The requirement is that I want to write an Akka streaming application that listens to continuous events from Kafka, then sessionizes the event data in a time frame, based on some id value embedded inside each event.
For example, let's say that my time frame window is two minutes, and in the first two minutes I get the four events below:
Input:
{"message-domain":"1234","id":1,"aaa":"bbb"}
{"message-domain":"1234","id":2,"aaa":"bbb"}
{"message-domain":"5678","id":4,"aaa":"bbb"}
{"message-domain":"1234","id":3,"aaa":"bbb"}
Then in the output, after grouping/sessionizing these events, I will have only two events based on their message-domain value.
Output:
{"message-domain":"1234",messsages:[{"id":1,"aaa":"bbb"},{"id":2,"aaa":"bbb"},{"id":4,"aaa":"bbb"}]}
{"message-domain":"5678",messsages:[{"id":3,"aaa":"bbb"}]}
And I want this to happen in real time. Any suggestions on how to achieve this?
To group the events within a time window you can use Flow.groupedWithin:
val maxCount : Int = Int.MaxValue
val timeWindow = FiniteDuration(2L, TimeUnit.MINUTES)
val timeWindowFlow : Flow[String, Seq[String]] =
Flow[String] groupedWithin (maxCount, timeWindow)
Related
I'm trying to use Flink to consume a bounded data from a message queue in a streaming passion. The data will be in the following format:
{"id":-1,"name":"Start"}
{"id":1,"name":"Foo 1"}
{"id":2,"name":"Foo 2"}
{"id":3,"name":"Foo 3"}
{"id":4,"name":"Foo 4"}
{"id":5,"name":"Foo 5"}
...
{"id":-2,"name":"End"}
The start and end of messages can be determined using the event id. I want to receive such batches and store the latest (by overwriting) batch on disk or in memory. I can write a custom window trigger to extract the events using the start and end flags as shown below:
DataStream<Foo> fooDataStream = ...
AllWindowedStream<Foo, GlobalWindow> fooWindow = fooDataStream.windowAll(GlobalWindows.create())
.trigger(new CustomTrigger<>())
.evictor(new Evictor<Foo, GlobalWindow>() {
#Override
public void evictBefore(Iterable<TimestampedValue<Foo>> elements, int size, GlobalWindow window, EvictorContext evictorContext) {
for (Iterator<TimestampedValue<Foo>> iterator = elements.iterator();
iterator.hasNext(); ) {
TimestampedValue<Foo> foo = iterator.next();
if (foo.getValue().getId() < 0) {
iterator.remove();
}
}
}
#Override
public void evictAfter(Iterable<TimestampedValue<Foo>> elements, int size, GlobalWindow window, EvictorContext evictorContext) {
}
});
but how can I persist the output of the latest window. One way would be using a ProcessAllWindowFunction to receive all the events and write them to disk manually but it feels like a hack. I'm also looking into the Table API with Flink CEP Pattern (like this question) but couldn't find a way to clear the Table after each batch to discard the events from the previous batch.
There are a couple of things getting in the way of what you want:
(1) Flink's window operators produce append streams, rather than update streams. They're not designed to update previously emitted results. CEP also doesn't produce update streams.
(2) Flink's file system abstraction does not support overwriting files. This is because object stores, like S3, don't support this operation very well.
I think your options are:
(1) Rework your job so that it produces an update (changelog) stream. You can do this with toChangelogStream, or by using Table/SQL operations that create update streams, such as GROUP BY (when it's used without a time window). On top of this, you'll need to choose a sink that supports retractions/updates, such as a database.
(2) Stick to producing an append stream and use something like the FileSink to write the results to a series of rolling files. Then do some scripting outside of Flink to get what you want out of this.
First and foremost:
I'm kind of new to Flink (Understand the principle and is able to create any basic streaming job I need to)
I'm using Kinesis Analytics to run my Flink job and by default it's using incremental checkpointing with a 1 minute interval.
The Flink job is reading event from a Kinesis stream using a FlinkKinesisConsumer and a custom deserailzer (deserialze the byte into a simple Java object which is used throughout the job)
What I would like to archieve is simply counting how many event of ENTITY_ID/FOO and ENTITY_ID/BAR there is for the past 24 hours. It is important that this count is as accurate as possible and this is why I'm using this Flink feature instead of doing a running sum myself on a 5 minute tumbling window.
I also want to be able to have a count of 'TOTAL' events from the start (and not just for the past 24h) so I also output in the result the count of events for the past 5 minutes so that the post porcessing app can simply takes these 5 minute of data and do a running sum. (This count doesn't have to be accurate and it's ok if there is an outage and I lose some count)
Now, this job was working pretty good up until last week where we had a surge (10 times more) in traffic. From that point on Flink went banana.
Checkpoint size starting to slowly grow from ~500MB to 20GB and checkpoint time were taking around 1 minutes and growing over time.
The application started failing and never was able to fully recover and the event iterator age shoot up never went back down so no new events were being consumed.
Since I'm new with Flink I'm not enterely sure if the way I'm doing the sliding count is completely un optimised or plain wrong.
This is a small snippet of the key part of the code:
The source (MyJsonDeserializationSchema extends AbstractDeserializationSchema and simply read byte and create the Event object):
SourceFunction<Event> source =
new FlinkKinesisConsumer<>("input-kinesis-stream", new MyJsonDeserializationSchema(), kinesisConsumerConfig);
The input event, simple java pojo which will be use in the Flink operators:
public class Event implements Serializable {
public String entityId;
public String entityType;
public String entityName;
public long eventTimestamp = System.currentTimeMillis();
}
env.setStreamTimeCharacteristic(TimeCharacteristic.EventTime);
DataStream<Event> eventsStream = kinesis
.assignTimestampsAndWatermarks(new BoundedOutOfOrdernessTimestampExtractor<Event>(Time.seconds(30)) {
#Override
public long extractTimestamp(Event event) {
return event.eventTimestamp;
}
})
DataStream<Event> fooStream = eventsStream
.filter(new FilterFunction<Event>() {
#Override
public boolean filter(Event event) throws Exception {
return "foo".equalsIgnoreCase(event.entityType);
}
})
DataStream<Event> barStream = eventsStream
.filter(new FilterFunction<Event>() {
#Override
public boolean filter(Event event) throws Exception {
return "bar".equalsIgnoreCase(event.entityType);
}
})
StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);
Table fooTable = tEnv.fromDataStream("fooStream, entityId, entityName, entityType, eventTimestame.rowtime");
tEnv.registerTable("Foo", fooTable);
Table barTable = tEnv.fromDataStream("barStream, entityId, entityName, entityType, eventTimestame.rowtime");
tEnv.registerTable("Bar", barTable);
Table slidingFooCountTable = fooTable
.window(Slide.over("24.hour").every("5.minute").on("eventTimestamp").as("minuteWindow"))
.groupBy("entityId, entityName, minuteWindow")
.select("concat(concat(entityId,'_'), entityName) as slidingFooId, entityid as slidingFooEntityid, entityName as slidingFooEntityName, entityType.count as slidingFooCount, minuteWindow.rowtime as slidingFooMinute");
Table slidingBarCountTable = barTable
.window(Slide.over("24.hout").every("5.minute").on("eventTimestamp").as("minuteWindow"))
.groupBy("entityId, entityName, minuteWindow")
.select("concat(concat(entityId,'_'), entityName) as slidingBarId, entityid as slidingBarEntityid, entityName as slidingBarEntityName, entityType.count as slidingBarCount, minuteWindow.rowtime as slidingBarMinute");
Table tumblingFooCountTable = fooTable
.window(Tumble.over(tumblingWindowTime).on("eventTimestamp").as("minuteWindow"))
.groupBy("entityid, entityName, minuteWindow")
.select("concat(concat(entityName,'_'), entityName) as tumblingFooId, entityId as tumblingFooEntityId, entityNamae as tumblingFooEntityName, entityType.count as tumblingFooCount, minuteWindow.rowtime as tumblingFooMinute");
Table tumblingBarCountTable = barTable
.window(Tumble.over(tumblingWindowTime).on("eventTimestamp").as("minuteWindow"))
.groupBy("entityid, entityName, minuteWindow")
.select("concat(concat(entityName,'_'), entityName) as tumblingBarId, entityId as tumblingBarEntityId, entityNamae as tumblingBarEntityName, entityType.count as tumblingBarCount, minuteWindow.rowtime as tumblingBarMinute");
Table aggregatedTable = slidingFooCountTable
.leftOuterJoin(slidingBarCountTable, "slidingFooId = slidingBarId && slidingFooMinute = slidingBarMinute")
.leftOuterJoin(tumblingFooCountTable, "slidingFooId = tumblingBarId && slidingFooMinute = tumblingBarMinute")
.leftOuterJoin(tumblingFooCountTable, "slidingFooId = tumblingFooId && slidingFooMinute = tumblingFooMinute")
.select("slidingFooMinute as timestamp, slidingFooCreativeId as entityId, slidingFooEntityName as entityName, slidingFooCount, slidingBarCount, tumblingFooCount, tumblingBarCount");
DataStream<Result> result = tEnv.toAppendStream(aggregatedTable, Result.class);
result.addSink(sink); // write to an output stream to be picked up by a lambda function
I would greatly appreciate if someone with more experience in working with Flink could comment on the way I have done my counting? Is my code completely over engineered? Is there a better and more efficient way of counting events over a 24h period?
I have read somewhere in Stackoverflow #DavidAnderson suggesting to create our own sliding window using map state and slicing the event by timestamp.
However I'm not exactly sure what this mean and I didn't find any code example to show it.
You are creating quite a few windows in there. If You are creating a sliding window with a size of 24h and slide of 5 mins this means that there will be a lot of open windows in there, so You may expect that all the data You have received in the given day will be checkpointed in at least one window if You think about it. So, it's certain that the size & time of the checkpoint will grow as the data itself grows.
To be able to get the answer if the code can be rewritten You would need to provide more details on what exactly are You trying to achieve here.
I am currently working on a problem in Flink, wherein I'll have to compute aggregate functions for three different sliding windows of window sizes 7 days,14 days and 1 month.
From what I've understood I'd have to run three different consumers parallelly having the above mentioned window sizes. Is there a way to implement three sliding windows for a single data stream all using a single consumer code?
Some code or reference to implement this using Flink is very appreciable.
What I know :
consumer 1 computes over a sliding window of size 7 days
consumer 2 computes over a sliding window of size 14 days
and so on.
What I want:
consumer 1 computing all these sliding windows simultaneously for a single data stream.
Is it possible to implement this in Flink?
The various windows can share a single stream produced by one kafka consumer, like this:
consumer = new FlinkKafkaConsumer<>("topic", new topicSchema(), kafkaProps);
stream = env.addSource(consumer);
w1 = stream.keyBy(key)
.window(SlidingEventTimeWindows.of(Time.days(7), Time.days(1))
.process(...)
w2 = stream.keyBy(key)
.window(SlidingEventTimeWindows.of(Time.days(14), Time.days(1))
.process(...)
Or to be more efficient, you might structure it like this:
consumer = new FlinkKafkaConsumer<>("topic", new topicSchema(), kafkaProps);
stream = env.addSource(consumer);
dayByDay = stream.keyBy(key)
.window(TumblingEventTimeWindows.of(Time.days(1))
.process(...)
w1 = dayByDay.keyBy(key)
.window(SlidingEventTimeWindows.of(Time.days(7), Time.days(1))
.process(...)
w2 = dayByDay.keyBy(key)
.window(SlidingEventTimeWindows.of(Time.days(14), Time.days(1))
.process(...)
Note, however, that there is no Time.months(), so if you want windows aligned to month boundaries, I guess you'll have to figure that part out.
I'm trying to count the elements in a stream while enriching the result with the end time of the window.
The events are received from Kafka using kafka10 consumer provided by flink. EventTime is used.
A simple KeyedStream.count( ... ) works fine.
The stream has a length of 4 minutes. By using a time window of 3 minutes only one output is received. There should be two. The results are written using a BucketingSink.
val count = stream.map( m => (m.getContext, 1) )
.keyBy( 0 )
.timeWindow( Time.minutes(3) )
.apply( new EndTimeWindow() )
.map( new JsonMapper() )
count.addSink( countSink )
class EndTimeWindow extends WindowFunction[(String,Int),(String, Int),Tuple, TimeWindow]{
override def apply(key: Tuple, window: TimeWindow, input: Iterable[(String, Int)], out: Collector[(String, Int)]): Unit = {
var sum: Int = 0
for( value <-input ) {
sum = sum + value._2;
}
out.collect( (window.getEnd.toString, new Integer(sum ) ))
}
}
By using a time window of 3 minutes only one output with a smaller amount of events is received. There should be two outputs.
To be more precise, an event time window closes when a suitable watermark arrives -- which, with a bounded-out-of-orderness watermark generator, will happen (1) if an event arrives that is sufficiently outside the window, or (2) if the events are coming from a finite source that reaches its end, because in that case Flink will send a watermark with a timestamp of Long.MAX_VALUE that will close all open event time windows. However, with Kafka as your source, that won't happen.
Ok, I think, I know what went wrong. The mistake happens, because I thought wrong about the problem.
Since I'm using Eventtime, the windows close, when an event arrives that has a timestamp greater than the window end time. When the stream ends there arrives no element anymore. It follows, that the window never closes.
I'm having troubles building a searchbox with reactive extensions.
My goal is to get the latest text available every X milliseconds, do my search and post results back on a UI grid (winforms). But i'm stuck with first step.
I can see by logging that multiple events are fired in 5000 milliseconds using Rx Sample, not only one! I expected 1 time every 5000 ms max.
My code is really simple and i strongly believed it worked:
EventLoopScheduler scheduler = new EventLoopScheduler(ts => new Thread(ts));
Observable.FromEventPattern<EventArgs>(this.textBox1, "TextChanged")
.Sample(new TimeSpan(5000), scheduler).ObserveOn(this).Subscribe
(
args =>
{
string text = ((TextBox)args.Sender).Text;
Console.WriteLine("Sample fired. Text: {0}", text);
}
);
I'm wiring up everything in the constructor of a Form.
Am i messing up with scheduler?
Thank you.
Instead of new TimeSpan(5000) which is 5000 ticks and not very long at all, use TimeSpan.FromSeconds(5).