def __init__(self,
name="default",
data_context=None,
data_asset_type=None,
generators=None,
**kwargs):
if generators is None:
# Provide a gentle way to build a datasource with a sane default,
# including ability to specify the base_directory
base_directory = kwargs.pop("base_directory", "/data")
reader_options = kwargs.pop("reader_options", {})
generators = {
"default": {
"type": "subdir_reader",
"base_directory": base_directory,
"reader_options": reader_options
}
}
if data_asset_type is None:
data_asset_type = ClassConfig(class_name="SparkDFDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
super(SparkDFDatasource,
self).__init__(name,
type_="spark",
data_context=data_context,
data_asset_type=data_asset_type,
generators=generators)
try:
self.spark = SparkSession.builder.getOrCreate()
except Exception:
logger.error(
"Unable to load spark context; install optional spark dependency for support."
)
self.spark = None
self._build_generators()
def build_configuration(cls,
data_asset_type=None,
generators=None,
**kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
if generators is None:
# Provide a gentle way to build a datasource with a sane default,
# including ability to specify the base_directory
base_directory = kwargs.pop("base_directory", "/data")
reader_options = kwargs.pop("reader_options", {})
generators = {
"default": {
"class_name": "SubdirReaderGenerator",
"base_directory": base_directory,
"reader_options": reader_options
}
}
if data_asset_type is None:
data_asset_type = ClassConfig(class_name="SparkDFDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
configuration = kwargs
configuration.update({
"data_asset_type": data_asset_type,
"generators": generators,
})
return configuration
def _get_data_asset(self, batch_kwargs, expectation_suite, **kwargs):
batch_kwargs.update(kwargs)
if "data_asset_type" in batch_kwargs:
# Sqlalchemy does not use reader_options or need to remove batch_kwargs since it does not pass
# options through to a later reader
data_asset_type_config = batch_kwargs["data_asset_type"]
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, SqlAlchemyDataset):
raise ValueError("SqlAlchemyDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of SqlAlchemyDataset." % data_asset_type.__name__)
# We need to build a batch_id to be used in the dataframe
batch_id = BatchId({
"timestamp": time.time()
})
if "schema" in batch_kwargs:
schema = batch_kwargs["schema"]
else:
schema = None
if "table" in batch_kwargs:
return data_asset_type(
table_name=batch_kwargs["table"],
engine=self.engine,
schema=schema,
data_context=self._data_context,
expectation_suite=expectation_suite,
batch_kwargs=batch_kwargs,
batch_id=batch_id
)
elif "query" in batch_kwargs:
query = Template(batch_kwargs["query"]).safe_substitute(**kwargs)
return data_asset_type(
custom_sql=query,
engine=self.engine,
data_context=self._data_context,
expectation_suite=expectation_suite,
batch_kwargs=batch_kwargs,
batch_id=batch_id
)
else:
raise ValueError("Invalid batch_kwargs: exactly one of 'table' or 'query' must be specified")
def build_configuration(cls,
data_asset_type=None,
generators=None,
**kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
# As of 0.9.0, we do not require generators be configured
# generators = {
# "default": {
# "class_name": "TableBatchKwargsGenerator"
# },
# "passthrough": {
# "class_name": "PassthroughGenerator",
# }
# }
if data_asset_type is None:
data_asset_type = ClassConfig(class_name="SqlAlchemyDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
configuration = kwargs
configuration["data_asset_type"] = data_asset_type
if generators is not None:
configuration["generators"] = generators
return configuration
def build_configuration(cls, data_asset_type=None, generators=None, boto3_options=None, reader_method=None,
reader_options=None, limit=None, **kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
boto3_options: Optional dictionary with key-value pairs to pass to boto3 during instantiation.
reader_method: Optional default reader_method for generated batches
reader_options: Optional default reader_options for generated batches
limit: Optional default limit for generated batches
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
if data_asset_type is None:
data_asset_type = {"class_name": "PandasDataset"}
else:
data_asset_type = classConfigSchema.dump(ClassConfig(**data_asset_type))
configuration = kwargs
configuration["data_asset_type"] = data_asset_type
if generators:
configuration["generators"] = generators
if boto3_options is not None:
if isinstance(boto3_options, dict):
configuration.update(boto3_options)
else:
raise ValueError("boto3_options must be a dictionary of key-value pairs to pass to boto3 upon "
"initialization.")
if reader_options is not None:
if isinstance(reader_options, dict):
configuration.update(reader_options)
else:
raise ValueError("boto3_options must be a dictionary of key-value pairs to pass to boto3 upon "
"initialization.")
if reader_method is not None:
configuration["reader_method"] = reader_method
if limit is not None:
configuration["limit"] = limit
return configuration
def __init__(self,
name="pandas",
data_context=None,
data_asset_type=None,
generators=None,
**kwargs):
if generators is None:
# Provide a gentle way to build a datasource with a sane default,
# including ability to specify the base_directory and reader_options
base_directory = kwargs.pop("base_directory", "/data")
# By default, use CSV sniffer to infer separator, which requires the python engine
reader_options = kwargs.pop("reader_options", {
"sep": None,
"engine": "python"
})
generators = {
"default": {
"type": "subdir_reader",
"base_directory": base_directory,
"reader_options": reader_options
}
}
if data_asset_type is None:
data_asset_type = ClassConfig(class_name="PandasDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
super(PandasDatasource, self).__init__(name,
type_="pandas",
data_context=data_context,
data_asset_type=data_asset_type,
generators=generators)
self._build_generators()
def build_configuration(cls, data_asset_type=None, generators=None, **kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
if generators is None:
generators = {
"default": {
"class_name": "TableGenerator"
}
}
if data_asset_type is None:
data_asset_type = ClassConfig(
class_name="SqlAlchemyDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
configuration = kwargs
configuration.update({
"data_asset_type": data_asset_type,
"generators": generators,
})
return configuration
def build_configuration(
cls,
data_asset_type=None,
batch_kwargs_generators=None,
spark_config=None,
force_reuse_spark_context=False,
**kwargs,
):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
batch_kwargs_generators: Generator configuration dictionary
spark_config: dictionary of key-value pairs to pass to the spark builder
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
if data_asset_type is None:
data_asset_type = {
"class_name": "SparkDFDataset",
"module_name": "great_expectations.dataset",
}
else:
data_asset_type = classConfigSchema.dump(
ClassConfig(**data_asset_type))
if spark_config is None:
spark_config = {}
configuration = kwargs
configuration.update({
"data_asset_type":
data_asset_type,
"spark_config":
spark_config,
"force_reuse_spark_context":
force_reuse_spark_context,
})
if batch_kwargs_generators:
configuration["batch_kwargs_generators"] = batch_kwargs_generators
return configuration
def __init__(self,
batch,
expectation_suite,
expectation_engine=None,
**kwargs):
self.batch = batch
self.expectation_suite = expectation_suite
if isinstance(expectation_engine, dict):
expectation_engine = ClassConfig(**expectation_engine)
if isinstance(expectation_engine, ClassConfig):
module_name = expectation_engine.module_name or "great_expectations.dataset"
verify_dynamic_loading_support(module_name=module_name)
expectation_engine = load_class(
class_name=expectation_engine.class_name,
module_name=module_name)
self.expectation_engine = expectation_engine
if self.expectation_engine is None:
# Guess the engine
try:
import pandas as pd
if isinstance(batch.data, pd.DataFrame):
self.expectation_engine = PandasDataset
except ImportError:
pass
if self.expectation_engine is None:
if isinstance(batch.data, SqlAlchemyBatchReference):
self.expectation_engine = SqlAlchemyDataset
if self.expectation_engine is None:
try:
import pyspark
if isinstance(batch.data, pyspark.sql.DataFrame):
self.expectation_engine = SparkDFDataset
except ImportError:
pass
if self.expectation_engine is None:
raise ValueError(
"Unable to identify expectation_engine. It must be a subclass of DataAsset."
)
self.init_kwargs = kwargs
def build_configuration(cls,
data_asset_type=None,
generators=None,
spark_config=None,
**kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
spark_config: dictionary of key-value pairs to pass to the spark builder
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
if data_asset_type is None:
data_asset_type = {"class_name": "SparkDFDataset"}
else:
data_asset_type = classConfigSchema.dump(
ClassConfig(**data_asset_type))
if spark_config is None:
spark_config = {}
configuration = kwargs
configuration.update({
"data_asset_type": data_asset_type,
"spark_config": spark_config
})
if generators:
configuration["generators"] = generators
return configuration
def build_configuration(cls, data_asset_type=None, generators=None, boto3_options=None, reader_method=None,
reader_options=None, limit=None, **kwargs):
"""
Build a full configuration object for a datasource, potentially including generators with defaults.
Args:
data_asset_type: A ClassConfig dictionary
generators: Generator configuration dictionary
boto3_options: Optional dictionary with key-value pairs to pass to boto3 during instantiation.
reader_method: Optional default reader_method for generated batches
reader_options: Optional default reader_options for generated batches
limit: Optional default limit for generated batches
**kwargs: Additional kwargs to be part of the datasource constructor's initialization
Returns:
A complete datasource configuration.
"""
# PENDING DELETION - JPC - 20200130
# if generators is None:
# Provide a gentle way to build a datasource with a sane default,
# including ability to specify the base_directory and reader_options
# base_directory = kwargs.pop("base_directory", "data")
# By default, use CSV sniffer to infer separator, which requires the python engine
# reader_options = kwargs.pop("reader_options", {
# "sep": None,
# "engine": "python"
# })
# generators = {
# # "default": {
# # "class_name": "SubdirReaderBatchKwargsGenerator",
# # "base_directory": base_directory,
# # "reader_options": reader_options
# # },
# # "passthrough": {
# # "class_name": "PassthroughGenerator",
# # }
# }
if data_asset_type is None:
data_asset_type = ClassConfig(
class_name="PandasDataset")
else:
try:
data_asset_type = ClassConfig(**data_asset_type)
except TypeError:
# In this case, we allow the passed config, for now, in case they're using a legacy string-only config
pass
configuration = kwargs
configuration["data_asset_type"] = data_asset_type
if generators:
configuration["generators"] = generators
if boto3_options is not None:
if isinstance(boto3_options, dict):
configuration.update(boto3_options)
else:
raise ValueError("boto3_options must be a dictionary of key-value pairs to pass to boto3 upon "
"initialization.")
if reader_options is not None:
if isinstance(reader_options, dict):
configuration.update(reader_options)
else:
raise ValueError("boto3_options must be a dictionary of key-value pairs to pass to boto3 upon "
"initialization.")
if reader_method is not None:
configuration["reader_method"] = reader_method
if limit is not None:
configuration["limit"] = limit
return configuration
def _get_data_asset(self,
batch_kwargs,
expectation_suite,
caching=True,
**kwargs):
"""class-private implementation of get_data_asset"""
if self.spark is None:
logger.error("No spark session available")
return None
batch_kwargs.update(kwargs)
reader_options = batch_kwargs.copy()
if "data_asset_type" in reader_options:
data_asset_type_config = reader_options.pop(
"data_asset_type") # Get and remove the config
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, SparkDFDataset):
raise ValueError(
"SparkDFDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of SparkDFDataset." %
data_asset_type.__name__)
if "path" in batch_kwargs or "s3" in batch_kwargs:
if "path" in batch_kwargs:
path = reader_options.pop(
"path"
) # We remove this so it is not used as a reader option
else:
path = reader_options.pop("s3")
reader_options.pop("timestamp",
"") # ditto timestamp (but missing ok)
reader_method = reader_options.pop("reader_method", None)
if reader_method is None:
reader_method = self._guess_reader_method_from_path(path)
if reader_method is None:
raise BatchKwargsError(
"Unable to determine reader for path: %s" % path,
batch_kwargs)
else:
try:
reader_method = ReaderMethods[reader_method]
except KeyError:
raise BatchKwargsError(
"Unknown reader method: %s" % reader_method,
batch_kwargs)
reader = self.spark.read
for option in reader_options.items():
reader = reader.option(*option)
if reader_method == ReaderMethods.CSV:
df = reader.csv(path)
elif reader_method == ReaderMethods.parquet:
df = reader.parquet(path)
elif reader_method == ReaderMethods.delta:
df = reader.format("delta").load(path)
else:
raise BatchKwargsError(
"Unsupported reader: %s" % reader_method.name,
batch_kwargs)
elif "query" in batch_kwargs:
df = self.spark.sql(batch_kwargs["query"])
elif "df" in batch_kwargs and isinstance(batch_kwargs["df"],
(DataFrame, SparkDFDataset)):
df = batch_kwargs.pop(
"df") # We don't want to store the actual DataFrame in kwargs
if isinstance(df, SparkDFDataset):
# Grab just the spark_df reference, since we want to override everything else
df = df.spark_df
batch_kwargs["SparkDFRef"] = True
else:
raise BatchKwargsError(
"Unrecognized batch_kwargs for spark_source", batch_kwargs)
return data_asset_type(df,
expectation_suite=expectation_suite,
data_context=self._data_context,
batch_kwargs=batch_kwargs,
caching=caching)
def _get_data_asset(self, batch_kwargs, expectation_suite, **kwargs):
batch_kwargs.update(kwargs)
reader_options = batch_kwargs.copy()
if "data_asset_type" in reader_options:
data_asset_type_config = reader_options.pop(
"data_asset_type") # Get and remove the config
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, PandasDataset):
raise ValueError(
"PandasDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of PandasDataset." %
data_asset_type.__name__)
if "path" in batch_kwargs:
path = reader_options.pop(
"path") # We need to remove from the reader
reader_options.pop("timestamp",
"") # ditto timestamp (but missing ok)
reader_method = reader_options.pop("reader_method", None)
if reader_method is None:
reader_method = self._guess_reader_method_from_path(path)
if reader_method is None:
raise BatchKwargsError(
"Unable to determine reader for path: %s" % path,
batch_kwargs)
else:
try:
reader_method = ReaderMethods[reader_method]
except KeyError:
raise BatchKwargsError(
"Unknown reader method: %s" % reader_method,
batch_kwargs)
if reader_method == ReaderMethods.CSV:
df = pd.read_csv(path, **reader_options)
elif reader_method == ReaderMethods.parquet:
df = pd.read_parquet(path, **reader_options)
elif reader_method == ReaderMethods.excel:
df = pd.read_excel(path, **reader_options)
elif reader_method == ReaderMethods.JSON:
df = pd.read_json(path, **reader_options)
else:
raise BatchKwargsError(
"Unsupported reader: %s" % reader_method.name,
batch_kwargs)
elif "df" in batch_kwargs and isinstance(batch_kwargs["df"],
(pd.DataFrame, pd.Series)):
df = batch_kwargs.pop(
"df") # We don't want to store the actual dataframe in kwargs
batch_kwargs["PandasInMemoryDF"] = True
else:
raise BatchKwargsError(
"Invalid batch_kwargs: path or df is required for a PandasDatasource",
batch_kwargs)
return data_asset_type(df,
expectation_suite=expectation_suite,
data_context=self._data_context,
batch_kwargs=batch_kwargs)
def _get_data_asset(self,
batch_kwargs,
expectation_suite,
caching=True,
**kwargs):
"""class-private implementation of get_data_asset"""
if self.spark is None:
logger.error("No spark session available")
return None
for k, v in kwargs.items():
if isinstance(v, dict):
if k in batch_kwargs and isinstance(batch_kwargs[k], dict):
batch_kwargs[k].update(v)
else:
batch_kwargs[k] = v
else:
batch_kwargs[k] = v
reader_options = batch_kwargs.get("reader_options", {})
# We need to build a batch_id to be used in the dataframe
batch_id = BatchId({"timestamp": time.time()})
if "data_asset_type" in batch_kwargs:
data_asset_type_config = reader_options.pop(
"data_asset_type") # Get and remove the config
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, SparkDFDataset):
raise ValueError(
"SparkDFDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of SparkDFDataset." %
data_asset_type.__name__)
if "path" in batch_kwargs or "s3" in batch_kwargs:
# If both are present, let s3 override
path = batch_kwargs.get("path")
path = batch_kwargs.get("s3", path)
reader_method = batch_kwargs.get("reader_method")
if reader_method is None:
reader_method = self._guess_reader_method_from_path(path)
if reader_method is None:
raise BatchKwargsError(
"Unable to determine reader for path: %s" % path,
batch_kwargs)
else:
try:
reader_method = ReaderMethods[reader_method]
except KeyError:
raise BatchKwargsError(
"Unknown reader method: %s" % reader_method,
batch_kwargs)
reader = self.spark.read
for option in reader_options.items():
reader = reader.option(*option)
if reader_method == ReaderMethods.CSV:
df = reader.csv(path)
elif reader_method == ReaderMethods.parquet:
df = reader.parquet(path)
elif reader_method == ReaderMethods.delta:
df = reader.format("delta").load(path)
else:
raise BatchKwargsError(
"Unsupported reader: %s" % reader_method.name,
batch_kwargs)
elif "query" in batch_kwargs:
df = self.spark.sql(batch_kwargs["query"])
elif "dataset" in batch_kwargs and isinstance(
batch_kwargs["dataset"], (DataFrame, SparkDFDataset)):
df = batch_kwargs.get("dataset")
# We don't want to store the actual dataframe in kwargs; copy the remaining batch_kwargs
batch_kwargs = {
k: batch_kwargs[k]
for k in batch_kwargs if k != 'dataset'
}
if isinstance(df, SparkDFDataset):
# Grab just the spark_df reference, since we want to override everything else
df = df.spark_df
# Record this in the kwargs *and* the id
batch_kwargs["SparkDFRef"] = True
batch_id["SparkDFRef"] = True
else:
raise BatchKwargsError(
"Unrecognized batch_kwargs for spark_source", batch_kwargs)
if "limit" in batch_kwargs:
df = df.limit(batch_kwargs['limit'])
return data_asset_type(df,
expectation_suite=expectation_suite,
data_context=self._data_context,
batch_kwargs=batch_kwargs,
caching=caching,
batch_id=batch_id)
def _get_data_asset(self, batch_kwargs, expectation_suite, **kwargs):
batch_kwargs.update(kwargs)
# pandas cannot take unicode as a delimiter, which can happen in py2. Handle this case explicitly.
# We handle it here so that the updated value will be in the batch_kwargs for transparency to the user.
if PY2 and "sep" in batch_kwargs and batch_kwargs["sep"] is not None:
batch_kwargs["sep"] = str(batch_kwargs["sep"])
# We will use and manipulate reader_options along the way
reader_options = batch_kwargs.copy()
# We need to build a batch_id to be used in the dataframe
batch_id = BatchId({"timestamp": time.time()})
if "data_asset_type" in batch_kwargs:
data_asset_type_config = reader_options.pop(
"data_asset_type") # Get and remove the config
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, PandasDataset):
raise ValueError(
"PandasDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of PandasDataset." %
data_asset_type.__name__)
if "path" in batch_kwargs:
path = reader_options.pop(
"path") # We remove this so it is not used as a reader option
reader_options.pop("timestamp",
"") # ditto timestamp (but missing ok)
reader_options.pop("partition_id", "")
reader_method = reader_options.pop("reader_method", None)
reader_fn, reader_fn_options = self._get_reader_fn(
reader_method, path, reader_options)
try:
df = getattr(pd, reader_fn)(path, **reader_fn_options)
except AttributeError:
raise BatchKwargsError(
"Unsupported reader: %s" % reader_method.name,
batch_kwargs)
elif "s3" in batch_kwargs:
try:
import boto3
s3 = boto3.client("s3")
except ImportError:
raise BatchKwargsError(
"Unable to load boto3 client to read s3 asset.",
batch_kwargs)
raw_url = reader_options.pop(
"s3") # We need to remove from the reader
reader_options.pop("timestamp",
"") # ditto timestamp (but missing ok)
reader_method = reader_options.pop("reader_method", None)
url = S3Url(raw_url)
logger.debug("Fetching s3 object. Bucket: %s Key: %s" %
(url.bucket, url.key))
s3_object = s3.get_object(Bucket=url.bucket, Key=url.key)
reader_fn, reader_fn_options = self._get_reader_fn(
reader_method, url.key, reader_options)
try:
df = getattr(pd, reader_fn)(StringIO(
s3_object["Body"].read().decode(
s3_object.get("ContentEncoding", "utf-8"))),
**reader_fn_options)
except AttributeError:
raise BatchKwargsError(
"Unsupported reader: %s" % reader_method.name,
batch_kwargs)
except IOError:
raise
elif "dataset" in batch_kwargs and isinstance(
batch_kwargs["dataset"], (pd.DataFrame, pd.Series)):
df = batch_kwargs.pop(
"dataset"
) # We don't want to store the actual dataframe in kwargs
# Record this in the kwargs *and* the id
batch_kwargs["PandasInMemoryDF"] = True
batch_id["PandasInMemoryDF"] = True
else:
raise BatchKwargsError(
"Invalid batch_kwargs: path, s3, or df is required for a PandasDatasource",
batch_kwargs)
if df.memory_usage().sum() < HASH_THRESHOLD:
batch_id["fingerprint"] = hashlib.md5(
pd.util.hash_pandas_object(df, index=True).values).hexdigest()
return data_asset_type(df,
expectation_suite=expectation_suite,
data_context=self._data_context,
batch_kwargs=batch_kwargs,
batch_id=batch_id)
def _get_data_asset(self, batch_kwargs, expectation_suite, **kwargs):
for k, v in kwargs.items():
if isinstance(v, dict):
if k in batch_kwargs and isinstance(batch_kwargs[k], dict):
batch_kwargs[k].update(v)
else:
batch_kwargs[k] = v
else:
batch_kwargs[k] = v
if "data_asset_type" in batch_kwargs:
# Sqlalchemy does not use reader_options or need to remove batch_kwargs since it does not pass
# options through to a later reader
data_asset_type_config = batch_kwargs["data_asset_type"]
try:
data_asset_type_config = ClassConfig(**data_asset_type_config)
except TypeError:
# We tried; we'll pass the config downstream, probably as a string, and handle an error later
pass
else:
data_asset_type_config = self._data_asset_type
data_asset_type = self._get_data_asset_class(data_asset_type_config)
if not issubclass(data_asset_type, SqlAlchemyDataset):
raise ValueError(
"SqlAlchemyDatasource cannot instantiate batch with data_asset_type: '%s'. It "
"must be a subclass of SqlAlchemyDataset." %
data_asset_type.__name__)
# We need to build a batch_id to be used in the dataframe
batch_id = BatchId({"timestamp": time.time()})
if "schema" in batch_kwargs:
schema = batch_kwargs["schema"]
else:
schema = None
if "table" in batch_kwargs:
limit = batch_kwargs.get('limit')
offset = batch_kwargs.get('offset')
if limit is not None or offset is not None:
logger.info(
"Generating query from table batch_kwargs based on limit and offset"
)
raw_query = sqlalchemy.select([sqlalchemy.text("*")])\
.select_from(sqlalchemy.schema.Table(batch_kwargs['table'], sqlalchemy.MetaData(), schema=schema))\
.offset(offset)\
.limit(limit)
query = str(
raw_query.compile(self.engine,
compile_kwargs={"literal_binds": True}))
return data_asset_type(custom_sql=query,
engine=self.engine,
data_context=self._data_context,
expectation_suite=expectation_suite,
batch_kwargs=batch_kwargs,
batch_id=batch_id)
else:
return data_asset_type(table_name=batch_kwargs["table"],
engine=self.engine,
schema=schema,
data_context=self._data_context,
expectation_suite=expectation_suite,
batch_kwargs=batch_kwargs,
batch_id=batch_id)
elif "query" in batch_kwargs:
if "limit" in batch_kwargs or "offset" in batch_kwargs:
logger.warning(
"Limit and offset parameters are ignored when using query-based batch_kwargs; consider "
"adding limit and offset directly to the generated query.")
if "bigquery_temp_table" in batch_kwargs:
table_name = batch_kwargs.get("bigquery_temp_table")
else:
table_name = None
query = Template(batch_kwargs["query"]).safe_substitute(**kwargs)
return data_asset_type(custom_sql=query,
engine=self.engine,
table_name=table_name,
data_context=self._data_context,
expectation_suite=expectation_suite,
batch_kwargs=batch_kwargs,
batch_id=batch_id)
else:
raise ValueError(
"Invalid batch_kwargs: exactly one of 'table' or 'query' must be specified"
)