flink如何将读取kafka时的偏移量存入redis

Flink可以使用FlinkKafkaConsumer来读取Kafka数据，并使用Flink的状态后端来保存偏移量。而要将偏移量存入Redis，可以在Flink中使用Redis作为状态后端，将偏移量存储在Redis中。

具体实现步骤如下：

1. 引入相关依赖

在Flink项目中引入以下依赖：

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-kafka_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
</dependency>

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-statebackend-redis</artifactId>
    <version>${flink.version}</version>
</dependency>

<dependency>
    <groupId>org.apache.flink</groupId>
    <artifactId>flink-connector-redis_${scala.binary.version}</artifactId>
    <version>${flink.version}</version>
</dependency>

2. 配置Redis状态后端

在Flink程序中配置Redis作为状态后端，可以参考以下代码：

import org.apache.flink.contrib.streaming.state.RocksDBStateBackend;
import org.apache.flink.contrib.streaming.state.RocksDBStateBackend.PriorityQueueStateType;
import org.apache.flink.runtime.state.StateBackend;
import org.apache.flink.runtime.state.memory.MemoryStateBackend;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.redis.RedisConfig;
import org.apache.flink.streaming.connectors.redis.RedisSink;
import org.apache.flink.streaming.connectors.redis.common.config.FlinkJedisPoolConfig;

public class RedisStateBackendExample {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 配置Redis连接池
        FlinkJedisPoolConfig jedisPoolConfig = new FlinkJedisPoolConfig.Builder()
                .setHost("localhost")
                .setPort(6379)
                .build();

        // 配置Redis状态后端
        StateBackend stateBackend = new RocksDBStateBackend("file:///tmp/checkpoints", true);
        env.setStateBackend(stateBackend);

        // 添加RedisSink
        RedisConfig redisConfig = new RedisConfig.Builder()
                .setHost("localhost")
                .setPort(6379)
                .build();
        RedisSink<String> redisSink = new RedisSink<>(redisConfig, new RedisSinkFunction<String>("my-redis-key"));
        env.addSink(redisSink);

        // 执行任务
        env.execute("Redis State Backend Example");
    }
}

注意：这里使用了RocksDBStateBackend作为状态后端，同时也添加了一个RedisSink。

3. 配置Kafka消费者

在Flink程序中使用FlinkKafkaConsumer来读取Kafka数据，并使用状态后端保存偏移量，可以参考以下代码：

import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer;
import org.apache.flink.streaming.connectors.kafka.FlinkKafkaConsumer.OffsetCommitMode;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaDeserializationSchemaWrapper;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchema;
import org.apache.flink.streaming.connectors.kafka.KafkaSerializationSchemaWrapper;
import org.apache.flink.streaming.connectors.kafka.KafkaTopicPartition;
import org.apache.flink.streaming.connectors.kafka.internals.KafkaTopicPartitionState;
import org.apache.flink.streaming.util.serialization.SimpleStringSchema;

import java.util.Properties;

public class KafkaToRedisExample {

    public static void main(String[] args) throws Exception {

        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // 配置Kafka消费者
        Properties properties = new Properties();
        properties.setProperty("bootstrap.servers", "localhost:9092");
        properties.setProperty("group.id", "my-group");
        FlinkKafkaConsumer<String> kafkaConsumer = new FlinkKafkaConsumer<>("my-topic", new SimpleStringSchema(), properties);

        // 配置Kafka反序列化器
        KafkaDeserializationSchema<String> kafkaDeserializationSchema = new KafkaDeserializationSchemaWrapper<>(new SimpleStringSchema());
        kafkaConsumer.setStartFromEarliest();
        kafkaConsumer.setCommitOffsetsOnCheckpoints(true);
        kafkaConsumer.setCommitOffsetOnCheckpoint(true);
        kafkaConsumer.setCommitMode(OffsetCommitMode.ON_CHECKPOINTS);
        kafkaConsumer.setDeserializationSchema(kafkaDeserializationSchema);

        // 读取Kafka数据
        DataStream<String> stream = env.addSource(kafkaConsumer);

        // 对数据进行处理
        stream.map(new MapFunction<String, String>() {
            @Override
            public String map(String value) throws Exception {
                return value.toUpperCase();
            }
        }).print();

        // 执行任务
        env.execute("Kafka to Redis Example");
    }
}

注意：这里使用了FlinkKafkaConsumer来读取Kafka数据，并配置了反序列化器和偏移量相关的参数。

4. 将偏移量存入Redis

要将偏移量存入Redis，可以在程序中使用RedisSink来实现。这里的RedisSinkFunction可以将偏移量存储在Redis中。

import org.apache.flink.streaming.connectors.redis.RedisCommands;
import org.apache.flink.streaming.connectors.redis.RedisSinkFunction;

public class RedisSinkFunction<T> implements RedisSinkFunction<T> {

    private final String redisKey;

    public RedisSinkFunction(String redisKey) {
        this.redisKey = redisKey;
    }

    @Override
    public void invoke(T value, Context context, RedisCommands redisCommands) throws Exception {
        // 将偏移量存储在Redis中
        for (KafkaTopicPartitionState<T> partition : context.getKafkaTopicPartitionStates()) {
            KafkaTopicPartition topicPartition = partition.getKafkaTopicPartition();
            String key = redisKey + "-" + topicPartition.getTopic() + "-" + topicPartition.getPartition();
            long offset = partition.getOffset();
            redisCommands.set(key, String.valueOf(offset));
        }
    }
}

注意：这里的RedisSinkFunction使用了Flink的上下文信息Context，可以获取到KafkaTopicPartitionState，从而可以获取到偏移量。