SQL Databases

Reliable relational data at scale with PostgreSQL and MySQL

Why Relational Databases Still Matter

In the NoSQL era, some proclaimed SQL databases were obsolete. Yet today, relational databases power the vast majority of critical systems: banking, e-commerce, healthcare, social networks. SQL's power comes from ACID transactions: Atomicity (all-or-nothing), Consistency (valid state), Isolation (concurrent safety), Durability (permanent). NoSQL traded these guarantees for scalability, but most applications don't need distributed scaling: they need correctness.

PostgreSQL and MySQL are modern, performant, battle-tested. PostgreSQL is a feature-rich database with JSON support, full-text search, spatial data, and advanced analytics built-in. MySQL is simpler, lighter, widely deployed. Both scale to terabytes of data. Both support replication, high availability, automated backups. Both have mature ecosystems of tools and hosting options.

We design optimal schemas, write efficient queries, optimize slow queries, handle replication and backups, manage migrations. Database performance directly impacts application performance. A slow query fetching data doesn't become fast when wrapped in cache. We fix the root cause: proper indexing, query optimization, schema normalization. The result: applications that scale without throwing hardware at problems.

Database Services

Schema Design

A well-designed schema is the foundation for application performance. Proper normalization reduces data duplication and ensures consistency. Appropriate data types (not storing phone numbers as TEXT). Strategic denormalization for read-heavy workloads. Proper constraints (primary keys, foreign keys) prevent invalid data. We design schemas that scale, maintain data integrity, and enable efficient queries.

Principles:

  • β€’ Normalization levels
  • β€’ Data type selection
  • β€’ Constraint design
  • β€’ Relationship modeling
  • β€’ Scalability planning

Design Tools:

  • β€’ Entity-relationship diagrams
  • β€’ Normalization analysis
  • β€’ Capacity planning
  • β€’ Access pattern review
  • β€’ Query simulation

Optimization:

  • β€’ Index strategy
  • β€’ Partitioning
  • β€’ Archive tables
  • β€’ Summary tables
  • β€’ Materialized views

Query Optimization

Slow queries are the primary cause of database performance problems. We analyze query execution plans, identify missing indexes, rewrite inefficient queries, and configure the query optimizer. N+1 query problems. Missing indexes causing full table scans. Inefficient JOINs. We fix these systematically: profiling slow queries, understanding access patterns, implementing solutions.

Tools: EXPLAIN ANALYZE, slow query logs, APM integration, query performance insights, index recommendations, statistics analysis.

Replication & High Availability

Single database server is a single point of failure. Replication creates standby copies. Read replicas distribute read traffic. Primary-replica failover ensures uptime. We configure streaming replication, promote replicas, monitor replication lag, handle network partitions. The result: databases stay online even during maintenance or hardware failures.

Strategies: Physical replication, logical replication, WAL streaming, synchronous replication, failover automation, connection pooling.

Backups & Recovery

Data loss is catastrophic. Backups must be automated, tested, and recoverable. We implement point-in-time recovery: backups plus transaction logs enable recovery to any specific moment. Automated backup scheduling. Backup encryption for security. Regular recovery testing ensures backups actually work. Disaster recovery planning.

Backup approaches: pg_dump, full backups, incremental backups, WAL archiving, physical backups, backup verification, retention policies.

PostgreSQL vs MySQL

PostgreSQL

Advanced open-source database with enterprise features. ACID compliance is rock-solid. JSON and JSONB for semi-structured data. Full-text search built-in. PostGIS extension for geographic data. Window functions for analytics. Extensibility through custom types, operators, functions. Perfect for complex queries and data integrity requirements.

Best For:

  • β€’ Complex schemas
  • β€’ Analytics queries
  • β€’ JSON data
  • β€’ Geographic data
  • β€’ Extensibility needs

MySQL

Simple, fast, widely deployed. InnoDB provides ACID compliance and reliable performance. Read replicas distribute traffic easily. Minimal configuration needed for basic deployments. Available everywhere (hosting, cloud, on-prem). Perfect for straightforward schemas and read-heavy workloads where simplicity matters more than advanced features.

Best For:

  • β€’ Simple schemas
  • β€’ Read-heavy workloads
  • β€’ WordPress & CMS
  • β€’ Rapid deployment
  • β€’ Wide hosting support

Database Best Practices

Primary Keys Always

Every table needs a primary key. Identifies records uniquely. Enables efficient updates and deletes. Foreign keys reference primary keys. Proper data modeling starts with identifying primary keys.

Strategic Indexing

Indexes speed reads but slow writes. Create indexes on columns used in WHERE, JOIN, ORDER BY clauses. Don't over-index. Monitor index usage. Remove unused indexes. Analyze query plans.

Data Type Precision

Use appropriate types: INT for integers, DECIMAL for money (never FLOAT), VARCHAR with max length, DATE for dates. Wrong types waste storage and cause bugs. MONEY fields must use DECIMAL.

Constraints for Data Integrity

NOT NULL, UNIQUE, CHECK, FOREIGN KEY. These prevent invalid data at the database level, not just application validation. Corrupt data detection catches bugs early.

Connection Pooling

Applications should never create raw database connections per request. Connection pools reuse connections. Dramatically improves performance under load. Configure pool size appropriately.

Monitor Performance

Slow queries logs catch problems. Query monitoring reveals bottlenecks. Replication lag alerts indicate issues. Monitor disk space, connection count, transaction rate.

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