Flink之数据去重

前言

flink 处理重复数据，经过实战，总结一下3个方式。

1. 去重方式一 流转表

核心代码逻辑

    // 计算iopv
        SingleOutputStreamOperator<FundIopvIndicators> streamOperator = EtfIopvFunction.calculateRealTimeIopv(stringKeyedStream);

        // 流转表
        Table table = tableEnv.fromDataStream(streamOperator, "fundCode,realTimeIopv,computationTime,strTime");
        // 去重
        Table duplicateRemoval = tableEnv.sqlQuery(" select strTime,fundCode,realTimeIopv,computationTime from ( "
                + " select  strTime,fundCode,realTimeIopv,computationTime, ROW_NUMBER() OVER "
                + " (PARTITION BY strTime ORDER BY strTime desc) AS rownum  from "
                + table
                + ") where rownum=1"
        );

2 去重方式二 flink sql

--- 去重查询
-- kafka source
CREATE TABLE user_log (
  user_id VARCHAR
  ,item_id VARCHAR
  ,category_id VARCHAR
  ,behavior INT
  ,ts TIMESTAMP(3)
  ,process_time as proctime()
  , WATERMARK FOR ts AS ts
) WITH (
  'connector' = 'kafka'
  ,'topic' = 'user_behavior'
  ,'properties.bootstrap.servers' = 'localhost:9092'
  ,'properties.group.id' = 'user_log'
  ,'scan.startup.mode' = 'group-offsets'
  ,'format' = 'json'
);

---sink table
CREATE TABLE user_log_sink (
  user_id VARCHAR
  ,item_id VARCHAR
  ,category_id VARCHAR
  ,behavior INT
  ,ts TIMESTAMP(3)
  ,num BIGINT
  ,primary key (user_id) not enforced
) WITH (
'connector' = 'upsert-kafka'
  ,'topic' = 'user_behavior_sink'
  ,'properties.bootstrap.servers' = 'localhost:9092'
  ,'properties.group.id' = 'user_log'
  ,'key.format' = 'json'
  ,'key.json.ignore-parse-errors' = 'true'
  ,'value.format' = 'json'
  ,'value.json.fail-on-missing-field' = 'false'
  ,'value.fields-include' = 'ALL'
);

-- insert
insert into user_log_sink(user_id, item_id, category_id,behavior,ts,num)
SELECT user_id, item_id, category_id,behavior,ts,rownum
FROM (
   SELECT user_id, item_id, category_id,behavior,ts,
     ROW_NUMBER() OVER (PARTITION BY category_id ORDER BY process_time desc) AS rownum -- desc use the latest one,
   FROM user_log)
WHERE rownum=1-- 只能使用 rownum=1，如果写 rownum=2（或<10），每个分区只会输出一条数据(小于是多条)rownum=2的，看起来基于全局去重了

3. 利用 flink 的 MapState

public class IopvDeduplicateProcessFunction extends RichFlatMapFunction<FundIopvIndicators, FundIopvIndicators> {
    
    
    private MapState<String, FundIopvIndicators> mapState;

    /***状态初始化*/
    @Override
    public void open(Configuration parameters) throws Exception {
    
    

        MapStateDescriptor descriptor = new MapStateDescriptor("MapDescriptor", String.class, FundIopvIndicators.class);
        mapState = getRuntimeContext().getMapState(descriptor);

    }

    @Override
    public void flatMap(FundIopvIndicators iopvIndicators, Collector<FundIopvIndicators> collector) throws Exception {
    
    
        String strTime = iopvIndicators.getStrTime();
        // 去重
        if (mapState.get(strTime) == null) {
    
    
            mapState.put(strTime, iopvIndicators);
            collector.collect(iopvIndicators);
        }
    }
}