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From James Hughes <jn...@virginia.edu>
Subject Re: Geospatial + Partitioned Index
Date Fri, 16 Jan 2015 21:50:25 GMT
Hi Russ,

We've had good success in GeoMesa's spatio-temporal index blending together
sharding, spatial, and temporal data for indexing and planning queries for
geo-time data.  As you noted, this approach can lead to a large number of
ranges; so far, we haven't seen any problems based on the number of ranges.

In general, if you are looking for a product which can handle geographic
data with time and other attributes, I'd suggest either GeoMesa or
GeoWave.  The two are similar, and we are hopeful that we further align
them in the future.

If rolling your own is the order of the day, you might check with Eugene to
see how his spatial range skipping iterator is going.  He may be using
GeoHashes/z-order curves rather than Hilbert curves.

Thanks,

Jim

On Fri, Jan 16, 2015 at 3:37 PM, Russ Weeks <rweeks@newbrightidea.com>
wrote:

> Hi, Josh,
>
> Thanks for your response. I think I should clarify something. When I said,
> "the client would just scan (-inf, +inf)", I didn't mean that the net
> effect would be to read all data. I just meant that my custom Iterator
> would seek() to ranges which are a function of its configuration and its
> knowledge of the partitioning scheme, just like the IntersectingIterator.
> Except that instead of its configuration defining a set of keyword terms,
> it would define a set of disjoint intervals on a space-filling curve.
>
> My understanding is that setting the scan range to (-inf,+inf) in this
> case is just a way to tell Accumulo, "run this scan across all tablets".
>
> -Russ
>
> On Fri, Jan 16, 2015 at 12:17 PM, Josh Elser <josh.elser@gmail.com> wrote:
>
>> Russ Weeks wrote:
>>
>>> Hey, all,
>>>
>>> I'm looking at switching my geospatial index to a partitioned index to
>>> smooth out some hotspots. So for any query, I'll have a bunch of ranges
>>> representing intervals on a Hilbert curve, plus a bunch of partitions,
>>> each of which needs to be scanned for every range.
>>>
>>> The way that the (excellent!) Accumulo Recipes geospatial store
>>> addresses this is to take the product of the partitions and the curve
>>> intervals[1]. It seems like an alternative would be to encode the curve
>>> intervals as a property of a custom iterator (I need one anyways to
>>> filter out extraneous points from the search area) and then the client
>>> would just scan (-inf, +inf), which I think is more typical when
>>> querying a partitioned index?
>>>
>>
>> I'm no expert on storing geo-spatial data, but having to scan (-inf,+inf)
>> on a table for a query is typically the reason people deal with the pain of
>> hot-spotting, although it is the easiest to implement.
>>
>> If you can be "tricky" in how you're encoding your data in the row such
>> that you can reduce the search space over your partitioned index, you can
>> try to get the best of both worlds (avoid reading all data and still get a
>> good distribution).
>>
>> Since that was extremely vague, here's an example: say you had a text
>> index and wanted to look up the word "the" and your index had 100
>> partitions, [0,99]. If you knew that it was only possible for "the" to show
>> up on partitions 5, 27 and 83 (typically by use of some hashing function),
>> you could drastically reduce your search space while still avoiding hot
>> spotting on a single server.
>>
>>  Can anybody comment on which approach is preferred? Is it common to
>>> expose the number of partitions in the index and the encoding of those
>>> partitions to client code? Am I needlessly worried that taking the
>>> product of the curve intervals and the partitions will produce too many
>>> ranges?
>>>
>>
>> In the trivial sense, the client doesn't need to know the partitions and
>> would just scan the entire index like you said earlier. You could also
>> track the partitions that you have created in a separate table and the
>> client could read that table to know ahead of time (if you have a reason to
>> do so in your implementation).
>>
>> Depending on the amount of data you have, lots of ranges to check could
>> take some time. YMMV
>>
>>
>>  Thanks,
>>> -Russ
>>>
>>> 1:
>>> https://github.com/calrissian/accumulo-recipes/blob/master/
>>> store/geospatial-store/src/main/java/org/calrissian/accumulorecipes/
>>> geospatialstore/impl/AccumuloGeoSpatialStore.java#L190
>>>
>>
>

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