I've created the following application that prints specific messages occurrences within 20sec windows:
public class SparkMain {
public static void main(String[] args) {
Map<String, Object> kafkaParams = new HashMap<>();
kafkaParams.put(BOOTSTRAP_SERVERS_CONFIG, "localhost:9092, localhost:9093");
kafkaParams.put(GROUP_ID_CONFIG, "spark-consumer-id");
kafkaParams.put(KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
kafkaParams.put(VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class.getName());
// events topic has 2 partitions
Collection<String> topics = Arrays.asList("events");
// local[*] Run Spark locally with as many worker threads as logical cores on your machine.
SparkConf conf = new SparkConf().setMaster("local[*]").setAppName("SsvpSparkStreaming");
// Create context with a 1 seconds batch interval
JavaStreamingContext streamingContext =
new JavaStreamingContext(conf, Durations.seconds(1));
JavaInputDStream<ConsumerRecord<String, String>> stream =
KafkaUtils.createDirectStream(
streamingContext,
LocationStrategies.PreferConsistent(),
ConsumerStrategies.<String, String>Subscribe(topics, kafkaParams)
);
// extract event name from record value
stream.map(new Function<ConsumerRecord<String, String>, String>() {
@Override
public String call(ConsumerRecord<String, String> rec) throws Exception {
return rec.value().substring(0, 5);
}})
// filter events
.filter(new Function<String, Boolean>() {
@Override
public Boolean call(String eventName) throws Exception {
return eventName.contains("msg");
}})
// count with 20sec window and 5 sec slide duration
.countByValueAndWindow(Durations.seconds(20), Durations.seconds(5))
.print();
streamingContext.checkpoint("c:\\projects\\spark\\");
streamingContext.start();
try {
streamingContext.awaitTermination();
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
After running the main method inside logs I see only single consumer initialization that gets both partitions:
2018-10-25 18:25:56,007 INFO [org.apache.kafka.common.utils.LogContext$KafkaLogger.info] - <[Consumer clientId=consumer-1, groupId=spark-consumer-id] Setting newly assigned partitions [events-0, events-1]>
Isn't the number of consumers should be equal to the number of spark workers? In accordance with https://spark.apache.org/docs/2.3.2/submitting-applications.html#master-urls
local[*] means - Run Spark locally with as many worker threads as logical cores on your machine.
I have 8 cores CPU, so I expect 8 consumers or at least 2 consumers should be created and each gets the partition of the 'events' topic(2 partitions).
It seems to me that I need to run a whole standalone spark master-worker cluster with 2 nodes where each node starts its own consumer...
