For most applications, that changes nothing. For a specific class of container, it changes the collector silently, with no warning in the logs and no failure at startup.

I had some spare minutes and built Quarkus Contribution City around this idea, using Java and Quarkus to generate an SVG from GitHub's contribution data. The visual inspiration came from Pink Pixel's skyline, and you can see a version with my real data on my GitHub profile. A scheduled GitHub Action updates the image at night.

That gap is why we built message search for Kafdrop at a hackathon. The setup was familiar from my day job: a platform moving data between pipeline stages over a pub/sub API, with no way to ask where a message went when something breaks. Kafdrop showed partitions and offsets. It couldn't search inside the messages.

In Part 1, we built a Quarkus MCP tool server. In Part 2, we secured it with agentgateway. In Part 3, we added distributed tracing. The architecture handles single-request tool calls well — but enterprise processes are rarely single-step.

Consider what happens when a developer tells Goose: "Migrate the users table to add an email column, then deploy the updated service to production." This triggers a chain of operations:

What Is Error Prone?

Error Prone is an open-source static analysis tool that Google built to fill this gap. Unlike most static analysis tools, it does not run as a separate step; it plugs into javac as a plugin. Using the information the compiler produces while analyzing the code, it looks for patterns that follow the rules of the language but are most likely wrong, and reports them as compiler warnings or errors.

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.

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!

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

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.

• 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.

That decision is harder than it sounds. A business operation that was once handled inside a single database transaction may cross several services after decomposition. An order can involve inventory, payment, pricing, customer limits, audit history, and notification. The architecture may divide these responsibilities cleanly, but the business still sees one operation: place the order.

Choosing between infrastructure instruments, such as Kotlin and Java, is an investment decision, and like any investment decision, it needs a thesis. Four business pressures should drive the discussion: time-to-market, operational risk, talent economics, and cross-platform consistency.

CEP is a built-in capability of Apache Flink, available as open source and deployable at scales the old proprietary platforms could never reach. Yet it remains underused and misunderstood. This article is a practical guide to what CEP is, when to use it, how Flink implements it, and where it fits in modern data architectures, including agentic AI, which is covered in depth in the companion article.

The Old Architecture

The reason AngularAndSpring was built using Spring Reactor was primarily the efficient use of operating system threads and memory under high load. Spring Reactor had a limited thread pool (usually the number of available CPU cores) and used OS threads only when the CPU had work to do. It did not let the operating system threads sleep. This used CPU resources much better and required far fewer OS threads, each of which needs around a megabyte of memory.

The Machine Is an Input, Even When the Repository Pretends Otherwise

A repository normally captures application source, configuration templates, and dependency manifests, but operating assumptions often remain outside it. A service compiled with one JDK can behave differently under another. Code that relies on the host time zone inherits machine configuration, and Java’s default time zone is derived from the environment in which the JVM runs; APIs that obtain the system default can therefore return different values across hosts. Locale has a similar characteristic because a JVM maintains a default locale that can influence locale-sensitive operations. 

• Part 1, Running Sentiment Analysis Inside Neo4j With a Java Plugin, used the VADER lexicon via a pure Java UDF – rule-based, fully local, high throughput.
• Part 2, Wasm Inside Neo4j: Building the Example That Didn't Exist, embedded a Wasm runtime inside a Java UDF using wasmtime-java — sandboxed, still local, lower throughput.

Part 3 takes a different approach. Instead of bundling a scoring model inside the plugin JAR, we call TypeSafe AI's Jev from inside a Java UDF. Jev is a System One model – it doesn't generate text. It takes a state (the text to evaluate) and a set of typed questions and returns structured decisions with explicit probability and confidence information. No prompt engineering, no JSON schema coercion, no parsing.

QuestDB appeals to enterprise developers by combining a time-series architecture with a familiar SQL interface. This technique reduces the learning curve for teams experienced in relational querying while providing a database optimized for append-heavy, chronological workloads. For systems needing recent-state queries, historical analysis, trend detection, and rapid ingestion, QuestDB delivers a practical solution.

IoTDB stands out for its focus on large-scale time-series workloads from devices and industrial systems. It is purpose-built for high-frequency data ingestion, historical analysis, and time-based queries. This makes it ideal for applications that require insight into both the current state and historical trends of devices or processes.

These recommendations are repeated throughout documentation, blog posts, and operational guides, yet their origins and the mechanisms that justify these specific values are rarely examined. Undoubtedly, they provide a reasonable and safe starting point or a safe upper bound in most of the cases, but a safe default is not necessarily an optimal configuration.