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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.
*/
// scalastyle:off println
package org.apache.spark.examples.sql.streaming
import org.apache.spark.sql.SparkSession
/**
* Consumes messages from one or more topics in Kafka and does wordcount.
* Usage: StructuredKafkaWordCount <bootstrap-servers> <subscribe-type> <topics>
* <bootstrap-servers> The Kafka "bootstrap.servers" configuration. A
* comma-separated list of host:port.
* <subscribe-type> There are three kinds of type, i.e. 'assign', 'subscribe',
* 'subscribePattern'.
* |- <assign> Specific TopicPartitions to consume. Json string
* | {"topicA":[0,1],"topicB":[2,4]}.
* |- <subscribe> The topic list to subscribe. A comma-separated list of
* | topics.
* |- <subscribePattern> The pattern used to subscribe to topic(s).
* | Java regex string.
* |- Only one of "assign, "subscribe" or "subscribePattern" options can be
* | specified for Kafka source.
* <topics> Different value format depends on the value of 'subscribe-type'.
*
* Example:
* `$ bin/run-example \
* sql.streaming.StructuredKafkaWordCount host1:port1,host2:port2 \
* subscribe topic1,topic2`
*/
object StructuredKafkaWordCount {
def main(args: Array[String]): Unit = {
if (args.length < 3) {
System.err.println("Usage: StructuredKafkaWordCount <bootstrap-servers> " +
"<subscribe-type> <topics>")
System.exit(1)
}
val Array(bootstrapServers, subscribeType, topics) = args
val spark = SparkSession
.builder
.appName("StructuredKafkaWordCount")
.getOrCreate()
import spark.implicits._
// Create DataSet representing the stream of input lines from kafka
val lines = spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", bootstrapServers)
.option(subscribeType, topics)
.load()
.selectExpr("CAST(value AS STRING)")
.as[String]
// Generate running word count
val wordCounts = lines.flatMap(_.split(" ")).groupBy("value").count()
// Start running the query that prints the running counts to the console
val query = wordCounts.writeStream
.outputMode("complete")
.format("console")
.start()
query.awaitTermination()
}
}
// scalastyle:on println
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