Generating Watermarks
This documentation is for an unreleased version of Apache Flink. We recommend you use the latest stable version.

Generating Watermarks #

In this section you will learn about the APIs that Flink provides for working with event time timestamps and watermarks. For an introduction to event time, processing time, and ingestion time, please refer to the introduction to event time.

Introduction to Watermark Strategies #

In order to work with event time, Flink needs to know the events timestamps, meaning each element in the stream needs to have its event timestamp assigned. This is usually done by accessing/extracting the timestamp from some field in the element by using a TimestampAssigner.

Timestamp assignment goes hand-in-hand with generating watermarks, which tell the system about progress in event time. You can configure this by specifying a WatermarkGenerator.

The Flink API expects a WatermarkStrategy that contains both a TimestampAssigner and WatermarkGenerator. A number of common strategies are available out of the box as static methods on WatermarkStrategy, but users can also build their own strategies when required.

Here is the interface for completeness’ sake:

public interface WatermarkStrategy<T> 
    extends TimestampAssignerSupplier<T>,
            WatermarkGeneratorSupplier<T>{

    /**
     * Instantiates a {@link TimestampAssigner} for assigning timestamps according to this
     * strategy.
     */
    @Override
    TimestampAssigner<T> createTimestampAssigner(TimestampAssignerSupplier.Context context);

    /**
     * Instantiates a WatermarkGenerator that generates watermarks according to this strategy.
     */
    @Override
    WatermarkGenerator<T> createWatermarkGenerator(WatermarkGeneratorSupplier.Context context);
}

As mentioned, you usually don’t implement this interface yourself but use the static helper methods on WatermarkStrategy for common watermark strategies or to bundle together a custom TimestampAssigner with a WatermarkGenerator. For example, to use bounded-out-of-orderness watermarks and a lambda function as a timestamp assigner you use this:

WatermarkStrategy
        .<Tuple2<Long, String>>forBoundedOutOfOrderness(Duration.ofSeconds(20))
        .withTimestampAssigner((event, timestamp) -> event.f0);
WatermarkStrategy
  .forBoundedOutOfOrderness[(Long, String)](Duration.ofSeconds(20))
  .withTimestampAssigner(new SerializableTimestampAssigner[(Long, String)] {
    override def extractTimestamp(element: (Long, String), recordTimestamp: Long): Long = element._1
  })
class FirstElementTimestampAssigner(TimestampAssigner):
   
    def extract_timestamp(self, value, record_timestamp):
        return value[0]


WatermarkStrategy \
    .for_bounded_out_of_orderness(Duration.of_seconds(20)) \
    .with_timestamp_assigner(FirstElementTimestampAssigner())

Specifying a TimestampAssigner is optional and in most cases you don’t actually want to specify one. For example, when using Kafka or Kinesis you would get timestamps directly from the Kafka/Kinesis records.

We will look at the WatermarkGenerator interface later in Writing WatermarkGenerators.

Attention: Both timestamps and watermarks are specified as milliseconds since the Java epoch of 1970-01-01T00:00:00Z.

Using Watermark Strategies #

There are two places in Flink applications where a WatermarkStrategy can be used: 1) directly on sources and 2) after non-source operation.

The first option is preferable, because it allows sources to exploit knowledge about shards/partitions/splits in the watermarking logic. Sources can usually then track watermarks at a finer level and the overall watermark produced by a source will be more accurate. Specifying a WatermarkStrategy directly on the source usually means you have to use a source specific interface/ Refer to Watermark Strategies and the Kafka Connector for how this works on a Kafka Connector and for more details about how per-partition watermarking works there.

The second option (setting a WatermarkStrategy after arbitrary operations) should only be used if you cannot set a strategy directly on the source:

final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

DataStream<MyEvent> stream = env.readFile(
        myFormat, myFilePath, FileProcessingMode.PROCESS_CONTINUOUSLY, 100,
        FilePathFilter.createDefaultFilter(), typeInfo);

DataStream<MyEvent> withTimestampsAndWatermarks = stream
        .filter( event -> event.severity() == WARNING )
        .assignTimestampsAndWatermarks(<watermark strategy>);

withTimestampsAndWatermarks
        .keyBy( (event) -> event.getGroup() )
        .window(TumblingEventTimeWindows.of(Time.seconds(10)))
        .reduce( (a, b) -> a.add(b) )
        .addSink(...);
val env = StreamExecutionEnvironment.getExecutionEnvironment

val stream: DataStream[MyEvent] = env.readFile(
         myFormat, myFilePath, FileProcessingMode.PROCESS_CONTINUOUSLY, 100,
         FilePathFilter.createDefaultFilter())

val withTimestampsAndWatermarks: DataStream[MyEvent] = stream
        .filter( _.severity == WARNING )
        .assignTimestampsAndWatermarks(<watermark strategy>)

withTimestampsAndWatermarks
        .keyBy( _.getGroup )
        .window(TumblingEventTimeWindows.of(Time.seconds(10)))
        .reduce( (a, b) => a.add(b) )
        .addSink(...)
env = StreamExecutionEnvironment.get_execution_environment()

# currently read_file is not supported in PyFlink
stream = env \
    .read_text_file(my_file_path, charset) \
    .map(lambda s: MyEvent.from_string(s))

with_timestamp_and_watermarks = stream \
    .filter(lambda e: e.severity() == WARNING) \
    .assign_timestamp_and_watermarks(<watermark strategy>)

with_timestamp_and_watermarks \
    .key_by(lambda e: e.get_group()) \
    .window(TumblingEventTimeWindows.of(Time.seconds(10))) \
    .reduce(lambda a, b: a.add(b)) \
    .add_sink(...)

Using a WatermarkStrategy this way takes a stream and produce a new stream with timestamped elements and watermarks. If the original stream had timestamps and/or watermarks already, the timestamp assigner overwrites them.

Dealing With Idle Sources #

If one of the input splits/partitions/shards does not carry events for a while this means that the WatermarkGenerator also does not get any new information on which to base a watermark. We call this an idle input or an idle source. This is a problem because it can happen that some of your partitions do still carry events. In that case, the watermark will be held back, because it is computed as the minimum over all the different parallel watermarks.

To deal with this, you can use a WatermarkStrategy that will detect idleness and mark an input as idle. WatermarkStrategy provides a convenience helper for this:

WatermarkStrategy
        .<Tuple2<Long, String>>forBoundedOutOfOrderness(Duration.ofSeconds(20))
        .withIdleness(Duration.ofMinutes(1));
WatermarkStrategy
  .forBoundedOutOfOrderness[(Long, String)](Duration.ofSeconds(20))
  .withIdleness(Duration.ofMinutes(1))
WatermarkStrategy \
    .for_bounded_out_of_orderness(Duration.of_seconds(20)) \
    .with_idleness(Duration.of_minutes(1))

Watermark alignment Beta #

In the previous paragraph we discussed a situation when splits/partitions/shards or sources are idle and can stall increasing watermarks. On the other side of the spectrum, a split/partition/shard or source may process records very fast and in turn increase its watermark relatively faster than the others. This on its own is not a problem per se. However, for downstream operators that are using watermarks to emit some data it can actually become a problem.

In this case, contrary to idle sources, the watermark of such downstream operator (like windowed joins on aggregations) can progress. However, such operator might need to buffer excessive amount of data coming from the fast inputs, as the minimal watermark from all of its inputs is held back by the lagging input. All records emitted by the fast input will hence have to be buffered in the said downstream operator state, which can lead into uncontrollable growth of the operator’s state.

In order to address the issue, you can enable watermark alignment, which will make sure no sources/splits/shards/partitions increase their watermarks too far ahead of the rest. You can enable alignment for every source separately:

WatermarkStrategy
        .<Tuple2<Long, String>>forBoundedOutOfOrderness(Duration.ofSeconds(20))
        .withWatermarkAlignment("alignment-group-1", Duration.ofSeconds(20), Duration.ofSeconds(1));
WatermarkStrategy
  .forBoundedOutOfOrderness[(Long, String)](Duration.ofSeconds(20))
  .withWatermarkAlignment("alignment-group-1", Duration.ofSeconds(20), Duration.ofSeconds(1))
WatermarkStrategy \
    .for_bounded_out_of_orderness(Duration.of_seconds(20)) \
    .with_watermark_alignment("alignment-group-1", Duration.of_seconds(20), Duration.of_seconds(1))
Note: You can enable watermark alignment only for FLIP-27 sources. It does not work for legacy or if applied after the source via DataStream#assignTimestampsAndWatermarks.

When enabling the alignment, you need to tell Flink, which group should the source belong. You do that by providing a label (e.g. alignment-group-1) which bind together all sources that share it. Moreover, you have to tell the maximal drift from the current minimal watermarks across all sources belonging to that group. The third parameter describes how often the current maximal watermark should be updated. The downside of frequent updates is that there will be more RPC messages travelling between TMs and the JM.

In order to achieve the alignment Flink will pause consuming from the source/task, which generated watermark that is too far into the future. In the meantime it will continue reading records from other sources/tasks which can move the combined watermark forward and that way unblock the faster one.

Note: As of 1.15, Flink supports aligning across tasks of the same source and/or different sources. It does not support aligning splits/partitions/shards in the same task.

In a case where there are e.g. two Kafka partitions that produce watermarks at different pace, that get assigned to the same task watermark might not behave as expected. Fortunately, worst case it should not perform worse than without alignment.

Given the limitation above, we suggest applying watermark alignment in two situations:

  1. You have two different sources (e.g. Kafka and File) that produce watermarks at different speeds
  2. You run your source with parallelism equal to the number of splits/shards/partitions, which results in every subtask being assigned a single unit of work.

Writing WatermarkGenerators #

A TimestampAssigner is a simple function that extracts a field from an event, we therefore don’t need to look at them in detail. A WatermarkGenerator, on the other hand, is a bit more complicated to write and we will look at how you can do that in the next two sections. This is the WatermarkGenerator interface:

/**
 * The {@code WatermarkGenerator} generates watermarks either based on events or
 * periodically (in a fixed interval).
 *
 * <p><b>Note:</b> This WatermarkGenerator subsumes the previous distinction between the
 * {@code AssignerWithPunctuatedWatermarks} and the {@code AssignerWithPeriodicWatermarks}.
 */
@Public
public interface WatermarkGenerator<T> {

    /**
     * Called for every event, allows the watermark generator to examine 
     * and remember the event timestamps, or to emit a watermark based on
     * the event itself.
     */
    void onEvent(T event, long eventTimestamp, WatermarkOutput output);

    /**
     * Called periodically, and might emit a new watermark, or not.
     *
     * <p>The interval in which this method is called and Watermarks 
     * are generated depends on {@link ExecutionConfig#getAutoWatermarkInterval()}.
     */
    void onPeriodicEmit(WatermarkOutput output);
}

There are two different styles of watermark generation: periodic and punctuated.

A periodic generator usually observes the incoming events via onEvent() and then emits a watermark when the framework calls onPeriodicEmit().

A puncutated generator will look at events in onEvent() and wait for special marker events or punctuations that carry watermark information in the stream. When it sees one of these events it emits a watermark immediately. Usually, punctuated generators don’t emit a watermark from onPeriodicEmit().

We will look at how to implement generators for each style next.

Writing a Periodic WatermarkGenerator #

A periodic generator observes stream events and generates watermarks periodically (possibly depending on the stream elements, or purely based on processing time).

The interval (every n milliseconds) in which the watermark will be generated is defined via ExecutionConfig.setAutoWatermarkInterval(...). The generators’s onPeriodicEmit() method will be called each time, and a new watermark will be emitted if the returned watermark is non-null and larger than the previous watermark.

Here we show two simple examples of watermark generators that use periodic watermark generation. Note that Flink ships with BoundedOutOfOrdernessWatermarks, which is a WatermarkGenerator that works similarly to the BoundedOutOfOrdernessGenerator shown below. You can read about using that here.

/**
 * This generator generates watermarks assuming that elements arrive out of order,
 * but only to a certain degree. The latest elements for a certain timestamp t will arrive
 * at most n milliseconds after the earliest elements for timestamp t.
 */
public class BoundedOutOfOrdernessGenerator implements WatermarkGenerator<MyEvent> {

    private final long maxOutOfOrderness = 3500; // 3.5 seconds

    private long currentMaxTimestamp;

    @Override
    public void onEvent(MyEvent event, long eventTimestamp, WatermarkOutput output) {
        currentMaxTimestamp = Math.max(currentMaxTimestamp, eventTimestamp);
    }

    @Override
    public void onPeriodicEmit(WatermarkOutput output) {
        // emit the watermark as current highest timestamp minus the out-of-orderness bound
        output.emitWatermark(new Watermark(currentMaxTimestamp - maxOutOfOrderness - 1));
    }

}

/**
 * This generator generates watermarks that are lagging behind processing time 
 * by a fixed amount. It assumes that elements arrive in Flink after a bounded delay.
 */
public class TimeLagWatermarkGenerator implements WatermarkGenerator<MyEvent> {

    private final long maxTimeLag = 5000; // 5 seconds

    @Override
    public void onEvent(MyEvent event, long eventTimestamp, WatermarkOutput output) {
        // don't need to do anything because we work on processing time
    }

    @Override
    public void onPeriodicEmit(WatermarkOutput output) {
        output.emitWatermark(new Watermark(System.currentTimeMillis() - maxTimeLag));
    }
}
/**
 * This generator generates watermarks assuming that elements arrive out of order,
 * but only to a certain degree. The latest elements for a certain timestamp t will arrive
 * at most n milliseconds after the earliest elements for timestamp t.
 */
class BoundedOutOfOrdernessGenerator extends WatermarkGenerator[MyEvent] {

    val maxOutOfOrderness = 3500L // 3.5 seconds

    var currentMaxTimestamp: Long = _

    override def onEvent(element: MyEvent, eventTimestamp: Long, output: WatermarkOutput): Unit = {
        currentMaxTimestamp = max(eventTimestamp, currentMaxTimestamp)
    }

    override def onPeriodicEmit(output: WatermarkOutput): Unit = {
        // emit the watermark as current highest timestamp minus the out-of-orderness bound
        output.emitWatermark(new Watermark(currentMaxTimestamp - maxOutOfOrderness - 1))
    }
}

/**
 * This generator generates watermarks that are lagging behind processing 
 * time by a fixed amount. It assumes that elements arrive in Flink after 
 * a bounded delay.
 */
class TimeLagWatermarkGenerator extends WatermarkGenerator[MyEvent] {

    val maxTimeLag = 5000L // 5 seconds

    override def onEvent(element: MyEvent, eventTimestamp: Long, output: WatermarkOutput): Unit = {
        // don't need to do anything because we work on processing time
    }

    override def onPeriodicEmit(output: WatermarkOutput): Unit = {
        output.emitWatermark(new Watermark(System.currentTimeMillis() - maxTimeLag))
    }
}
Still not supported in Python API.

Writing a Punctuated WatermarkGenerator #

A punctuated watermark generator will observe the stream of events and emit a watermark whenever it sees a special element that carries watermark information.

This is how you can implement a punctuated generator that emits a watermark whenever an event indicates that it carries a certain marker:

public class PunctuatedAssigner implements WatermarkGenerator<MyEvent> {

    @Override
    public void onEvent(MyEvent event, long eventTimestamp, WatermarkOutput output) {
        if (event.hasWatermarkMarker()) {
            output.emitWatermark(new Watermark(event.getWatermarkTimestamp()));
        }
    }

    @Override
    public void onPeriodicEmit(WatermarkOutput output) {
        // don't need to do anything because we emit in reaction to events above
    }
}
class PunctuatedAssigner extends WatermarkGenerator[MyEvent] {

    override def onEvent(element: MyEvent, eventTimestamp: Long): Unit = {
        if (event.hasWatermarkMarker()) {
            output.emitWatermark(new Watermark(event.getWatermarkTimestamp()))
        }
    }

    override def onPeriodicEmit(): Unit = {
        // don't need to do anything because we emit in reaction to events above
    }
}
Still not supported in Python API.
Note: It is possible to generate a watermark on every single event. However, because each watermark causes some computation downstream, an excessive number of watermarks degrades performance.

Watermark Strategies and the Kafka Connector #

When using Apache Kafka as a data source, each Kafka partition may have a simple event time pattern (ascending timestamps or bounded out-of-orderness). However, when consuming streams from Kafka, multiple partitions often get consumed in parallel, interleaving the events from the partitions and destroying the per-partition patterns (this is inherent in how Kafka’s consumer clients work).

In that case, you can use Flink’s Kafka-partition-aware watermark generation. Using that feature, watermarks are generated inside the Kafka consumer, per Kafka partition, and the per-partition watermarks are merged in the same way as watermarks are merged on stream shuffles.

For example, if event timestamps are strictly ascending per Kafka partition, generating per-partition watermarks with the ascending timestamps watermark generator will result in perfect overall watermarks. Note, that we don’t provide a TimestampAssigner in the example, the timestamps of the Kafka records themselves will be used instead.

The illustrations below show how to use the per-Kafka-partition watermark generation, and how watermarks propagate through the streaming dataflow in that case.

FlinkKafkaConsumer<MyType> kafkaSource = new FlinkKafkaConsumer<>("myTopic", schema, props);
kafkaSource.assignTimestampsAndWatermarks(
        WatermarkStrategy
                .forBoundedOutOfOrderness(Duration.ofSeconds(20)));

DataStream<MyType> stream = env.addSource(kafkaSource);
val kafkaSource = new FlinkKafkaConsumer[MyType]("myTopic", schema, props)
kafkaSource.assignTimestampsAndWatermarks(
  WatermarkStrategy
    .forBoundedOutOfOrderness(Duration.ofSeconds(20)))

val stream: DataStream[MyType] = env.addSource(kafkaSource)
kafka_source = FlinkKafkaConsumer("timer-stream-source", schema, props)

stream = env
    .add_source(kafka_source)
    .assign_timestamps_and_watermarks(
        WatermarkStrategy
            .for_bounded_out_of_orderness(Duration.of_seconds(20)))
Generating Watermarks with awareness for Kafka-partitions

How Operators Process Watermarks #

As a general rule, operators are required to completely process a given watermark before forwarding it downstream. For example, WindowOperator will first evaluate all windows that should be fired, and only after producing all of the output triggered by the watermark will the watermark itself be sent downstream. In other words, all elements produced due to occurrence of a watermark will be emitted before the watermark.

The same rule applies to TwoInputStreamOperator. However, in this case the current watermark of the operator is defined as the minimum of both of its inputs.

The details of this behavior are defined by the implementations of the OneInputStreamOperator#processWatermark, TwoInputStreamOperator#processWatermark1 and TwoInputStreamOperator#processWatermark2 methods.

The Deprecated AssignerWithPeriodicWatermarks and AssignerWithPunctuatedWatermarks #

Prior to introducing the current abstraction of WatermarkStrategy, TimestampAssigner, and WatermarkGenerator, Flink used AssignerWithPeriodicWatermarks and AssignerWithPunctuatedWatermarks. You will still see them in the API but it is recommended to use the new interfaces because they offer a clearer separation of concerns and also unify periodic and punctuated styles of watermark generation.

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