spark-instrumented-optimizer/python/pyspark/java_gateway.py
Tathagata Das 2cb976355c [SPARK-24565][SS] Add API for in Structured Streaming for exposing output rows of each microbatch as a DataFrame
## What changes were proposed in this pull request?

Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful.
- Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning).
- Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source).
- Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice.

The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`.

## How was this patch tested?
New unit tests.

Author: Tathagata Das <tathagata.das1565@gmail.com>

Closes #21571 from tdas/foreachBatch.
2018-06-19 13:56:51 -07:00

171 lines
7.3 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 shutil
import socket
import platform
import tempfile
import time
from subprocess import Popen, PIPE
if sys.version >= '3':
xrange = range
from py4j.java_gateway import java_import, JavaGateway, JavaObject, GatewayParameters
from pyspark.find_spark_home import _find_spark_home
from pyspark.serializers import read_int, write_with_length, UTF8Deserializer
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"])
gateway_secret = os.environ["PYSPARK_GATEWAY_SECRET"]
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)
# Create a temporary directory where the gateway server should write the connection
# information.
conn_info_dir = tempfile.mkdtemp()
try:
fd, conn_info_file = tempfile.mkstemp(dir=conn_info_dir)
os.close(fd)
os.unlink(conn_info_file)
env = dict(os.environ)
env["_PYSPARK_DRIVER_CONN_INFO_PATH"] = conn_info_file
# 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)
# Wait for the file to appear, or for the process to exit, whichever happens first.
while not proc.poll() and not os.path.isfile(conn_info_file):
time.sleep(0.1)
if not os.path.isfile(conn_info_file):
raise Exception("Java gateway process exited before sending its port number")
with open(conn_info_file, "rb") as info:
gateway_port = read_int(info)
gateway_secret = UTF8Deserializer().loads(info)
finally:
shutil.rmtree(conn_info_dir)
# 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(
gateway_parameters=GatewayParameters(port=gateway_port, auth_token=gateway_secret,
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
def do_server_auth(conn, auth_secret):
"""
Performs the authentication protocol defined by the SocketAuthHelper class on the given
file-like object 'conn'.
"""
write_with_length(auth_secret.encode("utf-8"), conn)
conn.flush()
reply = UTF8Deserializer().loads(conn)
if reply != "ok":
conn.close()
raise Exception("Unexpected reply from iterator server.")
def ensure_callback_server_started(gw):
"""
Start callback server if not already started. The callback server is needed if the Java
driver process needs to callback into the Python driver process to execute Python code.
"""
# getattr will fallback to JVM, so we cannot test by hasattr()
if "_callback_server" not in gw.__dict__ or gw._callback_server is None:
gw.callback_server_parameters.eager_load = True
gw.callback_server_parameters.daemonize = True
gw.callback_server_parameters.daemonize_connections = True
gw.callback_server_parameters.port = 0
gw.start_callback_server(gw.callback_server_parameters)
cbport = gw._callback_server.server_socket.getsockname()[1]
gw._callback_server.port = cbport
# gateway with real port
gw._python_proxy_port = gw._callback_server.port
# get the GatewayServer object in JVM by ID
jgws = JavaObject("GATEWAY_SERVER", gw._gateway_client)
# update the port of CallbackClient with real port
jgws.resetCallbackClient(jgws.getCallbackClient().getAddress(), gw._python_proxy_port)