Below are the results for the posted methods. Tested it on local and it gave no difference in timing between the method-2 andmethod-3. Failed to run in dbfiddle somehow.
Also I was initially worried if adding the trigger to the our target table, will worsen the performance as because , it will make all the execution to "row by row" rather a true batch insert(method-3 as posted) as there will be more number of context switches , but it seems it will still be doing the batch commits(like the way its in method-2). So as per that , we won't lose any performance as such. Is this understanding correct?
Method-1- 00:01:44.48
Method-2- 00:00:02.67
Method-3- 00:00:02.39
https://gist.github.com/databasetech0073/8e9106757d751358c0c0c65a2374dbc6
On Thu, Sep 19, 2024 at 6:42 PM Lok P <loknath.73@xxxxxxxxx> wrote:
On Thu, Sep 19, 2024 at 5:40 PM Ron Johnson <ronljohnsonjr@xxxxxxxxx> wrote:On Thu, Sep 19, 2024 at 5:24 AM Lok P <loknath.73@xxxxxxxxx> wrote:[snip]DO $$
DECLARE
num_inserts INTEGER := 100000;
batch_size INTEGER := 50;
start_time TIMESTAMP;
end_time TIMESTAMP;
elapsed_time INTERVAL;
i INTEGER;
BEGIN
-- Method 1: Individual Inserts with Commit after every Row
start_time := clock_timestamp();
FOR i IN 1..num_inserts LOOP
INSERT INTO parent_table VALUES (i, 'a');
COMMIT;
END LOOP;
end_time := clock_timestamp();
elapsed_time := end_time - start_time;
INSERT INTO debug_log (method1, start_time, end_time, elapsed_time)
VALUES ('Method 1: Individual Inserts with Commit after every Row', start_time, end_time, elapsed_time);
-- Method 2: Individual Inserts with Commit after 100 Rows
start_time := clock_timestamp();
FOR i IN 1..num_inserts LOOP
INSERT INTO parent_table2 VALUES (i, 'a');
-- Commit after every 100 rows
IF i % batch_size = 0 THEN
COMMIT;
END IF;
END LOOP;
-- Final commit if not already committed
commit;
end_time := clock_timestamp();
elapsed_time := end_time - start_time;
INSERT INTO debug_log (method1, start_time, end_time, elapsed_time)
VALUES ('Method 2: Individual Inserts with Commit after 100 Rows', start_time, end_time, elapsed_time);
-- Method 3: Batch Inserts with Commit after all
start_time := clock_timestamp();
FOR i IN 1..(num_inserts / batch_size) LOOP
INSERT INTO parent_table3 VALUES
(1 + (i - 1) * batch_size, 'a'),[snip](49 + (i - 1) * batch_size, 'a'),
(50 + (i - 1) * batch_size, 'a'));
COMMIT;
END LOOP;
COMMIT; -- Final commit for all
end_time := clock_timestamp();
elapsed_time := end_time - start_time;
INSERT INTO debug_log (method1, start_time, end_time, elapsed_time)
VALUES ('Method 3: Batch Inserts with Commit after All', start_time, end_time, elapsed_time);
END $$;Reproduce what behavior?Anyway, plpgsql functions (including anonymous DO statements) are -- to Postgresql -- single statements. Thus, they'll be faster than individual calls..An untrusted language like plpython3u might speed things up even more, if you have to read a heterogeneous external file and insert all the records into the db.Here if you see my script , the method-1 is doing commit after each row insert. And method-2 is doing a batch commit i.e. commit after every "50" row. And method-3 is doing a true batch insert i.e. combining all the 50 values in one insert statement and submitting to the database in oneshot and then COMMIT it, so the context switching will be a lot less. So I was expecting Method-3 to be the fastest way to insert the rows here, but the response time shows the same response time for Method-2 and method-3. Method-1 is the slowest through.
