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 <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.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 <bootstrap-servers> " +
"<subscribe-type> <topics>");
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<String> 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<Row> wordCounts = lines.flatMap(
(FlatMapFunction<String, String>) 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
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