Linux系統中KafKa安裝和使用方法 java客戶端連線kafka
阿新 • • 發佈:2019-01-09
kafka linux單機安裝
1 下載並安裝kafka
# tar zxvf kafka_2.12-1.1.0tgz # mv kafka_2.12-1.1.0 /usr/local/kafka # cd /usr/local/kafka
2 啟動服務
執行kafka需要使用Zookeeper,所以需要先啟動一個Zookeeper伺服器,如果沒有Zookeeper,可以使用kafka自帶打包和配置好的Zookeeper,&後臺程序
# bin/zookeeper-server-start.sh config/zookeeper.properties &
然後啟動kafka服務
# bin/kafka-server-start.sh config/server.properties &
3 新建一個topic
建立一個名為“test”的Topic,只有一個分割槽和一個備份:
# bin/kafka-topics.sh --create --zookeeper localhost:2182 --replication-factor 1 --partitions 1 --topic test
建立好之後,可以通過以下命令檢視已建立的topic資訊:
# bin/kafka-topics.sh --list --zookeeper localhost:2182 test
除手工建立topic外,也可以配置broker,當釋出一個不存在的topic時自動建立topic。
4 傳送訊息
Kafka提供了一個命令列工具,可以從輸入檔案或者命令列中讀取訊息併發送給Kafka叢集,每一行是一條訊息。執行producer,然後在控制檯輸入幾條訊息到伺服器
# bin/kafka-console-producer.sh --broker-list localhost:9092 --topic test This is a message This is another message
5 消費訊息
Kafka也提供了一個消費訊息的命令列工具
# bin/kafka-console-consumer.sh --zookeeper localhost:2182 --topic test --from-beginning This is a message This is another message
append:
listeners=PLAINTEXT://172.16
java 客服端連線程式碼
生產者程式碼
import java.util.Properties;
import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
public class KafkaProducer {
private final Producer<String, String> producer;
public final static String TOPIC = "test";
private KafkaProducer(){
Properties props = new Properties();
//此處配置的是kafka的埠
props.put("metadata.broker.list", "10.175.118.105:9092");
//配置value的序列化類
props.put("serializer.class", "kafka.serializer.StringEncoder");
//配置key的序列化類
props.put("key.serializer.class", "kafka.serializer.StringEncoder");
props.put("request.required.acks","-1");
producer = new Producer<String, String>(new ProducerConfig(props));
}
void produce() {
int messageNo = 1000;
final int COUNT = 10000;
while (messageNo < COUNT) {
String key = String.valueOf(messageNo);
String data = "hello kafka message " + key;
producer.send(new KeyedMessage<String, String>(TOPIC, key ,data));
System.out.println(data);
messageNo ++;
}
}
public static void main( String[] args )
{
new KafkaProducer().produce();
}
}
消費者程式碼
import java.util.HashMap;
import java.util.List;
import java.util.Map;
import java.util.Properties;
import com.huawei.hwclouds.dbs.ops.base.huatuo.diagnosis.service.impl.KafkaProducer;
import kafka.consumer.ConsumerConfig;
import kafka.consumer.ConsumerIterator;
import kafka.consumer.KafkaStream;
import kafka.javaapi.consumer.ConsumerConnector;
import kafka.serializer.StringDecoder;
import kafka.utils.VerifiableProperties;
public class KafkaConsumer {
private final ConsumerConnector consumer;
private KafkaConsumer() {
Properties props = new Properties();
//zookeeper 配置
props.put("zookeeper.connect", "10.175.118.105:2182");
//group 代表一個消費組
props.put("group.id", "test-consumer-group");
//zk連線超時
props.put("zookeeper.session.timeout.ms", "4000");
props.put("zookeeper.sync.time.ms", "200");
props.put("auto.commit.interval.ms", "1000");
props.put("auto.offset.reset", "smallest");//必須要加,如果要讀舊資料
//序列化類
props.put("serializer.class", "kafka.serializer.StringEncoder");
ConsumerConfig config = new ConsumerConfig(props);
consumer = kafka.consumer.Consumer.createJavaConsumerConnector(config);
}
void consume() {
Map<String, Integer> topicCountMap = new HashMap<String, Integer>();
topicCountMap.put(KafkaProducer.TOPIC, new Integer(1));
StringDecoder keyDecoder = new StringDecoder(new VerifiableProperties());
StringDecoder valueDecoder = new StringDecoder(new VerifiableProperties());
Map<String, List<KafkaStream<String, String>>> consumerMap =
consumer.createMessageStreams(topicCountMap,keyDecoder,valueDecoder);
KafkaStream<String, String> stream = consumerMap.get(KafkaProducer.TOPIC).get(0);
ConsumerIterator<String, String> it = stream.iterator();
while (it.hasNext())
System.out.println(it.next().message());
}
public static void main(String[] args) {
new KafkaConsumer().consume();
}
}