programming

13 Nov 2025

DZone Java Zone

When Memory Overflows: Too Many ApplicationContexts in Spring Integration Tests

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.

13 Nov 2025 5:00pm GMT

12 Nov 2025

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.

12 Nov 2025 5:00pm GMT

11 Nov 2025

DZone Java Zone

Debugging Performance Regressions in High-Scale Java Web Services: A Systematic Approach

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.

11 Nov 2025 7:00pm GMT