Fixed finding postaggregations (#4017)
This commit is contained in:
Jeff Niu
Maxime Beauchemin
parent
5bc581fd44
commit
cb7c5aa70c
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@ -786,73 +786,123 @@ class DruidDatasource(Model, BaseDatasource):
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return granularity
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@staticmethod
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def _metrics_and_post_aggs(metrics, metrics_dict):
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all_metrics = []
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post_aggs = {}
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def get_post_agg(mconf):
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"""
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For a metric specified as `postagg` returns the
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kind of post aggregation for pydruid.
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"""
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if mconf.get('type') == 'javascript':
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return JavascriptPostAggregator(
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name=mconf.get('name', ''),
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field_names=mconf.get('fieldNames', []),
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function=mconf.get('function', ''))
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elif mconf.get('type') == 'quantile':
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return Quantile(
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mconf.get('name', ''),
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mconf.get('probability', ''),
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)
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elif mconf.get('type') == 'quantiles':
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return Quantiles(
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mconf.get('name', ''),
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mconf.get('probabilities', ''),
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)
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elif mconf.get('type') == 'fieldAccess':
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return Field(mconf.get('name'))
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elif mconf.get('type') == 'constant':
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return Const(
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mconf.get('value'),
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output_name=mconf.get('name', ''),
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)
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elif mconf.get('type') == 'hyperUniqueCardinality':
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return HyperUniqueCardinality(
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mconf.get('name'),
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)
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elif mconf.get('type') == 'arithmetic':
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return Postaggregator(
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mconf.get('fn', '/'),
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mconf.get('fields', []),
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mconf.get('name', ''))
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else:
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return CustomPostAggregator(
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mconf.get('name', ''),
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mconf)
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def recursive_get_fields(_conf):
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_type = _conf.get('type')
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_field = _conf.get('field')
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_fields = _conf.get('fields')
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@staticmethod
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def find_postaggs_for(postagg_names, metrics_dict):
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"""Return a list of metrics that are post aggregations"""
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postagg_metrics = [
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metrics_dict[name] for name in postagg_names
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if metrics_dict[name].metric_type == 'postagg'
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]
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# Remove post aggregations that were found
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for postagg in postagg_metrics:
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postagg_names.remove(postagg.metric_name)
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return postagg_metrics
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field_names = []
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if _type in ['fieldAccess', 'hyperUniqueCardinality',
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'quantile', 'quantiles']:
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field_names.append(_conf.get('fieldName', ''))
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@staticmethod
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def recursive_get_fields(_conf):
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_type = _conf.get('type')
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_field = _conf.get('field')
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_fields = _conf.get('fields')
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field_names = []
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if _type in ['fieldAccess', 'hyperUniqueCardinality',
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'quantile', 'quantiles']:
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field_names.append(_conf.get('fieldName', ''))
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if _field:
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field_names += DruidDatasource.recursive_get_fields(_field)
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if _fields:
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for _f in _fields:
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field_names += DruidDatasource.recursive_get_fields(_f)
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return list(set(field_names))
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if _field:
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field_names += recursive_get_fields(_field)
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if _fields:
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for _f in _fields:
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field_names += recursive_get_fields(_f)
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return list(set(field_names))
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@staticmethod
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def resolve_postagg(postagg, post_aggs, agg_names, visited_postaggs, metrics_dict):
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mconf = postagg.json_obj
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required_fields = set(
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DruidDatasource.recursive_get_fields(mconf)
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+ mconf.get('fieldNames', []))
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# Check if the fields are already in aggs
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# or is a previous postagg
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required_fields = set([
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field for field in required_fields
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if field not in visited_postaggs and field not in agg_names
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])
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# First try to find postaggs that match
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if len(required_fields) > 0:
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missing_postaggs = DruidDatasource.find_postaggs_for(
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required_fields, metrics_dict)
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for missing_metric in required_fields:
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agg_names.add(missing_metric)
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for missing_postagg in missing_postaggs:
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# Add to visited first to avoid infinite recursion
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# if post aggregations are cyclicly dependent
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visited_postaggs.add(missing_postagg.metric_name)
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for missing_postagg in missing_postaggs:
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DruidDatasource.resolve_postagg(
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missing_postagg, post_aggs, agg_names, visited_postaggs, metrics_dict)
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post_aggs[postagg.metric_name] = DruidDatasource.get_post_agg(postagg.json_obj)
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@staticmethod
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def metrics_and_post_aggs(metrics, metrics_dict):
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# Separate metrics into those that are aggregations
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# and those that are post aggregations
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agg_names = set()
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postagg_names = []
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for metric_name in metrics:
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metric = metrics_dict[metric_name]
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if metric.metric_type != 'postagg':
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all_metrics.append(metric_name)
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if metrics_dict[metric_name].metric_type != 'postagg':
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agg_names.add(metric_name)
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else:
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mconf = metric.json_obj
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all_metrics += recursive_get_fields(mconf)
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all_metrics += mconf.get('fieldNames', [])
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if mconf.get('type') == 'javascript':
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post_aggs[metric_name] = JavascriptPostAggregator(
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name=mconf.get('name', ''),
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field_names=mconf.get('fieldNames', []),
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function=mconf.get('function', ''))
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elif mconf.get('type') == 'quantile':
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post_aggs[metric_name] = Quantile(
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mconf.get('name', ''),
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mconf.get('probability', ''),
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)
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elif mconf.get('type') == 'quantiles':
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post_aggs[metric_name] = Quantiles(
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mconf.get('name', ''),
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mconf.get('probabilities', ''),
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)
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elif mconf.get('type') == 'fieldAccess':
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post_aggs[metric_name] = Field(mconf.get('name'))
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elif mconf.get('type') == 'constant':
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post_aggs[metric_name] = Const(
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mconf.get('value'),
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output_name=mconf.get('name', ''),
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)
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elif mconf.get('type') == 'hyperUniqueCardinality':
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post_aggs[metric_name] = HyperUniqueCardinality(
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mconf.get('name'),
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)
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elif mconf.get('type') == 'arithmetic':
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post_aggs[metric_name] = Postaggregator(
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mconf.get('fn', '/'),
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mconf.get('fields', []),
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mconf.get('name', ''))
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else:
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post_aggs[metric_name] = CustomPostAggregator(
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mconf.get('name', ''),
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mconf)
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return all_metrics, post_aggs
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postagg_names.append(metric_name)
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# Create the post aggregations, maintain order since postaggs
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# may depend on previous ones
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post_aggs = OrderedDict()
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visited_postaggs = set()
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for postagg_name in postagg_names:
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postagg = metrics_dict[postagg_name]
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visited_postaggs.add(postagg_name)
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DruidDatasource.resolve_postagg(
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postagg, post_aggs, agg_names, visited_postaggs, metrics_dict)
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return list(agg_names), post_aggs
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def values_for_column(self,
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column_name,
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@ -940,7 +990,7 @@ class DruidDatasource(Model, BaseDatasource):
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columns_dict = {c.column_name: c for c in self.columns}
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all_metrics, post_aggs = self._metrics_and_post_aggs(
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all_metrics, post_aggs = DruidDatasource.metrics_and_post_aggs(
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metrics,
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metrics_dict)
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@ -2,12 +2,26 @@ import json
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import unittest
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from mock import Mock
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import pydruid.utils.postaggregator as postaggs
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import superset.connectors.druid.models as models
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from superset.connectors.druid.models import (
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DruidColumn, DruidDatasource, DruidMetric,
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)
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def mock_metric(metric_name, is_postagg=False):
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metric = Mock()
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metric.metric_name = metric_name
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metric.metric_type = 'postagg' if is_postagg else 'metric'
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return metric
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def emplace(metrics_dict, metric_name, is_postagg=False):
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metrics_dict[metric_name] = mock_metric(metric_name, is_postagg)
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# Unit tests that can be run without initializing base tests
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class DruidFuncTestCase(unittest.TestCase):
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def test_get_filters_ignores_invalid_filter_objects(self):
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@ -271,3 +285,273 @@ class DruidFuncTestCase(unittest.TestCase):
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called_args = client.groupby.call_args_list[0][1]
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self.assertIn('dimensions', called_args)
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self.assertEqual(['col1', 'col2'], called_args['dimensions'])
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def test_get_post_agg_returns_correct_agg_type(self):
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get_post_agg = DruidDatasource.get_post_agg
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# javascript PostAggregators
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function = 'function(field1, field2) { return field1 + field2; }'
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conf = {
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'type': 'javascript',
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'name': 'postagg_name',
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'fieldNames': ['field1', 'field2'],
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'function': function,
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, models.JavascriptPostAggregator))
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self.assertEqual(postagg.name, 'postagg_name')
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self.assertEqual(postagg.post_aggregator['type'], 'javascript')
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self.assertEqual(postagg.post_aggregator['fieldNames'], ['field1', 'field2'])
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self.assertEqual(postagg.post_aggregator['name'], 'postagg_name')
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self.assertEqual(postagg.post_aggregator['function'], function)
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# Quantile
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conf = {
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'type': 'quantile',
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'name': 'postagg_name',
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'probability': '0.5',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.Quantile))
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self.assertEqual(postagg.name, 'postagg_name')
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self.assertEqual(postagg.post_aggregator['probability'], '0.5')
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# Quantiles
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conf = {
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'type': 'quantiles',
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'name': 'postagg_name',
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'probabilities': '0.4,0.5,0.6',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.Quantiles))
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self.assertEqual(postagg.name, 'postagg_name')
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self.assertEqual(postagg.post_aggregator['probabilities'], '0.4,0.5,0.6')
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# FieldAccess
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conf = {
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'type': 'fieldAccess',
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'name': 'field_name',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.Field))
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self.assertEqual(postagg.name, 'field_name')
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# constant
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conf = {
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'type': 'constant',
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'value': 1234,
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'name': 'postagg_name',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.Const))
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self.assertEqual(postagg.name, 'postagg_name')
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self.assertEqual(postagg.post_aggregator['value'], 1234)
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# hyperUniqueCardinality
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conf = {
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'type': 'hyperUniqueCardinality',
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'name': 'unique_name',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.HyperUniqueCardinality))
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self.assertEqual(postagg.name, 'unique_name')
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# arithmetic
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conf = {
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'type': 'arithmetic',
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'fn': '+',
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'fields': ['field1', 'field2'],
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'name': 'postagg_name',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, postaggs.Postaggregator))
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self.assertEqual(postagg.name, 'postagg_name')
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self.assertEqual(postagg.post_aggregator['fn'], '+')
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self.assertEqual(postagg.post_aggregator['fields'], ['field1', 'field2'])
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# custom post aggregator
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conf = {
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'type': 'custom',
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'name': 'custom_name',
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'stuff': 'more_stuff',
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}
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postagg = get_post_agg(conf)
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self.assertTrue(isinstance(postagg, models.CustomPostAggregator))
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self.assertEqual(postagg.name, 'custom_name')
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self.assertEqual(postagg.post_aggregator['stuff'], 'more_stuff')
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def test_find_postaggs_for_returns_postaggs_and_removes(self):
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find_postaggs_for = DruidDatasource.find_postaggs_for
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postagg_names = set(['pa2', 'pa3', 'pa4', 'm1', 'm2', 'm3', 'm4'])
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metrics = {}
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for i in range(1, 6):
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emplace(metrics, 'pa' + str(i), True)
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emplace(metrics, 'm' + str(i), False)
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postagg_list = find_postaggs_for(postagg_names, metrics)
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self.assertEqual(3, len(postagg_list))
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self.assertEqual(4, len(postagg_names))
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expected_metrics = ['m1', 'm2', 'm3', 'm4']
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expected_postaggs = set(['pa2', 'pa3', 'pa4'])
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for postagg in postagg_list:
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expected_postaggs.remove(postagg.metric_name)
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for metric in expected_metrics:
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postagg_names.remove(metric)
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self.assertEqual(0, len(expected_postaggs))
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self.assertEqual(0, len(postagg_names))
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def test_recursive_get_fields(self):
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conf = {
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'type': 'quantile',
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'fieldName': 'f1',
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'field': {
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'type': 'custom',
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'fields': [{
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'type': 'fieldAccess',
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'fieldName': 'f2',
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}, {
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'type': 'fieldAccess',
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'fieldName': 'f3',
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}, {
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'type': 'quantiles',
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'fieldName': 'f4',
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'field': {
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'type': 'custom',
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},
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}, {
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'type': 'custom',
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'fields': [{
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'type': 'fieldAccess',
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'fieldName': 'f5',
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}, {
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'type': 'fieldAccess',
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'fieldName': 'f2',
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'fields': [{
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'type': 'fieldAccess',
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'fieldName': 'f3',
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}, {
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'type': 'fieldIgnoreMe',
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'fieldName': 'f6',
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}],
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}],
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}],
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},
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}
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fields = DruidDatasource.recursive_get_fields(conf)
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expected = set(['f1', 'f2', 'f3', 'f4', 'f5'])
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self.assertEqual(5, len(fields))
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for field in fields:
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expected.remove(field)
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self.assertEqual(0, len(expected))
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def test_metrics_and_post_aggs_tree(self):
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metrics = ['A', 'B', 'm1', 'm2']
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metrics_dict = {}
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for i in range(ord('A'), ord('K') + 1):
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emplace(metrics_dict, chr(i), True)
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for i in range(1, 10):
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emplace(metrics_dict, 'm' + str(i), False)
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def depends_on(index, fields):
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dependents = fields if isinstance(fields, list) else [fields]
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metrics_dict[index].json_obj = {'fieldNames': dependents}
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depends_on('A', ['m1', 'D', 'C'])
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depends_on('B', ['B', 'C', 'E', 'F', 'm3'])
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depends_on('C', ['H', 'I'])
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depends_on('D', ['m2', 'm5', 'G', 'C'])
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depends_on('E', ['H', 'I', 'J'])
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depends_on('F', ['J', 'm5'])
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depends_on('G', ['m4', 'm7', 'm6', 'A'])
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depends_on('H', ['A', 'm4', 'I'])
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depends_on('I', ['H', 'K'])
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depends_on('J', 'K')
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depends_on('K', ['m8', 'm9'])
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all_metrics, postaggs = DruidDatasource.metrics_and_post_aggs(
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metrics, metrics_dict)
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expected_metrics = set(all_metrics)
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self.assertEqual(9, len(all_metrics))
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for i in range(1, 10):
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expected_metrics.remove('m' + str(i))
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self.assertEqual(0, len(expected_metrics))
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self.assertEqual(11, len(postaggs))
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for i in range(ord('A'), ord('K') + 1):
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del postaggs[chr(i)]
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self.assertEqual(0, len(postaggs))
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def test_metrics_and_post_aggs(self):
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"""
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Test generation of metrics and post-aggregations from an initial list
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of superset metrics (which may include the results of either). This
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primarily tests that specifying a post-aggregator metric will also
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require the raw aggregation of the associated druid metric column.
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"""
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metrics_dict = {
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'unused_count': DruidMetric(
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metric_name='unused_count',
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verbose_name='COUNT(*)',
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metric_type='count',
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json=json.dumps({'type': 'count', 'name': 'unused_count'}),
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),
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'some_sum': DruidMetric(
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metric_name='some_sum',
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verbose_name='SUM(*)',
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metric_type='sum',
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json=json.dumps({'type': 'sum', 'name': 'sum'}),
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),
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'a_histogram': DruidMetric(
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metric_name='a_histogram',
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verbose_name='APPROXIMATE_HISTOGRAM(*)',
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metric_type='approxHistogramFold',
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json=json.dumps(
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{'type': 'approxHistogramFold', 'name': 'a_histogram'},
|
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),
|
||||
),
|
||||
'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
|
||||
|
|
|
|||
|
|
@ -12,7 +12,7 @@ from mock import Mock, patch
|
|||
|
||||
from superset import db, security, sm
|
||||
from superset.connectors.druid.models import (
|
||||
DruidCluster, DruidDatasource, DruidMetric,
|
||||
DruidCluster, DruidDatasource,
|
||||
)
|
||||
from .base_tests import SupersetTestCase
|
||||
|
||||
|
|
@ -328,91 +328,6 @@ class DruidTests(SupersetTestCase):
|
|||
permission=permission, view_menu=view_menu).first()
|
||||
assert pv is not None
|
||||
|
||||
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
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
|
|
|
|||
Loading…
Reference in New Issue