Author: Roger Pettett

A lot of what I do day-to-day is related to optimisation. Be it Perl code, SQL queries, Javascript or HTML there are usually at least a couple of cracking examples I find every week. On Friday I came across this:

SELECT cycle FROM goldcrest WHERE id_run = ?

This query is being used to find the number of the latest cycles (between 1 and 37 for each id_run) in a near-real-time tracking system and is used several times whenever a run report is viewed.

EXPLAIN SELECT cycle FROM goldcrest WHERE id_run = 231;
  
+----+-------------+-----------+------+---------------+---------+---------+-------+--------+-------------+
| id | select_type | table     | type | possible_keys | key     | key_len | ref   | rows   | Extra       |
+----+-------------+-----------+------+---------------+---------+---------+-------+--------+-------------+
|  1 | SIMPLE      | goldcrest | ref  | g_idrun       | g_idrun |       8 | const | 262792 | Using where |
+----+-------------+-----------+------+---------------+---------+---------+-------+--------+-------------+

In itself this would be fine but the goldcrest table in this instance contains several thousand rows for each id_run. So, for id_run, let’s say, 231 this query happens to return approximately 588,000 rows to determine that the latest cycle for run 231 is the number 34.

To clean this up we first try something like this:

SELECT MIN(cycle),MAX(cycle) FROM goldcrest WHERE id_run = ?

which still scans the 588000 rows (keyed on id_run incidentally) but doesn’t actually return them to the user, only one row containing both values we’re interested in. Fair enough, the CPU and disk access penalties are similar but the data transfer penalty is significantly improved.

Next I try adding an index against the id_run and cycle columns:

ALTER TABLE goldcrest ADD INDEX(id_run,cycle);
Query OK, 37589514 rows affected (23 min 6.17 sec)
Records: 37589514  Duplicates: 0  Warnings: 0

Now this of course takes a long time and, because the tuples are fairly redundant, creates a relatively inefficient index, also penalising future INSERTs. However, casually ignoring those facts, our query performance is now radically different:

EXPLAIN SELECT MIN(cycle),MAX(cycle) FROM goldcrest WHERE id_run = 231;
  
+----+-------------+-------+------+---------------+------+---------+------+------+------------------------------+
| id | select_type | table | type | possible_keys | key  | key_len | ref  | rows | Extra                        |
+----+-------------+-------+------+---------------+------+---------+------+------+------------------------------+
|  1 | SIMPLE      | NULL  | NULL | NULL          | NULL |    NULL | NULL | NULL | Select tables optimized away |
+----+-------------+-------+------+---------------+------+---------+------+------+------------------------------+
  
SELECT MIN(cycle),MAX(cycle) FROM goldcrest WHERE id_run = 231;
+------------+------------+
| MIN(cycle) | MAX(cycle) |
+------------+------------+
|          1 |         37 |
+------------+------------+
  
1 row in set (0.01 sec)

That looks a lot better to me now!

Generally I try to steer clear of the mysterious internal workings of database engines, but with much greater frequency come across examples like this:

sub clone_type {
  my ($self, $clone_type, $clone) = @_;
  my %clone_type;

  if($clone and $clone_type) {
    $clone_type{$clone} = $clone_type;
    return $clone_type{$clone};
  }

  return;
}

Thankfully this one’s pretty quick to figure out – they’re usually *much* more convoluted, but still.. Huh??

Pass in a clone_type scalar, create a local hash with the same name (Argh!), store the clone_type scalar in the hash keyed at position $clone, then return the same value we just stored.

I don’t get it… maybe a global hash or something else would make sense, but this works out the same:

sub clone_type {
  my ($self, $clone_type, $clone) = @_;

  if($clone and $clone_type) {
    return $clone_type;
  }
  return;
}

and I’m still not sure why you’d want to do that if you have the values on the way in already.

Programmers really need to think around the problem, not just through it. Thinking through may result in functionality but thinking around results in both function and performance which means a whole lot more in my book, and incidentally, why it seems so hard to hire good programmers.