I mentioned problems with InnoDB flushing in a previous post. Before getting to ideas on a solution, let’s define some terms and take a look into theory.

The two most important parameters for InnoDB performance are innodb_buffer_pool_size and innodb_log_file_size. InnoDB works with data in memory, and all changes to data are performed in memory. In order to survive a crash or system failure, InnoDB is logging changes into InnoDB transaction logs. The size of the InnoDB transaction log defines how many changed blocks we can have in memory for a given period of time. The obvious question is: Why can’t we simply have a gigantic InnoDB transaction log? The answer is that the size of the transaction log affects recovery time after a crash. The bigger the log, the longer the recovery time. …

In my previous post I introduced materialized view concepts. This post begins with an introduction to change data capture technology and describes some of the ways in which it can be leveraged for your benefit. This is followed by a description of FlexCDC, the change data capture tool included with Flexviews. It continues with an overview of how to install and run FlexCDC, and concludes with a demonstration of the utility.

As a reminder, the first post covered the following topics:

1. What is a materialized view(MV)?
2. It explained that an MV can pre-compute joins and may aggregate and summarize data.
3. Using the aggregated data can significantly improve query response times compared to accessing the non-aggregated data.
4. Keeping MVs up-to-date (refreshing) is …

If you know me, then you probably have heard of Flexviews. If not, then it might not be familiar to you. I’m giving a talk on it at the MySQL 2011 CE, and I figured I should blog about it before then. For those unfamiliar, Flexviews enables you to create and maintain incrementally refreshable materialized views.

You might be asking yourself “what is an incrementally refreshable materialized view?”. If so, then keep reading. This is the first in a multi-part series describing Flexviews.

edit:
You can find part 2 of the series here: http://www.mysqlperformanceblog.com/2011/03/25/using-flexviews-part-two-change-data-capture/

The output of …

Many software developers find they need to store hierarchical data, such as threaded comments, personnel org charts, or nested bill-of-materials. Sometimes it’s tricky to do this in SQL and still run efficient queries against the data. I’ll be presenting a webinar for Percona on February 28 at 9am PST. I’ll describe several solutions for storing and querying trees in an SQL database, including the design I call Closure Table.

In Closure Table, we store every path in a tree, not only direct parent-child references, but also grandparent-grandchild, and every other path, no matter how long. We even store paths of length zero, which means a node is its own parent. So if A is a parent of B, and B is a parent of C and C is a parent of D, we need to store the following paths: A-A, A-B, A-C, A-D, B-B, B-C, B-D, C-C, …

The “slow query log” is the single most valuable way to examine query execution on your MySQL server. Queries are logged with timing information, and in the case of Percona Server, a great deal of additional performance and other diagnostic information. But the execution time recorded in the log is the time the query took on the server, and the client that sent the query to the server might see something quite different. Sometimes it’s valuable to be able to see both views of query execution time.

Why would you want to see the query timing information from the application server or other client program? I have run into a handful of scenarios where this was desirable. For example, sometimes there is no access to the database server. I’ve seen this when access was forbidden by management, and when a server was so overloaded that nobody could get into it if they wanted to. Another reason for logging from the client is to log only …

Sometimes you just need some data to test and stress things. But randomly generated data is awful — it doesn’t have realistic distributions, and it isn’t easy to understand whether your results are meaningful and correct. Real or quasi-real data is best. Whether you’re looking for a couple of megabytes or many terabytes, the following sources of data might help you benchmark and test under more realistic conditions.

• The venerable sakila test database: small, fake database of movies.
• The employees test database: small, fake database of employees.
• The Wikipedia page-view statistics database: large, real website traffic data.
• The …