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[SPARK-16114] [SQL] structured streaming event time window example #13957
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EventTimeWindow example
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Merge remote-tracking branch 'upstream/master' into current
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java and python
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116
.../java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java
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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. | ||
*/ | ||
package org.apache.spark.examples.sql.streaming; | ||
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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; | ||
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import java.sql.Timestamp; | ||
import java.util.ArrayList; | ||
import java.util.Iterator; | ||
import java.util.List; | ||
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/** | ||
* 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 { | ||
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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); | ||
} | ||
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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"; | ||
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SparkSession spark = SparkSession | ||
.builder() | ||
.appName("JavaStructuredNetworkWordCountWindowed") | ||
.getOrCreate(); | ||
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// 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())); | ||
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// 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"); | ||
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// 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"); | ||
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// 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(); | ||
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query.awaitTermination(); | ||
} | ||
} |
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102 changes: 102 additions & 0 deletions
102
examples/src/main/python/sql/streaming/structured_network_wordcount_windowed.py
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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. | ||
# | ||
|
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""" | ||
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>]` | ||
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One recommended <window duration>, <slide duration> pair is 10, 5 | ||
""" | ||
from __future__ import print_function | ||
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import sys | ||
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from pyspark.sql import SparkSession | ||
from pyspark.sql.functions import explode | ||
from pyspark.sql.functions import split | ||
from pyspark.sql.functions import window | ||
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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) | ||
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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) | ||
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spark = SparkSession\ | ||
.builder\ | ||
.appName("StructuredNetworkWordCountWindowed")\ | ||
.getOrCreate() | ||
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# 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() | ||
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# 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 | ||
) | ||
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# 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') | ||
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# Start running the query that prints the windowed word counts to the console | ||
query = windowedCounts\ | ||
.writeStream\ | ||
.outputMode('complete')\ | ||
.format('console')\ | ||
.option('truncate', 'false')\ | ||
.start() | ||
|
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query.awaitTermination() |
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103 changes: 103 additions & 0 deletions
103
...in/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCountWindowed.scala
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@@ -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. | ||
*/ | ||
|
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// 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 { | ||
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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) | ||
} | ||
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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" | ||
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val spark = SparkSession | ||
.builder | ||
.appName("StructuredNetworkWordCountWindowed") | ||
.getOrCreate() | ||
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import spark.implicits._ | ||
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// 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)] | ||
|
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// Split the lines into words, retaining timestamps | ||
val words = lines.flatMap(line => | ||
line._1.split(" ").map(word => (word, line._2)) | ||
).toDF("word", "timestamp") | ||
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// 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") | ||
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// 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() | ||
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query.awaitTermination() | ||
} | ||
} | ||
// scalastyle:on println |
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good catch!