聊聊flink的ProcessFunction

序

本文主要研究一下flink的ProcessFunction

实例

import org.apache.flink.api.common.state.ValueState;
import org.apache.flink.api.common.state.ValueStateDescriptor;
import org.apache.flink.api.java.tuple.Tuple2;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.functions.ProcessFunction;
import org.apache.flink.streaming.api.functions.ProcessFunction.Context;
import org.apache.flink.streaming.api.functions.ProcessFunction.OnTimerContext;
import org.apache.flink.util.Collector;


// the source data stream
DataStream<Tuple2<String, String>> stream = ...;

// apply the process function onto a keyed stream
DataStream<Tuple2<String, Long>> result = stream
    .keyBy(0)
    .process(new CountWithTimeoutFunction());

/**
 * The data type stored in the state
 */
public class CountWithTimestamp {

    public String key;
    public long count;
    public long lastModified;
}

/**
 * The implementation of the ProcessFunction that maintains the count and timeouts
 */
public class CountWithTimeoutFunction extends ProcessFunction<Tuple2<String, String>, Tuple2<String, Long>> {

    /** The state that is maintained by this process function */
    private ValueState<CountWithTimestamp> state;

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

    @Override
    public void processElement(Tuple2<String, String> value, Context ctx, Collector<Tuple2<String, Long>> out)
            throws Exception {

        // retrieve the current count
        CountWithTimestamp current = state.value();
        if (current == null) {
            current = new CountWithTimestamp();
            current.key = value.f0;
        }

        // update the state's count
        current.count++;

        // set the state's timestamp to the record's assigned event time timestamp
        current.lastModified = ctx.timestamp();

        // write the state back
        state.update(current);

        // schedule the next timer 60 seconds from the current event time
        ctx.timerService().registerEventTimeTimer(current.lastModified + 60000);
    }

    @Override
    public void onTimer(long timestamp, OnTimerContext ctx, Collector<Tuple2<String, Long>> out)
            throws Exception {

        // get the state for the key that scheduled the timer
        CountWithTimestamp result = state.value();

        // check if this is an outdated timer or the latest timer
        if (timestamp == result.lastModified + 60000) {
            // emit the state on timeout
            out.collect(new Tuple2<String, Long>(result.key, result.count));
        }
    }
}

• 本实例展示了如何在ProcessFunction里头使用keyed state以及timer；process方法使用的ProcessFunction是CountWithTimeoutFunction
• CountWithTimeoutFunction的open方法创建了CountWithTimestamp类型的ValueState；processElement方法里头会更新该ValueState，用于记录每个key的element个数以及最后访问时间，然后注册一个EventTimeTimer，在当前eventTime时间的60秒后到达
• onTimer用于响应timer，它会判断如果该key在60秒内没有被update，则emit相关数据

ProcessFunction

flink-streaming-java_2.11-1.7.0-sources.jar!/org/apache/flink/streaming/api/functions/ProcessFunction.java

@PublicEvolving
public abstract class ProcessFunction<I, O> extends AbstractRichFunction {

    private static final long serialVersionUID = 1L;

    public abstract void processElement(I value, Context ctx, Collector<O> out) throws Exception;

    public void onTimer(long timestamp, OnTimerContext ctx, Collector<O> out) throws Exception {}

    public abstract class Context {

        public abstract Long timestamp();

        public abstract TimerService timerService();

        public abstract <X> void output(OutputTag<X> outputTag, X value);
    }

    public abstract class OnTimerContext extends Context {

        public abstract TimeDomain timeDomain();
    }

}

可以通过RuntimeContext获取keyed state

小结

可以通过RuntimeContext获取keyed state

doc

• Process Function (Low-level Operations)