On 5/8/13 3:54 AM, Heikki Linnakangas wrote:
On 24.04.2013 14:31, Florian Pflug wrote:
On Apr23, 2013, at 23:25 , Alexander Korotkov<aekorotkov@gmail.com>
I've taken a brief look on the paper and implementation. As I can
see iDistance implements some global building strategy. I mean, for
example, it selects some point, calculates distances from selected
point to all points in dataset etc. So, it uses the whole dataset
at the same time.

However you can try to implement global index building in GiST or
SP-GiST. In this case I think you should carefully estimate your
capabilities during single GSoC project. You would need to extend
GiST or SP-GiST interface and write completely new implementation
of tree building algorithm. Question of how to exactly extend GiST
or SP-GiST interface for this case could appear to be very hard
even theoretically.
+1. That seemed to be a major roadblock to me too when I read the

You could work around that by making partition identification a
separate step. You'd have a function

idist_analyze(cfg name, table name, field name)

which'd identify suitable partitions for the data distribution in
table.field and store them somewhere. Such a set of pre-identified
partitions would be akin to a tsearch configuration, i.e. all other
parts of the iDistance machinery would use it to map points to index
keys and queries to ranges of those keys. You'll want to look at how
tsearch handles that, and check if the method can indeed be applied
to iDistance.
You could perform that step as part of the index build. Before the index build starts to add tuples to the index, it could scan a random sample of the heap and identify the partitions based on that.

If you need to store the metadata, like a map of partitions, it becomes difficult to cajole this into a normal GiST or SP-GiST opclass. The API doesn't have any support for storing such metadata.
In a first cut, you'd probably only allow inserts into index which
don't change the maximum distances from the partition centers that
idist_analyze() found.
That seems like a pretty serious restriction. I'd try to write it so that you can insert any value, but if the new values are very different from any existing values, it would be OK for the index quality to degrade. For example, you could simply add any out-of-bounds values to a separate branch in the index, which would have no particular structure and would just have to be scanned on every query. You can probably do better than that, but that would be a trivial way to deal with it.
Or you could use the new insert to start a new partition.

Heck, maybe the focus should actually be on partitions and not individual records/points. ISTM the entire challenge here is figuring out a way to maintain a set of partitions that:

- Are limited enough in number that you can quickly perform operations/searches across all partitions
- Yet small enough that once you've narrowed down a set of partitions you don't have a ton of raw records to still look at

Before we had range types I experimented with representing time ranges as rectangles of varying size (ie: for (start, end), create rectangle(point(start,start), point(end,end)). The problem with that is you had to convert timestamp into a float, which was not exact. So when querying you could use a GiST index on all the rectangles to narrow your scope, but you still needed a set of exact clauses (ie: start >= now() - '1 year' AND end <= now()). Partitions would be similar in that they wouldn't be exact but could greatly narrow the search space (of course we'd want to handle the secondary exact checking internally instead of exposing the user to that).
Jim C. Nasby, Data Architect jim@nasby.net
512.569.9461 (cell) http://jim.nasby.net

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