Why Service Fabric?

This is a brief article which is not related to What is  Service Fabric. The intent is to add more pointers to this as we explore Service Fabric.

 Here in this article the focus is to study Why Service Fabric is needed. Following is a comparison study that I manage to collect talking to some Service Fabric devs and PMs. Having said that the intent of this blog is to open debate points.

  1. Why we want to use Service Fabric as Hadoop Map Reduce
Suppose we have 1 TB of data size, and the data is divided in many sections. Considering one section , if there is a failover during the execution. The processing start again from the beginning of the section in case of Hadoop Map Reduce. If we implement map reduce in Service Fabric, as the states are saved this will not be an issue.

Traditional Map Reduce is having mapper, combiner and reducer. Data gets copied from mapper to combiner and then Reducer. In case of Service Fabric we save the data from getting copied multiple times (Copy happens from Mapper directly to Reducer) 

  1. Distributed Data Structures
Service Fabric is providing Distributed data structures like Distributed Queue and Distributed Dictionary. These in built data structure helps one to quickly develop stateful services on windows fabric. This also ensures that your service never loses data inspite of cluster wide failures.

Unlike Azure Redis cache , Azure Table Store, Azure Queue the state is kept locally in service instance. Advantages are all reads are local, all  write incur minimum number of network.


  1. Interesting and I was thinking the same thing in regards to using Service Fabric in a Map Reduce model. This enables very easy deployment as it can all run locally in-house and if allowed, one can expand into the cloud. The question is... How does one start?


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