本文主要是以kafka 09的client为例子，详解kafka client的使用，包括kafka消费者的三种消费语义at-most-once, at-least-once, 和 exactly-once message ，生产者的使用等。
(一) 创建topic
代码语言：javascript复制bin/kafka-topics --zookeeper localhost:2181 --create --topic normal-topic --partitions 2 --replication-factor 1(二) 生产者
代码语言：javascript复制public class ProducerExample {
public static void main(String[] str) throws InterruptedException, IOException {
System.out.println("Starting ProducerExample ...");
sendMessages();
}
private static void sendMessages() throws InterruptedException, IOException {
Producer<String, String> producer = createProducer();
sendMessages(producer);
// Allow the producer to complete sending of the messages before program exit.
Thread.sleep(20);
}
private static Producer<String, String> createProducer() {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
props.put("acks", "all");
props.put("retries", 0);
// Controls how much bytes sender would wait to batch up before publishing to Kafka.
props.put("batch.size", 10);
props.put("linger.ms", 1);
props.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
props.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
return new KafkaProducer(props);
}
private static void sendMessages(Producer<String, String> producer) {
String topic = "normal-topic";
int partition = 0;
long record = 1;
for (int i = 1; i <= 10; i ) {
producer.send(
new ProducerRecord<String, String>(topic, partition, Long.toString(record),Long.toString(record )));
}
}
}（三）消费者
消费者注册到kafka有多种方式：
subscribe:这种方式在新增topic或者partition或者消费者增加或者消费者减少的时候，会进行消费者组内消费者的再平衡。
assign:这种方式注册的消费者不会进行rebalance。
上面两种方式都是可以实现，三种消费语义的。具体API的使用请看下文。
1. At-most-once Kafka Consumer
做多一次消费语义是kafka消费者的默认实现。配置这种消费者最简单的方式是
1). enable.auto.commit设置为true。
2). auto.commit.interval.ms设置为一个较低的时间范围。
3). consumer.commitSync()不要调用该方法。
由于上面的配置，就可以使得kafka有线程负责按照指定间隔提交offset。
但是这种方式会使得kafka消费者有两种消费语义：
a.最多一次语义->at-most-once
消费者的offset已经提交，但是消息还在处理，这个时候挂了，再重启的时候会从上次提交的offset处消费，导致上次在处理的消息部分丢失。
b. 最少一次消费语义->at-least-once
消费者已经处理完了，但是offset还没提交，那么这个时候消费者挂了，就会导致消费者重复消费消息处理。但是由于auto.commit.interval.ms设置为一个较低的时间范围，会降低这种情况出现的概率。
代码如下：
代码语言：javascript复制public class AtMostOnceConsumer {
public static void main(String[] str) throws InterruptedException {
System.out.println("Starting AtMostOnceConsumer ...");
execute();
}
private static void execute() throws InterruptedException {
KafkaConsumer<String, String> consumer = createConsumer();
// Subscribe to all partition in that topic. 'assign' could be used here
// instead of 'subscribe' to subscribe to specific partition.
consumer.subscribe(Arrays.asList("normal-topic"));
processRecords(consumer);
}
private static KafkaConsumer<String, String> createConsumer() {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
String consumeGroup = "cg1";
props.put("group.id", consumeGroup);
// Set this property, if auto commit should happen.
props.put("enable.auto.commit", "true");
// Auto commit interval, kafka would commit offset at this interval.
props.put("auto.commit.interval.ms", "101");
// This is how to control number of records being read in each poll
props.put("max.partition.fetch.bytes", "135");
// Set this if you want to always read from beginning.
// props.put("auto.offset.reset", "earliest");
props.put("heartbeat.interval.ms", "3000");
props.put("session.timeout.ms", "6001");
props.put("key.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer",
"org.apache.kafka.common.serialization.StringDeserializer");
return new KafkaConsumer<String, String>(props);
}
private static void processRecords(KafkaConsumer<String, String> consumer) {
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
long lastOffset = 0;
for (ConsumerRecord<String, String> record : records) {
System.out.printf("nroffset = %d, key = %s, value = %s", record.offset(), record.key(), record.value());
lastOffset = record.offset();
}
System.out.println("lastOffset read: " lastOffset);
process();
}
}
private static void process() throws InterruptedException {
// create some delay to simulate processing of the message.
Thread.sleep(20);
}
}2. At-least-once kafka consumer
实现最少一次消费语义的消费者也很简单。
1). 设置enable.auto.commit为false
2). 消息处理完之后手动调用consumer.commitSync()
这种方式就是要手动在处理完该次poll得到消息之后，调用offset异步提交函数consumer.commitSync()。建议是消费者内部实现密等，来避免消费者重复处理消息进而得到重复结果。最多一次发生的场景是消费者的消息处理完并输出到结果库（也可能是部分处理完），但是offset还没提交，这个时候消费者挂掉了，再重启的时候会重新消费并处理消息。
代码如下：
代码语言：javascript复制public class AtLeastOnceConsumer {
public static void main(String[] str) throws InterruptedException {
System.out.println("Starting AutoOffsetGuranteedAtLeastOnceConsumer ...");
execute();
}
private static void execute() throws InterruptedException {
KafkaConsumer<String, String> consumer = createConsumer();
// Subscribe to all partition in that topic. 'assign' could be used here
// instead of 'subscribe' to subscribe to specific partition.
consumer.subscribe(Arrays.asList("normal-topic"));
processRecords(consumer);
}
private static KafkaConsumer<String, String> createConsumer() {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
String consumeGroup = "cg1";
props.put("group.id", consumeGroup);
// Set this property, if auto commit should happen.
props.put("enable.auto.commit", "true");
// Make Auto commit interval to a big number so that auto commit does not happen,
// we are going to control the offset commit via consumer.commitSync(); after processing // message.
props.put("auto.commit.interval.ms", "999999999999");
// This is how to control number of messages being read in each poll
props.put("max.partition.fetch.bytes", "135");
props.put("heartbeat.interval.ms", "3000");
props.put("session.timeout.ms", "6001");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer","org.apache.kafka.common.serialization.StringDeserializer");
return new KafkaConsumer<String, String>(props);
}
private static void processRecords(KafkaConsumer<String, String> consumer) throws {
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
long lastOffset = 0;
for (ConsumerRecord<String, String> record : records) {
System.out.printf("nroffset = %d, key = %s, value = %s", record.offset(), record.key(), record.value());
lastOffset = record.offset();
}
System.out.println("lastOffset read: " lastOffset);
process();
// Below call is important to control the offset commit. Do this call after you
// finish processing the business process.
consumer.commitSync();
}
}
private static void process() throws InterruptedException {
// create some delay to simulate processing of the record.
Thread.sleep(20);
}
}3. 使用subscribe实现Exactly-once
使用subscribe实现Exactly-once 很简单，具体思路如下：
1). 将enable.auto.commit设置为false。
2). 不调用consumer.commitSync()。
3). 使用subcribe定于topic。
4). 实现一个ConsumerRebalanceListener，在该listener内部执行
consumer.seek(topicPartition,offset)，从指定的topic/partition的offset处启动。
5). 在处理消息的时候，要同时控制保存住每个消息的offset。以原子事务的方式保存offset和处理的消息结果。传统数据库实现原子事务比较简单。但对于非传统数据库，比如hdfs或者nosql，为了实现这个目标，只能将offset与消息保存在同一行。
6). 实现密等，作为保护层。
代码如下：
代码语言：javascript复制public class ExactlyOnceDynamicConsumer {
private static OffsetManager offsetManager = new OffsetManager("storage2");
public static void main(String[] str) throws InterruptedException {
System.out.println("Starting ExactlyOnceDynamicConsumer ...");
readMessages();
}
private static void readMessages() throws InterruptedException {
KafkaConsumer<String, String> consumer = createConsumer();
// Manually controlling offset but register consumer to topics to get dynamically
// assigned partitions. Inside MyConsumerRebalancerListener use
// consumer.seek(topicPartition,offset) to control offset which messages to be read.
consumer.subscribe(Arrays.asList("normal-topic"),
new MyConsumerRebalancerListener(consumer));
processRecords(consumer);
}
private static KafkaConsumer<String, String> createConsumer() {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
String consumeGroup = "cg3";
props.put("group.id", consumeGroup);
// Below is a key setting to turn off the auto commit.
props.put("enable.auto.commit", "false");
props.put("heartbeat.interval.ms", "2000");
props.put("session.timeout.ms", "6001");
// Control maximum data on each poll, make sure this value is bigger than the maximum // single message size
props.put("max.partition.fetch.bytes", "140");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
return new KafkaConsumer<String, String>(props);
}
private static void processRecords(KafkaConsumer<String, String> consumer) {
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records) {
System.out.printf("offset = %d, key = %s, value = %sn", record.offset(), record.key(), record.value());
// Save processed offset in external storage.
offsetManager.saveOffsetInExternalStore(record.topic(), record.partition(), record.offset());
}
}
}
}
public class MyConsumerRebalancerListener implements org.apache.kafka.clients.consumer.ConsumerRebalanceListener {
private OffsetManager offsetManager = new OffsetManager("storage2");
private Consumer<String, String> consumer;
public MyConsumerRebalancerListener(Consumer<String, String> consumer) {
this.consumer = consumer;
}
public void onPartitionsRevoked(Collection<TopicPartition> partitions) {
for (TopicPartition partition : partitions) {
offsetManager.saveOffsetInExternalStore(partition.topic(), partition.partition(), consumer.position(partition));
}
}
public void onPartitionsAssigned(Collection<TopicPartition> partitions) {
for (TopicPartition partition : partitions) {
consumer.seek(partition, offsetManager.readOffsetFromExternalStore(partition.topic(), partition.partition()));
}
}
}
/**
* The partition offset are stored in an external storage. In this case in a local file system where
* program runs.
*/
public class OffsetManager {
private String storagePrefix;
public OffsetManager(String storagePrefix) {
this.storagePrefix = storagePrefix;
}
/**
* Overwrite the offset for the topic in an external storage.
*
* @param topic - Topic name.
* @param partition - Partition of the topic.
* @param offset - offset to be stored.
*/
void saveOffsetInExternalStore(String topic, int partition, long offset) {
try {
FileWriter writer = new FileWriter(storageName(topic, partition), false);
BufferedWriter bufferedWriter = new BufferedWriter(writer);
bufferedWriter.write(offset "");
bufferedWriter.flush();
bufferedWriter.close();
} catch (Exception e) {
e.printStackTrace();
throw new RuntimeException(e);
}
}
/**
* @return he last offset 1 for the provided topic and partition.
*/
long readOffsetFromExternalStore(String topic, int partition) {
try {
Stream<String> stream = Files.lines(Paths.get(storageName(topic, partition)));
return Long.parseLong(stream.collect(Collectors.toList()).get(0)) 1;
} catch (Exception e) {
e.printStackTrace();
}
return 0;
}
private String storageName(String topic, int partition) {
return storagePrefix "-" topic "-" partition;
}
}4. 使用assign实现Exactly-once
使用assign实现Exactly-once 也很简单，具体思路如下：
1). 将enable.auto.commit设置为false。
2). 不调用consumer.commitSync()。
3). 调用assign注册kafka消费者到kafka
4). 初次启动的时候，调用consumer.seek(topicPartition，offset)来指定offset。
5). 在处理消息的时候，要同时控制保存住每个消息的offset。以原子事务的方式保存offset和处理的消息结果。传统数据库实现原子事务比较简单。但对于非传统数据库，比如hdfs或者nosql，为了实现这个目标，只能将offset与消息保存在同一行。
6). 实现密等，作为保护层。
代码如下：
代码语言：javascript复制public class ExactlyOnceStaticConsumer {
private static OffsetManager offsetManager = new OffsetManager("storage1");
public static void main(String[] str) throws InterruptedException, IOException {
System.out.println("Starting ExactlyOnceStaticConsumer ...");
readMessages();
}
private static void readMessages() throws InterruptedException, IOException {
KafkaConsumer<String, String> consumer = createConsumer();
String topic = "normal-topic";
int partition = 1;
TopicPartition topicPartition =
registerConsumerToSpecificPartition(consumer, topic, partition);
// Read the offset for the topic and partition from external storage.
long offset = offsetManager.readOffsetFromExternalStore(topic, partition);
// Use seek and go to exact offset for that topic and partition.
consumer.seek(topicPartition, offset);
processRecords(consumer);
}
private static KafkaConsumer<String, String> createConsumer() {
Properties props = new Properties();
props.put("bootstrap.servers", "localhost:9092");
String consumeGroup = "cg2";
props.put("group.id", consumeGroup);
// Below is a key setting to turn off the auto commit.
props.put("enable.auto.commit", "false");
props.put("heartbeat.interval.ms", "2000");
props.put("session.timeout.ms", "6001");
// control maximum data on each poll, make sure this value is bigger than the maximum // single message size
props.put("max.partition.fetch.bytes", "140");
props.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
props.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
return new KafkaConsumer<String, String>(props);
}
/**
* Manually listens for specific topic partition. But, if you are looking for example of how to * dynamically listens to partition and want to manually control offset then see
* ExactlyOnceDynamicConsumer.java
*/
private static TopicPartition registerConsumerToSpecificPartition(
KafkaConsumer<String, String> consumer, String topic, int partition) {
TopicPartition topicPartition = new TopicPartition(topic, partition);
List<TopicPartition> partitions = Arrays.asList(topicPartition);
consumer.assign(partitions);
return topicPartition;
}
/**
* Process data and store offset in external store. Best practice is to do these operations
* atomically.
*/
private static void processRecords(KafkaConsumer<String, String> consumer) throws {
while (true) {
ConsumerRecords<String, String> records = consumer.poll(100);
for (ConsumerRecord<String, String> record : records) {
System.out.printf("offset = %d, key = %s, value = %sn", record.offset(), record.key(), record.value());
offsetManager.saveOffsetInExternalStore(record.topic(), record.partition(), record.offset());
}
}
}
}
