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From ayan guha <guha.a...@gmail.com>
Subject Re: Spark or Storm
Date Wed, 17 Jun 2015 14:13:14 GMT
Thanks for this. It's kcl based kinesis application. But because its just a
Java application we are thinking to use spark on EMR or storm for fault
tolerance and load balancing. Is it a correct approach?
On 17 Jun 2015 23:07, "Enno Shioji" <eshioji@gmail.com> wrote:

> Hi Ayan,
>
> Admittedly I haven't done much with Kinesis, but if I'm not mistaken you
> should be able to use their "processor" interface for that. In this
> example, it's incrementing a counter:
> https://github.com/awslabs/amazon-kinesis-data-visualization-sample/blob/master/src/main/java/com/amazonaws/services/kinesis/samples/datavis/kcl/CountingRecordProcessor.java
>
> Instead of incrementing a counter, you could do your transformation and
> send it to HBase.
>
>
>
>
>
>
> On Wed, Jun 17, 2015 at 1:40 PM, ayan guha <guha.ayan@gmail.com> wrote:
>
>> Great discussion!!
>>
>> One qs about some comment: 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
>>
>> - Do you mean KCL application? Or some kind of processing withinKineis?
>>
>> Can you kindly share a link? I would definitely pursue this route as our
>> transformations are really simple.
>>
>> Best
>>
>> On Wed, Jun 17, 2015 at 10:26 PM, Ashish Soni <asoni.learn@gmail.com>
>> wrote:
>>
>>> My Use case is below
>>>
>>> We are going to receive lot of event as stream ( basically Kafka Stream
>>> ) and then we need to process and compute
>>>
>>> Consider you have a phone contract with ATT and every call / sms / data
>>> useage you do is an event and then it needs  to calculate your bill on real
>>> time basis so when you login to your account you can see all those variable
>>> as how much you used and how much is left and what is your bill till date
>>> ,Also there are different rules which need to be considered when you
>>> calculate the total bill one simple rule will be 0-500 min it is free but
>>> above it is $1 a min.
>>>
>>> How do i maintain a shared state  ( total amount , total min , total
>>> data etc ) so that i know how much i accumulated at any given point as
>>> events for same phone can go to any node / executor.
>>>
>>> Can some one please tell me how can i achieve this is spark as in storm
>>> i can have a bolt which can do this ?
>>>
>>> Thanks,
>>>
>>>
>>>
>>> On Wed, Jun 17, 2015 at 4:52 AM, Enno Shioji <eshioji@gmail.com> wrote:
>>>
>>>> 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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>>>>>
>>>>>
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>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>
>>>
>>
>>
>> --
>> Best Regards,
>> Ayan Guha
>>
>
>

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