3使用Flink CDC 将用户表user_info、省份表base_provice同步到Redis中做旁路缓存。5分

需要先按照以下步骤进行配置：

1. 配置Flink CDC连接到MySQL数据库，读取数据变化。

2. 配置Flink连接到Redis，将数据同步到Redis中。

3. 配置好CDC和Flink的运行环境，启动CDC和Flink任务。

代码示例：

// CDC配置
bin/flink-cdc.sh \
    --mode mysql-binlog \
    --mysql.server-id=1 \
    --mysql.host=localhost \
    --mysql.port=3306 \
    --mysql.username=root \
    --mysql.password=123456 \
    --mysql.database.name=user_db \
    --cdc.table.whitelist=user_info,base_province \
    --cdc.startup.mode=latest-offset \
    --cdc.startup.timestamp-millis=0 \
    --sink.format=changelog-json \
    --sink.topic=user_info,base_province \
    --sink.partition-key-fields=user_id,province_id \
    --properties-file /path/to/flink-cdc.properties

// Flink配置
StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setParallelism(1);

Properties properties = new Properties();
properties.setProperty("bootstrap.servers", "localhost:9092");
properties.setProperty("group.id", "flink-cdc-redis");
properties.setProperty("key.serializer", "org.apache.kafka.common.serialization.StringSerializer");
properties.setProperty("value.serializer", "org.apache.kafka.common.serialization.StringSerializer");

FlinkKafkaConsumer<String> consumer = new FlinkKafkaConsumer<>("user_info,base_province", new SimpleStringSchema(), properties);
consumer.setStartFromLatest();

DataStream<String> stream = env.addSource(consumer);

stream.map(new MapFunction<String, Tuple2<String, String>>() {
    @Override
    public Tuple2<String, String> map(String value) throws Exception {
        JSONObject json = JSON.parseObject(value);
        String table = json.getString("table");
        String key = json.getString("key");
        String type = json.getString("type");
        String data = json.getString("data");

        if ("user_info".equals(table)) {
            JSONObject user = JSON.parseObject(data);
            String userId = user.getString("user_id");
            String userName = user.getString("user_name");
            String userAge = user.getString("user_age");
            String userGender = user.getString("user_gender");
            return new Tuple2<>("user:" + userId, userName + "," + userAge + "," + userGender);
        } else if ("base_province".equals(table)) {
            JSONObject province = JSON.parseObject(data);
            String provinceId = province.getString("province_id");
            String provinceName = province.getString("province_name");
            return new Tuple2<>("province:" + provinceId, provinceName);
        } else {
            return null;
        }
    }
})
.filter(Objects::nonNull)
.addSink(new RedisSink<>(new FlinkJedisPoolConfig.Builder().setHost("localhost").build(), new RedisMapper<Tuple2<String, String>>() {
    @Override
    public RedisCommandDescription getCommandDescription() {
        return new RedisCommandDescription(RedisCommand.SET);
    }

    @Override
    public String getKeyFromData(Tuple2<String, String> data) {
        return data.f0;
    }

    @Override
    public String getValueFromData(Tuple2<String, String> data) {
        return data.f1;
    }
})).setParallelism(1);

env.execute("Flink CDC to Redis");

上述代码中，首先使用Flink CDC从MySQL中读取user_info和base_province表的数据，并将其写入Kafka中。然后使用Flink从Kafka中读取数据，将其解析后写入Redis中，缓存到Redis中。其中，使用了Flink提供的RedisSink来将数据写入Redis中