kafka java consumer example

some time ago in the Kafka QQ Group was asked about this--about how Java consumer dynamically modify topic subscription issues. It's really a good question to think about it, because if you simply hold the consumer instance in another thread and then call subscribe to modify it, the

Error phenomenon:Java client programming as the consumer of Kafka, connecting Kafka's broker error650) this.width=650; "Src=" https://s4.51cto.com/wyfs02/M00/91/6A/wKiom1j12BGgUkKgAACUSA5Q0tU565.png-wh_500x0-wm_ 3-wmp_4-s_64493172.png "title=" Qq20170418170758.png "alt=" Wkiom1j12bggukkgaacusa5q0tu565.png-wh_50 "/>Error reason analysis:When the server configuration or network environment is poor, there will

)); Stringdecoder Keydecoder = new Stringdecoder (new Verifiableproperties ());Stringdecoder Valuedecoder = new Stringdecoder (new Verifiableproperties ()); MapConsumer.createmessagestreams (Topiccountmap,keydecoder,valuedecoder);kafkastreamConsumeriterator int messagecount = 0;while (It.hasnext ()) {System.out.println (It.next (). message ());messagecount++;if (Messagecount = = 100) {SYSTEM.OUT.PRINTLN ("Consumer end of the total consumption of" + Me

java.util.map;import Java.util.properties;import Java.util.concurrent.executorservice;import Java.util.concurrent.executors;import Kafka.consumer.consumer;import Kafka.consumer.consumerconfig;import Kafka.consumer.consumeriterator;import Kafka.consumer.kafkastream;import Kafka.javaapi.consumer.consumerconnector;import Kafka.message.messageandmetadata;public class Logconsumer {private Consumerconfig config; Private String topic; private int partitionsnum; Private Messageexecutor exec

responded to this change (although many people are asking them to change, see HTTPS://GITHUB.COM/QUANTIFIND/KAFKAOFFSETMONITOR/ISSUES/79), So it's probably because you're using a new version of consumer that you can't see. As for the old and new versions, here is a unified explanation: kafka0.9 before the consumer was written in Scala, the package name structure is kafka.consumer.*, divided into high-level

later in detail). So, if more than one topic partition, theoretically the entire cluster can achieve the greater throughput. But is the number of partitions as good as possible? Obviously not, because each partition has its own overhead: one, the more memory that the client/server needs to use the client-side scenario first. Kafka 0.8.2 later introduced the Java version of the new producer, this produc

Transferred from: HTTP://WWW.TUICOOL.COM/ARTICLES/AJ6FAJ3How to determine the number of partitions, keys, and consumer threads for Kafka in the QQ group of the Kafak Chinese community, the proportion of the problem mentioned is quite high, which is one of the most frequently encountered problems for Kafka users. This paper, combined with

have the same number of threads, so we modify the example above, assuming that each consumer has 2 threads. One of the main differences between the round robin strategy and range is that you can't predict the result of the assignment until redistribution-because it uses hash modulo to randomize the sort order. if you want to adopt a Roundrobin strategy, you must first meet two conditions: The

reproduced original: http://www.cnblogs.com/huxi2b/p/4757098.html How to determine the number of partitions, key, and consumer threads for Kafka In the QQ group of the Kafak Chinese community, the proportion of the problem mentioned is quite high, which is one of the most common problems Kafka users encounter. This article unifies the

number of message streams to return. number of streams to be returned * @ param keyDecoder a decoder that decodes the message key can be decoded Key decoder * @ param valueDecoder a decoder that decodes the message itself can decode the decoder of the message itself * @ return a list of KafkaStream. each stream supports an * iterator over its MessageAndMetadata elements. returns the KafkaStream list. Each stream supports an iterator Based on the MessagesAndMetadata element. */ public /*** Cre

Recently want to test the performance of Kafka, toss a lot of genius to Kafka installed to the window. The entire process of installation is provided below, which is absolutely usable and complete, while providing complete Kafka Java client code to communicate with Kafka. He

;ImportKafka.javaapi.consumer.ConsumerConnector;ImportKafka.serializer.StringDecoder;Importkafka.utils.VerifiableProperties; Public classKafkaconsumer {Private FinalConsumerconnector Consumer; PublicKafkaconsumer () {Properties props=NewProperties (); //Zookeeper ConfigurationProps.put ("Zookeeper.connect", "192.168.91.231:2181"); //Group represents a consumer groupProps.put ("Group.id", "Jd-group"); //ZK

Https://github.com/edenhill/librdkafka/wiki/Broker-version-compatibilityIf you are using the broker version of 0.8, you will need to set the-X broker.version.fallback=0.8.x.y if you run the routine or you cannot runFor example, my example:My Kafka version is 0.9.1.Unzip Librdkafka-master.zipCD Librdkafka-master./configure make make installCD examples./rdkafka_consumer_example-b 192.168.10.10:9092 One_way_

the two partition allocation policies built into Kafka. This article assumes that we have a theme named T1, which contains 10 partitions, and then we have two consumers (C1,C2)To consume data from these 10 partitions, and C1 's num.streams = 1,c2 's Num.streams = 2.Range strategyThe range policy is for each topic, first sorting the partitions within the same topic by ordinal and sorting the consumers alphabetically. In our

(true) {String message = "message-" + ++count;//消息主题是testKeyedMessagenew KeyedMessage"test", message);//message可以带key, 根据key来将消息分配到指定区, 如果没有key则随机分配到某个区// KeyedMessageproducer.send(keyedMessage);System.out.println("send: " + message);try {Thread.sleep(1000);} catch (InterruptedException e) {e.printStackTrace();}}// producer.close();} }/*** 自定义分区类**/class MyPartition implements Partitioner {public int partition(Object key, int numPartitions) {return key.hashCode()%numPartitions;}} From fo

Kafka Producer Consumer, kafkaproducerProducer API Org. apache. kafka. clients. producer. KafkaProducer 1 props.put("bootstrap.servers", "192.168.1.128:9092"); 2 props.put("acks", "all"); 3 props.put("retries", 0); 4 props.put("batch.size", 16384); 5 props.put("linger.ms", 1); 6 props.put("buffer.memory", 33554432); 7 props.put("key.serializer", "org.apache.kafk

complete, the Commitsync is dead, the server restarts again, the message will still be repeated consumption. What is the solution to the problem? The answer is to save committed offset, instead of relying on Kafka's cluster to save committed offset, to manipulate the message and save offset into an atomic operation. In the official document of Kafka, the following 2 types of usage scenarios for saving offset are listed: relational databases, accessed

();}finally{Lock.unlock ();}}Public Void Get (){Lock.lock ();try {While (!flag)consumer_con.await ();System.out.println (Thread.CurrentThread (). GetName () + ".... Consumers ... "+this.name);Flag=false;producer_con.signal ();} catch (Interruptedexception e) {//TODO auto-generated catch blocke.printstacktrace ();}finally{Lock.unlock ();}}}class Producer implements Runnable{Resource R=null;Public Producer (Resource R){This.r=r;}public void Run () {TODO auto-generated method stubswhile (true)R.se