Database Performance Optimization

What gets fixed

• Slow queries and timeouts
• High CPU or I/O usage on database
• Locking and contention issues
• Unstable performance under load
• Inefficient joins and scans
• Queries that degrade as data grows

How it is done

• Analyze real queries (not synthetic benchmarks)
• Review execution plans
• Identify dominant cost drivers
• Apply minimal targeted changes
• Validate with real workload

No blind indexing. No guesswork.

Typical databases

• Microsoft SQL Server
• MySQL / MariaDB
• AWS RDS (all types except Oracle)
• PostgreSQL

Focus is on behavior, not vendor-specific tricks.

Results

• Faster query execution
• Reduced database load
• More stable performance under growth
• Lower infrastructure cost
• Predictable behavior under load

When this is NOT needed

• Database load is low and stable
• Queries are already optimized
• No performance-related impact

Outcome

SQL queries become efficient.

Database load becomes predictable.

Performance scales with data.

What you get

Query optimization

• Rewrite inefficient queries
• Reduce unnecessary scans and joins
• Improve filtering and aggregation

Index strategy

• Add missing indexes
• Remove redundant or harmful ones
• Align indexes with real query patterns

Execution plan analysis

• Identify why the database chooses bad plans
• Fix cardinality and statistics issues
• Stabilize performance across datasets

Typical areas

• Complex joins and nested queries
• Large tables and missing indexes
• High write load and locking
• Inefficient ORM-generated queries
• Reporting queries impacting production
• Query plans changing unpredictably

When this is a good fit

• Database is a bottleneck
• Queries are slow or inconsistent
• CPU/I/O usage is high without clear reason
• Performance degrades as data grows
• Previous tuning attempts did not help

Engagement format

• Query and workload analysis
• Execution plan review
• Targeted optimization
• Before/after validation

No schema rewrites unless absolutely necessary.