apache flink 0.10 如何从无界输入数据流中第一次出现复合键？

Question

I am a newbie with apache flink. I have an unbound data stream in my input (fed into flink 0.10 via kakfa).

I want to get the 1st occurence of each primary key (the primary key is the contract_num and the event_dt).
These "duplicates" occur nearly immediately after each other. The source system cannot filter this for me, so flink has to do it.

Here is my input data:

contract_num, event_dt, attr 
A1, 2016-02-24 10:25:08, X
A1, 2016-02-24 10:25:08, Y
A1, 2016-02-24 10:25:09, Z
A2, 2016-02-24 10:25:10, C

Here is the output data I want:

A1, 2016-02-24 10:25:08, X
A1, 2016-02-24 10:25:09, Z
A2, 2016-02-24 10:25:10, C

Note the 2nd row has been removed as the key combination of A001 and '2016-02-24 10:25:08' already occurred in the 1st row.

How can I do this with flink 0.10?

I was thinking about using keyBy(0,1) but after that I don't know what to do!

(I used joda-time and org.flinkspector to setup these tests).

@Test
public void test() {
    DateTime threeSecondsAgo = (new DateTime()).minusSeconds(3);
    DateTime twoSecondsAgo = (new DateTime()).minusSeconds(2);
    DateTime oneSecondsAgo = (new DateTime()).minusSeconds(2);

    DataStream<Tuple3<String, Date, String>> testStream =
            createTimedTestStreamWith(
                    Tuple3.of("A1", threeSecondsAgo.toDate(), "X"))
            .emit(Tuple3.of("A1", threeSecondsAgo.toDate(), "Y"), after(0, TimeUnit.NANOSECONDS))
            .emit(Tuple3.of("A1", twoSecondsAgo.toDate(), "Z"), after(0, TimeUnit.NANOSECONDS))
            .emit(Tuple3.of("A2", oneSecondsAgo.toDate(), "C"), after(0, TimeUnit.NANOSECONDS))
            .close();
    
    testStream.keyBy(0,1);
}

Answer 1

Filtering duplicates over an infinite stream will eventually fail if your key space is larger than your available storage space. The reason is that you have to store the already seen keys somewhere to filter out the duplicates. Thus, it would be good to define a time window after which you can purge the current set of seen keys.

If you're aware of this problem but want to try it anyway, you can do it by applying a stateful flatMap operation after the keyBy call. The stateful mapper uses Flink's state abstraction to store whether it has already seen an element with this key or not. That way, you will also benefit from Flink's fault tolerance mechanism because your state will be automatically checkpointed.

A Flink program doing your job could look like

public static void main(String[] args) throws Exception {
    StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

    DataStream<Tuple3<String, Date, String>> input = env.fromElements(Tuple3.of("foo", new Date(1000), "bar"), Tuple3.of("foo", new Date(1000), "foobar"));

    input.keyBy(0, 1).flatMap(new DuplicateFilter()).print();

    env.execute("Test");
}

where the implementation of DuplicateFilter depends on the version of Flink.

Version >= 1.0 implementation

public static class DuplicateFilter extends RichFlatMapFunction<Tuple3<String, Date, String>, Tuple3<String, Date, String>> {

    static final ValueStateDescriptor<Boolean> descriptor = new ValueStateDescriptor<>("seen", Boolean.class, false);
    private ValueState<Boolean> operatorState;

    @Override
    public void open(Configuration configuration) {
        operatorState = this.getRuntimeContext().getState(descriptor);
    }

    @Override
    public void flatMap(Tuple3<String, Date, String> value, Collector<Tuple3<String, Date, String>> out) throws Exception {
        if (!operatorState.value()) {
            // we haven't seen the element yet
            out.collect(value);
            // set operator state to true so that we don't emit elements with this key again
            operatorState.update(true);
        }
    }
}

Version 0.10 implementation

public static class DuplicateFilter extends RichFlatMapFunction<Tuple3<String, Date, String>, Tuple3<String, Date, String>> {

    private OperatorState<Boolean> operatorState;

    @Override
    public void open(Configuration configuration) {
        operatorState = this.getRuntimeContext().getKeyValueState("seen", Boolean.class, false);
    }

    @Override
    public void flatMap(Tuple3<String, Date, String> value, Collector<Tuple3<String, Date, String>> out) throws Exception {
        if (!operatorState.value()) {
            // we haven't seen the element yet
            out.collect(value);
            operatorState.update(true);
        }
    }
}

Update: Using a tumbling time window

input.keyBy(0, 1).timeWindow(Time.seconds(1)).apply(new WindowFunction<Iterable<Tuple3<String,Date,String>>, Tuple3<String, Date, String>, Tuple, TimeWindow>() {
    @Override
    public void apply(Tuple tuple, TimeWindow window, Iterable<Tuple3<String, Date, String>> input, Collector<Tuple3<String, Date, String>> out) throws Exception {
        out.collect(input.iterator().next());
    }
})

Answer 2

Here's another way to do this that I happen to have just written. It has the disadvantage that it's a bit more custom code since it doesn't use the built-in Flink windowing functions but it doesn't have the latency penalty that Till mentioned. Full example on GitHub .

package com.dataartisans.filters;

import com.google.common.cache.CacheBuilder;
import com.google.common.cache.CacheLoader;
import com.google.common.cache.LoadingCache;
import org.apache.flink.api.common.functions.RichFilterFunction;
import org.apache.flink.api.java.functions.KeySelector;
import org.apache.flink.configuration.Configuration;
import org.apache.flink.streaming.api.checkpoint.CheckpointedAsynchronously;

import java.io.Serializable;
import java.util.HashSet;
import java.util.concurrent.TimeUnit;


/**
  * This class filters duplicates that occur within a configurable time of each other in a data stream.
  */
public class DedupeFilterFunction<T, K extends Serializable> extends RichFilterFunction<T> implements CheckpointedAsynchronously<HashSet<K>> {

  private LoadingCache<K, Boolean> dedupeCache;
  private final KeySelector<T, K> keySelector;
  private final long cacheExpirationTimeMs;

  /**
    * @param cacheExpirationTimeMs The expiration time for elements in the cache
    */
  public DedupeFilterFunction(KeySelector<T, K> keySelector, long cacheExpirationTimeMs){
    this.keySelector = keySelector;
    this.cacheExpirationTimeMs = cacheExpirationTimeMs;
  }

  @Override
  public void open(Configuration parameters) throws Exception {
    createDedupeCache();
  }


  @Override
  public boolean filter(T value) throws Exception {
    K key = keySelector.getKey(value);
    boolean seen = dedupeCache.get(key);
    if (!seen) {
      dedupeCache.put(key, true);
      return true;
    } else {
      return false;
    }
  }

  @Override
  public HashSet<K> snapshotState(long checkpointId, long checkpointTimestamp) throws Exception {
    return new HashSet<>(dedupeCache.asMap().keySet());
  }

  @Override
  public void restoreState(HashSet<K> state) throws Exception {
    createDedupeCache();
    for (K key : state) {
      dedupeCache.put(key, true);
    }
  }

  private void createDedupeCache() {
    dedupeCache = CacheBuilder.newBuilder()
      .expireAfterWrite(cacheExpirationTimeMs, TimeUnit.MILLISECONDS)
      .build(new CacheLoader<K, Boolean>() {
        @Override
        public Boolean load(K k) throws Exception {
          return false;
        }
      });
  }
}