Python Target.get_agg_key方法代码示例

# 需要导入模块: from target import Target [as 别名]
# 或者: from target.Target import get_agg_key [as 别名]
def test_agg_key():
    t = Target({
        'variables': {
            'foo': 'bar',
            'target_type': 'rate',
            'region': 'us-east-1'
        }})

    # catchall bucket
    assert t.get_agg_key({'foo': ['']}) == 'foo:__region=us-east-1,target_type=rate'

    # non catchall bucket
    assert t.get_agg_key({'foo': ['ba', ''], 'bar': ['']}) == 'foo:ba__region=us-east-1,target_type=rate'

    struct = {
        'n3': ['bucketmatch1', 'bucketmatch2'],
        'othertag': ['']
    }
    # none of the structs applies
    assert t.get_agg_key(struct) == '__foo=bar,region=us-east-1,target_type=rate'

    struct = {
        'target_type': [''],
        'region': ['us-east', 'us-west', '']
    }
    # one catchall, the other matches
    assert t.get_agg_key(struct) == 'region:us-east,target_type:__foo=bar'

# 需要导入模块: from target import Target [as 别名]
# 或者: from target.Target import get_agg_key [as 别名]
def build_graphs_from_targets(targets, query):
    graphs = {}
    if not targets:
        return (graphs, query)
    group_by = query['group_by']
    sum_by = query['sum_by']
    avg_by = query['avg_by']
    avg_over = query['avg_over']
    # i'm gonna assume you never use second and your datapoints are stored with
    # minutely resolution. later on we can use config options for this (or
    # better: somehow query graphite about it)
    # note, the day/week/month numbers are not technically accurate, but
    # since we're doing movingAvg that's ok
    averaging = {
        'M': 1,
        'h': 60,
        'd': 60 * 24,
        'w': 60 * 24 * 7,
        'mo': 60 * 24 * 30
    }
    if avg_over is not None:
        avg_over_amount = avg_over[0]
        avg_over_unit = avg_over[1]
        if avg_over_unit in averaging.keys():
            multiplier = averaging[avg_over_unit]
            query['target_modifiers'].append(
                Query.graphite_function_applier('movingAverage', avg_over_amount * multiplier))

    # for each group_by bucket, make 1 graph.
    # so for each graph, we have:
    # the "constants": tags in the group_by
    # the "variables": tags not in the group_by, which can have arbitrary
    # values, or different values from a group_by tag that match the same
    # bucket pattern
    # go through all targets and group them into graphs:
    for _target_id, target_data in sorted(targets.items()):
        # FWIW. has an 'id' which timeserieswidget doesn't care about
        target = Target(target_data)
        target['target'] = target['id']

        (graph_key, constants) = target.get_graph_info(group_by)
        if graph_key not in graphs:
            graph = {'from': query['from'], 'until': query['to']}
            graph.update({'constants': constants, 'targets': []})
            graphs[graph_key] = graph
        graphs[graph_key]['targets'].append(target)

    # ok so now we have a graphs dictionary with a graph for every appropriate
    # combination of group_by tags, and each graph contains all targets that
    # should be shown on it.  but the user may have asked to aggregate certain
    # targets together, by summing and/or averaging across different values of
    # (a) certain tag(s). let's process the aggregations now.
    if (sum_by or avg_by):
        for (graph_key, graph_config) in graphs.items():
            graph_config['targets_sum_candidates'] = {}
            graph_config['targets_avg_candidates'] = {}
            graph_config['normal_targets'] = []

            for target in graph_config['targets']:
                sum_id = target.get_agg_key(sum_by)
                if sum_id:
                    if sum_id not in graph_config['targets_sum_candidates']:
                        graphs[graph_key]['targets_sum_candidates'][sum_id] = []
                    graph_config['targets_sum_candidates'][sum_id].append(target)

            for (sum_id, targets) in graph_config['targets_sum_candidates'].items():
                if len(targets) > 1:
                    for t in targets:
                        graph_config['targets'].remove(t)
                    graph_config['targets'].append(
                        graphite_func_aggregate(targets, sum_by, "sumSeries"))

            for target in graph_config['targets']:
                # Now that any summing is done, we look at aggregating by
                # averaging because avg(foo+bar+baz) is more efficient
                # than avg(foo)+avg(bar)+avg(baz)
                # aggregate targets (whether those are sums or regular ones)
                avg_id = target.get_agg_key(avg_by)
                if avg_id:
                    if avg_id not in graph_config['targets_avg_candidates']:
                        graph_config['targets_avg_candidates'][avg_id] = []
                    graph_config['targets_avg_candidates'][avg_id].append(target)

            for (avg_id, targets) in graph_config['targets_avg_candidates'].items():
                if len(targets) > 1:
                    for t in targets:
                        graph_config['targets'].remove(t)
                    graph_config['targets'].append(
                        graphite_func_aggregate(targets, avg_by, "averageSeries"))

    # remove targets/graphs over the limit
    graphs = graphs_limit_targets(graphs, query['limit_targets'])

    # Apply target modifiers (like movingAverage, summarize, ...)
    for (graph_key, graph_config) in graphs.items():
        for target in graph_config['targets']:
            for target_modifier in query['target_modifiers']:
                target_modifier(target, graph_config)

    # if in a graph all targets have a tag with the same value, they are
#.........这里部分代码省略.........