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Asked: 2026-05-23T23:33:15+00:00

hope someone can figure out what I’m doing wrong here. The task seems quite

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hope someone can figure out what I’m doing wrong here. The task seems quite simple, but is apparently beyond me.

I have two tables and I am trying to append precipitation data. Three conditions need to match in the two tables to get the correct precipitation data for each field site, e.g. year, latitude, and longitude. I tried by using the following queries (among other failed queries that I can’t even remember any more): 

SELECT f.*, g.* 
  FROM fieldSites f  LEFT OUTER JOIN gpcp_precipitation2 g
    ON f.date = g.year 
   AND f.d_lat = g.lat
 WHERE f.d_lon = g.lon;

this one timed out

and: 

SELECT *
FROM fieldSites
INNER JOIN gpcp_precipitation2 
  ON (fieldSites.d_lon = gpcp_precipitation2.lon 
  AND fieldSites.d_lat = gpcp_precipitation2.lat 
  AND fieldSites.date = gpcp_precipitation2.year);

this one also timed out on me.

I’d like to dump to a .csv file, but right now I just want to execute a successful query.

Here are my tables:

left table: fieldSites

siteId  d_lat  d_lon   year  data1  data2  country      
  1     -13.75  18.75   2009  0.598  0.351  Angola       
  1     -13.75  18.75   2008  0.654  0.330  Angola       
  1     -13.75  18.75   2007  0.489  0.381  Angola       
  1     -13.75  18.75   2006  0.554  0.389  Angola       
  1     -13.75  18.75   2005  0.321  0.321  Angola       
  1     -13.75  18.75   2004  0.598  0.351  Angola       
  1     -13.75  18.75   2003  0.654  0.330  Angola       
  1     -13.75  18.75   2002  0.489  0.381  Angola       
  1     -13.75  18.75   2001  0.554  0.389  Angola       
  2     -78.75  163.75  2009  0.285  0.155  Antarctica   
  2     -78.75  163.75  2008  0.285  0.155  Antarctica   
  2     -78.75  163.75  2007  0.285  0.155  Antarctica   
  2     -78.75  163.75  2006  0.285  0.155  Antarctica   
  2     -78.75  163.75  2005  0.285  0.155  Antarctica   
...1052 sites, 11 years, 11496 rows

right table: gpcp_precipitation2

siteId   lat    lon   year  precipitation
1        81.5   1.25  2009  93.36571912   
1        81.5   1.25  2008  93.36571912   
1        81.5   1.25  2007  93.36571912   
1        81.5   1.25  2006  93.36571912   
1        81.5   1.25  2005  93.36571912   
1        81.5   1.25  2004  93.36571912   
1        81.5   1.25  2003  93.36571912   
1        81.5   1.25  2002  93.36571912   
1        81.5   1.25  2001  93.36571912   
1        81.5   1.25  2000  93.36571912   
1        81.5   3.75  2009  93.36571912 
1        81.5   3.75  2008  93.36571912   
1        81.5   3.75  2007  93.36571912

... 92300 rows

What I want is this: 

siteId  d_lat  d_lon   year  data1  data2  country      precipitation  
  1     13.75  18.75   2009  0.598  0.351  Angola       144.286
  1     13.75  18.75   2008  0.654  0.330  Angola       114.970
  1     13.75  18.75   2007  0.489  0.381  Angola       70.000
  1     13.75  18.75   2006  0.554  0.389  Angola       174.179
  1     13.75  18.75   2005  0.321  0.321  Angola       174.743
  1     13.75  18.75   2004  0.598  0.351  Angola       70.506
  1     13.75  18.75   2003  0.654  0.330  Angola       173.716
  1     13.75  18.75   2002  0.489  0.381  Angola       74.162
  1     13.75  18.75   2001  0.554  0.389  Angola       139.445
  2     78.75  163.75  2009  0.285  0.155  Antarctica   0
  2     78.75  163.75  2008  0.285  0.155  Antarctica   0
  2     78.75  163.75  2007  0.285  0.155  Antarctica   0
  2     78.75  163.75  2006  0.285  0.155  Antarctica   0

Am I doing something completely stupid? I am stumped.
Thanks so much for any advice.

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1 Answer

  1. Editorial Team
    Select fieldSites.*, precipitation.*
From fieldSites
Inner Join gpcp_precipitation2 As precipitation On precipitation.siteId = fieldSites.siteId
Where
    fieldSites.d_year = precipitation.year And
    fieldSites.d_lat = precipitation.lat And
    fieldSites.d_lon = precipitation.lon

    If that query is timing out, you have an indexing problem, not necessarily a query problem. This gives you several predicates in the where clause to filter on, so it should reduce your joins quite a bit, but you may need an index that includes siteId, year, lat, and lon on both tables.

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