Flink keyBy算子的分区规则

阿东在路上2024-12-14 15:39

demo代码

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String worlds = "flink,spark,hadoop,zk,kafka";

streamSource.flatMap(new RichFlatMapFunction<String, String>() {
            @Override
            public void flatMap(String value, Collector<String> collector) throws Exception {
                String[] worlds = value.split(",");
                for (String world : worlds) {
                    collector.collect(world);
                }
            }
        }).keyBy(new KeySelector<String, String>() {
            @Override
            public String getKey(String key) throws Exception {
                return key;
            }
        }).process(new KeyedProcessFunction<String, String, String>() {
            @Override
            public void processElement(String key,KeyedProcessFunction<String, String, String>.Context ctx,Collector<String> out) throws Exception {
                out.collect(key);
            }
        }).print("->");

        env.execute();

运行结果：

源码查看

我们进去keyBy算子，看看计算逻辑是怎样的：

根据调用逻辑，我们抽象出分区数据的计算逻辑：

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(MathUtils.murmurHash(key.hashCode()) % maxParallelism) * parallelism / maxParallelism ;

其中：

maxParallelism：默认128
parallelism：并行度数
key：分区键

验证

我们把抽出来的逻辑加到计算结果中查看

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process(new KeyedProcessFunction<String, String, String>() {
            @Override
            public void processElement(String key,
                                       KeyedProcessFunction<String, String, String>.Context ctx,
                                       Collector<String> out) throws Exception {
                String currentKey = ctx.getCurrentKey();
                int i = (MathUtils.murmurHash(key.hashCode()) % 128) * parallelism / 128 ;
                out.collect(key + "_" +i);
                out.collect(key);
            }

处理结果：