Recently I was doing a little work for a client who has MyISAM tables with many columns (the same one Peter wrote about recently). The client's performance is suffering in part because of the number of columns, which is over 200. The queries are generally pretty simple (sums of columns), but they're ad-hoc (can access any columns) and it seems tailor-made for a column-oriented database.

I decided it was time to actually give Infobright a try. They have an open-source community edition, which is crippled but not enough to matter for this test. The "Knowledge Grid" architecture seems ideal for the types of queries the client runs. But hey, why not also try MonetDB, another open-source column-oriented database I've been …

MySQL backup soon becomes an important matter when the database is used in production. The pain-point comes from the fact that while backuping the database is not available to respond to client requests anymore. With mysqldump - the standard tool for performing MySQL backups - and a large database the operation can go over many tenth of minutes if not hours. If I am running my business on line this is simply not acceptable.

The classical approach to workaround this problem is to take advantage of MySQL replication. I set up a master/slave configuration where the slave acts as copy of the master. Then, when needed, I run mysqldump on the slave without any service interruption on the master.

But ZFS snapshosts bring a new straightforward approach that avoids the pain and the complexity of a master/slave replication.

Snapshots are a key feature of ZFS that allows me to save a copy of …

It is pretty understood the tables which have long rows tend to be slower than tables with short rows. I was interested to check if the row length is the only thing what matters or if number of columns we have to work with also have an important role. I was interested in peak row processing speed so I looked at full table scan in case data fits in OS cache completely. I created 3 tables - First containing single tinyint column which is almost shortest type possible (CHAR(0) could be taking less space), table with 1 tinyint column and char(99) column and table with 100 tinyint columns. The former two tables have the same row length but have number of column different 50 times. Finally I have created 4th table which is also 100 columns but one of them is VARCHAR causes raw format to be dynamic.

More specially:

PLAIN TEXT SQL:

1. CREATE TABLE `t1` (
…

I spent Friday examining the systems for a more traditional consulting gig (in case you did not know, Pythian has offered more traditional consulting and emergency services in addition to our remote DBA offering for our entire 12 year history). It is a familiar story to many people — the database performance was unacceptable.

The company had a few log tables that had huge amounts of inserts and foreign keys, so they used InnoDB. Unfortunately, they also used a GUID as a primary key (which is getting more and more common these days, but long primary keys with InnoDB slow down INSERT, UPDATE and DELETE commands a great deal) — varchar(32) and utf8.

That’s right — their primary key for many of these tables was 96 bytes long (32 characters * 3 bytes per character), and as an InnoDB table, the primary key is clustered with …

It is rather typical for systems to start as MyISAM but as system growths to move to Innodb. The reason of the move could be just desire for better data consistency guaranty or being bitten repairing multiple GB MyISAM table few times, though Table Locks is probably the most important issue - with modern multi core servers not only the fact you can't well mix SELECTs and UPDATEs but also the fact only one update can be happening at the time can be the problem, not to mention Key Cache which often becomes serious contention issue.

The problem we often run into during migration is Full Text Search indexes which are not supported for Innodb tables. So what can you do ?

Leave Tables as …

There is a little-known setting, myisam_block_size in MySQL. This affects the block size used in the indexes of MyISAM tables stored in .MYI files on disc and in the key buffer.

The default value is 1k, this is possibly too small for best performance on modern systems; in particular, many filesystems used a bigger block size, so writing a single index block requires a read followed by a write. Random reads are really slow on hard discs (writes are mostly fast as they go into your battery-backed raid controller which has lots of RAM).

I am currently in the process of experimenting with myisam_block_size, and so far have determined the following:

• myisam_block_size is settable only at server start time, either in my.cnf or on the command-line
• myisam_block_size only affects newly created tables or tables rebuilt using ALTER TABLE; existing MyISAM tables keep their old index block size and …

MySQL Full Text Search Limitations

Suppose you have a MyISAM table containing a column with a full text index. This table starts to grow to a significant size (millions of rows) and gets updated fairly frequently. Chances are that you’ll start to see some bottlenecks when accessing this table, since without row level locking, the reading and writing operations will be blocking each other.

A solution that many people would suggest right away is to use the master for writes and a slave for reads, but this only masks the problem, and it won’t take long before enough read traffic on the slave starts causing slave lags.

Why Sphinx?

The main difference between the Sphinx search engine and other alternatives is its close integration with MySQL. For example, it can be used as a storage engine.  In this way, Sphinx’s impact on existing application code …

One of the ways to import data into MySQL is using the LOAD DATA INFILE. It is a faster method than recovering from a dump, as it’s raw data instead of SQL sentences.

The import time depends on the table engine, for example, MyISAM can be 40 times faster than Innodb. Let’s benchmark this:

Preparation

I’m gonna make some benchmarking using MySQL 5.1.36 (64 bits MacOS X). I’ll need a big table, so I’ll take City from the World Database and create a huge table called “city_huge”:

CREATE TABLE city_huge LIKE CITY;

INSERT INTO city_huge 
    SELECT NULL, name, CountryCode, District, Population FROM city;
# Run this sentence 100 times,
# so city_huge table will be 100 times bigger than city.
# Tip: use a script, temporary table, stored procedure...
# or tell your monkey to do so.

SELECT COUNT(*) FROM …

Seen in #maatkit on Freenode:

I never realized just how terrible recovering MyISAM from a crash can be

Sad but true — it can be pretty painful. This is one of the reasons I pretty much recommend InnoDB (okay, okay, XtraDB) for most data unless it’s read-only.

Related posts:

1. Hindsight on a scalable replacement for InnoDB A while ag
2. What is the scalable replacement for InnoDB? A while ba
3. Xtrabackup is for InnoDB tables too, not just XtraDB Just thoug

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As I've wrote few times using multiple key caches is a great way to get CPU scalability if you're using MyISAM. It is however very annoying - this feature really looks half baked to me.

The problem with multiple key caches and mapping of tables to the different files is - there is no way to see the existing key caches, their mapping and stats. The only thing you can access is key cache parameters - via structured variables

In particular I would like to:

See the list of created caches Right now I can create key caches with random names causing invisible resource consumption. It is possible to make an error in key cache creation but it is not possible to later find out such key …