This blog covers
⚈ PARTITIONing uses and non-uses
⚈ How to Maintain a time-series PARTITIONed table
⚈ AUTO_INCREMENT secrets
First, my Opinions on PARTITIONing
Taken from Rick's RoTs - Rules of Thumb for MySQL
⚈ #1: Don't use PARTITIONing until you know how and why it will help.
⚈ Don't use PARTITION unless you will have >1M rows
⚈ No more than 50 PARTITIONs on a table (open, show table status, etc, are impacted) (fixed in 5.6.6?; a better fix coming eventually in 5.7)
⚈ PARTITION BY RANGE is the only useful method.
⚈ SUBPARTITIONs are not useful.
⚈ The partition field should not be the field first in any key.
⚈ It is OK to have an AUTO_INCREMENT as the first part of a compound key, or in a non-UNIQUE index.
It is so tempting to believe that PARTITIONing will solve performance problems. But it is so often wrong.
PARTITIONing splits up one table into several smaller tables. But table size is rarely a performance issue. Instead, I/O time and indexes are the issues.
A common fallacy: "Partitioning will make my queries run faster". It won't. Ponder what it takes for a 'point query'. Without partitioning, but with an appropriate index, there is a BTree (the index) to drill down to find the desired row. For a billion rows, this might be 5 levels deep. With partitioning, first the partition is chosen and "opened", then a smaller BTree (of say 4 levels) is drilled down. Well, the savings of the shallower BTree is consumed by having to open the partition. Similarly, if you look at the disk blocks that need to be touched, and which of those are likely to be cached, you come to the conclusion that about the same number of disk hits is likely. Since disk hits are the main cost in a query, Partitioning does not gain any performance (at least for this typical case). The 2D case (below) gives the main contradiction to this discussion.
Use Cases for PARTITIONing
Use case #1 -- time series. Perhaps the most common use case where PARTITIONing shines is in a dataset where "old" data is peroidically deleted from the table. RANGE PARTITIONing by day (or other unit of time) lets you do a nearly instantaneous DROP PARTITION plus REORGANIZE PARTITION instead of a much slower DELETE. Much of this blog is focused on this use case. This use case is also discussed in Big DELETEs
The big win for Case #1: DROP PARTITION is a lot faster than DELETEing a lot of rows.
Use case #2 -- 2-D index. INDEXes are inherently one-dimensional. If you need two "ranges" in the WHERE clause, try to migrate one of them to PARTITIONing.
Finding the nearest 10 pizza parlors on a map needs a 2D index. Partition pruning sort of gives a second dimension. See Latitude/Longitude Indexing
That uses PARTITION BY RANGE(latitude) together with PRIMARY KEY(longitude, ...)
The big win for Case #2: Scanning fewer rows.
Use case #3 -- hot spot. This is a bit complicated to explain. Given this combination:
⚈ A table's index is too big to be cached, but the index for one partition is cacheable, and
⚈ The index is randomly accessed, and
⚈ Data ingestion would normally be I/O bound due to updating the index
Partitioning can keep all the index "hot" in RAM, thereby avoiding a lot of I/O.
The big win for Case #3: Improving caching to decrease I/O to speed up operations.
Use case #4 -- transportable tablespace. Using EXPORT/IMPORT partition for quickly archiving or importing data. (IMPORTing could be tricky because of the partition key.) See also Transportable Tablespaces for InnoDB Partitions
That link talks about 5.7, but has a section "But how to do this in 5.6?"
See also FLUSH TABLES ... FOR EXPORT, which was not supported for Partitioned InnoDB tables until 5.6.17.
The big win for Case #4: Quickly moving a partition in between tables (or servers).
Use case #5 -- I have yet to find a 5th use case.
Note that almost always, these use cases involve RANGE partitioning, not the other forms.
AUTO_INCREMENT in PARTITION
⚈ For AUTO_INCREMENT to work (in any table), it must be the first field in some index. Period. There are no other requirements on indexing it.
⚈ Being the first field in some index lets the engine find the 'next' value when opening the table.
⚈ AUTO_INCREMENT need not be UNIQUE. What you lose: prevention of explicitly inserting a duplicate id. (This is rarely needed, anyway.)
Examples (where id is AUTO_INCREMENT):
⚈ PRIMARY KEY (...), INDEX(id)
⚈ PRIMARY KEY (...), UNIQUE(id, partition_key) -- not useful
⚈ INDEX(id), INDEX(...) (but no UNIQUE keys)
⚈ PRIMARY KEY(id), ... -- works only if id is the partition key (not very useful)
PARTITION Maintenance for the Time-Series Case
Let's focus on the maintenance task involved in Case #1, as described above.
You have a large table that is growing on one end and being pruned on the other. Examples include news, logs, and other transient information. PARTITION BY RANGE is an excellent vehicle for such a table.
⚈ DROP PARTITION is much faster than DELETE. (This is the big reason for doing this flavor of partitioning.)
⚈ Queries often limit themselves to 'recent' data, thereby taking advantage of "partition pruning".
Depending on the type of data, and how long before it expires, you might have daily or weekly or hourly (etc) partitions.
There is no simple SQL statement to "drop partitions older than 30 days" or "add a new partition for tomorrow". It would be tedious to do this by hand every day.
High Level View of the Code
ALTER TABLE tbl
DROP PARTITION from20120314;
ALTER TABLE tbl
REORGANIZE PARTITION future INTO
from20120415 VALUES LESS THAN (TO_DAYS('2012-04-16')),
future VALUES LESS THAN MAXVALUE;
After which you have...
CREATE TABLE tbl (
dt DATETIME NOT NULL, -- or DATE
PRIMARY KEY (..., dt),
UNIQUE KEY (..., dt),
PARTITION BY RANGE (TO_DAYS(dt)) (
start VALUES LESS THAN (0),
from20120315 VALUES LESS THAN (TO_DAYS('2012-03-16')),
from20120316 VALUES LESS THAN (TO_DAYS('2012-03-17')),
from20120414 VALUES LESS THAN (TO_DAYS('2012-04-15')),
from20120415 VALUES LESS THAN (TO_DAYS('2012-04-16')),
future VALUES LESS THAN MAXVALUE
Perhaps you noticed some odd things in the example. Let me explain them.
⚈ Partition naming: Make them useful.
⚈ from20120415 ... 04-16: Note that the LESS THAN is the next day's date
⚈ The "start" partition: See paragraph below.
⚈ The "future" partition: This is normally empty, but it can catch overflows; more later.
⚈ The range key (dt) must be included in any PRIMARY or UNIQUE key.
⚈ The range key (dt) should be last in any keys it is in -- You have already "pruned" with it; it is almost useless in the index, especially at the beginning.
⚈ DATETIME, etc -- I picked this datatype because it is typical for a time series. Newer MySQL versions allow TIMESTAMP. INT could be used; etc.
⚈ There is an extra day (03-16 thru 04-16): The latest day is only partially full.
Why the bogus "start" partition? If an invalid datetime (Feb 31) were to be used, the datetime would turn into NULL. NULLs are put into the first partition. Since any SELECT could have an invalid date (yeah, this stretching things), the partition pruner always includes the first partition in the resulting set of partitions to search. So, if the SELECT must scan the first partition, it would be slightly more efficient if that partition were empty. Hence the bogus "start" partition. Longer discussion, by The Data Charmer
5.5 eliminates the bogus check, but only if you switch to a new syntax:
PARTITION BY RANGE COLUMNS(dt) ( PARTITION day_20100226 VALUES LESS THAN ('2010-02-27'), ...
ALTER TABLE tbl REORGANIZE PARTITION future INTO ( PARTITION from20150606 VALUES LESS THAN (736121), PARTITION future VALUES LESS THAN MAXVALUE ) ALTER TABLE tbl DROP PARTITION from20150603