Kafka-API

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new 它的实现类
ctrl+r替换
格式化ctrl+alt+l

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Kafka生产者Java API

创建生产者

不带回调函数的

public class CustomProducer {
    public static void main(String[] args) throws InterruptedException {
        Properties properties = new Properties();
        //kafka地址
        properties.put("bootstrap.servers", "hadoop101:9092, hadoop102:9092, hadoop103:9092");
        //acks=-1
        properties.put("acks", "all");
        properties.put("retries", 0);
        //基于大小的批处理
        properties.put(ProducerConfig.BATCH_SIZE_CONFIG, 16384);
        //基于时间的批处理
        properties.put("linger.ms", 1);
        //客户端缓存大小
        properties.put("buffer.memory", 33554432);
        //k v序列化
        properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        Producer<String, String> producer = new KafkaProducer<String, String>(properties);

        for (int i = 0; i < 9; i++){
            producer.send(new ProducerRecord<String, String>("first","" + i, "Hello" + i ));
        }
        //Thread.sleep(1000);
        producer.close(); //忘记close关了,它就是基于批处理的条件( 基于大小的批处理； 基于时间的批处理，看是否达到，没有达到就不会send；)

    }

}

 new producer<String, String>( "主题"， 分区int， " key“, "value" ）

带回调函数

带回调函数的producer， 每发一条消息调用一次回调函数
不管有没有发送成功

public class CustomProducerCompletion {
    public static void main(String[] args) {
        Properties properties = new Properties();
        properties.put("bootstrap.servers", "hadoop101:9092, hadoop102:9092, hadoop103:9092");
        properties.put("acks", "all");
        properties.put("retries", 2);
        properties.put("batch.size", 16384);
        properties.put("linger.ms", 1);
        properties.put("buffer.memory", 33554432);
        properties.put("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        properties.put("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");
        //自定义分区 ProducerConfig.PARTITIONER_CLASS_CONFIG
        //properties.put("partitioner.class", "com.atguigu.kafka.producer.CustomPartitioner");
        //拦截器
        properties.put(ProducerConfig.INTERCEPTOR_CLASSES_CONFIG,
                Arrays.asList("com.atguigu.kafka.interceptor.TimeStampInterceptor","com.atguigu.kafka.interceptor.CountInterceptor"));

        KafkaProducer<String, String> kafkaProducer = new KafkaProducer<String, String>(properties);
        for (int i = 0; i < 9; i++){
            kafkaProducer.send(new ProducerRecord<String, String>("first", "1", "Hi" + i), new Callback() {
                public void onCompletion(RecordMetadata recordMetadata, Exception e) {
                    if (recordMetadata != null){
                        System.out.println("Topic:" + recordMetadata.topic() + "\t" +
                                "Partition:" + recordMetadata.partition() + "\t" + "offset:" + recordMetadata.offset()
                                );
                    }
                }
            });
        }
        kafkaProducer.close();

    }
}

自定义分区

指定分区重写key的规则

public class CustomPartitioner implements Partitioner {
    public int partition(String topic, Object key, byte[] keyBytes, Object value, byte[] valueBytes, Cluster cluster) {
        return 0; //控制分区
    }

    public void close() {

    }

    /**
     * 可以添加属性
     * @param config
     */
    public void configure(Map<String, ?> config) {

    }
}

Kafka消费者Java API

高级API

不需手动管理offset

poll 超时时间
subscribe订阅主题
可同时消费多个主题
数组-Arrays.asList->集合

1) 高级API优点

高级API 写起来简单

不需要自行去管理offset，系统通过zookeeper自行管理。

不需要管理分区，副本等情况，系统自动管理。

消费者断线会自动根据上一次记录在zookeeper中的offset去接着获取数据；可以使用group来区分对同一个topic 的不同程序访问分离开来（不同的group记录不同的offset，这样不同程序读取同一个topic才不会因为offset互相影响）

2）高级API缺点

不能自行控制offset（对于某些特殊需求来说）

不能细化控制如分区、副本、zk等

//高级API
public class CustomConsumer  {
    public static void main(String[] args) {
        Properties properties = new Properties();
        //定义kafka集群地址
        properties.put("bootstrap.servers", "hadoop101:9092, hadoop102:9092, hadoop103:9092");
        //消费者组id
        properties.put(ConsumerConfig.GROUP_ID_CONFIG, "kris");
        //是否自动提交偏移量：(kafka集群管理)
        properties.put("enable.auto.commit", "true");
        //间隔多长时间提交一次offset
        properties.put(ConsumerConfig.AUTO_COMMIT_INTERVAL_MS_CONFIG, "1000");
        //key，value的反序列化
        properties.put("key.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");
        properties.put("value.deserializer", "org.apache.kafka.common.serialization.StringDeserializer");

        KafkaConsumer<String, String> kafkaConsumer = new KafkaConsumer<String, String>(properties);
        kafkaConsumer.subscribe(Arrays.asList("first"));  //订阅主题
        while (true){
            ConsumerRecords<String, String> records = kafkaConsumer.poll(100); //定义Consumer, poll拉数据
            for (ConsumerRecord<String, String> record : records) {
                System.out.println("Topic:" + record.topic() + "\t" +
                        "Partition:" + record.partition() + "\t" + "Offset:" +record.offset()
                        + "\t" + "key:" + record.key() + "\t" + "value:" + record.value());
            }
        }
    }
}

低级API

leader
　　offset
　　保存offset
消息

public class LowLevelConsumer {
    public static void main(String[] args) {
        //1.集群
        ArrayList<String> list = new ArrayList<>();
        list.add("hadoop101");
        list.add("hadoop102");
        list.add("hadoop103");
        //2.主题
        String topic = "first";
        //3.分区
        int partition = 2;
        //4.offset
        long offset = 10;

        //5.获取leader
        String leader = getLeader(list, topic, partition);
        //6.连接leader获取数据
        getData(leader, topic, partition, offset);

    }

    private static void getData(String leader, String topic, int partition, long offset) {
        //1.创建SimpleConsumer
        SimpleConsumer consumer = new SimpleConsumer(leader, 9092, 2000, 1024 * 1024 * 2, "getData");
        //2.发送请求
        //3.构建请求对象FetchRequestBuilder
        FetchRequestBuilder builder = new FetchRequestBuilder();
        FetchRequestBuilder requestBuilder = builder.addFetch(topic, partition, offset, 1024 * 1024);
        FetchRequest fetchRequest = requestBuilder.build();
        //4.获取响应
        FetchResponse fetchResponse = consumer.fetch(fetchRequest);
        //5.解析响应
        ByteBufferMessageSet messageAndOffsets = fetchResponse.messageSet(topic, partition);
        //6.遍历
        for (MessageAndOffset messageAndOffset : messageAndOffsets) {
            long message_offset = messageAndOffset.offset();
            Message message = messageAndOffset.message();
            //7.解析message
            ByteBuffer byteBuffer = message.payload(); //payload是有效负载
            byte[] bytes = new byte[byteBuffer.limit()];
            byteBuffer.get(bytes);
            //8.获取数据
            System.out.println("offset:" + message_offset + "\t" + "value:" + new String(bytes));

        }

    }

    private static String getLeader(ArrayList<String> list, String topic, int partition) {
        //1.循环发送请求，获取leader
        for (String host : list) {
            //2.创建SimpleConsumer对象
            SimpleConsumer consumer = new SimpleConsumer(
                    host,
                    9092,
                    2000,
                    1024*1024,
                    "getLeader"
            );
            //3.发送获取leader请求
            //4.构造请求TopicMetadataRequest
            TopicMetadataRequest request = new TopicMetadataRequest(Arrays.asList(topic));
            //5.获取响应TopicMetadataResponse
            TopicMetadataResponse response = consumer.send(request);
            //6.解析响应
            List<TopicMetadata> topicsMetadata = response.topicsMetadata();
            //7.遍历topicsMetadata
            for (TopicMetadata topicMetadata : topicsMetadata) {
                List<PartitionMetadata> partitionsMetadata = topicMetadata.partitionsMetadata();
                //8.遍历partitionsMetadata
                for (PartitionMetadata partitionMetadata : partitionsMetadata) {
                    //9.判断
                    if (partitionMetadata.partitionId() == partition){
                        BrokerEndPoint endPoint = partitionMetadata.leader();
                        return endPoint.host();

                    }
                }

            }

        }

        return null;
    }
}

Kafka producer拦截器

flume-事件
flume的拦截器链：
kafka-消息

每发送一条数据调用一次onSend方法
接收数据调用回调函数之前调用onAcknoeledgement

https://blog.csdn.net/stark_summer/article/details/50144591

Kafka与Flume比较

在企业中必须要清楚流式数据采集框架flume和kafka的定位是什么：

flume：cloudera公司研发:

适合多个生产者；

适合下游数据消费者不多的情况；

适合数据安全性要求不高的操作；

适合与Hadoop生态圈对接的操作。

kafka：linkedin公司研发:

适合数据下游消费众多的情况；

适合数据安全性要求较高的操作，支持replication。

因此我们常用的一种模型是：

线上数据 --> flume --> kafka --> flume(根据情景增删该流程) --> HDFS

vim flume-kafka.conf
# define
a1.sources = r1
a1.sinks = k1
a1.channels = c1

# source
a1.sources.r1.type = exec
a1.sources.r1.command = tail -F -c +0 /opt/module/datas/flume.log
a1.sources.r1.shell = /bin/bash -c

# sink
a1.sinks.k1.type = org.apache.flume.sink.kafka.KafkaSink
a1.sinks.k1.kafka.bootstrap.servers = hadoop101:9092,hadoop102:9092,hadoop103:9092
a1.sinks.k1.kafka.topic = first
a1.sinks.k1.kafka.flumeBatchSize = 20
a1.sinks.k1.kafka.producer.acks = 1
a1.sinks.k1.kafka.producer.linger.ms = 1

# channel
a1.channels.c1.type = memory
a1.channels.c1.capacity = 1000
a1.channels.c1.transactionCapacity = 100

# bind
a1.sources.r1.channels = c1
a1.sinks.k1.channel = c1

tail -F动态实时  -c 0从0行开始监控

[kris@hadoop101 flume]$ bin/flume-ng agent -c conf/ -n a1 -f job/flume-kafka.conf 

[kris@hadoop101 datas]$ cat > flume.log
Hello

[kris@hadoop101 kafka]$ bin/kafka-console-consumer.sh --bootstrap-server hadoop101: --topic first
Hello