/
query_object.py
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/
query_object.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=R
import hashlib
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Union
import simplejson as json
from superset import app
from superset.utils import core as utils
from superset.views.utils import get_time_range_endpoints
# TODO: Type Metrics dictionary with TypedDict when it becomes a vanilla python type
# https://github.com/python/mypy/issues/5288
class QueryObject:
"""
The query object's schema matches the interfaces of DB connectors like sqla
and druid. The query objects are constructed on the client.
"""
granularity: str
from_dttm: datetime
to_dttm: datetime
is_timeseries: bool
time_shift: Optional[timedelta]
groupby: List[str]
metrics: List[Union[Dict, str]]
row_limit: int
filter: List[str]
timeseries_limit: int
timeseries_limit_metric: Optional[Dict]
order_desc: bool
extras: Dict
columns: List[str]
orderby: List[List]
def __init__(
self,
granularity: str,
metrics: List[Union[Dict, str]],
groupby: Optional[List[str]] = None,
filters: Optional[List[str]] = None,
time_range: Optional[str] = None,
time_shift: Optional[str] = None,
is_timeseries: bool = False,
timeseries_limit: int = 0,
row_limit: int = app.config["ROW_LIMIT"],
timeseries_limit_metric: Optional[Dict] = None,
order_desc: bool = True,
extras: Optional[Dict] = None,
columns: Optional[List[str]] = None,
orderby: Optional[List[List]] = None,
relative_start: str = app.config["DEFAULT_RELATIVE_START_TIME"],
relative_end: str = app.config["DEFAULT_RELATIVE_END_TIME"],
):
self.granularity = granularity
self.from_dttm, self.to_dttm = utils.get_since_until(
relative_start=relative_start,
relative_end=relative_end,
time_range=time_range,
time_shift=time_shift,
)
self.is_timeseries = is_timeseries
self.time_range = time_range
self.time_shift = utils.parse_human_timedelta(time_shift)
self.groupby = groupby or []
# Temporal solution for backward compatability issue due the new format of
# non-ad-hoc metric which needs to adhere to superset-ui per
# https://git.io/Jvm7P.
self.metrics = [
metric if "expressionType" in metric else metric["label"] # type: ignore
for metric in metrics
]
self.row_limit = row_limit
self.filter = filters or []
self.timeseries_limit = timeseries_limit
self.timeseries_limit_metric = timeseries_limit_metric
self.order_desc = order_desc
self.extras = extras or {}
if app.config["SIP_15_ENABLED"] and "time_range_endpoints" not in self.extras:
self.extras["time_range_endpoints"] = get_time_range_endpoints(form_data={})
self.columns = columns or []
self.orderby = orderby or []
def to_dict(self) -> Dict[str, Any]:
query_object_dict = {
"granularity": self.granularity,
"from_dttm": self.from_dttm,
"to_dttm": self.to_dttm,
"is_timeseries": self.is_timeseries,
"groupby": self.groupby,
"metrics": self.metrics,
"row_limit": self.row_limit,
"filter": self.filter,
"timeseries_limit": self.timeseries_limit,
"timeseries_limit_metric": self.timeseries_limit_metric,
"order_desc": self.order_desc,
"extras": self.extras,
"columns": self.columns,
"orderby": self.orderby,
}
return query_object_dict
def cache_key(self, **extra: Any) -> str:
"""
The cache key is made out of the key/values from to_dict(), plus any
other key/values in `extra`
We remove datetime bounds that are hard values, and replace them with
the use-provided inputs to bounds, which may be time-relative (as in
"5 days ago" or "now").
"""
cache_dict = self.to_dict()
cache_dict.update(extra)
for k in ["from_dttm", "to_dttm"]:
del cache_dict[k]
if self.time_range:
cache_dict["time_range"] = self.time_range
json_data = self.json_dumps(cache_dict, sort_keys=True)
return hashlib.md5(json_data.encode("utf-8")).hexdigest()
def json_dumps(self, obj: Any, sort_keys: bool = False) -> str:
return json.dumps(
obj, default=utils.json_int_dttm_ser, ignore_nan=True, sort_keys=sort_keys
)