Kubernetes

CI/CD with GitLab + Kubernetes: deploy in 10 minutes

CI/CD automation: from 2 hours to 10 minutes per deploy Client E-commerce startup, 5-person engineering team Challenge Manual deployment took 2 hours Frequent release errors No fast rollback path Required: CI/CD automation, GitOps, fast rollback Solution Self-hosted GitLab GitLab CI pipeline (Build → Test → Deploy) Managed Kubernetes in Yandex Cloud Flux CD for GitOps Technologies GitLab Docker Kubernetes Helm Flux CD Results ✅ Deploy time: from 2 hours to 10 minutes (12x) ✅ Errors: −90% ✅ Deploy frequency: from 1/week to 10+/day ✅ Rollback time: from 1 hour to 2 minutes

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Geo-Distributed Blockchain Node Cluster in Kubernetes

Geo-Distributed Blockchain Node Cluster in Kubernetes Client Cryptocurrency platform (Web3 / DeFi) Challenge The client needed fault-tolerant infrastructure to run ETH and BSC full nodes across four regions (EU, US, AP, LatAm) with minimal latency for end users, DDoS and RPC spam protection, secure HSM-based transaction signing, and centralized observability. Cold node sync takes 2–3 weeks — a fast-bootstrap solution was required. Solution 1. Infrastructure & IaC (Terraform + EKS) Terraform modules: VPC, subnets, security groups for 4 regions (Frankfurt, Virginia, Singapore, São Paulo) Managed Kubernetes (EKS 1.29+) per region with dedicated node pools: full nodes, archive nodes, signing service NVMe StorageClass (gp3) via CSI driver for high-performance chaindata storage Helm charts for geth / bsc-node with per-region custom values 2. GitOps: Flux CD Multi-Cluster Flux CD v2 with Kustomization per region — single source of truth for all clusters Secrets management: HashiCorp Vault + External Secrets Operator (ESO) All infrastructure changes applied via git push — no direct cluster access required 3. Load Balancing & Anti-Spam HAProxy 2.8: sticky sessions, health checks via eth_syncing — traffic routed only to fully synced nodes Nginx Ingress: rate limiting, IP reputation filtering (Lua-based, fail2ban-style) Cloudflare Workers: geo-routing + L7 DDoS protection Custom Go sidecar: health endpoint returns ready only when node is fully synced 4. HSM Integration for Transaction Signing AWS CloudHSM (prod) / YubiHSM2 (staging) for private key storage Go microservice with PKCS#11 abstraction — swap HSM vendor without rewriting code Isolated K8s namespace + NetworkPolicy: no egress except to HSM endpoint gRPC API for backend: sign tx, get pubkey Full audit log of all signing operations → Loki 5. Monitoring & Alerting Custom Prometheus exporter (Go): eth_blockNumber, eth_syncing, peer count per node Grafana dashboards: sync lag, block height per region, RPC latency, SLO 99.9% Alertmanager → PagerDuty: alerts on block lag > N blocks, node down, peer count below threshold Loki + Promtail: structured logs from all nodes with region/pod correlation 6. Operations & Disaster Recovery Snapshot bootstrap: chaindata from S3 via rclone — node ready in hours instead of weeks DR playbook: step-by-step runbooks for regional recovery Chaos Engineering (Chaos Mesh): node kill, network partition, pod failure tests Architecture Decision Records (ADR) for all key design choices Technologies Kubernetes Terraform Helm Flux CD Prometheus Grafana Results ✅ Geo-routing: latency reduced by routing users to the nearest region ✅ Fast bootstrap: node ready in hours via S3 snapshot instead of 2–3 weeks of sync ✅ Anti-spam: rate limiting + IP reputation — public RPC handles bot load without degradation ✅ HSM: private keys never leave the hardware module ✅ GitOps: every infrastructure change goes through git with a full audit trail ✅ SLO 99.9%: tracked in Grafana, PagerDuty alerts on any degradation

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Prometheus + Grafana Monitoring Stack

Observability Stack for Microservices Architecture Client Early-stage startup Challenge After migrating to a microservices architecture (15+ services), the team had no centralized monitoring in place. Issues were only discovered through user complaints — typically 30+ minutes after they occurred. A full observability stack was needed to detect and diagnose problems proactively. Solution 1. Monitoring Architecture Prometheus for metrics collection Grafana for visualization Loki for centralized log aggregation Jaeger for distributed tracing Alertmanager for notifications 2. Metrics Collection Automatic service discovery in Kubernetes Application-level custom metrics System metrics via node-exporter Database metrics via postgres-exporter and redis-exporter 3. Grafana Dashboards Per-service dashboards for each microservice Unified infrastructure overview dashboard SLA/SLO tracking metrics Business metrics (RPS, conversion rate) 4. Centralized Logging (Loki) Log aggregation across all services Full-text log search via Grafana Log-to-metric correlation 5. Distributed Tracing (Jaeger) HTTP request tracing across services Call chain visualization Bottleneck identification Per-service latency analysis 6. Alerting Alerts delivered to Slack / PagerDuty / custom webhooks Critical issue escalation On-call rotation support Automatic incident creation Technologies Prometheus Grafana Kubernetes Docker Helm Linux Results ✅ MTTD: reduced from 30 minutes to under 1 minute ✅ MTTR: recovery time reduced by 60% ✅ Alerts: proactive notifications before users are impacted ✅ Visibility: full observability across all services ✅ Capacity planning: data-driven resource forecasting

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