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Posted to user@spark.apache.org by "Afshin, Bardia" <Ba...@capitalone.com> on 2017/04/24 17:07:07 UTC
community feedback on RedShift with Spark
I wanted to reach out to the community to get a understanding of what everyones experience is in regardst to maximizing performance as in decreasing load time on loading multiple large datasets to RedShift.
Two approaches:
1. Spark writes file to S3, RedShift COPY INTO from S3 bucket.
2. Spark directly writes results to RedShfit via JDBC
JDBC is known for poor performance, and RedShift (wihtout any provided examples) claims you can speed up loading from s3 buckets via different queues set up in your RedShift Workload Management.
What’s the communities experience with desiging processes which large datasets are needed to be pushed into RedShfit and doing it in minimal time taken to load the data to RedShift?
________________________________________________________
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Re: community feedback on RedShift with Spark
Posted by Aakash Basu <aa...@gmail.com>.
Hey afshin,
Your point 1 is innumerably faster than the latter.
It further shoots up the speed if you know how to properly use distKey and
sortKey on the tables being loaded.
Thanks,
Aakash.
https://www.linkedin.com/in/aakash-basu-5278b363
On 24-Apr-2017 10:37 PM, "Afshin, Bardia" <Ba...@capitalone.com>
wrote:
I wanted to reach out to the community to get a understanding of what
everyones experience is in regardst to maximizing performance as in
decreasing load time on loading multiple large datasets to RedShift.
Two approaches:
1. Spark writes file to S3, RedShift COPY INTO from S3 bucket.
2. Spark directly writes results to RedShfit via JDBC
JDBC is known for poor performance, and RedShift (wihtout any provided
examples) claims you can speed up loading from s3 buckets via different
queues set up in your RedShift Workload Management.
What’s the communities experience with desiging processes which large
datasets are needed to be pushed into RedShfit and doing it in minimal time
taken to load the data to RedShift?
------------------------------
The information contained in this e-mail is confidential and/or proprietary
to Capital One and/or its affiliates and may only be used solely in
performance of work or services for Capital One. The information
transmitted herewith is intended only for use by the individual or entity
to which it is addressed. If the reader of this message is not the intended
recipient, you are hereby notified that any review, retransmission,
dissemination, distribution, copying or other use of, or taking of any
action in reliance upon this information is strictly prohibited. If you
have received this communication in error, please contact the sender and
delete the material from your computer.
Re: community feedback on RedShift with Spark
Posted by Matt Deaver <ma...@gmail.com>.
Redshift COPY is immensely faster than trying to do insert statements. I
did some rough testing of inserting data using INSERT and COPY and COPY is
vastly superior to the point that if speed is at all an issue to your
process you shouldn't even consider using INSERT.
On Mon, Apr 24, 2017 at 11:07 AM, Afshin, Bardia <
Bardia.Afshin@capitalone.com> wrote:
> I wanted to reach out to the community to get a understanding of what
> everyones experience is in regardst to maximizing performance as in
> decreasing load time on loading multiple large datasets to RedShift.
>
>
>
> Two approaches:
>
> 1. Spark writes file to S3, RedShift COPY INTO from S3 bucket.
>
> 2. Spark directly writes results to RedShfit via JDBC
>
>
>
> JDBC is known for poor performance, and RedShift (wihtout any provided
> examples) claims you can speed up loading from s3 buckets via different
> queues set up in your RedShift Workload Management.
>
>
>
> What’s the communities experience with desiging processes which large
> datasets are needed to be pushed into RedShfit and doing it in minimal time
> taken to load the data to RedShift?
>
> ------------------------------
>
> The information contained in this e-mail is confidential and/or
> proprietary to Capital One and/or its affiliates and may only be used
> solely in performance of work or services for Capital One. The information
> transmitted herewith is intended only for use by the individual or entity
> to which it is addressed. If the reader of this message is not the intended
> recipient, you are hereby notified that any review, retransmission,
> dissemination, distribution, copying or other use of, or taking of any
> action in reliance upon this information is strictly prohibited. If you
> have received this communication in error, please contact the sender and
> delete the material from your computer.
>
--
Regards,
Matt
Data Engineer
https://www.linkedin.com/in/mdeaver
http://mattdeav.pythonanywhere.com/