We recently saw that we can benefit from using JSON documents with MySQL HeatWave cluster in OCI (HeatWave accelerator for MySQL Database Service).

However sometimes the data can’t be loaded to HeatWave Cluster or the query cannot use it.

And this is not always easy to understand why. Let’s get familiar on how to find the info.

Data not loaded in HW Cluster

Let’s see an example with this table (collection):

SQL > show create table listingsAndReviews\G
*************************** 1. row ***************************
       Table: listingsAndReviews
Create Table: CREATE TABLE `listingsAndReviews` (
  `doc` json DEFAULT NULL,
  `_id` char(28) GENERATED ALWAYS AS
 (json_unquote(json_extract(`doc`,_utf8mb4'$._id'))) STORED NOT NULL,
  `_json_schema` json GENERATED ALWAYS AS 
  (_utf8mb4'{"type":"object"}') VIRTUAL,
  PRIMARY KEY (`_id`),
  CONSTRAINT `$val_strict_B70EB65BDDBAB20B0EE5BB7532C9060C422A06F8` …

Since the release of MySQL 8.0, the MySQL X Dev API has provided users with the convenient ability to utilize MySQL without the need to write a single line of SQL!

MySQL X Dev API brings the support for CRUD operations on JSON documents that are stored in MySQL. MySQL Document Store is ACID compliant and is compatible with everything MySQL like replication, InnoDB Cluster, …

The MySQL X Dev API is available using the MySQL X Protocol, listening by default on port 33060.

If you are interested in learning more about MySQL Document Store, please refer to the these presentations [1], [2], [ …

Some time ago, we saw how we could deploy WordPress on OCI using MySQL HeatWave Database Service with Read Replicas. We had to modify WordPress to use a specific plugin that configures the Read/Write Splitting on the application (WordPress): LudicrousDB.

Today, we will not modify WordPress to split the Read and Write operations, but we will use MySQL Router 8.2.0 (see [1], [2], [3]).

Architecture

The …

A quick configuration change may do the trick in improving the performance of your AWS RDS for MySQL instance. Here, we will discuss a notable new feature in Amazon RDS, the Dedicated Log Volume (DLV), that has been introduced to boost database performance. While this discussion primarily targets MySQL instances, the principles are also relevant to […]

The ability to store data on Object Storage and retrieve it dynamically when necessary is a notable advantage of Lakehouse when managing MySQL historical data we would like to archive.

Let’s illustrate this with the following table:

CREATE TABLE `temperature_history` (
  `id` bigint unsigned NOT NULL AUTO_INCREMENT,
  `time_stamp` timestamp NULL DEFAULT CURRENT_TIMESTAMP,
  `device_id` varchar(30) DEFAULT NULL,
  `value` decimal(5,2) NOT NULL DEFAULT '0.00',
  `day_date` date GENERATED ALWAYS AS (cast(`time_stamp` as date)) STORED NOT NULL,
  PRIMARY KEY (`id`,`day_date`),
  KEY `device_id_idx` (`device_id`)
) ENGINE=InnoDB AUTO_INCREMENT=129428417 DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci SECONDARY_ENGINE=RAPID
/*!50500 PARTITION BY RANGE  COLUMNS(day_date)
(PARTITION p0_before2023_11 VALUES LESS THAN ('2023-11-01') ENGINE = InnoDB,
 PARTITION p2023_12 VALUES LESS THAN ('2023-12-01') ENGINE = InnoDB,
 PARTITION p2024_01 VALUES LESS THAN …

You may have come across numerous presentations showcasing MySQL HeatWave as a Query Accelerator for MySQL. However, if you have not yet had the opportunity to test it yourself (if you have, you already know the answer), allow us to conduct a test using actual data to determine the potential benefits of utilizing a MySQL HeatWave Cluster.

Data & Queries

The data is simple, some arduinos with a DHT22 sensor sending temperature and humidity.

So first let’s have a look at the amount of collected data:

select format_bytes(sum(data_length)) DATA,
       format_bytes(sum(index_length)) INDEXES,
       format_bytes(sum(data_length + index_length)) 'TOTAL SIZE'
from information_schema.TABLES order by data_length + index_length;
+-----------+-----------+------------+
| DATA      | INDEXES   | TOTAL SIZE |
+-----------+-----------+------------+
| 21.89 GiB | 14.06 GiB | 35.95 GiB  |
+-----------+-----------+------------+
1 row …

Since MySQL Shell 8.1, it’s even easier to create a logical backup of your MySQL instance and store it directly in Object Storage, an internet-scale, high-performance storage platform in Oracle Cloud Infrastructure (OCI).

MySQL Shell now offers the option of dumping to Object Storage Bucket using PAR (Pre-Authenticated Request).

Bucket Creation

The first step is to create an Object Storage Bucket in the OCI Console:

Let’s call it lefred-mysql-backups:

When created, we can click on the three-dots and create a new PAR:

In this blog post, we’ll review how to run Linux profilers such as perf and produce flame graphs on Kubernetes environments.

Flame graphs are a graphical representation of function calls. It shows which code paths are more busy on the CPU in given samples. They can be generated with any OS profiler that contains stack traces such as perf, eBPF, and SystemTap.

An example of a flame graph can be found below:

Each box is a function in the stack, and wider boxes mean more time the system was busy on CPU on these functions.

Kubernetes limitations

In Linux, by default, performance system events can’t be collected by unprivileged users. In regular environments, this can be easily worked around by running the profiler with a sudo privilege.

On the other hand, in Kubernetes environments, pods are the smallest deployable unit that consists of one or more containers. Exploits are generally targeted to …

Let’s see how to deploy WordPress and MySQL on a Kubernetes Cluster. The Kubernets cluster we are using is OKE (Oracle Kubernetes Engine) in OCI (Oracle Cloud Infrastructure):

OKE Cluster

We start by creating a Kubernetes Cluster on OCI using the Console:

We select the Quick create mode:

We need to name our cluster and make some choices:

When created, we can find it in the OKE Clusters list:

And we can see the pool of workers nodes and the workers:

kubectl

I like to use kubectl directly on my latop to manage my K8s Cluster.

On my Linux Desktop, I need to install kubernetes-client package (rpm).

Then on the K8s Cluster details, you can click on Access Cluster to get all the commands to use:

We need to copy them on our terminal and then, I like to also enable the bash completion for …

Performing an operation is always challenging when dealing with K8s.

When on-prem or DBaaS like RDS or Cloud SQL, it is relatively straightforward to apply a change. You can perform a DIRECT ALTER, use a tool such as pt-osc, or even, for certain cases where async replication is in use, perform changes on replicas and failover.

In this blog post, I’ll provide some thoughts on how schema changes can be performed when running MySQL on Kubernetes

I won’t focus on DIRECT ALTERs as it is pretty straightforward to apply them. You can just connect to the MySQL service POD and perform the ALTER.

But how can we apply changes in more complex scenarios where we may want to benefit from pt-osc, gain better control over the operation, or take advantage of the K8s features?

One convenient way that I’ve found …