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#
# 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.
#
"""
Counts words in text encoded with UTF8 received from the network every second.
Usage: recoverable_network_wordcount.py <hostname> <port> <checkpoint-directory> <output-file>
<hostname> and <port> describe the TCP server that Spark Streaming would connect to receive
data. <checkpoint-directory> directory to HDFS-compatible file system which checkpoint data
<output-file> file to which the word counts will be appended
To run this on your local machine, you need to first run a Netcat server
`$ nc -lk 9999`
and then run the example
`$ bin/spark-submit examples/src/main/python/streaming/recoverable_network_wordcount.py \
localhost 9999 ~/checkpoint/ ~/out`
If the directory ~/checkpoint/ does not exist (e.g. running for the first time), it will create
a new StreamingContext (will print "Creating new context" to the console). Otherwise, if
checkpoint data exists in ~/checkpoint/, then it will create StreamingContext from
the checkpoint data.
"""
from __future__ import print_function
import os
import sys
from pyspark import SparkContext
from pyspark.streaming import StreamingContext
# Get or register a Broadcast variable
def getWordBlacklist(sparkContext):
if ('wordBlacklist' not in globals()):
globals()['wordBlacklist'] = sparkContext.broadcast(["a", "b", "c"])
return globals()['wordBlacklist']
# Get or register an Accumulator
def getDroppedWordsCounter(sparkContext):
if ('droppedWordsCounter' not in globals()):
globals()['droppedWordsCounter'] = sparkContext.accumulator(0)
return globals()['droppedWordsCounter']
def createContext(host, port, outputPath):
# If you do not see this printed, that means the StreamingContext has been loaded
# from the new checkpoint
print("Creating new context")
if os.path.exists(outputPath):
os.remove(outputPath)
sc = SparkContext(appName="PythonStreamingRecoverableNetworkWordCount")
ssc = StreamingContext(sc, 1)
# Create a socket stream on target ip:port and count the
# words in input stream of \n delimited text (eg. generated by 'nc')
lines = ssc.socketTextStream(host, port)
words = lines.flatMap(lambda line: line.split(" "))
wordCounts = words.map(lambda x: (x, 1)).reduceByKey(lambda x, y: x + y)
def echo(time, rdd):
# Get or register the blacklist Broadcast
blacklist = getWordBlacklist(rdd.context)
# Get or register the droppedWordsCounter Accumulator
droppedWordsCounter = getDroppedWordsCounter(rdd.context)
# Use blacklist to drop words and use droppedWordsCounter to count them
def filterFunc(wordCount):
if wordCount[0] in blacklist.value:
droppedWordsCounter.add(wordCount[1])
False
else:
True
counts = "Counts at time %s %s" % (time, rdd.filter(filterFunc).collect())
print(counts)
print("Dropped %d word(s) totally" % droppedWordsCounter.value)
print("Appending to " + os.path.abspath(outputPath))
with open(outputPath, 'a') as f:
f.write(counts + "\n")
wordCounts.foreachRDD(echo)
return ssc
if __name__ == "__main__":
if len(sys.argv) != 5:
print("Usage: recoverable_network_wordcount.py <hostname> <port> "
"<checkpoint-directory> <output-file>", file=sys.stderr)
sys.exit(-1)
host, port, checkpoint, output = sys.argv[1:]
ssc = StreamingContext.getOrCreate(checkpoint,
lambda: createContext(host, int(port), output))
ssc.start()
ssc.awaitTermination()