MySQL Partitioning & OPTIMIZE TABLE

MySQL table Partitioning can be used in various way to improve performance. I wanted to get some idea of how this would affect database maintenance operations like OPTIMIZE TABLE.

For these tests I used a default install of MySQL 5.6.15, with a key_buffer_size of 2GB, and executed OPTIMIZE TABLE table_name after a series of actions (detailed below).

Here are the results of an OPTIMIZE TABLE statement after an initial load of 2.8GB of data. The 2.8 GB of data produced 4.5GB of indices (slightly more for the HASH partitioned table due to the PK modification).

Table Type Optimize Time (m, s)
Non Partitioned 17 min 20.09 sec
Hash-Partitioned (10 partitions) 2 min 38.61 sec
Range-Partitioned (by auto increment id) 1 min 37.27 sec

Non-Partitioned Table

CREATE TABLE `table1` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `second_id` mediumint(11) unsigned NOT NULL,
  `third_id` tinyint(11) unsigned NOT NULL,
  `fourth_id` mediumint(11) unsigned NOT NULL,
  `fifth_id` tinyint(11) unsigned NOT NULL,
  `value` double DEFAULT NULL,
  PRIMARY KEY (`id`),
  KEY `second_refs_id_` (`second`),
  KEY `third_id_refs_id` (`third_id`),
  KEY `fifth_id_refs_id` (`fifth_id`)
) ENGINE=MyISAM

Hash-Partitioned Table

CREATE TABLE `table2` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `second_id` mediumint(11) unsigned NOT NULL,
  `third_id` tinyint(11) unsigned NOT NULL,
  `fourth_id` mediumint(11) unsigned NOT NULL,
  `fifth_id` tinyint(11) unsigned NOT NULL,
  `value` double DEFAULT NULL,
  PRIMARY KEY (`id`, fifth_id),
  KEY `second_refs_id_` (`second`),
  KEY `third_id_refs_id` (`third_id`),
  KEY `fifth_id_refs_id` (`fifth_id`)
) ENGINE=MyISAM
PARTITION BY HASH(fifth_id)
PARTITIONS 10;

Range-Partitioned Table

CREATE TABLE `table1` (
  `id` int(11) unsigned NOT NULL AUTO_INCREMENT,
  `second_id` mediumint(11) unsigned NOT NULL,
  `third_id` tinyint(11) unsigned NOT NULL,
  `fourth_id` mediumint(11) unsigned NOT NULL,
  `fifth_id` tinyint(11) unsigned NOT NULL,
  `value` double DEFAULT NULL,
  PRIMARY KEY (`id`, fifth_id),
  KEY `second_refs_id_` (`second`),
  KEY `third_id_refs_id` (`third_id`),
  KEY `fifth_id_refs_id` (`fifth_id`)
) ENGINE=MyISAM
PARTITION BY RANGE(id)
(
    PARTITION p0 VALUES LESS THAN (14203359),
    PARTITION p1 VALUES LESS THAN (28406718),
    PARTITION p2 VALUES LESS THAN (42610077),
    PARTITION p3 VALUES LESS THAN (56813436),
    PARTITION p4 VALUES LESS THAN (71016795),
    PARTITION p5 VALUES LESS THAN (85220153),
    PARTITION p6 VALUES LESS THAN (99423512),
    PARTITION p7 VALUES LESS THAN (113626871),
    PARTITION p8 VALUES LESS THAN (127830230),
    PARTITION p9 VALUES LESS THAN (142033590),
    PARTITION p10 VALUES LESS THAN MAXVALUE
);

Now let’s delete one row from each of the table.

DELETE FROM table1 LIMIT 1;
DELETE FROM table2 LIMIT 1;
DELETE FROM table2 LIMIT 1;

What does this do to our optimize times?

Table Type Optimize Time (m, s)
Non Partitioned 16 min 53.11 sec
Hash-Partitioned (10 partitions) 0.70 sec
Range-Partitioned (by auto increment id) 1 min 4.87 sec

How about a random delete of ten rows?

What does this do to our optimize times?

Table Type Optimize Time (m, s)
Non Partitioned 15 min 44.20 sec
Hash-Partitioned (10 partitions) 12 min 47.28 sec
Range-Partitioned (by auto increment id) 5 min 55.09 sec

Please note for this final test your own mileage may vary according to which partitions the delete touches. It’s obvious here MySQL has some kind of internal flag to see if an index has been changed. If the indexes on a partition have not changed then it doesn’t need to optimize it.

I’m still not entirely sure why the HASH partition test performed so well in the second test. I received the same result for multiple iteration of this test. If anyone has any ideas then I’m all ears!


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