Structured-Streaming 学习二—WordCount-kafka

再来分析一下structured streaming是如何与kafka结合的。
注意，你可以在本地安装一个单机的kafka来进行测试，测试期间请打开producer，一行一行输入要wordcount单词。
之后我会介绍如何写producer的代码。

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/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: JavaStructuredKafkaWordCount   
 *    The Kafka "bootstrap.servers" configuration. A
 *   comma-separated list of host:port.
 *    There are three kinds of type, i.e. 'assign', 'subscribe',
 *   'subscribePattern'.
 *   |-  Specific TopicPartitions to consume. Json string
 *   |  {"topicA":[0,1],"topicB":[2,4]}.
 *   |-  The topic list to subscribe. A comma-separated list of
 *   |  topics.
 *   |-  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.
 *    Different value format depends on the value of 'subscribe-type'.
 *
 * Example:
 *    `$ bin/run-example \
 *      sql.streaming.JavaStructuredKafkaWordCount host1:port1,host2:port2 \
 *      subscribe topic1,topic2`
 */
public final class WC_kafka{

    public static void main(String[] args) throws Exception {
        args = (String[]) Arrays.asList("host:port","subscribe","topic50").toArray();
        if (args.length < 3) {
            System.err.println("Usage: JavaStructuredKafkaWordCount  " +
                    " ");
            System.exit(1);
        }

        String bootstrapServers = args[0];
        String subscribeType = args[1];
        String topics = args[2];


        SparkSession spark = SparkSession
                .builder()
                .master("local")
                .appName("JavaStructuredKafkaWordCount")
                .getOrCreate();

        // Create DataSet representing the stream of input lines from kafka
        Dataset lines = spark
                .readStream()
                .format("kafka")
                .option("kafka.bootstrap.servers", bootstrapServers)
                .option(subscribeType, topics)
                .option("startingOffsets","earliest")   //有输入时可源源不断地输出结果[Structured Streaming + Kafka](https://spark.apache.org/docs/latest/structured-streaming-kafka-integration.html)
                .load()
                .selectExpr("CAST(value AS STRING)")
                .as(Encoders.STRING());

        // Generate running word count
        Dataset wordCounts = lines.flatMap(
                (FlatMapFunction) x -> Arrays.asList(x.split(" ")).iterator(),
                Encoders.STRING()).groupBy("value").count();
        // Start running the query that prints the running counts to the console
        StreamingQuery query = wordCounts.writeStream()
                //.format("kafka")
                //.option("kafka.bootstrap.servers", bootstrapServers)
                //.option(subscribeType, topics)
                .outputMode("complete")
                .format("console")
                .option("numRows",100)　　　//扩展显示的行数[Structured-Streaming Programming](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-modes)
//里的console Sink
                .start();

        query.awaitTermination();
    }
}

Structured Streaming + Kafka
Structured-Streaming Programming