python/pyspark/sql/worker/python_streaming_sink_runner.py

#
# 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.
#

import os
import sys
from typing import IO

from pyspark.accumulators import _accumulatorRegistry
from pyspark.errors import PySparkAssertionError, PySparkRuntimeError
from pyspark.serializers import (
    read_bool,
    read_int,
    read_long,
    write_int,
    SpecialLengths,
)
from pyspark.sql.datasource import DataSource, WriterCommitMessage
from pyspark.sql.types import (
    _parse_datatype_json_string,
    StructType,
)
from pyspark.util import handle_worker_exception, local_connect_and_auth
from pyspark.worker_util import (
    check_python_version,
    read_command,
    pickleSer,
    send_accumulator_updates,
    setup_broadcasts,
    setup_memory_limits,
    setup_spark_files,
    utf8_deserializer,
)


def main(infile: IO, outfile: IO) -> None:
    """
    Main method for committing or aborting a data source streaming write operation.

    This process is invoked from the `PythonStreamingSinkCommitRunner.runInPython`
    method in the StreamingWrite implementation of the PythonDataSourceV2. It is
    responsible for invoking either the `commit` or the `abort` method on a data source
    writer instance, given a list of commit messages.
    """
    try:
        check_python_version(infile)
        setup_spark_files(infile)
        setup_broadcasts(infile)

        memory_limit_mb = int(os.environ.get("PYSPARK_PLANNER_MEMORY_MB", "-1"))
        setup_memory_limits(memory_limit_mb)

        _accumulatorRegistry.clear()

        # Receive the data source instance.
        data_source = read_command(pickleSer, infile)

        if not isinstance(data_source, DataSource):
            raise PySparkAssertionError(
                error_class="PYTHON_DATA_SOURCE_TYPE_MISMATCH",
                message_parameters={
                    "expected": "a Python data source instance of type 'DataSource'",
                    "actual": f"'{type(data_source).__name__}'",
                },
            )
        # Receive the data source output schema.
        schema_json = utf8_deserializer.loads(infile)
        schema = _parse_datatype_json_string(schema_json)
        if not isinstance(schema, StructType):
            raise PySparkAssertionError(
                error_class="PYTHON_DATA_SOURCE_TYPE_MISMATCH",
                message_parameters={
                    "expected": "an output schema of type 'StructType'",
                    "actual": f"'{type(schema).__name__}'",
                },
            )
        # Receive the `overwrite` flag.
        overwrite = read_bool(infile)
        # Instantiate data source reader.
        try:
            # Create the data source writer instance.
            writer = data_source.streamWriter(schema=schema, overwrite=overwrite)

            # Receive the commit messages.
            num_messages = read_int(infile)
            commit_messages = []
            for _ in range(num_messages):
                message = pickleSer._read_with_length(infile)
                if message is not None and not isinstance(message, WriterCommitMessage):
                    raise PySparkAssertionError(
                        error_class="PYTHON_DATA_SOURCE_TYPE_MISMATCH",
                        message_parameters={
                            "expected": "an instance of WriterCommitMessage",
                            "actual": f"'{type(message).__name__}'",
                        },
                    )
                commit_messages.append(message)

            batch_id = read_long(infile)
            abort = read_bool(infile)

            # Commit or abort the Python data source write.
            # Note the commit messages can be None if there are failed tasks.
            if abort:
                writer.abort(commit_messages, batch_id)
            else:
                writer.commit(commit_messages, batch_id)
            # Send a status code back to JVM.
            write_int(0, outfile)
            outfile.flush()
        except Exception as e:
            error_msg = "data source {} throw exception: {}".format(data_source.name, e)
            raise PySparkRuntimeError(
                error_class="PYTHON_STREAMING_DATA_SOURCE_RUNTIME_ERROR",
                message_parameters={"action": "commitOrAbort", "error": error_msg},
            )
    except BaseException as e:
        handle_worker_exception(e, outfile)
        sys.exit(-1)
    send_accumulator_updates(outfile)

    # check end of stream
    if read_int(infile) == SpecialLengths.END_OF_STREAM:
        write_int(SpecialLengths.END_OF_STREAM, outfile)
    else:
        # write a different value to tell JVM to not reuse this worker
        write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
        sys.exit(-1)


if __name__ == "__main__":
    # Read information about how to connect back to the JVM from the environment.
    java_port = int(os.environ["PYTHON_WORKER_FACTORY_PORT"])
    auth_secret = os.environ["PYTHON_WORKER_FACTORY_SECRET"]
    (sock_file, _) = local_connect_and_auth(java_port, auth_secret)
    write_int(os.getpid(), sock_file)
    sock_file.flush()
    main(sock_file, sock_file)