def test_split_on_date_parts_single_date_parts(
batch_identifiers_for_column, date_parts, sa
):
"""What does this test and why?
split_on_date_parts should still build the correct query when passed a single element list
date_parts that is a string, DatePart enum objects, mixed case string.
To match our interface it should accept a dateutil parseable string as the batch identifier
or a datetime and also fail when parameters are invalid.
"""
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter()
column_name: str = "column_name"
result: sa.sql.elements.BooleanClauseList = data_splitter.split_on_date_parts(
column_name=column_name,
batch_identifiers={column_name: batch_identifiers_for_column},
date_parts=date_parts,
)
# using mock-alchemy
assert ExpressionMatcher(result) == ExpressionMatcher(
sa.and_(
sa.extract("month", sa.column(column_name)) == 10,
)
)
# using values
assert isinstance(result, sa.sql.elements.BinaryExpression)
assert isinstance(result.comparator.type, sa.Boolean)
assert isinstance(result.left, sa.sql.elements.Extract)
assert result.left.field == "month"
assert result.left.expr.name == column_name
assert result.right.effective_value == 10
def test_named_date_part_methods(
mock_split_on_date_parts: mock.MagicMock,
splitter_method_name: str,
called_with_date_parts: List[DatePart],
):
"""Test that a partially pre-filled version of split_on_date_parts() was called with the appropriate params.
For example, split_on_year.
"""
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter(
dialect="sqlite")
column_name: str = "column_name"
batch_identifiers: dict = {
column_name: {
"year": 2018,
"month": 10,
"day": 31
}
}
getattr(data_splitter, splitter_method_name)(
column_name=column_name,
batch_identifiers=batch_identifiers,
)
mock_split_on_date_parts.assert_called_with(
column_name=column_name,
batch_identifiers=batch_identifiers,
date_parts=called_with_date_parts,
)
def test_get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts_multiple_date_parts(
date_parts, sa):
"""What does this test and why?
get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts should
return the correct query when passed any valid set of parameters including multiple date parts.
"""
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter(
dialect="sqlite")
table_name: str = "table_name"
column_name: str = "column_name"
result: sa.sql.elements.BooleanClauseList = data_splitter.get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts(
table_name=table_name,
column_name=column_name,
date_parts=date_parts,
)
assert isinstance(result, sa.sql.Select)
expected_query_str: str = "SELECT distinct(concat(concat('', CAST(EXTRACT(year FROM column_name) AS VARCHAR)), CAST(EXTRACT(month FROM column_name) AS VARCHAR))) AS concat_distinct_values, CAST(EXTRACT(year FROM column_name) AS INTEGER) AS year, CAST(EXTRACT(month FROM column_name) AS INTEGER) AS month FROM table_name"
actual_query_str: str = (str(
result.compile(compile_kwargs={"literal_binds": True})).replace(
"\n", "").replace(" ", "").lower())
assert (actual_query_str == expected_query_str.replace("\n", "").replace(
" ", "").lower())
def test_get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts_single_date_parts(
date_parts, sa):
"""What does this test and why?
get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts should still build the correct
query when passed a single element list of date_parts that is a string, DatePart enum object, or mixed case string.
"""
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter(
dialect="sqlite")
table_name: str = "table_name"
column_name: str = "column_name"
result: sa.sql.elements.BooleanClauseList = data_splitter.get_split_query_for_data_for_batch_identifiers_for_split_on_date_parts(
table_name=table_name,
column_name=column_name,
date_parts=date_parts,
)
assert isinstance(result, sa.sql.Select)
query_str: str = (str(
result.compile(compile_kwargs={"literal_binds": True})).replace(
"\n", "").replace(" ", "").lower())
assert (query_str == (
"SELECT distinct(EXTRACT(month FROM column_name)) AS concat_distinct_values, "
"CAST(EXTRACT(month FROM column_name) AS INTEGER) AS month FROM table_name"
).replace("\n", "").replace(" ", "").lower())
def test_get_splitter_method(underscore_prefix: str, splitter_method_name: str):
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter()
splitter_method_name_with_prefix = f"{underscore_prefix}{splitter_method_name}"
assert data_splitter.get_splitter_method(
splitter_method_name_with_prefix
) == getattr(data_splitter, splitter_method_name)
def test_get_data_for_batch_identifiers_year_and_month_and_day(
mock_execution_engine: mock.MagicMock,
mock_get_data_for_batch_identifiers_for_split_on_date_parts: mock.MagicMock,
):
"""test that get_data_for_batch_identifiers_for_split_on_date_parts() was called with the appropriate params."""
data_splitter: SqlAlchemyDataSplitter = SqlAlchemyDataSplitter()
table_name: str = "table_name"
column_name: str = "column_name"
data_splitter.get_data_for_batch_identifiers_year_and_month_and_day(
execution_engine=mock_execution_engine,
table_name=table_name,
column_name=column_name,
)
mock_get_data_for_batch_identifiers_for_split_on_date_parts.assert_called_with(
execution_engine=mock_execution_engine,
table_name=table_name,
column_name=column_name,
date_parts=[DatePart.YEAR, DatePart.MONTH, DatePart.DAY],
)
def __init__(
self,
name: Optional[str] = None,
credentials: Optional[dict] = None,
data_context: Optional[Any] = None,
engine=None,
connection_string: Optional[str] = None,
url: Optional[str] = None,
batch_data_dict: Optional[dict] = None,
create_temp_table: bool = True,
concurrency: Optional[ConcurrencyConfig] = None,
**kwargs, # These will be passed as optional parameters to the SQLAlchemy engine, **not** the ExecutionEngine
) -> None:
"""Builds a SqlAlchemyExecutionEngine, using a provided connection string/url/engine/credentials to access the
desired database. Also initializes the dialect to be used and configures usage statistics.
Args:
name (str): \
The name of the SqlAlchemyExecutionEngine
credentials: \
If the Execution Engine is not provided, the credentials can be used to build the Execution
Engine. If the Engine is provided, it will be used instead
data_context (DataContext): \
An object representing a Great Expectations project that can be used to access Expectation
Suites and the Project Data itself
engine (Engine): \
A SqlAlchemy Engine used to set the SqlAlchemyExecutionEngine being configured, useful if an
Engine has already been configured and should be reused. Will override Credentials
if provided.
connection_string (string): \
If neither the engines nor the credentials have been provided, a connection string can be used
to access the data. This will be overridden by both the engine and credentials if those are
provided.
url (string): \
If neither the engines, the credentials, nor the connection_string have been provided,
a url can be used to access the data. This will be overridden by all other configuration
options if any are provided.
concurrency (ConcurrencyConfig): Concurrency config used to configure the sqlalchemy engine.
"""
super().__init__(name=name, batch_data_dict=batch_data_dict)
self._name = name
self._credentials = credentials
self._connection_string = connection_string
self._url = url
self._create_temp_table = create_temp_table
if engine is not None:
if credentials is not None:
logger.warning(
"Both credentials and engine were provided during initialization of SqlAlchemyExecutionEngine. "
"Ignoring credentials."
)
self.engine = engine
else:
concurrency: ConcurrencyConfig
if data_context is None or data_context.concurrency is None:
concurrency = ConcurrencyConfig()
else:
concurrency = data_context.concurrency
concurrency.add_sqlalchemy_create_engine_parameters(kwargs)
if credentials is not None:
self.engine = self._build_engine(credentials=credentials, **kwargs)
elif connection_string is not None:
self.engine = sa.create_engine(connection_string, **kwargs)
elif url is not None:
parsed_url = make_url(url)
self.drivername = parsed_url.drivername
self.engine = sa.create_engine(url, **kwargs)
else:
raise InvalidConfigError(
"Credentials or an engine are required for a SqlAlchemyExecutionEngine."
)
# these are two backends where temp_table_creation is not supported we set the default value to False.
if self.engine.dialect.name.lower() in [
"trino",
"awsathena", # WKS 202201 - AWS Athena currently doesn't support temp_tables.
]:
self._create_temp_table = False
# Get the dialect **for purposes of identifying types**
if self.engine.dialect.name.lower() in [
"postgresql",
"mysql",
"sqlite",
"oracle",
"mssql",
]:
# These are the officially included and supported dialects by sqlalchemy
self.dialect_module = import_library_module(
module_name=f"sqlalchemy.dialects.{self.engine.dialect.name}"
)
elif self.engine.dialect.name.lower() == "snowflake":
self.dialect_module = import_library_module(
module_name="snowflake.sqlalchemy.snowdialect"
)
elif self.engine.dialect.name.lower() == "dremio":
# WARNING: Dremio Support is experimental, functionality is not fully under test
self.dialect_module = import_library_module(
module_name="sqlalchemy_dremio.pyodbc"
)
elif self.engine.dialect.name.lower() == "redshift":
self.dialect_module = import_library_module(
module_name="sqlalchemy_redshift.dialect"
)
elif self.engine.dialect.name.lower() == "bigquery":
self.dialect_module = import_library_module(
module_name=_BIGQUERY_MODULE_NAME
)
elif self.engine.dialect.name.lower() == "teradatasql":
# WARNING: Teradata Support is experimental, functionality is not fully under test
self.dialect_module = import_library_module(
module_name="teradatasqlalchemy.dialect"
)
else:
self.dialect_module = None
# <WILL> 20210726 - engine_backup is used by the snowflake connector, which requires connection and engine
# to be closed and disposed separately. Currently self.engine can refer to either a Connection or Engine,
# depending on the backend. This will need to be cleaned up in an upcoming refactor, so that Engine and
# Connection can be handled separately.
self._engine_backup = None
if self.engine and self.engine.dialect.name.lower() in [
"sqlite",
"mssql",
"snowflake",
"mysql",
]:
self._engine_backup = self.engine
# sqlite/mssql temp tables only persist within a connection so override the engine
self.engine = self.engine.connect()
# Send a connect event to provide dialect type
if data_context is not None and getattr(
data_context, "_usage_statistics_handler", None
):
handler = data_context._usage_statistics_handler
handler.send_usage_message(
event=UsageStatsEvents.EXECUTION_ENGINE_SQLALCHEMY_CONNECT.value,
event_payload={
"anonymized_name": handler.anonymizer.anonymize(self.name),
"sqlalchemy_dialect": self.engine.name,
},
success=True,
)
# Gather the call arguments of the present function (and add the "class_name"), filter out the Falsy values,
# and set the instance "_config" variable equal to the resulting dictionary.
self._config = {
"name": name,
"credentials": credentials,
"data_context": data_context,
"engine": engine,
"connection_string": connection_string,
"url": url,
"batch_data_dict": batch_data_dict,
"module_name": self.__class__.__module__,
"class_name": self.__class__.__name__,
}
self._config.update(kwargs)
filter_properties_dict(properties=self._config, clean_falsy=True, inplace=True)
self._data_splitter = SqlAlchemyDataSplitter()
self._data_sampler = SqlAlchemyDataSampler()
class SqlAlchemyExecutionEngine(ExecutionEngine):
def __init__(
self,
name: Optional[str] = None,
credentials: Optional[dict] = None,
data_context: Optional[Any] = None,
engine=None,
connection_string: Optional[str] = None,
url: Optional[str] = None,
batch_data_dict: Optional[dict] = None,
create_temp_table: bool = True,
concurrency: Optional[ConcurrencyConfig] = None,
**kwargs, # These will be passed as optional parameters to the SQLAlchemy engine, **not** the ExecutionEngine
) -> None:
"""Builds a SqlAlchemyExecutionEngine, using a provided connection string/url/engine/credentials to access the
desired database. Also initializes the dialect to be used and configures usage statistics.
Args:
name (str): \
The name of the SqlAlchemyExecutionEngine
credentials: \
If the Execution Engine is not provided, the credentials can be used to build the Execution
Engine. If the Engine is provided, it will be used instead
data_context (DataContext): \
An object representing a Great Expectations project that can be used to access Expectation
Suites and the Project Data itself
engine (Engine): \
A SqlAlchemy Engine used to set the SqlAlchemyExecutionEngine being configured, useful if an
Engine has already been configured and should be reused. Will override Credentials
if provided.
connection_string (string): \
If neither the engines nor the credentials have been provided, a connection string can be used
to access the data. This will be overridden by both the engine and credentials if those are
provided.
url (string): \
If neither the engines, the credentials, nor the connection_string have been provided,
a url can be used to access the data. This will be overridden by all other configuration
options if any are provided.
concurrency (ConcurrencyConfig): Concurrency config used to configure the sqlalchemy engine.
"""
super().__init__(name=name, batch_data_dict=batch_data_dict)
self._name = name
self._credentials = credentials
self._connection_string = connection_string
self._url = url
self._create_temp_table = create_temp_table
if engine is not None:
if credentials is not None:
logger.warning(
"Both credentials and engine were provided during initialization of SqlAlchemyExecutionEngine. "
"Ignoring credentials."
)
self.engine = engine
else:
concurrency: ConcurrencyConfig
if data_context is None or data_context.concurrency is None:
concurrency = ConcurrencyConfig()
else:
concurrency = data_context.concurrency
concurrency.add_sqlalchemy_create_engine_parameters(kwargs)
if credentials is not None:
self.engine = self._build_engine(credentials=credentials, **kwargs)
elif connection_string is not None:
self.engine = sa.create_engine(connection_string, **kwargs)
elif url is not None:
parsed_url = make_url(url)
self.drivername = parsed_url.drivername
self.engine = sa.create_engine(url, **kwargs)
else:
raise InvalidConfigError(
"Credentials or an engine are required for a SqlAlchemyExecutionEngine."
)
# these are two backends where temp_table_creation is not supported we set the default value to False.
if self.engine.dialect.name.lower() in [
"trino",
"awsathena", # WKS 202201 - AWS Athena currently doesn't support temp_tables.
]:
self._create_temp_table = False
# Get the dialect **for purposes of identifying types**
if self.engine.dialect.name.lower() in [
"postgresql",
"mysql",
"sqlite",
"oracle",
"mssql",
]:
# These are the officially included and supported dialects by sqlalchemy
self.dialect_module = import_library_module(
module_name=f"sqlalchemy.dialects.{self.engine.dialect.name}"
)
elif self.engine.dialect.name.lower() == "snowflake":
self.dialect_module = import_library_module(
module_name="snowflake.sqlalchemy.snowdialect"
)
elif self.engine.dialect.name.lower() == "dremio":
# WARNING: Dremio Support is experimental, functionality is not fully under test
self.dialect_module = import_library_module(
module_name="sqlalchemy_dremio.pyodbc"
)
elif self.engine.dialect.name.lower() == "redshift":
self.dialect_module = import_library_module(
module_name="sqlalchemy_redshift.dialect"
)
elif self.engine.dialect.name.lower() == "bigquery":
self.dialect_module = import_library_module(
module_name=_BIGQUERY_MODULE_NAME
)
elif self.engine.dialect.name.lower() == "teradatasql":
# WARNING: Teradata Support is experimental, functionality is not fully under test
self.dialect_module = import_library_module(
module_name="teradatasqlalchemy.dialect"
)
else:
self.dialect_module = None
# <WILL> 20210726 - engine_backup is used by the snowflake connector, which requires connection and engine
# to be closed and disposed separately. Currently self.engine can refer to either a Connection or Engine,
# depending on the backend. This will need to be cleaned up in an upcoming refactor, so that Engine and
# Connection can be handled separately.
self._engine_backup = None
if self.engine and self.engine.dialect.name.lower() in [
"sqlite",
"mssql",
"snowflake",
"mysql",
]:
self._engine_backup = self.engine
# sqlite/mssql temp tables only persist within a connection so override the engine
self.engine = self.engine.connect()
# Send a connect event to provide dialect type
if data_context is not None and getattr(
data_context, "_usage_statistics_handler", None
):
handler = data_context._usage_statistics_handler
handler.send_usage_message(
event=UsageStatsEvents.EXECUTION_ENGINE_SQLALCHEMY_CONNECT.value,
event_payload={
"anonymized_name": handler.anonymizer.anonymize(self.name),
"sqlalchemy_dialect": self.engine.name,
},
success=True,
)
# Gather the call arguments of the present function (and add the "class_name"), filter out the Falsy values,
# and set the instance "_config" variable equal to the resulting dictionary.
self._config = {
"name": name,
"credentials": credentials,
"data_context": data_context,
"engine": engine,
"connection_string": connection_string,
"url": url,
"batch_data_dict": batch_data_dict,
"module_name": self.__class__.__module__,
"class_name": self.__class__.__name__,
}
self._config.update(kwargs)
filter_properties_dict(properties=self._config, clean_falsy=True, inplace=True)
self._data_splitter = SqlAlchemyDataSplitter()
self._data_sampler = SqlAlchemyDataSampler()
@property
def credentials(self) -> Optional[dict]:
return self._credentials
@property
def connection_string(self) -> Optional[str]:
return self._connection_string
@property
def url(self) -> Optional[str]:
return self._url
@property
def dialect(self) -> Dialect:
return self.engine.dialect
@property
def dialect_name(self) -> str:
"""Retrieve the string name of the engine dialect in lowercase e.g. "postgresql".
Returns:
String representation of the sql dialect.
"""
return self.engine.dialect.name.lower()
def _build_engine(self, credentials: dict, **kwargs) -> "sa.engine.Engine":
"""
Using a set of given credentials, constructs an Execution Engine , connecting to a database using a URL or a
private key path.
"""
# Update credentials with anything passed during connection time
drivername = credentials.pop("drivername")
schema_name = credentials.pop("schema_name", None)
if schema_name is not None:
logger.warning(
"schema_name specified creating a URL with schema is not supported. Set a default "
"schema on the user connecting to your database."
)
create_engine_kwargs = kwargs
connect_args = credentials.pop("connect_args", None)
if connect_args:
create_engine_kwargs["connect_args"] = connect_args
if "private_key_path" in credentials:
options, create_engine_kwargs = self._get_sqlalchemy_key_pair_auth_url(
drivername, credentials
)
else:
options = get_sqlalchemy_url(drivername, **credentials)
self.drivername = drivername
engine = sa.create_engine(options, **create_engine_kwargs)
return engine
def _get_sqlalchemy_key_pair_auth_url(
self, drivername: str, credentials: dict
) -> Tuple["sa.engine.url.URL", Dict]:
"""
Utilizing a private key path and a passphrase in a given credentials dictionary, attempts to encode the provided
values into a private key. If passphrase is incorrect, this will fail and an exception is raised.
Args:
drivername(str) - The name of the driver class
credentials(dict) - A dictionary of database credentials used to access the database
Returns:
a tuple consisting of a url with the serialized key-pair authentication, and a dictionary of engine kwargs.
"""
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import serialization
private_key_path = credentials.pop("private_key_path")
private_key_passphrase = credentials.pop("private_key_passphrase")
with Path(private_key_path).expanduser().resolve().open(mode="rb") as key:
try:
p_key = serialization.load_pem_private_key(
key.read(),
password=private_key_passphrase.encode()
if private_key_passphrase
else None,
backend=default_backend(),
)
except ValueError as e:
if "incorrect password" in str(e).lower():
raise DatasourceKeyPairAuthBadPassphraseError(
datasource_name="SqlAlchemyDatasource",
message="Decryption of key failed, was the passphrase incorrect?",
) from e
else:
raise e
pkb = p_key.private_bytes(
encoding=serialization.Encoding.DER,
format=serialization.PrivateFormat.PKCS8,
encryption_algorithm=serialization.NoEncryption(),
)
credentials_driver_name = credentials.pop("drivername", None)
create_engine_kwargs = {"connect_args": {"private_key": pkb}}
return (
get_sqlalchemy_url(drivername or credentials_driver_name, **credentials),
create_engine_kwargs,
)
def get_domain_records(
self,
domain_kwargs: Dict,
) -> Selectable:
"""
Uses the given domain kwargs (which include row_condition, condition_parser, and ignore_row_if directives) to
obtain and/or query a batch. Returns in the format of an SqlAlchemy table/column(s) object.
Args:
domain_kwargs (dict) - A dictionary consisting of the domain kwargs specifying which data to obtain
Returns:
An SqlAlchemy table/column(s) (the selectable object for obtaining data on which to compute)
"""
batch_id = domain_kwargs.get("batch_id")
if batch_id is None:
# We allow no batch id specified if there is only one batch
if self.active_batch_data:
data_object = self.active_batch_data
else:
raise GreatExpectationsError(
"No batch is specified, but could not identify a loaded batch."
)
else:
if batch_id in self.loaded_batch_data_dict:
data_object = self.loaded_batch_data_dict[batch_id]
else:
raise GreatExpectationsError(
f"Unable to find batch with batch_id {batch_id}"
)
selectable: Selectable
if "table" in domain_kwargs and domain_kwargs["table"] is not None:
# TODO: Add logic to handle record_set_name once implemented
# (i.e. multiple record sets (tables) in one batch
if domain_kwargs["table"] != data_object.selectable.name:
selectable = sa.Table(
domain_kwargs["table"],
sa.MetaData(),
schema=data_object._schema_name,
)
else:
selectable = data_object.selectable
elif "query" in domain_kwargs:
raise ValueError(
"query is not currently supported by SqlAlchemyExecutionEngine"
)
else:
selectable = data_object.selectable
"""
If a custom query is passed, selectable will be TextClause and not formatted
as a subquery wrapped in "(subquery) alias". TextClause must first be converted
to TextualSelect using sa.columns() before it can be converted to type Subquery
"""
if TextClause and isinstance(selectable, TextClause):
selectable = selectable.columns().subquery()
# Filtering by row condition.
if (
"row_condition" in domain_kwargs
and domain_kwargs["row_condition"] is not None
):
condition_parser = domain_kwargs["condition_parser"]
if condition_parser == "great_expectations__experimental__":
parsed_condition = parse_condition_to_sqlalchemy(
domain_kwargs["row_condition"]
)
selectable = (
sa.select([sa.text("*")])
.select_from(selectable)
.where(parsed_condition)
)
else:
raise GreatExpectationsError(
"SqlAlchemyExecutionEngine only supports the great_expectations condition_parser."
)
# Filtering by filter_conditions
filter_conditions: List[RowCondition] = domain_kwargs.get(
"filter_conditions", []
)
# For SqlAlchemyExecutionEngine only one filter condition is allowed
if len(filter_conditions) == 1:
filter_condition = filter_conditions[0]
assert (
filter_condition.condition_type == RowConditionParserType.GE
), "filter_condition must be of type GE for SqlAlchemyExecutionEngine"
selectable = (
sa.select([sa.text("*")])
.select_from(selectable)
.where(parse_condition_to_sqlalchemy(filter_condition.condition))
)
elif len(filter_conditions) > 1:
raise GreatExpectationsError(
"SqlAlchemyExecutionEngine currently only supports a single filter condition."
)
if "column" in domain_kwargs:
return selectable
# Filtering by ignore_row_if directive
if (
"column_A" in domain_kwargs
and "column_B" in domain_kwargs
and "ignore_row_if" in domain_kwargs
):
if self.active_batch_data.use_quoted_name:
# Checking if case-sensitive and using appropriate name
# noinspection PyPep8Naming
column_A_name = quoted_name(domain_kwargs["column_A"], quote=True)
# noinspection PyPep8Naming
column_B_name = quoted_name(domain_kwargs["column_B"], quote=True)
else:
# noinspection PyPep8Naming
column_A_name = domain_kwargs["column_A"]
# noinspection PyPep8Naming
column_B_name = domain_kwargs["column_B"]
ignore_row_if = domain_kwargs["ignore_row_if"]
if ignore_row_if == "both_values_are_missing":
selectable = get_sqlalchemy_selectable(
sa.select([sa.text("*")])
.select_from(get_sqlalchemy_selectable(selectable))
.where(
sa.not_(
sa.and_(
sa.column(column_A_name) == None,
sa.column(column_B_name) == None,
)
)
)
)
elif ignore_row_if == "either_value_is_missing":
selectable = get_sqlalchemy_selectable(
sa.select([sa.text("*")])
.select_from(get_sqlalchemy_selectable(selectable))
.where(
sa.not_(
sa.or_(
sa.column(column_A_name) == None,
sa.column(column_B_name) == None,
)
)
)
)
else:
if ignore_row_if not in ["neither", "never"]:
raise ValueError(
f'Unrecognized value of ignore_row_if ("{ignore_row_if}").'
)
if ignore_row_if == "never":
# deprecated-v0.13.29
warnings.warn(
f"""The correct "no-action" value of the "ignore_row_if" directive for the column pair case is \
"neither" (the use of "{ignore_row_if}" is deprecated as of v0.13.29 and will be removed in v0.16). Please use "neither" moving forward.
""",
DeprecationWarning,
)
return selectable
if "column_list" in domain_kwargs and "ignore_row_if" in domain_kwargs:
if self.active_batch_data.use_quoted_name:
# Checking if case-sensitive and using appropriate name
column_list = [
quoted_name(domain_kwargs[column_name], quote=True)
for column_name in domain_kwargs["column_list"]
]
else:
column_list = domain_kwargs["column_list"]
ignore_row_if = domain_kwargs["ignore_row_if"]
if ignore_row_if == "all_values_are_missing":
selectable = get_sqlalchemy_selectable(
sa.select([sa.text("*")])
.select_from(get_sqlalchemy_selectable(selectable))
.where(
sa.not_(
sa.and_(
*(
sa.column(column_name) == None
for column_name in column_list
)
)
)
)
)
elif ignore_row_if == "any_value_is_missing":
selectable = get_sqlalchemy_selectable(
sa.select([sa.text("*")])
.select_from(get_sqlalchemy_selectable(selectable))
.where(
sa.not_(
sa.or_(
*(
sa.column(column_name) == None
for column_name in column_list
)
)
)
)
)
else:
if ignore_row_if != "never":
raise ValueError(
f'Unrecognized value of ignore_row_if ("{ignore_row_if}").'
)
return selectable
return selectable
def get_compute_domain(
self,
domain_kwargs: Dict,
domain_type: Union[str, MetricDomainTypes],
accessor_keys: Optional[Iterable[str]] = None,
) -> Tuple[Selectable, dict, dict]:
"""Uses a given batch dictionary and domain kwargs to obtain a SqlAlchemy column object.
Args:
domain_kwargs (dict) - A dictionary consisting of the domain kwargs specifying which data to obtain
domain_type (str or MetricDomainTypes) - an Enum value indicating which metric domain the user would
like to be using, or a corresponding string value representing it. String types include "identity",
"column", "column_pair", "table" and "other". Enum types include capitalized versions of these from the
class MetricDomainTypes.
accessor_keys (str iterable) - keys that are part of the compute domain but should be ignored when
describing the domain and simply transferred with their associated values into accessor_domain_kwargs.
Returns:
SqlAlchemy column
"""
selectable = self.get_domain_records(domain_kwargs)
split_domain_kwargs = self._split_domain_kwargs(
domain_kwargs, domain_type, accessor_keys
)
return selectable, split_domain_kwargs.compute, split_domain_kwargs.accessor
def _split_column_metric_domain_kwargs(
self,
domain_kwargs: Dict,
domain_type: MetricDomainTypes,
) -> SplitDomainKwargs:
"""Split domain_kwargs for column domain types into compute and accessor domain kwargs.
Args:
domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
domain_type: an Enum value indicating which metric domain the user would
like to be using.
Returns:
compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
"""
assert (
domain_type == MetricDomainTypes.COLUMN
), "This method only supports MetricDomainTypes.COLUMN"
compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
accessor_domain_kwargs: Dict = {}
if "column" not in compute_domain_kwargs:
raise ge_exceptions.GreatExpectationsError(
"Column not provided in compute_domain_kwargs"
)
# Checking if case-sensitive and using appropriate name
if self.active_batch_data.use_quoted_name:
accessor_domain_kwargs["column"] = quoted_name(
compute_domain_kwargs.pop("column"), quote=True
)
else:
accessor_domain_kwargs["column"] = compute_domain_kwargs.pop("column")
return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
def _split_column_pair_metric_domain_kwargs(
self,
domain_kwargs: Dict,
domain_type: MetricDomainTypes,
) -> SplitDomainKwargs:
"""Split domain_kwargs for column pair domain types into compute and accessor domain kwargs.
Args:
domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
domain_type: an Enum value indicating which metric domain the user would
like to be using.
Returns:
compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
"""
assert (
domain_type == MetricDomainTypes.COLUMN_PAIR
), "This method only supports MetricDomainTypes.COLUMN_PAIR"
compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
accessor_domain_kwargs: Dict = {}
if not (
"column_A" in compute_domain_kwargs and "column_B" in compute_domain_kwargs
):
raise ge_exceptions.GreatExpectationsError(
"column_A or column_B not found within compute_domain_kwargs"
)
# Checking if case-sensitive and using appropriate name
if self.active_batch_data.use_quoted_name:
accessor_domain_kwargs["column_A"] = quoted_name(
compute_domain_kwargs.pop("column_A"), quote=True
)
accessor_domain_kwargs["column_B"] = quoted_name(
compute_domain_kwargs.pop("column_B"), quote=True
)
else:
accessor_domain_kwargs["column_A"] = compute_domain_kwargs.pop("column_A")
accessor_domain_kwargs["column_B"] = compute_domain_kwargs.pop("column_B")
return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
def _split_multi_column_metric_domain_kwargs(
self,
domain_kwargs: Dict,
domain_type: MetricDomainTypes,
) -> SplitDomainKwargs:
"""Split domain_kwargs for multicolumn domain types into compute and accessor domain kwargs.
Args:
domain_kwargs: A dictionary consisting of the domain kwargs specifying which data to obtain
domain_type: an Enum value indicating which metric domain the user would
like to be using.
Returns:
compute_domain_kwargs, accessor_domain_kwargs split from domain_kwargs
The union of compute_domain_kwargs, accessor_domain_kwargs is the input domain_kwargs
"""
assert (
domain_type == MetricDomainTypes.MULTICOLUMN
), "This method only supports MetricDomainTypes.MULTICOLUMN"
compute_domain_kwargs: Dict = copy.deepcopy(domain_kwargs)
accessor_domain_kwargs: Dict = {}
if "column_list" not in domain_kwargs:
raise GreatExpectationsError("column_list not found within domain_kwargs")
column_list = compute_domain_kwargs.pop("column_list")
if len(column_list) < 2:
raise GreatExpectationsError("column_list must contain at least 2 columns")
# Checking if case-sensitive and using appropriate name
if self.active_batch_data.use_quoted_name:
accessor_domain_kwargs["column_list"] = [
quoted_name(column_name, quote=True) for column_name in column_list
]
else:
accessor_domain_kwargs["column_list"] = column_list
return SplitDomainKwargs(compute_domain_kwargs, accessor_domain_kwargs)
def resolve_metric_bundle(
self,
metric_fn_bundle: Iterable[Tuple[MetricConfiguration, Any, dict, dict]],
) -> Dict[Tuple[str, str, str], Any]:
"""For every metric in a set of Metrics to resolve, obtains necessary metric keyword arguments and builds
bundles of the metrics into one large query dictionary so that they are all executed simultaneously. Will fail
if bundling the metrics together is not possible.
Args:
metric_fn_bundle (Iterable[Tuple[MetricConfiguration, Callable, dict]): \
A Dictionary containing a MetricProvider's MetricConfiguration (its unique identifier), its metric provider function
(the function that actually executes the metric), and the arguments to pass to the metric provider function.
A dictionary of metrics defined in the registry and corresponding arguments
Returns:
A dictionary of metric names and their corresponding now-queried values.
"""
resolved_metrics = {}
# We need a different query for each domain (where clause).
queries: Dict[Tuple, dict] = {}
for (
metric_to_resolve,
engine_fn,
compute_domain_kwargs,
accessor_domain_kwargs,
metric_provider_kwargs,
) in metric_fn_bundle:
if not isinstance(compute_domain_kwargs, IDDict):
compute_domain_kwargs = IDDict(compute_domain_kwargs)
domain_id = compute_domain_kwargs.to_id()
if domain_id not in queries:
queries[domain_id] = {
"select": [],
"ids": [],
"domain_kwargs": compute_domain_kwargs,
}
if self.engine.dialect.name == "clickhouse":
queries[domain_id]["select"].append(
engine_fn.label(
metric_to_resolve.metric_name.join(
random.choices(string.ascii_lowercase, k=2)
)
)
)
else:
queries[domain_id]["select"].append(
engine_fn.label(metric_to_resolve.metric_name)
)
queries[domain_id]["ids"].append(metric_to_resolve.id)
for query in queries.values():
domain_kwargs = query["domain_kwargs"]
selectable = self.get_domain_records(
domain_kwargs=domain_kwargs,
)
assert len(query["select"]) == len(query["ids"])
try:
"""
If a custom query is passed, selectable will be TextClause and not formatted
as a subquery wrapped in "(subquery) alias". TextClause must first be converted
to TextualSelect using sa.columns() before it can be converted to type Subquery
"""
if TextClause and isinstance(selectable, TextClause):
res = self.engine.execute(
sa.select(query["select"]).select_from(
selectable.columns().subquery()
)
).fetchall()
else:
res = self.engine.execute(
sa.select(query["select"]).select_from(selectable)
).fetchall()
logger.debug(
f"SqlAlchemyExecutionEngine computed {len(res[0])} metrics on domain_id {IDDict(domain_kwargs).to_id()}"
)
except OperationalError as oe:
exception_message: str = "An SQL execution Exception occurred. "
exception_traceback: str = traceback.format_exc()
exception_message += f'{type(oe).__name__}: "{str(oe)}". Traceback: "{exception_traceback}".'
logger.error(exception_message)
raise ExecutionEngineError(message=exception_message)
assert (
len(res) == 1
), "all bundle-computed metrics must be single-value statistics"
assert len(query["ids"]) == len(
res[0]
), "unexpected number of metrics returned"
for idx, id in enumerate(query["ids"]):
resolved_metrics[id] = convert_to_json_serializable(res[0][idx])
return resolved_metrics
def close(self) -> None:
"""
Note: Will 20210729
This is a helper function that will close and dispose Sqlalchemy objects that are used to connect to a database.
Databases like Snowflake require the connection and engine to be instantiated and closed separately, and not
doing so has caused problems with hanging connections.
Currently the ExecutionEngine does not support handling connections and engine separately, and will actually
override the engine with a connection in some cases, obfuscating what object is used to actually used by the
ExecutionEngine to connect to the external database. This will be handled in an upcoming refactor, which will
allow this function to eventually become:
self.connection.close()
self.engine.dispose()
More background can be found here: https://github.com/great-expectations/great_expectations/pull/3104/
"""
if self._engine_backup:
self.engine.close()
self._engine_backup.dispose()
else:
self.engine.dispose()
def _get_splitter_method(self, splitter_method_name: str) -> Callable:
"""Get the appropriate splitter method from the method name.
Args:
splitter_method_name: name of the splitter to retrieve.
Returns:
splitter method.
"""
return self._data_splitter.get_splitter_method(splitter_method_name)
def execute_split_query(self, split_query: Selectable) -> List[Row]:
"""Use the execution engine to run the split query and fetch all of the results.
Args:
split_query: Query to be executed as a sqlalchemy Selectable.
Returns:
List of row results.
"""
if self.engine.dialect.name.lower() == "awsathena":
# Note: Athena does not support casting to string, only to varchar
# but sqlalchemy currently generates a query as `CAST(colname AS STRING)` instead
# of `CAST(colname AS VARCHAR)` with other dialects.
split_query: str = str(
split_query.compile(self.engine, compile_kwargs={"literal_binds": True})
)
pattern = re.compile(r"(CAST\(EXTRACT\(.*?\))( AS STRING\))", re.IGNORECASE)
split_query = re.sub(pattern, r"\1 AS VARCHAR)", split_query)
return self.engine.execute(split_query).fetchall()
def get_data_for_batch_identifiers(
self, table_name: str, splitter_method_name: str, splitter_kwargs: dict
) -> List[dict]:
"""Build data used to construct batch identifiers for the input table using the provided splitter config.
Sql splitter configurations yield the unique values that comprise a batch by introspecting your data.
Args:
table_name: Table to split.
splitter_method_name: Desired splitter method to use.
splitter_kwargs: Dict of directives used by the splitter method as keyword arguments of key=value.
Returns:
List of dicts of the form [{column_name: {"key": value}}]
"""
return self._data_splitter.get_data_for_batch_identifiers(
execution_engine=self,
table_name=table_name,
splitter_method_name=splitter_method_name,
splitter_kwargs=splitter_kwargs,
)
def _build_selectable_from_batch_spec(
self, batch_spec: BatchSpec
) -> Union[Selectable, str]:
if "splitter_method" in batch_spec:
splitter_fn: Callable = self._get_splitter_method(
splitter_method_name=batch_spec["splitter_method"]
)
split_clause = splitter_fn(
batch_identifiers=batch_spec["batch_identifiers"],
**batch_spec["splitter_kwargs"],
)
else:
split_clause = True
table_name: str = batch_spec["table_name"]
sampling_method: Optional[str] = batch_spec.get("sampling_method")
if sampling_method is not None:
if sampling_method in [
"_sample_using_limit",
"sample_using_limit",
"_sample_using_random",
"sample_using_random",
]:
sampler_fn = self._data_sampler.get_sampler_method(sampling_method)
return sampler_fn(
execution_engine=self,
batch_spec=batch_spec,
where_clause=split_clause,
)
else:
sampler_fn = self._data_sampler.get_sampler_method(sampling_method)
return (
sa.select("*")
.select_from(
sa.table(table_name, schema=batch_spec.get("schema_name", None))
)
.where(
sa.and_(
split_clause,
sampler_fn(batch_spec),
)
)
)
return (
sa.select("*")
.select_from(
sa.table(table_name, schema=batch_spec.get("schema_name", None))
)
.where(split_clause)
)
def get_batch_data_and_markers(
self, batch_spec: BatchSpec
) -> Tuple[Any, BatchMarkers]:
if not isinstance(
batch_spec, (SqlAlchemyDatasourceBatchSpec, RuntimeQueryBatchSpec)
):
raise InvalidBatchSpecError(
f"""SqlAlchemyExecutionEngine accepts batch_spec only of type SqlAlchemyDatasourceBatchSpec or
RuntimeQueryBatchSpec (illegal type "{str(type(batch_spec))}" was received).
"""
)
batch_data: Optional[SqlAlchemyBatchData] = None
batch_markers: BatchMarkers = BatchMarkers(
{
"ge_load_time": datetime.datetime.now(datetime.timezone.utc).strftime(
"%Y%m%dT%H%M%S.%fZ"
)
}
)
source_schema_name: str = batch_spec.get("schema_name", None)
source_table_name: str = batch_spec.get("table_name", None)
temp_table_schema_name: Optional[str] = batch_spec.get("temp_table_schema_name")
if batch_spec.get("bigquery_temp_table"):
# deprecated-v0.15.3
warnings.warn(
"BigQuery tables that are created as the result of a query are no longer created as "
"permanent tables. Thus, a named permanent table through the `bigquery_temp_table`"
"parameter is not required. The `bigquery_temp_table` parameter is deprecated as of"
"v0.15.3 and will be removed in v0.18.",
DeprecationWarning,
)
create_temp_table: bool = batch_spec.get(
"create_temp_table", self._create_temp_table
)
if isinstance(batch_spec, RuntimeQueryBatchSpec):
# query != None is already checked when RuntimeQueryBatchSpec is instantiated
query: str = batch_spec.query
batch_spec.query = "SQLQuery"
batch_data = SqlAlchemyBatchData(
execution_engine=self,
query=query,
temp_table_schema_name=temp_table_schema_name,
create_temp_table=create_temp_table,
source_table_name=source_table_name,
source_schema_name=source_schema_name,
)
elif isinstance(batch_spec, SqlAlchemyDatasourceBatchSpec):
if self.engine.dialect.name.lower() == "oracle":
selectable: str = self._build_selectable_from_batch_spec(
batch_spec=batch_spec
)
else:
selectable: Selectable = self._build_selectable_from_batch_spec(
batch_spec=batch_spec
)
batch_data = SqlAlchemyBatchData(
execution_engine=self,
selectable=selectable,
create_temp_table=create_temp_table,
source_table_name=source_table_name,
source_schema_name=source_schema_name,
)
return batch_data, batch_markers