spark-instrumented-optimizer/python/pyspark/java_gateway.py
Bryan Cutler 209b9361ac [SPARK-20791][PYSPARK] Use Arrow to create Spark DataFrame from Pandas
## What changes were proposed in this pull request?

This change uses Arrow to optimize the creation of a Spark DataFrame from a Pandas DataFrame. The input df is sliced according to the default parallelism. The optimization is enabled with the existing conf "spark.sql.execution.arrow.enabled" and is disabled by default.

## How was this patch tested?

Added new unit test to create DataFrame with and without the optimization enabled, then compare results.

Author: Bryan Cutler <cutlerb@gmail.com>
Author: Takuya UESHIN <ueshin@databricks.com>

Closes #19459 from BryanCutler/arrow-createDataFrame-from_pandas-SPARK-20791.
2017-11-13 13:16:01 +09:00

129 lines
5.8 KiB
Python

#
# 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 atexit
import os
import sys
import select
import signal
import shlex
import socket
import platform
from subprocess import Popen, PIPE
if sys.version >= '3':
xrange = range
from py4j.java_gateway import java_import, JavaGateway, GatewayClient
from pyspark.find_spark_home import _find_spark_home
from pyspark.serializers import read_int
def launch_gateway(conf=None):
"""
launch jvm gateway
:param conf: spark configuration passed to spark-submit
:return:
"""
if "PYSPARK_GATEWAY_PORT" in os.environ:
gateway_port = int(os.environ["PYSPARK_GATEWAY_PORT"])
else:
SPARK_HOME = _find_spark_home()
# Launch the Py4j gateway using Spark's run command so that we pick up the
# proper classpath and settings from spark-env.sh
on_windows = platform.system() == "Windows"
script = "./bin/spark-submit.cmd" if on_windows else "./bin/spark-submit"
command = [os.path.join(SPARK_HOME, script)]
if conf:
for k, v in conf.getAll():
command += ['--conf', '%s=%s' % (k, v)]
submit_args = os.environ.get("PYSPARK_SUBMIT_ARGS", "pyspark-shell")
if os.environ.get("SPARK_TESTING"):
submit_args = ' '.join([
"--conf spark.ui.enabled=false",
submit_args
])
command = command + shlex.split(submit_args)
# Start a socket that will be used by PythonGatewayServer to communicate its port to us
callback_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
callback_socket.bind(('127.0.0.1', 0))
callback_socket.listen(1)
callback_host, callback_port = callback_socket.getsockname()
env = dict(os.environ)
env['_PYSPARK_DRIVER_CALLBACK_HOST'] = callback_host
env['_PYSPARK_DRIVER_CALLBACK_PORT'] = str(callback_port)
# Launch the Java gateway.
# We open a pipe to stdin so that the Java gateway can die when the pipe is broken
if not on_windows:
# Don't send ctrl-c / SIGINT to the Java gateway:
def preexec_func():
signal.signal(signal.SIGINT, signal.SIG_IGN)
proc = Popen(command, stdin=PIPE, preexec_fn=preexec_func, env=env)
else:
# preexec_fn not supported on Windows
proc = Popen(command, stdin=PIPE, env=env)
gateway_port = None
# We use select() here in order to avoid blocking indefinitely if the subprocess dies
# before connecting
while gateway_port is None and proc.poll() is None:
timeout = 1 # (seconds)
readable, _, _ = select.select([callback_socket], [], [], timeout)
if callback_socket in readable:
gateway_connection = callback_socket.accept()[0]
# Determine which ephemeral port the server started on:
gateway_port = read_int(gateway_connection.makefile(mode="rb"))
gateway_connection.close()
callback_socket.close()
if gateway_port is None:
raise Exception("Java gateway process exited before sending the driver its port number")
# In Windows, ensure the Java child processes do not linger after Python has exited.
# In UNIX-based systems, the child process can kill itself on broken pipe (i.e. when
# the parent process' stdin sends an EOF). In Windows, however, this is not possible
# because java.lang.Process reads directly from the parent process' stdin, contending
# with any opportunity to read an EOF from the parent. Note that this is only best
# effort and will not take effect if the python process is violently terminated.
if on_windows:
# In Windows, the child process here is "spark-submit.cmd", not the JVM itself
# (because the UNIX "exec" command is not available). This means we cannot simply
# call proc.kill(), which kills only the "spark-submit.cmd" process but not the
# JVMs. Instead, we use "taskkill" with the tree-kill option "/t" to terminate all
# child processes in the tree (http://technet.microsoft.com/en-us/library/bb491009.aspx)
def killChild():
Popen(["cmd", "/c", "taskkill", "/f", "/t", "/pid", str(proc.pid)])
atexit.register(killChild)
# Connect to the gateway
gateway = JavaGateway(GatewayClient(port=gateway_port), auto_convert=True)
# Import the classes used by PySpark
java_import(gateway.jvm, "org.apache.spark.SparkConf")
java_import(gateway.jvm, "org.apache.spark.api.java.*")
java_import(gateway.jvm, "org.apache.spark.api.python.*")
java_import(gateway.jvm, "org.apache.spark.ml.python.*")
java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*")
# TODO(davies): move into sql
java_import(gateway.jvm, "org.apache.spark.sql.*")
java_import(gateway.jvm, "org.apache.spark.sql.api.python.*")
java_import(gateway.jvm, "org.apache.spark.sql.hive.*")
java_import(gateway.jvm, "scala.Tuple2")
return gateway