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From dgoldenberg <dgoldenberg...@gmail.com>
Subject Spark Streaming and reducing latency
Date Sun, 17 May 2015 13:51:12 GMT
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?



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