How can Flink deduplicate data in Kafka?

In Flink, deduplicating data from Kafka can be achieved using the following methods:

1. Organize by key
2. decrease
3. combine
4. To bend or crease something to form pleats or layers.

DataStream<MyData> stream = env.addSource(new FlinkKafkaConsumer<>(...));

DataStream<MyData> deduplicatedStream = stream
    .keyBy(data -> data.getId())  // 按照 id 字段进行分组
    .reduce((data1, data2) -> data1);  // 使用 reduce 操作符将相同 id 的数据去重

1. group by key
2. Functioning process
3. Function that can be used to flat map data and process it efficiently, typically used for handling rich data structures.

DataStream<MyData> stream = env.addSource(new FlinkKafkaConsumer<>(...));

DataStream<MyData> deduplicatedStream = stream
    .keyBy(data -> data.getUniqueId())  // 按照唯一标识符进行分组
    .process(new DeduplicateFunction());  // 自定义 ProcessFunction 实现去重逻辑

public static class DeduplicateFunction extends ProcessFunction<MyData, MyData> {
    private ValueState<Boolean> seen;

    @Override
    public void open(Configuration parameters) throws Exception {
        seen = getRuntimeContext().getState(new ValueStateDescriptor<>("seen", Boolean.class));
    }

    @Override
    public void processElement(MyData data, Context ctx, Collector<MyData> out) throws Exception {
        if (seen.value() == null) {
            seen.update(true);
            out.collect(data);
        }
    }
}

It is important to note that the above method can only remove duplicates from adjacent data. If the data volume is large or the data distribution is uneven, it may result in performance issues. If you need to deduplicate data from the entire Kafka, consider using Flink’s state backend like RocksDB to store processed data identifiers in the state and regularly clean up expired data.