[postgis-users] Slow Query Times for Split Tiles

[elliott]

I know that the tiles are indexed.  How do I tell if the query is using 
the index?

On 11/1/2011 5:34 PM, Pierre Racine wrote:
> Did you check if your query is using the index?
>
>    
>> -----Original Message-----
>> From: postgis-users-bounces at postgis.refractions.net [mailto:postgis-users-
>> bounces at postgis.refractions.net] On Behalf Of elliott
>> Sent: Tuesday, November 01, 2011 4:48 PM
>> To: PostGIS Users Discussion
>> Subject: Re: [postgis-users] Slow Query Times for Split Tiles
>>
>> On 11/1/2011 4:27 PM, Mateusz Łoskot wrote:
>>      
>>> On 1 November 2011 20:23, elliott<elliott at cpi.com>   wrote:
>>>
>>>        
>>>> Should splitting raster files into tiles increase query performance?
>>>>
>>>> I have a two 1201x1201 raster tiles that were loaded by the following
>>>> scripts:
>>>>
>>>> raster2pgsql.py -a -r N37E126.hgt -F N37E126 -t srtm3 -s 4326
>>>> -k1201x1201>  srtm.sql psql -f srtm.sql TEST
>>>>
>>>> raster2pgsql.py -a -r N37E127.hgt -F N37E127 -t srtm3 -s 4327
>>>> -k50x50>  srtm.sql psql -f srtm.sql TEST
>>>>
>>>> Querying these raster files appears to produce approximately the same
>>>> timing results.  Should the 50x50 have better performance than the
>>>>          
>> 1201x1201?
>>      
>>>> Taking almost 3 minutes to query 10000 points seems to be very slow.
>>>>
>>>>          
>>> Do you query tiles or points (what points?)?
>>>
>>>        
>>           Querying tiles for altitudes at lat/lon pairs.
>>      
>>> Do you measure itme of only query only or query and raster blob
>>> processing/rendering?
>>>
>>>        
>>           Time is for query only.
>>      
>>> What's your actual SQL command?
>>>
>>>        
>>           This query is run for 10000 lat/lon pairs -
>>
>>           select ST_Value(rast, ST_Transform(ST_SetSRID(ST_Point( lon,lat), 4326),
>> ST_SRID(rast))) from srtm3 where filename='filename'
>> AND ST_Intersects(rast, ST_Transform(ST_SetSRID(ST_Point(lat,lon),
>> 4326), ST_SRID(rast)));
>>      
>>> Best regards,
>>>
>>>        
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>>      
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