MySQL is one of the most widely used databases in the world, second only to the Oracle database, and MySQL Heatwave is also available in all major clouds (Azure, AWS, GCP, and OCI). MySQL-HW is an extension of MySQL that allows one to swiftly resolve queries across large volumes of in-memory data, up to several hundreds […]

This blog post illustrates how to use AutoML on MySQL Heatwave to train an optimized machine learning model to forecast crime

In a post from last year, I talked about how we can view the SQL queries that are run whenever we use the MySQL Document Store API to return JSON documents. If you did not know, when using the Document Store API, the commands are translated into SQL and executed against the database. Sometimes, it is helpful to view […]

Vindex # Vitess uses Vindexes (short for Vitess Index) to associate rows in a table with a designated address known as Keyspace ID. This allows Vitess to direct a row to its intended destination, typically a shard within the cluster. Vindexes play a dual role: enabling data sharding through Primary Vindexes and facilitating global indexing via Secondary Vindexes. Through this mechanism, Vindexes serve as an indispensable tool for routing queries in a sharded database, ensuring optimal performance and scalability.

The Oracle Cloud Infrastructure (OCI) command line interface (CLI) allows users to manage OCI resources. In this post, we will discuss how to use the OCI CLI to create a MySQL HeatWave inbound replication channel.

This is the sixth (and final) post in a series dedicated to showing how to use the OCI CLI to manage MySQL HeatWave resources. This post will discuss how to create a MySQL HeatWave inbound replication channel. Prerequisites Before you can run any of the examples below, you need to install the OCI CLI. If you do not have […]

If you're using MySQL, you likely have indexes that are powered by B-trees.The B-tree is a powerful data structure, and is frequently used to construct indexes in relational databases.If you are using the InnoDB storage engine, it is the only choice for your index, save for spatial indexes.However, MySQL has a secret weapon for making lookups with these types of indexes even faster: the Adaptive Hash Index, or AHI.Before jumping into how this works, let's take a few moments to review B-trees, the InnoDB buffer pool, and how these work together during index lookups. B-Tree indexes The B-tree data structure has been used by computer systems for decades.It is particularly useful in the context of file system and data storage applications, due to the fact that each node can store many values.This is useful in algorithms that interface with storage systems, as the size of each node can be set to work well with the unit(s) of storage, for example aligning …

When talking about storage performance, we often hear three terms that are important in describing storage performance. They are

• bandwidth
• latency
• I/O operations per second (IOPS)

When you talk to storage vendors and cloud providers, they will gladly provide you with numbers on bandwidth and IOPS, but latency numbers will be hard to come by. To evaluate storage, especially for MySQL, you need just one number, and that is the 99.5 percentile commit latency for a random 16 KB disk write. But let’s start at the beginning.

Bandwidth

The bandwidth of an IO subsystem is the amount of data it can read or write per second. For good benchmarking numbers, this is usually measured while doing large sequential writes. A benchmark would, for example, write and read megabyte sized …

A support question: Somebody had a WordPress installation, in which a table had entries with an id column that contained multiple entries with 0. The table was supposed to undergo a schema change where id becomes an actual primary key, and auto_increment. They needed to find all rows WHERE id=0 and assign them unique id values.

Here is a test table:

mysql> create table b ( id integer not null, d varchar(255));
Query OK, 0 rows affected (0.25 sec)

kris> insert into b values (0, "1"), (0, "2"), (3, "3");
Query OK, 3 rows affected (0.06 sec)
Records: 3  Duplicates: 0  Warnings: 0

kris> select * from b;
+----+------+
| id | d    |
+----+------+
|  0 | 1    |
|  0 | 2    |
|  3 | 3    |
+----+------+
3 rows in set (0.00 sec)

…

Introduction In today’s increasingly complex software landscape, understanding and debugging the inner workings of distributed applications are more challenging than ever before. As applications become more distributed, spanning multiple services and environments, traditional monitoring approaches often fall short of providing comprehensive insights into system behavior and performance. This is where observability comes into play. Observability […]