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structured_network_wordcount.R
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structured_network_wordcount.R
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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 UTF8 encoded, '\n' delimited text received from the network.
# 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/r/streaming/structured_network_wordcount.R localhost 9999
# Load SparkR library into your R session
library(SparkR)
# Initialize SparkSession
sparkR.session(appName = "SparkR-Streaming-structured-network-wordcount-example")
args <- commandArgs(trailing = TRUE)
if (length(args) != 2) {
print("Usage: structured_network_wordcount.R <hostname> <port>")
print("<hostname> and <port> describe the TCP server that Structured Streaming")
print("would connect to receive data.")
q("no")
}
hostname <- args[[1]]
port <- as.integer(args[[2]])
# Create DataFrame representing the stream of input lines from connection to localhost:9999
lines <- read.stream("socket", host = hostname, port = port)
# Split the lines into words
words <- selectExpr(lines, "explode(split(value, ' ')) as word")
# Generate running word count
wordCounts <- count(groupBy(words, "word"))
# Start running the query that prints the running counts to the console
query <- write.stream(wordCounts, "console", outputMode = "complete")
awaitTermination(query)
sparkR.session.stop()