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e4ca44e95f
* Typo fix in CONTRIBUTING.md
* Alter references to config.get('FOO') to use preferred config['FOO']
* Set missing configuration constants in superset/config.py
* Misc. CI fixes
* Add type annotation for FEATURE_FLATGS
140 lines
5.0 KiB
Python
140 lines
5.0 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# pylint: disable=R
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import hashlib
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from datetime import datetime, timedelta
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from typing import Any, Dict, List, Optional, Union
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import simplejson as json
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from superset import app
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from superset.utils import core as utils
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# TODO: Type Metrics dictionary with TypedDict when it becomes a vanilla python type
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# https://github.com/python/mypy/issues/5288
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class QueryObject:
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"""
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The query object's schema matches the interfaces of DB connectors like sqla
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and druid. The query objects are constructed on the client.
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"""
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granularity: str
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from_dttm: datetime
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to_dttm: datetime
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is_timeseries: bool
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time_shift: Optional[timedelta]
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groupby: List[str]
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metrics: List[Union[Dict, str]]
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row_limit: int
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filter: List[str]
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timeseries_limit: int
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timeseries_limit_metric: Optional[Dict]
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order_desc: bool
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extras: Dict
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columns: List[str]
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orderby: List[List]
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def __init__(
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self,
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granularity: str,
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metrics: List[Union[Dict, str]],
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groupby: Optional[List[str]] = None,
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filters: Optional[List[str]] = None,
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time_range: Optional[str] = None,
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time_shift: Optional[str] = None,
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is_timeseries: bool = False,
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timeseries_limit: int = 0,
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row_limit: int = app.config["ROW_LIMIT"],
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timeseries_limit_metric: Optional[Dict] = None,
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order_desc: bool = True,
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extras: Optional[Dict] = None,
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columns: Optional[List[str]] = None,
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orderby: Optional[List[List]] = None,
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relative_start: str = app.config["DEFAULT_RELATIVE_START_TIME"],
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relative_end: str = app.config["DEFAULT_RELATIVE_END_TIME"],
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):
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self.granularity = granularity
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self.from_dttm, self.to_dttm = utils.get_since_until(
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relative_start=relative_start,
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relative_end=relative_end,
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time_range=time_range,
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time_shift=time_shift,
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)
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self.is_timeseries = is_timeseries
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self.time_range = time_range
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self.time_shift = utils.parse_human_timedelta(time_shift)
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self.groupby = groupby or []
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# Temporal solution for backward compatability issue
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# due the new format of non-ad-hoc metric.
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self.metrics = [
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metric if "expressionType" in metric else metric["label"] # type: ignore
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for metric in metrics
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]
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self.row_limit = row_limit
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self.filter = filters or []
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self.timeseries_limit = timeseries_limit
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self.timeseries_limit_metric = timeseries_limit_metric
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self.order_desc = order_desc
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self.extras = extras or {}
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self.columns = columns or []
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self.orderby = orderby or []
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def to_dict(self) -> Dict[str, Any]:
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query_object_dict = {
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"granularity": self.granularity,
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"from_dttm": self.from_dttm,
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"to_dttm": self.to_dttm,
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"is_timeseries": self.is_timeseries,
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"groupby": self.groupby,
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"metrics": self.metrics,
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"row_limit": self.row_limit,
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"filter": self.filter,
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"timeseries_limit": self.timeseries_limit,
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"timeseries_limit_metric": self.timeseries_limit_metric,
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"order_desc": self.order_desc,
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"extras": self.extras,
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"columns": self.columns,
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"orderby": self.orderby,
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}
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return query_object_dict
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def cache_key(self, **extra) -> str:
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"""
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The cache key is made out of the key/values from to_dict(), plus any
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other key/values in `extra`
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We remove datetime bounds that are hard values, and replace them with
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the use-provided inputs to bounds, which may be time-relative (as in
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"5 days ago" or "now").
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"""
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cache_dict = self.to_dict()
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cache_dict.update(extra)
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for k in ["from_dttm", "to_dttm"]:
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del cache_dict[k]
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if self.time_range:
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cache_dict["time_range"] = self.time_range
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json_data = self.json_dumps(cache_dict, sort_keys=True)
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return hashlib.md5(json_data.encode("utf-8")).hexdigest()
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def json_dumps(self, obj: Any, sort_keys: bool = False) -> str:
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return json.dumps(
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obj, default=utils.json_int_dttm_ser, ignore_nan=True, sort_keys=sort_keys
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)
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