Python Data Source API

Overview

The Python Data Source API is a new feature introduced in Spark 4.0, enabling developers to read from custom data sources and write to custom data sinks in Python. This guide provides a comprehensive overview of the API and instructions on how to create, use, and manage Python data sources.

Creating a Python Data Source

To create a custom Python data source, you’ll need to subclass the DataSource base classes and implement the necessary methods for reading and writing data.

This example demonstrates creating a simple data source to generate synthetic data using the faker library. Ensure the faker library is installed and accessible in your Python environment.

Define the Data Source

Start by creating a new subclass of DataSource with the source name, schema.

In order to be used as source or sink in batch or streaming query, corresponding method of DataSource needs to be implemented.

Method that needs to be implemented for a capability:

	source	sink
batch	reader()	writer()
streaming	streamReader() or simpleStreamReader()	streamWriter()

from pyspark.sql.datasource import DataSource, DataSourceReader
from pyspark.sql.types import StructType

class FakeDataSource(DataSource):
    """
    A fake data source for PySpark to generate synthetic data using the `faker` library.
    Options:
    - numRows: specify number of rows to generate. Default value is 3.
    """

    @classmethod
    def name(cls):
        return "fake"

    def schema(self):
        return "name string, date string, zipcode string, state string"

    def reader(self, schema: StructType):
        return FakeDataSourceReader(schema, self.options)

    def streamReader(self, schema: StructType):
        return FakeStreamReader(schema, self.options)

    # Please skip the implementation of this method if streamReader has been implemented.
    def simpleStreamReader(self, schema: StructType):
        return SimpleStreamReader()

    def streamWriter(self, schema: StructType, overwrite: bool):
        return FakeStreamWriter(self.options)

Implementing Reader for Python Data Source

Implement the Reader

Define the reader logic to generate synthetic data. Use the faker library to populate each field in the schema.

class FakeDataSourceReader(DataSourceReader):

    def __init__(self, schema, options):
        self.schema: StructType = schema
        self.options = options

    def read(self, partition):
        from faker import Faker
        fake = Faker()
        # Note: every value in this `self.options` dictionary is a string.
        num_rows = int(self.options.get("numRows", 3))
        for _ in range(num_rows):
            row = []
            for field in self.schema.fields:
                value = getattr(fake, field.name)()
                row.append(value)
            yield tuple(row)

Implementing Streaming Reader and Writer for Python Data Source

Implement the Stream Reader

This is a dummy streaming data reader that generate 2 rows in every microbatch. The streamReader instance has a integer offset that increase by 2 in every microbatch.

class RangePartition(InputPartition):
    def __init__(self, start, end):
        self.start = start
        self.end = end

class FakeStreamReader(DataSourceStreamReader):
    def __init__(self, schema, options):
        self.current = 0

    def initialOffset(self) -> dict:
        """
        Return the initial start offset of the reader.
        """
        return {"offset": 0}

    def latestOffset(self) -> dict:
        """
        Return the current latest offset that the next microbatch will read to.
        """
        self.current += 2
        return {"offset": self.current}

    def partitions(self, start: dict, end: dict):
        """
        Plans the partitioning of the current microbatch defined by start and end offset,
        it needs to return a sequence of :class:`InputPartition` object.
        """
        return [RangePartition(start["offset"], end["offset"])]

    def commit(self, end: dict):
        """
        This is invoked when the query has finished processing data before end offset, this can be used to clean up resource.
        """
        pass

    def read(self, partition) -> Iterator[Tuple]:
        """
        Takes a partition as an input and read an iterator of tuples from the data source.
        """
        start, end = partition.start, partition.end
        for i in range(start, end):
            yield (i, str(i))

Implement the Simple Stream Reader

If the data source has low throughput and doesn’t require partitioning, you can implement SimpleDataSourceStreamReader instead of DataSourceStreamReader.

One of simpleStreamReader() and streamReader() must be implemented for readable streaming data source. And simpleStreamReader() will only be invoked when streamReader() is not implemented.

This is the same dummy streaming reader that generate 2 rows every batch implemented with SimpleDataSourceStreamReader interface.

class SimpleStreamReader(SimpleDataSourceStreamReader):
    def initialOffset(self):
        """
        Return the initial start offset of the reader.
        """
        return {"offset": 0}

    def read(self, start: dict) -> (Iterator[Tuple], dict):
        """
        Takes start offset as an input, return an iterator of tuples and the start offset of next read.
        """
        start_idx = start["offset"]
        it = iter([(i,) for i in range(start_idx, start_idx + 2)])
        return (it, {"offset": start_idx + 2})

    def readBetweenOffsets(self, start: dict, end: dict) -> Iterator[Tuple]:
        """
        Takes start and end offset as input and read an iterator of data deterministically.
        This is called whe query replay batches during restart or after failure.
        """
        start_idx = start["offset"]
        end_idx = end["offset"]
        return iter([(i,) for i in range(start_idx, end_idx)])

    def commit(self, end):
        """
        This is invoked when the query has finished processing data before end offset, this can be used to clean up resource.
        """
        pass

Implement the Stream Writer

This is a streaming data writer that write the metadata information of each microbatch to a local path.

class SimpleCommitMessage(WriterCommitMessage):
   partition_id: int
   count: int

class FakeStreamWriter(DataSourceStreamWriter):
   def __init__(self, options):
       self.options = options
       self.path = self.options.get("path")
       assert self.path is not None

   def write(self, iterator):
       """
       Write the data and return the commit message of that partition
       """
       from pyspark import TaskContext
       context = TaskContext.get()
       partition_id = context.partitionId()
       cnt = 0
       for row in iterator:
           cnt += 1
       return SimpleCommitMessage(partition_id=partition_id, count=cnt)

   def commit(self, messages, batchId) -> None:
       """
       Receives a sequence of :class:`WriterCommitMessage` when all write tasks succeed and decides what to do with it.
       In this FakeStreamWriter, we write the metadata of the microbatch(number of rows and partitions) into a json file inside commit().
       """
       status = dict(num_partitions=len(messages), rows=sum(m.count for m in messages))
       with open(os.path.join(self.path, f"{batchId}.json"), "a") as file:
           file.write(json.dumps(status) + "\n")

   def abort(self, messages, batchId) -> None:
       """
       Receives a sequence of :class:`WriterCommitMessage` from successful tasks when some tasks fail and decides what to do with it.
       In this FakeStreamWriter, we write a failure message into a txt file inside abort().
       """
       with open(os.path.join(self.path, f"{batchId}.txt"), "w") as file:
           file.write(f"failed in batch {batchId}")

Serialization Requirement

User defined DataSource, DataSourceReader, DataSourceWriter, DataSourceStreamReader and DataSourceStreamWriter and their methods must be able to be serialized by pickle.

For library that are used inside a method, it must be imported inside the method. For example, TaskContext must be imported inside the read() method in the code below.

def read(self, partition):
    from pyspark import TaskContext
    context = TaskContext.get()

Using a Python Data Source

Use a Python Data Source in Batch Query

After defining your data source, it must be registered before usage.

spark.dataSource.register(FakeDataSource)

Use the fake datasource with the default schema and options:

spark.read.format("fake").load().show()

# +-----------+----------+-------+-------+
# |       name|      date|zipcode|  state|
# +-----------+----------+-------+-------+
# |Carlos Cobb|2018-07-15|  73003|Indiana|
# | Eric Scott|1991-08-22|  10085|  Idaho|
# | Amy Martin|1988-10-28|  68076| Oregon|
# +-----------+----------+-------+-------+

Use the fake datasource with a custom schema:

spark.read.format("fake").schema("name string, company string").load().show()

# +---------------------+--------------+
# |name                 |company       |
# +---------------------+--------------+
# |Tanner Brennan       |Adams Group   |
# |Leslie Maxwell       |Santiago Group|
# |Mrs. Jacqueline Brown|Maynard Inc   |
# +---------------------+--------------+

Use the fake datasource with a different number of rows:

spark.read.format("fake").option("numRows", 5).load().show()

# +--------------+----------+-------+------------+
# |          name|      date|zipcode|       state|
# +--------------+----------+-------+------------+
# |  Pam Mitchell|1988-10-20|  23788|   Tennessee|
# |Melissa Turner|1996-06-14|  30851|      Nevada|
# |  Brian Ramsey|2021-08-21|  55277|  Washington|
# |  Caitlin Reed|1983-06-22|  89813|Pennsylvania|
# | Douglas James|2007-01-18|  46226|     Alabama|
# +--------------+----------+-------+------------+

Use a Python Data Source in Streaming Query

Once we register the python data source, we can also use it in streaming queries as source of readStream() or sink of writeStream() by passing short name or full name to format().

Start a query that read from fake python data source and write to console

query = spark.readStream.format("fake").load().writeStream().format("console").start()

# +---+
# | id|
# +---+
# |  0|
# |  1|
# +---+
# +---+
# | id|
# +---+
# |  2|
# |  3|
# +---+

We can also use the same data source in streaming reader and writer

query = spark.readStream.format("fake").load().writeStream().format("fake").start("/output_path")