Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/13957#discussion_r69353021
--- Diff:
examples/src/main/scala/org/apache/spark/examples/sql/streaming/StructuredNetworkWordCountWindowed.scala
---
@@ -0,0 +1,100 @@
+/*
+ * 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.
+ *
+ * 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> <slide duration>`
+ *
+ * One recommended <window duration>, <slide duration> pair is 60, 30
+ */
+object StructuredNetworkWordCountWindowed {
+
+ def main(args: Array[String]) {
+ if (args.length < 4) {
+ 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 = args(3).toInt
+ if (slideSize > windowSize) {
+ System.err.println("<slide duration> must be less than or equal to
<window duration>")
+ }
+
+ 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(words.col("timestamp"), s"$windowSize seconds", s"$slideSize
seconds"),
--- End diff --
Also move the "$windowSize seconds" higher up .. similar to the python, so
that this piece of code looks simpler, and can be exactly copied over to the
guide.
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