Flink状态应用测试程序示例

1. 创建执行环境

        // 1. 创建执行环境StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();env.setParallelism(1);   

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2. 创建数据流

        // 2. 创建数据流DataStream<Tuple2<String, Integer>> inputStream = env.fromElements(Tuple2.of("a", 1),Tuple2.of("b", 2),Tuple2.of("c", 3),Tuple2.of("a", 4),Tuple2.of("b", 5),Tuple2.of("c", 6)).uid("source").name("source");

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3. 对数据流进行keyBy()操作

        // 3. 对数据流进行keyBy()操作DataStream<Tuple2<String, Integer>> keyedStream = inputStream.keyBy(new KeySelector<Tuple2<String, Integer>, String>() {@Overridepublic String getKey(Tuple2<String, Integer> stringIntegerTuple2) throws Exception {return stringIntegerTuple2.f0;}});

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4. 使用RichFlatMapFunction来实现状态的维护

        // 4. 使用RichFlatMapFunction来实现状态的维护keyedStream.flatMap(new RichFlatMapFunction<Tuple2<String, Integer>, Tuple2<String, Integer>>() {// 定义状态变量private ValueState<Integer> countState;@Overridepublic void open(Configuration parameters) throws Exception {// 初始化状态变量ValueStateDescriptor<Integer> descriptor = new ValueStateDescriptor<>("count", Integer.class);countState = getRuntimeContext().getState(descriptor);}@Overridepublic void flatMap(Tuple2<String, Integer> input, Collector<Tuple2<String, Integer>> out) throws Exception {// 获取状态变量的值Integer currentCount = countState.value();// 更新状态变量的值if (currentCount == null) {currentCount = 0;}currentCount++;countState.update(currentCount);// 输出结果out.collect(Tuple2.of(input.f0, currentCount));}}).print(); 

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5.执行任务

        // 5.执行任务env.execute("Flink State Test"); 

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6.执行结果

(a,1)
(b,1)
(c,1)
(a,2)
(b,2)
(c,2)Process finished with exit code 0

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