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v3.0.4 — Stable release with all bugfixes

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@DevSpecOps DevSpecOps released this 29 Jun 13:26
v3.0.4
80bda78

🚀 Release Notes — v3.0.4

Stable release with all bugfixes applied

We are happy to announce the release of v3.0.4 — a fully stable and production‑ready version of Pipeline-Prod-AIOps.
This release closes all issues identified during external review and brings the project to a state ready for demonstration and deployment.

✅ What's New & Fixed

🔧 Critical Fixes

  • Kafka advertised listener — now uses internal kafka:9092 address, ensuring reliable connectivity between producer, consumer, and broker.
  • Producer reliability — added flush() to guarantee messages are actually sent to Kafka (previously they were buffered and never delivered).
  • Database column unification — renamed total_amount → amount across producer, consumer, API, and dashboard queries. No more mismatches.

📊 Grafana & Monitoring

  • Datasource provisioning — fixed ClickHouse datasource with overwrite: true and explicit host field. No more manual tweaks after deployment.
  • Dashboard portability — all panels now use fixed UID clickhouse, making the dashboard reusable across environments.
  • Time filters removed — dashboard shows all available data immediately, no need to adjust time ranges.
  • Pie chart fixed — revenue by channel now displays all three channels (online_web, online_app, offline_store).

🧪 Testing & CI

  • Tests rewritten — replaced slow testcontainers with direct connection tests for speed and reliability.
  • CI pipeline enhanced — now spins up full Docker Compose stack before running pytest, ensuring tests run against real services.
  • All tests pass — API health, ClickHouse connection, Kafka producer connection — all green.

📦 Infrastructure

  • Grafana provisioning — datasource and dashboard now fully configured via YAML, no manual setup required after docker-compose up.
  • Producer data model — realistic sports retail data with balanced channels, loyalty cards, discounts, and A/B test groups.

🛠️ How to Upgrade

git pull origin main
docker-compose down -v
docker-compose up -d --build

Wait ~1 minute for services to initialize, then open:

📊 What You'll See

  • Data flowing — real‑time order generation into ClickHouse
  • Grafana dashboard — revenue, A/B test results, channel breakdown, top categories, promo analysis
  • Prometheus metrics — API health, cache hits, predictions count
  • CI/CD pipeline — automated tests on every push

🎯 What's Next (Roadmap)

We are already planning the next major release — v4.0.0 — which will include:

  • Kubernetes (minikube) deployment with Helm charts
  • Dead Letter Queue (DLQ) for Kafka
  • MLflow for experiment tracking and model registry
  • Data drift monitoring with Evidently AI

🙏 Special Thanks

To everyone who contributed feedback, tested the system, and helped make this release stable.

📎 Links

Released on: 2026-06-29
Stable · Production‑ready · Fully documented

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