mirror of
https://github.com/apache/superset.git
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c1712e5d10
* Add relative start param for time filters * Fix typo and add types to parse_human_datetime * Add relative start/end to query_object * Fix linting error
132 lines
5.0 KiB
Python
132 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 typing import 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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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: List[str] = None,
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filters: 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.get('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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prequeries: Optional[List[Dict]] = None,
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is_prequery: bool = False,
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columns: List[str] = None,
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orderby: List[List] = None,
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relative_start: str = app.config.get('DEFAULT_RELATIVE_START_TIME', 'today'),
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relative_end: str = app.config.get('DEFAULT_RELATIVE_END_TIME', 'today'),
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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 if groupby is not None else []
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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'] # noqa: T484
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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 if filters is not None else []
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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.prequeries = prequeries if prequeries is not None else []
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self.is_prequery = is_prequery
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self.extras = extras if extras is not None else {}
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self.columns = columns if columns is not None else []
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self.orderby = orderby if orderby is not None else []
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def to_dict(self):
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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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'prequeries': self.prequeries,
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'is_prequery': self.is_prequery,
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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):
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"""
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The cache key is made out of the key/values in `query_obj`, 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, sort_keys=False):
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return json.dumps(
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obj,
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default=utils.json_int_dttm_ser,
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ignore_nan=True,
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sort_keys=sort_keys,
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)
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