Re: How batch processing works

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(dot)73(at)gmail(dot)com> wrote:

>
>
> On Thu, Sep 19, 2024 at 5:40 PM Ron Johnson <ronljohnsonjr(at)gmail(dot)com>
> wrote:
>
>> On Thu, Sep 19, 2024 at 5:24 AM Lok P <loknath(dot)73(at)gmail(dot)com> 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.
>