Java Web Development (JSP/Servlets) Services

In a three-article series published recently on this site (Part 1, Part 2, Part 3), I've been demonstrating the power of the AWS Cloud Development Kit (CDK) in the Infrastructure as Code (IaC) area, especially when coupled with the ubiquitous Java and its supersonic/subatomic cloud-native stack: Quarkus.

While focusing on the CDK fundamentals in Java, like Stack and Construct, together with their Quarkus implementations, this series was a bit frugal as far as the infrastructure elements were concerned. Indeed, for the sake of clarity and simplification, the infrastructure used to illustrate how to use the CDK with Java and Quarkus was inherently consensual. Hence, the idea for a new series, of which this article is the first, is a series less concerned with CDK internals and more dedicated to the infrastructure itself.

Troubleshooting memory problems, such as memory leaks and OutOfMemoryError, can be an intimidating task even for experienced engineers. In this post, we would like to share simple tips, tools, and tricks so that even a novice engineer can isolate memory problems and resolve them quickly.

What Are Common Signs of a Java Memory Leak That Might Lead to OutOfMemoryError?

Before your application throws an OutOfMemoryError, it usually gives you a few warning signs. If you catch them early, you can prevent downtime and customer impact. Here’s what you should keep an eye on:

In Spring, the ApplicationContext is the central container object that manages all beans (i.e., components, services, repositories, etc.).

Its tasks include reading the configuration (Java Config, XML, annotations), creating and managing bean instances, handling dependency injection, and running the application lifecycle.

It isn’t seldom when software products need to easily and efficiently manage the direct storage and handling of JSON content directly into the underlying database. The purpose of this article is to exemplify how such tasks can be conveniently accomplished via the asentinel-orm, a lightweight ORM tool built on top of Spring JDBC, which possesses most of the features one would expect from such a project.

We will start by defining a simple entity that contains a JSONB column. Then, we will configure a sample application that uses the asentinel-orm to handle its data access towards a PostgreSQL database that stores such entities. Lastly, we will exemplify and emphasize how the actual JSON data can be queried and stored properly.

High-scale, real-time services live under unforgiving economics. Ad tech and similar platforms push millions of requests through Java web services, where a handful of milliseconds either unlock profitable throughput or sink margins under excess compute. Regressions in latency and resource usage rarely arrive with sirens; they slip in alongside routine refactors, dependency upgrades, or subtle shifts in traffic shape. What looks like a harmless tweak in a unit test can magnify into elevated CPU, long garbage collection pauses, or thread starvation once it meets production load. The work of debugging these regressions is less about isolated heroics and more about following a disciplined trail from symptoms to causes, correlating signals across the JVM, and validating fixes under real heat.

Industry-wide, the cost of performance regressions is notoriously high, though rarely measured with public precision. In environments like ad tech, where margins are directly tied to throughput and latency, even a minor, sustained performance degradation can translate to significant operational expense and lost revenue. Teams that adopt systematic debugging and profiling practices don't just resolve incidents faster; they build a culture of performance awareness that prevents regressions from being deployed in the first place. The resulting efficiency gains, often manifesting as reduced cloud spend or the ability to handle more traffic on the same hardware, directly improve the bottom line. This article examines how that discipline works in practice for Java services running on Tomcat.