MySQL

MySQL has powered the web for decades, and I have run it across large and long-lived systems — from tight schemas to the careful indexing that keeps OLTP workloads fast. I treat compatibility, replication, and predictable performance as its strengths.

My practice covers both MySQL and its drop-in fork MariaDB, so migrations and mixed estates are no obstacle. I keep schema migrations versioned, use transactions where they must be atomic, and rely on read replicas and connection pooling for scale.

Performance work is about the plan: slow-query logs, EXPLAIN analysis, composite index design, and caching at the right layer — Memcached/Redis above, careful buffer tuning below.

Schema design is deliberate and forward-safe. I model tightly around the workload, choose the right column types and charset, and keep migrations versioned — so the schema evolves predictably instead of accumulating drift.

Indexing is where OLTP performance is won. Rather than adding indexes reactively, I design composite indexes around the actual query patterns, and let `EXPLAIN` confirm the plan before trusting it under load.

Transactions are used where they must hold. Endpoints that need atomicity get transactional guarantees explicitly, while read-heavy paths that do not need it are kept free of pointless transaction overhead.

Scale is a deliberate progression. Connection pooling absorbs burst, read replicas offload reads, and the hot data path is protected by caching above the database — so the engine is not asked to work harder than its design wants.

The MySQL/MariaDB parity matters in real estates. I design the schema and queries to be portable across both, so mixed environments and migrations never become a file of surprises.

Performance work always starts with evidence. Slow-query logs and `EXPLAIN` pinpoint the real bottleneck before anything changes, so optimisation is targeted and verifiable instead of speculative.

I design the schema around the workload. Tight column types, the right charset, and forward-safe migrations keep the model predictable instead of drifting.

Indexes are built around the real query patterns. Composite and covering indexes serve the actual access paths, and `EXPLAIN` confirms the plan before load proves it.

Transactions are used where they must hold. Endpoints that need atomicity get explicit guarantees; read-heavy paths that do not are kept free of pointless overhead.

Scale is a deliberate progression. Connection pooling absorbs bursts, read replicas offload reads, and caching above protects the hot data path.

The MySQL/MariaDB parity is kept portable. I design schema and queries to run on both, so mixed estates and migrations never become a file of surprises.

Slow-query logs and `EXPLAIN` drive optimisation. I pinpoint the real bottleneck before changing anything and verify the win after.

Backups and point-in-time recovery are part of the design, not an afterthought, so the data is restorable when it matters.

The key and data sizes stay sensible. Over-indexing and bloated tables are caught early so the engine keeps serving predictably.

For OLTP web and product load, MySQL/MariaDB is the proven, low-operational-surface choice, and I tune it to the actual workload rather than defending a preference.