Re: Help with extracting large volumes of records across related tables

There's a very simple solution using cursors.

As an example :

create table categories ( id serial primary key, name text );
create table items ( id serial primary key, cat_id integer references
categories(id), name text );
create index items_cat_idx on items( cat_id );

insert stuff...

select * from categories;
id | name
----+----------
1 | tools
2 | supplies
3 | food
(3 lignes)

select * from items;
id | cat_id | name
----+--------+--------------
1 | 1 | hammer
2 | 1 | screwdriver
3 | 2 | nails
4 | 2 | screws
5 | 1 | wrench
6 | 2 | bolts
7 | 2 | cement
8 | 3 | beer
9 | 3 | burgers
10 | 3 | french fries
(10 lignes)

Now (supposing you use Python) you use the extremely simple sample
program below :

import psycopg
db = psycopg.connect("host=localhost dbname=rencontres user=rencontres
password=.........")

# Simple. Let's make some cursors.
cursor = db.cursor()
cursor.execute( "BEGIN;" )
cursor.execute( "declare cat_cursor no scroll cursor without hold for
select * from categories order by id for read only;" )
cursor.execute( "declare items_cursor no scroll cursor without hold for
select * from items order by cat_id for read only;" )

# set up some generators
def qcursor( cursor, psql_cursor_name ):
while True:
cursor.execute( "fetch 2 from %s;" % psql_cursor_name )guess
if not cursor.rowcount:
break
# print "%s fetched %d rows." % (psql_cursor_name, cursor.rowcount)
for row in cursor.dictfetchall():
yield row
print "%s exhausted." % psql_cursor_name

# use the generators
categories = qcursor( cursor, "cat_cursor" )
items = qcursor( cursor, "items_cursor" )

current_item = items.next()
for cat in categories:
print "Category : ", cat

# if no items (or all items in category are done) skip to next category
if cat['id'] < current_item['cat_id']:
continue

# case of items without category (should not happen)
while cat['id'] > current_item['cat_id']:
current_item = items.next()

while current_item['cat_id'] == cat['id']:
print "\t", current_item
current_item = items.next()

It produces the following output :

Category : {'id': 1, 'name': 'tools'}
{'cat_id': 1, 'id': 1, 'name': 'hammer'}
{'cat_id': 1, 'id': 2, 'name': 'screwdriver'}
{'cat_id': 1, 'id': 5, 'name': 'wrench'}
Category : {'id': 2, 'name': 'supplies'}
{'cat_id': 2, 'id': 3, 'name': 'nails'}
{'cat_id': 2, 'id': 4, 'name': 'screws'}
{'cat_id': 2, 'id': 6, 'name': 'bolts'}
{'cat_id': 2, 'id': 7, 'name': 'cement'}
Category : {'id': 3, 'name': 'food'}
{'cat_id': 3, 'id': 8, 'name': 'beer'}
{'cat_id': 3, 'id': 9, 'name': 'burgers'}
{'cat_id': 3, 'id': 10, 'name': 'french fries'}

This simple code, with "fetch 1000" instead of "fetch 2", dumps a database
of several million rows, where each categories contains generally 1 but
often 2-4 items, at the speed of about 10.000 items/s.

Satisfied ?