One of the biggest transitions from mid-level backend engineering to senior backend engineering is realizing that Garbage Collection is not just a JVM internals topic — it is a latency, scalability, and reliability topic.
In modern backend systems, every API request creates temporary objects:
• Request/response DTOs
• JSON serialization objects
• Hibernate entities and proxies
• Validation objects
• Thread-local allocations
• Logging and tracing metadata
At scale, this means millions of short-lived objects are created every minute.
The JVM handles cleanup automatically, but the way it performs that cleanup can directly affect production behavior.
In distributed systems, even a 200ms GC pause can amplify into:
• Elevated API latency
• Timeout failures
• Retry storms from load balancers
• Thread pool saturation
• Cascading downstream failures
This is why GC selection is not just a JVM decision — it is an architecture decision.
My practical view of the three most important modern collectors:
• G1 GC → Best starting point for most Spring Boot and microservice workloads. Strong balance between throughput and predictable pause times.
• ZGC → Ideal for ultra-low latency systems where pause times need to stay consistently low, even with very large heaps.
• Shenandoah → Valuable for Kubernetes and cloud-native environments where workload patterns and heap pressure change rapidly.
One of the biggest mistakes engineers make is assuming lower pause times always mean better performance.
That is not always true.
Choosing ZGC for a smaller service can increase CPU usage without delivering meaningful latency improvements. In many cases, G1 gives better overall efficiency because the workload does not justify the extra GC overhead.
On one service I worked on, moving from Parallel GC to G1 reduced p99 latency spikes during peak traffic by more than 40%.
Garbage Collection also cannot solve poor memory hygiene.
Static cache growth, ThreadLocal misuse, unbounded collections, and object retention issues will still create memory pressure regardless of the collector you choose.
Senior engineers do not just write code that works.
They understand how the JVM behaves under real production traffic, how memory pressure affects latency, and how infrastructure decisions shape end-user experience.
#Java #JVM #GarbageCollection #SpringBoot #Microservices #BackendEngineering #DistributedSystems #PerformanceEngineering #Scalability #SystemDesign
My rule now: Before adding retries, queues, or more services, I ask one question: Did we actually fix the bottleneck, or did we just spread it around?