flink的ProcessWindowFunction函数的三种状态

背景

在处理窗口函数时，ProcessWindowFunction处理函数可以定义三个状态： 富函数getRuntimeContext.getState,

每个key+每个窗口的状态context.windowState(),每个key的状态context.globalState，那么这几个状态之间有什么关系呢？

ProcessWindowFunction处理函数三种状态之间的关系：

1.getRuntimeContext.getState这个定义的状态是每个key维度的，也就是可以跨时间窗口并维持状态的

2.context.windowState()这个定义的状态是和每个key以及窗口相关的，也就是虽然key相同，但是时间窗口不同，他们的值也不一样.

3.context.globalState这个定义的状态是和每个key相关的，也就是和getRuntimeContext.getState的定义一样，可以跨窗口维护状态

验证代码如下所示：

package wikiedits.func;


import org.apache.flink.api.common.state.ValueState;

import org.apache.flink.api.common.state.ValueStateDescriptor;

import org.apache.flink.api.java.tuple.Tuple2;

import org.apache.flink.configuration.Configuration;

import org.apache.flink.streaming.api.TimeCharacteristic;

import org.apache.flink.streaming.api.datastream.DataStream;

import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;

import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;

import org.apache.flink.streaming.api.functions.source.SourceFunction;

import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;

import org.apache.flink.streaming.api.windowing.time.Time;

import org.apache.flink.streaming.api.windowing.windows.TimeWindow;

import org.apache.flink.util.Collector;
import wikiedits.func.model.KeyCount;

import java.text.SimpleDateFormat;

import java.util.Date;



public class ProcessWindowFunctionDemo {

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

        final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        // 使用处理时间
        env.setStreamTimeCharacteristic(TimeCharacteristic.ProcessingTime);
        // 并行度为1
        env.setParallelism(1);
        // 设置数据源，一共三个元素
        DataStream<Tuple2<String, Integer>> dataStream = env.addSource(new SourceFunction<Tuple2<String, Integer>>() {
            @Override
            public void run(SourceContext<Tuple2<String, Integer>> ctx) throws Exception {
                int xxxNum = 0;
                int yyyNum = 0;
                for (int i = 1; i < Integer.MAX_VALUE; i++) {
                    // 只有XXX和YYY两种name
                    String name = (0 == i % 2) ? "XXX" : "YYY";
                    //更新aaa和bbb元素的总数
                    if (0 == i % 2) {
                        xxxNum++;
                    } else {
                        yyyNum++;
                    }
                    // 使用当前时间作为时间戳
                    long timeStamp = System.currentTimeMillis();
                    // 将数据和时间戳打印出来，用来验证数据
                    System.out.println(String.format("source，%s, %s,    XXX total : %d,    YYY total : %d\n",
                            name,
                            time(timeStamp),
                            xxxNum,
                            yyyNum));
                    // 发射一个元素，并且戴上了时间戳
                    ctx.collectWithTimestamp(new Tuple2<String, Integer>(name, 1), timeStamp);
                    // 每发射一次就延时1秒
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {
            }
        });

        // 将数据用5秒的滚动窗口做划分，再用ProcessWindowFunction
        SingleOutputStreamOperator<String> mainDataStream = dataStream
                // 以Tuple2的f0字段作为key，本例中实际上key只有aaa和bbb两种
                .keyBy(value -> value.f0)
                // 5秒一次的滚动窗口
                .timeWindow(Time.seconds(5))
                // 统计每个key当前窗口内的元素数量，然后把key、数量、窗口起止时间整理成字符串发送给下游算子
                .process(new ProcessWindowFunction<Tuple2<String, Integer>, String, String, TimeWindow>() {
                    // 自定义状态
                    private ValueState<KeyCount> state;
                    @Override
                    public void open(Configuration parameters) throws Exception {
                        // 初始化状态，name是myState
                        state = getRuntimeContext().getState(new ValueStateDescriptor<>("myState", KeyCount.class));
                    }

                    public void clear(Context context){
                        ValueState<KeyCount> contextWindowValueState = context.windowState().getState(new ValueStateDescriptor<>("myWindowState", KeyCount.class));
                        contextWindowValueState.clear();
                    }

                    @Override
                    public void process(String s, Context context, Iterable<Tuple2<String, Integer>> iterable,
                            Collector<String> collector) throws Exception {
                        // 从backend取得当前单词的myState状态
                        KeyCount current = state.value();
                        // 如果myState还从未没有赋值过，就在此初始化
                        if (current == null) {
                            current = new KeyCount();
                            current.key = s;
                            current.count = 0;
                        }
                        int count = 0;
                        // iterable可以访问该key当前窗口内的所有数据，
                        // 这里简单处理，只统计了元素数量
                        for (Tuple2<String, Integer> tuple2 : iterable) {
                            count++;
                        }
                        // 更新当前key的元素总数
                        current.count += count;
                        // 更新状态到backend
                        state.update(current);
                        System.out.println("getRuntimeContext() == context :" + (getRuntimeContext() == context));
                        ValueState<KeyCount> contextWindowValueState = context.windowState().getState(new ValueStateDescriptor<>("myWindowState", KeyCount.class));
                        ValueState<KeyCount> contextGlobalValueState = context.globalState().getState(new ValueStateDescriptor<>("myGlobalState", KeyCount.class));
                        KeyCount windowValue = contextWindowValueState.value();
                        if (windowValue == null) {
                            windowValue = new KeyCount();
                            windowValue.key = s;
                            windowValue.count = 0;
                        }
                        windowValue.count += count;
                        contextWindowValueState.update(windowValue);

                        KeyCount globalValue = contextGlobalValueState.value();
                        if (globalValue == null) {
                            globalValue = new KeyCount();
                            globalValue.key = s;
                            globalValue.count = 0;
                        }
                        globalValue.count += count;
                        contextGlobalValueState.update(globalValue);

                        ValueState<KeyCount> contextWindowSameNameState =
                                context.windowState().getState(new ValueStateDescriptor<>("myState", KeyCount.class));
                        ValueState<KeyCount> contextGlobalSameNameState =
                                context.globalState().getState(new ValueStateDescriptor<>("myState", KeyCount.class));
                        System.out.println("contextWindowSameNameState == contextGlobalSameNameState :" + (
                                contextWindowSameNameState == contextGlobalSameNameState));
                        System.out.println(
                                "state == contextGlobalSameNameState :" + (state == contextGlobalSameNameState));

                        // 将当前key及其窗口的元素数量，还有窗口的起止时间整理成字符串
                        String value = String.format("window, %s, %s - %s, %d,    total : %d, windowStateCount :%s, globalStateCount :%s\n",
                                // 当前key
                                s,
                                // 当前窗口的起始时间
                                time(context.window().getStart()),
                                // 当前窗口的结束时间
                                time(context.window().getEnd()),
                                // 当前key在当前窗口内元素总数
                                count,
                                // 当前key出现的总数
                                current.count,
                                contextWindowValueState.value(),
                                contextGlobalValueState.value());

                        // 发射到下游算子
                        collector.collect(value);
                    }
                });

        // 打印结果，通过分析打印信息，检查ProcessWindowFunction中可以处理所有key的整个窗口的数据
        mainDataStream.print();

        env.execute("processfunction demo : processwindowfunction");

    }



    public static String time(long timeStamp) {
        return new SimpleDateFormat("hh:mm:ss").format(new Date(timeStamp));
    }




}

输出结果：

window, XXX, 08:34:45 - 08:34:50, 3,    total : 22, windowStateCount :KeyCount{key='XXX', count=3}, globalStateCount :KeyCount{key='XXX', count=22}
window, YYY, 08:34:45 - 08:34:50, 2,    total : 22, windowStateCount :KeyCount{key='YYY', count=2}, globalStateCount :KeyCount{key='YYY', count=22}

从结果可以验证以上的结论，此外需要特别注意的一点是context.windowState()的状态需要在clear方法中清理掉，因为一旦时间窗口结束，就再也没有机会清理了
从这个例子中还发现一个比较有趣的现象：

ValueState<KeyCount> state = getRuntimeContext().getState(new ValueStateDescriptor<>("myState", KeyCount.class));
ValueState<KeyCount> contextWindowSameNameState =
        context.windowState().getState(new ValueStateDescriptor<>("myState", KeyCount.class));
ValueState<KeyCount> contextGlobalSameNameState =
        context.globalState().getState(new ValueStateDescriptor<>("myState", KeyCount.class));

在open中通过getRuntimeContext().getState定义的状态竟然可以通过 context.windowState()/ context.globalState()访问到，并且他们指向的都是同一个变量，可以参见代码的输出：

System.out.println("contextWindowSameNameState == contextGlobalSameNameState :" + (
        contextWindowSameNameState == contextGlobalSameNameState));
System.out.println(
        "state == contextGlobalSameNameState :" + (state == contextGlobalSameNameState));

结果如下：

contextWindowSameNameState == contextGlobalSameNameState :true
state == contextGlobalSameNameState :true

参考文献：

https://cloud.tencent.com/developer/article/1815079