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[SPARK-16114] [SQL] structured streaming event time window example #13957

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Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@
import java.util.Iterator;

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
* Counts words in UTF8 encoded, '\n' delimited text received from the network.
*
* Usage: JavaStructuredNetworkWordCount <hostname> <port>
* <hostname> and <port> describe the TCP server that Structured Streaming
Expand All @@ -40,7 +40,7 @@ public final class JavaStructuredNetworkWordCount {

public static void main(String[] args) throws Exception {
if (args.length < 2) {
System.err.println("Usage: JavaNetworkWordCount <hostname> <port>");
System.err.println("Usage: JavaStructuredNetworkWordCount <hostname> <port>");
System.exit(1);
}

Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,116 @@
/*
* 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.
*/
package org.apache.spark.examples.sql.streaming;

import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.sql.*;
import org.apache.spark.sql.functions;
import org.apache.spark.sql.streaming.StreamingQuery;
import scala.Tuple2;

import java.sql.Timestamp;
import java.util.ArrayList;
import java.util.Iterator;
import java.util.List;

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network over a
* sliding window of configurable duration. Each line from the network is tagged
* with a timestamp that is used to determine the windows into which it falls.
*
* Usage: JavaStructuredNetworkWordCountWindowed <hostname> <port> <window duration>
* [<slide duration>]
* <hostname> and <port> describe the TCP server that Structured Streaming
* would connect to receive data.
* <window duration> gives the size of window, specified as integer number of seconds
* <slide duration> gives the amount of time successive windows are offset from one another,
* given in the same units as above. <slide duration> should be less than or equal to
* <window duration>. If the two are equal, successive windows have no overlap. If
* <slide duration> is not provided, it defaults to <window duration>.
*
* To run this on your local machine, you need to first run a Netcat server
* `$ nc -lk 9999`
* and then run the example
* `$ bin/run-example sql.streaming.JavaStructuredNetworkWordCountWindowed
* localhost 9999 <window duration in seconds> [<slide duration in seconds>]`
*
* One recommended <window duration>, <slide duration> pair is 10, 5
*/
public final class JavaStructuredNetworkWordCountWindowed {

public static void main(String[] args) throws Exception {
if (args.length < 3) {
System.err.println("Usage: JavaStructuredNetworkWordCountWindowed <hostname> <port>" +
" <window duration in seconds> [<slide duration in seconds>]");
System.exit(1);
}

String host = args[0];
int port = Integer.parseInt(args[1]);
int windowSize = Integer.parseInt(args[2]);
int slideSize = (args.length == 3) ? windowSize : Integer.parseInt(args[3]);
if (slideSize > windowSize) {
System.err.println("<slide duration> must be less than or equal to <window duration>");
}
String windowDuration = windowSize + " seconds";
String slideDuration = slideSize + " seconds";

SparkSession spark = SparkSession
.builder()
.appName("JavaStructuredNetworkWordCountWindowed")
.getOrCreate();

// Create DataFrame representing the stream of input lines from connection to host:port
Dataset<Tuple2<String, Timestamp>> lines = spark
.readStream()
.format("socket")
.option("host", host)
.option("port", port)
.option("includeTimestamp", true)
.load().as(Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP()));

// Split the lines into words, retaining timestamps
Dataset<Row> words = lines.flatMap(
new FlatMapFunction<Tuple2<String, Timestamp>, Tuple2<String, Timestamp>>() {
@Override
public Iterator<Tuple2<String, Timestamp>> call(Tuple2<String, Timestamp> t) {
List<Tuple2<String, Timestamp>> result = new ArrayList<>();
for (String word : t._1.split(" ")) {
result.add(new Tuple2<>(word, t._2));
}
return result.iterator();
}
},
Encoders.tuple(Encoders.STRING(), Encoders.TIMESTAMP())
).toDF("word", "timestamp");

// Group the data by window and word and compute the count of each group
Dataset<Row> windowedCounts = words.groupBy(
functions.window(words.col("timestamp"), windowDuration, slideDuration),
words.col("word")
).count().orderBy("window");

// Start running the query that prints the windowed word counts to the console
StreamingQuery query = windowedCounts.writeStream()
.outputMode("complete")
.format("console")
.option("truncate", "false")
.start();

query.awaitTermination();
}
}
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@
#

"""
Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
Counts words in UTF8 encoded, '\n' delimited text received from the network.
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Contributor

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good catch!

Usage: structured_network_wordcount.py <hostname> <port>
<hostname> and <port> describe the TCP server that Structured Streaming
would connect to receive data.
Expand Down Expand Up @@ -58,6 +58,7 @@

# Split the lines into words
words = lines.select(
# explode turns each item in an array into a separate row
explode(
split(lines.value, ' ')
).alias('word')
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,102 @@
#
# 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 over a
sliding window of configurable duration. Each line from the network is tagged
with a timestamp that is used to determine the windows into which it falls.

Usage: structured_network_wordcount_windowed.py <hostname> <port> <window duration>
[<slide duration>]
<hostname> and <port> describe the TCP server that Structured Streaming
would connect to receive data.
<window duration> gives the size of window, specified as integer number of seconds
<slide duration> gives the amount of time successive windows are offset from one another,
given in the same units as above. <slide duration> should be less than or equal to
<window duration>. If the two are equal, successive windows have no overlap. If
<slide duration> is not provided, it defaults to <window duration>.

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/sql/streaming/structured_network_wordcount_windowed.py
localhost 9999 <window duration> [<slide duration>]`

One recommended <window duration>, <slide duration> pair is 10, 5
"""
from __future__ import print_function

import sys

from pyspark.sql import SparkSession
from pyspark.sql.functions import explode
from pyspark.sql.functions import split
from pyspark.sql.functions import window

if __name__ == "__main__":
if len(sys.argv) != 5 and len(sys.argv) != 4:
msg = ("Usage: structured_network_wordcount_windowed.py <hostname> <port> "
"<window duration in seconds> [<slide duration in seconds>]")
print(msg, file=sys.stderr)
exit(-1)

host = sys.argv[1]
port = int(sys.argv[2])
windowSize = int(sys.argv[3])
slideSize = int(sys.argv[4]) if (len(sys.argv) == 5) else windowSize
if slideSize > windowSize:
print("<slide duration> must be less than or equal to <window duration>", file=sys.stderr)
windowDuration = '{} seconds'.format(windowSize)
slideDuration = '{} seconds'.format(slideSize)

spark = SparkSession\
.builder\
.appName("StructuredNetworkWordCountWindowed")\
.getOrCreate()

# Create DataFrame representing the stream of input lines from connection to host:port
lines = spark\
.readStream\
.format('socket')\
.option('host', host)\
.option('port', port)\
.option('includeTimestamp', 'true')\
.load()

# Split the lines into words, retaining timestamps
# split() splits each line into an array, and explode() turns the array into multiple rows
words = lines.select(
explode(split(lines.value, ' ')).alias('word'),
lines.timestamp
)

# Group the data by window and word and compute the count of each group
windowedCounts = words.groupBy(
window(words.timestamp, windowDuration, slideDuration),
words.word
).count().orderBy('window')

# Start running the query that prints the windowed word counts to the console
query = windowedCounts\
.writeStream\
.outputMode('complete')\
.format('console')\
.option('truncate', 'false')\
.start()

query.awaitTermination()
Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ import org.apache.spark.sql.functions._
import org.apache.spark.sql.SparkSession

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
* Counts words in UTF8 encoded, '\n' delimited text received from the network.
*
* Usage: StructuredNetworkWordCount <hostname> <port>
* <hostname> and <port> describe the TCP server that Structured Streaming
Expand Down
Original file line number Diff line number Diff line change
@@ -0,0 +1,103 @@
/*
* 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.
*/

// scalastyle:off println
package org.apache.spark.examples.sql.streaming

import java.sql.Timestamp

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

/**
* Counts words in UTF8 encoded, '\n' delimited text received from the network over a
* sliding window of configurable duration. Each line from the network is tagged
* with a timestamp that is used to determine the windows into which it falls.
*
* Usage: StructuredNetworkWordCountWindowed <hostname> <port> <window duration>
* [<slide duration>]
* <hostname> and <port> describe the TCP server that Structured Streaming
* would connect to receive data.
* <window duration> gives the size of window, specified as integer number of seconds
* <slide duration> gives the amount of time successive windows are offset from one another,
* given in the same units as above. <slide duration> should be less than or equal to
* <window duration>. If the two are equal, successive windows have no overlap. If
* <slide duration> is not provided, it defaults to <window duration>.
*
* To run this on your local machine, you need to first run a Netcat server
* `$ nc -lk 9999`
* and then run the example
* `$ bin/run-example sql.streaming.StructuredNetworkWordCountWindowed
* localhost 9999 <window duration in seconds> [<slide duration in seconds>]`
*
* One recommended <window duration>, <slide duration> pair is 10, 5
*/
object StructuredNetworkWordCountWindowed {

def main(args: Array[String]) {
if (args.length < 3) {
System.err.println("Usage: StructuredNetworkWordCountWindowed <hostname> <port>" +
" <window duration in seconds> [<slide duration in seconds>]")
System.exit(1)
}

val host = args(0)
val port = args(1).toInt
val windowSize = args(2).toInt
val slideSize = if (args.length == 3) windowSize else args(3).toInt
if (slideSize > windowSize) {
System.err.println("<slide duration> must be less than or equal to <window duration>")
}
val windowDuration = s"$windowSize seconds"
val slideDuration = s"$slideSize seconds"

val spark = SparkSession
.builder
.appName("StructuredNetworkWordCountWindowed")
.getOrCreate()

import spark.implicits._

// Create DataFrame representing the stream of input lines from connection to host:port
val lines = spark.readStream
.format("socket")
.option("host", host)
.option("port", port)
.option("includeTimestamp", true)
.load().as[(String, Timestamp)]

// Split the lines into words, retaining timestamps
val words = lines.flatMap(line =>
line._1.split(" ").map(word => (word, line._2))
).toDF("word", "timestamp")

// Group the data by window and word and compute the count of each group
val windowedCounts = words.groupBy(
window($"timestamp", windowDuration, slideDuration), $"word"
).count().orderBy("window")

// Start running the query that prints the windowed word counts to the console
val query = windowedCounts.writeStream
.outputMode("complete")
.format("console")
.option("truncate", "false")
.start()

query.awaitTermination()
}
}
// scalastyle:on println
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