Using Elastic Search for geo-spatial search

Over the past few months we have been quietly working on the platform. As I have mentioned in a previous post, we are using elastic search as a key value store and that’s working pretty nicely for us. In addition to that we are also using it as a geospatial search engine. is going to be all about local and that means geospatial data and lots of it. That’s not just POIs (points of interest) but also streets, cities, and areas of interest. In geospatial terms that means shapes: points, paths, and polygons. Doing geospatial search means searching through documents that have geospatial data associated with it using a query that also contains geospatial data. So given a shape, find every document with a shape that overlaps or intersects with it.

Since elastic search is still very new and rapidly evolving (especially the geospatial functionality), I had some worries about whether it would work as advertised. So, after months of coding it was about time to see if it could actually take a decent data set and work as advertised instead of falling over and dying in a horrible way.

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Another Porsche

1/2 TB ought to be enough for anyone. So I bought another one 🙂 For backup, you know.

At 100€, it’s not really something to consider for very long. Data must be safe, so more is better.

Bought a porsche

I bought myself a nice porsche :-). The 1/2 TB type.

Currently the ntfs drive is formatting. I seems to be really quiet, compared to my maxtor external drive or my internal drive. Altogether I now have 1.1 TB of storage available. Should keep me happy for a while.