「Flink」使用Managed Keyed State实现计数窗口功能

先上代码：

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

        // 初始化测试单词数据流
        DataStreamSource lineDS = env.addSource(new RichSourceFunction() {
            private boolean isCanaled = false;

            @Override
            public void run(SourceContext ctx) throws Exception {
                while(!isCanaled) {
                    ctx.collect("hadoop flink spark");
                    Thread.sleep(1000);
                }
            }

            @Override
            public void cancel() {
                isCanaled = true;
            }
        });

        // 切割单词，并转换为元组
        SingleOutputStreamOperator> wordTupleDS = lineDS.flatMap((String line, Collector> ctx) -> {
            Arrays.stream(line.split(" ")).forEach(word -> ctx.collect(Tuple2.of(word, 1)));
        }).returns(Types.TUPLE(Types.STRING, Types.INT));

        // 按照单词进行分组
        KeyedStream, Integer> keyedWordTupleDS = wordTupleDS.keyBy(t -> t.f1);

        // 对单词进行计数
        keyedWordTupleDS.flatMap(new RichFlatMapFunction, Tuple2>() {

            private transient ValueState> countSumValueState;

            @Override
            public void open(Configuration parameters) throws Exception {
                // 初始化ValueState
                ValueStateDescriptor> countSumValueStateDesc = new ValueStateDescriptor("countSumValueState",
                        TypeInformation.of(new TypeHint>() {})
                );
                countSumValueState = getRuntimeContext().getState(countSumValueStateDesc);
            }

            @Override
            public void flatMap(Tuple2 value, Collector> out) throws Exception {
                if(countSumValueState.value() == null) {
                    countSumValueState.update(Tuple2.of(0, 0));
                }

                Integer count = countSumValueState.value().f0;
                count++;
                Integer valueSum = countSumValueState.value().f1;
                valueSum += value.f1;

                countSumValueState.update(Tuple2.of(count, valueSum));

                // 每当达到3次，发送到下游
                if(count > 3) {
                    out.collect(Tuple2.of(value.f0, valueSum));
                    // 清除计数
                    countSumValueState.update(Tuple2.of(0, valueSum));
                }
            }
        }).print();

        env.execute("KeyedState State");
    }
}

代码说明：

1、构建测试数据源，每秒钟发送一次文本，为了测试方便，这里就发一个包含三个单词的文本行

2、对句子按照空格切分，并将单词转换为元组，每个单词初始出现的次数为1

3、按照单词进行分组

4、自定义FlatMap

初始化ValueState，注意：ValueState只能在KeyedStream中使用，而且每一个ValueState都对一个一个key。每当一个并发处理ValueState，都会从上下文获取到Key的取值，所以每个处理逻辑拿到的ValueStated都是对应指定key的ValueState，这个部分是由Flink自动完成的。

注意：

带默认初始值的ValueStateDescriptor已经过期了，官方推荐让我们手动在处理时检查是否为空

instead and manually manage the default value by checking whether the contents of the state is null.

”

/**
 * Creates a new {@code ValueStateDescriptor} with the given name, default value, and the specific
 * serializer.
 *
 * @deprecated Use {@link #ValueStateDescriptor(String, TypeSerializer)} instead and manually
 * manage the default value by checking whether the contents of the state is {@code null}.
 *
 * @param name The (unique) name for the state.
 * @param typeSerializer The type serializer of the values in the state.
 * @param defaultValue The default value that will be set when requesting state without setting
 *                     a value before.
 */
@Deprecated
public ValueStateDescriptor(String name, TypeSerializer typeSerializer, T defaultValue) {
   super(name, typeSerializer, defaultValue);
}

5、逻辑实现

在flatMap逻辑中判断ValueState是否已经初始化，如果没有手动给一个初始值。并进行累加后更新。每当count > 3发送计算结果到下游，并清空计数。