If you’re after some benchmark that someone else has already run to help
estimate sizing, we pretty regularly publish benchmarking on various cloud
provider instances.

For example, see:
https://www.instaclustr.com/announcing-instaclustr-support-for-aws-i3en-instances/
and https://www.instaclustr.com/certified-apache-cassandra/

Cheers
Ben

---


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On Wed, 21 Aug 2019 at 02:42, Marc Selwan <marc.sel...@datastax.com> wrote:

> Cassandra, being a scale-out database, can load any arbitrary number of
> records per hour.
>
> The best way to do this is for your given data model, find what your max
> throughput is on a single node by scaling the number of clients until you
> start seeing errors (or hit your latency SLA) then pull back by 15-20%.
> From there, it's a matter of linearly scaling clients and nodes until you
> hit your desired throughput.
>
> I recommend taking a look at TLP-Stress as it's a bit easier to use and
> understand:
> https://thelastpickle.com/blog/2018/10/31/tlp-stress-intro.html
>
> Best.
> *Marc Selwan | *DataStax *| *PM, Server Team *|* *(925) 413-7079* *|*
> Twitter <https://twitter.com/MarcSelwan>
>
> *  Quick links | *DataStax <http://www.datastax.com> *| *Training
> <http://www.academy.datastax.com> *| *Documentation
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>  *| *Downloads <http://www.datastax.com/download>
>
>
>
> On Tue, Aug 20, 2019 at 7:16 AM Surbhi Gupta <surbhi.gupt...@gmail.com>
> wrote:
>
>> Have you tried ycsa?
>> It is a tool from yahoo for stress testing nosql databases.
>>
>> On Tue, Aug 20, 2019 at 3:34 AM <yanga.zuke...@condorgreen.com> wrote:
>>
>>> Hi Everyone,
>>>
>>>
>>>
>>> Anyone before who have bused Cassandra-stress. I want to test if it’s
>>> possible to load 600 milllions records per hour in Cassandra or
>>>
>>> Find a better way to optimize Cassandra for this case.
>>>
>>> Any help will be highly appreciated.
>>>
>>>
>>>
>>> Sent from Mail
>>> <https://urldefense.proofpoint.com/v2/url?u=https-3A__go.microsoft.com_fwlink_-3FLinkId-3D550986&d=DwMFaQ&c=adz96Xi0w1RHqtPMowiL2g&r=E6NVfMr2TIhW42QMfARTvsfCLtdF-oEA3KfAQRfVZdk&m=qz4MqEErkPhY1u6JLqEJUgJmIIjmnMQjptddjTPJE_M&s=87TbqmPgsIH-JP0fbsUYHhpSQyxeHVdqioQud3BHygc&e=>
>>> for Window
>>>
>>

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