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From "Stadin, Benjamin" <Benjamin.Sta...@heidelberg-mobil.com>
Subject Re: Is Storm the right tool for me?
Date Mon, 01 Dec 2014 09:38:52 GMT
Thanks for your response.
Shark doesn’t seem to be something I want / need. The custom data handler is performance
critical, file based (SQLite file) and already highly optimized (e.g. File sync is off, giving.
And this db is associated to a single user sessions and should not be replicated but rather
be a local temporary source existing only on the executing node – otherwise replicating
these files will become a bottle neck. But maybe this is still possible to configure with
Shark?


Von: Vladi Feigin <vladif86@gmail.com<mailto:vladif86@gmail.com>>
Antworten an: "user@storm.apache.org<mailto:user@storm.apache.org>" <user@storm.apache.org<mailto:user@storm.apache.org>>
Datum: Montag, 1. Dezember 2014 06:16
An: "user@storm.apache.org<mailto:user@storm.apache.org>" <user@storm.apache.org<mailto:user@storm.apache.org>>
Betreff: Re: Is Storm the right tool for me?


Hi
Sounds to me you need an ETL offline process MR/Shark offline to get the processed data to
db.
Storm fits the use cases when you have continous data stream and the processing time with
a low latency..

On 1 Dec 2014 04:26, "Stadin, Benjamin" <Benjamin.Stadin@heidelberg-mobil.com<mailto:Benjamin.Stadin@heidelberg-mobil.com>>
wrote:
Hi all,

I need some advise whether Storm is the right tool for my purpose. My requirements share commonalities
with „big data“, workflow coordination and „reactive“ event driven data processing
(as in for example Haskell Arrows), which doesn’t make it any easier to find the right tool
set.

To explain my needs it’s probably best to give an example scenario:

 *   A user uploads small files (typically 1-200 files, file size typically 2-10MB per file)
 *   Files should be converted in parallel and on available nodes. The conversion is actually
done via native tools, so there is not so much big data processing required, but dynamic parallelization
(so for example to split the conversion step into as many conversion tasks as files are available).
The conversion typically takes between several minutes and a few hours.
 *   The converted files gathered and are stored in a single database (containing geometries
for rendering)
 *   Once the db is ready, a web map server is (re-)configured and the user can make small
updates to the data set via a web UI.
 *   … Some other data processing steps which I leave away for brevity …
 *   There will be initially only a few concurrent users, but the system shall be able to
scale if needed

My current thoughts:

 *   I should avoid to upload files into the distributed storage during conversion, but probably
should rather have each conversion filter download the file it is actually converting from
a shared place. Other wise it’s bad for scalability reasons (too many redundant copies of
same temporary files if there are many concurrent users and many cluster nodes).
 *   Apache Oozie seems an option to chain together my pipes into a workflow. But is it a
good fit with Storm?
 *   Apache Crunch seems to make it easy to dynamically parallelize tasks (Oozie itself can’t
do this). But I may not need crunch after all if I have Storm, and it also doesn’t seem
to fit to my last problem following.
 *   The part that causes me the most headache is the user interactive db update: I consider
to use Kafka as message bus to broker between the web UI and a custom db handler (nb, the
db is a SQLite file). Here I see Storm would serve my purpose better than Spark (Streaming)
since it should have immediate update responsiveness and the handler is probably best implemented
as a long running continuing task. But does Storm allow to create such long running tasks
dynamically, so that when another (web) user starts a new task a new long-running task is
created? Also, is it possible to identify a running task, so that a long running task can
be bound to a session (db handler working on local db updates, until task done)?

~Ben
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