Decreasing performance in table partitioning - Mailing list pgsql-general
| From | Herouth Maoz |
|---|---|
| Subject | Decreasing performance in table partitioning |
| Date | |
| Msg-id | 1BAC89D2-2CE9-4968-9418-57E25540AFA1@unicell.co.il Whole thread Raw |
| Responses |
Re: Decreasing performance in table partitioning
Re: Decreasing performance in table partitioning |
| List | pgsql-general |
Hello all.
I have created a function that partitions a large table into monthly partitions. Since the name of the table, target schema for partitions, name of the date field etc. are all passed as strings, the function is heavily based on EXECUTE statements.
My problem is the main loop, in which data for one month is moved from the old table to the partition table.
(1)
EXECUTE FORMAT (
'WITH del AS (
DELETE FROM %1$I.%2$I
WHERE %3$I >= %4$L AND %3$I < %5$L
RETURNING *
)
INSERT INTO %6$I.%7$I
SELECT * FROM del',
p_main_schema,
p_table_name,
p_date_field_name,
v_curr_month_str,
v_curr_month_to_str,
p_partition_schema,
v_partition_name
);
In the first few iterations, this runs in very good times. But as iterations progress, performance drops, despite the size of the date for each month being more or less the same. Eventually I end up with iterations that run for hours, when I started with only a few minutes. The odd thing is that the last iteration, which is actually for a month not yet inserted into that table (0 records to move) it took 6 hours for the above statement to run!
I tried to improve this, by first testing whether there are any records for the current month in the table, adding:
(2)
EXECUTE FORMAT (
'SELECT true
FROM %1$I.%2$I
WHERE %3$I >= %4$L AND %3$I < %5$L
LIMIT 1',
p_main_schema,
p_table_name,
p_date_field_name,
v_curr_month_str,
v_curr_month_to_str
) INTO v_exists;
Before the above statement, and putting it in an IF statement on v_exists. Also, after each move, I added:
EXECUTE FORMAT ( 'ANALYZE %I.%I', p_main_schema, p_table_name );
But to no avail. In fact, in each iteration, the execution of statement 2 above takes more and more time.
Here is the number of rows in each month for the table I was trying to partition:
count | the_month
----------+---------------------
10907117 | 2013-08-01 00:00:00
12715234 | 2013-09-01 00:00:00
14902928 | 2013-10-01 00:00:00
10933566 | 2013-11-01 00:00:00
11394906 | 2013-12-01 00:00:00
9181051 | 2014-01-01 00:00:00
8487028 | 2014-02-01 00:00:00
9892981 | 2014-03-01 00:00:00
8830191 | 2014-04-01 00:00:00
8368638 | 2014-05-01 00:00:00
8014685 | 2014-06-01 00:00:00
6780589 | 2014-07-01 00:00:00
And the times for each iteration:
Month Statement 2 Statement 1
2013-08 3 sec 3 min
2013-09 2 min 17 min
2013-10 4 min 21 min
2013-11 8 min 20 min
2013-12 9 min 32 min
2014-01 16 min 21 min
2014-02 19 min 20 min
2014-03 14 min 23 min
For April I had to cancel it in the middle. My problem is that I can't let this run into the evening, when we have backup followed by large data collection. These times are just for the given statements, and additional time is spent creating indexes on the partitions and so on. So this thing ran from 11:24 until I had to cancel it at around 6PM.
Can anybody explain the performance deterioration and/or offer a suggestion for a different design?
TIA,
Herouth
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