kafka的api操作（官网http://kafka.apache.org/documentation/#producerapi）

Kafka API 简单用法

本篇会用到以下依赖：（本人包和这个不同，去maven里查找）

<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-clients</artifactId>
<version>0.10.2.0</version>
</dependency>

<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka-streams</artifactId>
<version>0.10.2.0</version>
</dependency>

package com.yjsj.kafka;
import java.util.Properties;
import kafka.javaapi.producer.Producer;
import kafka.producer.KeyedMessage;
import kafka.producer.ProducerConfig;
public class OldProducer {
@SuppressWarnings("deprecation")
public static void main(String[] args) {
Properties properties = new Properties();
properties.put("metadata.broker.list", "master:9092");
properties.put("request.required.acks", "1");
properties.put("serializer.class",
"kafka.serializer.StringEncoder");

Producer<Integer, String> producer = new Producer<Integer,String>(new
ProducerConfig(properties));
KeyedMessage<Integer, String> message = new KeyedMessage<Integer,
String>("first", "hello world");
producer.send(message );
}
}

package com.yjsj.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
public class NewProducer {
public static void main(String[] args) {
Properties props = new Properties();
// Kafka 服务端的主机名和端口号
props.put("bootstrap.servers", "master:9092");
// 等待所有副本节点的应答
props.put("acks", "all");
// 消息发送最大尝试次数
props.put("retries", 0);
// 一批消息处理大小
props.put("batch.size", 16384);
// 请求延时
props.put("linger.ms", 1);
// 发送缓存区内存大小
props.put("buffer.memory", 33554432);
// key 序列化
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
// value 序列化
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
KafkaProducer<String, String> producer = new KafkaProducer<>(props);
for (int i = 0; i < 50; i++) {
producer.send(new ProducerRecord<String, String>("first",

Integer.toString(i), "hello world-" + i));
}
producer.close();
}
}

for (int i = 0; i < 50; i++) {
kafkaProducer.send(new ProducerRecord<String, String>("first",

"hello" + i), new Callback() {
@Override
public void onCompletion(RecordMetadata metadata, Exception
exception) {
if (metadata != null) {
System.out.println(metadata.partition() + "---" +
metadata.offset());
}
}
});
}
kafkaProducer.close();
}
}

package com.yjsj.kafka;
import java.util.Map;
import org.apache.kafka.clients.producer.Partitioner;

import org.apache.kafka.common.Cluster;
public class CustomPartitioner implements Partitioner {
@Override
public void configure(Map<String, ?> configs) {
}
@Override
public int partition(String topic, Object key, byte[] keyBytes, Object
value, byte[] valueBytes, Cluster cluster) {
// 控制分区
return 0;
}
@Override
public void close() {
}
}

package com.yjsj.kafka;
import java.util.Properties;
import org.apache.kafka.clients.producer.KafkaProducer;
import org.apache.kafka.clients.producer.Producer;
import org.apache.kafka.clients.producer.ProducerRecord;
public class PartitionerProducer {
public static void main(String[] args) {
Properties props = new Properties();
// Kafka 服务端的主机名和端口号
props.put("bootstrap.servers", "node1:9092");
// 等待所有副本节点的应答
props.put("acks", "all");
// 消息发送最大尝试次数
props.put("retries", 0);
// 一批消息处理大小
props.put("batch.size", 16384);
// 增加服务端请求延时
props.put("linger.ms", 1);
// 发送缓存区内存大小

props.put("buffer.memory", 33554432);
// key 序列化
props.put("key.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
// value 序列化
props.put("value.serializer",
"org.apache.kafka.common.serialization.StringSerializer");
// 自定义分区
props.put("partitioner.class", "com.yjsj.kafka.CustomPartitioner");
Producer<String, String> producer = new KafkaProducer<>(props);
producer.send(new ProducerRecord<String, String>("first", "1",
"hadoop"));
producer.close();
}
}

package com.yjsj.kafka.consume;
import java.util.Arrays;
import java.util.Properties;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.clients.consumer.ConsumerRecords;
import org.apache.kafka.clients.consumer.KafkaConsumer;
public class CustomNewConsumer {

public static void main(String[] args) {
Properties props = new Properties();
// 定义 kakfa 服务的地址，不需要将所有 broker 指定上
props.put("bootstrap.servers", "master:9092");
// 制定 consumer group
props.put("group.id", "test");
// 是否自动确认 offset
props.put("enable.auto.commit", "true");
// 自动确认 offset 的时间间隔
props.put("auto.commit.interval.ms", "1000");
// key 的序列化类
props.put("key.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
// value 的序列化类
props.put("value.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
// 定义 consumer
KafkaConsumer<String, String> consumer = new KafkaConsumer<>(props);
// 消费者订阅的 topic, 可同时订阅多个
consumer.subscribe(Arrays.asList("first", "second","third"));
while (true) {
// 读取数据，读取超时时间为 100ms
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records)
System.out.printf("offset = %d, key = %s, value = %s%n",
record.offset(), record.key(), record.value());
}
}
}

package com.yjsj.kafka.stream;
import java.util.Properties;
import org.apache.kafka.streams.KafkaStreams;
import org.apache.kafka.streams.StreamsConfig;
import org.apache.kafka.streams.processor.Processor;
import org.apache.kafka.streams.processor.ProcessorSupplier;
import org.apache.kafka.streams.processor.TopologyBuilder;
public class Application {
public static void main(String[] args) {
// 定义输入的 topic
String from = "first";
// 定义输出的 topic
String to = "second";
// 设置参数
Properties settings = new Properties();
settings.put(StreamsConfig.APPLICATION_ID_CONFIG, "logFilter");
settings.put(StreamsConfig.BOOTSTRAP_SERVERS_CONFIG, "master:9092");
StreamsConfig config = new StreamsConfig(settings);
// 构建拓扑

TopologyBuilder builder = new TopologyBuilder();
builder.addSource("SOURCE", from)
.addProcessor("PROCESS", new ProcessorSupplier<byte[],
byte[]>() {
@Override
public Processor<byte[], byte[]> get() {
// 具体分析处理
return new LogProcessor();
}
}, "SOURCE")
.addSink("SINK", to, "PROCESS");
// 创建 kafka stream
KafkaStreams streams = new KafkaStreams(builder, config);
streams.start();
}
}

具体业务处理

package com.yjsj.kafka.stream;
import org.apache.kafka.streams.processor.Processor;
import org.apache.kafka.streams.processor.ProcessorContext;
public class LogProcessor implements Processor<byte[], byte[]> {
private ProcessorContext context;
@Override
public void init(ProcessorContext context) {
this.context = context;
}
@Override
public void process(byte[] key, byte[] value) {
String input = new String(value);
// 如果包含“>>>”则只保留该标记后面的内容
if (input.contains(">>>")) {
input = input.split(">>>")[1].trim();
// 输出到下一个 topic
context.forward("logProcessor".getBytes(), input.getBytes());
}else{
context.forward("logProcessor".getBytes(), input.getBytes());

}
}
@Override
public void punctuate(long timestamp) {
}
@Override
public void close() {
}
}