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From Abhishek Verma <>
Subject Re: Pluggable throttling of read and write queries
Date Thu, 23 Feb 2017 01:14:06 GMT
On Wed, Feb 22, 2017 at 4:01 PM, Jay Zhuang <> wrote:

> Here is the Scheduler interface:
> andra/blob/cassandra-3.11/conf/cassandra.yaml#L978

> Seems like it could be used for this case.

The IRequestScheduler is only used in the thrift Cassandra server code

Almost all of our customers are using CQL instead of thrift protocol, so we
wont be able to use the request scheduler.

It is removed in 4.x with thrift, not sure why:
> a70925bf6ebedb70e7705fc

Since it only works from thrift, it makes sense that it is being removed as
a part of that commit.


On 2/22/17 3:39 PM, Eric Stevens wrote:
>> We’ve actually had several customers where we’ve done the opposite -
>> split large clusters apart to separate uses cases
>> We do something similar but for a single application.  We're
>> functionally sharding data to different clusters from a single
>> application.  We can have different server classes for different types
>> of workloads, we can grow and size clusters accordingly, and we also do
>> things like time sharding so that we can let at-rest data go to cheaper
>> storage options.
>> I agree with the general sentiment here that (at least as it stands
>> today) a monolithic cluster for many applications does not compete to
>> per-application clusters unless cost is no issue.  At our scale, the
>> terabytes of C* data we take in per day means that even very small cost
>> savings really add up at scale.  And even where cost is no issue, the
>> additional isolation and workload tailoring is still highly valuable.
>> On Wed, Feb 22, 2017 at 12:01 PM Edward Capriolo <
>> <>> wrote:
>>     On Wed, Feb 22, 2017 at 1:20 PM, Abhishek Verma <
>>     <>> wrote:
>>         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
>>         <
>>         <>> wrote:
>>             Aren't you using mesos Cassandra framework to manage your
>>             multiple clusters ? (Seen a presentation in cass summit)
>>         Yes we are
>>         using and
>>         contribute heavily to it. I am aware of the presentation
>>         ( 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"
>>             < <>> 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
>>                 <> 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
>>                     <> wrote:
>>                         On Sat, Feb 18, 2017 at 3:12 AM, Abhishek Verma
>>                         <> 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.
>>     I agree with these assertions. On one hand I think about a "managed
>>     service" like say Amazon DynamoDB. They likely start with
>>     very/very/very large footprints. IE they commission huge clusters of
>>     the fastest SSD hardware. Next every application/user has a quota.
>>     They always can control the basic load because they control the quota.
>>     Control on the hardware level makes sense, but then your unit of
>>     management is "a cluster" . Users do not have a unified API anymore,
>>     they have switch statements, this data in cluster x, this data
>>     cluster y. You still end up in cases where degenerate usage patterns
>>     affect others.
>>     With Cassandra it would be nice if these controls were build into
>>     the API. This could also help you build your own charge back model
>>     in the enterprise. Sure as someone pointed out rejecting reads
>>     stinks for that user. But then again someone has to decide who and
>>     how pays for the hardware.
>>     For example, imagine a company with 19 business units all using the
>>     same Cassandra cluster. One business unit might account for 90% of
>>     the storage, but 1% of the requests. Another business unit might be
>>     95% of the requests, but 1% the data. How do you come up with a
>>     billing model? For the customer with 95% of the requests their
>>     "cost" on the systems is young generation GC, network.
>>     Datastax enterprise had/has a concept of  "the analytic dc". The
>>     concept is "real time goes here" and "analytic goes there" with the
>>     right resource controls you could get much more fine grained then
>>     that. It will never be perfect there will always be that random
>>     abuser with the "aggregate allow filtering query" but there are ways
>>     to move in a more managed direction.

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