mirror of https://github.com/apache/superset.git
Druid support via SQLAlchemy (#4163)
* Use druiddb * Remove auto formatting * Show prequeries * Fix subtle bug with lists * Move arguments to query object * Fix druid run_query
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@ -1022,7 +1022,10 @@ class DruidDatasource(Model, BaseDatasource):
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orderby=None,
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extras=None, # noqa
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columns=None, phase=2, client=None, form_data=None,
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order_desc=True):
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order_desc=True,
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prequeries=None,
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is_prequery=False,
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):
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"""Runs a query against Druid and returns a dataframe.
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"""
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# TODO refactor into using a TBD Query object
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@ -370,6 +370,8 @@ class SqlaTable(Model, BaseDatasource):
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)
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logging.info(sql)
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sql = sqlparse.format(sql, reindent=True)
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if query_obj['is_prequery']:
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query_obj['prequeries'].append(sql)
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return sql
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def get_sqla_table(self):
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@ -405,7 +407,10 @@ class SqlaTable(Model, BaseDatasource):
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extras=None,
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columns=None,
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form_data=None,
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order_desc=True):
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order_desc=True,
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prequeries=None,
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is_prequery=False,
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):
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"""Querying any sqla table from this common interface"""
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template_kwargs = {
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'from_dttm': from_dttm,
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@ -564,37 +569,73 @@ class SqlaTable(Model, BaseDatasource):
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if is_timeseries and \
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timeseries_limit and groupby and not time_groupby_inline:
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# some sql dialects require for order by expressions
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# to also be in the select clause -- others, e.g. vertica,
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# require a unique inner alias
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inner_main_metric_expr = main_metric_expr.label('mme_inner__')
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inner_select_exprs += [inner_main_metric_expr]
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subq = select(inner_select_exprs)
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subq = subq.select_from(tbl)
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inner_time_filter = dttm_col.get_time_filter(
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inner_from_dttm or from_dttm,
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inner_to_dttm or to_dttm,
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)
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subq = subq.where(and_(*(where_clause_and + [inner_time_filter])))
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subq = subq.group_by(*inner_groupby_exprs)
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if self.database.db_engine_spec.inner_joins:
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# some sql dialects require for order by expressions
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# to also be in the select clause -- others, e.g. vertica,
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# require a unique inner alias
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inner_main_metric_expr = main_metric_expr.label('mme_inner__')
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inner_select_exprs += [inner_main_metric_expr]
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subq = select(inner_select_exprs)
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subq = subq.select_from(tbl)
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inner_time_filter = dttm_col.get_time_filter(
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inner_from_dttm or from_dttm,
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inner_to_dttm or to_dttm,
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)
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subq = subq.where(and_(*(where_clause_and + [inner_time_filter])))
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subq = subq.group_by(*inner_groupby_exprs)
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ob = inner_main_metric_expr
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if timeseries_limit_metric:
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timeseries_limit_metric = metrics_dict.get(timeseries_limit_metric)
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ob = timeseries_limit_metric.sqla_col
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direction = desc if order_desc else asc
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subq = subq.order_by(direction(ob))
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subq = subq.limit(timeseries_limit)
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ob = inner_main_metric_expr
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if timeseries_limit_metric:
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timeseries_limit_metric = metrics_dict.get(timeseries_limit_metric)
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ob = timeseries_limit_metric.sqla_col
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direction = desc if order_desc else asc
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subq = subq.order_by(direction(ob))
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subq = subq.limit(timeseries_limit)
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on_clause = []
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for i, gb in enumerate(groupby):
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on_clause.append(
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groupby_exprs[i] == column(gb + '__'))
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on_clause = []
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for i, gb in enumerate(groupby):
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on_clause.append(
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groupby_exprs[i] == column(gb + '__'))
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tbl = tbl.join(subq.alias(), and_(*on_clause))
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tbl = tbl.join(subq.alias(), and_(*on_clause))
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else:
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# run subquery to get top groups
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subquery_obj = {
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'prequeries': prequeries,
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'is_prequery': True,
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'is_timeseries': False,
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'row_limit': timeseries_limit,
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'groupby': groupby,
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'metrics': metrics,
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'granularity': granularity,
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'from_dttm': inner_from_dttm or from_dttm,
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'to_dttm': inner_to_dttm or to_dttm,
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'filter': filter,
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'orderby': orderby,
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'extras': extras,
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'columns': columns,
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'form_data': form_data,
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'order_desc': True,
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}
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result = self.query(subquery_obj)
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dimensions = [c for c in result.df.columns if c not in metrics]
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top_groups = self._get_top_groups(result.df, dimensions)
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qry = qry.where(top_groups)
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return qry.select_from(tbl)
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def _get_top_groups(self, df, dimensions):
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cols = {col.column_name: col for col in self.columns}
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groups = []
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for unused, row in df.iterrows():
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group = []
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for dimension in dimensions:
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col_obj = cols.get(dimension)
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group.append(col_obj.sqla_col == row[dimension])
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groups.append(and_(*group))
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return or_(*groups)
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def query(self, query_obj):
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qry_start_dttm = datetime.now()
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sql = self.get_query_str(query_obj)
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@ -609,6 +650,12 @@ class SqlaTable(Model, BaseDatasource):
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error_message = (
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self.database.db_engine_spec.extract_error_message(e))
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# if this is a main query with prequeries, combine them together
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if not query_obj['is_prequery']:
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query_obj['prequeries'].append(sql)
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sql = ';\n\n'.join(query_obj['prequeries'])
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sql += ';'
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return QueryResult(
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status=status,
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df=df,
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@ -62,6 +62,7 @@ class BaseEngineSpec(object):
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time_groupby_inline = False
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limit_method = LimitMethod.FETCH_MANY
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time_secondary_columns = False
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inner_joins = True
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@classmethod
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def fetch_data(cls, cursor, limit):
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@ -1229,6 +1230,7 @@ class DruidEngineSpec(BaseEngineSpec):
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"""Engine spec for Druid.io"""
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engine = 'druid'
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limit_method = LimitMethod.FETCH_MANY
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inner_joins = False
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engines = {
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@ -991,6 +991,12 @@ class Superset(BaseSupersetView):
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query = viz_obj.datasource.get_query_str(query_obj)
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except Exception as e:
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return json_error_response(e)
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if query_obj['prequeries']:
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query_obj['prequeries'].append(query)
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query = ';\n\n'.join(query_obj['prequeries'])
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query += ';'
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return Response(
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json.dumps({
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'query': query,
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@ -205,6 +205,8 @@ class BaseViz(object):
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'timeseries_limit_metric': timeseries_limit_metric,
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'form_data': form_data,
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'order_desc': order_desc,
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'prequeries': [],
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'is_prequery': False,
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}
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return d
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