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Read from Delta

SparkDeltaSource

Bases: SourceInterface

The Spark Delta Source is used to read data from a Delta table.

Example

#Delta Source for Streaming Queries

from rtdip_sdk.pipelines.sources import SparkDeltaSource
from rtdip_sdk.pipelines.utilities import SparkSessionUtility

# Not required if using Databricks
spark = SparkSessionUtility(config={}).execute()

delta_source = SparkDeltaSource(
    spark=spark,
    options={
        "maxFilesPerTrigger": 1000,
        "ignoreChanges: True,
        "startingVersion": 0
    },
    table_name="{YOUR-DELTA-TABLE-PATH}"
)

delta_source.read_stream()

#Delta Source for Batch Queries

from rtdip_sdk.pipelines.sources import SparkDeltaSource
from rtdip_sdk.pipelines.utilities import SparkSessionUtility

# Not required if using Databricks
spark = SparkSessionUtility(config={}).execute()

delta_source = SparkDeltaSource(
    spark=spark,
    options={
        "versionAsOf": 0,
        "timestampAsOf": "yyyy-mm-dd hh:mm:ss[.fffffffff]"
    },
    table_name="{YOUR-DELTA-TABLE-PATH}"
)

delta_source.read_batch()

Parameters:

Name Type Description Default
spark SparkSession

Spark Session required to read data from a Delta table.

 required
options dict

Options that can be specified for a Delta Table read operation (See Attributes table below). Further information on the options is available for batch and streaming.

 required
table_name str

Name of the Hive Metastore or Unity Catalog Delta Table

 required

Attributes:

Name Type Description
maxFilesPerTrigger int

How many new files to be considered in every micro-batch. The default is 1000. (Streaming)
maxBytesPerTrigger int

How much data gets processed in each micro-batch. (Streaming)
ignoreDeletes bool str

Ignore transactions that delete data at partition boundaries. (Streaming)
ignoreChanges bool str

Pre-process updates if files had to be rewritten in the source table due to a data changing operation. (Streaming)
startingVersion int str

The Delta Lake version to start from. (Streaming)
startingTimestamp datetime str

The timestamp to start from. (Streaming)
withEventTimeOrder bool str

Whether the initial snapshot should be processed with event time order. (Streaming)
timestampAsOf datetime str

Query the Delta Table from a specific point in time. (Batch)
versionAsOf int str

Query the Delta Table from a specific version. (Batch)
Source code in src/sdk/python/rtdip_sdk/pipelines/sources/spark/delta.py 
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class SparkDeltaSource(SourceInterface):
    """
    The Spark Delta Source is used to read data from a Delta table.

    Example
    --------
    ```python
    #Delta Source for Streaming Queries

    from rtdip_sdk.pipelines.sources import SparkDeltaSource
    from rtdip_sdk.pipelines.utilities import SparkSessionUtility

    # Not required if using Databricks
    spark = SparkSessionUtility(config={}).execute()

    delta_source = SparkDeltaSource(
        spark=spark,
        options={
            "maxFilesPerTrigger": 1000,
            "ignoreChanges: True,
            "startingVersion": 0
        },
        table_name="{YOUR-DELTA-TABLE-PATH}"
    )

    delta_source.read_stream()
    ```
    ```python
    #Delta Source for Batch Queries

    from rtdip_sdk.pipelines.sources import SparkDeltaSource
    from rtdip_sdk.pipelines.utilities import SparkSessionUtility

    # Not required if using Databricks
    spark = SparkSessionUtility(config={}).execute()

    delta_source = SparkDeltaSource(
        spark=spark,
        options={
            "versionAsOf": 0,
            "timestampAsOf": "yyyy-mm-dd hh:mm:ss[.fffffffff]"
        },
        table_name="{YOUR-DELTA-TABLE-PATH}"
    )

    delta_source.read_batch()
    ```

    Parameters:
        spark (SparkSession): Spark Session required to read data from a Delta table.
        options (dict): Options that can be specified for a Delta Table read operation (See Attributes table below). Further information on the options is available for [batch](https://docs.delta.io/latest/delta-batch.html#read-a-table){ target="_blank" } and [streaming](https://docs.delta.io/latest/delta-streaming.html#delta-table-as-a-source){ target="_blank" }.
        table_name (str): Name of the Hive Metastore or Unity Catalog Delta Table

    Attributes:
        maxFilesPerTrigger (int): How many new files to be considered in every micro-batch. The default is 1000. (Streaming)
        maxBytesPerTrigger (int): How much data gets processed in each micro-batch. (Streaming)
        ignoreDeletes (bool str): Ignore transactions that delete data at partition boundaries. (Streaming)
        ignoreChanges (bool str): Pre-process updates if files had to be rewritten in the source table due to a data changing operation. (Streaming)
        startingVersion (int str): The Delta Lake version to start from. (Streaming)
        startingTimestamp (datetime str): The timestamp to start from. (Streaming)
        withEventTimeOrder (bool str): Whether the initial snapshot should be processed with event time order. (Streaming)
        timestampAsOf (datetime str): Query the Delta Table from a specific point in time. (Batch)
        versionAsOf (int str): Query the Delta Table from a specific version. (Batch)
    """

    spark: SparkSession
    options: dict
    table_name: str

    def __init__(self, spark: SparkSession, options: dict, table_name: str) -> None:
        self.spark = spark
        self.options = options
        self.table_name = table_name

    @staticmethod
    def system_type():
        """
        Attributes:
            SystemType (Environment): Requires PYSPARK
        """
        return SystemType.PYSPARK

    @staticmethod
    def libraries():
        libraries = Libraries()
        libraries.add_maven_library(get_default_package("spark_delta_core"))
        return libraries

    @staticmethod
    def settings() -> dict:
        return {}

    def pre_read_validation(self):
        return True

    def post_read_validation(self):
        return True

    def read_batch(self):
        """
        Reads batch data from Delta. Most of the options provided by the Apache Spark DataFrame read API are supported for performing batch reads on Delta tables.
        """
        try:
            return (
                self.spark.read.format("delta")
                .options(**self.options)
                .table(self.table_name)
            )

        except Py4JJavaError as e:
            logging.exception(e.errmsg)
            raise e
        except Exception as e:
            logging.exception(str(e))
            raise e

    def read_stream(self) -> DataFrame:
        """
        Reads streaming data from Delta. All of the data in the table is processed as well as any new data that arrives after the stream started. .load() can take table name or path.
        """
        try:
            return (
                self.spark.readStream.format("delta")
                .options(**self.options)
                .load(self.table_name)
            )

        except Py4JJavaError as e:
            logging.exception(e.errmsg)
            raise e
        except Exception as e:
            logging.exception(str(e))
            raise e

system_type() staticmethod

Attributes:

Name Type Description
SystemType Environment

Requires PYSPARK
Source code in src/sdk/python/rtdip_sdk/pipelines/sources/spark/delta.py 
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@staticmethod
def system_type():
    """
    Attributes:
        SystemType (Environment): Requires PYSPARK
    """
    return SystemType.PYSPARK

read_batch()

Reads batch data from Delta. Most of the options provided by the Apache Spark DataFrame read API are supported for performing batch reads on Delta tables.

Source code in src/sdk/python/rtdip_sdk/pipelines/sources/spark/delta.py 
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def read_batch(self):
    """
    Reads batch data from Delta. Most of the options provided by the Apache Spark DataFrame read API are supported for performing batch reads on Delta tables.
    """
    try:
        return (
            self.spark.read.format("delta")
            .options(**self.options)
            .table(self.table_name)
        )

    except Py4JJavaError as e:
        logging.exception(e.errmsg)
        raise e
    except Exception as e:
        logging.exception(str(e))
        raise e

read_stream()

Reads streaming data from Delta. All of the data in the table is processed as well as any new data that arrives after the stream started. .load() can take table name or path.

Source code in src/sdk/python/rtdip_sdk/pipelines/sources/spark/delta.py 
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def read_stream(self) -> DataFrame:
    """
    Reads streaming data from Delta. All of the data in the table is processed as well as any new data that arrives after the stream started. .load() can take table name or path.
    """
    try:
        return (
            self.spark.readStream.format("delta")
            .options(**self.options)
            .load(self.table_name)
        )

    except Py4JJavaError as e:
        logging.exception(e.errmsg)
        raise e
    except Exception as e:
        logging.exception(str(e))
        raise e