Operations | Monitoring | ITSM | DevOps | Cloud

Spring Boot version upgrades—whether moving from 2.x to 3.x, 3.x to 4.x, or even minor bumps like 3.2.5 to 3.3.1—regularly introduce subtle, breaking changes that unit and integration tests miss. JSON serialization shifts, autoconfiguration reordering, and transitive dependency conflicts can silently alter your API contract.

Every few months a new "we migrated off Spring Boot" post washes across r/java or DZone. The numbers are always impressive. 60% memory reduction, 85% faster startup, cloud bill cut in half. The comments are always full of developers convinced they should be doing the same thing. Is Spring Boot really that bad now? I decided to do my own research. I read every credible public migration case study I could find. I ran benchmarks. I built the internal business case for switching two of our services.

‍ When you're architecting an enterprise Java application, one decision quietly shapes everything downstream: runtime footprint, deployment pipelines, and how your platform team handles incidents at 3 a.m. For two decades, that decision was framed as Java SE vs Java EE. In 2026, that framing has quietly inverted.

Drop a JAR on the JVM. Get distributed tracing, RxJava context propagation, log-trace correlation, and Vert.x internal metrics. No code changes. No Maven dependency. Java 8–21. Inside the design of last9/vertx-opentelemetry v2.3.4. Prathamesh works as an evangelist at Last9, runs SRE stories - where SRE and DevOps folks share their stories, and maintains o11y.wiki - a glossary of all terms related to observability.

Coroot already does eBPF-based CPU profiling for Java. It catches CPU hotspots well, but that's all it can do. Every time we looked at a GC pressure issue or a latency spike caused by lock contention, we could see something was wrong but not what. We wanted memory allocation and lock contention profiling. So we decided to add async-profiler support to coroot-node-agent. The goal: memory allocation and lock contention profiles for any HotSpot JVM, with zero code changes. Here's how we got there.

Coroot's node agent uses eBPF to capture network traffic at the kernel level. It hooks into syscalls like read and write, reads the first bytes of each payload, and detects the protocol: HTTP, MySQL, PostgreSQL, Redis, Kafka, and others. This works for any language and any framework without touching application code. For encrypted traffic, we attach eBPF uprobes to TLS library functions like SSL_write and SSL_read in OpenSSL, crypto/tls in Go, and rustls in Rust.

Enterprise Spring Boot APIs should be tested at three levels: unit tests for business logic, integration tests for external service behavior, and traffic replay for production edge cases. Most teams only do the first. This guide shows all three using a real Spring Boot application that calls external APIs (SpaceX, US Treasury) with JWT authentication. The kind of service that looks simple in development and breaks in production.

Achieving end-to-end observability for applications is a top priority for organizations today, but instrumenting for both frontend and backend monitoring can be a significant hurdle. What complicates matters is that the SREs and DevOps teams responsible for deploying monitoring tools typically don’t own frontend code or have the context needed to safely modify it.

One of the most common infrastructure changes Java developers and operators are dealing with today is the migration from Oracle Java to OpenJDK. The reason is the licensing changes made by Oracle and the maturity of the OpenJDK distributions. The migration process is quite simple: replace the JDK, recompile the code, and redeploy the application. However, the differences between the two runtimes can lead to unexpected issues that are not caught by unit tests.