We have lots of dedicated Cassandra clusters for large use cases, but we have a long tail of (~100) of internal customers who want to store < 200GB of data with < 5k qps and non-critical data. It does not make sense to create a 3 node dedicated cluster for each of these small use cases. So we have a shared cluster into which we onboard these users.
But once in a while, one of the customers will run a ingest job from HDFS which will pound the shared cluster and break our SLA for the cluster for all the other customers. Currently, I don't see anyway to signal back pressure to the ingestion jobs or throttle their requests. Another example is one customer doing a large number of range queries which has the same effect. A simple way to avoid this is to throttle the read or write requests based on some quota limits for each keyspace or user. Please see replies inlined: On Mon, Feb 20, 2017 at 11:46 PM, vincent gromakowski < vincent.gromakow...@gmail.com> wrote: > Aren't you using mesos Cassandra framework to manage your multiple > clusters ? (Seen a presentation in cass summit) > Yes we are using https://github.com/mesosphere/dcos-cassandra-service and contribute heavily to it. I am aware of the presentation ( https://www.youtube.com/watch?v=4Ap-1VT2ChU) at the Cassandra summit as I was the one who gave it :) This has helped us automate the creation and management of these clusters. > What's wrong with your current mesos approach ? > Hardware efficiency: Spinning up dedicated clusters for each use case wastes a lot of hardware resources. One of the approaches we have taken is spinning up multiple Cassandra nodes belonging to different clusters on the same physical machine. However, we still have overhead of managing these separate multi-tenant clusters. > I am also thinking it's better to split a large cluster into smallers > except if you also manage client layer that query cass and you can put some > backpressure or rate limit in it. > We have an internal storage API layer that some of the clients use, but there are many customers who use the vanilla DataStax Java or Python driver. Implementing throttling in each of those clients does not seem like a viable approach. Le 21 févr. 2017 2:46 AM, "Edward Capriolo" <edlinuxg...@gmail.com> a > écrit : > >> Older versions had a request scheduler api. > > I am not aware of the history behind it. Can you please point me to the JIRA tickets and/or why it was removed? On Monday, February 20, 2017, Ben Slater <ben.sla...@instaclustr.com> wrote: >> >>> 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. >>> >> There have been several instances where we have moved customers out of the shared cluster to their own dedicated clusters because they outgrew our limitations. But I don't think it makes sense to move all the small use cases into their separate clusters. On Mon, 20 Feb 2017 at 22:21 Oleksandr Shulgin <oleksandr.shul...@zalando.de> >>> wrote: >>> >>>> On Sat, Feb 18, 2017 at 3:12 AM, Abhishek Verma <ve...@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? >>>>> >>>> >>>> Hi, >>>> >>>> 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 if you have a limited number of baskets and several eggs which are not critical if they break rarely. > What potential benefits do you see? >>>> >>> The main benefit of sharing a single cluster among several small use cases is increasing the hardware efficiency and decreasing the management overhead of a large number of clusters. Thanks everyone for your replies and questions. -Abhishek.
