Java Web Development (JSP/Servlets) Services

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.

Java 8, released more than a decade ago, is a major milestone. With this version, Java brought a fundamental shift from only being an object-oriented programming (OOP) to a combination of OOP and functional programming (FP) as well. To achieve this, Java 8 came in with support of lambdas, stream APIs, etc., as core language features.

Stream API is influenced and modeled after the collection pipeline. A typical stream has three stages, viz., source, intermediate operations, and terminal operations.

In high-performance computing, we are trained to hunt for bottlenecks in our code, our algorithms, or our infrastructure. But my favorite bug was not in any of those. It was an invisible interaction between the JVM's garbage collector and the server's disk, resulting in 15+ second, stop-the-world (STW) pauses on a service handling millions of requests per second.

The Mystery: The 503 Spikes

I was working on a large-scale Java service handling millions of user requests per second. The system was designed for extreme throughput, but we were plagued by intermittent spikes in load balancer timeouts, causing 503 responses to be returned to the users. 

JPlus is a Java superset language running on the JVM that enhances developer productivity while staying fully compatible with the Java ecosystem.

Why Support JPlus?

JPlus fills a unique gap in the Java ecosystem: