Java Web Development (JSP/Servlets) Services

Modern enterprise applications must process increasing data volumes while guaranteeing low latency, high availability, and predictable performance. While relational databases are essential for transactional workloads and complex queries, some applications do not fit the relational model. Systems that manage millions of user engagements, financial events, or IoT data often need a persistence solution built for distributed environments and horizontal scalability. In these cases, ScyllaDB affords a compelling option for enterprise architects.

ScyllaDB is a high-performance, distributed NoSQL database based on the wide-column data model. Inspired by Apache Cassandra and compatible with its query language (CQL), ScyllaDB uses a shard-per-core architecture to maximize hardware efficiency and preserve consistent performance under intensive workloads. When should ScyllaDB be chosen over traditional relational databases or other NoSQL options? This article examines its column-family model, compares it with Cassandra, and shows how to integrate ScyllaDB into a Java enterprise application.

What does it take to build and sustain an open-source project for more than a decade? For Shai Almog, creator of Codename One, it takes a willingness to challenge conventional approaches to software development and a continued commitment to the developers who rely on the work.

I recently caught up with Shai to discuss the inspiration behind Codename One, the realities of maintaining an open-source project, and his perspective on how the Java ecosystem has evolved over the years. Check it out below!

If you've been following the database world lately, you'll have noticed DuckDB turning up in some unexpected places. One of the more interesting examples is pg_duckdb, a Postgres extension that embeds DuckDB directly into the Postgres process, letting analytical queries run through DuckDB's engine without leaving the database. It's a clever idea: keep your data where it is, but bring a faster analytical engine.

That got us wondering: could we do something similar with Neo4j?

Teams considering or committed to adopting Kotlin often plan a migration as a series of rewrites, swapping Java services for Kotlin counterparts one microservice at a time. In practice, that kind of plan tends to stall before it produces any shipped code.

Kotlin's role in the JVM ecosystem is expanding because it doesn't require that kind of commitment. Companies such as Netflix, Adobe, and DoorDash have adopted it incrementally, alongside existing JVM systems, rather than through a full rewrite. The lowest-risk starting point is tests. They give you Kotlin's language benefits, including null-safety, data classes, and expressive lambdas, without touching anything that runs in production.

Virtual threads let us keep ordinary blocking code while many more requests wait concurrently. Under load, however, that brings three critical questions into focus:

• How much work do we admit? Cheap threads can still build an expensive queue.
• What belongs inside a lock? A sleeping lock holder still makes everyone else wait.
• Who stops abandoned work? A caller's timeout does not cancel its task.

The examples below isolate these questions with separate before-and-after programs. Each program is complete: save its code block under the filename shown and run the accompanying command, without arguments. All you need is JDK 25. They use its compact source-file syntax, so no class declaration, build tool, external library, or preview flag is required.