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.
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.
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.


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 ,
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