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 ? >>>>>> >>>>>> >>>>> >>>> >>> >> >
