Be careful with Flink.  IME it's got a long way to go.  The core model is 
fantastic but there's a lot of low-hanging fruit that needs to be fixed.

On Tuesday, May 2, 2017 at 4:03:12 PM UTC-6, Evan Chan wrote:
>
> Hi Shiva,
>
> Spark will likely be too high latency for you.  Practical minimal batch 
> size is a couple seconds.
>
> Think of Akka as a best fit for if you want to deploy individual apps each 
> reading from some set of fixed Kafka partitions.  Each one could then write 
> to HDFS.  However you would need to handle failover, state, etc. etc.
> It is likely that Flink has much more built in for you - HDFS integration, 
> checkpointing, failover, shuffling/routing of messages, integration with 
> Kafka, etc.
>
> You might want to look into Intel Gearpump - this is Akka-based very low 
> latency dynamic stream processing, and they have handled distribution 
> already.
>
> -Evan
>
> On Friday, April 28, 2017 at 7:52:50 AM UTC-7, Shivakumar Ramagopal wrote:
>>
>> Viktor,
>>
>>
>> On Fri, Apr 28, 2017 at 5:03 PM, Viktor Klang <viktor...@gmail.com> 
>> wrote:
>>
>>>
>>>
>>> On Fri, Apr 28, 2017 at 1:12 PM, Shiva Ramagopal <tr....@gmail.com> 
>>> wrote:
>>>
>>>> Hi Viktor,
>>>>
>>>> On Fri, Apr 28, 2017 at 2:55 PM, Viktor Klang <viktor...@gmail.com> 
>>>> wrote:
>>>>
>>>>> Hi Shiva,
>>>>>
>>>>> On Fri, Apr 28, 2017 at 11:20 AM, Shiva Ramagopal <tr....@gmail.com> 
>>>>> wrote:
>>>>>
>>>>>> I'm looking to compare Kafka Streams vs Akka Streams in two areas: 
>>>>>>
>>>>>> 1. For ingesting between Kafka and HDFS/RDBMS 
>>>>>>
>>>>>> Requirements are mainly around performance and latency. A Kafka topic 
>>>>>> can have several million events, each corresponding to a database change 
>>>>>> capture. When ingesting this topic into HDFS I'm also looking to 
>>>>>> partition 
>>>>>> the data by day, typically based on a timestamp field in the event 
>>>>>> record, 
>>>>>> aggregations on-the-fly (say by a userid field) and parquetizing 
>>>>>> (preferably on the fly to optimize performance by reducing two I/O 
>>>>>> operations).
>>>>>>
>>>>>
>>>>> Looking forward to see your benchmark!
>>>>>
>>>>
>>>> Hey, you wanted requirements! :) 
>>>>
>>>>>  
>>>>>
>>>>>>
>>>>>> 2. Low-latency processing
>>>>>>
>>>>>> Experiences around performances of Storm/Flink and Akka Streams would 
>>>>>> be *very* nice. Typical use-cases are de-duping and enrichment with 
>>>>>> metrics 
>>>>>> computation (# duplicate events/records, aggregate metrics etc). Low 
>>>>>> latency and scalability are main considerations
>>>>>>
>>>>>
>>>>> Low latency is not a metric, and scalability is not a profile. :)
>>>>> In other words: What latency distribution are you targeting and what 
>>>>> scalability curve?
>>>>>
>>>>
>>>> I know latency is a nuanced topic. I'm just looking for broad 
>>>> experiences on performance comparisons if anyone has done that. Currently 
>>>> we have a Storm topology over 5 nodes doing enrichment of events from 
>>>> Kafka 
>>>> that involves a lookup into a db per event. 90th percentile of latency of 
>>>> this processing is under 200 ms and we are happy with this. While Storm is 
>>>> mature, Akka Streams seems more general purpose than Storm. I'd like to 
>>>> use 
>>>> Akka Streams for this reason if performance is comparable to Storm. 
>>>>
>>>
>>> There's one important architectural difference here tho: Akka Streams 
>>> are local-only (as in materialization). You can of course materialize Akka 
>>> Streams on multiple nodes and use a transport to coordinate data 
>>> processing. Interestingly it doesn't lock you in to a particular backend 
>>> such that Kafka Streams or even Storm would do.
>>>
>>
>> The materialization part was something I was not aware of. Thanks for 
>> pointing it out, really appreciate it.
>>
>>>
>>> All of this boils down to requirements. Something like Flink or Google 
>>> Beam could be viable options here as well. In your situation I'd look at 
>>> the requirements and make a couple of prototypes before picking a winner.
>>>
>>
>> I was also looking for something to unify batch and streaming - which is 
>> how I came to look at SMACK. I'd really like to keep the tech stack small 
>> and have the parts integrate tightly with each other. Guess I have to pick 
>> between Spark, Flink, Beam and Storm. 
>>
>>>  
>>>
>>>>  
>>>>
>>>>>  
>>>>>
>>>>>>
>>>>>> On Thu, Apr 27, 2017 at 8:31 PM, Viktor Klang <viktor...@gmail.com> 
>>>>>> wrote:
>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> On Thu, Apr 27, 2017 at 10:39 AM, Shiva Ramagopal <tr....@gmail.com> 
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> I have read through multiple articles describing the SMACK stack 
>>>>>>>> but I'm having difficulty understanding the role of Akka in the stack. 
>>>>>>>> How 
>>>>>>>> does Akka fit in?
>>>>>>>>
>>>>>>>
>>>>>>> Akka is for building the application itself.
>>>>>>>  
>>>>>>>
>>>>>>>>
>>>>>>>> Also I would like to know of experiences in using Akka Streams vs 
>>>>>>>> Kafka Connect for ingesting from Kafka into HDFS (Hive) and RDBMS. Has 
>>>>>>>> anyone used Akka Streams for say, dynamic partitioning of events from 
>>>>>>>> a 
>>>>>>>> Kafka topic into HDFS?
>>>>>>>>
>>>>>>>
>>>>>>> Based on what requirements?
>>>>>>>  
>>>>>>>
>>>>>>>>
>>>>>>>> TIA
>>>>>>>> -Shiva
>>>>>>>>
>>>>>>>> -- 
>>>>>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>>>>> >>>>>>>>>> Check the FAQ: 
>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>>> >>>>>>>>>> Search the archives: 
>>>>>>>> https://groups.google.com/group/akka-user
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>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> -- 
>>>>>>> Cheers,
>>>>>>> √
>>>>>>>
>>>>>>> -- 
>>>>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>>>> >>>>>>>>>> Check the FAQ: 
>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>> >>>>>>>>>> Search the archives: 
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>>>>>>>
>>>>>>
>>>>>> -- 
>>>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>>> >>>>>>>>>> Check the FAQ: 
>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>> >>>>>>>>>> Search the archives: 
>>>>>> https://groups.google.com/group/akka-user
>>>>>> --- 
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>>>>>> For more options, visit https://groups.google.com/d/optout.
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>> -- 
>>>>> Cheers,
>>>>> √
>>>>>
>>>>> -- 
>>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>>> >>>>>>>>>> Check the FAQ: 
>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>> >>>>>>>>>> Search the archives: 
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>>>>>
>>>>
>>>> -- 
>>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>>> >>>>>>>>>> Check the FAQ: 
>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>> >>>>>>>>>> Search the archives: 
>>>> https://groups.google.com/group/akka-user
>>>> --- 
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>>>> For more options, visit https://groups.google.com/d/optout.
>>>>
>>>
>>>
>>>
>>> -- 
>>> Cheers,
>>> √
>>>
>>> -- 
>>> >>>>>>>>>> Read the docs: http://akka.io/docs/
>>> >>>>>>>>>> Check the FAQ: 
>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>> >>>>>>>>>> Search the archives: 
>>> https://groups.google.com/group/akka-user
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>>>
>>
>>

-- 
>>>>>>>>>>      Read the docs: http://akka.io/docs/
>>>>>>>>>>      Check the FAQ: 
>>>>>>>>>> http://doc.akka.io/docs/akka/current/additional/faq.html
>>>>>>>>>>      Search the archives: https://groups.google.com/group/akka-user
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