Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/13957#discussion_r69362732
--- Diff:
examples/src/main/java/org/apache/spark/examples/sql/streaming/JavaStructuredNetworkWordCountWindowed.java
---
@@ -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>
+ * <optional 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> <optional slide duration>`
+ *
+ * One recommended <window duration>, <slide duration> pair is 60, 30
+ */
+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> <optional 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 windowArg = windowSize + " seconds";
+ String slideArg = 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"), windowArg, slideArg),
--- End diff --
windowArg --> windowDuration
slideArg --> slideInterval
So that this code snippet in the guide is self explanatory.
And make sure its consistent in other languages.
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at infrastruct...@apache.org or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org
For additional commands, e-mail: reviews-h...@spark.apache.org
