We’ve actually had several customers where we’ve done the opposite - split large clusters apart to separate uses cases. We found that this allowed us to better align hardware with use case requirements (for example using AWS c3.2xlarge for very hot data at low latency, m4.xlarge for more general purpose data) we can also tune JVM settings, etc to meet those uses cases.


On Mon, 20 Feb 2017 at 22:21 Oleksandr Shulgin <oleksandr.shulgin@zalando.de> wrote:
On Sat, Feb 18, 2017 at 3:12 AM, Abhishek Verma <verma@uber.com> wrote:
Cassandra is being used on a large scale at Uber. We usually create dedicated clusters for each of our internal use cases, however that is difficult to scale and manage.

We are investigating the approach of using a single shared cluster with 100s of nodes and handle 10s to 100s of different use cases for different products in the same cluster. We can define different keyspaces for each of them, but that does not help in case of noisy neighbors. 

Does anybody in the community have similar large shared clusters and/or face noisy neighbor issues?


We've never tried this approach and given my limited experience I would find this a terrible idea from the perspective of maintenance (remember the old saying about basket and eggs?)

What potential benefits do you see?


Ben Slater
Chief Product Officer
Instaclustr: Cassandra + Spark - Managed | Consulting | Support
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