dtenedor commented on code in PR #43360: URL: https://github.com/apache/spark/pull/43360#discussion_r1358666425
########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,168 @@ +# +# 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: + 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 Review Comment: If we want to create a user-defined Python materialization in the future that has no corresponding reader implementation, this API would preclude this. Should we instead change the invariant to "at least one of 'reader' and/or 'writer' must be implemented"? ########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,168 @@ +# +# 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: Review Comment: ```suggestion class DataSource: """ This represents a user-defined table implemented in Python. Subsequent Spark programs can then query from and/or write to the table. This class defines the table's schema and declares an interface to define logic to generate the rows for a scan from the table, and to respond to writes of rows into the table later. """ ``` ########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,168 @@ +# +# 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: + 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 in the Spark planner + to generate a list of partitions. Note, partition values must be serializable. Review Comment: should we mention which serialization protocol must be supported? Is it the default Python serialization? ########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,168 @@ +# +# 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: + 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 in the Spark planner + to generate a list of partitions. Note, partition values must be serializable. + + The planner then creates an RDD for each partition. Each partition value will be Review Comment: optional: we're trying to move away from RDDs for future use of Spark, maybe just mention that if this method returns N partitions, then the query execution will create N instances of this class, each initialized with one of the partition buffers created earlier? ########## python/pyspark/sql/datasource.py: ########## @@ -0,0 +1,168 @@ +# +# 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: + 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 in the Spark planner + to generate a list of partitions. Note, partition values must be serializable. + + The planner then creates an RDD for each partition. Each partition value will be + passed to `read(partition)` to read the data from this data source. + + If this method is not implemented, or returns an empty list, Spark will create one Review Comment: I would recommend to make this method required. User-defined data sources without partitioning support for Spark don't make much sense, and forcing users to implement this method acts as a safeguard that performance will remain high because the data source evaluation can take place using multiple executors concurrently. If users just want to return a single stream of values, they can create a UDTF that accepts scalar arguments. -- 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. 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