v1.3
Generating Timestamps / Watermarks
This section is relevant for programs running on event time. For an introduction to event time, processing time, and ingestion time, please refer to the introduction to event time.
To work with event time, streaming programs need to set the time characteristic accordingly.
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)Assigning Timestamps
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.
Timestamp assignment goes hand-in-hand with generating watermarks, which tell the system about progress in event time.
There are two ways to assign timestamps and generate watermarks:
- Directly in the data stream source
- Via a timestamp assigner / watermark generator: in Flink timestamp assigners also define the watermarks to be emitted
Attention Both timestamps and watermarks are specified as milliseconds since the Java epoch of 1970-01-01T00:00:00Z.
Source Functions with Timestamps and Watermarks
Stream sources can also directly assign timestamps to the elements they produce, and they can also emit watermarks. When this is done, no timestamp assigner is needed. Note that if a timestamp assigner is used, any timestamps and watermarks provided by the source will be overwritten.
To assign a timestamp to an element in the source directly, the source must use the collectWithTimestamp(...)
method on the SourceContext. To generate watermarks, the source must call the emitWatermark(Watermark) function.
Below is a simple example of a (non-checkpointed) source that assigns timestamps and generates watermarks:
@Override
public void run(SourceContext<MyType> ctx) throws Exception {
while (/* condition */) {
MyType next = getNext();
ctx.collectWithTimestamp(next, next.getEventTimestamp());
if (next.hasWatermarkTime()) {
ctx.emitWatermark(new Watermark(next.getWatermarkTime()));
}
}
}override def run(ctx: SourceContext[MyType]): Unit = {
while (/* condition */) {
val next: MyType = getNext()
ctx.collectWithTimestamp(next, next.eventTimestamp)
if (next.hasWatermarkTime) {
ctx.emitWatermark(new Watermark(next.getWatermarkTime))
}
}
}Timestamp Assigners / Watermark Generators
Timestamp assigners take 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.
Timestamp assigners are usually specified immediately after the data source, but it is not strictly required to do so. A common pattern, for example, is to parse (MapFunction) and filter (FilterFunction) before the timestamp assigner. In any case, the timestamp assigner needs to be specified before the first operation on event time (such as the first window operation). As a special case, when using Kafka as the source of a streaming job, Flink allows the specification of a timestamp assigner / watermark emitter inside the source (or consumer) itself. More information on how to do so can be found in the Kafka Connector documentation.
NOTE: The remainder of this section presents the main interfaces a programmer has to implement in order to create her own timestamp extractors/watermark emitters. To see the pre-implemented extractors that ship with Flink, please refer to the Pre-defined Timestamp Extractors / Watermark Emitters page.
final StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
DataStream<MyEvent> stream = env.readFile(
myFormat, myFilePath, FileProcessingMode.PROCESS_CONTINUOUSLY, 100,
FilePathFilter.createDefaultFilter(), typeInfo);
DataStream<MyEvent> withTimestampsAndWatermarks = stream
.filter( event -> event.severity() == WARNING )
.assignTimestampsAndWatermarks(new MyTimestampsAndWatermarks());
withTimestampsAndWatermarks
.keyBy( (event) -> event.getGroup() )
.timeWindow(Time.seconds(10))
.reduce( (a, b) -> a.add(b) )
.addSink(...);val env = StreamExecutionEnvironment.getExecutionEnvironment
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime)
val stream: DataStream[MyEvent] = env.readFile(
myFormat, myFilePath, FileProcessingMode.PROCESS_CONTINUOUSLY, 100,
FilePathFilter.createDefaultFilter())
val withTimestampsAndWatermarks: DataStream[MyEvent] = stream
.filter( _.severity == WARNING )
.assignTimestampsAndWatermarks(new MyTimestampsAndWatermarks())
withTimestampsAndWatermarks
.keyBy( _.getGroup )
.timeWindow(Time.seconds(10))
.reduce( (a, b) => a.add(b) )
.addSink(...)With Periodic Watermarks
AssignerWithPeriodicWatermarks assigns timestamps 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 assigner’s getCurrentWatermark() 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.
Two simple examples of timestamp assigners with periodic watermark generation are below.
/**
* 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 extends AssignerWithPeriodicWatermarks<MyEvent> {
private final long maxOutOfOrderness = 3500; // 3.5 seconds
private long currentMaxTimestamp;
@Override
public long extractTimestamp(MyEvent element, long previousElementTimestamp) {
long timestamp = element.getCreationTime();
currentMaxTimestamp = Math.max(timestamp, currentMaxTimestamp);
return timestamp;
}
@Override
public Watermark getCurrentWatermark() {
// return the watermark as current highest timestamp minus the out-of-orderness bound
return new Watermark(currentMaxTimestamp - maxOutOfOrderness);
}
}
/**
* 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 extends AssignerWithPeriodicWatermarks<MyEvent> {
private final long maxTimeLag = 5000; // 5 seconds
@Override
public long extractTimestamp(MyEvent element, long previousElementTimestamp) {
return element.getCreationTime();
}
@Override
public Watermark getCurrentWatermark() {
// return the watermark as current time minus the maximum time lag
return 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 AssignerWithPeriodicWatermarks[MyEvent] {
val maxOutOfOrderness = 3500L // 3.5 seconds
var currentMaxTimestamp: Long
override def extractTimestamp(element: MyEvent, previousElementTimestamp: Long): Long = {
val timestamp = element.getCreationTime()
currentMaxTimestamp = max(timestamp, currentMaxTimestamp)
timestamp
}
override def getCurrentWatermark(): Watermark = {
// return the watermark as current highest timestamp minus the out-of-orderness bound
new Watermark(currentMaxTimestamp - maxOutOfOrderness)
}
}
/**
* 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 AssignerWithPeriodicWatermarks[MyEvent] {
val maxTimeLag = 5000L // 5 seconds
override def extractTimestamp(element: MyEvent, previousElementTimestamp: Long): Long = {
element.getCreationTime
}
override def getCurrentWatermark(): Watermark = {
// return the watermark as current time minus the maximum time lag
new Watermark(System.currentTimeMillis() - maxTimeLag)
}
}With Punctuated Watermarks
To generate watermarks whenever a certain event indicates that a new watermark might be generated, use
AssignerWithPunctuatedWatermarks. For this class Flink will first call the extractTimestamp(...) method
to assign the element a timestamp, and then immediately call the
checkAndGetNextWatermark(...) method on that element.
The checkAndGetNextWatermark(...) method is passed the timestamp that was assigned in the extractTimestamp(...)
method, and can decide whether it wants to generate a watermark. Whenever the checkAndGetNextWatermark(...)
method returns a non-null watermark, and that watermark is larger than the latest previous watermark, that
new watermark will be emitted.
public class PunctuatedAssigner extends AssignerWithPunctuatedWatermarks<MyEvent> {
@Override
public long extractTimestamp(MyEvent element, long previousElementTimestamp) {
return element.getCreationTime();
}
@Override
public Watermark checkAndGetNextWatermark(MyEvent lastElement, long extractedTimestamp) {
return lastElement.hasWatermarkMarker() ? new Watermark(extractedTimestamp) : null;
}
}class PunctuatedAssigner extends AssignerWithPunctuatedWatermarks[MyEvent] {
override def extractTimestamp(element: MyEvent, previousElementTimestamp: Long): Long = {
element.getCreationTime
}
override def checkAndGetNextWatermark(lastElement: MyEvent, extractedTimestamp: Long): Watermark = {
if (lastElement.hasWatermarkMarker()) new Watermark(extractedTimestamp) else null
}
}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.
Timestamps per Kafka Partition
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.
The illustrations below show how to use the per-Kafka-partition watermark generation, and how watermarks propagate through the streaming dataflow in that case.
FlinkKafkaConsumer09<MyType> kafkaSource = new FlinkKafkaConsumer09<>("myTopic", schema, props);
kafkaSource.assignTimestampsAndWatermarks(new AscendingTimestampExtractor<MyType>() {
@Override
public long extractAscendingTimestamp(MyType element) {
return element.eventTimestamp();
}
});
DataStream<MyType> stream = env.addSource(kafkaSource);val kafkaSource = new FlinkKafkaConsumer09[MyType]("myTopic", schema, props)
kafkaSource.assignTimestampsAndWatermarks(new AscendingTimestampExtractor[MyType] {
def extractAscendingTimestamp(element: MyType): Long = element.eventTimestamp
})
val stream: DataStream[MyType] = env.addSource(kafkaSource)