StateBackend

package com.atguigu.state;

import com.atguigu.bean.WaterSensor;
import com.atguigu.functions.WaterSensorMapFunction;
import org.apache.flink.api.common.eventtime.WatermarkStrategy;
import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.contrib.streaming.state.EmbeddedRocksDBStateBackend;
import org.apache.flink.runtime.state.hashmap.HashMapStateBackend;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.KeyedProcessFunction;
import org.apache.flink.util.Collector;

import java.time.Duration;

public class StateBackendDemo {
    public static void main(String[] args) throws Exception {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        /**
         * TODO 状态后端
         * 1、负责管理 本地状态
         * 2、 hashmap
         *          存在 TM的 JVM的堆内存，  读写快，缺点是存不了太多（受限与TaskManager的内存）
         *     rocksdb
         *          存在 TM所在节点的rocksdb数据库，存到磁盘中，  写--序列化，读--反序列化
         *          读写相对慢一些，可以存很大的状态
         *
         * 3、配置方式
         *    1）配置文件 默认值  flink-conf.yaml
         *    2）代码中指定
         *    3）提交参数指定
         *    flink run-application -t yarn-application
         *    -p 3
         *    -Dstate.backend.type=rocksdb
         *    -c 全类名
         *    jar包
          */

        // 1. 使用 hashmap状态后端
        HashMapStateBackend hashMapStateBackend = new HashMapStateBackend();
        env.setStateBackend(hashMapStateBackend);
        // 2. 使用 rocksdb状态后端
        EmbeddedRocksDBStateBackend embeddedRocksDBStateBackend = new EmbeddedRocksDBStateBackend();
        env.setStateBackend(embeddedRocksDBStateBackend);

        SingleOutputStreamOperator<WaterSensor> sensorDS = env
                .socketTextStream("192.168.1.7", 9091)
                .map(new WaterSensorMapFunction())
                .assignTimestampsAndWatermarks(
                        WatermarkStrategy
                                .<WaterSensor>forBoundedOutOfOrderness(Duration.ofSeconds(3))
                                .withTimestampAssigner((element, ts) -> element.getTs() * 1000L)
                );

        sensorDS.keyBy(r -> r.getId())
                .process(
                        new KeyedProcessFunction<String, WaterSensor, String>() {
                            ValueState<Integer> lastVcState;
                            @Override
                            public void open(Configuration parameters) throws Exception {
                                super.open(parameters);
                                lastVcState = getRuntimeContext().getState(new ValueStateDescriptor<Integer>("lastVcState", Types.INT));
                            }

                            @Override
                            public void processElement(WaterSensor value, Context ctx, Collector<String> out) throws Exception {
                                int lastVc = lastVcState.value() == null ? 0 : lastVcState.value();
                                Integer vc = value.getVc();
                                if (Math.abs(vc - lastVc) > 10) {
                                    out.collect("传感器=" + value.getId() + "==>当前水位值=" + vc + ",与上一条水位值=" + lastVc + ",相差超过10！！！！");
                                }
                                lastVcState.update(vc);
                            }
                        }
                )
                .print();

        env.execute();
    }
}