MySQL databases serve as the foundation of countless applications, powering everything from simple blogs to massive enterprise platforms. But as these applications evolve and user bases expand, MySQL performance issues tend to pop up and ensuring smooth database performance becomes crucial. Fast queries mean happy users, while sluggish performance can lead to frustration and lost […]

In this blog, I will demonstrate how to use Percona Monitoring and Management (PMM) to find out the reason why the MySQL server is stalling. I will use only one typical situation for the MySQL server stall in this example, but the same dashboards, graphs, and principles will help you in all other cases.

Nobody wants it but database servers may stop handling connections at some point. As a result, the application will slow down and then will stop responding.

It is always better to know about the stall from a monitoring instrument rather than from your own customers.

PMM is a great help in this case. If you look at its graphs and notice that many of them started showing unusual behavior, you need to react. In the case of stalls, you will see that either some activity went to 0 or, otherwise, it increased to high …

Monitoring your MySQL database performance in real-time helps you immediately identify problems and other factors that could be causing issues now or in the future. It’s also a good way to determine which components of the database can be enhanced or optimized to increase your efficiency and performance. This is usually done through monitoring software and tools either built-in to the database management software or installed from third-party providers.

Prometheus is an open-source software application used for event monitoring and alerting. It can be used along with a visualization tool like Grafana to easily create and edit dashboards, query, visualize, alert on, and understand your metrics. ScaleGrid provides full admin access to your MySQL deployments – this makes it …

Please join Percona CEO Peter Zaitsev as he presents “Performance Analyses and Troubleshooting Technologies for Databases” on Wednesday, August 7th, 2019 at 11:00 AM PDT (UTC-7).

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Have you heard about the USE Method (Utilization – Saturation – Errors), RED (Rate – Errors – Duration) or Golden Signals (Latency – Traffic – Errors – Saturations)?

In this presentation, we will talk briefly about these different-but-similar “focuses” and discuss how we can apply them to data infrastructure performance analysis troubleshooting and monitoring.

We will use MySQL as an example, but most of this talk applies to other database technologies as well.

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Next in our Benefits of SQL Diagnostic Manager for MySQL (formerly Monyog) blog series, we discuss monitoring and understanding performance trends using visual analytics and the display dashboard of SQL Diagnostic Manager for MySQL. If you missed it, you can read last week’s blog on identifying and analyzing problematic SQL queries.

View and Understand Trends By Analyzing Historical Data

Configure the time duration for storing the data collected by SQL Diagnostic Manager for MySQL. It stores the data in a high-performance database (that is, the embedded relational database management system SQLite). By analyzing historical data, quickly obtain answers to questions like:

• How many times and when did database servers go down during the last six months? Which …

Sysbench has long been established as the de facto standard when it comes to benchmarking MySQL performance. Percona relies on it daily, and even Oracle uses it when blogging about new features in MySQL 8. Sysbench comes with several pre-defined benchmarking tests. These tests are written in an easy-to-understand scripting language called Lua. Some of these tests are called: oltp_read_write, oltp_point_select, tpcc, oltp_insert. There are over ten such scripts to emulate various behaviors found in standard OLTP applications.

But what if your application does not fit the pattern of traditional OLTP? How can you continue to utilize the power of load-testing, benchmarking, …

Wondering which databases are trending in 2019? We asked hundreds of developers, engineers, software architects, dev teams, and IT leaders at DeveloperWeek to discover the current NoSQL vs. SQL usage, most popular databases, important metrics to track, and their most time-consuming database management tasks. Get the latest insights on MySQL, MongoDB, PostgreSQL, Redis, and many others to see which database management systems are most favored this year.

SQL vs. NoSQL

As any database administrator knows, the first question you have to ask yourself is whether to use a SQL or NoSQL database for your application. …

MySQL 8.0.15 performs worse in sysbench oltp_read_write than MySQL 5.7.25

Initially I was testing group replication performance and was puzzled why MySQL 8.0.15 performs consistently worse than MySQL 5.7.25.

It appears that a single server instance is affected by a performance degradation.

My testing setup

Hardware details:
Bare metal server provided by packet.net, instance size: c2.medium.x86
24 Physical Cores @ 2.2 GHz
(1 X AMD EPYC 7401P)
Memory: 64 GB of ECC RAM

Storage : INTEL® SSD DC S4500, 480GB

This is a server grade SATA SSD.

Benchmark

sysbench oltp_read_write --report-interval=1 --time=1800 --threads=24 --tables=10 --table-size=10000000 --mysql-user=root --mysql-socket=/tmp/mysql.sock run

In the following summary I used these combinations:

• innodb_flush_log_at_trx_commit=0 or 1
• Binlog: …

Over the last year, I have been pursuing a part time hobby project exploring ways to squeeze as much data as possible in MySQL. As you will see, there are quite a few different ways. Of course things like compression ratio matters a lot but, other items like performance of inserts, selects and updates, along with the total amount of bytes written are also important. When you start combining all the possibilities, you end up with a large set of compression options and, of course, I am surely missing a ton. This project has been a great learning opportunity and I hope you’ll enjoy reading about my results. Given the volume of results, I’ll have to write a series of posts. This post is the first of the series. I also have to mention that some of my work overlaps work done by one of my colleague, Yura Sorokin, in a …

The main focus of a previous blog post was the performance of MyRocks when using fast SSD devices. However, I figured that MyRocks would be beneficial for use in cloud workloads, where storage is either slow or expensive.

In that earlier post, we demonstrated the benefits of MyRocks, especially for heavy IO workloads. Meanwhile, Mark wrote in his blog that the CPU overhead in MyRocks might be significant for CPU-bound workloads, but this should not be the issue for IO-bound workloads.

In the cloud the cost of resources is a major consideration. Let’s review the annual cost for the processing and storage …