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222 lines
7.3 KiB
Python
222 lines
7.3 KiB
Python
"""Defines the templating context for SQL Lab"""
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from datetime import datetime, timedelta
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import inspect
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import json
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import random
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import time
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from typing import Any, List, Optional, Tuple
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import uuid
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from dateutil.relativedelta import relativedelta
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from flask import g, request
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from jinja2.sandbox import SandboxedEnvironment
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from myapp import app
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# 模板渲染的上下午函数
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conf = app.config
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BASE_CONTEXT = {
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"datetime": datetime,
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"random": random,
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"relativedelta": relativedelta,
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"time": time,
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"timedelta": timedelta,
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"uuid": uuid,
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}
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BASE_CONTEXT.update(conf.get("JINJA_CONTEXT_ADDONS", {}))
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def url_param(param: str, default: Optional[str] = None) -> Optional[Any]:
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"""Read a url or post parameter and use it in your SQL Lab query
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When in SQL Lab, it's possible to add arbitrary URL "query string"
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parameters, and use those in your SQL code. For instance you can
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alter your url and add `?foo=bar`, as in
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`{domain}/myapp/sqllab?foo=bar`. Then if your query is something like
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SELECT * FROM foo = '{{ url_param('foo') }}', it will be parsed at
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runtime and replaced by the value in the URL.
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As you create a visualization form this SQL Lab query, you can pass
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parameters in the explore view as well as from the dashboard, and
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it should carry through to your queries.
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:param param: the parameter to lookup
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:param default: the value to return in the absence of the parameter
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"""
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if request.args.get(param):
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return request.args.get(param, default)
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# Supporting POST as well as get
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form_data = request.form.get("form_data")
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if isinstance(form_data, str):
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form_data = json.loads(form_data)
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url_params = form_data.get("url_params") or {}
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return url_params.get(param, default)
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return default
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def current_user_id() -> Optional[int]:
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"""The id of the user who is currently logged in"""
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if hasattr(g, "user") and g.user:
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return g.user.id
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return None
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def current_username() -> Optional[str]:
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"""The username of the user who is currently logged in"""
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if g.user:
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return g.user.username
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return None
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def filter_values(column: str, default: Optional[str] = None) -> List[str]:
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""" Gets a values for a particular filter as a list
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This is useful if:
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- you want to use a filter box to filter a query where the name of filter box
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column doesn't match the one in the select statement
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- you want to have the ability for filter inside the main query for speed
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purposes
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This searches for "filters" and "extra_filters" in ``form_data`` for a match
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Usage example::
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SELECT action, count(*) as times
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FROM logs
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WHERE action in ( {{ "'" + "','".join(filter_values('action_type')) + "'" }} )
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GROUP BY action
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:param column: column/filter name to lookup
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:param default: default value to return if there's no matching columns
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:return: returns a list of filter values
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"""
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form_data = json.loads(request.form.get("form_data", "{}"))
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return_val = []
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for filter_type in ["filters", "extra_filters"]:
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if filter_type not in form_data:
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continue
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for f in form_data[filter_type]:
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if f["col"] == column:
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if isinstance(f["val"], list):
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for v in f["val"]:
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return_val.append(v)
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else:
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return_val.append(f["val"])
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if return_val:
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return return_val
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if default:
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return [default]
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else:
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return []
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class CacheKeyWrapper:
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""" Dummy class that exposes a method used to store additional values used in
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calculation of query object cache keys"""
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def __init__(self, extra_cache_keys: Optional[List[Any]] = None):
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self.extra_cache_keys = extra_cache_keys
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def cache_key_wrapper(self, key: Any) -> Any:
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""" Adds values to a list that is added to the query object used for calculating
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a cache key.
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This is needed if the following applies:
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- Caching is enabled
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- The query is dynamically generated using a jinja template
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- A username or similar is used as a filter in the query
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Example when using a SQL query as a data source ::
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SELECT action, count(*) as times
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FROM logs
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WHERE logged_in_user = '{{ cache_key_wrapper(current_username()) }}'
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GROUP BY action
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This will ensure that the query results that were cached by `user_1` will
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**not** be seen by `user_2`, as the `cache_key` for the query will be
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different. ``cache_key_wrapper`` can be used similarly for regular table data
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sources by adding a `Custom SQL` filter.
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:param key: Any value that should be considered when calculating the cache key
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:return: the original value ``key`` passed to the function
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"""
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if self.extra_cache_keys is not None:
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self.extra_cache_keys.append(key)
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return key
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class BaseTemplateProcessor:
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"""Base class for database-specific jinja context
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There's this bit of magic in ``process_template`` that instantiates only
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the database context for the active database as a ``models.Database``
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object binds it to the context object, so that object methods
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have access to
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that context. This way, {{ hive.latest_partition('mytable') }} just
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knows about the database it is operating in.
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This means that object methods are only available for the active database
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and are given access to the ``models.Database`` object and schema
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name. For globally available methods use ``@classmethod``.
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"""
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engine: Optional[str] = None
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def __init__(
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self,
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database=None,
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query=None,
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table=None,
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extra_cache_keys: Optional[List[Any]] = None,
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**kwargs
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):
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self.database = database
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self.query = query
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self.schema = None
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if query and query.schema:
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self.schema = query.schema
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elif table:
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self.schema = table.schema
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self.context = {
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"url_param": url_param,
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"current_user_id": current_user_id,
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"current_username": current_username,
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"cache_key_wrapper": CacheKeyWrapper(extra_cache_keys).cache_key_wrapper,
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"filter_values": filter_values,
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"form_data": {},
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}
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self.context.update(kwargs)
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self.context.update(BASE_CONTEXT)
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if self.engine:
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self.context[self.engine] = self
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self.env = SandboxedEnvironment()
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def process_template(self, sql: str, **kwargs) -> str:
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"""Processes a sql template
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>>> sql = "SELECT '{{ datetime(2017, 1, 1).isoformat() }}'"
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>>> process_template(sql)
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"SELECT '2017-01-01T00:00:00'"
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"""
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template = self.env.from_string(sql)
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kwargs.update(self.context)
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return template.render(kwargs)
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template_processors = {}
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keys = tuple(globals().keys())
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for k in keys:
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o = globals()[k]
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if o and inspect.isclass(o) and issubclass(o, BaseTemplateProcessor):
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template_processors[o.engine] = o
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def get_template_processor(database, table=None, query=None, **kwargs):
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TP = template_processors.get(database.backend, BaseTemplateProcessor)
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return TP(database=database, table=table, query=query, **kwargs)
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