Request Review for [SPARK-46992]Fix cache consistency

[Jay Han]

Hi community,
 I've fixed the issue about consistency of cache: SPARK-46992
 long
time ago. I'll appreciate if someone could help review this pr!


-- 
Best,
Jay

Re: A proposal for creating a Knowledge Sharing Hub for Apache Spark Community

[Jay Han]

+1. It sounds awesome!

Kiran Kumar Dusi  于2024年3月21日周四 14:16写道：

> +1
>
> On Thu, 21 Mar 2024 at 7:46 AM, Farshid Ashouri <
> farsheed.asho...@gmail.com> wrote:
>
>> +1
>>
>> On Mon, 18 Mar 2024, 11:00 Mich Talebzadeh, 
>> wrote:
>>
>>> Some of you may be aware that Databricks community Home | Databricks
>>> have just launched a knowledge sharing hub. I thought it would be a
>>> good idea for the Apache Spark user group to have the same, especially
>>> for repeat questions on Spark core, Spark SQL, Spark Structured
>>> Streaming, Spark Mlib and so forth.
>>>
>>> Apache Spark user and dev groups have been around for a good while.
>>> They are serving their purpose . We went through creating a slack
>>> community that managed to create more more heat than light.. This is
>>> what Databricks community came up with and I quote
>>>
>>> "Knowledge Sharing Hub
>>> Dive into a collaborative space where members like YOU can exchange
>>> knowledge, tips, and best practices. Join the conversation today and
>>> unlock a wealth of collective wisdom to enhance your experience and
>>> drive success."
>>>
>>> I don't know the logistics of setting it up.but I am sure that should
>>> not be that difficult. If anyone is supportive of this proposal, let
>>> the usual +1, 0, -1 decide
>>>
>>> HTH
>>>
>>> Mich Talebzadeh,
>>> Dad | Technologist | Solutions Architect | Engineer
>>> London
>>> United Kingdom
>>>
>>>
>>>view my Linkedin profile
>>>
>>>
>>>  https://en.everybodywiki.com/Mich_Talebzadeh
>>>
>>>
>>>
>>> Disclaimer: The information provided is correct to the best of my
>>> knowledge but of course cannot be guaranteed . It is essential to note
>>> that, as with any advice, quote "one test result is worth one-thousand
>>> expert opinions (Werner Von Braun)".
>>>
>>> -
>>> To unsubscribe e-mail: user-unsubscr...@spark.apache.org
>>>
>>>

Re: [Spark-Core] Improving Reliability of spark when Executors OOM

[Jay Han]

Hi,
what about supporting for solving the disk space problem of "device space
isn't enough"? I think it's same as OOM exception.

kalyan  于2024年1月27日周六 13:00写道：

> Hi all,
>
> Sorry for the delay in getting the first draft of (my first) SPIP out.
>
> https://docs.google.com/document/d/1hxEPUirf3eYwNfMOmUHpuI5dIt_HJErCdo7_yr9htQc/edit?pli=1
>
> Let me know what you think.
>
> Regards
> kalyan.
>
> On Sat, Jan 20, 2024 at 8:19 AM Ashish Singh  wrote:
>
>> Hey all,
>>
>> Thanks for this discussion, the timing of this couldn't be better!
>>
>> At Pinterest, we recently started to look into reducing OOM failures
>> while also reducing memory consumption of spark applications. We considered
>> the following options.
>> 1. Changing core count on executor to change memory available per task in
>> the executor.
>> 2. Changing resource profile based on task failures and gc metrics to
>> grow or shrink executor memory size. We do this at application level based
>> on the app's past runs today.
>> 3. K8s vertical pod autoscaler
>> 
>>
>> Internally, we are mostly getting aligned on option 2. We would love to
>> make this happen and are looking forward to the SPIP.
>>
>>
>> On Wed, Jan 17, 2024 at 9:34 AM Mridul Muralidharan 
>> wrote:
>>
>>>
>>> Hi,
>>>
>>>   We are internally exploring adding support for dynamically changing
>>> the resource profile of a stage based on runtime characteristics.
>>> This includes failures due to OOM and the like, slowness due to
>>> excessive GC, resource wastage due to excessive overprovisioning, etc.
>>> Essentially handles scale up and scale down of resources.
>>> Instead of baking these into the scheduler directly (which is already
>>> complex), we are modeling it as a plugin - so that the 'business logic' of
>>> how to handle task events and mutate state is pluggable.
>>>
>>> The main limitation I find with mutating only the cores is the limits it
>>> places on what kind of problems can be solved with it - and mutating
>>> resource profiles is a much more natural way to handle this
>>> (spark.task.cpus predates RP).
>>>
>>> Regards,
>>> Mridul
>>>
>>> On Wed, Jan 17, 2024 at 9:18 AM Tom Graves 
>>> wrote:
>>>
 It is interesting. I think there are definitely some discussion points
 around this.  reliability vs performance is always a trade off and its
 great it doesn't fail but if it doesn't meet someone's SLA now that could
 be as bad if its hard to figure out why.   I think if something like this
 kicks in, it needs to be very obvious to the user so they can see that it
 occurred.  Do you have something in place on UI or something that indicates
 this? The nice thing is also you aren't wasting memory by increasing it for
 all tasks when maybe you only need it for one or two.  The downside is you
 are only finding out after failure.

 I do also worry a little bit that in your blog post, the error you
 pointed out isn't a java OOM but an off heap memory issue (overhead + heap
 usage).  You don't really address heap memory vs off heap in that article.
 Only thing I see mentioned is spark.executor.memory which is heap memory.
 Obviously adjusting to only run one task is going to give that task more
 overall memory but the reasons its running out in the first place could be
 different.  If it was on heap memory for instance with more tasks I would
 expect to see more GC and not executor OOM.  If you are getting executor
 OOM you are likely using more off heap memory/stack space, etc then you
 allocated.   Ultimately it would be nice to know why that is happening and
 see if we can address it to not fail in the first place.  That could be
 extremely difficult though, especially if using software outside Spark that
 is using that memory.

 As Holden said,  we need to make sure this would play nice with the
 resource profiles, or potentially if we can use the resource profile
 functionality.  Theoretically you could extend this to try to get new
 executor if using dynamic allocation for instance.

 I agree doing a SPIP would be a good place to start to have more
 discussions.

 Tom

 On Wednesday, January 17, 2024 at 12:47:51 AM CST, kalyan <
 justfors...@gmail.com> wrote:


 Hello All,

 At Uber, we had recently, done some work on improving the reliability
 of spark applications in scenarios of fatter executors going out of memory
 and leading to application failure. Fatter executors are those that have
 more than 1 task running on it at a given time concurrently. This has
 significantly improved the reliability of many spark applications for us at
 Uber. We made a blog about this recently. Link:
 https://www.uber.com/en-US/blog/dynamic-executor-core-resizing-in-spark/

 At a high level, we have done the below