8ddbc431d8
## What changes were proposed in this pull request? There's a latent corner-case bug in PySpark UDF evaluation where executing a `BatchPythonEvaluation` with a single multi-argument UDF where _at least one argument value is repeated_ will crash at execution with a confusing error. This problem was introduced in #12057: the code there has a fast path for handling a "batch UDF evaluation consisting of a single Python UDF", but that branch incorrectly assumes that a single UDF won't have repeated arguments and therefore skips the code for unpacking arguments from the input row (whose schema may not necessarily match the UDF inputs due to de-duplication of repeated arguments which occurred in the JVM before sending UDF inputs to Python). This fix here is simply to remove this special-casing: it turns out that the code in the "multiple UDFs" branch just so happens to work for the single-UDF case because Python treats `(x)` as equivalent to `x`, not as a single-argument tuple. ## How was this patch tested? New regression test in `pyspark.python.sql.tests` module (tested and confirmed that it fails before my fix). Author: Josh Rosen <joshrosen@databricks.com> Closes #17927 from JoshRosen/SPARK-20685.
217 lines
7.8 KiB
Python
217 lines
7.8 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Worker that receives input from Piped RDD.
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"""
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from __future__ import print_function
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import os
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import sys
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import time
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import socket
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import traceback
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from pyspark.accumulators import _accumulatorRegistry
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from pyspark.broadcast import Broadcast, _broadcastRegistry
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from pyspark.taskcontext import TaskContext
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from pyspark.files import SparkFiles
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from pyspark.serializers import write_with_length, write_int, read_long, \
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write_long, read_int, SpecialLengths, UTF8Deserializer, PickleSerializer, BatchedSerializer
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from pyspark import shuffle
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pickleSer = PickleSerializer()
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utf8_deserializer = UTF8Deserializer()
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def report_times(outfile, boot, init, finish):
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write_int(SpecialLengths.TIMING_DATA, outfile)
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write_long(int(1000 * boot), outfile)
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write_long(int(1000 * init), outfile)
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write_long(int(1000 * finish), outfile)
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def add_path(path):
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# worker can be used, so donot add path multiple times
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if path not in sys.path:
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# overwrite system packages
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sys.path.insert(1, path)
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def read_command(serializer, file):
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command = serializer._read_with_length(file)
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if isinstance(command, Broadcast):
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command = serializer.loads(command.value)
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return command
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def chain(f, g):
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"""chain two function together """
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return lambda *a: g(f(*a))
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def wrap_udf(f, return_type):
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if return_type.needConversion():
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toInternal = return_type.toInternal
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return lambda *a: toInternal(f(*a))
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else:
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return lambda *a: f(*a)
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def read_single_udf(pickleSer, infile):
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num_arg = read_int(infile)
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arg_offsets = [read_int(infile) for i in range(num_arg)]
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row_func = None
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for i in range(read_int(infile)):
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f, return_type = read_command(pickleSer, infile)
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if row_func is None:
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row_func = f
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else:
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row_func = chain(row_func, f)
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# the last returnType will be the return type of UDF
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return arg_offsets, wrap_udf(row_func, return_type)
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def read_udfs(pickleSer, infile):
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num_udfs = read_int(infile)
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udfs = {}
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call_udf = []
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for i in range(num_udfs):
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arg_offsets, udf = read_single_udf(pickleSer, infile)
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udfs['f%d' % i] = udf
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args = ["a[%d]" % o for o in arg_offsets]
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call_udf.append("f%d(%s)" % (i, ", ".join(args)))
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# Create function like this:
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# lambda a: (f0(a0), f1(a1, a2), f2(a3))
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# In the special case of a single UDF this will return a single result rather
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# than a tuple of results; this is the format that the JVM side expects.
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mapper_str = "lambda a: (%s)" % (", ".join(call_udf))
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mapper = eval(mapper_str, udfs)
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func = lambda _, it: map(mapper, it)
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ser = BatchedSerializer(PickleSerializer(), 100)
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# profiling is not supported for UDF
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return func, None, ser, ser
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def main(infile, outfile):
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try:
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boot_time = time.time()
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split_index = read_int(infile)
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if split_index == -1: # for unit tests
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exit(-1)
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version = utf8_deserializer.loads(infile)
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if version != "%d.%d" % sys.version_info[:2]:
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raise Exception(("Python in worker has different version %s than that in " +
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"driver %s, PySpark cannot run with different minor versions." +
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"Please check environment variables PYSPARK_PYTHON and " +
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"PYSPARK_DRIVER_PYTHON are correctly set.") %
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("%d.%d" % sys.version_info[:2], version))
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# initialize global state
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taskContext = TaskContext._getOrCreate()
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taskContext._stageId = read_int(infile)
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taskContext._partitionId = read_int(infile)
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taskContext._attemptNumber = read_int(infile)
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taskContext._taskAttemptId = read_long(infile)
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shuffle.MemoryBytesSpilled = 0
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shuffle.DiskBytesSpilled = 0
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_accumulatorRegistry.clear()
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# fetch name of workdir
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spark_files_dir = utf8_deserializer.loads(infile)
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SparkFiles._root_directory = spark_files_dir
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SparkFiles._is_running_on_worker = True
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# fetch names of includes (*.zip and *.egg files) and construct PYTHONPATH
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add_path(spark_files_dir) # *.py files that were added will be copied here
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num_python_includes = read_int(infile)
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for _ in range(num_python_includes):
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filename = utf8_deserializer.loads(infile)
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add_path(os.path.join(spark_files_dir, filename))
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if sys.version > '3':
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import importlib
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importlib.invalidate_caches()
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# fetch names and values of broadcast variables
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num_broadcast_variables = read_int(infile)
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for _ in range(num_broadcast_variables):
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bid = read_long(infile)
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if bid >= 0:
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path = utf8_deserializer.loads(infile)
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_broadcastRegistry[bid] = Broadcast(path=path)
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else:
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bid = - bid - 1
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_broadcastRegistry.pop(bid)
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_accumulatorRegistry.clear()
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is_sql_udf = read_int(infile)
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if is_sql_udf:
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func, profiler, deserializer, serializer = read_udfs(pickleSer, infile)
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else:
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func, profiler, deserializer, serializer = read_command(pickleSer, infile)
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init_time = time.time()
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def process():
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iterator = deserializer.load_stream(infile)
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serializer.dump_stream(func(split_index, iterator), outfile)
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if profiler:
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profiler.profile(process)
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else:
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process()
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except Exception:
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try:
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write_int(SpecialLengths.PYTHON_EXCEPTION_THROWN, outfile)
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write_with_length(traceback.format_exc().encode("utf-8"), outfile)
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except IOError:
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# JVM close the socket
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pass
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except Exception:
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# Write the error to stderr if it happened while serializing
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print("PySpark worker failed with exception:", file=sys.stderr)
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print(traceback.format_exc(), file=sys.stderr)
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exit(-1)
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finish_time = time.time()
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report_times(outfile, boot_time, init_time, finish_time)
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write_long(shuffle.MemoryBytesSpilled, outfile)
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write_long(shuffle.DiskBytesSpilled, outfile)
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# Mark the beginning of the accumulators section of the output
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write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
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write_int(len(_accumulatorRegistry), outfile)
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for (aid, accum) in _accumulatorRegistry.items():
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pickleSer._write_with_length((aid, accum._value), outfile)
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# check end of stream
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if read_int(infile) == SpecialLengths.END_OF_STREAM:
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write_int(SpecialLengths.END_OF_STREAM, outfile)
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else:
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# write a different value to tell JVM to not reuse this worker
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write_int(SpecialLengths.END_OF_DATA_SECTION, outfile)
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exit(-1)
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if __name__ == '__main__':
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# Read a local port to connect to from stdin
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java_port = int(sys.stdin.readline())
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sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
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sock.connect(("127.0.0.1", java_port))
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sock_file = sock.makefile("rwb", 65536)
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main(sock_file, sock_file)
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