superset models 源码
superset models 代码
文件路径：/superset/connectors/sqla/models.py
# 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=too-many-lines, redefined-outer-name
import dataclasses
import json
import logging
import re
from collections import defaultdict
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import (
Any,
Callable,
cast,
Dict,
Hashable,
List,
NamedTuple,
Optional,
Set,
Tuple,
Type,
Union,
)
from uuid import uuid4
import dateutil.parser
import numpy as np
import pandas as pd
import sqlalchemy as sa
import sqlparse
from flask import escape, Markup
from flask_appbuilder import Model
from flask_babel import lazy_gettext as _
from jinja2.exceptions import TemplateError
from sqlalchemy import (
and_,
asc,
Boolean,
Column,
DateTime,
desc,
Enum,
ForeignKey,
inspect,
Integer,
or_,
select,
String,
Table,
Text,
update,
)
from sqlalchemy.engine.base import Connection
from sqlalchemy.ext.hybrid import hybrid_property
from sqlalchemy.orm import backref, Query, relationship, RelationshipProperty, Session
from sqlalchemy.orm.exc import NoResultFound
from sqlalchemy.orm.mapper import Mapper
from sqlalchemy.schema import UniqueConstraint
from sqlalchemy.sql import column, ColumnElement, literal_column, table
from sqlalchemy.sql.elements import ColumnClause, TextClause
from sqlalchemy.sql.expression import Label, Select, TextAsFrom
from sqlalchemy.sql.selectable import Alias, TableClause
from superset import app, db, is_feature_enabled, security_manager
from superset.advanced_data_type.types import AdvancedDataTypeResponse
from superset.columns.models import Column as NewColumn, UNKOWN_TYPE
from superset.common.db_query_status import QueryStatus
from superset.connectors.base.models import BaseColumn, BaseDatasource, BaseMetric
from superset.connectors.sqla.utils import (
find_cached_objects_in_session,
get_columns_description,
get_physical_table_metadata,
get_virtual_table_metadata,
validate_adhoc_subquery,
)
from superset.datasets.models import Dataset as NewDataset
from superset.db_engine_specs.base import BaseEngineSpec, CTE_ALIAS, TimestampExpression
from superset.exceptions import (
AdvancedDataTypeResponseError,
DatasetInvalidPermissionEvaluationException,
QueryClauseValidationException,
QueryObjectValidationError,
SupersetSecurityException,
)
from superset.extensions import feature_flag_manager
from superset.jinja_context import (
BaseTemplateProcessor,
ExtraCache,
get_template_processor,
)
from superset.models.annotations import Annotation
from superset.models.core import Database
from superset.models.helpers import (
AuditMixinNullable,
CertificationMixin,
clone_model,
QueryResult,
)
from superset.sql_parse import (
extract_table_references,
ParsedQuery,
sanitize_clause,
Table as TableName,
)
from superset.superset_typing import (
AdhocColumn,
AdhocMetric,
Column as ColumnTyping,
Metric,
OrderBy,
QueryObjectDict,
)
from superset.tables.models import Table as NewTable
from superset.utils import core as utils
from superset.utils.core import (
GenericDataType,
get_column_name,
get_username,
is_adhoc_column,
MediumText,
QueryObjectFilterClause,
remove_duplicates,
)
config = app.config
metadata = Model.metadata # pylint: disable=no-member
logger = logging.getLogger(__name__)
ADVANCED_DATA_TYPES = config["ADVANCED_DATA_TYPES"]
VIRTUAL_TABLE_ALIAS = "virtual_table"
# a non-exhaustive set of additive metrics
ADDITIVE_METRIC_TYPES = {
"count",
"sum",
"doubleSum",
}
ADDITIVE_METRIC_TYPES_LOWER = {op.lower() for op in ADDITIVE_METRIC_TYPES}
class SqlaQuery(NamedTuple):
applied_template_filters: List[str]
cte: Optional[str]
extra_cache_keys: List[Any]
labels_expected: List[str]
prequeries: List[str]
sqla_query: Select
class QueryStringExtended(NamedTuple):
applied_template_filters: Optional[List[str]]
labels_expected: List[str]
prequeries: List[str]
sql: str
@dataclass
class MetadataResult:
added: List[str] = field(default_factory=list)
removed: List[str] = field(default_factory=list)
modified: List[str] = field(default_factory=list)
class AnnotationDatasource(BaseDatasource):
"""Dummy object so we can query annotations using 'Viz' objects just like
regular datasources.
"""
cache_timeout = 0
changed_on = None
type = "annotation"
column_names = [
"created_on",
"changed_on",
"id",
"start_dttm",
"end_dttm",
"layer_id",
"short_descr",
"long_descr",
"json_metadata",
"created_by_fk",
"changed_by_fk",
]
def query(self, query_obj: QueryObjectDict) -> QueryResult:
error_message = None
qry = db.session.query(Annotation)
qry = qry.filter(Annotation.layer_id == query_obj["filter"][0]["val"])
if query_obj["from_dttm"]:
qry = qry.filter(Annotation.start_dttm >= query_obj["from_dttm"])
if query_obj["to_dttm"]:
qry = qry.filter(Annotation.end_dttm <= query_obj["to_dttm"])
status = QueryStatus.SUCCESS
try:
df = pd.read_sql_query(qry.statement, db.engine)
except Exception as ex: # pylint: disable=broad-except
df = pd.DataFrame()
status = QueryStatus.FAILED
logger.exception(ex)
error_message = utils.error_msg_from_exception(ex)
return QueryResult(
status=status,
df=df,
duration=timedelta(0),
query="",
error_message=error_message,
)
def get_query_str(self, query_obj: QueryObjectDict) -> str:
raise NotImplementedError()
def values_for_column(self, column_name: str, limit: int = 10000) -> List[Any]:
raise NotImplementedError()
class TableColumn(Model, BaseColumn, CertificationMixin):
"""ORM object for table columns, each table can have multiple columns"""
__tablename__ = "table_columns"
__table_args__ = (UniqueConstraint("table_id", "column_name"),)
table_id = Column(Integer, ForeignKey("tables.id"))
table: "SqlaTable" = relationship(
"SqlaTable",
backref=backref("columns", cascade="all, delete-orphan"),
foreign_keys=[table_id],
)
is_dttm = Column(Boolean, default=False)
expression = Column(MediumText())
python_date_format = Column(String(255))
extra = Column(Text)
export_fields = [
"table_id",
"column_name",
"verbose_name",
"is_dttm",
"is_active",
"type",
"advanced_data_type",
"groupby",
"filterable",
"expression",
"description",
"python_date_format",
"extra",
]
update_from_object_fields = [s for s in export_fields if s not in ("table_id",)]
export_parent = "table"
@property
def is_boolean(self) -> bool:
"""
Check if the column has a boolean datatype.
"""
return self.type_generic == GenericDataType.BOOLEAN
@property
def is_numeric(self) -> bool:
"""
Check if the column has a numeric datatype.
"""
return self.type_generic == GenericDataType.NUMERIC
@property
def is_string(self) -> bool:
"""
Check if the column has a string datatype.
"""
return self.type_generic == GenericDataType.STRING
@property
def is_temporal(self) -> bool:
"""
Check if the column has a temporal datatype. If column has been set as
temporal/non-temporal (`is_dttm` is True or False respectively), return that
value. This usually happens during initial metadata fetching or when a column
is manually set as temporal (for this `python_date_format` needs to be set).
"""
if self.is_dttm is not None:
return self.is_dttm
return self.type_generic == GenericDataType.TEMPORAL
@property
def db_engine_spec(self) -> Type[BaseEngineSpec]:
return self.table.db_engine_spec
@property
def db_extra(self) -> Dict[str, Any]:
return self.table.database.get_extra()
@property
def type_generic(self) -> Optional[utils.GenericDataType]:
if self.is_dttm:
return GenericDataType.TEMPORAL
column_spec = self.db_engine_spec.get_column_spec(
self.type, db_extra=self.db_extra
)
return column_spec.generic_type if column_spec else None
def get_sqla_col(self, label: Optional[str] = None) -> Column:
label = label or self.column_name
db_engine_spec = self.db_engine_spec
column_spec = db_engine_spec.get_column_spec(self.type, db_extra=self.db_extra)
type_ = column_spec.sqla_type if column_spec else None
if self.expression:
tp = self.table.get_template_processor()
expression = tp.process_template(self.expression)
col = literal_column(expression, type_=type_)
else:
col = column(self.column_name, type_=type_)
col = self.table.make_sqla_column_compatible(col, label)
return col
@property
def datasource(self) -> RelationshipProperty:
return self.table
def get_time_filter(
self,
start_dttm: DateTime,
end_dttm: DateTime,
) -> ColumnElement:
col = self.get_sqla_col(label="__time")
l = []
if start_dttm:
l.append(col >= self.table.text(self.dttm_sql_literal(start_dttm)))
if end_dttm:
l.append(col < self.table.text(self.dttm_sql_literal(end_dttm)))
return and_(*l)
def get_timestamp_expression(
self,
time_grain: Optional[str],
label: Optional[str] = None,
template_processor: Optional[BaseTemplateProcessor] = None,
) -> Union[TimestampExpression, Label]:
"""
Return a SQLAlchemy Core element representation of self to be used in a query.
:param time_grain: Optional time grain, e.g. P1Y
:param label: alias/label that column is expected to have
:param template_processor: template processor
:return: A TimeExpression object wrapped in a Label if supported by db
"""
label = label or utils.DTTM_ALIAS
pdf = self.python_date_format
is_epoch = pdf in ("epoch_s", "epoch_ms")
column_spec = self.db_engine_spec.get_column_spec(
self.type, db_extra=self.db_extra
)
type_ = column_spec.sqla_type if column_spec else DateTime
if not self.expression and not time_grain and not is_epoch:
sqla_col = column(self.column_name, type_=type_)
return self.table.make_sqla_column_compatible(sqla_col, label)
if self.expression:
expression = self.expression
if template_processor:
expression = template_processor.process_template(self.expression)
col = literal_column(expression, type_=type_)
else:
col = column(self.column_name, type_=type_)
time_expr = self.db_engine_spec.get_timestamp_expr(col, pdf, time_grain)
return self.table.make_sqla_column_compatible(time_expr, label)
def dttm_sql_literal(self, dttm: DateTime) -> str:
"""Convert datetime object to a SQL expression string"""
sql = (
self.db_engine_spec.convert_dttm(self.type, dttm, db_extra=self.db_extra)
if self.type
else None
)
if sql:
return sql
tf = self.python_date_format
# Fallback to the default format (if defined).
if not tf:
tf = self.db_extra.get("python_date_format_by_column_name", {}).get(
self.column_name
)
if tf:
if tf in ["epoch_ms", "epoch_s"]:
seconds_since_epoch = int(dttm.timestamp())
if tf == "epoch_s":
return str(seconds_since_epoch)
return str(seconds_since_epoch * 1000)
return f"'{dttm.strftime(tf)}'"
# TODO(john-bodley): SIP-15 will explicitly require a type conversion.
return f"""'{dttm.strftime("%Y-%m-%d %H:%M:%S.%f")}'"""
@property
def data(self) -> Dict[str, Any]:
attrs = (
"id",
"column_name",
"verbose_name",
"description",
"expression",
"filterable",
"groupby",
"is_dttm",
"type",
"type_generic",
"advanced_data_type",
"python_date_format",
"is_certified",
"certified_by",
"certification_details",
"warning_markdown",
)
attr_dict = {s: getattr(self, s) for s in attrs if hasattr(self, s)}
attr_dict.update(super().data)
return attr_dict
def to_sl_column(
self, known_columns: Optional[Dict[str, NewColumn]] = None
) -> NewColumn:
"""Convert a TableColumn to NewColumn"""
session: Session = inspect(self).session
column = known_columns.get(self.uuid) if known_columns else None
if not column:
column = NewColumn()
extra_json = self.get_extra_dict()
for attr in {
"verbose_name",
"python_date_format",
}:
value = getattr(self, attr)
if value:
extra_json[attr] = value
# column id is primary key, so make sure that we check uuid against
# the id as well
if not column.id:
with session.no_autoflush:
saved_column: NewColumn = (
session.query(NewColumn).filter_by(uuid=self.uuid).one_or_none()
)
if saved_column is not None:
logger.warning(
"sl_column already exists. Using this row for db update %s",
self,
)
# overwrite the existing column instead of creating a new one
column = saved_column
column.uuid = self.uuid
column.created_on = self.created_on
column.changed_on = self.changed_on
column.created_by = self.created_by
column.changed_by = self.changed_by
column.name = self.column_name
column.type = self.type or UNKOWN_TYPE
column.expression = self.expression or self.table.quote_identifier(
self.column_name
)
column.description = self.description
column.is_aggregation = False
column.is_dimensional = self.groupby
column.is_filterable = self.filterable
column.is_increase_desired = True
column.is_managed_externally = self.table.is_managed_externally
column.is_partition = False
column.is_physical = not self.expression
column.is_spatial = False
column.is_temporal = self.is_dttm
column.extra_json = json.dumps(extra_json) if extra_json else None
column.external_url = self.table.external_url
return column
@staticmethod
def after_delete( # pylint: disable=unused-argument
mapper: Mapper,
connection: Connection,
target: "TableColumn",
) -> None:
session = inspect(target).session
column = session.query(NewColumn).filter_by(uuid=target.uuid).one_or_none()
if column:
session.delete(column)
class SqlMetric(Model, BaseMetric, CertificationMixin):
"""ORM object for metrics, each table can have multiple metrics"""
__tablename__ = "sql_metrics"
__table_args__ = (UniqueConstraint("table_id", "metric_name"),)
table_id = Column(Integer, ForeignKey("tables.id"))
table = relationship(
"SqlaTable",
backref=backref("metrics", cascade="all, delete-orphan"),
foreign_keys=[table_id],
)
expression = Column(MediumText(), nullable=False)
extra = Column(Text)
export_fields = [
"metric_name",
"verbose_name",
"metric_type",
"table_id",
"expression",
"description",
"d3format",
"extra",
"warning_text",
]
update_from_object_fields = list(s for s in export_fields if s != "table_id")
export_parent = "table"
def __repr__(self) -> str:
return str(self.metric_name)
def get_sqla_col(self, label: Optional[str] = None) -> Column:
label = label or self.metric_name
tp = self.table.get_template_processor()
sqla_col: ColumnClause = literal_column(tp.process_template(self.expression))
return self.table.make_sqla_column_compatible(sqla_col, label)
@property
def perm(self) -> Optional[str]:
return (
("{parent_name}.[{obj.metric_name}](id:{obj.id})").format(
obj=self, parent_name=self.table.full_name
)
if self.table
else None
)
def get_perm(self) -> Optional[str]:
return self.perm
@property
def data(self) -> Dict[str, Any]:
attrs = (
"is_certified",
"certified_by",
"certification_details",
"warning_markdown",
)
attr_dict = {s: getattr(self, s) for s in attrs}
attr_dict.update(super().data)
return attr_dict
def to_sl_column(
self, known_columns: Optional[Dict[str, NewColumn]] = None
) -> NewColumn:
"""Convert a SqlMetric to NewColumn. Find and update existing or
create a new one."""
session: Session = inspect(self).session
column = known_columns.get(self.uuid) if known_columns else None
if not column:
column = NewColumn()
extra_json = self.get_extra_dict()
for attr in {"verbose_name", "metric_type", "d3format"}:
value = getattr(self, attr)
if value is not None:
extra_json[attr] = value
is_additive = (
self.metric_type and self.metric_type.lower() in ADDITIVE_METRIC_TYPES_LOWER
)
# column id is primary key, so make sure that we check uuid against
# the id as well
if not column.id:
with session.no_autoflush:
saved_column: NewColumn = (
session.query(NewColumn).filter_by(uuid=self.uuid).one_or_none()
)
if saved_column is not None:
logger.warning(
"sl_column already exists. Using this row for db update %s",
self,
)
# overwrite the existing column instead of creating a new one
column = saved_column
column.uuid = self.uuid
column.name = self.metric_name
column.created_on = self.created_on
column.changed_on = self.changed_on
column.created_by = self.created_by
column.changed_by = self.changed_by
column.type = UNKOWN_TYPE
column.expression = self.expression
column.warning_text = self.warning_text
column.description = self.description
column.is_aggregation = True
column.is_additive = is_additive
column.is_filterable = False
column.is_increase_desired = True
column.is_managed_externally = self.table.is_managed_externally
column.is_partition = False
column.is_physical = False
column.is_spatial = False
column.extra_json = json.dumps(extra_json) if extra_json else None
column.external_url = self.table.external_url
return column
@staticmethod
def after_delete( # pylint: disable=unused-argument
mapper: Mapper,
connection: Connection,
target: "SqlMetric",
) -> None:
session = inspect(target).session
column = session.query(NewColumn).filter_by(uuid=target.uuid).one_or_none()
if column:
session.delete(column)
sqlatable_user = Table(
"sqlatable_user",
metadata,
Column("id", Integer, primary_key=True),
Column("user_id", Integer, ForeignKey("ab_user.id")),
Column("table_id", Integer, ForeignKey("tables.id")),
)
def _process_sql_expression(
expression: Optional[str],
database_id: int,
schema: str,
template_processor: Optional[BaseTemplateProcessor] = None,
) -> Optional[str]:
if template_processor and expression:
expression = template_processor.process_template(expression)
if expression:
try:
expression = validate_adhoc_subquery(
expression,
database_id,
schema,
)
expression = sanitize_clause(expression)
except (QueryClauseValidationException, SupersetSecurityException) as ex:
raise QueryObjectValidationError(ex.message) from ex
return expression
class SqlaTable(Model, BaseDatasource): # pylint: disable=too-many-public-methods
"""An ORM object for SqlAlchemy table references"""
type = "table"
query_language = "sql"
is_rls_supported = True
columns: List[TableColumn] = []
metrics: List[SqlMetric] = []
metric_class = SqlMetric
column_class = TableColumn
owner_class = security_manager.user_model
__tablename__ = "tables"
# Note this uniqueness constraint is not part of the physical schema, i.e., it does
# not exist in the migrations, but is required by `import_from_dict` to ensure the
# correct filters are applied in order to identify uniqueness.
#
# The reason it does not physically exist is MySQL, PostgreSQL, etc. have a
# different interpretation of uniqueness when it comes to NULL which is problematic
# given the schema is optional.
__table_args__ = (UniqueConstraint("database_id", "schema", "table_name"),)
table_name = Column(String(250), nullable=False)
main_dttm_col = Column(String(250))
database_id = Column(Integer, ForeignKey("dbs.id"), nullable=False)
fetch_values_predicate = Column(Text)
owners = relationship(owner_class, secondary=sqlatable_user, backref="tables")
database: Database = relationship(
"Database",
backref=backref("tables", cascade="all, delete-orphan"),
foreign_keys=[database_id],
)
schema = Column(String(255))
sql = Column(MediumText())
is_sqllab_view = Column(Boolean, default=False)
template_params = Column(Text)
extra = Column(Text)
baselink = "tablemodelview"
export_fields = [
"table_name",
"main_dttm_col",
"description",
"default_endpoint",
"database_id",
"offset",
"cache_timeout",
"schema",
"sql",
"params",
"template_params",
"filter_select_enabled",
"fetch_values_predicate",
"extra",
]
update_from_object_fields = [f for f in export_fields if f != "database_id"]
export_parent = "database"
export_children = ["metrics", "columns"]
sqla_aggregations = {
"COUNT_DISTINCT": lambda column_name: sa.func.COUNT(sa.distinct(column_name)),
"COUNT": sa.func.COUNT,
"SUM": sa.func.SUM,
"AVG": sa.func.AVG,
"MIN": sa.func.MIN,
"MAX": sa.func.MAX,
}
def __repr__(self) -> str: # pylint: disable=invalid-repr-returned
return self.name
@staticmethod
def _apply_cte(sql: str, cte: Optional[str]) -> str:
"""
Append a CTE before the SELECT statement if defined
:param sql: SELECT statement
:param cte: CTE statement
:return:
"""
if cte:
sql = f"{cte}\n{sql}"
return sql
@property
def db_engine_spec(self) -> Type[BaseEngineSpec]:
return self.database.db_engine_spec
@property
def changed_by_name(self) -> str:
if not self.changed_by:
return ""
return str(self.changed_by)
@property
def changed_by_url(self) -> str:
if not self.changed_by:
return ""
return f"/superset/profile/{self.changed_by.username}"
@property
def connection(self) -> str:
return str(self.database)
@property
def description_markeddown(self) -> str:
return utils.markdown(self.description)
@property
def datasource_name(self) -> str:
return self.table_name
@property
def datasource_type(self) -> str:
return self.type
@property
def database_name(self) -> str:
return self.database.name
@classmethod
def get_datasource_by_name(
cls,
session: Session,
datasource_name: str,
schema: Optional[str],
database_name: str,
) -> Optional["SqlaTable"]:
schema = schema or None
query = (
session.query(cls)
.join(Database)
.filter(cls.table_name == datasource_name)
.filter(Database.database_name == database_name)
)
# Handling schema being '' or None, which is easier to handle
# in python than in the SQLA query in a multi-dialect way
for tbl in query.all():
if schema == (tbl.schema or None):
return tbl
return None
@property
def link(self) -> Markup:
name = escape(self.name)
anchor = f'<a target="_blank" href="{self.explore_url}">{name}</a>'
return Markup(anchor)
def get_schema_perm(self) -> Optional[str]:
"""Returns schema permission if present, database one otherwise."""
return security_manager.get_schema_perm(self.database, self.schema)
def get_perm(self) -> str:
"""
Return this dataset permission name
:return: dataset permission name
:raises DatasetInvalidPermissionEvaluationException: When database is missing
"""
if self.database is None:
raise DatasetInvalidPermissionEvaluationException()
return f"[{self.database}].[{self.table_name}](id:{self.id})"
@hybrid_property
def name(self) -> str: # pylint: disable=invalid-overridden-method
return self.schema + "." + self.table_name if self.schema else self.table_name
@property
def full_name(self) -> str:
return utils.get_datasource_full_name(
self.database, self.table_name, schema=self.schema
)
@property
def dttm_cols(self) -> List[str]:
l = [c.column_name for c in self.columns if c.is_dttm]
if self.main_dttm_col and self.main_dttm_col not in l:
l.append(self.main_dttm_col)
return l
@property
def num_cols(self) -> List[str]:
return [c.column_name for c in self.columns if c.is_numeric]
@property
def any_dttm_col(self) -> Optional[str]:
cols = self.dttm_cols
return cols[0] if cols else None
@property
def html(self) -> str:
df = pd.DataFrame((c.column_name, c.type) for c in self.columns)
df.columns = ["field", "type"]
return df.to_html(
index=False,
classes=("dataframe table table-striped table-bordered " "table-condensed"),
)
@property
def sql_url(self) -> str:
return self.database.sql_url + "?table_name=" + str(self.table_name)
def external_metadata(self) -> List[Dict[str, str]]:
# todo(yongjie): create a pysical table column type in seprated PR
if self.sql:
return get_virtual_table_metadata(dataset=self) # type: ignore
return get_physical_table_metadata(
database=self.database,
table_name=self.table_name,
schema_name=self.schema,
)
@property
def time_column_grains(self) -> Dict[str, Any]:
return {
"time_columns": self.dttm_cols,
"time_grains": [grain.name for grain in self.database.grains()],
}
@property
def select_star(self) -> Optional[str]:
# show_cols and latest_partition set to false to avoid
# the expensive cost of inspecting the DB
return self.database.select_star(
self.table_name, schema=self.schema, show_cols=False, latest_partition=False
)
@property
def health_check_message(self) -> Optional[str]:
check = config["DATASET_HEALTH_CHECK"]
return check(self) if check else None
@property
def data(self) -> Dict[str, Any]:
data_ = super().data
if self.type == "table":
data_["granularity_sqla"] = utils.choicify(self.dttm_cols)
data_["time_grain_sqla"] = [
(g.duration, g.name) for g in self.database.grains() or []
]
data_["main_dttm_col"] = self.main_dttm_col
data_["fetch_values_predicate"] = self.fetch_values_predicate
data_["template_params"] = self.template_params
data_["is_sqllab_view"] = self.is_sqllab_view
data_["health_check_message"] = self.health_check_message
data_["extra"] = self.extra
data_["owners"] = self.owners_data
return data_
@property
def extra_dict(self) -> Dict[str, Any]:
try:
return json.loads(self.extra)
except (TypeError, json.JSONDecodeError):
return {}
def get_fetch_values_predicate(self) -> TextClause:
tp = self.get_template_processor()
try:
return self.text(tp.process_template(self.fetch_values_predicate))
except TemplateError as ex:
raise QueryObjectValidationError(
_(
"Error in jinja expression in fetch values predicate: %(msg)s",
msg=ex.message,
)
) from ex
def values_for_column(self, column_name: str, limit: int = 10000) -> List[Any]:
"""Runs query against sqla to retrieve some
sample values for the given column.
"""
cols = {col.column_name: col for col in self.columns}
target_col = cols[column_name]
tp = self.get_template_processor()
tbl, cte = self.get_from_clause(tp)
qry = select([target_col.get_sqla_col()]).select_from(tbl).distinct()
if limit:
qry = qry.limit(limit)
if self.fetch_values_predicate:
qry = qry.where(self.get_fetch_values_predicate())
engine = self.database.get_sqla_engine()
sql = qry.compile(engine, compile_kwargs={"literal_binds": True})
sql = self._apply_cte(sql, cte)
sql = self.mutate_query_from_config(sql)
df = pd.read_sql_query(sql=sql, con=engine)
return df[column_name].to_list()
def mutate_query_from_config(self, sql: str) -> str:
"""Apply config's SQL_QUERY_MUTATOR
Typically adds comments to the query with context"""
sql_query_mutator = config["SQL_QUERY_MUTATOR"]
if sql_query_mutator:
sql = sql_query_mutator(
sql,
# TODO(john-bodley): Deprecate in 3.0.
user_name=get_username(),
security_manager=security_manager,
database=self.database,
)
return sql
def get_template_processor(self, **kwargs: Any) -> BaseTemplateProcessor:
return get_template_processor(table=self, database=self.database, **kwargs)
def get_query_str_extended(self, query_obj: QueryObjectDict) -> QueryStringExtended:
sqlaq = self.get_sqla_query(**query_obj)
sql = self.database.compile_sqla_query(sqlaq.sqla_query)
sql = self._apply_cte(sql, sqlaq.cte)
sql = sqlparse.format(sql, reindent=True)
sql = self.mutate_query_from_config(sql)
return QueryStringExtended(
applied_template_filters=sqlaq.applied_template_filters,
labels_expected=sqlaq.labels_expected,
prequeries=sqlaq.prequeries,
sql=sql,
)
def get_query_str(self, query_obj: QueryObjectDict) -> str:
query_str_ext = self.get_query_str_extended(query_obj)
all_queries = query_str_ext.prequeries + [query_str_ext.sql]
return ";\n\n".join(all_queries) + ";"
def get_sqla_table(self) -> TableClause:
tbl = table(self.table_name)
if self.schema:
tbl.schema = self.schema
return tbl
def get_from_clause(
self, template_processor: Optional[BaseTemplateProcessor] = None
) -> Tuple[Union[TableClause, Alias], Optional[str]]:
"""
Return where to select the columns and metrics from. Either a physical table
or a virtual table with it's own subquery. If the FROM is referencing a
CTE, the CTE is returned as the second value in the return tuple.
"""
if not self.is_virtual:
return self.get_sqla_table(), None
from_sql = self.get_rendered_sql(template_processor)
parsed_query = ParsedQuery(from_sql)
if not (
parsed_query.is_unknown()
or self.db_engine_spec.is_readonly_query(parsed_query)
):
raise QueryObjectValidationError(
_("Virtual dataset query must be read-only")
)
cte = self.db_engine_spec.get_cte_query(from_sql)
from_clause = (
table(CTE_ALIAS)
if cte
else TextAsFrom(self.text(from_sql), []).alias(VIRTUAL_TABLE_ALIAS)
)
return from_clause, cte
def get_rendered_sql(
self, template_processor: Optional[BaseTemplateProcessor] = None
) -> str:
"""
Render sql with template engine (Jinja).
"""
sql = self.sql
if template_processor:
try:
sql = template_processor.process_template(sql)
except TemplateError as ex:
raise QueryObjectValidationError(
_(
"Error while rendering virtual dataset query: %(msg)s",
msg=ex.message,
)
) from ex
sql = sqlparse.format(sql.strip("\t\r\n; "), strip_comments=True)
if not sql:
raise QueryObjectValidationError(_("Virtual dataset query cannot be empty"))
if len(sqlparse.split(sql)) > 1:
raise QueryObjectValidationError(
_("Virtual dataset query cannot consist of multiple statements")
)
return sql
def adhoc_metric_to_sqla(
self,
metric: AdhocMetric,
columns_by_name: Dict[str, TableColumn],
template_processor: Optional[BaseTemplateProcessor] = None,
) -> ColumnElement:
"""
Turn an adhoc metric into a sqlalchemy column.
:param dict metric: Adhoc metric definition
:param dict columns_by_name: Columns for the current table
:param template_processor: template_processor instance
:returns: The metric defined as a sqlalchemy column
:rtype: sqlalchemy.sql.column
"""
expression_type = metric.get("expressionType")
label = utils.get_metric_name(metric)
if expression_type == utils.AdhocMetricExpressionType.SIMPLE:
metric_column = metric.get("column") or {}
column_name = cast(str, metric_column.get("column_name"))
table_column: Optional[TableColumn] = columns_by_name.get(column_name)
if table_column:
sqla_column = table_column.get_sqla_col()
else:
sqla_column = column(column_name)
sqla_metric = self.sqla_aggregations[metric["aggregate"]](sqla_column)
elif expression_type == utils.AdhocMetricExpressionType.SQL:
expression = _process_sql_expression(
expression=metric["sqlExpression"],
database_id=self.database_id,
schema=self.schema,
template_processor=template_processor,
)
sqla_metric = literal_column(expression)
else:
raise QueryObjectValidationError("Adhoc metric expressionType is invalid")
return self.make_sqla_column_compatible(sqla_metric, label)
def adhoc_column_to_sqla(
self,
col: AdhocColumn,
template_processor: Optional[BaseTemplateProcessor] = None,
) -> ColumnElement:
"""
Turn an adhoc column into a sqlalchemy column.
:param col: Adhoc column definition
:param template_processor: template_processor instance
:returns: The metric defined as a sqlalchemy column
:rtype: sqlalchemy.sql.column
"""
label = utils.get_column_name(col)
expression = _process_sql_expression(
expression=col["sqlExpression"],
database_id=self.database_id,
schema=self.schema,
template_processor=template_processor,
)
col_in_metadata = self.get_column(expression)
if col_in_metadata:
sqla_column = col_in_metadata.get_sqla_col()
is_dttm = col_in_metadata.is_temporal
else:
sqla_column = literal_column(expression)
# probe adhoc column type
tbl, _ = self.get_from_clause(template_processor)
qry = sa.select([sqla_column]).limit(1).select_from(tbl)
sql = self.database.compile_sqla_query(qry)
col_desc = get_columns_description(self.database, sql)
is_dttm = col_desc[0]["is_dttm"]
if (
is_dttm
and col.get("columnType") == "BASE_AXIS"
and (time_grain := col.get("timeGrain"))
):
sqla_column = self.db_engine_spec.get_timestamp_expr(
col=sqla_column,
pdf=None,
time_grain=time_grain,
)
return self.make_sqla_column_compatible(sqla_column, label)
def make_sqla_column_compatible(
self, sqla_col: ColumnElement, label: Optional[str] = None
) -> ColumnElement:
"""Takes a sqlalchemy column object and adds label info if supported by engine.
:param sqla_col: sqlalchemy column instance
:param label: alias/label that column is expected to have
:return: either a sql alchemy column or label instance if supported by engine
"""
label_expected = label or sqla_col.name
db_engine_spec = self.db_engine_spec
# add quotes to tables
if db_engine_spec.allows_alias_in_select:
label = db_engine_spec.make_label_compatible(label_expected)
sqla_col = sqla_col.label(label)
sqla_col.key = label_expected
return sqla_col
def make_orderby_compatible(
self, select_exprs: List[ColumnElement], orderby_exprs: List[ColumnElement]
) -> None:
"""
If needed, make sure aliases for selected columns are not used in
`ORDER BY`.
In some databases (e.g. Presto), `ORDER BY` clause is not able to
automatically pick the source column if a `SELECT` clause alias is named
the same as a source column. In this case, we update the SELECT alias to
another name to avoid the conflict.
"""
if self.db_engine_spec.allows_alias_to_source_column:
return
def is_alias_used_in_orderby(col: ColumnElement) -> bool:
if not isinstance(col, Label):
return False
regexp = re.compile(f"\\(.*\\b{re.escape(col.name)}\\b.*\\)", re.IGNORECASE)
return any(regexp.search(str(x)) for x in orderby_exprs)
# Iterate through selected columns, if column alias appears in orderby
# use another `alias`. The final output columns will still use the
# original names, because they are updated by `labels_expected` after
# querying.
for col in select_exprs:
if is_alias_used_in_orderby(col):
col.name = f"{col.name}__"
def get_sqla_row_level_filters(
self,
template_processor: BaseTemplateProcessor,
) -> List[TextClause]:
"""
Return the appropriate row level security filters for this table and the
current user. A custom username can be passed when the user is not present in the
Flask global namespace.
:param template_processor: The template processor to apply to the filters.
:returns: A list of SQL clauses to be ANDed together.
"""
all_filters: List[TextClause] = []
filter_groups: Dict[Union[int, str], List[TextClause]] = defaultdict(list)
try:
for filter_ in security_manager.get_rls_filters(self):
clause = self.text(
f"({template_processor.process_template(filter_.clause)})"
)
if filter_.group_key:
filter_groups[filter_.group_key].append(clause)
else:
all_filters.append(clause)
if is_feature_enabled("EMBEDDED_SUPERSET"):
for rule in security_manager.get_guest_rls_filters(self):
clause = self.text(
f"({template_processor.process_template(rule['clause'])})"
)
all_filters.append(clause)
grouped_filters = [or_(*clauses) for clauses in filter_groups.values()]
all_filters.extend(grouped_filters)
return all_filters
except TemplateError as ex:
raise QueryObjectValidationError(
_(
"Error in jinja expression in RLS filters: %(msg)s",
msg=ex.message,
)
) from ex
def text(self, clause: str) -> TextClause:
return self.db_engine_spec.get_text_clause(clause)
def get_sqla_query( # pylint: disable=too-many-arguments,too-many-locals,too-many-branches,too-many-statements
self,
apply_fetch_values_predicate: bool = False,
columns: Optional[List[ColumnTyping]] = None,
extras: Optional[Dict[str, Any]] = None,
filter: Optional[ # pylint: disable=redefined-builtin
List[QueryObjectFilterClause]
] = None,
from_dttm: Optional[datetime] = None,
granularity: Optional[str] = None,
groupby: Optional[List[Column]] = None,
inner_from_dttm: Optional[datetime] = None,
inner_to_dttm: Optional[datetime] = None,
is_rowcount: bool = False,
is_timeseries: bool = True,
metrics: Optional[List[Metric]] = None,
orderby: Optional[List[OrderBy]] = None,
order_desc: bool = True,
to_dttm: Optional[datetime] = None,
series_columns: Optional[List[Column]] = None,
series_limit: Optional[int] = None,
series_limit_metric: Optional[Metric] = None,
row_limit: Optional[int] = None,
row_offset: Optional[int] = None,
timeseries_limit: Optional[int] = None,
timeseries_limit_metric: Optional[Metric] = None,
) -> SqlaQuery:
"""Querying any sqla table from this common interface"""
if granularity not in self.dttm_cols and granularity is not None:
granularity = self.main_dttm_col
extras = extras or {}
time_grain = extras.get("time_grain_sqla")
template_kwargs = {
"columns": columns,
"from_dttm": from_dttm.isoformat() if from_dttm else None,
"groupby": groupby,
"metrics": metrics,
"row_limit": row_limit,
"row_offset": row_offset,
"time_column": granularity,
"time_grain": time_grain,
"to_dttm": to_dttm.isoformat() if to_dttm else None,
"table_columns": [col.column_name for col in self.columns],
"filter": filter,
}
columns = columns or []
groupby = groupby or []
series_column_names = utils.get_column_names(series_columns or [])
# deprecated, to be removed in 2.0
if is_timeseries and timeseries_limit:
series_limit = timeseries_limit
series_limit_metric = series_limit_metric or timeseries_limit_metric
template_kwargs.update(self.template_params_dict)
extra_cache_keys: List[Any] = []
template_kwargs["extra_cache_keys"] = extra_cache_keys
removed_filters: List[str] = []
applied_template_filters: List[str] = []
template_kwargs["removed_filters"] = removed_filters
template_kwargs["applied_filters"] = applied_template_filters
template_processor = self.get_template_processor(**template_kwargs)
db_engine_spec = self.db_engine_spec
prequeries: List[str] = []
orderby = orderby or []
need_groupby = bool(metrics is not None or groupby)
metrics = metrics or []
# For backward compatibility
if granularity not in self.dttm_cols and granularity is not None:
granularity = self.main_dttm_col
columns_by_name: Dict[str, TableColumn] = {
col.column_name: col for col in self.columns
}
metrics_by_name: Dict[str, SqlMetric] = {m.metric_name: m for m in self.metrics}
if not granularity and is_timeseries:
raise QueryObjectValidationError(
_(
"Datetime column not provided as part table configuration "
"and is required by this type of chart"
)
)
if not metrics and not columns and not groupby:
raise QueryObjectValidationError(_("Empty query?"))
metrics_exprs: List[ColumnElement] = []
for metric in metrics:
if utils.is_adhoc_metric(metric):
assert isinstance(metric, dict)
metrics_exprs.append(
self.adhoc_metric_to_sqla(
metric=metric,
columns_by_name=columns_by_name,
template_processor=template_processor,
)
)
elif isinstance(metric, str) and metric in metrics_by_name:
metrics_exprs.append(metrics_by_name[metric].get_sqla_col())
else:
raise QueryObjectValidationError(
_("Metric '%(metric)s' does not exist", metric=metric)
)
if metrics_exprs:
main_metric_expr = metrics_exprs[0]
else:
main_metric_expr, label = literal_column("COUNT(*)"), "ccount"
main_metric_expr = self.make_sqla_column_compatible(main_metric_expr, label)
# To ensure correct handling of the ORDER BY labeling we need to reference the
# metric instance if defined in the SELECT clause.
# use the key of the ColumnClause for the expected label
metrics_exprs_by_label = {m.key: m for m in metrics_exprs}
metrics_exprs_by_expr = {str(m): m for m in metrics_exprs}
# Since orderby may use adhoc metrics, too; we need to process them first
orderby_exprs: List[ColumnElement] = []
for orig_col, ascending in orderby:
col: Union[AdhocMetric, ColumnElement] = orig_col
if isinstance(col, dict):
col = cast(AdhocMetric, col)
if col.get("sqlExpression"):
col["sqlExpression"] = _process_sql_expression(
expression=col["sqlExpression"],
database_id=self.database_id,
schema=self.schema,
template_processor=template_processor,
)
if utils.is_adhoc_metric(col):
# add adhoc sort by column to columns_by_name if not exists
col = self.adhoc_metric_to_sqla(col, columns_by_name)
# if the adhoc metric has been defined before
# use the existing instance.
col = metrics_exprs_by_expr.get(str(col), col)
need_groupby = True
elif col in columns_by_name:
col = columns_by_name[col].get_sqla_col()
elif col in metrics_exprs_by_label:
col = metrics_exprs_by_label[col]
need_groupby = True
elif col in metrics_by_name:
col = metrics_by_name[col].get_sqla_col()
need_groupby = True
if isinstance(col, ColumnElement):
orderby_exprs.append(col)
else:
# Could not convert a column reference to valid ColumnElement
raise QueryObjectValidationError(
_("Unknown column used in orderby: %(col)s", col=orig_col)
)
select_exprs: List[Union[Column, Label]] = []
groupby_all_columns = {}
groupby_series_columns = {}
# filter out the pseudo column __timestamp from columns
columns = [col for col in columns if col != utils.DTTM_ALIAS]
dttm_col = columns_by_name.get(granularity) if granularity else None
if need_groupby:
# dedup columns while preserving order
columns = groupby or columns
for selected in columns:
if isinstance(selected, str):
# if groupby field/expr equals granularity field/expr
if selected == granularity:
table_col = columns_by_name[selected]
outer = table_col.get_timestamp_expression(
time_grain=time_grain,
label=selected,
template_processor=template_processor,
)
# if groupby field equals a selected column
elif selected in columns_by_name:
outer = columns_by_name[selected].get_sqla_col()
else:
selected = validate_adhoc_subquery(
selected,
self.database_id,
self.schema,
)
outer = literal_column(f"({selected})")
outer = self.make_sqla_column_compatible(outer, selected)
else:
outer = self.adhoc_column_to_sqla(
col=selected, template_processor=template_processor
)
groupby_all_columns[outer.name] = outer
if (
is_timeseries and not series_column_names
) or outer.name in series_column_names:
groupby_series_columns[outer.name] = outer
select_exprs.append(outer)
elif columns:
for selected in columns:
if is_adhoc_column(selected):
_sql = selected["sqlExpression"]
_column_label = selected["label"]
elif isinstance(selected, str):
_sql = selected
_column_label = selected
selected = validate_adhoc_subquery(
_sql,
self.database_id,
self.schema,
)
select_exprs.append(
columns_by_name[selected].get_sqla_col()
if isinstance(selected, str) and selected in columns_by_name
else self.make_sqla_column_compatible(
literal_column(selected), _column_label
)
)
metrics_exprs = []
if granularity:
if granularity not in columns_by_name or not dttm_col:
raise QueryObjectValidationError(
_(
'Time column "%(col)s" does not exist in dataset',
col=granularity,
)
)
time_filters = []
if is_timeseries:
timestamp = dttm_col.get_timestamp_expression(
time_grain=time_grain, template_processor=template_processor
)
# always put timestamp as the first column
select_exprs.insert(0, timestamp)
groupby_all_columns[timestamp.name] = timestamp
# Use main dttm column to support index with secondary dttm columns.
if (
db_engine_spec.time_secondary_columns
and self.main_dttm_col in self.dttm_cols
and self.main_dttm_col != dttm_col.column_name
):
time_filters.append(
columns_by_name[self.main_dttm_col].get_time_filter(
from_dttm,
to_dttm,
)
)
time_filters.append(dttm_col.get_time_filter(from_dttm, to_dttm))
# Always remove duplicates by column name, as sometimes `metrics_exprs`
# can have the same name as a groupby column (e.g. when users use
# raw columns as custom SQL adhoc metric).
select_exprs = remove_duplicates(
select_exprs + metrics_exprs, key=lambda x: x.name
)
# Expected output columns
labels_expected = [c.key for c in select_exprs]
# Order by columns are "hidden" columns, some databases require them
# always be present in SELECT if an aggregation function is used
if not db_engine_spec.allows_hidden_ordeby_agg:
select_exprs = remove_duplicates(select_exprs + orderby_exprs)
qry = sa.select(select_exprs)
tbl, cte = self.get_from_clause(template_processor)
if groupby_all_columns:
qry = qry.group_by(*groupby_all_columns.values())
where_clause_and = []
having_clause_and = []
for flt in filter: # type: ignore
if not all(flt.get(s) for s in ["col", "op"]):
continue
flt_col = flt["col"]
val = flt.get("val")
op = flt["op"].upper()
col_obj: Optional[TableColumn] = None
sqla_col: Optional[Column] = None
if flt_col == utils.DTTM_ALIAS and is_timeseries and dttm_col:
col_obj = dttm_col
elif is_adhoc_column(flt_col):
sqla_col = self.adhoc_column_to_sqla(flt_col)
else:
col_obj = columns_by_name.get(flt_col)
filter_grain = flt.get("grain")
if is_feature_enabled("ENABLE_TEMPLATE_REMOVE_FILTERS"):
if get_column_name(flt_col) in removed_filters:
# Skip generating SQLA filter when the jinja template handles it.
continue
if col_obj or sqla_col is not None:
if sqla_col is not None:
pass
elif col_obj and filter_grain:
sqla_col = col_obj.get_timestamp_expression(
time_grain=filter_grain, template_processor=template_processor
)
elif col_obj:
sqla_col = col_obj.get_sqla_col()
col_type = col_obj.type if col_obj else None
col_spec = db_engine_spec.get_column_spec(
native_type=col_type,
db_extra=self.database.get_extra(),
)
is_list_target = op in (
utils.FilterOperator.IN.value,
utils.FilterOperator.NOT_IN.value,
)
col_advanced_data_type = col_obj.advanced_data_type if col_obj else ""
if col_spec and not col_advanced_data_type:
target_generic_type = col_spec.generic_type
else:
target_generic_type = GenericDataType.STRING
eq = self.filter_values_handler(
values=val,
target_generic_type=target_generic_type,
target_native_type=col_type,
is_list_target=is_list_target,
db_engine_spec=db_engine_spec,
db_extra=self.database.get_extra(),
)
if (
col_advanced_data_type != ""
and feature_flag_manager.is_feature_enabled(
"ENABLE_ADVANCED_DATA_TYPES"
)
and col_advanced_data_type in ADVANCED_DATA_TYPES
):
values = eq if is_list_target else [eq] # type: ignore
bus_resp: AdvancedDataTypeResponse = ADVANCED_DATA_TYPES[
col_advanced_data_type
].translate_type(
{
"type": col_advanced_data_type,
"values": values,
}
)
if bus_resp["error_message"]:
raise AdvancedDataTypeResponseError(
_(bus_resp["error_message"])
)
where_clause_and.append(
ADVANCED_DATA_TYPES[col_advanced_data_type].translate_filter(
sqla_col, op, bus_resp["values"]
)
)
elif is_list_target:
assert isinstance(eq, (tuple, list))
if len(eq) == 0:
raise QueryObjectValidationError(
_("Filter value list cannot be empty")
)
if len(eq) > len(
eq_without_none := [x for x in eq if x is not None]
):
is_null_cond = sqla_col.is_(None)
if eq:
cond = or_(is_null_cond, sqla_col.in_(eq_without_none))
else:
cond = is_null_cond
else:
cond = sqla_col.in_(eq)
if op == utils.FilterOperator.NOT_IN.value:
cond = ~cond
where_clause_and.append(cond)
elif op == utils.FilterOperator.IS_NULL.value:
where_clause_and.append(sqla_col.is_(None))
elif op == utils.FilterOperator.IS_NOT_NULL.value:
where_clause_and.append(sqla_col.isnot(None))
elif op == utils.FilterOperator.IS_TRUE.value:
where_clause_and.append(sqla_col.is_(True))
elif op == utils.FilterOperator.IS_FALSE.value:
where_clause_and.append(sqla_col.is_(False))
else:
if (
op
not in {
utils.FilterOperator.EQUALS.value,
utils.FilterOperator.NOT_EQUALS.value,
}
and eq is None
):
raise QueryObjectValidationError(
_(
"Must specify a value for filters "
"with comparison operators"
)
)
if op == utils.FilterOperator.EQUALS.value:
where_clause_and.append(sqla_col == eq)
elif op == utils.FilterOperator.NOT_EQUALS.value:
where_clause_and.append(sqla_col != eq)
elif op == utils.FilterOperator.GREATER_THAN.value:
where_clause_and.append(sqla_col > eq)
elif op == utils.FilterOperator.LESS_THAN.value:
where_clause_and.append(sqla_col < eq)
elif op == utils.FilterOperator.GREATER_THAN_OR_EQUALS.value:
where_clause_and.append(sqla_col >= eq)
elif op == utils.FilterOperator.LESS_THAN_OR_EQUALS.value:
where_clause_and.append(sqla_col <= eq)
elif op == utils.FilterOperator.LIKE.value:
where_clause_and.append(sqla_col.like(eq))
elif op == utils.FilterOperator.ILIKE.value:
where_clause_and.append(sqla_col.ilike(eq))
else:
raise QueryObjectValidationError(
_("Invalid filter operation type: %(op)s", op=op)
)
where_clause_and += self.get_sqla_row_level_filters(template_processor)
if extras:
where = extras.get("where")
if where:
try:
where = template_processor.process_template(f"({where})")
except TemplateError as ex:
raise QueryObjectValidationError(
_(
"Error in jinja expression in WHERE clause: %(msg)s",
msg=ex.message,
)
) from ex
where = _process_sql_expression(
expression=where,
database_id=self.database_id,
schema=self.schema,
)
where_clause_and += [self.text(where)]
having = extras.get("having")
if having:
try:
having = template_processor.process_template(f"({having})")
except TemplateError as ex:
raise QueryObjectValidationError(
_(
"Error in jinja expression in HAVING clause: %(msg)s",
msg=ex.message,
)
) from ex
having = _process_sql_expression(
expression=having,
database_id=self.database_id,
schema=self.schema,
)
having_clause_and += [self.text(having)]
if apply_fetch_values_predicate and self.fetch_values_predicate:
qry = qry.where(self.get_fetch_values_predicate())
if granularity:
qry = qry.where(and_(*(time_filters + where_clause_and)))
else:
qry = qry.where(and_(*where_clause_and))
qry = qry.having(and_(*having_clause_and))
self.make_orderby_compatible(select_exprs, orderby_exprs)
for col, (orig_col, ascending) in zip(orderby_exprs, orderby):
if not db_engine_spec.allows_alias_in_orderby and isinstance(col, Label):
# if engine does not allow using SELECT alias in ORDER BY
# revert to the underlying column
col = col.element
if (
db_engine_spec.allows_alias_in_select
and db_engine_spec.allows_hidden_cc_in_orderby
and col.name in [select_col.name for select_col in select_exprs]
):
col = literal_column(col.name)
direction = asc if ascending else desc
qry = qry.order_by(direction(col))
if row_limit:
qry = qry.limit(row_limit)
if row_offset:
qry = qry.offset(row_offset)
if series_limit and groupby_series_columns:
if db_engine_spec.allows_joins and db_engine_spec.allows_subqueries:
# some sql dialects require for order by expressions
# to also be in the select clause -- others, e.g. vertica,
# require a unique inner alias
inner_main_metric_expr = self.make_sqla_column_compatible(
main_metric_expr, "mme_inner__"
)
inner_groupby_exprs = []
inner_select_exprs = []
for gby_name, gby_obj in groupby_series_columns.items():
label = get_column_name(gby_name)
inner = self.make_sqla_column_compatible(gby_obj, gby_name + "__")
inner_groupby_exprs.append(inner)
inner_select_exprs.append(inner)
inner_select_exprs += [inner_main_metric_expr]
subq = select(inner_select_exprs).select_from(tbl)
inner_time_filter = []
if dttm_col and not db_engine_spec.time_groupby_inline:
inner_time_filter = [
dttm_col.get_time_filter(
inner_from_dttm or from_dttm,
inner_to_dttm or to_dttm,
)
]
subq = subq.where(and_(*(where_clause_and + inner_time_filter)))
subq = subq.group_by(*inner_groupby_exprs)
ob = inner_main_metric_expr
if series_limit_metric:
ob = self._get_series_orderby(
series_limit_metric, metrics_by_name, columns_by_name
)
direction = desc if order_desc else asc
subq = subq.order_by(direction(ob))
subq = subq.limit(series_limit)
on_clause = []
for gby_name, gby_obj in groupby_series_columns.items():
# in this case the column name, not the alias, needs to be
# conditionally mutated, as it refers to the column alias in
# the inner query
col_name = db_engine_spec.make_label_compatible(gby_name + "__")
on_clause.append(gby_obj == column(col_name))
tbl = tbl.join(subq.alias(), and_(*on_clause))
else:
if series_limit_metric:
orderby = [
(
self._get_series_orderby(
series_limit_metric,
metrics_by_name,
columns_by_name,
),
not order_desc,
)
]
# run prequery to get top groups
prequery_obj = {
"is_timeseries": False,
"row_limit": series_limit,
"metrics": metrics,
"granularity": granularity,
"groupby": groupby,
"from_dttm": inner_from_dttm or from_dttm,
"to_dttm": inner_to_dttm or to_dttm,
"filter": filter,
"orderby": orderby,
"extras": extras,
"columns": columns,
"order_desc": True,
}
result = self.query(prequery_obj)
prequeries.append(result.query)
dimensions = [
c
for c in result.df.columns
if c not in metrics and c in groupby_series_columns
]
top_groups = self._get_top_groups(
result.df, dimensions, groupby_series_columns, columns_by_name
)
qry = qry.where(top_groups)
qry = qry.select_from(tbl)
if is_rowcount:
if not db_engine_spec.allows_subqueries:
raise QueryObjectValidationError(
_("Database does not support subqueries")
)
label = "rowcount"
col = self.make_sqla_column_compatible(literal_column("COUNT(*)"), label)
qry = select([col]).select_from(qry.alias("rowcount_qry"))
labels_expected = [label]
return SqlaQuery(
applied_template_filters=applied_template_filters,
cte=cte,
extra_cache_keys=extra_cache_keys,
labels_expected=labels_expected,
sqla_query=qry,
prequeries=prequeries,
)
def _get_series_orderby(
self,
series_limit_metric: Metric,
metrics_by_name: Dict[str, SqlMetric],
columns_by_name: Dict[str, TableColumn],
) -> Column:
if utils.is_adhoc_metric(series_limit_metric):
assert isinstance(series_limit_metric, dict)
ob = self.adhoc_metric_to_sqla(series_limit_metric, columns_by_name)
elif (
isinstance(series_limit_metric, str)
and series_limit_metric in metrics_by_name
):
ob = metrics_by_name[series_limit_metric].get_sqla_col()
else:
raise QueryObjectValidationError(
_("Metric '%(metric)s' does not exist", metric=series_limit_metric)
)
return ob
def _normalize_prequery_result_type(
self,
row: pd.Series,
dimension: str,
columns_by_name: Dict[str, TableColumn],
) -> Union[str, int, float, bool, Text]:
"""
Convert a prequery result type to its equivalent Python type.
Some databases like Druid will return timestamps as strings, but do not perform
automatic casting when comparing these strings to a timestamp. For cases like
this we convert the value via the appropriate SQL transform.
:param row: A prequery record
:param dimension: The dimension name
:param columns_by_name: The mapping of columns by name
:return: equivalent primitive python type
"""
value = row[dimension]
if isinstance(value, np.generic):
value = value.item()
column_ = columns_by_name[dimension]
db_extra: Dict[str, Any] = self.database.get_extra()
if column_.type and column_.is_temporal and isinstance(value, str):
sql = self.db_engine_spec.convert_dttm(
column_.type, dateutil.parser.parse(value), db_extra=db_extra
)
if sql:
value = self.text(sql)
return value
def _get_top_groups(
self,
df: pd.DataFrame,
dimensions: List[str],
groupby_exprs: Dict[str, Any],
columns_by_name: Dict[str, TableColumn],
) -> ColumnElement:
groups = []
for _unused, row in df.iterrows():
group = []
for dimension in dimensions:
value = self._normalize_prequery_result_type(
row,
dimension,
columns_by_name,
)
group.append(groupby_exprs[dimension] == value)
groups.append(and_(*group))
return or_(*groups)
def query(self, query_obj: QueryObjectDict) -> QueryResult:
qry_start_dttm = datetime.now()
query_str_ext = self.get_query_str_extended(query_obj)
sql = query_str_ext.sql
status = QueryStatus.SUCCESS
errors = None
error_message = None
def assign_column_label(df: pd.DataFrame) -> Optional[pd.DataFrame]:
"""
Some engines change the case or generate bespoke column names, either by
default or due to lack of support for aliasing. This function ensures that
the column names in the DataFrame correspond to what is expected by
the viz components.
Sometimes a query may also contain only order by columns that are not used
as metrics or groupby columns, but need to present in the SQL `select`,
filtering by `labels_expected` make sure we only return columns users want.
:param df: Original DataFrame returned by the engine
:return: Mutated DataFrame
"""
labels_expected = query_str_ext.labels_expected
if df is not None and not df.empty:
if len(df.columns) < len(labels_expected):
raise QueryObjectValidationError(
_("Db engine did not return all queried columns")
)
if len(df.columns) > len(labels_expected):
df = df.iloc[:, 0 : len(labels_expected)]
df.columns = labels_expected
return df
try:
df = self.database.get_df(sql, self.schema, mutator=assign_column_label)
except Exception as ex: # pylint: disable=broad-except
df = pd.DataFrame()
status = QueryStatus.FAILED
logger.warning(
"Query %s on schema %s failed", sql, self.schema, exc_info=True
)
db_engine_spec = self.db_engine_spec
errors = [
dataclasses.asdict(error) for error in db_engine_spec.extract_errors(ex)
]
error_message = utils.error_msg_from_exception(ex)
return QueryResult(
applied_template_filters=query_str_ext.applied_template_filters,
status=status,
df=df,
duration=datetime.now() - qry_start_dttm,
query=sql,
errors=errors,
error_message=error_message,
)
def get_sqla_table_object(self) -> Table:
return self.database.get_table(self.table_name, schema=self.schema)
def fetch_metadata(self, commit: bool = True) -> MetadataResult:
"""
Fetches the metadata for the table and merges it in
:param commit: should the changes be committed or not.
:return: Tuple with lists of added, removed and modified column names.
"""
new_columns = self.external_metadata()
metrics = [
SqlMetric(**metric)
for metric in self.database.get_metrics(self.table_name, self.schema)
]
any_date_col = None
db_engine_spec = self.db_engine_spec
# If no `self.id`, then this is a new table, no need to fetch columns
# from db. Passing in `self.id` to query will actually automatically
# generate a new id, which can be tricky during certain transactions.
old_columns = (
(
db.session.query(TableColumn)
.filter(TableColumn.table_id == self.id)
.all()
)
if self.id
else self.columns
)
old_columns_by_name: Dict[str, TableColumn] = {
col.column_name: col for col in old_columns
}
results = MetadataResult(
removed=[
col
for col in old_columns_by_name
if col not in {col["name"] for col in new_columns}
]
)
# clear old columns before adding modified columns back
columns = []
for col in new_columns:
old_column = old_columns_by_name.pop(col["name"], None)
if not old_column:
results.added.append(col["name"])
new_column = TableColumn(
column_name=col["name"],
type=col["type"],
table=self,
)
new_column.is_dttm = new_column.is_temporal
db_engine_spec.alter_new_orm_column(new_column)
else:
new_column = old_column
if new_column.type != col["type"]:
results.modified.append(col["name"])
new_column.type = col["type"]
new_column.expression = ""
new_column.groupby = True
new_column.filterable = True
columns.append(new_column)
if not any_date_col and new_column.is_temporal:
any_date_col = col["name"]
# add back calculated (virtual) columns
columns.extend([col for col in old_columns if col.expression])
self.columns = columns
if not self.main_dttm_col:
self.main_dttm_col = any_date_col
self.add_missing_metrics(metrics)
# Apply config supplied mutations.
config["SQLA_TABLE_MUTATOR"](self)
db.session.merge(self)
if commit:
db.session.commit()
return results
@classmethod
def query_datasources_by_name(
cls,
session: Session,
database: Database,
datasource_name: str,
schema: Optional[str] = None,
) -> List["SqlaTable"]:
query = (
session.query(cls)
.filter_by(database_id=database.id)
.filter_by(table_name=datasource_name)
)
if schema:
query = query.filter_by(schema=schema)
return query.all()
@classmethod
def query_datasources_by_permissions( # pylint: disable=invalid-name
cls,
session: Session,
database: Database,
permissions: Set[str],
schema_perms: Set[str],
) -> List["SqlaTable"]:
# TODO(hughhhh): add unit test
return (
session.query(cls)
.filter_by(database_id=database.id)
.filter(
or_(
SqlaTable.perm.in_(permissions),
SqlaTable.schema_perm.in_(schema_perms),
)
)
.all()
)
@classmethod
def get_eager_sqlatable_datasource(
cls, session: Session, datasource_id: int
) -> "SqlaTable":
"""Returns SqlaTable with columns and metrics."""
return (
session.query(cls)
.options(
sa.orm.subqueryload(cls.columns),
sa.orm.subqueryload(cls.metrics),
)
.filter_by(id=datasource_id)
.one()
)
@classmethod
def get_all_datasources(cls, session: Session) -> List["SqlaTable"]:
qry = session.query(cls)
qry = cls.default_query(qry)
return qry.all()
@staticmethod
def default_query(qry: Query) -> Query:
return qry.filter_by(is_sqllab_view=False)
def has_extra_cache_key_calls(self, query_obj: QueryObjectDict) -> bool:
"""
Detects the presence of calls to `ExtraCache` methods in items in query_obj that
can be templated. If any are present, the query must be evaluated to extract
additional keys for the cache key. This method is needed to avoid executing the
template code unnecessarily, as it may contain expensive calls, e.g. to extract
the latest partition of a database.
:param query_obj: query object to analyze
:return: True if there are call(s) to an `ExtraCache` method, False otherwise
"""
templatable_statements: List[str] = []
if self.sql:
templatable_statements.append(self.sql)
if self.fetch_values_predicate:
templatable_statements.append(self.fetch_values_predicate)
extras = query_obj.get("extras", {})
if "where" in extras:
templatable_statements.append(extras["where"])
if "having" in extras:
templatable_statements.append(extras["having"])
if self.is_rls_supported:
templatable_statements += [
f.clause for f in security_manager.get_rls_filters(self)
]
for statement in templatable_statements:
if ExtraCache.regex.search(statement):
return True
return False
def get_extra_cache_keys(self, query_obj: QueryObjectDict) -> List[Hashable]:
"""
The cache key of a SqlaTable needs to consider any keys added by the parent
class and any keys added via `ExtraCache`.
:param query_obj: query object to analyze
:return: The extra cache keys
"""
extra_cache_keys = super().get_extra_cache_keys(query_obj)
if self.has_extra_cache_key_calls(query_obj):
sqla_query = self.get_sqla_query(**query_obj)
extra_cache_keys += sqla_query.extra_cache_keys
return extra_cache_keys
@property
def quote_identifier(self) -> Callable[[str], str]:
return self.database.quote_identifier
@staticmethod
def before_update(
mapper: Mapper, # pylint: disable=unused-argument
connection: Connection, # pylint: disable=unused-argument
target: "SqlaTable",
) -> None:
"""
Check before update if the target table already exists.
Note this listener is called when any fields are being updated and thus it is
necessary to first check whether the reference table is being updated.
Note this logic is temporary, given uniqueness is handled via the dataset DAO,
but is necessary until both the legacy datasource editor and datasource/save
endpoints are deprecated.
:param mapper: The table mapper
:param connection: The DB-API connection
:param target: The mapped instance being persisted
:raises Exception: If the target table is not unique
"""
# pylint: disable=import-outside-toplevel
from superset.datasets.commands.exceptions import get_dataset_exist_error_msg
from superset.datasets.dao import DatasetDAO
# Check whether the relevant attributes have changed.
state = db.inspect(target) # pylint: disable=no-member
for attr in ["database_id", "schema", "table_name"]:
history = state.get_history(attr, True)
if history.has_changes():
break
else:
return None
if not DatasetDAO.validate_uniqueness(
target.database_id, target.schema, target.table_name, target.id
):
raise Exception(get_dataset_exist_error_msg(target.full_name))
def get_sl_columns(self) -> List[NewColumn]:
"""
Convert `SqlaTable.columns` and `SqlaTable.metrics` to the new Column model
"""
session: Session = inspect(self).session
uuids = set()
for column_or_metric in self.columns + self.metrics:
# pre-assign uuid after new columns or metrics are inserted so
# the related `NewColumn` can have a deterministic uuid, too
if not column_or_metric.uuid:
column_or_metric.uuid = uuid4()
else:
uuids.add(column_or_metric.uuid)
# load existing columns from cached session states first
existing_columns = set(
find_cached_objects_in_session(session, NewColumn, uuids=uuids)
)
for column in existing_columns:
uuids.remove(column.uuid)
if uuids:
with session.no_autoflush:
# load those not found from db
existing_columns |= set(
session.query(NewColumn).filter(NewColumn.uuid.in_(uuids))
)
known_columns = {column.uuid: column for column in existing_columns}
return [
item.to_sl_column(known_columns) for item in self.columns + self.metrics
]
@staticmethod
def update_column( # pylint: disable=unused-argument
mapper: Mapper, connection: Connection, target: Union[SqlMetric, TableColumn]
) -> None:
"""
:param mapper: Unused.
:param connection: Unused.
:param target: The metric or column that was updated.
"""
inspector = inspect(target)
session = inspector.session
# Forces an update to the table's changed_on value when a metric or column on the
# table is updated. This busts the cache key for all charts that use the table.
session.execute(update(SqlaTable).where(SqlaTable.id == target.table.id))
# if table itself has changed, shadow-writing will happen in `after_update` anyway
if target.table not in session.dirty:
dataset: NewDataset = (
session.query(NewDataset)
.filter_by(uuid=target.table.uuid)
.one_or_none()
)
# Update shadow dataset and columns
# did we find the dataset?
if not dataset:
# if dataset is not found create a new copy
target.table.write_shadow_dataset()
return
# update changed_on timestamp
session.execute(update(NewDataset).where(NewDataset.id == dataset.id))
try:
with session.no_autoflush:
column = session.query(NewColumn).filter_by(uuid=target.uuid).one()
# update `Column` model as well
session.merge(target.to_sl_column({target.uuid: column}))
except NoResultFound:
logger.warning("No column was found for %s", target)
# see if the column is in cache
column = next(
find_cached_objects_in_session(
session, NewColumn, uuids=[target.uuid]
),
None,
)
if column:
logger.warning("New column was found in cache: %s", column)
else:
# to be safe, use a different uuid and create a new column
uuid = uuid4()
target.uuid = uuid
session.add(target.to_sl_column())
@staticmethod
def after_insert(
mapper: Mapper,
connection: Connection,
sqla_table: "SqlaTable",
) -> None:
"""
Shadow write the dataset to new models.
The ``SqlaTable`` model is currently being migrated to two new models, ``Table``
and ``Dataset``. In the first phase of the migration the new models are populated
whenever ``SqlaTable`` is modified (created, updated, or deleted).
In the second phase of the migration reads will be done from the new models.
Finally, in the third phase of the migration the old models will be removed.
For more context: https://github.com/apache/superset/issues/14909
"""
security_manager.dataset_after_insert(mapper, connection, sqla_table)
sqla_table.write_shadow_dataset()
@staticmethod
def after_delete(
mapper: Mapper,
connection: Connection,
sqla_table: "SqlaTable",
) -> None:
"""
Shadow write the dataset to new models.
The ``SqlaTable`` model is currently being migrated to two new models, ``Table``
and ``Dataset``. In the first phase of the migration the new models are populated
whenever ``SqlaTable`` is modified (created, updated, or deleted).
In the second phase of the migration reads will be done from the new models.
Finally, in the third phase of the migration the old models will be removed.
For more context: https://github.com/apache/superset/issues/14909
"""
security_manager.dataset_after_delete(mapper, connection, sqla_table)
session = inspect(sqla_table).session
dataset = (
session.query(NewDataset).filter_by(uuid=sqla_table.uuid).one_or_none()
)
if dataset:
session.delete(dataset)
@staticmethod
def after_update(
mapper: Mapper,
connection: Connection,
sqla_table: "SqlaTable",
) -> None:
"""
Shadow write the dataset to new models.
The ``SqlaTable`` model is currently being migrated to two new models, ``Table``
and ``Dataset``. In the first phase of the migration the new models are populated
whenever ``SqlaTable`` is modified (created, updated, or deleted).
In the second phase of the migration reads will be done from the new models.
Finally, in the third phase of the migration the old models will be removed.
For more context: https://github.com/apache/superset/issues/14909
"""
# set permissions
security_manager.dataset_after_update(mapper, connection, sqla_table)
inspector = inspect(sqla_table)
session = inspector.session
# double-check that ``UPDATE``s are actually pending (this method is called even
# for instances that have no net changes to their column-based attributes)
if not session.is_modified(sqla_table, include_collections=True):
return
# find the dataset from the known instance list first
# (it could be either from a previous query or newly created)
dataset = next(
find_cached_objects_in_session(
session, NewDataset, uuids=[sqla_table.uuid]
),
None,
)
# if not found, pull from database
if not dataset:
dataset = (
session.query(NewDataset).filter_by(uuid=sqla_table.uuid).one_or_none()
)
if not dataset:
sqla_table.write_shadow_dataset()
return
# sync column list and delete removed columns
if (
inspector.attrs.columns.history.has_changes()
or inspector.attrs.metrics.history.has_changes()
):
# add pending new columns to known columns list, too, so if calling
# `after_update` twice before changes are persisted will not create
# two duplicate columns with the same uuids.
dataset.columns = sqla_table.get_sl_columns()
# physical dataset
if not sqla_table.sql:
# if the table name changed we should relink the dataset to another table
# (and create one if necessary)
if (
inspector.attrs.table_name.history.has_changes()
or inspector.attrs.schema.history.has_changes()
or inspector.attrs.database.history.has_changes()
):
tables = NewTable.bulk_load_or_create(
sqla_table.database,
[TableName(schema=sqla_table.schema, table=sqla_table.table_name)],
sync_columns=False,
default_props=dict(
changed_by=sqla_table.changed_by,
created_by=sqla_table.created_by,
is_managed_externally=sqla_table.is_managed_externally,
external_url=sqla_table.external_url,
),
)
if not tables[0].id:
# dataset columns will only be assigned to newly created tables
# existing tables should manage column syncing in another process
physical_columns = [
clone_model(
column, ignore=["uuid"], keep_relations=["changed_by"]
)
for column in dataset.columns
if column.is_physical
]
tables[0].columns = physical_columns
dataset.tables = tables
# virtual dataset
else:
# mark all columns as virtual (not physical)
for column in dataset.columns:
column.is_physical = False
# update referenced tables if SQL changed
if sqla_table.sql and inspector.attrs.sql.history.has_changes():
referenced_tables = extract_table_references(
sqla_table.sql, sqla_table.database.get_dialect().name
)
dataset.tables = NewTable.bulk_load_or_create(
sqla_table.database,
referenced_tables,
default_schema=sqla_table.schema,
# sync metadata is expensive, we'll do it in another process
# e.g. when users open a Table page
sync_columns=False,
default_props=dict(
changed_by=sqla_table.changed_by,
created_by=sqla_table.created_by,
is_managed_externally=sqla_table.is_managed_externally,
external_url=sqla_table.external_url,
),
)
# update other attributes
dataset.name = sqla_table.table_name
dataset.expression = sqla_table.sql or sqla_table.quote_identifier(
sqla_table.table_name
)
dataset.is_physical = not sqla_table.sql
def write_shadow_dataset(
self: "SqlaTable",
) -> None:
"""
Shadow write the dataset to new models.
The ``SqlaTable`` model is currently being migrated to two new models, ``Table``
and ``Dataset``. In the first phase of the migration the new models are populated
whenever ``SqlaTable`` is modified (created, updated, or deleted).
In the second phase of the migration reads will be done from the new models.
Finally, in the third phase of the migration the old models will be removed.
For more context: https://github.com/apache/superset/issues/14909
"""
session = inspect(self).session
# make sure database points to the right instance, in case only
# `table.database_id` is updated and the changes haven't been
# consolidated by SQLA
if self.database_id and (
not self.database or self.database.id != self.database_id
):
self.database = session.query(Database).filter_by(id=self.database_id).one()
# create columns
columns = []
for item in self.columns + self.metrics:
item.created_by = self.created_by
item.changed_by = self.changed_by
# on `SqlaTable.after_insert`` event, although the table itself
# already has a `uuid`, the associated columns will not.
# Here we pre-assign a uuid so they can still be matched to the new
# Column after creation.
if not item.uuid:
item.uuid = uuid4()
columns.append(item.to_sl_column())
# physical dataset
if not self.sql:
# always create separate column entries for Dataset and Table
# so updating a dataset would not update columns in the related table
physical_columns = [
clone_model(
column,
ignore=["uuid"],
# `created_by` will always be left empty because it'd always
# be created via some sort of automated system.
# But keep `changed_by` in case someone manually changes
# column attributes such as `is_dttm`.
keep_relations=["changed_by"],
)
for column in columns
if column.is_physical
]
tables = NewTable.bulk_load_or_create(
self.database,
[TableName(schema=self.schema, table=self.table_name)],
sync_columns=False,
default_props=dict(
created_by=self.created_by,
changed_by=self.changed_by,
is_managed_externally=self.is_managed_externally,
external_url=self.external_url,
),
)
tables[0].columns = physical_columns
# virtual dataset
else:
# mark all columns as virtual (not physical)
for column in columns:
column.is_physical = False
# find referenced tables
referenced_tables = extract_table_references(
self.sql, self.database.get_dialect().name
)
tables = NewTable.bulk_load_or_create(
self.database,
referenced_tables,
default_schema=self.schema,
# syncing table columns can be slow so we are not doing it here
sync_columns=False,
default_props=dict(
created_by=self.created_by,
changed_by=self.changed_by,
is_managed_externally=self.is_managed_externally,
external_url=self.external_url,
),
)
# create the new dataset
new_dataset = NewDataset(
uuid=self.uuid,
database_id=self.database_id,
created_on=self.created_on,
created_by=self.created_by,
changed_by=self.changed_by,
changed_on=self.changed_on,
owners=self.owners,
name=self.table_name,
expression=self.sql or self.quote_identifier(self.table_name),
tables=tables,
columns=columns,
is_physical=not self.sql,
is_managed_externally=self.is_managed_externally,
external_url=self.external_url,
)
session.add(new_dataset)
sa.event.listen(SqlaTable, "before_update", SqlaTable.before_update)
sa.event.listen(SqlaTable, "after_insert", SqlaTable.after_insert)
sa.event.listen(SqlaTable, "after_delete", SqlaTable.after_delete)
sa.event.listen(SqlaTable, "after_update", SqlaTable.after_update)
sa.event.listen(SqlMetric, "after_update", SqlaTable.update_column)
sa.event.listen(SqlMetric, "after_delete", SqlMetric.after_delete)
sa.event.listen(TableColumn, "after_update", SqlaTable.update_column)
sa.event.listen(TableColumn, "after_delete", TableColumn.after_delete)
RLSFilterRoles = Table(
"rls_filter_roles",
metadata,
Column("id", Integer, primary_key=True),
Column("role_id", Integer, ForeignKey("ab_role.id"), nullable=False),
Column("rls_filter_id", Integer, ForeignKey("row_level_security_filters.id")),
)
RLSFilterTables = Table(
"rls_filter_tables",
metadata,
Column("id", Integer, primary_key=True),
Column("table_id", Integer, ForeignKey("tables.id")),
Column("rls_filter_id", Integer, ForeignKey("row_level_security_filters.id")),
)
class RowLevelSecurityFilter(Model, AuditMixinNullable):
"""
Custom where clauses attached to Tables and Roles.
"""
__tablename__ = "row_level_security_filters"
id = Column(Integer, primary_key=True)
name = Column(String(255), unique=True, nullable=False)
description = Column(Text)
filter_type = Column(
Enum(*[filter_type.value for filter_type in utils.RowLevelSecurityFilterType])
)
group_key = Column(String(255), nullable=True)
roles = relationship(
security_manager.role_model,
secondary=RLSFilterRoles,
backref="row_level_security_filters",
)
tables = relationship(
SqlaTable, secondary=RLSFilterTables, backref="row_level_security_filters"
)
clause = Column(Text, nullable=False)
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