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Posted to user@drill.apache.org by PROJJWAL SAHA <pr...@gmail.com> on 2017/10/19 05:26:33 UTC
Benchmark numbers using Drill
Hi,
Is there any public performance benchmark that users have achieved using
Drill in production scenarios ? It would be useful if someone can pass me
any links for customer user stories.
Regards
Re: Benchmark numbers using Drill
Posted by PROJJWAL SAHA <pr...@gmail.com>.
Thanks for this very useful info..
On 19 Oct 2017 11:28 pm, "Saurabh Mahapatra" <sa...@gmail.com>
wrote:
> I do not think you will get such information about benchmarks from
> customers on production workloads. But from the customers I have worked
> with who have taken Drill to production, here is some information that may
> be of use to you:
>
> 1. The trend universally has been to use beefier machines for in-memory
> query engines. We see 256GB RAM and 32 cores as the most frequent
> configuration. On the network side, it is 2x10GbE.
>
> 2. The most commonly sized dedicated cluster for starting out with Drill in
> production has been around 16-20 nodes with the above configuration. I have
> several customers who have deployed this on 200+ nodes as well but in those
> scenarios, it is a service among many.
>
> 3. The concurrency we see in the above settings is a function of the size
> of the dataset and the complexity of the customer query. In general,
> Little's law holds. The smaller the chunk of work is to be processed, the
> faster will be the throughput. Our understanding of this changes further
> with the new releases of Drill where spill to disk features will make it
> more of a pessimistic execution engine. Also, the use of queues can also
> change this understanding.
>
> 4. From my company side, we do have TPCH and TPCDS benchmarks that I do
> share with customers. But such benchmarks are flawed because they come from
> the world of traditional warehousing where the competition was among
> general purpose query engines. For example, our tests show that at higher
> and higher data scale, Drill beats Impala on these benchmarks. The same is
> touted by the Hive LLAP folks as well. But they do not necessarily imply
> that it is the best tool choice for the production environment. It is a
> reason why I am resistant getting into the war of the query engines in
> which every query engine beats the other under a given set of primed
> conditions.
>
> 5. It is an absolute most that you understand the query patterns that the
> system will have to withstand with the data characteristics specific to
> your use case. I would only trust that. Big data systems are going to be
> application specific and will require tuning. Which also means that you
> have to revisit the kinds of analytics you would like your end users to
> have. Which again raises the question-what kinds of analytics truly
> generate value for the BI user?
>
> Best,
> Saurabh
>
> On Wed, Oct 18, 2017 at 10:26 PM, PROJJWAL SAHA <pr...@gmail.com>
> wrote:
>
> > Hi,
> >
> > Is there any public performance benchmark that users have achieved using
> > Drill in production scenarios ? It would be useful if someone can pass me
> > any links for customer user stories.
> >
> > Regards
> >
>
Re: Benchmark numbers using Drill
Posted by Saurabh Mahapatra <sa...@gmail.com>.
Thanks Divya. Why dont you go ahead and create a JIRA (with the above info)
for this and assign it to Bridget Bevens.
I can create one but I would rather have someone from the community ask for
it.
Best,
Saurabh
On Mon, Oct 23, 2017 at 7:14 PM, Divya Gehlot <di...@gmail.com>
wrote:
> Yes a very good info which helps a lots of ppl like me who is using Drill
> as one of their production environment
> cant we share this information as recommendation to Dril users on the
> Apache Drill KB ?
>
> On 20 October 2017 at 01:58, Saurabh Mahapatra <
> saurabhmahapatra94@gmail.com
> > wrote:
>
> > I do not think you will get such information about benchmarks from
> > customers on production workloads. But from the customers I have worked
> > with who have taken Drill to production, here is some information that
> may
> > be of use to you:
> >
> > 1. The trend universally has been to use beefier machines for in-memory
> > query engines. We see 256GB RAM and 32 cores as the most frequent
> > configuration. On the network side, it is 2x10GbE.
> >
> > 2. The most commonly sized dedicated cluster for starting out with Drill
> in
> > production has been around 16-20 nodes with the above configuration. I
> have
> > several customers who have deployed this on 200+ nodes as well but in
> those
> > scenarios, it is a service among many.
> >
> > 3. The concurrency we see in the above settings is a function of the size
> > of the dataset and the complexity of the customer query. In general,
> > Little's law holds. The smaller the chunk of work is to be processed, the
> > faster will be the throughput. Our understanding of this changes further
> > with the new releases of Drill where spill to disk features will make it
> > more of a pessimistic execution engine. Also, the use of queues can also
> > change this understanding.
> >
> > 4. From my company side, we do have TPCH and TPCDS benchmarks that I do
> > share with customers. But such benchmarks are flawed because they come
> from
> > the world of traditional warehousing where the competition was among
> > general purpose query engines. For example, our tests show that at higher
> > and higher data scale, Drill beats Impala on these benchmarks. The same
> is
> > touted by the Hive LLAP folks as well. But they do not necessarily imply
> > that it is the best tool choice for the production environment. It is a
> > reason why I am resistant getting into the war of the query engines in
> > which every query engine beats the other under a given set of primed
> > conditions.
> >
> > 5. It is an absolute most that you understand the query patterns that the
> > system will have to withstand with the data characteristics specific to
> > your use case. I would only trust that. Big data systems are going to be
> > application specific and will require tuning. Which also means that you
> > have to revisit the kinds of analytics you would like your end users to
> > have. Which again raises the question-what kinds of analytics truly
> > generate value for the BI user?
> >
> > Best,
> > Saurabh
> >
> > On Wed, Oct 18, 2017 at 10:26 PM, PROJJWAL SAHA <pr...@gmail.com>
> > wrote:
> >
> > > Hi,
> > >
> > > Is there any public performance benchmark that users have achieved
> using
> > > Drill in production scenarios ? It would be useful if someone can pass
> me
> > > any links for customer user stories.
> > >
> > > Regards
> > >
> >
>
Re: Benchmark numbers using Drill
Posted by Divya Gehlot <di...@gmail.com>.
Yes a very good info which helps a lots of ppl like me who is using Drill
as one of their production environment
cant we share this information as recommendation to Dril users on the
Apache Drill KB ?
On 20 October 2017 at 01:58, Saurabh Mahapatra <saurabhmahapatra94@gmail.com
> wrote:
> I do not think you will get such information about benchmarks from
> customers on production workloads. But from the customers I have worked
> with who have taken Drill to production, here is some information that may
> be of use to you:
>
> 1. The trend universally has been to use beefier machines for in-memory
> query engines. We see 256GB RAM and 32 cores as the most frequent
> configuration. On the network side, it is 2x10GbE.
>
> 2. The most commonly sized dedicated cluster for starting out with Drill in
> production has been around 16-20 nodes with the above configuration. I have
> several customers who have deployed this on 200+ nodes as well but in those
> scenarios, it is a service among many.
>
> 3. The concurrency we see in the above settings is a function of the size
> of the dataset and the complexity of the customer query. In general,
> Little's law holds. The smaller the chunk of work is to be processed, the
> faster will be the throughput. Our understanding of this changes further
> with the new releases of Drill where spill to disk features will make it
> more of a pessimistic execution engine. Also, the use of queues can also
> change this understanding.
>
> 4. From my company side, we do have TPCH and TPCDS benchmarks that I do
> share with customers. But such benchmarks are flawed because they come from
> the world of traditional warehousing where the competition was among
> general purpose query engines. For example, our tests show that at higher
> and higher data scale, Drill beats Impala on these benchmarks. The same is
> touted by the Hive LLAP folks as well. But they do not necessarily imply
> that it is the best tool choice for the production environment. It is a
> reason why I am resistant getting into the war of the query engines in
> which every query engine beats the other under a given set of primed
> conditions.
>
> 5. It is an absolute most that you understand the query patterns that the
> system will have to withstand with the data characteristics specific to
> your use case. I would only trust that. Big data systems are going to be
> application specific and will require tuning. Which also means that you
> have to revisit the kinds of analytics you would like your end users to
> have. Which again raises the question-what kinds of analytics truly
> generate value for the BI user?
>
> Best,
> Saurabh
>
> On Wed, Oct 18, 2017 at 10:26 PM, PROJJWAL SAHA <pr...@gmail.com>
> wrote:
>
> > Hi,
> >
> > Is there any public performance benchmark that users have achieved using
> > Drill in production scenarios ? It would be useful if someone can pass me
> > any links for customer user stories.
> >
> > Regards
> >
>
Re: Benchmark numbers using Drill
Posted by Saurabh Mahapatra <sa...@gmail.com>.
I do not think you will get such information about benchmarks from
customers on production workloads. But from the customers I have worked
with who have taken Drill to production, here is some information that may
be of use to you:
1. The trend universally has been to use beefier machines for in-memory
query engines. We see 256GB RAM and 32 cores as the most frequent
configuration. On the network side, it is 2x10GbE.
2. The most commonly sized dedicated cluster for starting out with Drill in
production has been around 16-20 nodes with the above configuration. I have
several customers who have deployed this on 200+ nodes as well but in those
scenarios, it is a service among many.
3. The concurrency we see in the above settings is a function of the size
of the dataset and the complexity of the customer query. In general,
Little's law holds. The smaller the chunk of work is to be processed, the
faster will be the throughput. Our understanding of this changes further
with the new releases of Drill where spill to disk features will make it
more of a pessimistic execution engine. Also, the use of queues can also
change this understanding.
4. From my company side, we do have TPCH and TPCDS benchmarks that I do
share with customers. But such benchmarks are flawed because they come from
the world of traditional warehousing where the competition was among
general purpose query engines. For example, our tests show that at higher
and higher data scale, Drill beats Impala on these benchmarks. The same is
touted by the Hive LLAP folks as well. But they do not necessarily imply
that it is the best tool choice for the production environment. It is a
reason why I am resistant getting into the war of the query engines in
which every query engine beats the other under a given set of primed
conditions.
5. It is an absolute most that you understand the query patterns that the
system will have to withstand with the data characteristics specific to
your use case. I would only trust that. Big data systems are going to be
application specific and will require tuning. Which also means that you
have to revisit the kinds of analytics you would like your end users to
have. Which again raises the question-what kinds of analytics truly
generate value for the BI user?
Best,
Saurabh
On Wed, Oct 18, 2017 at 10:26 PM, PROJJWAL SAHA <pr...@gmail.com> wrote:
> Hi,
>
> Is there any public performance benchmark that users have achieved using
> Drill in production scenarios ? It would be useful if someone can pass me
> any links for customer user stories.
>
> Regards
>