[postgis-users] Nearest Neighbor on Large Datasets

[Pierre Racine]

Try this:

SELECT grid.pk_uid, point.pk_uid AS el_id, point.elevation
FROM grid_rail_lines grid, elev_rail_combined point
WHERE point.pk_uid  = (SELECT point.pk_uid id2
        FROM grid_rail_lines point
        ORDER BY point.geom <-> grid.geom
        LIMIT 1)
ORDER BY grid.pk_uid

From: postgis-users-bounces at lists.osgeo.org [mailto:postgis-users-bounces at lists.osgeo.org] On Behalf Of Spencer Gardner
Sent: Monday, April 08, 2013 11:08 AM
To: 'PostGIS Users Discussion'
Subject: [postgis-users] Nearest Neighbor on Large Datasets

I have a layer of grid cells and a layer of discreet points representing elevation samples. My grid layer has a total of about 430,000 cells, the elevation data contains about 320,000 points, and both datasets have spatial indexes on them. I need to join each grid cell with the nearest elevation point (within at most 30 feet of the cell's center point). To accomplish this, I created the following query:

SELECT DISTINCT ON (grid_rail_lines.pk_uid)
  grid_rail_lines.pk_uid
, elev_rail_combined.pk_uid AS el_id
, elev_rail_combined.elevation
FROM
  grid_rail_lines JOIN elev_rail_combined
    ON grid_rail_lines.the_geom<->elev_rail_combined.the_geom < 30
ORDER BY
  grid_rail_lines.pk_uid ASC
, grid_rail_lines.the_geom<->elev_rail_combined.the_geom ASC


EXPLAIN provides the following information (also available at depesz<http://explain.depesz.com/s/Vsm>):

Unique  (cost=141973943356.82..142206589733.23 rows=434170 width=624)
  ->  Sort  (cost=141973943356.82..142090266545.03 rows=46529275283 width=624)
        Sort Key: grid_rail_lines.pk_uid, ((grid_rail_lines.the_geom <-> elev_rail_combined.the_geom))
        ->  Nested Loop  (cost=0.00..4910712887.16 rows=46529275283 width=624)
              Join Filter: ((grid_rail_lines.the_geom <-> elev_rail_combined.the_geom) < 30::double precision)
              ->  Seq Scan on grid_rail_lines  (cost=0.00..13202.70 rows=434170 width=484)
              ->  Seq Scan on elev_rail_combined  (cost=0.00..6220.05 rows=321505 width=140)


I let this query run over the weekend and it took a total of 21 hours. These datasets are rather large so I expect it to take a long time, but I wonder if there is a more efficient way to conduct the join. As far as I can tell, I've structured the query as recommended in the documentation. Does anyone have ideas for how to improve performance?

Thanks,
Spencer
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