Thanks Adrian,
In option 2, when you say "this is automated in an external Python script", do you mean that you use something like psycopg2 to perform the queries the database (e.g. for comparing data in the holding table with the older table)?2015-03-30 9:53 GMT-04:00 Adrian Klaver <adrian.klaver@xxxxxxxxxxx>:
I guess it depends on what end purpose of the above is? If you are just trying to keep relatively update to date information from the DBF sources, would it not be easier just to load them into a new table?On 03/30/2015 06:04 AM, Guillaume Drolet wrote:
Hello,
I need your help speeding up the procedure I will explain below. I am
looking for improvements to my method or different approaches/ideas to
would help in this matter.
I have a set of DBF files that I load into my database using a plpython
function and a call to ogr2ogr (http://www.gdal.org/drv_pg.html). Once
in a while, I'll have to load updated versions of these tables to get
the latest additions and possible corrections to older versions.
In my plpython script, if a table is loaded for the first time, I first
load it empty, then I create a trigger function on insert (execute on
row) that will check for duplicates on each insert. Depending on the
type of data I load, my trigger first checks for equality in a subset of
columns (between 1 and 3 columns that would be like my primary key(s))
and if true, I check if all columns are equal between NEW and the
matching row from my table. When this condition is true, I return null,
else I store rows (i.e. NEW.* and matching row(s) in a new table called
"duplicate" for further manual investigation. Here's an example for one
table:
CREATE OR REPLACE FUNCTION check_naipf_insert()
RETURNS trigger AS
' BEGIN
IF EXISTS (SELECT 1
FROM public.naipf
WHERE id_pet_mes IS NOT DISTINCT FROM NEW.id_pet_mes
AND etage IS NOT DISTINCT FROM NEW.etage) THEN
IF EXISTS (SELECT 1
FROM public.naipf
WHERE id_pet_mes IS NOT DISTINCT FROM
NEW.id_pet_mes
AND etage IS NOT DISTINCT FROM NEW.etage
AND type_couv IS NOT DISTINCT FROM NEW.type_couv
AND densite IS NOT DISTINCT FROM NEW.densite
AND hauteur IS NOT DISTINCT FROM NEW.hauteur
AND cl_age IS NOT DISTINCT FROM NEW.cl_age) THEN
RETURN NULL;
ELSE
INSERT INTO public.duplic_naipf SELECT NEW.*;
INSERT INTO public.duplic_naipf (SELECT *
FROM
public.naipf
WHERE
id_pet_mes IS NOT DISTINCT FROM NEW.id_pet_mes
AND etage
IS NOT DISTINCT FROM NEW.etage );
RETURN NULL;
END IF;
END IF;
RETURN NEW;
END; '
LANGUAGE plpgsql VOLATILE COST 100;
CREATE TRIGGER check_insert_naipf
BEFORE INSERT
ON public.pet4_naipf
FOR EACH ROW
EXECUTE PROCEDURE check_naipf_insert();
(in this case, duplicate rows that need investigation are rows that may
have changed relative to older version of the DBF file, but that have no
change in what I call their primary keys although they are not really
PKs since I don't want to raise errors at loading)
Once this is done, ogr2ogr is called a second time to load the data. It
is quite fast for small tables (tens of thousands of rows, tens of
columns) but for large tables it takes forever. For example, I started
loading a table with 3.5 million rows/33 columns last Friday at 3PM and
this now, Monday morning at 9PM some 3 million rows have been loaded.
My question is: what are the other approaches that would make this
procedure faster? How is this kind of task usually implemented in
postgresql? Would it be better to load everything with no check and then
apply some functions to find duplicate rows (although this would involve
more manual work)?
So, where existing table is some_dbf_data:
1) CREATE TABLE new_some_dbf_data(...)
2) Dump DBF file into new_some_dbf_data
3)In transaction rename/drop some_dbf_data, rename new_some_dbf_data to some_dbf_data
Option 2 is what I do for a similar procedure:
1) Dump DBF data into holding table.
2) Use SQL in function(s) to compare old/new table and make appropriate adjustments. Doing SQL in bulk is a lot faster then checking each row, or least that is what I found. In any case the way you are doing it looks to involve 3.5 million inserts with a trigger action on each, that is bound to be slow:)
3) This is automated in an external Python script.
Option 3
Use dbf(https://pypi.python.org/pypi/dbf/0.88.16) and do the comparisons in the DBF files outside Postgres and only import what has changed.--
Thanks a lot for your help!
Adrian Klaver
adrian.klaver@xxxxxxxxxxx
