Messages in partitions are logically the same. Topic is a logical group of 
partitions for messages that are of same type and/or from same source e.g. if 
you have two web-apps producing data, you will like to tell data from one 
source that from another. These are candidates for a topic each. If you see 
increase in data over time, you may want to consider increasing partitions to 
achieve better throughputs. As Dave said, partitions help in distributing / 
dividing the load from a source into multiple parallel streams, for faster 
throughputs. This means each partition deals with less (part of) data and 
maintains its replica state on different nodes.

Same thing on the consumer end, you can start as many parallel processes (or 
threads) as there are partitions of a topic, to achieve better throughputs. 

-Zakee

> On Nov 16, 2016, at 9:03 AM, Tauzell, Dave <dave.tauz...@surescripts.com> 
> wrote:
> 
> Partitions are used to distribute the messages in a topic between several 
> different broker instances.   This provides higher throughput.  Partitions 
> can also be replicate which allows for high availability.
> 
> -Dave
> 
> From: Doyle, Keith [mailto:keith.do...@greenwayhealth.com]
> Sent: Wednesday, November 16, 2016 11:01 AM
> To: users@kafka.apache.org
> Subject: Topics vs Partitions
> 
> 
> Trying to wrap my head around some of the concepts...
> 
> 
> 
> For semantic partitioning, is there any rationale for choosing to divide a = 
> single topic into multiple partitions as opposed to just creating multiple = 
> topics each with a single partition?
> 
> 
> 
> 
> --
> 
> [Greenway_Health_PNG_large_NO_tag]
> 
> Keith Doyle  |  Senior Software Engineer
> Greenway Health  |  4301 W. Boy Scout Blvd., Suite 800, Tampa, FL 33607
> (702) 256-9911 office  |  GreenwayHealth.com<http://www.greenwayhealth.com/>
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