Join Optimizations in MySQL 5.6 and MariaDB 5.5

This is the third blog post in the series of blog posts leading up to the talk comparing the optimizer enhancements in MySQL 5.6 and MariaDB 5.5. This blog post is targeted at the join related optimizations introduced in the optimizer. These optimizations are available in both MySQL 5.6 and MariaDB 5.5, and MariaDB 5.5

Profiling your slow queries using pt-query-digest and some love from Percona Server

This guide will get you up and running with how to identify the bottleneck queries using the excellent tool pt-query-digest. You will learn how to use and analyze the output returned by pt-query-digest. You will also learn some differences between slow query logging in various MySQL versions. Later on in the post I will also show you how to make use of the extra diagnostic data available with Percona Server.

On Covering Indexes and Their Impact on Performance

The purpose of this post is to describe what covering indexes are and how they can be used to improve the performance of queries. People mostly use indexes to filter or sort the results but not much thought is given to actually reduce the disk reads by using proper indexes. So I will show you how to reduce disk reads and hence improve the performance of queries by utilizing indexes properly.

Statement-based vs Row-based Replication

Replication as most people know it, has mostly been SQL statement propagation from master to slave. This is known as "statement-based" replication. But there is also another kind of replication that is available, "the row-based replication" and that has quite a lot of benefits. In this post I intend on highlighting the advantages and disadvantages of both the types of replication to help you choose the best one. I also follow up with my own recommendation.

MySQL Query Optimization – Tip # 1 – Avoid using wildcard character at the start of a LIKE pattern.

The more I go through others SQL, there are some common mistakes that I see developers making over and over again, so I thought why not start a series of tips that can help developers optimize their queries and avoid common pitfalls. So this post is a part of that series of tips, and this is the first tip "Avoid using a wild card character at the start of a LIKE pattern".

MySQL Paginated displays – How to kill performance vs How to improve performance!

Pagination is used very frequently in many websites, be it search results or most popular posts they are seen everywhere. But the way how it is typically implemented is naive and prone to performance degradation. In this article I attempt on explaining the performance implications of poorly designed pagination implementation. I have also analyzed how Google, Yahoo and Facebook handle pagination implementation. Then finally i present my suggestion which will greatly improve the performance related to pagination.

The effect of network latency on the performance of Apache pre-fork web server model

Web server performance is a complex topic, involving many variables. The most frequent of the variables that come to mind are database, CPU, memory and disk. The forgotten one is what we call "network latency". In this post I attempt to describe how important a part network latency plays in the performance of a web server, most importantly when we are talking about web servers that follow the pre-fork model, such as the immensely popular Apache Web Server.

Youtube batch processing – less costly requests

The need: YouTube is a very popular video hosting service as everyone knows. And there are many others like me who use this service to host videos. Then when needed these videos are searched based on user request and different levels of information are displayed. While this seems to be a trivial task and yes YouTube has a very nice API with all the functionality needed right there, and the documentation is really very comprehensive. On a side note I

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The “Shadow Table” trick.

The need: Often there is a requirement where data in a particular table has to be processed, and the data processing might be slow, while the table might be a one that is used by your application extensively. For example, a logging table that logs page hits. Or there might be an archiving operation that has to be performed on a particular table. Archiving / processing / aggregating records, all these operations are slow and can really blog down a

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Performance tuning using vertical partitioning.

Does having small data-sets really help? Of course it does! Are memory lookups faster that disk lookups. Of course ! So many times I have seen people complain about queries taking too long now, while they were not taking that long earlier. There is one big reason for this, earlier the size of data-set was small so it could fit into memory. Now that the data-set has grown large enough that it cannot fit entirely into memory, the disk seeks

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