On 03/30/2015 01:43 PM, Guillaume Drolet wrote:
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)?
Yes. Basically I use the dbf module I mentioned previously to read the
DBF files, output the data I need, writing it into an in memory CSV file
which I then use the psycopg2 COPY functions to dump into the Postgres
database. The files I am working with are not as the large the ones you
mention, so doing all this in memory is workable. The script then calls
a series of user functions in Postgres to do the comparing and
manipulating. At the time I did this plpythonu was less featured then
it is now, so to do what I wanted made more sense in an external script.
Also the script pulls the DBF files from elsewhere and I felt more
comfortable doing that outside the database then in.
Though more and more I seem to be using pandas(pandas.pydata.org) to do
data conversions. Saves a lot of the steps in the above. In this case
you would still need to get the data out of the DBF files.
Thanks.
2015-03-30 9:53 GMT-04:00 Adrian Klaver <adrian.kla...@aklaver.com
<mailto:adrian.kla...@aklaver.com>>:
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
<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)?
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?
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
<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.kla...@aklaver.com <mailto:adrian.kla...@aklaver.com>
--
Adrian Klaver
adrian.kla...@aklaver.com
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