#!/usr/bin/env python

# All Rights Reserved.
#
#    Licensed under the Apache License, Version 2.0 (the "License"); you may
#    not use this file except in compliance with the License. You may obtain
#    a copy of the License at
#
#         http://www.apache.org/licenses/LICENSE-2.0
#
#    Unless required by applicable law or agreed to in writing, software
#    distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
#    WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
#    License for the specific language governing permissions and limitations
#    under the License.

import argparse

import elastic_recheck.elasticRecheck as er


def get_options():
    parser = argparse.ArgumentParser(description='Edit hiera yaml.')
    parser.add_argument('--file', '-f', help="Queries file",
                        default="queries.yaml")
    return parser.parse_args()


def collect_metrics(classifier):
    data = {}
    for q in classifier.queries:
        results = classifier.hits_by_query(q['query'], size=3000)
        rate = {}
        for x in results['hits']['hits']:
            uuid = x['_source']['@fields']['build_uuid']
            if type(uuid) == list:
                uuid = uuid[0]
            success = x['_source']['@fields']['build_status']
            if type(success) == list:
                success = success[0]

            # use of sets to ensure we aren't finding more than one
            # fail per build
            if success not in rate:
                rate[success] = set(uuid)
            else:
                rate[success].add(uuid)

        data[q['bug']] = {
            'fails': len(rate["FAILURE"]),
            'hits': rate,
            'query': q['query']
            }

    return data


def print_metrics(data):
    print "Elastic recheck known issues"
    sorted_data = sorted(data.iteritems(),
                         key=lambda x: -len(x[1]['hits']['FAILURE']))
    for d in sorted_data:
        print "Bug: %s => %s" % (d[0], d[1]['query'].rstrip())
        for s in d[1]['hits'].keys():
            print "  %s: %s" % (s, len(d[1]['hits'][s]))
        print


def main():
    opts = get_options()
    classifier = er.Classifier(opts.file)
    data = collect_metrics(classifier)
    print_metrics(data)


if __name__ == "__main__":
    main()