Merge "Add run_time_meta arge to specify values to use in graph"
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@ -137,6 +137,27 @@ will generate a graph like:
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.. image:: graph-dailycount.png
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Run Time Grouped By Run Metadata
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--------------------------------
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This graph generates a box and whiskers plot to show the distribution of run
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times for runs with each plot grouped by values to a provided run_metadata key
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For example, running something like::
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subunit2sql-graph --title "Run times by Job Name" --database-connection mysql://test:test@localhost/subunit2sql -o test.png run_time_meta build_name --filter_list gate-tempest-dsvm-neutron-full-ssh,gate-tempest-dsvm-full-ubuntu-xenial,gate-tempest-dsvm-full-ubuntu-trusty,gate-tempest-dsvm-py35-ubuntu-xenial
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will generate a graph like:
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.. image:: graph-run_time_meta.png
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:width: 115%
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It's also worth noting the --filter_list argument used in the command above.
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In some cases, especially larger data sets, there are too many distinct values
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for a given run_metadata key to make the graph useful. To workaround this you
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specify a list of values that will be used to filter the output so that only
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matches of that key will be in the output.
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subunit2sql-graph plugin interface
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==================================
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@ -28,10 +28,16 @@ matplotlib.style.use('ggplot')
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def set_cli_opts(parser):
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parser.add_argument('metadata_key',
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help="The run_metadata key to group the runs by")
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parser.add_argument('--filter_list', '-f',
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help='A comma seperated list of values to use')
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def generate_series():
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session = api.get_session()
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if CONF.command.filter_list:
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filter_list = CONF.command.filter_list.split(',')
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else:
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filter_list = None
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if CONF.start_date:
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start_date = datetime.datetime.strptime(CONF.start_date, '%Y-%m-%d')
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else:
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@ -43,8 +49,13 @@ def generate_series():
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run_times = api.get_run_times_grouped_by_run_metadata_key(
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CONF.command.metadata_key, start_date=start_date,
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stop_date=stop_date, session=session)
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df = pd.DataFrame(dict(
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[(k, pd.Series(v)) for k, v in run_times.iteritems()]))
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if not filter_list:
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df = pd.DataFrame(dict(
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[(k, pd.Series(v)) for k, v in run_times.iteritems()]))
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else:
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df = pd.DataFrame(dict(
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[(k, pd.Series(v)) for k, v in run_times.iteritems()
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if k in filter_list]))
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if not CONF.title:
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title = "Run aggregate run time grouped by metadata"
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else:
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