Primary Key design is an important thing for InnoDB performance, and choosing a poor PK definition will have an impact on performance and also write propagation in databases. When this comes to Aurora, this impact is even worse than you may notice.

In short, we consider a poor definition of a Primary Key in InnoDB as “anything but quasi sequential values”, which may cause very random access to data and thus increase the IO dependency.

In this post, I’ll try to demonstrate the potential impact of the primary key design when running on Aurora, and how a bad design can lead to a 15x write latency penalty (or more).

The Analysis

Recently I worked on a case where a customer was having issues with scaling writes in Aurora MySQL. While this is a known limitation in Aurora considering how the distributed storage layer syncs out data among all nodes of the cluster, we observed additional latency occurring when more …

The new MyDumper 0.11.1 version, which includes many new features and bug fixes, is now available.  You can download the code from here.

For this release, there are three main changes: 1) we added config file functionality which allows users to set session-level variables (one of the most requested features!), 2) we developed a better and robust import mechanism, and 3) we fixed all the filename related issues.  Those changes and mostly the last one forced us to change the version number from 0.10.9 to 0.11.1 as a backup taken in 0.10.x will not work in 0.11.x and vice versa.

New Features:

• Adding order by part functionality #388
…

When doing migrations or failovers in MySQL, there is usually a need to do a topology change and repoint replica servers to obtain replication data from a different server.

For example, given servers {A, B, and C} and the following topology:

If you need to repoint C to be a replica of B, i.e:

You can follow the next steps:

Note: log_replica_updates should be enabled on the soon-to-be primary as it is a prerequisite for chain replication.

Note: It is assumed that both replicas only stream from Server A and there are no conflicting replication filters in place that might break replication later on.

If Using File/Position-Based Replication:

1) Stop B and C

STOP REPLICA;

2) If replicas are multi-threaded, correct MTS gaps and make them …

Here’s a second post focusing on the performance of MySQL on ZFS in cloud environments. In the first post, MySQL/ZFS Performance Update, we compared the performances of ZFS and ext4. This time we’ll look at the benefits of using ephemeral storage devices. These devices, called ephemeral in AWS, local in Google cloud, and temporary in Azure, are provided directly by the virtualization host. They are not network-attached and are not IO throttled, at least compared to regular storage. Not only can they handle a high number of IOPs, but their IO latency is also very low. For simplicity, we’ll name these devices local ephemeral. They can be quite large: Azure lsv2, Google Cloud n2, and AWS i3 instance types offer TBs of fast NVMe local ephemeral storage.

The main drawback of local ephemeral …

Storing your data locally can impose security and availability risks. Major cloud providers have object storage services available to allow you to upload and distribute data across different regions using various retention and restore policies.

Percona XtraBackup delivers the xbcloud binary – an auxiliary tool to allow users to upload backups to different cloud providers directly.

Today we are glad to announce the introduction of the Exponential Backoff feature to xbcloud.

In short, this new feature will allow your backup upload/download to work better with unstable network connections by retrying each chunk and adding an exponential wait time in between retries, increasing the chances of completion in case of an unstable connection or network glitch.

This new functionality is available on today’s release of Percona …

During the development of the myloader –innodb-optimize-keys option, which was released in version 0.10.7, we found several issues and opportunities to improve the process. We had to change the approach, reimplement some of the core functionality and add a couple of data structures. That allowed us to implement, at a really low cost, a feature that executes the files that contain INSERT statements, sorted by Primary Key. This is desirable to reduce page splits, which cause on-disk tablespace fragmentation.

In this blog post, I will present the differences in data fragmentation for each version.

Test Details

These are local vm tests as there is no intention to show performance gain.

The table that I used is: …

Database architecture and design are becoming an increasingly lost art. With new technologies and the push towards faster development cycles, people continue to take shortcuts, often to the detriment of long-term performance, scalability, and security. Designing how your application stores, accesses, and processes data is so fundamentally important, it can not be overlooked. I want people to understand that early design choices can have a profound impact on their applications. To that end, I will be exploring database design principles and practices over the next several months. I am starting with every developer’s favorite data format: JSON!

It seems that almost every database over the last few years has introduced various degrees of support for storing and interacting with JSON objects directly. While these features are designed to make it easier for application developers to write code faster, the implementations of each implementation …

The Problem

State Snapshot Transfer can be a very long and expensive process, depending on the size of your Percona XtraDB Cluster (PXC)/Galera cluster, as well as network and disk bandwidth. There are situations where it is needed though, like after long enough node separation, where the gcache on other members was too small to keep all the needed transactions.

Let’s see how we can avoid SST, yet recover fast and without even the need for doing a full backup from another node.

Below, I will present a simple scenario, where one of the cluster nodes was having a broken network for long enough that it will make …

Problem Statement

Having backups of binary logs is fairly normal these days. The more recent binary logs are copied offsite, the better RPO (Recovery Point Objective) can be achieved. I was asked multiple times recently if something could be done to “stream” the binary logs to S3 as close to real-time as possible. Unfortunately, there is no readily available solution that would do that. Here, I show what can be done and also show a proof of concept implementation, which is not suitable for production use.

In this example, the instance has two binary log files (mysql-bin.000001 and mysql-bin.000002) already closed and mysql-bin.000003 being written. A trivial solution for backing up these binary log files would be to back up just the closed ones (the one that is not written). The default size of the binary log file is 1 GB. This means with this solution we would have a 1 GB binlog not backed up in the worst-case scenario. On …

As some of you likely know, I have a favorable view of ZFS and especially of MySQL on ZFS. As I published a few years ago, the argument for ZFS was less about performance than its useful features like data compression and snapshots. At the time, ZFS was significantly slower than xfs and ext4 except when the L2ARC was used.

Since then, however, ZFS on Linux has progressed a lot and I also learned how to better tune it. Also, I found out the sysbench benchmark I used at the time was not a fair choice since the dataset it generates compresses much less than a realistic one. For all these reasons, I believe that it is time to revisit the performance aspect of MySQL on ZFS.

ZFS Evolution

In 2018, I reported ZFS performance results based on version 0.6.5.6, the default version available in Ubuntu Xenial. The present post is using …