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Version: 0.17.19

SparkDFExecutionEngine

class great_expectations.execution_engine.SparkDFExecutionEngine(*args, persist=True, spark_config=None, force_reuse_spark_context=True, **kwargs)#

SparkDFExecutionEngine instantiates the ExecutionEngine API to support computations using Spark platform.

This class holds an attribute spark_df which is a spark.sql.DataFrame.

Constructor builds a SparkDFExecutionEngine, using provided configuration parameters.

Parameters:

  • *args – Positional arguments for configuring SparkDFExecutionEngine

  • persist – If True (default), then creation of the Spark DataFrame is done outside this class

  • spark_config – Dictionary of Spark configuration options

  • force_reuse_spark_context – If True then utilize existing SparkSession if it exists and is active

  • **kwargs – Keyword arguments for configuring SparkDFExecutionEngine

For example:

    name: str = "great_expectations-ee-config"
    spark_config: Dict[str, str] = {
    "spark.app.name": name,
    "spark.sql.catalogImplementation": "hive",
    "spark.executor.memory": "512m",
    }
    execution_engine = SparkDFExecutionEngine(spark_config=spark_config)
    spark_session: SparkSession = execution_engine.spark

get_compute_domain(domain_kwargs: dict, domain_type: Union[str, great_expectations.core.metric_domain_types.MetricDomainTypes], accessor_keys: Optional[Iterable[str]] = None) → Tuple[pyspark.DataFrame, dict, dict]#

Uses a DataFrame and Domain kwargs (which include a row condition and a condition parser) to obtain and/or query a Batch of data.

Returns in the format of a Spark DataFrame along with Domain arguments required for computing. If the Domain is a single column, this is added to 'accessor Domain kwargs' and used for later access.

Parameters:

  • 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:

  • a DataFrame (the data on which to compute)

  • a dictionary of compute_domain_kwargs, describing the DataFrame

  • a dictionary of accessor_domain_kwargs, describing any accessors needed to identify the Domain within the compute domain

Return type:

A tuple including

get_domain_records(domain_kwargs: dict) → pyspark.DataFrame#

Uses the given Domain kwargs (which include row_condition, condition_parser, and ignore_row_if directives) to obtain and/or query a batch.

Parameters:

domain_kwargs (dict) –

Returns:

A DataFrame (the data on which to compute returned in the format of a Spark DataFrame)