I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work

A safer operating model treats each agent as an isolated contributor with a dedicated Git worktree, an explicit file-level contract, deterministic validation commands, and no authority to integrate directly into the protected branch. Git worktrees provide multiple linked working trees for one repository, while modern coding-agent platforms independently reinforce the same principle through isolated sandboxes, scoped write access, and controlled network permissions. 

The repository's demo reports 30x+ actor-to-worker oversubscription for that sample workload, with sub-second activation. It is a demonstration, not a production capacity guarantee. 

The Ampere® PMU Profiler (APP) is a Python-based tool designed to provide deep insight into the microarchitectural behavior of applications running on Ampere CPUs (e.g., Ampere® Altra® and AmpereOne®). Unlike standard profilers that identify where time is spent (e.g., which functions consume CPU time), the PMU Profiler explains why time is being spent by measuring low-level hardware events associated with the CPU pipeline and execution behavior.

A key outcome of APP is that it enables performance engineers to move from coarse symptoms to actionable causes. For example, while application-level profiling can show an expensive code path, APP can help identify whether the expense stems from inefficient instruction fetching, data cache misses, or other microarchitectural factors that are difficult or impossible to isolate using application-level tools alone. The document outlines a top-down performance analysis methodology and positions APP as an essential final step for expert-level tuning, particularly on Ampere platforms, where you must understand hardware-level bottlenecks and then apply targeted code optimizations.

A reliable CDC design has to account for those failure modes from the beginning. That means combining Kafka and Debezium with idempotent writes, deterministic projections, controlled replay workflows, reconciliation checks, and enough recovery evidence to explain what happened when something goes wrong.

"The daily digest the pipeline sends, with new postings badged."

Then I started building pipelines that fed automated billing and revenue recognition systems. The rules changed completely.

If you deploy AWS Lambdas through Terraform, you almost certainly use archive_file. With enough lambdas, a single terraform apply can kill the CI runner with OOM. The trickiest part is that you will not see any errors in Terraform output and have no clue what just happened.

I noticed this when my lambdas started failing — every first terraform apply after a routine change. SIGKILL from the kernel OOM killer and nothing in Terraform logs. The strange part is that reapply sometimes worked — not always on the first try, but eventually it went through. I've named the ticket "Flaky CI," and two weeks of investigation was focused on the CI itself: runner memory, parallel jobs, Docker leaks. terraform apply was the last suspect — from my perspective, there was no way or reason for it to consume so much memory.

This is not a theoretical race. It shows up in any system where a long-running task publishes to shared storage, and the orchestrator uses rolling deployments, which is to say most production batch pipelines. The failure is quiet. Nothing crashes. Logs show two successful task completions. The corruption lives entirely in the output, and it surfaces later as a downstream decision made on data that never existed as a coherent snapshot.

But feature flags are not limited to simple on/off switches. They can support gradual rollouts, experimentation, access control, operational safeguards, and runtime configuration. Each of these use cases has a different purpose and requires a different way of designing and managing flags.

What first got you interested in digging into complex infrastructure and systems problems?

In this blog, you will take a closer look at the different exchange types that can be used in RabbitMQ. All are demonstrated by means of examples in a Spring Boot application. Enjoy!

Introduction

In the previous blog, you learned the basic concepts of RabbitMQ and how to use it in a Spring Boot application. However, you only scratched the surface of it, so now it is time to dig a bit deeper into the different exchange types.

A Familiar Routine

If you build data pipelines for a living, you've lived this story. Your PySpark job runs perfectly in a cloud notebook. You productionize it, push it through CI, deploy it to the cluster, and it fails. A dependency mismatch. A different Spark minor version. A Delta Lake protocol feature your local wheel doesn't know about. A timezone default nobody set.

At launch, we set a simple quality target. From historical user behavior on other surfaces, we knew that about 9% of served items would receive a user vote. Among those votes, we wanted 70% to be thumbs up. We expected that approval rate to show whether the pipeline was improving.

Session capture, per-line attribution, and selection-bias-free agent benchmarks, on top of the Git host you already use.

A technical overview for engineers and engineering leaders evaluating how much of their codebase is now written by AI agents - and who is accountable for it.

]]> Mon, 24 Aug 2026 17:00:01 GMT https://dzone.com/articles/3669854 Troian Serhii https://feeds.dzone.com/link/23568/17424572/downtime-unlocked-front-door Anyone who has carried a pager long enough develops a professional numbness. A queue backs up, a p99 drifts past budget, a deploy does something stupid at 40% rollout. You fix it, you write it up, you go back to sleep. The stakes are real but abstract: revenue per minute, an SLA credit, a churn number on somebody's spreadsheet.

That numbness doesn't survive contact with a product people rely on for safety.

]]> Thu, 20 Aug 2026 16:00:00 GMT https://dzone.com/articles/3672079 Naveen Goel https://feeds.dzone.com/link/23568/17423142/docker-ai-development-platform It stopped being just a packaging tool the day our onboarding doc got shorter instead of longer.

Three weeks into a new ML platform job, I asked a coworker why the 'getting started' doc had a section called 'If conda breaks, try the alternative.' He laughed in a way that told me it wasn't a joke. Every new hire spent their first two days fighting Python versions, CUDA driver mismatches, and a vector database that someone had installed locally in 2022 and nobody dared touch. We had four individuals on the team, each with distinct working setups, and "it works on my machine" was no longer a mere punchline; it had become a regular agenda item during our daily standup meetings. 

]]> Wed, 19 Aug 2026 17:00:00 GMT https://dzone.com/articles/3663882 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23568/17423058/docker-llm-containerization The first time I containerized a fine-tuned Llama model for a client's internal search tool, the build finished at 38 gigabytes. I remember staring at the terminal thinking there was no way that was right. It was right. The image included a CUDA base, PyTorch with every backend compiled in, model weights baked directly into the layer, and a pip cache that had not been cleaned. 

Pushing that to our registry took eleven minutes on a good connection. Pulling it onto a fresh node during an autoscale event took even longer, and by the time the pod was ready, the traffic spike it was supposed to handle had already passed. That's the moment I stopped treating LLM containers like regular application containers, because they are not the same animal at all.

]]> Wed, 19 Aug 2026 14:00:01 GMT https://dzone.com/articles/3663880 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23568/17422953/docker-engine-swarm-traffic If different Docker Engine versions are running simultaneously in a Docker Swarm cluster, this may lead not to an obvious service outage but to a more subtle scenario: partial traffic degradation on individual nodes. In this case, the issue appeared on one of the manager nodes, Traefik started reporting an unavailable status (health=0) for the router-app service, and the cause, according to the working hypothesis, was related to differences in iptables rules and overlay networking between Docker 28.1.1 and 28.2.2.

On June 22, 2025, this exact scenario occurred in the production cluster of the backend infrastructure for a socially significant public transportation mobile application. The system serves about 2 million users, several tens of thousands of daily active users, and a total load of around 1000–1600 RPS, so even partial degradation at a single entry point affected a high-load segment of traffic and could have had a noticeable impact on SLA metrics if it had not been localized in time.

]]> Wed, 19 Aug 2026 13:00:00 GMT https://dzone.com/articles/3665843 Denis Tiumentsev https://feeds.dzone.com/link/23568/17422499/local-first-risk-detection-pipeline Enterprise risk problems often start as data-platform issues. Challenges include fragmented signals, inconsistent definitions, missing context, weak lineage, and untimely alerts. Whether evaluating transactions, support messages, images, or metrics, the main question is: how can we turn imperfect evidence into explainable, defensible decisions?

Observations from a hackathon revealed recurring design pressures in digital safety projects. Local language and context significantly influenced outcomes. Network access was often unreliable. No single detector proved sufficient. Risk scores without clear justifications were challenging to interpret. Although not a universal solution, these insights support a practical guideline: maintain decision paths that are local, modular, and auditable.

]]> Tue, 18 Aug 2026 19:00:00 GMT https://dzone.com/articles/3662917 Naga Hemanth Badabagni