Re: Pluggable throttling of read and write queries

[Abhishek Verma]

On Wed, Feb 22, 2017 at 4:01 PM, Jay Zhuang  wrote:

> Here is the Scheduler interface: https://github.com/apache/cass
> 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
path:
https://github.com/apache/cassandra/blob/cassandra-3.0/src/java/org/apache/cassandra/thrift/CassandraServer.java#L1870


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:
> https://github.com/apache/cassandra/commit/4881d9c308ccd6b5c
> a70925bf6ebedb70e7705fc

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

-Abhishek.

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 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"
>> > a
>> écrit :
>>
>>
>> Older versions had a request scheduler api.
>>
>> I am 

Re: Pluggable throttling of read and write queries

[Jay Zhuang]

Here is the Scheduler interface: 
https://github.com/apache/cassandra/blob/cassandra-3.11/conf/cassandra.yaml#L978


Seems like it could be used for this case.

It is removed in 4.x with thrift, not sure why: 
https://github.com/apache/cassandra/commit/4881d9c308ccd6b5ca70925bf6ebedb70e7705fc


Thanks,
Jay

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 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"
> 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

Re: Pluggable throttling of read and write queries

[Eric Stevens]

> 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 <
> 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"  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 <
> oleksandr.shul...@zalando.de> 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 

Re: Pluggable throttling of read and write queries

[Edward Capriolo]

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 <
> 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"  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 <
 oleksandr.shul...@zalando.de> 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 

Re: Pluggable throttling of read and write queries

[Abhishek Verma]

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"  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.

Re: Pluggable throttling of read and write queries

[vincent gromakowski]

Aren't you using mesos Cassandra framework to manage your multiple clusters
? (Seen a presentation in cass summit)
What's wrong with your current mesos approach ?
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.

Le 21 févr. 2017 2:46 AM, "Edward Capriolo"  a
écrit :

> Older versions had a request scheduler api.
>
> 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.
>>
>> Cheers
>> Ben
>>
>> 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  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 potential benefits do you see?
>>>
>>> Regards,
>>> --
>>> Alex
>>>
>>> --
>> 
>> Ben Slater
>> Chief Product Officer
>> Instaclustr: Cassandra + Spark - Managed | Consulting | Support
>> +61 437 929 798 <+61%20437%20929%20798>
>>
>
>
> --
> Sorry this was sent from mobile. Will do less grammar and spell check than
> usual.
>

Re: Pluggable throttling of read and write queries

[Ben Slater]

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.

Cheers
Ben

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 potential benefits do you see?
>
> Regards,
> --
> Alex
>
> --

Ben Slater
Chief Product Officer
Instaclustr: Cassandra + Spark - Managed | Consulting | Support
+61 437 929 798

Re: Pluggable throttling of read and write queries

[Oleksandr Shulgin]

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 potential benefits do you see?

Regards,
--
Alex