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From Mich Talebzadeh <mich.talebza...@gmail.com>
Subject Re: Bechmarks on Spark running on Yarn versus Spark on K8s
Date Mon, 05 Jul 2021 19:37:03 GMT
Thanks Aditya for the link. I will have a look.

Cheers


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On Mon, 5 Jul 2021 at 20:27, Madaditya .Maddy <w47sneakin@gmail.com> wrote:

> I came across an article that benchmarked spark on k8s vs yarn by
> Datamechanics.
>
> Link :
> https://www.datamechanics.co/blog-post/apache-spark-performance-benchmarks-show-kubernetes-has-caught-up-with-yarn
>
> -Regards
> Aditya
>
> On Mon, Jul 5, 2021, 23:49 Mich Talebzadeh <mich.talebzadeh@gmail.com>
> wrote:
>
>> Thanks Yuri. Those are very valid points.
>>
>> Let me clarify my point. Let us assume that we will be using Yarn versus
>> K8s doing the same job. Spark-submit will use Yarn at first instance and
>> will then switch to using k8s for the same task.
>>
>>
>>    1. Have there been such benchmarks?
>>    2. When should I choose PaaS versus k8s for example for small to
>>    medium size jobs
>>    3. I can see the flexibility of running Spark on Dataproc, then some
>>    may argue that k8s are the way forward
>>    4. Bear in mind that I am only considering Spark. For example for
>>    Kafka and Zookeeper we opt for dockers as they do a single function.
>>
>>
>> Cheers,
>>
>> Mich
>>
>>
>>    view my Linkedin profile
>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>
>>
>>
>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>> any loss, damage or destruction of data or any other property which may
>> arise from relying on this email's technical content is explicitly
>> disclaimed. The author will in no case be liable for any monetary damages
>> arising from such loss, damage or destruction.
>>
>>
>>
>>
>> ‪On Mon, 5 Jul 2021 at 19:06, ‫"Yuri Oleynikov (‫יורי אולייניקוב‬‎)"‬‎
<
>> yurkao@gmail.com> wrote:‬
>>
>>> Not a big expert on Spark, but I’m not really understand how you are
>>> going to compare and what? Reading-writing to and from Hdfs? How does it
>>> related to yarn and k8s… these are recourse managers (YARN yet another
>>> resource manager) : what and how much to allocate and when… (cpu, ram).
>>> Local Disk spilling? Depends on disk throughput…
>>> So what you are going to measure?
>>>
>>>
>>>
>>>
>>> Best regards
>>>
>>> On 5 Jul 2021, at 20:43, Mich Talebzadeh <mich.talebzadeh@gmail.com>
>>> wrote:
>>>
>>> 
>>>
>>> I was curious to know if there are benchmarks around on comparison
>>> between Spark on Yarn compared to Kubernetes.
>>>
>>>
>>> This question arose because traditionally in Google Cloud we have been
>>> using Spark on Dataproc clusters. Dataproc
>>> <https://cloud.google.com/dataproc>  provides Spark, Hadoop plus others
>>> (optional install) for data and analytic processing. It is PaaS
>>>
>>>
>>> Now they have GKE clusters as well and also introduced Apache Spark
>>> with Cloud Dataproc on Kubernetes
>>> <https://cloud.google.com/blog/products/data-analytics/modernize-apache-spark-with-cloud-dataproc-on-kubernetes>
which
>>> allows one to submit Spark jobs to k8s using Dataproc stub as a platform to
>>> submit the job as below from cloud console or local
>>>
>>>
>>> gcloud dataproc jobs submit pyspark --cluster="dataproc-for-gke"
>>> gs://bucket/testme.py --region="europe-west2" --py-files
>>> gs://bucket/DSBQ.zip
>>> Job [e5fc19b62cf744f0b13f3e6d9cc66c19] submitted.
>>> Waiting for job output...
>>>
>>>
>>> At the moment it is a struggle to see what merits using k8s instead of
>>> dataproc bar notebooks etc. Actually there is not much literature around
>>> with PySpark on k8s.
>>>
>>>
>>> For me Spark on bare metal is the preferred option as I cannot see how
>>> one can pigeon hole Spark into a container and make it performant but I may
>>> be totally wrong.
>>>
>>>
>>> Thanks
>>>
>>>
>>>    view my Linkedin profile
>>> <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/>
>>>
>>>
>>>
>>> *Disclaimer:* Use it at your own risk. Any and all responsibility for
>>> any loss, damage or destruction of data or any other property which may
>>> arise from relying on this email's technical content is explicitly
>>> disclaimed. The author will in no case be liable for any monetary damages
>>> arising from such loss, damage or destruction.
>>>
>>>
>>>
>>>

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