programming

05 Oct 2026

DZone Java Zone

Building Time-Series Applications With Java and InfluxDB

InfluxDB is essential for applications that analyze continuously changing data. In IoT, this includes tracking temperature, pressure, energy use, or machine telemetry over time. Financial systems use similar models for market prices, exchange rates, trading activity, portfolio values, and risk metrics. The key requirement is the ability to ingest large volumes of timestamped data and efficiently query current, historical, and evolving values.

This versatility makes InfluxDB valuable beyond traditional monitoring. Enterprises use it for observability, infrastructure metrics, logistics, industrial systems, customer activity, fraud detection, transaction trends, and business KPIs. When time is central to data storage and queries, a dedicated time-series database simplifies architecture and enables more intuitive queries.

05 Oct 2026 1:00pm GMT

02 Oct 2026

DZone Java Zone

Wasm Inside Neo4j: Building the Example That Didn't Exist

In a recent DZone article, Running Sentiment Analysis Inside Neo4j With a Java Plugin, we explored several approaches to running sentiment analysis inside the Neo4j database engine. One of those approaches - embedding a Wasm runtime inside a Java UDF - was described like this:

Theoretically, we could embed a Wasm runtime such as wasmtime inside a Java UDF and execute the VADER Wasm module from within Neo4j, getting Wasm's sandbox guarantees inside Neo4j's plugin model. It's technically feasible but no published working example appears to exist and the complexity cost is high relative to the alternatives. An interesting idea to watch, but not practical today.

02 Oct 2026 6:00pm GMT

Part 3: End-to-End Tracing and Observability Across Goose, agentgateway, and Quarkus

Enterprise context - Acme FinServ. SOC 2 CC7 (system monitoring) requires that Acme can detect and investigate anomalous activity. When an agent-driven workflow touches customer data at 2 AM, "we have logs somewhere" is not an answer an auditor accepts. The distributed trace built in this part is the forensic evidence trail: a single trace ID that ties the Goose prompt to every agentgateway policy decision and every Quarkus tool call, so a post-incident review can reconstruct exactly which agent did what, in what order, and how long each governed hop took.

The Core Problem

In Part 1, we built a Quarkus MCP tool server. In Part 2, we secured it with agentgateway's JWT authentication, RBAC, and ExtMCP guardrails. The architecture works - but when something goes wrong in production, you're flying blind.

02 Oct 2026 1:00pm GMT