How do I make a MySQL database run completely in memory?
Assuming you understand the consequences of using the MEMORY engine as mentioned in comments, and here, as well as some others you'll find by searching about (no transaction safety, locking issues, etc) - you can proceed as follows:
MEMORY tables are stored differently than InnoDB, so you'll need to use an export/import strategy. First dump each table separately to a file using SELECT * FROM tablename INTO OUTFILE 'table_filename'. Create the MEMORY database and recreate the tables you'll be using with this syntax: CREATE TABLE tablename (...) ENGINE = MEMORY;. You can then import your data using LOAD DATA INFILE 'table_filename' INTO TABLE tablename for each table.
It is also possible to place the MySQL data directory in a tmpfs in thus speeding up the database write and read calls. It might not be the most efficient way to do this but sometimes you can't just change the storage engine.
Here is my fstab entry for my MySQL data directory
none /opt/mysql/server-5.6/data tmpfs defaults,size=1000M,uid=999,gid=1000,mode=0700 0 0
You may also want to take a look at the innodb_flush_log_at_trx_commit=2 setting. Maybe this will speedup your MySQL sufficently.
innodb_flush_log_at_trx_commit changes the mysql disk flush behaviour. When set to 2 it will only flush the buffer every second. By default each insert will cause a flush and thus cause more IO load.
Memory Engine is not the solution you're looking for. You lose everything that you went to a database for in the first place (i.e. ACID).
Here are some better alternatives:
Don't use joins - very few large apps do this (i.e Google, Flickr, NetFlix), because it sucks for large sets of joins.
A LEFT [OUTER] JOIN can be faster than an equivalent subquery because the server might be able to optimize it better—a fact that is not specific to MySQL Server alone.
-The MySQL Manual
- Make sure the columns you're querying against have indexes. Use EXPLAIN to confirm they are being used.
- Use and increase your Query_Cache and memory space for your indexes to get them in memory and store frequent lookups.
- Denormalize your schema, especially for simple joins (i.e. get fooId from barMap).
The last point is key. I used to love joins, but then had to run joins on a few tables with 100M+ rows. No good. Better off insert the data you're joining against into that target table (if it's not too much) and query against indexed columns and you'll get your query in a few ms.
I hope those help.