSF's 'bacct' database, parses it and stores in 'lsf_report' table.
Based on the start time of the first Worker and end time of the last Worker (as recorded in pipeline DB),
it pulls the relevant data out of your Meadow (runs 'bacct' script in case of LSF), parses the report and stores in 'worker_resource_usage' table.
You can join this table to 'worker' table USING(meadow_name,process_id) in the usual MySQL way
to filter by analysis_id, do various stats, etc.
You can optionally ask the script to dump the 'bacct' database in a dump file,
or fill in the 'lsf_report' table from an existing dump file (most time is taken by querying bacct).
Please note the script may additionally pull information about LSF processes that you ran simultaneously
with running the pipeline. It is easy to ignore them by joining into 'worker' table.</code></pre>
You can optionally provide an an external filename or command to get the data from it (don't forget to append a '|' to the end!)
and then the data will be taken from your source and parsed from there.</code></pre>
<h1id="USAGE-EXAMPLES">USAGE EXAMPLES</h1>
<pre><code> # Just run it the usual way: query 'bacct' and load the relevant data into 'lsf_report' table: