Github user tdas commented on a diff in the pull request:
https://github.com/apache/spark/pull/13957#discussion_r69053315
--- Diff:
examples/src/main/scala/org/apache/spark/examples/sql/streaming/EventTimeWindow.scala
---
@@ -0,0 +1,114 @@
+/*
+ * 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 org.apache.spark.sql.SparkSession
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types.{DoubleType, TimestampType}
+
+/**
+ * Computes the average signal from IoT device readings over a sliding
window of
+ * configurable duration. The readings are received over the network and
must be
+ * UTF8-encoded and separated by '\n'.
+ *
+ * A single reading should take the format
+ * <device name (string)>, <reading (double)>
+ *
+ * Usage: EventTimeWindow <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, minutes,
+ * or days, e.g. "1 minute", "2 seconds"
+ * <slide duration> gives the amount of time successive windows are offset
from one another,
+ * given in the same units as above
+ * (<window duration> and <slide duration> must be enclosed by quotes to
ensure that
+ * they are processed as individual arguments)
+ *
+ * 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.EventTimeWindow
+ * localhost 9999 <window duration> <slide duration>`
+ *
+ * Type device readings in the format given above into Netcat.
+ *
+ * An example sequence of device readings:
+ * dev0,7.0
+ * dev1,8.0
+ * dev0,5.0
+ * dev1,3.0
+ */
+object EventTimeWindow {
+
+ def main(args: Array[String]) {
+ if (args.length < 4) {
+ System.err.println("Usage: EventTimeWindow <hostname> <port> <window
duration>" +
+ " <slide duration>")
+ System.exit(1)
+ }
+
+ val host = args(0)
+ val port = args(1).toInt
+ val windowSize = args(2)
+ val slideSize = args(3)
+
+ val spark = SparkSession
+ .builder
+ .appName("EventTimeWindow")
+ .getOrCreate()
+
+ // Create DataFrame representing the stream of input readings from
connection to host:port
+ val lines = spark.readStream
+ .format("socket")
+ .option("host", host)
+ .option("port", port)
+ .option("includeTimestamp", true)
+ .load()
+
+ // Split the readings into their individual components
+ val splitLines = lines.select(
+ split(lines.col("value"), ",").alias("pieces"),
--- End diff --
as i said offline this is very complex example. rather just do streaming
windowed word count.
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