allisonwang-db commented on code in PR #43360: URL: https://github.com/apache/spark/pull/43360#discussion_r1369086390
########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,185 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +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 + 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. + + 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 short name of this 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") + """ + ... Review Comment: Actually, we should raise the not implemented error here since the return type of `schema` should not be None ########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,185 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +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 + 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. + + 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 short name of this 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") + """ + ... + + def reader(self, schema: StructType) -> "DataSourceReader": + """ + Returns a DataSourceReader instance for reading data. + + This method is required for readable data sources. It will be called once during + the physical planning stage in the Spark planner. + + 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 a list of partitions for this data source. + + This method is called once during the physical planning stage to generate a list + of partitions. If the method returns N partitions, then the planner will create + N tasks. Each task will then execute ``read(partition)`` in parallel, using each + partition value to read the data from this data source. + + If this method is not implemented, or returns an empty list, Spark will create one + partition for the result DataFrame and use one single task to read the data. + + Returns + ------- + partitions : list + A list of partitions for this data source. The partition can be any arbitrary + 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)] + """ + ... + + @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 by Spark tasks to read the data. + You can initialize any non-serializable resources required for reading data from + the data source within this method. + + Implementing this method is required for readable data sources. + + Parameters + ---------- + partition : object + The partition to read. It must be one of the partition values returned by + ``partitions()`` or None if ``partitions()`` method is not implemented. Review Comment: Updated the default implementation of `partitions()` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected] --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
