python/pyspark/sql/datasource.py

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from abc import ABC, abstractmethod
from typing import Any, Dict, Iterator, Tuple, Union, TYPE_CHECKING

from pyspark.sql import Row
from pyspark.sql.types import StructType

if TYPE_CHECKING:
    from pyspark.sql._typing import OptionalPrimitiveType


__all__ = ["DataSource", "DataSourceReader"]


class DataSource(ABC):
    """
    A base class for data sources.

    This class represents a custom data source that allows for reading from and/or
    writing to it. The data source provides methods to create readers and writers
    for reading and writing data, respectively. At least one of the methods ``reader``
    or ``writer`` must be implemented by any subclass to make the data source either
    readable or writable (or both).

    After implementing this interface, you can start to load your data source using
    ``spark.read.format(...).load()`` and save data using ``df.write.format(...).save()``.
    """

    def __init__(self, options: Dict[str, "OptionalPrimitiveType"]):
        """
        Initializes the data source with user-provided options.

        Parameters
        ----------
        options : dict
            A dictionary representing the options for this data source.

        Notes
        -----
        This method should not contain any non-serializable objects.
        """
        self.options = options

    @property
    def name(self) -> str:
        """
        Returns a string represents the format name of this data source.

        By default, it is the class name of the data source. It can be overridden to
        provide a customized short name for the data source.

        Examples
        --------
        >>> def name(self):
        ...     return "my_data_source"
        """
        return self.__class__.__name__

    def schema(self) -> Union[StructType, str]:
        """
        Returns the schema of the data source.

        It can reference the ``options`` field to infer the data source's schema when
        users do not explicitly specify it. This method is invoked once when calling
        ``spark.read.format(...).load()`` to get the schema for a data source read
        operation. If this method is not implemented, and a user does not provide a
        schema when reading the data source, an exception will be thrown.

        Returns
        -------
        schema : StructType or str
            The schema of this data source or a DDL string represents the schema

        Examples
        --------
        Returns a DDL string:

        >>> def schema(self):
        ...    return "a INT, b STRING"

        Returns a StructType:

        >>> def schema(self):
        ...   return StructType().add("a", "int").add("b", "string")
        """
        raise NotImplementedError

    def reader(self, schema: StructType) -> "DataSourceReader":
        """
        Returns a ``DataSourceReader`` instance for reading data.

        The implementation is required for readable data sources.

        Parameters
        ----------
        schema : StructType
            The schema of the data to be read.

        Returns
        -------
        reader : DataSourceReader
            A reader instance for this data source.
        """
        raise NotImplementedError


class DataSourceReader(ABC):
    """
    A base class for data source readers. Data source readers are responsible for
    outputting data from a data source.
    """

    def partitions(self) -> Iterator[Any]:
        """
        Returns an iterator of partitions for this data source.

        Partitions are used to split data reading operations into parallel tasks.
        If this method returns N partitions, the query planner will create N tasks.
        Each task will execute ``read(partition)`` in parallel, using the respective
        partition value to read the data.

        This method is called once during query planning. By default, it returns a
        single partition with the value ``None``. Subclasses can override this method
        to return multiple partitions.

        It's recommended to override this method for better performance when reading
        large datasets.

        Returns
        -------
        Iterator[Any]
            An iterator of partitions for this data source. The partition value can be
            any serializable objects.

        Notes
        -----
        This method should not return any un-picklable objects.

        Examples
        --------
        Returns a list of integers:

        >>> def partitions(self):
        ...     return [1, 2, 3]

        Returns a list of string:

        >>> def partitions(self):
        ...     return ["a", "b", "c"]

        Returns a list of tuples:

        >>> def partitions(self):
        ...     return [("a", 1), ("b", 2), ("c", 3)]

        Returns a list of dictionaries:

        >>> def partitions(self):
        ...     return [{"a": 1}, {"b": 2}, {"c": 3}]
        """
        yield None

    @abstractmethod
    def read(self, partition: Any) -> Iterator[Union[Tuple, Row]]:
        """
        Generates data for a given partition and returns an iterator of tuples or rows.

        This method is invoked once per partition to read the data. Implementing
        this method is required for readable data sources. You can initialize any
        non-serializable resources required for reading data from the data source
        within this method.

        Parameters
        ----------
        partition : object
            The partition to read. It must be one of the partition values returned by
            ``partitions()``.

        Returns
        -------
        Iterator[Tuple] or Iterator[Row]
            An iterator of tuples or rows. Each tuple or row will be converted to a row
            in the final DataFrame.

        Examples
        --------
        Yields a list of tuples:

        >>> def read(self, partition):
        ...     yield (partition, 0)
        ...     yield (partition, 1)

        Yields a list of rows:

        >>> def read(self, partition):
        ...     yield Row(partition=partition, value=0)
        ...     yield Row(partition=partition, value=1)
        """
        ...