> On Jan 9, 2017, at 1:54 PM, Kevin Grittner wrote:
>
> On Mon, Jan 9, 2017 at 11:49 AM, Israel Brewster
> wrote:
>
>> [load of new data]
>
>> Limit (cost=354643835.82..354643835.83 rows=1 width=9) (actual
>> time=225998.319..225998.320 rows=1
On Mon, Jan 9, 2017 at 11:49 AM, Israel Brewster wrote:
> [load of new data]
> Limit (cost=354643835.82..354643835.83 rows=1 width=9) (actual
> time=225998.319..225998.320 rows=1 loops=1)
> [...] I ran the query again [...]
> Limit (cost=354643835.82..354643835.83
Hey,
I like your curiosity !
At the billion range, you __have__ to use pgpointcloud,
pyramid raster solution (actually the more common way to perform this task)
or another database (hello monetdb).
Cheers,
Rémi-C
2017-01-09 20:11 GMT+01:00 Jonathan Vanasco :
>
> On Jan 9,
On Jan 9, 2017, at 12:49 PM, Israel Brewster wrote:
> Planning time: 4.554 ms
> Execution time: 225998.839 ms
> (20 rows)
>
> So a little less than four minutes. Not bad (given the size of the database),
> or so I thought.
>
> This morning (so a couple of days later) I ran the query again
At BILLIONS, you're getting to a point where the point index is probably
(a) very large and (b) very deep, so you might want to do something
different with your data storage, like loading the data in spatially
compact patches of several 10s of points. Then the index will float more
nicely in
So just for interests sake, to kick things up a notch (and out of sheer morbid curiosity), I loaded a higher-resolution dataset (Elevation data for the state of Alaska, 2 arc second resolution, as opposed to 100 meter resolution before). Same structure/indexes and everything, just higher
On Jan 5, 2017, at 1:38 PM, Rémi Cura wrote:
>
> Hey,
> 1 sec seems really good in this case,
> and I'm assuming you tuned postgres so the main index fits into ram (work_mem
> and all other stuff).
>
> You could avoid a CTE by mixing both cte.
>
> WITH pts AS (
>
Ah, yes indeed. Upping the segment length to 1,000 brings the execution time down to 642 ms, and further upping it to 10,000 brings the execution time down again to 442.104 ms. I'll have to play around with it and see where the minimum is. Would that be likely to vary depending on initial path
Hey,
1 sec seems really good in this case,
and I'm assuming you tuned postgres so the main index fits into ram
(work_mem and all other stuff).
You could avoid a CTE by mixing both cte.
WITH pts AS (
SELECT (pt).geom, (pt).path[1] as vert
FROM
ST_DumpPoints(
ST_Segmentize(
Varying the segment length upwards might have a salutary effect for a
while, as the efficiency improvement of fewer inner loops battles with the
inefficiency of having more points selected by the index filter. Worth an
experiment.
P
On Thu, Jan 5, 2017 at 1:00 PM, Israel Brewster
> On Jan 5, 2017, at 10:38 AM, Paul Ramsey wrote:
>
> Yes, you did. You want a query that spits out a tupleset of goemetries (one
> each for each wee segment), and then you can join that set to your main table
> using st_dwithin() as the join clause.
> So start by
Yes, you did. You want a query that spits out a tupleset of goemetries (one
each for each wee segment), and then you can join that set to your main
table using st_dwithin() as the join clause.
So start by ditching the main table and just work on a query that generates
a pile of wee segments.
On
On Jan 5, 2017, at 8:50 AM, Paul Ramsey wrote:
>
> The index filters using bounding boxes. A long, diagonal route will have a
> large bounding box, relative to the area you actually care about (within a
> narrow strip of the route). Use ST_Segmentize() to add points
The index filters using bounding boxes. A long, diagonal route will have a
large bounding box, relative to the area you actually care about (within a
narrow strip of the route). Use ST_Segmentize() to add points to your
route, ST_DumpPoints() to dump those out as point and ST_MakeLine to
generate
I have a database (PostgreSQL 9.6.1) containing 62,702,675 rows of latitude (numeric), longitude(numeric), elevation(integer) data, along with a PostGIS (2.3.0) geometry column (location), running on a CentOS 6.8 box with 64GB RAM and a RAID10 SSD data drive. I'm trying to get the maximum
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