Scaling GIS Data in Non-relational Data Stores
by Mike Malone on Mar 30, 2010
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As the amount of GIS data we need to keep track of increases, the amount of devices accessing it increases, and the amount of GIS writes increase, we’re finding that, much like real-time web applicat...
As the amount of GIS data we need to keep track of increases, the amount of devices accessing it increases, and the amount of GIS writes increase, we’re finding that, much like real-time web applications, normal RDBMS’s are not well suited to scaling. This talk covers why GIS data is hard to scale in a normal RDBMS, what nonrelational stores exist out there, and some basic examples of how to do spatial queries within a nonrelational store.
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You can design a scalable architecture that maintains some of the ACID characteristics of a typical relational datastore, but eventually you’ll have to relax some of these constraints.
Note that consistency, as defined here, does not mean the same thing as “consistency” in “ACID” - before it meant that all data constraints were met.
This is a gross simplification, and the approaches data stores take to perform node recovery, rebalancing, and repair are often their most distinguishing characteristics. This is actually why we chose Cassandra - the distributed cluster logic is more robust than any other store I’ve seen.