Re: Spark streaming. Strict discretizing by time
Hi, I changed auto.offset.reset to largest. The result 30, 50, 40, 40, 35, 30 seconds... Instead of 10 seconds. It looks like attempt to react on backpressure but very slow. In any case it is far from any real time tasks including soft real time. And ok, I agreed with Spark usage with data flows without peaks and with hot reserves of hardware. If it is interesting for you I added Flink test with same logic. Just run it by ./gradlew test_flink. Just as a reference. https://github.com/rssdev10/spark-kafka-streaming Cheers 2016-07-06 20:12 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > > Yes and I sent you results. It is appropriate only with known parameters > of input data stream. > > No, as far as I can tell from your posts in this thread and your > linked project, you only tested with auto.offset.reset smallest and a > large backlog. That's not what I advised you to do. Don't draw > inaccurate conclusions about Spark DStreams from that test. The > reason you need to specify maxRatePerPartition is because you're > starting with a large backlog and thus a large first batch. If you > were testing an ongoing stream with auto.offset.reset largest, > backpressure alone should be sufficient. > > > > On Wed, Jul 6, 2016 at 12:23 PM, rss rss <rssde...@gmail.com> wrote: > >> If you aren't processing messages as fast as you receive them, you're > >> going to run out of kafka retention regardless of whether you're using > >> Spark or Flink. Again, physics. It's just a question of what > >> compromises you choose. > > > > > > Yes. I wrote about it. But in case of Flink you will have output strictly > > after specified time. If it is impossible to process 1000 messages per 1 > > second but possible process 500, then Flink makes an output for 500. If > only > > 1 message processed, Flink produced an output for one only but after 1 > > second. At the same time Spark processes all 1000 but much longer that 1 > > second in this case. > > > >> that's what backpressure > >> and maxRatePerPartition are for. As long as those are set reasonably, > >> you'll have a reasonably fixed output interval. Have you actually > >> tested this in the way I suggested? > > > > > > Yes and I sent you results. It is appropriate only with known parameters > of > > input data stream. I'm not able to estimate bounds of Sparks usage in > > general. And I'm not about it. I'm about Spark has these limitations. And > > most problem is when a calculation time depends on input data. That is if > > you want to have a stable period of output data generation you have to > use > > computational system with free resources in hot reserve. > > > > In any case thanks, now I understand how to use Spark. > > > > PS: I will continue work with Spark but to minimize emails stream I plan > to > > unsubscribe from this mail list > > > > 2016-07-06 18:55 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > >> > >> If you aren't processing messages as fast as you receive them, you're > >> going to run out of kafka retention regardless of whether you're using > >> Spark or Flink. Again, physics. It's just a question of what > >> compromises you choose. > >> > >> If by "growing of a processing window time of Spark" you mean a > >> processing time that exceeds batch time... that's what backpressure > >> and maxRatePerPartition are for. As long as those are set reasonably, > >> you'll have a reasonably fixed output interval. Have you actually > >> tested this in the way I suggested? > >> > >> On Wed, Jul 6, 2016 at 11:38 AM, rss rss <rssde...@gmail.com> wrote: > >> > Ok, thanks. But really this is not full decision. In case of growing > of > >> > processing time I will have growing of window time. That is really > with > >> > Spark I have 2 points of a latency growing. First is a delay of > >> > processing > >> > of messages in Kafka queue due to physical limitation of a computer > >> > system. > >> > And second one is growing of a processing window time of Spark. In > case > >> > of > >> > Flink there is only first point of delay but time intervals of output > >> > data > >> > are fixed. It is really looks like limitation of Spark. That is if > >> > dataflow > >> > is stable, all is ok. If there are peaks of loading more than > >> > possibility of > >> > computational system or data dependent time of calculation, Spark is > not > >
Re: Spark streaming. Strict discretizing by time
> > If you aren't processing messages as fast as you receive them, you're > going to run out of kafka retention regardless of whether you're using > Spark or Flink. Again, physics. It's just a question of what > compromises you choose. Yes. I wrote about it. But in case of Flink you will have output strictly after specified time. If it is impossible to process 1000 messages per 1 second but possible process 500, then Flink makes an output for 500. If only 1 message processed, Flink produced an output for one only but after 1 second. At the same time Spark processes all 1000 but much longer that 1 second in this case. that's what backpressure > and maxRatePerPartition are for. As long as those are set reasonably, > you'll have a reasonably fixed output interval. Have you actually > tested this in the way I suggested? Yes and I sent you results. It is appropriate only with known parameters of input data stream. I'm not able to estimate bounds of Sparks usage in general. And I'm not about it. I'm about Spark has these limitations. And most problem is when a calculation time depends on input data. That is if you want to have a stable period of output data generation you have to use computational system with free resources in hot reserve. In any case thanks, now I understand how to use Spark. PS: I will continue work with Spark but to minimize emails stream I plan to unsubscribe from this mail list 2016-07-06 18:55 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > If you aren't processing messages as fast as you receive them, you're > going to run out of kafka retention regardless of whether you're using > Spark or Flink. Again, physics. It's just a question of what > compromises you choose. > > If by "growing of a processing window time of Spark" you mean a > processing time that exceeds batch time... that's what backpressure > and maxRatePerPartition are for. As long as those are set reasonably, > you'll have a reasonably fixed output interval. Have you actually > tested this in the way I suggested? > > On Wed, Jul 6, 2016 at 11:38 AM, rss rss <rssde...@gmail.com> wrote: > > Ok, thanks. But really this is not full decision. In case of growing of > > processing time I will have growing of window time. That is really with > > Spark I have 2 points of a latency growing. First is a delay of > processing > > of messages in Kafka queue due to physical limitation of a computer > system. > > And second one is growing of a processing window time of Spark. In case > of > > Flink there is only first point of delay but time intervals of output > data > > are fixed. It is really looks like limitation of Spark. That is if > dataflow > > is stable, all is ok. If there are peaks of loading more than > possibility of > > computational system or data dependent time of calculation, Spark is not > > able to provide a periodically stable results output. Sometimes this is > > appropriate but sometime this is not appropriate. > > > > 2016-07-06 18:11 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > >> > >> Then double the upper limit you have set until the processing time > >> approaches the batch time. > >> > >> On Wed, Jul 6, 2016 at 11:06 AM, rss rss <rssde...@gmail.com> wrote: > >> > Ok, with: > >> > > >> > .set("spark.streaming.backpressure.enabled","true") > >> > .set("spark.streaming.receiver.maxRate", "1") > >> > .set("spark.streaming.kafka.maxRatePerPartition", "1") > >> > > >> > I have something like > >> > > >> > > >> > > *** > >> > Processing time: 5626 > >> > Expected time: 1 > >> > Processed messages: 10 > >> > Message example: {"message": 950002, > >> > "uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} > >> > Recovered json: > >> > {"message":950002,"uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} > >> > > >> > That is yes, it works but throughput is much less than without > >> > limitations > >> > because of this is an absolute upper limit. And time of processing is > >> > half > >> > of available. > >> > > >> > Regarding Spark 2.0 structured streaming I will look it some later. > Now > >> > I > >> > don't know how to strictly measure throughput and latency of this high > >> > level > >> > API. My aim now is to compare
Re: Spark streaming. Strict discretizing by time
Ok, thanks. But really this is not full decision. In case of growing of processing time I will have growing of window time. That is really with Spark I have 2 points of a latency growing. First is a delay of processing of messages in Kafka queue due to physical limitation of a computer system. And second one is growing of a processing window time of Spark. In case of Flink there is only first point of delay but time intervals of output data are fixed. It is really looks like limitation of Spark. That is if dataflow is stable, all is ok. If there are peaks of loading more than possibility of computational system or *data dependent time of calculation*, Spark is not able to provide a periodically stable results output. Sometimes this is appropriate but sometime this is not appropriate. 2016-07-06 18:11 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > Then double the upper limit you have set until the processing time > approaches the batch time. > > On Wed, Jul 6, 2016 at 11:06 AM, rss rss <rssde...@gmail.com> wrote: > > Ok, with: > > > > .set("spark.streaming.backpressure.enabled","true") > > .set("spark.streaming.receiver.maxRate", "1") > > .set("spark.streaming.kafka.maxRatePerPartition", "1") > > > > I have something like > > > > > *** > > Processing time: 5626 > > Expected time: 1 > > Processed messages: 10 > > Message example: {"message": 950002, > > "uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} > > Recovered json: > > {"message":950002,"uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} > > > > That is yes, it works but throughput is much less than without > limitations > > because of this is an absolute upper limit. And time of processing is > half > > of available. > > > > Regarding Spark 2.0 structured streaming I will look it some later. Now I > > don't know how to strictly measure throughput and latency of this high > level > > API. My aim now is to compare streaming processors. > > > > > > 2016-07-06 17:41 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > >> > >> The configuration you set is spark.streaming.receiver.maxRate. The > >> direct stream is not a receiver. As I said in my first message in > >> this thread, and as the pages at > >> > >> > http://spark.apache.org/docs/latest/streaming-kafka-integration.html#approach-2-direct-approach-no-receivers > >> and > http://spark.apache.org/docs/latest/configuration.html#spark-streaming > >> also say, use maxRatePerPartition for the direct stream. > >> > >> Bottom line, if you have more information than your system can process > >> in X amount of time, after X amount of time you can either give the > >> wrong answer, or take longer to process. Flink can't violate the laws > >> of physics. If the tradeoffs that Flink make are better for your use > >> case than the tradeoffs that DStreams make, you may be better off > >> using Flink (or testing out spark 2.0 structured streaming, although > >> there's no kafka integration available for that yet) > >> > >> On Wed, Jul 6, 2016 at 10:25 AM, rss rss <rssde...@gmail.com> wrote: > >> > ok, thanks. I tried to set minimum max rate for beginning. However in > >> > general I don't know initial throughput. BTW it would be very useful > to > >> > explain it in > >> > > >> > > https://spark.apache.org/docs/latest/streaming-programming-guide.html#performance-tuning > >> > > >> > And really with > >> > > >> > .set("spark.streaming.backpressure.enabled","true") > >> > .set("spark.streaming.receiver.maxRate", "1") > >> > > >> > I have same problem: > >> > > >> > > *** > >> > Processing time: 36994 > >> > Expected time: 1 > >> > Processed messages: 3015830 > >> > Message example: {"message": 1, > >> > "uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > >> > Recovered json: > >> > {"message":1,"uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > >> > > >> > > >> > Regarding auto.offset.reset smallest, now it is because of a test and > I > >> > want > >> >
Re: Spark streaming. Strict discretizing by time
Ok, with: .set("spark.streaming.backpressure.enabled","true") .set("spark.streaming.receiver.maxRate", "1") .set("spark.streaming.kafka.maxRatePerPartition", "1") I have something like *** Processing time: 5626 Expected time: 1 Processed messages: 10 Message example: {"message": 950002, "uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} Recovered json: {"message":950002,"uid":"81e2d447-69f2-4ce6-a13d-50a1a8b569a0"} That is yes, it works but throughput is much less than without limitations because of this is an absolute upper limit. And time of processing is half of available. Regarding Spark 2.0 structured streaming I will look it some later. Now I don't know how to strictly measure throughput and latency of this high level API. My aim now is to compare streaming processors. 2016-07-06 17:41 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > The configuration you set is spark.streaming.receiver.maxRate. The > direct stream is not a receiver. As I said in my first message in > this thread, and as the pages at > > http://spark.apache.org/docs/latest/streaming-kafka-integration.html#approach-2-direct-approach-no-receivers > and http://spark.apache.org/docs/latest/configuration.html#spark-streaming > also say, use maxRatePerPartition for the direct stream. > > Bottom line, if you have more information than your system can process > in X amount of time, after X amount of time you can either give the > wrong answer, or take longer to process. Flink can't violate the laws > of physics. If the tradeoffs that Flink make are better for your use > case than the tradeoffs that DStreams make, you may be better off > using Flink (or testing out spark 2.0 structured streaming, although > there's no kafka integration available for that yet) > > On Wed, Jul 6, 2016 at 10:25 AM, rss rss <rssde...@gmail.com> wrote: > > ok, thanks. I tried to set minimum max rate for beginning. However in > > general I don't know initial throughput. BTW it would be very useful to > > explain it in > > > https://spark.apache.org/docs/latest/streaming-programming-guide.html#performance-tuning > > > > And really with > > > > .set("spark.streaming.backpressure.enabled","true") > > .set("spark.streaming.receiver.maxRate", "1") > > > > I have same problem: > > > *** > > Processing time: 36994 > > Expected time: 1 > > Processed messages: 3015830 > > Message example: {"message": 1, > > "uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > > Recovered json: > {"message":1,"uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > > > > > > Regarding auto.offset.reset smallest, now it is because of a test and I > want > > to get same messages for each run. But in any case I expect to read all > new > > messages from queue. > > > > Regarding backpressure detection. What is to do then a process time is > much > > more then input rate? Now I see growing time of processing instead of > stable > > 10 second and decreasing number of processed messages. Where is a limit > of > > of backpressure algorithm? > > > > Regarding Flink. I don't know how works core of Flink but you can check > self > > that Flink will strictly terminate processing of messages by time. > Deviation > > of the time window from 10 seconds to several minutes is impossible. > > > > PS: I prepared this example to make possible easy observe the problem and > > fix it if it is a bug. For me it is obvious. May I ask you to be near to > > this simple source code? In other case I have to think that this is a > > technical limitation of Spark to work with unstable data flows. > > > > Cheers > > > > 2016-07-06 16:40 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > >> > >> The direct stream determines batch sizes on the driver, in advance of > >> processing. If you haven't specified a maximum batch size, how would > >> you suggest the backpressure code determine how to limit the first > >> batch? It has no data on throughput until at least one batch is > >> completed. > >> > >> Again, this is why I'm saying test by producing messages into kafka at > >> a rate comparable to production, not loading a ton of messages in and > >> starting from auto.offset.reset smallest. > >>
Re: Spark streaming. Strict discretizing by time
ok, thanks. I tried to set minimum max rate for beginning. However in general I don't know initial throughput. BTW it would be very useful to explain it in https://spark.apache.org/docs/latest/streaming-programming-guide.html#performance-tuning And really with .set("spark.streaming.backpressure.enabled","true") .set("spark.streaming.receiver.maxRate", "1") I have same problem: *** Processing time: *36994* Expected time: 1 Processed messages: *3015830* Message example: {"message": 1, "uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} Recovered json: {"message":1,"uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} Regarding auto.offset.reset smallest, now it is because of a test and I want to get same messages for each run. But in any case I expect to read all new messages from queue. Regarding backpressure detection. What is to do then a process time is much more then input rate? Now I see growing time of processing instead of stable 10 second and decreasing number of processed messages. Where is a limit of of backpressure algorithm? Regarding Flink. I don't know how works core of Flink but you can check self that Flink will strictly terminate processing of messages by time. Deviation of the time window from 10 seconds to several minutes is impossible. PS: I prepared this example to make possible easy observe the problem and fix it if it is a bug. For me it is obvious. May I ask you to be near to this simple source code? In other case I have to think that this is a technical limitation of Spark to work with unstable data flows. Cheers 2016-07-06 16:40 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > The direct stream determines batch sizes on the driver, in advance of > processing. If you haven't specified a maximum batch size, how would > you suggest the backpressure code determine how to limit the first > batch? It has no data on throughput until at least one batch is > completed. > > Again, this is why I'm saying test by producing messages into kafka at > a rate comparable to production, not loading a ton of messages in and > starting from auto.offset.reset smallest. > > Even if you're unwilling to take that advice for some reason, and > unwilling to empirically determine a reasonable maximum partition > size, you should be able to estimate an upper bound such that the > first batch does not encompass your entire kafka retention. > Backpressure will kick in once it has some information to work with. > > On Wed, Jul 6, 2016 at 8:45 AM, rss rss <rssde...@gmail.com> wrote: > > Hello, > > > > thanks, I tried to .set("spark.streaming.backpressure.enabled","true") > but > > result is negative. Therefore I have prepared small test > > https://github.com/rssdev10/spark-kafka-streaming > > > > How to run: > > git clone https://github.com/rssdev10/spark-kafka-streaming.git > > cd spark-kafka-streaming > > > > # downloads kafka and zookeeper > > ./gradlew setup > > > > # run zookeeper, kafka, and run messages generation > > ./gradlew test_data_prepare > > > > And in other console just run: > >./gradlew test_spark > > > > It is easy to break data generation by CTRL-C. And continue by same > command > > ./gradlew test_data_prepare > > > > To stop all run: > > ./gradlew stop_all > > > > Spark test must generate messages each 10 seconds like: > > > *** > > Processing time: 33477 > > Expected time: 1 > > Processed messages: 2897866 > > Message example: {"message": 1, > > "uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > > Recovered json: > {"message":1,"uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} > > > > > > message is number of fist message in the window. Time values are in > > milliseconds. > > > > Brief results: > > > > Spark always reads all messaged from Kafka after first connection > > independently on dstream or window size time. It looks like a bug. > > When processing speed in Spark's app is near to speed of data generation > all > > is ok. > > I added delayFactor in > > > https://github.com/rssdev10/spark-kafka-streaming/blob/master/src/main/java/SparkStreamingConsumer.java > > to emulate slow processing. And streaming process is in degradation. When > > delayFactor=0 it looks like stable process. > > > > > > Cheers > > > > > > 2016-07-05 17:51 GMT+02:00 Cody
Re: Spark streaming. Strict discretizing by time
Hello, thanks, I tried to .set("spark.streaming.backpressure.enabled","true") but result is negative. Therefore I have prepared small test https://github.com/rssdev10/spark-kafka-streaming How to run: * git clone https://github.com/rssdev10/spark-kafka-streaming.git <https://github.com/rssdev10/spark-kafka-streaming.git> cd spark-kafka-streaming* # downloads kafka and zookeeper * ./gradlew setup * # run zookeeper, kafka, and run messages generation * ./gradlew test_data_prepare * And in other console just run: * ./gradlew test_spark* It is easy to break data generation by CTRL-C. And continue by same command *./gradlew test_data_prepare* To stop all run: *./gradlew stop_all* Spark test must generate messages each 10 seconds like: *** Processing time: 33477 Expected time: 1 Processed messages: 2897866 Message example: {"message": 1, "uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} Recovered json: {"message":1,"uid":"dde09b16-248b-4a2b-8936-109c72eb64cc"} *message* is number of fist message in the window. Time values are in milliseconds. Brief results: 1. Spark always reads all messaged from Kafka after first connection independently on dstream or window size time. It looks like a bug. 2. When processing speed in Spark's app is near to speed of data generation all is ok. 3. I added delayFactor in https://github.com/rssdev10/spark-kafka-streaming/blob/master/src/main/java/SparkStreamingConsumer.java to emulate slow processing. And streaming process is in degradation. When delayFactor=0 it looks like stable process. Cheers 2016-07-05 17:51 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > Test by producing messages into kafka at a rate comparable to what you > expect in production. > > Test with backpressure turned on, it doesn't require you to specify a > fixed limit on number of messages and will do its best to maintain > batch timing. Or you could empirically determine a reasonable fixed > limit. > > Setting up a kafka topic with way more static messages in it than your > system can handle in one batch, and then starting a stream from the > beginning of it without turning on backpressure or limiting the number > of messages... isn't a reasonable way to test steady state > performance. Flink can't magically give you a correct answer under > those circumstances either. > > On Tue, Jul 5, 2016 at 10:41 AM, rss rss <rssde...@gmail.com> wrote: > > Hi, thanks. > > > >I know about possibility to limit number of messages. But the problem > is > > I don't know number of messages which the system able to process. It > depends > > on data. The example is very simple. I need a strict response after > > specified time. Something like soft real time. In case of Flink I able to > > setup strict time of processing like this: > > > > KeyedStream<Event, Integer> keyed = > > eventStream.keyBy(event.userId.getBytes()[0] % partNum); > > WindowedStream<Event, Integer, TimeWindow> uniqUsersWin = > keyed.timeWindow( > > Time.seconds(10) ); > > DataStream uniqUsers = > > uniq.trigger(ProcessingTimeTrigger.create()) > > .fold(new Aggregator(), new FoldFunction<Event, Aggregator>() { > > @Override > > public Aggregator fold(Aggregator accumulator, Event value) > > throws Exception { > > accumulator.add(event.userId); > > return accumulator; > > } > > }); > > > > uniq.print(); > > > > And I can see results every 10 seconds independently on input data > stream. > > Is it possible something in Spark? > > > > Regarding zeros in my example the reason I have prepared message queue in > > Kafka for the tests. If I add some messages after I able to see new > > messages. But in any case I need first response after 10 second. Not > minutes > > or hours after. > > > > Thanks. > > > > > > > > 2016-07-05 17:12 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > >> > >> If you're talking about limiting the number of messages per batch to > >> try and keep from exceeding batch time, see > >> > >> http://spark.apache.org/docs/latest/configuration.html > >> > >> look for backpressure and maxRatePerParition > >> > >> > >> But if you're only seeing zeros after your job runs for a minute, it > >> sounds like something else is wrong. > >> > >> > >> On Tue, Jul 5, 2016 at 10:02 AM, rss r
Re: Spark streaming. Strict discretizing by time
Hi, thanks. I know about possibility to limit number of messages. But the problem is I don't know number of messages which the system able to process. It depends on data. The example is very simple. I need a strict response after specified time. Something like soft real time. In case of Flink I able to setup strict time of processing like this: KeyedStream<Event, Integer> keyed = eventStream.keyBy(event.userId.getBytes()[0] % partNum); WindowedStream<Event, Integer, TimeWindow> uniqUsersWin = keyed.timeWindow( *Time.seconds(10*) ); DataStream uniqUsers = uniq.trigger(*ProcessingTimeTrigger*.create()) .fold(new Aggregator(), new FoldFunction<Event, Aggregator>() { @Override public Aggregator fold(Aggregator accumulator, Event value) throws Exception { accumulator.add(event.userId); return accumulator; } }); uniq.print(); And I can see results every 10 seconds independently on input data stream. Is it possible something in Spark? Regarding zeros in my example the reason I have prepared message queue in Kafka for the tests. If I add some messages after I able to see new messages. But in any case I need first response after 10 second. Not minutes or hours after. Thanks. 2016-07-05 17:12 GMT+02:00 Cody Koeninger <c...@koeninger.org>: > If you're talking about limiting the number of messages per batch to > try and keep from exceeding batch time, see > > http://spark.apache.org/docs/latest/configuration.html > > look for backpressure and maxRatePerParition > > > But if you're only seeing zeros after your job runs for a minute, it > sounds like something else is wrong. > > > On Tue, Jul 5, 2016 at 10:02 AM, rss rss <rssde...@gmail.com> wrote: > > Hello, > > > > I'm trying to organize processing of messages from Kafka. And there is > a > > typical case when a number of messages in kafka's queue is more then > Spark > > app's possibilities to process. But I need a strong time limit to prepare > > result for at least for a part of data. > > > > Code example: > > > > SparkConf sparkConf = new SparkConf() > > .setAppName("Spark") > > .setMaster("local"); > > > > JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, > > Milliseconds.apply(5000)); > > > > jssc.checkpoint("/tmp/spark_checkpoint"); > > > > Set topicMap = new > > HashSet<>(Arrays.asList(topicList.split(","))); > > Map<String, String> kafkaParams = new HashMap<String, String>() { > > { > > put("metadata.broker.list", bootstrapServers); > > put("auto.offset.reset", "smallest"); > > } > > }; > > > > JavaPairInputDStream<String, String> messages = > > KafkaUtils.createDirectStream(jssc, > > String.class, > > String.class, > > StringDecoder.class, > > StringDecoder.class, > > kafkaParams, > > topicMap); > > > > messages.countByWindow(Seconds.apply(10), > Milliseconds.apply(5000)) > > .map(x -> {System.out.println(x); return x;}) > > .dstream().saveAsTextFiles("/tmp/spark", > "spark-streaming"); > > > > > > I need to see a result of window operation each 10 seconds (this is > only > > simplest example). But really with my test data ~10M messages I have > first > > result a minute after and further I see only zeros. Is a way to limit > > processing time to guarantee a response in specified time like Apache > > Flink's triggers? > > > > Thanks. >
Spark streaming. Strict discretizing by time
Hello, I'm trying to organize processing of messages from Kafka. And there is a typical case when a number of messages in kafka's queue is more then Spark app's possibilities to process. But I need a strong time limit to prepare result for at least for a part of data. Code example: SparkConf sparkConf = new SparkConf() .setAppName("Spark") .setMaster("local"); JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Milliseconds.apply(5000)); jssc.checkpoint("/tmp/spark_checkpoint"); Set topicMap = new HashSet<>(Arrays.asList(topicList.split(","))); MapkafkaParams = new HashMap () { { put("metadata.broker.list", bootstrapServers); put("auto.offset.reset", "smallest"); } }; JavaPairInputDStream messages = KafkaUtils.createDirectStream(jssc, String.class, String.class, StringDecoder.class, StringDecoder.class, kafkaParams, topicMap); messages.countByWindow(Seconds.apply(10), Milliseconds.apply(5000)) *.map(x -> {System.out.println(x); return x;})* .dstream().saveAsTextFiles("/tmp/spark", "spark-streaming"); I need to see a result of window operation each 10 seconds (this is only simplest example). But really with my test data ~10M messages I have first result a minute after and further I see only zeros. Is a way to limit processing time to guarantee a response in specified time like Apache Flink's triggers? Thanks.