This post was remaining in stand-by for a long time, specially that I was expecting that observed issues will be fixed soon. But time is going, and the problems are remaining. And I'm constantly asked "why, Dimitri, you're suggesting now to use XFS, while in the past you always suggested EXT4 ??" -- hope the following article will clarify you the "why" and maybe motivate you to do your own evaluations to see how well the things are working for you on your own systems under your own workloads..

NOTE : this will also clarify why the new Double Write did not appear in MySQL 8.0 in 2018, as it was planned, but only recently (http://dimitrik.free.fr/blog/posts/mysql-80-perf-new-dblwr.html)

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On a busy server, the binary logs can end up being one of the largest contributors to amount of disk space used. That means higher I/O, larger backups (you are backing up your binary logs, right?), potentially more network traffic when replicas fetch the logs, and so on. In general, binary logs compress well, so it has been a long time wish for a feature that allowed you to compress the logs while MySQL are still using them. Starting from MySQL 8.0.20 that is now possible. I will take a look at the new feature in this post.

Configuration

The binary log compression feature is controlled by two variables, one for enabling the feature and …

The new MySQL-8.0.20 release is coming with re-designed InnoDB Double Write Buffer (DBLWR), and, indeed, it's one huge historical PITA less.. -- why it was so painful and cost us much blood in the past, I could not better explain than already done it in the following article yet from 2018 about MySQL on IO-bound workloads.. The story is not complete, as it's missing the 2019's chapter (will tell it later, np) -- but if you'll (re)read the mentioned above article first, you'll better understand the next ;-))

But at least the current post is only about good news now -- the new DBLWR and how it helps to solve historical MySQL performance problems ! -- and as one picture is better than million words, I'll try to save 3M words here (as there are 3 pictures in this article ;-))

Well, I'll also skip all new design details …

We have just released MySQL 8.0.20. And it has some interesting replication enhancements. In particular one big and exciting feature: binary log compression. Here is the list of things in this release:

• Binary Log Compression (WL#3549). This work done by Luís Soares implements binary log compression, making use of the popular compression algorithm ZSTD.

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To celebrate the publishing of my new book MySQL 8 Query Performance Tuning, the Apress team invited me (thanks Jonathan and Liz) to write a post for the Apress blog. I decided to write about my top six best practices:

• Be wary of best practices
• Monitor
• Work methodically
• Consider the full stack
• Make small, incremental changes
• Understand the change

Yes, my first best practice is to be wary of best practices. Read why I added that and the other best practices at …

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MySQL introduced optimizer hints in version 5.7 and greatly extended the feature in MySQL 8. One thing that has been missing though is the ability to specify index hints using the syntax of optimizer hints. This has been improved of in MySQL 8.0.20 with the introduction of index-level optimizer hints for the FORCE and IGNORE versions of the index hints. This blog will look at the new index hint syntax.

Warning

Do not add index hints – neither using the old or new style – unless you really need them. When you add index hints, you limit the options of the optimizer which can prevent the optimizer obtaining the optimal query plan as new optimizer improvements are implemented or the data changes.

On …

Usually one would expect that ALTER TABLE with ALGORITHM=COPY will be slower than the default ALGORITHM=INPLACE. In this blog post we describe the case when this is not so.

One of the reasons for such behavior is the lesser known limitation of ALTER TABLE (with default ALGORITHM=INPLACE) that avoids REDO operations. As a result, all dirty pages of the altered table/tablespace have to be flushed before the ALTER TABLE completion.

Some history

A long time ago, all “ALTER TABLE” (DDLs) operations in MySQL were implemented by creating a new table with the new structure, then copying the content of the original table to the new table, and finally renaming the table. During this operation the table was locked to prevent data inconsistency.

Then, for InnoDB tables, the new algorithms were introduced, which do not involve the full table copy and some operations do not apply the table level lock – first the online …

A few days ago one of our intern @mydbops reached me with a SQL query. The query scans only a row according to the execution plan. But query does not seems optimally performing.

Below is the SQL query and its explain plan. ( MySQL 5.7 )

select username, role from user_roles where username= '9977223389' ORDER BY role_id DESC LIMIT 1;

Execution plan and table structure

*************************** 1. row ***************************
           id: 1
  select_type: SIMPLE
        table: user_roles
   partitions: NULL
         type: index
possible_keys: NULL
          key: PRIMARY
      key_len: 4
          ref: NULL
         rows: 1
     filtered: 10.00
        Extra: Using where
1 row in set, 1 warning (0.00 sec)

show create table user_roles\G
*************************** 1. row ***************************
       Table: …

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I have over the last few years been fortunate to have two books published through Apress, Pro MySQL NDB Cluster which I wrote together with Mikiya Okuno and MySQL Connector/Python Revealed. With the release of MySQL 8 around a year ago, I started to think of how many changes there has been in the last few MySQL versions. Since MySQL 5.6 was released as GA in early 2013, some of the major features related to performance tuning includes the Performance Schema which was greatly changed in 5.6, histograms, EXPLAIN ANALYZE, hash …

In this fourth post in our MySQL Use Case Blog Series we look at a customer of ours who was able to grow their Saas business from tens of customers initially to thousands of enterprise customers once they achieved continuous MySQL operations with Continuent Tungsten.

This particular customer, based in California, develops and sells automation software for account-based marketing as well as other marketing services and products such as SEO and content marketing. They provide solutions tailored for large enterprises and fast-growing, small businesses alike covering all industry types from technology all the way to higher education.

So how did this fast growing marketing automation SaaS provider scale from tens of customers to thousands of enterprise customers using Tungsten Clustering (currently with 600+ MySQL instances)?

The Challenge

SaaS and other web applications are inherently 24/7/365 operations, thus they …