Spring Data Performance Clinic
KRW 360,000 · informational list price
Profile queries, shape repositories, and keep persistence layers observable under load.
You learn to read execution plans without heroics, batch writes responsibly, and expose metrics that help teams notice regressions early. Labs use realistic datasets sized for laptops.
What you build and study
- Lazy versus eager loading decisions
- Batch inserts with chunk sizing
- Second-level cache evaluation
- Query logging with noise control
- Index hints and when to avoid them
- Paging under concurrent updates
- Load harness scripts with guardrails
Outcomes
- Cut cold-start query counts on a sample service
- Write a concise performance note for reviewers
- Pair metrics with reproduction steps
Mentor
Rina Cho
Security-focused Spring contributor; emphasizes measurable controls.
Questions
PostgreSQL is the default; translation notes exist for MySQL teams.
Brief overview only; focus stays on data access patterns.
16GB RAM recommended for generated datasets; 8GB works with reduced sample sizes.
Recent reflections
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“Batching lab made our nightly sync less chatty without magic flags.”