MariaDB

MariaDB is a relational database used in web applications, sales systems and the back ends of digital services. It stores data in tables, enforces relationships and supports transactions. For your business, this means a predictable way of working with orders, customers, documents and other information that has to stay consistent.

Choosing a database doesn’t solve data problems on its own, though. You need to design the schema, indexes, backups, permissions and how you will recover after a failure. We can set up a new MariaDB environment, move an existing database or find the cause of slow queries in a running application.

We start with how the data is used: how often it is written, which reports put load on the system, how much downtime is acceptable and how much data you could afford to lose in the worst case. Only these answers let us choose the right replication, backup and infrastructure.

MariaDB works well when data has clear relationships and the application needs transactions and SQL. It can power an online store, a CRM system, a B2B portal, a content site or an internal application. It is a particularly natural choice for teams already working with the MySQL ecosystem.

It is worth considering if you value open source software, broad library support and the option to run the database on your own infrastructure or in the cloud. Its replication features let you spread read traffic and build resilience against a single server failing.

If your data has a highly variable structure, or the project is mainly about searching documents, another database may model the problem better. We don’t choose MariaDB just because the team knows SQL. First we look at the data model, the types of queries and the operational requirements.

MariaDB started as a fork of MySQL and remains largely compatible with it, so many applications can use similar drivers and SQL syntax. However, the two projects are developed independently. They differ in versions, features, the query optimiser, tooling and support policy.

Before a migration, we check the data types, SQL functions, table engines, stored procedures, replication setup and ORM behaviour in use. Then we do a trial migration and test the most important queries. Calling a migration a “drop-in replacement” without these checks creates unnecessary risk.

If your current MySQL setup works well and is properly supported, migrating doesn’t have to be a priority. MariaDB makes sense when it follows from technical, licensing or infrastructure requirements, or from a standard adopted by your organisation.

A good database starts with a model that reflects what the application actually does. Orders, order lines, payments and customers should have clearly defined relationships and constraints. Foreign keys, unique indexes and transactions help stop bad data before it reaches a report or an accounting process.

Not every piece of information needs to be normalised into lots of small tables. An overly fragmented schema makes reading data harder, while copying the same values into several places leads to inconsistency. We design the structure around the specific writes, searches and reports, while leaving room to grow.

We roll out schema changes through versioned migrations. With large tables, we check how long locks will last and whether the operation can be done in stages. This way, updating the application doesn’t have to mean an unplanned break in sales.

A slow application doesn’t always need a bigger server. The cause might be a missing index, a query that fetches too much data, a transaction lock or a poorly designed table model. We start by measuring: we analyse the execution plan, query times, memory usage and how the database behaves under load.

An index speeds up reads, but it takes up space and makes writes more expensive. That’s why we don’t add indexes to every column. We choose them to match the queries the application runs and check that the optimiser actually uses them.

With heavier read traffic, part of it can be sent to replicas. Reporting can also be separated from the main database. This kind of architecture only makes sense once simpler problems are fixed, because replication adds lag, monitoring and failover procedures.

Replication copies changes from the primary server to one or more replicas. You can use it to spread reads, prepare a standby server or run analysis without loading the main instance. However, it doesn’t automatically give you high availability.

You need to decide how failures are detected, how a replica is promoted and how the application is redirected. Depending on the requirements, we may consider a classic primary-replica setup, Galera Cluster or a solution with a proxy such as MaxScale. Each option involves a trade-off between consistency, lag, cost and how complex it is to run.

The failover procedure should be tested regularly. A document describing failover is no help if, a year later, it turns out the replica has different permissions or the application is still connecting to the old address.

A replica is not a backup. A deleted table or a faulty data update can be copied to the other servers. You need separate backups, a retention policy and a copy kept outside the main environment.

We build the strategy around RPO and RTO. RPO (recovery point objective) defines how much of the most recent data you can afford to lose, and RTO (recovery time objective) how quickly the system should be back up. These values affect how often backups are taken, how logs are stored and which tools are chosen.

The most important test of a backup is restoring it. We can automate backup checks and regularly restore copies in a separate environment. That way you know the file exists, can be decrypted and contains the data the application needs.

The database should be accessible only to the services and people who really need it. We set up separate accounts, minimal permissions, encrypted connections, credential rotation and logging of important operations. Exposing the database port to the public internet isn’t a handy shortcut; it is an unnecessary risk.

MariaDB can run on a server, in a container, in a Kubernetes cluster or as a managed service. Docker makes environments easy to reproduce, but it doesn’t solve persistent storage, backup or monitoring. A cloud service takes over some of the operations, but it affects costs, the features available and how you migrate.

We choose the environment to match the scale of the project and the skills of your team. A small application doesn’t need a complex cluster from day one, and a critical system shouldn’t rely on a single instance without proven recovery.

MariaDB and MySQL are a natural choice for many PHP applications and for teams that already have tools and skills in this ecosystem. PostgreSQL is often chosen for complex queries, data types and SQL features. Even so, the quality of a system depends more on the data model and how it is maintained than on the name of the database engine.

We compare compatibility with the application, replication requirements, availability of specialists, cloud services and the cost of migration. If switching engines doesn’t solve a specific problem, it is better to improve the queries, the schema or the backup process first.

Let’s talk about your database and the risks you want to reduce. We can start with a performance audit, a migration plan or the design of a new environment.