Consider the following two Python code examples that achieve the same, but with a significant and surprising performance difference.
import psycopg2, time
conn = psycopg2.connect("dbname=mydatabase user=postgres")
cur = conn.cursor('cursor_unique_name')
cur2 = conn.cursor()
startTime = time.clock()
cur.execute("SELECT * FROM test for update;")
print ("Finished: SELECT * FROM test for update;: " + str(time.clock() - startTime));
for i in range (100000):
cur.fetchone()
cur2.execute("update test set num = num + 1 where current of cursor_unique_name;")
print ("Finished: update starting commit: " + str(time.clock() - startTime));
conn.commit()
print ("Finished: update : " + str(time.clock() - startTime));
cur2.close()
conn.close()
and
import psycopg2, time
conn = psycopg2.connect("dbname=mydatabase user=postgres")
cur = conn.cursor('cursor_unique_name')
cur2 = conn.cursor()
startTime = time.clock()
for i in range (100000):
cur2.execute("update test set num = num + 1 where id = " + str(i) + ";")
print ("Finished: update starting commit: " + str(time.clock() - startTime));
conn.commit()
print ("Finished: update : " + str(time.clock() - startTime));
cur2.close()
conn.close()
The create statement for a table test:
CREATE TABLE test (id serial PRIMARY KEY, num integer, data varchar);
And this table contains 100,000 rows and VACUUM ANALYZE TEST; was launched.
I got the following results sequentially with several attempts.
First code example:
Finished: SELECT * FROM test for update;: 0.00609304950429
Finished: update starting commit: 37.3272754429
Finished: update : 37.4449708474
Second code example:
Finished: update starting commit: 24.574401185
Finished committing: 24.7331461431
This is very surprising to me, as I think it should be the exact opposite, which means updating using the cursor should be significantly faster according to this answer .
David