Java Web Development (JSP/Servlets) Services

In the previous articles, we discussed how to perform data-driven API automation testing with different approaches, including object arrays, iterators, CSV files, and JSON files.

An Excel file can also be used to perform data-driven API testing. It allows testers to store multiple test data in one place, where we can easily add, update, or remove test cases without changing the automation code. It allows non-technical members, such as Business Analysts and Product owners, to understand and edit the test data to perform robust testing.

"Modernize the legacy stack" is a phrase that strikes dread into every senior engineer's heart — and for good reason. Migration projects fail at a notoriously high rate. They balloon in scope, break running systems, and produce tech debt that rivals what they replaced. I led successful migrations of critical microservices to modern runtimes, containerized deployments, and event-driven architectures — on time, without downtime, and with measurable gains in performance and reliability.

This article distills the frameworks, patterns, and hard lessons from those engagements into a practical guide for teams facing similar challenges.

In the first part of this two-part series on tuning Java applications for Ampere®- powered cloud instances, we concentrated on tuning your Java environment for cloud applications, including picking the right Java version, tuning your default heap and garbage collector, and some options that enable your application to take advantage of underlying Arm64 features. In this article, we will look more closely at the operating system and Kubernetes configuration. In particular, we take a deep dive into container awareness in recent versions of Java, how to restrict the system resources made available to Java containers, and some common Linux configuration options to optimize your system for specific workloads. Much of the advice related to operating system tuning and workload placement applies to all workloads, not just JVM workloads, but since our focus is on the deployment of Java applications on Arm64 to Kubernetes, we will focus on that use-case here.

Resource Allocation in Kubernetes

In this section, we’ll step outside the JVM and look at the infrastructure layer. Understanding how Kubernetes allocates resources, and how your Java application perceives those allocations, is fundamental to ensuring that you allocate the right amount of resources to your JVM.

As AI inference moves from prototype to production, Java services must handle high-concurrency workloads without disrupting existing APIs. This article examines patterns for scaling AI model serving in Java while preserving API contracts. Here, we compare synchronous and asynchronous approaches, including modern virtual threads and reactive streams, and discuss when to use in-process JNI/FFM calls versus network calls, gRPC/REST. We also present concrete guidelines for API versioning, timeouts, circuit breakers, bulkheads, rate limiting, graceful degradation, and observability using tools like Resilience4j, Micrometer, and OpenTelemetry. 

Detailed Java code examples illustrate each pattern from a blocking wrapper with a thread pool and queue to a non-blocking implementation using CompletableFuture and virtual threads to a Reactor-based example. We also show a gRPC client/server stub, a batching implementation, Resilience4j integration, and Micrometer/OpenTelemetry instrumentation, as well as performance considerations and deployment best practices. Finally, we offer a benchmarking strategy and a migration checklist with anti-patterns to avoid.

An Architect's Guide to 100GB+ Heaps in the Era of Agency