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Re: [geomesa-users] Geohashing vs R-tree
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Luca,
I'd agree more or less. When Accumulo splits a tablet, that is
basically rebalancing in terms of the B^+ tree structure being
used/presented. Splitting/rebalancing for R-trees is generally more
interesting and computationally intensive.
To unpack it a little bit, one could consider the time to insert a
record and/or build the index from scratch. For a B^+-tree, inserts are
O(log n). For an R-tree, the worst case of O(n) can be realized with
hotspotting. As a real world example cited in that paper, it is noted
that millions of GDELT data are located a handful of points. (Figure 10
shows that over 6 million points are associated with (0,0) and another 6
million with Washington, DC.) That can give an R-tree some headaches.*
For my money, I'd love to a distributed database toolkit which would
help one use more complex data structures. It'd be a quite challenging
project though...
Great question; let us know what else we can expand on,
Jim
* While working on that paper, we also tried out PostGIS with the same
dataset. We didn't report on that since we were rather unsure of our
results. Building the GIST index took days. We did try to jitter the
data to avoid hotspotting, and I think that may have helped some.
On 05/18/2015 06:00 PM, Luca Morandini wrote:
Folks,
I am drafting a presentation on Big Data spatial DBMSes, including
GeoMesa. I read some material, including the "Spatio-temporal Indexing
in Non-relational Distributed Databases" paper, but one question still
lingers in my mind: isn't the main advantage of geohashing over R-tree
the absence of index rebalancing?
Regards,
Luca Morandini
Data Architect - AURIN project
Melbourne eResearch Group
Department of Computing and Information Systems
University of Melbourne
Tel. +61 03 903 58 380
Skype: lmorandini
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