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From Enno Shioji <eshi...@gmail.com>
Subject Re: Spark or Storm
Date Wed, 17 Jun 2015 08:52:02 GMT
I guess both. In terms of syntax, I was comparing it with Trident.

If you are joining, Spark Streaming actually does offer windowed join out
of the box. We couldn't use this though as our event stream can grow
"out-of-sync", so we had to implement something on top of Storm. If your
event streams don't become out of sync, you may find the built-in join in
Spark Streaming useful. Storm also has a join keyword but its semantics are
different.


> Also, what do you mean by "No Back Pressure" ?

So when a topology is overloaded, Storm is designed so that it will stop
reading from the source. Spark on the other hand, will keep reading from
the source and spilling it internally. This maybe fine, in fairness, but it
does mean you have to worry about the persistent store usage in the
processing cluster, whereas with Storm you don't have to worry because the
messages just remain in the data store.

Spark came up with the idea of rate limiting, but I don't feel this is as
nice as back pressure because it's very difficult to tune it such that you
don't cap the cluster's processing power but yet so that it will prevent
the persistent storage to get used up.


On Wed, Jun 17, 2015 at 9:33 AM, Spark Enthusiast <sparkenthusiast@yahoo.in>
wrote:

> When you say Storm, did you mean Storm with Trident or Storm?
>
> My use case does not have simple transformation. There are complex events
> that need to be generated by joining the incoming event stream.
>
> Also, what do you mean by "No Back PRessure" ?
>
>
>
>
>
>   On Wednesday, 17 June 2015 11:57 AM, Enno Shioji <eshioji@gmail.com>
> wrote:
>
>
> We've evaluated Spark Streaming vs. Storm and ended up sticking with Storm.
>
> Some of the important draw backs are:
> Spark has no back pressure (receiver rate limit can alleviate this to a
> certain point, but it's far from ideal)
> There is also no exactly-once semantics. (updateStateByKey can achieve
> this semantics, but is not practical if you have any significant amount of
> state because it does so by dumping the entire state on every checkpointing)
>
> There are also some minor drawbacks that I'm sure will be fixed quickly,
> like no task timeout, not being able to read from Kafka using multiple
> nodes, data loss hazard with Kafka.
>
> It's also not possible to attain very low latency in Spark, if that's what
> you need.
>
> The pos for Spark is the concise and IMO more intuitive syntax, especially
> if you compare it with Storm's Java API.
>
> I admit I might be a bit biased towards Storm tho as I'm more familiar
> with it.
>
> Also, you can do some processing with Kinesis. If all you need to do is
> straight forward transformation and you are reading from Kinesis to begin
> with, it might be an easier option to just do the transformation in Kinesis.
>
>
>
>
>
> On Wed, Jun 17, 2015 at 7:15 AM, Sabarish Sasidharan <
> sabarish.sasidharan@manthan.com> wrote:
>
> Whatever you write in bolts would be the logic you want to apply on your
> events. In Spark, that logic would be coded in map() or similar such
> transformations and/or actions. Spark doesn't enforce a structure for
> capturing your processing logic like Storm does.
> Regards
> Sab
> Probably overloading the question a bit.
>
> In Storm, Bolts have the functionality of getting triggered on events. Is
> that kind of functionality possible with Spark streaming? During each phase
> of the data processing, the transformed data is stored to the database and
> this transformed data should then be sent to a new pipeline for further
> processing
>
> How can this be achieved using Spark?
>
>
>
> On Wed, Jun 17, 2015 at 10:10 AM, Spark Enthusiast <
> sparkenthusiast@yahoo.in> wrote:
>
> I have a use-case where a stream of Incoming events have to be aggregated
> and joined to create Complex events. The aggregation will have to happen at
> an interval of 1 minute (or less).
>
> The pipeline is :
>                                   send events
>              enrich event
> Upstream services -------------------> KAFKA ---------> event Stream
> Processor ------------> Complex Event Processor ------------> Elastic
> Search.
>
> From what I understand, Storm will make a very good ESP and Spark
> Streaming will make a good CEP.
>
> But, we are also evaluating Storm with Trident.
>
> How does Spark Streaming compare with Storm with Trident?
>
> Sridhar Chellappa
>
>
>
>
>
>
>
>   On Wednesday, 17 June 2015 10:02 AM, ayan guha <guha.ayan@gmail.com>
> wrote:
>
>
> I have a similar scenario where we need to bring data from kinesis to
> hbase. Data volecity is 20k per 10 mins. Little manipulation of data will
> be required but that's regardless of the tool so we will be writing that
> piece in Java pojo.
> All env is on aws. Hbase is on a long running EMR and kinesis on a
> separate cluster.
> TIA.
> Best
> Ayan
> On 17 Jun 2015 12:13, "Will Briggs" <wrbriggs@gmail.com> wrote:
>
> The programming models for the two frameworks are conceptually rather
> different; I haven't worked with Storm for quite some time, but based on my
> old experience with it, I would equate Spark Streaming more with Storm's
> Trident API, rather than with the raw Bolt API. Even then, there are
> significant differences, but it's a bit closer.
>
> If you can share your use case, we might be able to provide better
> guidance.
>
> Regards,
> Will
>
> On June 16, 2015, at 9:46 PM, asoni.learn@gmail.com wrote:
>
> Hi All,
>
> I am evaluating spark VS storm ( spark streaming  ) and i am not able to
> see what is equivalent of Bolt in storm inside spark.
>
> Any help will be appreciated on this ?
>
> Thanks ,
> Ashish
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