🚀 1. Real-Time System Monitoring & Optimization Platform

A robust backend system like New Relic / Datadog / Prometheus

🌐 Description

A Spring Boot service that collects CPU, memory, disk, network, service health metrics from multiple machines/servers in real time — and uses rules/AI to suggest optimizations.

🧱 Tech Stack

• Spring Boot (WebFlux or MVC)
• Spring Data JPA / MongoDB
• Redis (real-time caching)
• Kafka / RabbitMQ (streaming)
• WebSockets (real-time dashboard)
• Docker / Kubernetes
• Grafana-like dashboard (optional)

🔥 Modules

• Agent Service: Small script that sends machine metrics every X seconds

• Collector Service: Spring Boot microservice that receives metrics

• Rules Engine:

  • High CPU? Suggest killing process
  • High memory? Suggest GC tuning

• Alert Module: Email/SMS/Webhook alerts

• Optimization Recommendations

⭐ Why this is industry-level?

• Real-time data
• Microservices
• Streaming
• Production-like complexity
• Perfect for DevOps + backend profile

1) High-level overview (one-liner)

A distributed platform that ingests telemetry from lightweight agents, streams it into a scalable backend, stores short-term high-resolution and long-term aggregated metrics, applies rules/ML to detect anomalies and produce optimization recommendations, and drives real-time dashboards & alerting.

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2) Core components

1. Agent (Edge)

   • Lightweight program (Java/Go/Python) installed on hosts/containers.
   • Collects CPU, memory, disk, network, process list, JVM/GC, custom app metrics.
   • Sends batched telemetry via HTTPS/gRPC/MQTT or pushes to an ingress gateway.

2. Ingress / API Gateway

   • Rate limits, auth, TLS termination.
   • Accepts agent telemetry and client API calls.

3. Collector / Ingest Service (Spring Boot)

   • Validates, enriches, tags (host, env, service), and publishes events to messaging layer (Kafka).
   • Also supports direct write for low-volume setups.

4. Streaming Pipeline (Kafka / Kafka Streams / Flink)

   • Real-time aggregation, downsampling, anomaly detection triggers, enrichment for dashboard.

5. Time-series Storage (TSDB)

   • Short-term high-resolution: Prometheus remote write, InfluxDB, or TimescaleDB.
   • Long-term aggregated store: Postgres/ClickHouse/S3 for cold storage.

6. Metadata DB

   • PostgreSQL or MongoDB for hosts, agents, alerts, users, policies.

7. Rules Engine / Recommendation Engine

   • Deterministic rules (thresholds, rate of change, composite conditions).
   • ML module for anomaly detection and optimization suggestions (optional): e.g., auto-tune GC flags, recommend scaling, suggest process restarts, CPU or memory resizing.

8. Alerting Service

   • Generates alerts, supports escalation (email, Slack, SMS, webhooks), deduplication, suppression windows.

9. Dashboard & Real-time UI

   • WebSocket or server-sent events for live metrics. Grafana-like charts + drilldowns + topology view.

10. Auth & RBAC

    • OAuth2 / OpenID Connect for users, API keys for agents, fine-grained roles.

11. Observability & Telemetry

    • Tracing (Jaeger/Zipkin), logs (ELK/EFK), metrics for platform health.

12. CI/CD & Infra

    • Docker, Helm charts, Kubernetes, GitHub Actions/GitLab CI, IaC (Terraform).

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3) Recommended tech stack (industry realistic)

• Backend: Spring Boot (WebFlux for reactive ingest)
• Messaging: Kafka (or RabbitMQ for simpler setups)
• Time-series DB: TimescaleDB or InfluxDB
• Metadata/Relational: PostgreSQL
• Cache: Redis (fast leaderboards, recent alerts)
• Frontend: React + WebSockets (or reuse Grafana panels)
• Agent: Go (small binary) or Java (if JVM metrics required)
• Deployment: Docker + Kubernetes
• Monitoring: Prometheus + Grafana for platform itself
• Tracing: Jaeger
• CI/CD: GitHub Actions

---

4) Data model & example schemas

Agent telemetry event (JSON)

{
  "agentId": "host-123",
  "timestamp": 1699999999000,
  "tags": {"env":"prod","service":"orders","region":"ap-south-1"},
  "metrics": {
    "cpu": {"user":12.3, "system":3.1, "idle":84.6, "cores":4, "load1":0.8},
    "memory": {"total":16777216, "used":8234560, "free":8542656},
    "disk": [{"mount":"/","total":100000000, "used":45234534}],
    "network": {"rx_bytes":123456, "tx_bytes":54321},
    "processes": [{"pid":1234,"name":"java","rss":256000000}]
  }
}

Relational tables (Postgres)

• agents(agent_id PK, hostname, ip, version, last_seen, status, tags JSONB)
• policies(policy_id PK, name, conditions JSONB, actions JSONB)
• alerts(alert_id PK, agent_id, metric, severity, start_ts, end_ts, status)

Time-series data: use TSDB schema (native).

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5) APIs (selected — implement early)

• POST /api/v1/telemetry — agent pushes batched events (auth: API key).
• GET /api/v1/agents — list agents, filter by tags.
• GET /api/v1/metrics?agent=host-123&metric=cpu&from=...&to=...&step=10s
• POST /api/v1/policies — create rule/policy.
• GET /api/v1/alerts — query alerts.
• POST /api/v1/actions/execute — trigger remediation (e.g., restart service) — requires secure auth and audit logs.

Example POST /api/v1/telemetry should return 202 Accepted with a list of received IDs.

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6) Rules & Recommendation Engine design

Deterministic rules

• Simple JSON rule structure: conditions (metric operator threshold), duration, severity, actions.
• Example: IF cpu.user > 85% for 2 minutes THEN severity=critical, action=alert+suggest_scale_up

ML/Heuristic suggestions

• Use unsupervised models (Isolation Forest, Seasonal-ESD) on metric time-series to detect anomalies.
• Use historical patterns to estimate expected resource usage and suggest rightsizing: “Host X is underutilized: reduce vCPU from 4 -> 2”.
• For JVM apps: detect frequent GC pauses, recommend GC tuning flags or heap size adjustments.

Action types

• Notify (email, Slack)
• Run remediation script (via secure agent RPC)
• Create incident ticket (JIRA)
• Auto-scale (if integrated with infra)

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7) Agent design details

• Minimal resource usage, configurable polling interval.
• Collectors: /proc, platform APIs (Windows WMI), container stats (cgroups).
• Security: agent communicates over TLS with mutual auth (mTLS) or signed JWT.
• Offline mode: buffered storage and retry/backoff.
• Auto-update mechanism or versioning.

---

8) Ingest & storage strategy

• Agents send high-resolution (1–10s) samples.

• Ingest service writes raw to Kafka.

• Streaming job:

  • persists raw to short-term TSDB (7–30 days),
  • computes 1m/5m/1h aggregates stored in long-term store,
  • writes anomaly events to metadata DB/alerting queue.

• Cold retention: aggregated hourly/daily into object storage (S3) for compliance.

---

9) Scalability & reliability patterns

• Horizontal scale: multiple collector pods behind a Kubernetes service; Kafka for buffering.
• Backpressure: WebFlux + bounded executor pools to avoid OOM under surge.
• Partitioning: Kafka topics partitioned by host or tenant.
• High availability: replicate critical services (Postgres with replicas, Kafka cluster).
• Resilience: Retry policies, circuit breakers, exponential backoff, dead-letter topics.

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10) Security & multi-tenancy

• Agents authenticate via API keys + mTLS.
• RBAC for users; tenant isolation via tenant_id tag + separate Kafka topics / schemas or row-level security on PostgreSQL.
• Audit logging for all remediation actions.
• Secrets management: HashiCorp Vault or Kubernetes Secrets.

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11) Observability (platform self-monitoring)

• Instrument all services with Prometheus metrics.
• Have SLOs: ingestion latency, error rate, alert false positive rate.
• Health checks and readiness probes for Kubernetes.

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12) CI/CD, infra & deployment checklist

• Containerized apps → Docker.
• Helm charts for k8s deployment; values for environments.
• Terraform for infra (EKS/GKE/AKS or EC2 + managed Kafka).
• Pipeline: build → unit tests → static analysis → container build → push → deploy to staging → smoke tests → promote to prod.
• Canary releases for collector and ingest components.

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13) Testing strategy

• Unit tests for rules, parsers.
• Integration tests: use a local Kafka & TSDB environment (Testcontainers).
• Load tests: generate synthetic agent traffic (k6 or Gatling) to validate ingestion throughput.
• Chaos testing: simulate node failures and network partitions.

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14) MVP scope (what to build first)

Focus on a minimal, demonstrable pipeline you can extend:

1. Agent (simple) — sends CPU, memory, disk every 10s (HTTP POST).
2. Ingress & Collector (Spring Boot) — accepts telemetry & publishes to Kafka (or in-memory queue for MVP).
3. Simple Storage — TimescaleDB or even Postgres time-series table to persist metrics.
4. Basic Aggregator — compute 1m averages and store.
5. Dashboard — simple React UI showing last N minutes of CPU/memory graphs, list of agents.
6. Rules Engine (basic) — threshold rules that create alerts and deliver via email/Slack.

This is enough to demo streaming, storage, alerting, and UI.

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15) Stretch features (post-MVP)

• ML anomaly detection & automated recommendations.
• Auto-remediation (secure remote commands).
• Multi-tenant support and per-tenant RBAC.
• Integrations: Kubernetes metrics, cloud provider metrics, APM tracing.
• Billing & usage metering, audit trail.

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16) Deliverables you can show in interviews / portfolio

• Architecture diagram (drawn in Lucid/Diagrams.net).
• Sequence flow for telemetry ingestion → alert → action.
• API spec (OpenAPI / Swagger).
• DB schema and sample queries (e.g., how to compute 95th percentile).
• A short demo recording showing an agent, raising an alert, and a dashboard.
• Load test results and lessons learned (throughput, bottlenecks).
• Code repo with README + helm charts + sample data generator.

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17) Concrete next steps (what you can implement right now)

I recommend starting with a 3-day code plan (no time estimates here — just ordered tasks):

1. Create a Spring Boot project (WebFlux) with one endpoint POST /api/v1/telemetry.
2. Build a minimal agent script that posts telemetry to that endpoint.
3. Persist incoming events to Postgres (or TimescaleDB) with a simple metrics table.
4. Build a dashboard page that reads latest metrics and renders charts (use Chart.js or Recharts).
5. Add a simple threshold-based policy engine that scans stored metrics and creates alerts.
6. Put the whole stack into Docker Compose for local dev.

If you want, I can:

• generate the Spring Boot starter code (controllers, DTOs, repo) right now, or
• produce an OpenAPI spec for the APIs, or
• sketch the database schema + example SQL for TimescaleDB, or
• write the agent in Go/Python that posts telemetry.

Tell me which of those you'd like me to generate first and I’ll produce the code / spec immediately.