So I can explicitly specify no of receivers and executors in receiver based streaming? Can you share a sample program if any?
Also in Low level non receiver based , will data be fetched by same worker executor node and processed ? Also if I have concurrent jobs set to 1- so in low level fetching and processing will be delayed till next job starts ,say a situation where I have 1 sec of stream interval but my job1 takes 5 sec to complete , hence job2 starts at end of 5 sec, so now will it process all data from sec1 to sec 5 in low level non receiver streaming or only for interval sec1-sec2 ? And if it processes data for complete duration sec1-sec5.Is there any option to suppress start of other queued jobs(for interval sec2-3, sec3-4,sec4-5) since there work is already done by job2 ? On Wed, May 20, 2015 at 12:36 PM, Akhil Das <ak...@sigmoidanalytics.com> wrote: > One receiver basically runs on 1 core, so if your single node is having 4 > cores, there are still 3 cores left for the processing (for executors). And > yes receiver remains on the same machine unless some failure happens. > > Thanks > Best Regards > > On Tue, May 19, 2015 at 10:57 PM, Shushant Arora < > shushantaror...@gmail.com> wrote: > >> Thanks Akhil andDibyendu. >> >> Does in high level receiver based streaming executors run on receivers >> itself to have data localisation ? Or its always data is transferred to >> executor nodes and executor nodes differ in each run of job but receiver >> node remains same(same machines) throughout life of streaming application >> unless node failure happens? >> >> >> >> On Tue, May 19, 2015 at 9:29 PM, Dibyendu Bhattacharya < >> dibyendu.bhattach...@gmail.com> wrote: >> >>> Just to add, there is a Receiver based Kafka consumer which uses Kafka >>> Low Level Consumer API. >>> >>> http://spark-packages.org/package/dibbhatt/kafka-spark-consumer >>> >>> >>> Regards, >>> Dibyendu >>> >>> On Tue, May 19, 2015 at 9:00 PM, Akhil Das <ak...@sigmoidanalytics.com> >>> wrote: >>> >>>> >>>> On Tue, May 19, 2015 at 8:10 PM, Shushant Arora < >>>> shushantaror...@gmail.com> wrote: >>>> >>>>> So for Kafka+spark streaming, Receiver based streaming used highlevel >>>>> api and non receiver based streaming used low level api. >>>>> >>>>> 1.In high level receiver based streaming does it registers consumers >>>>> at each job start(whenever a new job is launched by streaming application >>>>> say at each second)? >>>>> >>>> >>>> -> Receiver based streaming will always have the receiver running >>>> parallel while your job is running, So by default for every 200ms >>>> (spark.streaming.blockInterval) the receiver will generate a block of data >>>> which is read from Kafka. >>>> >>>> >>>> >>>>> 2.No of executors in highlevel receiver based jobs will always equal >>>>> to no of partitions in topic ? >>>>> >>>> >>>> -> Not sure from where did you came up with this. For the non stream >>>> based one, i think the number of partitions in spark will be equal to the >>>> number of kafka partitions for the given topic. >>>> >>>> >>>> >>>>> 3.Will data from a single topic be consumed by executors in parllel or >>>>> only one receiver consumes in multiple threads and assign to executors in >>>>> high level receiver based approach ? >>>>> >>>>> -> They will consume the data parallel. For the receiver based >>>> approach, you can actually specify the number of receiver that you want to >>>> spawn for consuming the messages. >>>> >>>>> >>>>> >>>>> >>>>> On Tue, May 19, 2015 at 2:38 PM, Akhil Das <ak...@sigmoidanalytics.com >>>>> > wrote: >>>>> >>>>>> spark.streaming.concurrentJobs takes an integer value, not boolean. >>>>>> If you set it as 2 then 2 jobs will run parallel. Default value is 1 and >>>>>> the next job will start once it completes the current one. >>>>>> >>>>>> >>>>>>> Actually, in the current implementation of Spark Streaming and under >>>>>>> default configuration, only job is active (i.e. under execution) at any >>>>>>> point of time. So if one batch's processing takes longer than 10 >>>>>>> seconds, >>>>>>> then then next batch's jobs will stay queued. >>>>>>> This can be changed with an experimental Spark property >>>>>>> "spark.streaming.concurrentJobs" which is by default set to 1. Its not >>>>>>> currently documented (maybe I should add it). >>>>>>> The reason it is set to 1 is that concurrent jobs can potentially >>>>>>> lead to weird sharing of resources and which can make it hard to debug >>>>>>> the >>>>>>> whether there is sufficient resources in the system to process the >>>>>>> ingested >>>>>>> data fast enough. With only 1 job running at a time, it is easy to see >>>>>>> that >>>>>>> if batch processing time < batch interval, then the system will be >>>>>>> stable. >>>>>>> Granted that this may not be the most efficient use of resources under >>>>>>> certain conditions. We definitely hope to improve this in the future. >>>>>> >>>>>> >>>>>> Copied from TD's answer written in SO >>>>>> <http://stackoverflow.com/questions/23528006/how-jobs-are-assigned-to-executors-in-spark-streaming> >>>>>> . >>>>>> >>>>>> Non-receiver based streaming for example you can say are the >>>>>> fileStream, directStream ones. You can read a bit of information from >>>>>> here >>>>>> https://spark.apache.org/docs/1.3.1/streaming-kafka-integration.html >>>>>> >>>>>> Thanks >>>>>> Best Regards >>>>>> >>>>>> On Tue, May 19, 2015 at 2:13 PM, Shushant Arora < >>>>>> shushantaror...@gmail.com> wrote: >>>>>> >>>>>>> Thanks Akhil. >>>>>>> When I don't set spark.streaming.concurrentJobs to true. Will the >>>>>>> all pending jobs starts one by one after 1 jobs completes,or it does not >>>>>>> creates jobs which could not be started at its desired interval. >>>>>>> >>>>>>> And Whats the difference and usage of Receiver vs non-receiver based >>>>>>> streaming. Is there any documentation for that? >>>>>>> >>>>>>> On Tue, May 19, 2015 at 1:35 PM, Akhil Das < >>>>>>> ak...@sigmoidanalytics.com> wrote: >>>>>>> >>>>>>>> It will be a single job running at a time by default (you can also >>>>>>>> configure the spark.streaming.concurrentJobs to run jobs parallel >>>>>>>> which is >>>>>>>> not recommended to put in production). >>>>>>>> >>>>>>>> Now, your batch duration being 1 sec and processing time being 2 >>>>>>>> minutes, if you are using a receiver based streaming then ideally those >>>>>>>> receivers will keep on receiving data while the job is running (which >>>>>>>> will >>>>>>>> accumulate in memory if you set StorageLevel as MEMORY_ONLY and end up >>>>>>>> in >>>>>>>> block not found exceptions as spark drops some blocks which are yet to >>>>>>>> process to accumulate new blocks). If you are using a non-receiver >>>>>>>> based >>>>>>>> approach, you will not have this problem of dropping blocks. >>>>>>>> >>>>>>>> Ideally, if your data is small and you have enough memory to hold >>>>>>>> your data then it will run smoothly without any issues. >>>>>>>> >>>>>>>> Thanks >>>>>>>> Best Regards >>>>>>>> >>>>>>>> On Tue, May 19, 2015 at 1:23 PM, Shushant Arora < >>>>>>>> shushantaror...@gmail.com> wrote: >>>>>>>> >>>>>>>>> What happnes if in a streaming application one job is not yet >>>>>>>>> finished and stream interval reaches. Does it starts next job or wait >>>>>>>>> for >>>>>>>>> first to finish and rest jobs will keep on accumulating in queue. >>>>>>>>> >>>>>>>>> >>>>>>>>> Say I have a streaming application with stream interval of 1 sec, >>>>>>>>> but my job takes 2 min to process 1 sec stream , what will happen ? >>>>>>>>> At any >>>>>>>>> time there will be only one job running or multiple ? >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
