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Cost/Benefit Analysis of a MySQL Index

We all know that if we add a MySQL index to speed up a read, we end up making writes slower. How often do we do the analysis to look at how much more work is done?

Recently, a developer came to me and wanted to add an index to a very large table (hundreds of gigabytes) to speed up a query. We did some testing on a moderately used server:

Set long_query_time to 0 and turn slow query logging on
Turn slow query logging off after 30 minutes.

Add the index (was on a single field)

Repeat the slow query logging for 30 minutes at a similar time frame (in our case, we did middle of the day usage on a Tuesday and Wednesday, when the database is heavily used).

Then I looked at the write analysis – there were no DELETEs, no UPDATEs that updated the indexed field, and no UPDATEs that used the indexed field in the filtering. There were only INSERTs, and with the help of pt-query-digest, here’s what I found: …

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Why does the MySQL optimizer not do what I think it should?

In May, I presented two talks – one called “Are you getting the best out of your indexes?” and “Optimizing Queries Using EXPLAIN”. I now have slides and video for both of them.

The first talk about indexing should probably be titled “Why is MySQL doing this?!!?!!?” It gives insight into why the MySQL optimizer chooses indexes that you do not expect; especially when it does not use an index you expect it to.

The talk has something for everyone – for beginners it explains B-trees and how they work, and for the more seasoned DBA it explains concepts like average value group size, and how the optimizer uses those concepts applied to metadata to make decisions.

Slides are at
Click the slide image below to go to the video at …

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Correct Index Choices for Equality + LIKE Query Optimization

As part of our support services, we do a lot of query optimization. This is where most performance gains come from. Here’s an example of the work we do.

Some days ago a customer arrived with the following table:

CREATE TABLE `infamous_table` (
  `member_id` int(11) NOT NULL DEFAULT '0',
  `email` varchar(200) NOT NULL DEFAULT '',
  `msg_type` varchar(255) NOT NULL DEFAULT '',
  `t2send` int(11) NOT NULL DEFAULT '0',
  `flag` char(1) NOT NULL DEFAULT '',
  `sent` varchar(100) NOT NULL DEFAULT '',
  PRIMARY KEY (`id`),
  KEY `f` (`flag`),
  KEY `email` (`email`),
  KEY `msg_type` (`msg_type`(5)),
  KEY `t_msg` (`t2send`,`msg_type`(5))

And a query that looked like this:

  FROM `infamous_table`
 WHERE `t2send` > 1234 …
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Thoughts on MySQL 8.0 Invisible Indexes

MySQL 8.0 has a new feature called “invisible indexes,” which allow you to quickly enable/disable indexes from being used by the MySQL Optimizer.

I wanted to share some of my first experiences and thoughts about this new feature.

Why is it good for us?

There are a couple of use cases. One of them is if you want to drop an index, but want to know the effect beforehand. You can make it invisible to the optimizer. It is a quick metadata change to make an index invisible. Once you are sure there is no performance degradation, you can then drop the index.

The main point is that the invisible index is unavailable for use by the optimizer, but it is still present and kept up-to-date by write operations. The optimizer won’t use it, even if we try to “FORCE INDEX”. I think we should be …

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Understanding Bulk Index Creation in InnoDB (and innodb_sort_buffer_size)

In a previous post, I presented an Unexpected Memory Consumption for Bulk Index Creation in InnoDB.  This was triggered by an increased innodb_sort_buffer_size and as stated in another post: "the sorting algorithm does not scale well with large sort buffers".  In this post, I will present why it does not scale well and I will suggest solutions.

This post also answers feedback request for the

Unexpected Memory Consumption for Bulk Index Creation in InnoDB (MySQL)

In my last Hackathon, I worked on MyISAM vs InnoDB for data loading (LOAD DATA IN FILE) and bulk index creation.  My motivation was the following: knowing that some are still using MyISAM for this particular use-case, I wanted to verify/understand if/why InnoDB is slower than MyISAM.  I do not yet have complete results on this specific subject but I found some interesting things that

Percona Live Presentation: Improving Performance With Better Indexes

The slides for my Improving Performance With Better Indexes presentation at Percona Live 2015 MySQL Conference and Expo are now available.

In this presentation I discuss how to identify, review and analyze SQL statements in order to create better indexes for your queries. This includes understanding the EXPLAIN syntax and how to create and identify covering and partial column indexes.

Download PDF Presentation

This presentation is based on the work with a customer showing the 95% improvement of a key 15 table join query running 15,000 QPS in a 25 billion SQL statements per day …

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JSON Labs Release: Effective Functional Indexes in InnoDB

In MySQL 5.7.6, we added a new feature called Generated Columns. In the initial work all Generated Columns were materialized, even virtual ones. This not only resulted in unnecessary disk space being used and disk I/O being done, but it also meant that any table alteration required that the full table be rebuilt. In the new MySQL 5.7.7 JSON Lab release, we have resolved all of these issues by implementing new features that not only allow users to create non-materialized virtual …

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EXPLAIN minor wishlist

While we always want better performance and more and larger features for MySQL, those cannot just “magically appear” from one version to another, requiring deep architecture changes and lots of lines of code. However, there are sometimes smaller features and fixes that could be implemented by an intern or an external contributor, mainly at SQL layer, and that could make the MySQL ecosystem friendlier to newbies and non-experts. Making a piece of software easier to use is sometimes overlooked, but it is incredibly important -not everybody using MySQL is a DBA, and the more people adopting it, more people will be able to live from it, both upstream and as third party providers.

Here it is my own personal list of fixes for EXPLAIN messages. If you are an experienced MySQL user you are probably aware of their meaning, but that doesn’t solve the problem for beginners. The reason why I am writing a blog post is to gather …

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Advanced JSON for MySQL

What is JSON

JSON is an text based, human readable format for transmitting data between systems, for serializing objects and for storing document store data for documents that have different attributes/schema for each document. Popular document store databases use JSON (and the related BSON) for storing and transmitting data.

Problems with JSON in MySQL

It is difficult to inter-operate between MySQL and MongoDB (or other document databases) because JSON has traditionally been very difficult to work with. Up until recently, JSON is just a TEXT document. I said up until recently, so what has changed? The biggest thing is that there are new JSON UDF by Sveta Smirnova, which are part of the MySQL 5.7 Labs releases. Currently the JSON UDF are up to version 0.0.4. While these new UDF are a welcome edition to the MySQL database, they don’t solve the really tough …

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