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Database Optimization and Modernization

We improve database performance, stability, and scalability for growing products, so critical services stay fast and reliable as traffic and data volume increase.

What It Is

Database optimization is a structured process of improving schema design, indexing strategy, query execution, storage layout, and operational reliability.

We optimize not only isolated queries, but the full data path:

  • Query patterns and execution plans
  • Index design (single, composite, covering)
  • Transaction behavior and lock contention
  • Read/write traffic distribution
  • Backup, restore, and disaster recovery readiness

For scaling and availability, we implement Primary-Replica replication (formerly Master-Slave terminology), enabling read scaling, maintenance flexibility, and resilience against node failures.

Business Benefits

  • Faster user-facing pages and API responses
  • Stable operation during peak traffic and batch workloads
  • Predictable scaling without service degradation
  • Lower infrastructure cost per request and per transaction
  • Faster analytics, dashboards, and reporting jobs
  • Reduced outage risk and better operational continuity

Technical Benefits

  • Optimized SQL queries and execution plans (EXPLAIN, ANALYZE)
  • Proper index strategy including composite and covering indexes
  • Read/write separation with replica routing
  • Reduced lock contention and deadlock frequency
  • Partitioning and archival strategy for very large tables
  • Connection pooling and query/result caching
  • Replication lag monitoring and tuning
  • Safer backups, point-in-time recovery (PITR), and restore drills

Typical Modernization Scenarios

  • High API latency caused by inefficient joins and missing indexes
  • Write bottlenecks and lock contention in hot tables
  • Reporting queries impacting OLTP performance
  • Monolithic single-node DB needing horizontal read scaling
  • Legacy schema growth with poor retention and archival policy
  • Recovery process that is untested or too slow for SLA targets

How We Work

  1. Baseline metrics and load analysis
    Capture latency, throughput, CPU/IO, lock waits, replication status, and error rates.

  2. Slow query and index audit
    Analyze slow query logs, top resource consumers, and index effectiveness.

  3. Schema and query optimization
    Refactor high-impact queries, redesign indexes, and improve table/storage patterns.

  4. Replication and failover design
    Implement Primary-Replica topology, read routing, lag controls, and failover procedures.

  5. Monitoring, alerting, and ongoing tuning
    Deploy observability, SLO-driven alerts, and a continuous performance improvement cycle.

Reliability and Data Safety

  • Backup policy design by RPO/RTO targets
  • Automated full and incremental backups
  • Point-in-time recovery setup and validation
  • Restore playbooks and periodic disaster recovery tests
  • Replication health checks and incident runbooks
  • Controlled migration strategy with rollback planning

Technologies

  • MySQL 8
  • PostgreSQL 16+
  • Primary-Replica replication
  • Partitioning and data lifecycle policies
  • Query plan analysis and profiling tools
  • Backup automation and PITR
  • Monitoring and alerting stacks

Result

  • Lower latency and better throughput under load
  • Reliable data storage, replication, and recovery
  • More scaling headroom with predictable performance
  • Improved operational visibility and incident readiness

Let’s Discuss Your Project

Share your current database engine, workload profile, and pain points, and we will propose an optimization and modernization roadmap with expected performance and reliability gains.