This is the nature of Spark Streaming as a System Architecture:
1. It is a batch processing system architecture (Spark Batch) optimized for Streaming Data 2. In terms of sources of Latency in such System Architecture, bear in mind that besides “batching”, there is also the Central “Driver” function/module, which is essentially a Central Job/Task Manager (ie running on a dedicated node, which doesn’t sit on the Path of the Messages), which even in a Streaming Data scenario, FOR EACH Streaming BATCH schedules tasks (as per the DAG for the streaming job), sends them to the workers, receives the results, then schedules and sends more tasks (as per the DAG for the job) and so on and so forth In terms of Parallel Programming Patterns/Architecture, the above is known as Data Parallel Architecture with Central Job/Task Manager. There are other alternatives for achieving lower latency and in terms of Parallel Programming Patterns they are known as Pipelines or Task Parallel Architecture – essentially every messages streams individually through an assembly line of Tasks. As the tasks can be run on multiple cores of one box or in a distributed environment. Storm for example implements this pattern or you can just put together your own solution From: Akhil Das [mailto:ak...@sigmoidanalytics.com] Sent: Sunday, May 17, 2015 4:04 PM To: dgoldenberg Cc: user@spark.apache.org Subject: Re: Spark Streaming and reducing latency With receiver based streaming, you can actually specify spark.streaming.blockInterval which is the interval at which the receiver will fetch data from the source. Default value is 200ms and hence if your batch duration is 1 second, it will produce 5 blocks of data. And yes, with sparkstreaming when your processing time goes beyond your batch duration and you are having a higher data consumption then you will overwhelm the receiver's memory and hence will throw up block not found exceptions. Thanks Best Regards On Sun, May 17, 2015 at 7:21 PM, dgoldenberg <dgoldenberg...@gmail.com> wrote: I keep hearing the argument that the way Discretized Streams work with Spark Streaming is a lot more of a batch processing algorithm than true streaming. For streaming, one would expect a new item, e.g. in a Kafka topic, to be available to the streaming consumer immediately. With the discretized streams, streaming is done with batch intervals i.e. the consumer has to wait the interval to be able to get at the new items. If one wants to reduce latency it seems the only way to do this would be by reducing the batch interval window. However, that may lead to a great deal of churn, with many requests going into Kafka out of the consumers, potentially with no results whatsoever as there's nothing new in the topic at the moment. Is there a counter-argument to this reasoning? What are some of the general approaches to reduce latency folks might recommend? Or, perhaps there are ways of dealing with this at the streaming API level? If latency is of great concern, is it better to look into streaming from something like Flume where data is pushed to consumers rather than pulled by them? Are there techniques, in that case, to ensure the consumers don't get overwhelmed with new data? -- View this message in context: http://apache-spark-user-list.1001560.n3.nabble.com/Spark-Streaming-and-reducing-latency-tp22922.html Sent from the Apache Spark User List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
