Enhance your JVM diagnostics and optimization skills — analyze real failures, master dump analysis and learn to resolve performance issues as quickly as a Senior-level engineer. The course is available as recordings, so you can learn at your own pace.
Who this course is for
Mid and Senior-level Engineers
The program is designed for specialists working with high-load Java systems, aiming to gain a deeper understanding of JVM behavior.
- Java developers and architects: For those who want to understand the internal mechanisms of JVM and write truly fast, stable, and scalable code.
- QA Performance Engineers: For those who strive to identify bottlenecks in applications before they reach production.
- SRE / DevOps Engineers: For those responsible for production stability and want to reduce the time taken to diagnose critical incidents.
Skills and learning outcomes
In-depth understanding of the JVM
- JVM Architecture: Threads, heap, metaspace, native memory, JIT, and HotSpot optimizations.
- GC mechanisms: Algorithms, types of collectors, pause analysis, and optimization for specific loads.
Practical optimization
- Garbage collection tuning: Reducing GC pauses, optimizing throughput and latency.
- Application scaling: Approaches that truly work when increasing load.
Professional data analysis
- GC logs: Learn to find causes of sharp pauses and latency spikes.
- Thread dumps: Identifying deadlock, contention, long-running operations.
- Heap dumps: Finding memory leaks, analyzing object states and their relationships.
Working with real cases
- Analysis of incidents from large companies: What happened, why, and how it was fixed.
- Diagnostic templates: Universal approaches applicable in any JVM environment.
- Identifying bottlenecks: Techniques for detecting "bottlenecks" in code and infrastructure.
Special bonus for the course
Electronic book of 275 pages
You will receive an extended guide on the JVM, including practical recommendations and exclusive materials.
- In-depth analyses: GC, memory management, threads, profiling, and performance.
- Practical examples: Optimization cases from real projects.
- Introduction to Chaos Engineering: How to model errors to enhance system resilience.