Four Tables, One Screen

Picture a support screen for a small online shop. When a customer calls, the agent needs the order's customer, items, prices, and status in one place.

• kubectl rollout restart deployment/my-app and eat the disruption.
• Install Reloader so it restarts your pods for you when a Secret changes.
• Mount the Secret as a volume and cross your fingers that your app re-reads the file (spoiler: most apps read it once at boot).

All three share the same flaw: the way you "pick up" a config change is to throw away the process and start a new one. For a rotating database password, that means dropping every in-flight request, cold connection pools, and a latency spike — all to change one string.

It's a genuinely good design. But it collides with how most Go services consume secrets: you call AccessSecretVersion("latest") exactly once, at startup, and cache the result. From that moment your process is pinned to whatever latest meant at boot. Add version 4, disable version 3, and your service happily keeps using 3 until someone restarts it — which, at best, is a scheduled redeploy and, at worst, an incident when the disabled version stops resolving.

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.

If you are trying to learn how to fix Laravel performance issues in production, resist the urge to begin with Redis, Octane, or a larger server. First, find the work the request shouldn't be doing. Removing wasted work usually produces a bigger, safer improvement than adding infrastructure around it.

And yet the same old gap is there. Your Go service calls GetSecret once at startup, decodes the value into a struct, and treats it as immutable for the life of the process. When someone rotates the secret in Key Vault — a new version, the old one disabled — your service has no idea. It keeps using the value it read at boot until the next deploy or pod restart. The rotation was seamless in Azure and invisible to your app, which is exactly the wrong combination.

There's just one problem, and it's the one nobody mentions in the demo: your application has no idea it happened.

By the end you have a bespoke ConfigManager in every repo: a loader, a fetcher, a refresh loop, a mutex, and a prayer. It is never quite the same twice, it is rarely tested well, and it is exactly the kind of code nobody wants to own.

That’s where the Builder Pattern, combined with Faker, can really help. It lets us create clean, flexible, and realistic test data with ease.

• 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.

The Contradiction You Have Probably Already Measured

If you run infrastructure for a large ML organization, you have likely seen a dashboard that says something uncomfortable. The fleet is 90-plus percent allocated. Teams are filing tickets about blocked capacity. And a sampled profile of the same fleet shows a meaningful fraction of devices doing no useful work.

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.

It is not enough to simply say that wrong SQL can be produced. It’s that an ungoverned text-to-SQL layer may join tables it shouldn’t, return columns that should have been masked. As a result of a lack of oversight, a marketing analyst could receive a query containing raw account numbers, since none of the components of the stack told the system to do otherwise. Prompt-level guardrails (“please don’t show PII”) are not a security control. They’re just a suggestion, and a model under adversarial pressure ot just a confusing prompt will ignore a suggestion.

The framing is wrong. These formats were built for different jobs in different eras, and the more useful question is which one fits the job in front of you. So here is the history, the trade-offs, and where each one still wins, including what changes now that LLMs and agents read and write structured data too.

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.

• Both Embabel and LangGraph4j let a Java developer build multi-step AI agents without leaving the JVM.
• Embabel hands the framework a goal and a bag of typed actions, and lets a planner decide the order on its own.
• LangGraph4j asks the developer to draw the exact graph of nodes and edges by hand.
• We will understand both philosophies through a real Embabel agent, a small Kolkata street-crossing example, a comparison table, and finally a bigger question — is Java catching up with Python in enterprise AI work?

Where the Story Starts

If you are a Java developer today, you are watching two worlds collide. On one side are large language models, which grew up almost entirely in Python. On the other side is enterprise Java, which has spent twenty-five years learning to build systems that banks, insurance companies, and hospitals can actually trust.

Two frameworks are now trying to bring these two worlds together on the JVM: Embabel and LangGraph4j. Both help you build an "agent" — a piece of software that uses an LLM to complete a task in several steps, rather than in one single prompt. But the way they think about "steps" is completely different. That difference is what this article is about.

“How does a map work in Go?” is one of my favorite interview questions. It sounds simple, but it opens up a conversation about hashing, collisions, memory layout, and why two implementations with the same average O(1) lookup complexity can behave quite differently. With Go 1.24, that conversation got more interesting: the map implementation switched to Swiss Tables. Let’s walk through how the old implementation worked, what changed, and how the new design finds your keys.

TL;DR

Go 1.24, released in February 2025, completely replaced the internal implementation of the built-in `map` with a design based on Google's Swiss Tables. The syntax and the behavior guaranteed by the Go specification did not change, so existing programs required no migration. In microbenchmarks, some map operations became up to 60% faster. Across the Go team's full-application benchmark suite, geometric mean CPU time improved by about 1.5%. Datadog reported roughly 70% less memory for one exceptionally large map—an impressive case study, not a universal promise.

]]> Thu, 01 Oct 2026 15:00:00 GMT https://dzone.com/articles/3684611 Ilia Ivankin https://feeds.dzone.com/link/23565/17480936/self-healing-sql-pipelines-llms A self-healing SQL pipeline should not mean autonomous SQL generation followed by privileged execution. In production, the safer interpretation is narrower, where a language model proposes a repair, while deterministic controls decide whether that repair is syntactically valid, semantically plausible, operationally safe, and eligible for execution. This distinction matters because the same mechanism that corrects a renamed column can also generate an unintended DELETE, widen a join, or scan an unexpectedly large dataset. 

Structured-output features can constrain an LLM response to a defined schema, but schema conformance is not equivalent to database correctness or authorization. OpenAI’s Structured Outputs is designed to make generated output conform to supplied JSON Schemas, and it does not validate SQL semantics or execution safety. 

]]> Wed, 30 Sep 2026 12:00:04 GMT https://dzone.com/articles/3681517 Uthej Mopathi https://feeds.dzone.com/link/23565/17477595/jakarta-batch-enterprise-processing Batch processing remains vital because many business operations aren't suited to interactive requests. Tasks such as recalculating prices, reconciling transactions, migrating records, generating reports, processing invoices, reclassifying customers, or applying rules across millions of records may require considerable time. Handling these as standard requests leads to fragile systems, increased user wait times, frequent timeouts, challenging retries, and possible data inconsistencies.

A batch model handles large workloads predictably, incrementally, and with control over progress and recovery. Rather than processing a massive operation as a single loop, batch processing uses jobs, steps, chunks, checkpoints, filtering, and restartability. This approach separates long-running data tasks from the user experience while delivering a structured execution model. In this article, we will focus on Jakarta Batch and examine its sustained relevance for modern enterprise applications.

]]> Tue, 29 Sep 2026 14:00:00 GMT https://dzone.com/articles/3684607 Otavio Santana