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From "Meier, Caleb" <Caleb.Me...@parsons.com>
Subject fluo accumulo table tablet servers not keeping up with application
Date Thu, 26 Oct 2017 15:34:37 GMT
Hello Fluo Devs,

We have implemented an incremental query evaluation service for Apache Rya that leverages
Apache Fluo.  We’ve been doing some benchmarking and we’ve found that the Accumulo Tablet
servers for the Fluo table are falling behind pretty quickly for our application.  We’ve
tried splitting the Accumulo Table so that we have more Tablet Servers, but that doesn’t
really buy us too much.  Our application is fairly scan intensive—we have a metadata framework
in place that allows us to pass query results through the query tree, and each observer needs
to look up metadata to determine which observer to route its data to after processing.  To
give you some indication of our scan rates, our Join Observer does about 6 lookups, builds
a scanner to do one RowColumn restricted scan, and then does many writes.  So an obvious way
to alleviate the burden on the TableServer is to cut down on the number of scans.

One approach that we are considering is to import all of our metadata into memory.  Essentially,
each Observer would need access to an in memory metadata cache.  We’re considering using
the Observer context, but this cache needs to be mutable because a user needs to be able to
register new queries.  Is it possible to update the context, or would we need to restart the
application to do that?  I guess other options would be to create a static cache for each
Observer that stores the metadata, or to store it in Zookeeper.  Have any of you devs ever
had create a solution to share state between Observers that doesn’t rely on the Fluo table?

In addition to cutting down on the scan rate, are there any other approaches that you would
consider?  I assume that the problem lies primarily with how we’ve implemented our application,
but I’m also wondering if there is anything we can do from a configuration point of view
to reduce the burden on the Tablet servers.  Would reducing the number of workers/worker threads
to cut down on the number of times a single observation is processed be helpful?  It seems
like this approach would cut out some redundant scans as well, but it might be more of a second
order optimization. In general, any insight that you might have on this problem would be greatly

Caleb Meier

Caleb A. Meier, Ph.D.
Senior Software Engineer ♦ Analyst
Parsons Corporation
1911 N. Fort Myer Drive, Suite 800 ♦ Arlington, VA 22209
Office:  (703)797-3066
Caleb.Meier@Parsons.com<mailto:Caleb.Meier@Parsons.com> ♦ www.parsons.com<https://webportal.parsons.com/,DanaInfo=www.parsons.com+>

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