120 lines
4.0 KiB
Python
Executable File
120 lines
4.0 KiB
Python
Executable File
#!/usr/bin/env python
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# Copyright 2013 OpenStack Foundation
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#
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# Licensed under the Apache License, Version 2.0 (the "License"); you may
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# not use this file except in compliance with the License. You may obtain
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# a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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# License for the specific language governing permissions and limitations
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# under the License.
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import argparse
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import base64
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from datetime import datetime
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import json
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import os
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from launchpadlib import launchpad
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import elastic_recheck.elasticRecheck as er
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from elastic_recheck import results as er_results
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STEP = 3600000
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LPCACHEDIR = os.path.expanduser('~/.launchpadlib/cache')
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def get_launchpad_bug(bug):
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lp = launchpad.Launchpad.login_anonymously('grabbing bugs',
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'production',
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LPCACHEDIR)
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lp_bug = lp.bugs[bug]
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bugdata = {'name': lp_bug.title}
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projects = ", ".join(map(lambda x: "(%s - %s)" %
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(x.bug_target_name, x.status),
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lp_bug.bug_tasks))
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bugdata['affects'] = projects
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return bugdata
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def main():
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parser = argparse.ArgumentParser(description='Generate data for graphs.')
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parser.add_argument(dest='queries',
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help='path to query file')
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parser.add_argument('-o', dest='output',
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help='output filename')
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args = parser.parse_args()
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classifier = er.Classifier(args.queries)
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buglist = []
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epoch = datetime.utcfromtimestamp(0)
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ts = datetime.now()
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ts = datetime(ts.year, ts.month, ts.day, ts.hour)
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# ms since epoch
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now = int(((ts - epoch).total_seconds()) * 1000)
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start = now - (14 * 24 * STEP)
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for query in classifier.queries:
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urlq = dict(search=query['query'],
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fields=[],
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offset=0,
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timeframe="604800",
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graphmode="count")
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logstash_query = base64.urlsafe_b64encode(json.dumps(urlq))
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bug_data = get_launchpad_bug(query['bug'])
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bug = dict(number=query['bug'],
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query=query['query'],
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logstash_query=logstash_query,
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bug_data=bug_data,
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fails=0,
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fails24=0,
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data=[])
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buglist.append(bug)
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results = classifier.hits_by_query(query['query'], size=3000)
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facets_for_fail = er_results.FacetSet()
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facets_for_fail.detect_facets(results,
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["build_status", "build_uuid"])
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if "FAILURE" in facets_for_fail:
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bug['fails'] = len(facets_for_fail['FAILURE'])
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facets = er_results.FacetSet()
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facets.detect_facets(results,
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["build_status", "timestamp", "build_uuid"])
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for status in facets.keys():
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data = []
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for ts in range(start, now, STEP):
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if ts in facets[status]:
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fails = len(facets[status][ts])
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data.append([ts, fails])
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# get the last 24 hr count as well, can't wait to have
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# the pandas code and able to do it that way
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if ts > (now - (24 * STEP)):
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bug['fails24'] += fails
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else:
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data.append([ts, 0])
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bug["data"].append(dict(label=status, data=data))
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# the sort order is a little odd, but basically sort by failures in
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# the last 24 hours, then with all failures for ones that we haven't
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# seen in the last 24 hours.
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buglist = sorted(buglist,
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key=lambda bug: -(bug['fails24'] * 100000 + bug['fails']))
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out = open(args.output, 'w')
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out.write(json.dumps(buglist))
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out.close()
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if __name__ == "__main__":
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main()
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