def test_run_query_with_adhoc_metric(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
all_metrics = []
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(all_metrics, post_aggs))
groupby = []
metrics = [{
'expressionType': 'SIMPLE',
'column': {
'type': 'DOUBLE',
'column_name': 'col1'
},
'aggregate': 'SUM',
'label': 'My Adhoc Metric',
}]
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
# no groupby calls client.timeseries
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
filter=[],
row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(1, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.timeseries.call_args_list[0][1]
self.assertNotIn('dimensions', called_args)
self.assertIn('post_aggregations', called_args)
def test_run_query_with_adhoc_metric(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value="from")
to_dttm.isoformat = Mock(return_value="to")
timezone = "timezone"
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name="datasource")
metric1 = DruidMetric(metric_name="metric1")
metric2 = DruidMetric(metric_name="metric2")
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name="col1")
col2 = DruidColumn(column_name="col2")
ds.columns = [col1, col2]
all_metrics = []
post_aggs = ["some_agg"]
ds._metrics_and_post_aggs = Mock(return_value=(all_metrics, post_aggs))
groupby = []
metrics = [{
"expressionType": "SIMPLE",
"column": {
"type": "DOUBLE",
"column_name": "col1"
},
"aggregate": "SUM",
"label": "My Adhoc Metric",
}]
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {"mock": 0}
# no groupby calls client.timeseries
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
filter=[],
row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(1, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.timeseries.call_args_list[0][1]
self.assertNotIn("dimensions", called_args)
self.assertIn("post_aggregations", called_args)
def test_run_query_multiple_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value="from")
to_dttm.isoformat = Mock(return_value="to")
timezone = "timezone"
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name="datasource")
metric1 = DruidMetric(metric_name="metric1")
metric2 = DruidMetric(metric_name="metric2")
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name="col1")
col2 = DruidColumn(column_name="col2")
ds.columns = [col1, col2]
aggs = []
post_aggs = ["some_agg"]
ds._metrics_and_post_aggs = Mock(return_value=(aggs, post_aggs))
groupby = ["col1", "col2"]
metrics = ["metric1"]
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {"mock": 0}
# no groupby calls client.timeseries
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
row_limit=100,
filter=[],
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.groupby.call_args_list[0][1]
self.assertIn("dimensions", called_args)
self.assertEqual(["col1", "col2"], called_args["dimensions"])
def test_run_query_multiple_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
aggs = []
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(aggs, post_aggs))
groupby = ['col1', 'col2']
metrics = ['metric1']
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
# no groupby calls client.timeseries
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
row_limit=100,
filter=[],
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.groupby.call_args_list[0][1]
self.assertIn('dimensions', called_args)
self.assertEqual(['col1', 'col2'], called_args['dimensions'])
def test_run_query_with_adhoc_metric(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
all_metrics = []
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(all_metrics, post_aggs))
groupby = []
metrics = [{
'expressionType': 'SIMPLE',
'column': {'type': 'DOUBLE', 'column_name': 'col1'},
'aggregate': 'SUM',
'label': 'My Adhoc Metric',
}]
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
# no groupby calls client.timeseries
ds.run_query(
groupby, metrics, None, from_dttm,
to_dttm, client=client, filter=[], row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(1, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.timeseries.call_args_list[0][1]
self.assertNotIn('dimensions', called_args)
self.assertIn('post_aggregations', called_args)
def test_run_query_multiple_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
all_metrics = []
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(all_metrics, post_aggs))
groupby = ['col1', 'col2']
metrics = ['metric1']
ds.get_having_filters = Mock(return_value=[])
client.query_builder = Mock()
client.query_builder.last_query = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
# no groupby calls client.timeseries
ds.run_query(
groupby, metrics, None, from_dttm,
to_dttm, client=client, row_limit=100,
filter=[],
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args = client.groupby.call_args_list[0][1]
self.assertIn('dimensions', called_args)
self.assertEqual(['col1', 'col2'], called_args['dimensions'])
def test_run_query_single_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value="from")
to_dttm.isoformat = Mock(return_value="to")
timezone = "timezone"
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name="datasource")
metric1 = DruidMetric(metric_name="metric1")
metric2 = DruidMetric(metric_name="metric2")
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name="col1")
col2 = DruidColumn(column_name="col2")
ds.columns = [col1, col2]
aggs = ["metric1"]
post_aggs = ["some_agg"]
ds._metrics_and_post_aggs = Mock(return_value=(aggs, post_aggs))
groupby = ["col1"]
metrics = ["metric1"]
ds.get_having_filters = Mock(return_value=[])
client.query_builder.last_query.query_dict = {"mock": 0}
# client.topn is called twice
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
timeseries_limit=100,
client=client,
order_desc=True,
filter=[],
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args_pre = client.topn.call_args_list[0][1]
self.assertNotIn("dimensions", called_args_pre)
self.assertIn("dimension", called_args_pre)
called_args = client.topn.call_args_list[1][1]
self.assertIn("dimension", called_args)
self.assertEqual("col1", called_args["dimension"])
# not order_desc
client = Mock()
client.query_builder.last_query.query_dict = {"mock": 0}
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
order_desc=False,
filter=[],
row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn("dimensions", client.groupby.call_args_list[0][1])
self.assertEqual(["col1"],
client.groupby.call_args_list[0][1]["dimensions"])
# order_desc but timeseries and dimension spec
# calls topn with single dimension spec 'dimension'
spec = {"outputName": "hello", "dimension": "matcho"}
spec_json = json.dumps(spec)
col3 = DruidColumn(column_name="col3", dimension_spec_json=spec_json)
ds.columns.append(col3)
groupby = ["col3"]
client = Mock()
client.query_builder.last_query.query_dict = {"mock": 0}
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
order_desc=True,
timeseries_limit=5,
filter=[],
row_limit=100,
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn("dimension", client.topn.call_args_list[0][1])
self.assertIn("dimension", client.topn.call_args_list[1][1])
# uses dimension for pre query and full spec for final query
self.assertEqual("matcho",
client.topn.call_args_list[0][1]["dimension"])
self.assertEqual(spec, client.topn.call_args_list[1][1]["dimension"])
def test_run_query_single_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
aggs = ['metric1']
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(aggs, post_aggs))
groupby = ['col1']
metrics = ['metric1']
ds.get_having_filters = Mock(return_value=[])
client.query_builder.last_query.query_dict = {'mock': 0}
# client.topn is called twice
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
timeseries_limit=100,
client=client,
order_desc=True,
filter=[],
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args_pre = client.topn.call_args_list[0][1]
self.assertNotIn('dimensions', called_args_pre)
self.assertIn('dimension', called_args_pre)
called_args = client.topn.call_args_list[1][1]
self.assertIn('dimension', called_args)
self.assertEqual('col1', called_args['dimension'])
# not order_desc
client = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
order_desc=False,
filter=[],
row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn('dimensions', client.groupby.call_args_list[0][1])
self.assertEqual(['col1'],
client.groupby.call_args_list[0][1]['dimensions'])
# order_desc but timeseries and dimension spec
# calls topn with single dimension spec 'dimension'
spec = {'outputName': 'hello', 'dimension': 'matcho'}
spec_json = json.dumps(spec)
col3 = DruidColumn(column_name='col3', dimension_spec_json=spec_json)
ds.columns.append(col3)
groupby = ['col3']
client = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
ds.run_query(
groupby,
metrics,
None,
from_dttm,
to_dttm,
client=client,
order_desc=True,
timeseries_limit=5,
filter=[],
row_limit=100,
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn('dimension', client.topn.call_args_list[0][1])
self.assertIn('dimension', client.topn.call_args_list[1][1])
# uses dimension for pre query and full spec for final query
self.assertEqual('matcho',
client.topn.call_args_list[0][1]['dimension'])
self.assertEqual(spec, client.topn.call_args_list[1][1]['dimension'])
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
def test_run_query_single_groupby(self):
client = Mock()
from_dttm = Mock()
to_dttm = Mock()
from_dttm.replace = Mock(return_value=from_dttm)
to_dttm.replace = Mock(return_value=to_dttm)
from_dttm.isoformat = Mock(return_value='from')
to_dttm.isoformat = Mock(return_value='to')
timezone = 'timezone'
from_dttm.tzname = Mock(return_value=timezone)
ds = DruidDatasource(datasource_name='datasource')
metric1 = DruidMetric(metric_name='metric1')
metric2 = DruidMetric(metric_name='metric2')
ds.metrics = [metric1, metric2]
col1 = DruidColumn(column_name='col1')
col2 = DruidColumn(column_name='col2')
ds.columns = [col1, col2]
all_metrics = ['metric1']
post_aggs = ['some_agg']
ds._metrics_and_post_aggs = Mock(return_value=(all_metrics, post_aggs))
groupby = ['col1']
metrics = ['metric1']
ds.get_having_filters = Mock(return_value=[])
client.query_builder.last_query.query_dict = {'mock': 0}
# client.topn is called twice
ds.run_query(
groupby, metrics, None, from_dttm, to_dttm, timeseries_limit=100,
client=client, order_desc=True, filter=[],
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
# check that there is no dimensions entry
called_args_pre = client.topn.call_args_list[0][1]
self.assertNotIn('dimensions', called_args_pre)
self.assertIn('dimension', called_args_pre)
called_args = client.topn.call_args_list[1][1]
self.assertIn('dimension', called_args)
self.assertEqual('col1', called_args['dimension'])
# not order_desc
client = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
ds.run_query(
groupby, metrics, None, from_dttm, to_dttm, client=client,
order_desc=False, filter=[], row_limit=100,
)
self.assertEqual(0, len(client.topn.call_args_list))
self.assertEqual(1, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn('dimensions', client.groupby.call_args_list[0][1])
self.assertEqual(['col1'], client.groupby.call_args_list[0][1]['dimensions'])
# order_desc but timeseries and dimension spec
# calls topn with single dimension spec 'dimension'
spec = {'outputName': 'hello', 'dimension': 'matcho'}
spec_json = json.dumps(spec)
col3 = DruidColumn(column_name='col3', dimension_spec_json=spec_json)
ds.columns.append(col3)
groupby = ['col3']
client = Mock()
client.query_builder.last_query.query_dict = {'mock': 0}
ds.run_query(
groupby, metrics, None, from_dttm, to_dttm,
client=client, order_desc=True, timeseries_limit=5,
filter=[], row_limit=100,
)
self.assertEqual(2, len(client.topn.call_args_list))
self.assertEqual(0, len(client.groupby.call_args_list))
self.assertEqual(0, len(client.timeseries.call_args_list))
self.assertIn('dimension', client.topn.call_args_list[0][1])
self.assertIn('dimension', client.topn.call_args_list[1][1])
# uses dimension for pre query and full spec for final query
self.assertEqual('matcho', client.topn.call_args_list[0][1]['dimension'])
self.assertEqual(spec, client.topn.call_args_list[1][1]['dimension'])
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
