本文整理汇总了Python中superset.connectors.druid.models.DruidDatasource.metrics_and_post_aggs方法的典型用法代码示例。如果您正苦于以下问题：Python DruidDatasource.metrics_and_post_aggs方法的具体用法？Python DruidDatasource.metrics_and_post_aggs怎么用？Python DruidDatasource.metrics_and_post_aggs使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类superset.connectors.druid.models.DruidDatasource的用法示例。
在下文中一共展示了DruidDatasource.metrics_and_post_aggs方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_metrics_and_post_aggs_tree
# 需要导入模块: from superset.connectors.druid.models import DruidDatasource [as 别名]
# 或者: from superset.connectors.druid.models.DruidDatasource import metrics_and_post_aggs [as 别名]
def test_metrics_and_post_aggs_tree(self):
metrics = ['A', 'B', 'm1', 'm2']
metrics_dict = {}
for i in range(ord('A'), ord('K') + 1):
emplace(metrics_dict, chr(i), True)
for i in range(1, 10):
emplace(metrics_dict, 'm' + str(i), False)
def depends_on(index, fields):
dependents = fields if isinstance(fields, list) else [fields]
metrics_dict[index].json_obj = {'fieldNames': dependents}
depends_on('A', ['m1', 'D', 'C'])
depends_on('B', ['B', 'C', 'E', 'F', 'm3'])
depends_on('C', ['H', 'I'])
depends_on('D', ['m2', 'm5', 'G', 'C'])
depends_on('E', ['H', 'I', 'J'])
depends_on('F', ['J', 'm5'])
depends_on('G', ['m4', 'm7', 'm6', 'A'])
depends_on('H', ['A', 'm4', 'I'])
depends_on('I', ['H', 'K'])
depends_on('J', 'K')
depends_on('K', ['m8', 'm9'])
all_metrics, postaggs = DruidDatasource.metrics_and_post_aggs(
metrics, metrics_dict)
expected_metrics = set(all_metrics)
self.assertEqual(9, len(all_metrics))
for i in range(1, 10):
expected_metrics.remove('m' + str(i))
self.assertEqual(0, len(expected_metrics))
self.assertEqual(11, len(postaggs))
for i in range(ord('A'), ord('K') + 1):
del postaggs[chr(i)]
self.assertEqual(0, len(postaggs))示例2: test_metrics_and_post_aggs
# 需要导入模块: from superset.connectors.druid.models import DruidDatasource [as 别名]
# 或者: from superset.connectors.druid.models.DruidDatasource import metrics_and_post_aggs [as 别名]
def test_metrics_and_post_aggs(self):
"""
Test generation of metrics and post-aggregations from an initial list
of superset metrics (which may include the results of either). This
primarily tests that specifying a post-aggregator metric will also
require the raw aggregation of the associated druid metric column.
"""
metrics_dict = {
'unused_count': DruidMetric(
metric_name='unused_count',
verbose_name='COUNT(*)',
metric_type='count',
json=json.dumps({'type': 'count', 'name': 'unused_count'}),
),
'some_sum': DruidMetric(
metric_name='some_sum',
verbose_name='SUM(*)',
metric_type='sum',
json=json.dumps({'type': 'sum', 'name': 'sum'}),
),
'a_histogram': DruidMetric(
metric_name='a_histogram',
verbose_name='APPROXIMATE_HISTOGRAM(*)',
metric_type='approxHistogramFold',
json=json.dumps(
{'type': 'approxHistogramFold', 'name': 'a_histogram'},
),
),
'aCustomMetric': DruidMetric(
metric_name='aCustomMetric',
verbose_name='MY_AWESOME_METRIC(*)',
metric_type='aCustomType',
json=json.dumps(
{'type': 'customMetric', 'name': 'aCustomMetric'},
),
),
'quantile_p95': DruidMetric(
metric_name='quantile_p95',
verbose_name='P95(*)',
metric_type='postagg',
json=json.dumps({
'type': 'quantile',
'probability': 0.95,
'name': 'p95',
'fieldName': 'a_histogram',
}),
),
'aCustomPostAgg': DruidMetric(
metric_name='aCustomPostAgg',
verbose_name='CUSTOM_POST_AGG(*)',
metric_type='postagg',
json=json.dumps({
'type': 'customPostAgg',
'name': 'aCustomPostAgg',
'field': {
'type': 'fieldAccess',
'fieldName': 'aCustomMetric',
},
}),
),
}
metrics = ['some_sum']
all_metrics, post_aggs = DruidDatasource.metrics_and_post_aggs(
metrics, metrics_dict)
assert all_metrics == ['some_sum']
assert post_aggs == {}
metrics = ['quantile_p95']
all_metrics, post_aggs = DruidDatasource.metrics_and_post_aggs(
metrics, metrics_dict)
result_postaggs = set(['quantile_p95'])
assert all_metrics == ['a_histogram']
assert set(post_aggs.keys()) == result_postaggs
metrics = ['aCustomPostAgg']
all_metrics, post_aggs = DruidDatasource.metrics_and_post_aggs(
metrics, metrics_dict)
result_postaggs = set(['aCustomPostAgg'])
assert all_metrics == ['aCustomMetric']
assert set(post_aggs.keys()) == result_postaggs