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From "Stadin, Benjamin" <>
Subject Re: Is Spark the right tool for me?
Date Mon, 01 Dec 2014 15:36:40 GMT
Yes, the processing causes the most stress. But this is parallizeable by splitting the input
source. My problem is that once the heavy preprocessing is done, I’m in a „micro-update“
mode so to say (user-interactive part of the whole workflow). Then the map is rendered directly
from the SQLite file by the map server instance on that machine – this is actually a favorable
setup for me for resource consumption and implementation costs (I just need to tell the web
ui to refresh after something was written to the db, and the map server will render the updates
without me changing / coding anything). So my workflow requires to break out of parallel processing
for some time.

Do you think for my my generalized workflow and tool chain can be like so?

 1.  Pre-Process many files in a parallel way. Gather all results, deploy them on one single
machine. => Spark coalesce() + Crunch (for splitting input files into separate tasks)
 2.  On the machine where preprocessed results are on, configure a map server to connect to
the local SQLite source. Do user-interactive micro-updates on that file (web UI gets updated).
 3.  Post-process the files in parallel. => Spark + Crunch
 4.  Design all of the above as a workflow, runnable (or assignable) as part of a user session.
=> Oozie

Do you think this is ok?


Von: andy petrella <<>>
Datum: Montag, 1. Dezember 2014 15:48
An: Benjamin Stadin <<>>,
"<>" <<>>
Betreff: Re: Is Spark the right tool for me?

Indeed. However, I guess the important load and stress is in the processing of the 3D data
(DEM or alike) into geometries/shades/whatever.
Hence you can use spark (geotrellis can be tricky for 3D, poke @lossyrob for more info) to
perform these operations then keep an RDD of only the resulting geometries.
Those geometries won't probably that heavy, hence it might be possible to coalesce(1, true)
to have to whole thing on one node (or if your driver is more beefy, do a collect/foreach)
to create the index.
You could also create a GeoJSON of the geometries and create the r-tree on it (not sure about
this one).

On Mon Dec 01 2014 at 3:38:00 PM Stadin, Benjamin <<>>
Thank you for mentioning GeoTrellis. I haven’t heard of this before. We have many custom
tools and steps, I’ll check our tools fit in. The end result after is actually a 3D map
for native OpenGL based rendering on iOS / Android [1].

I’m using GeoPackage which is basically SQLite with R-Tree and a small library around it
(more lightweight than SpatialLite). I want to avoid accessing the SQLite db from any other
machine or task, that’s where I thought I can use a long running task which is the only
process responsible to update a local-only stored SQLite db file. As you also said SQLite
 (or mostly any other file based db) won’t work well over network. This isn’t only limited
to R-Tree but expected limitation because of file locking issues as documented also by SQLite.

I also thought to do the same thing when rendering the (web) maps. In combination with the
db handler which does the actual changes, I thought to run a map server instance on each node,
configure it to add the database location as map source once the task starts.



Von: andy petrella <<>>
Datum: Montag, 1. Dezember 2014 15:07
An: Benjamin Stadin <<>>,
"<>" <<>>
Betreff: Re: Is Spark the right tool for me?

Not quite sure which geo processing you're doing are they raster, vector? More info will be
appreciated for me to help you further.

Meanwhile I can try to give some hints, for instance, did you considered GeoMesa<>?
Since you need a WMS (or alike), did you considered GeoTrellis<>
(go to the batch processing)?

When you say SQLite, you mean that you're using Spatialite? Or your db is not a geo one, and
it's simple SQLite. In case you need an r-tree (or related) index, you're headaches will come
from congestion within your database transaction... unless you go to a dedicated database
like Vertica (just mentioning)


On Mon Dec 01 2014 at 2:49:44 PM Stadin, Benjamin <<>>
Hi all,

I need some advise whether Spark is the right tool for my zoo. 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 decide on a tool

NB: I have asked a similar question on the Storm mailing list, but have been deferred to Spark.
I previously thought Storm was closer to my needs – but maybe neither is.

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 Spark? What options do I have with Spark to chain a workflow from pipes?
 *   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 Spark, 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). But how about update responsiveness, isn’t it that Spark will cause
some lags (as opposed to Storm)?
 *   The db handler probably has to be implemented as a long running continuing task, so when
a user sends some changes the handler writes these to the db file. However, I want this to
be decoupled from the job. So file these updates should be done locally only on the machine
that started the job for the whole lifetime of this user interaction. Does Spark 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 and run on the same node, which eventually ends and triggers
the next task? 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), and eventually
restarted / recreated on failure?

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