## 🧭 Overall Goal
A **central analytics and monitoring system** that:
* Collects **real-time data** (logs and database events) from all your VPSs.
* Stores it safely for analysis and backup.
* Lets admin **visualize everything live** from one place.
* Keeps existing app servers lightweight and independent.
---
## 🧱 Architecture Summary
### 🔹 Existing VPSs (each running a web service)
* Each one has its own **MySQL database** and web app.
* Each runs a lightweight **Maxwell Daemon** (to stream MySQL binlog events).
* Optionally, they can also send **application / Nginx logs** using **Fluent Bit** or **Filebeat**.
* All these send data to a **central analytics server**.
### 🔹 New Analytics VPS (the “control tower”)
This machine will host:
1. **Apache Kafka** – message bus
* Receives streams from Maxwell or log forwarders on all other servers.
* Acts as a buffer and queue, so data isn’t lost if ClickHouse or Grafana restart.
2. **ClickHouse** – analytical database
* Consumes Kafka topics (e.g. logs, events).
* Stores them efficiently for querying and dashboards.
* Handles large volumes and time-series analytics.
3. **Grafana** – dashboard and visualization tool
* Connects to ClickHouse via HTTP.
* Displays charts, tables, and alerts in real time.
---
## ⚙️ Implementation Details
### Analytics VPS
* All three services run together via a **Docker Compose** stack:
* Each service has its data/configs **mounted to host folders** so you can edit and back them up normally.
* You can start/stop everything with `docker compose up -d` / `down`.
* Exposed ports:
* **9092** → Kafka (for incoming data)
* **8123** → ClickHouse HTTP
* **3000** → Grafana UI
### Your Other VPSs
* Install **Maxwell Daemon** for MySQL → Kafka streaming.
```bash
./bin/maxwell --producer=kafka --kafka.bootstrap.servers=ANALYTICS_IP:9092
```
* Optionally install **Fluent Bit** or **Filebeat** for log forwarding:
* Send logs to Kafka topic `app.logs`.
* (Later, can add Prometheus or Loki exporters for system metrics and logs.)
---
## 🔄 Data Flow
```
[ Web App + MySQL ] --binlog--> [ Maxwell ] ┐
[ Nginx / App Logs ] ----------> [ Fluent Bit ] ┘
│
▼
Apache Kafka (on Analytics VPS)
│
▼
ClickHouse (stores data)
│
▼
Grafana (real-time dashboards)
```
---
## 🧠 Management & Maintenance
| Task | Frequency | Notes |
| ----------------------- | ------------ | --------------------------------------------- |
| Check Docker stack | occasionally | `docker ps`, `docker logs` |
| Backup `/opt/analytics` | weekly | contains all configs and data |
| Update images | occasionally | `docker compose pull && up -d` |
| Add new dashboards | anytime | via Grafana web UI |
| Send new data types | anytime | just add Kafka topics / new ClickHouse tables |
---
## 💡 Advantages of This Design
✅ Centralized real-time analytics for all services
✅ Lightweight per-app setup (just Maxwell or log shipper)
✅ Scalable and fault-tolerant (Kafka buffers events)
✅ Easy backups and migration (one folder per service)
✅ Minimal ongoing maintenance (~1 hour/month)
✅ Fully private and runs on own VPS
---
## 🔮 Optional Future Upgrades
* Add **Prometheus + node_exporter** → for CPU/memory metrics in Grafana.
* Add **Loki** → to centralize plain text logs.
* Add **Kafka MirrorMaker** → for geographic redundancy.
* Replicate ClickHouse or back it up to S3 for disaster recovery.
---
### 🧭 In short:
Have one **Analytics VPS** running **Kafka + ClickHouse + Grafana (via Docker Compose)**,
and all your other servers will **stream their logs and database events** to it for **centralized visualization and analysis**.
A **central analytics and monitoring system** that:
* Collects **real-time data** (logs and database events) from all your VPSs.
* Stores it safely for analysis and backup.
* Lets admin **visualize everything live** from one place.
* Keeps existing app servers lightweight and independent.
---
## 🧱 Architecture Summary
### 🔹 Existing VPSs (each running a web service)
* Each one has its own **MySQL database** and web app.
* Each runs a lightweight **Maxwell Daemon** (to stream MySQL binlog events).
* Optionally, they can also send **application / Nginx logs** using **Fluent Bit** or **Filebeat**.
* All these send data to a **central analytics server**.
### 🔹 New Analytics VPS (the “control tower”)
This machine will host:
1. **Apache Kafka** – message bus
* Receives streams from Maxwell or log forwarders on all other servers.
* Acts as a buffer and queue, so data isn’t lost if ClickHouse or Grafana restart.
2. **ClickHouse** – analytical database
* Consumes Kafka topics (e.g. logs, events).
* Stores them efficiently for querying and dashboards.
* Handles large volumes and time-series analytics.
3. **Grafana** – dashboard and visualization tool
* Connects to ClickHouse via HTTP.
* Displays charts, tables, and alerts in real time.
---
## ⚙️ Implementation Details
### Analytics VPS
* All three services run together via a **Docker Compose** stack:
* Each service has its data/configs **mounted to host folders** so you can edit and back them up normally.
* You can start/stop everything with `docker compose up -d` / `down`.
* Exposed ports:
* **9092** → Kafka (for incoming data)
* **8123** → ClickHouse HTTP
* **3000** → Grafana UI
### Your Other VPSs
* Install **Maxwell Daemon** for MySQL → Kafka streaming.
```bash
./bin/maxwell --producer=kafka --kafka.bootstrap.servers=ANALYTICS_IP:9092
```
* Optionally install **Fluent Bit** or **Filebeat** for log forwarding:
* Send logs to Kafka topic `app.logs`.
* (Later, can add Prometheus or Loki exporters for system metrics and logs.)
---
## 🔄 Data Flow
```
[ Web App + MySQL ] --binlog--> [ Maxwell ] ┐
[ Nginx / App Logs ] ----------> [ Fluent Bit ] ┘
│
▼
Apache Kafka (on Analytics VPS)
│
▼
ClickHouse (stores data)
│
▼
Grafana (real-time dashboards)
```
---
## 🧠 Management & Maintenance
| Task | Frequency | Notes |
| ----------------------- | ------------ | --------------------------------------------- |
| Check Docker stack | occasionally | `docker ps`, `docker logs` |
| Backup `/opt/analytics` | weekly | contains all configs and data |
| Update images | occasionally | `docker compose pull && up -d` |
| Add new dashboards | anytime | via Grafana web UI |
| Send new data types | anytime | just add Kafka topics / new ClickHouse tables |
---
## 💡 Advantages of This Design
✅ Centralized real-time analytics for all services
✅ Lightweight per-app setup (just Maxwell or log shipper)
✅ Scalable and fault-tolerant (Kafka buffers events)
✅ Easy backups and migration (one folder per service)
✅ Minimal ongoing maintenance (~1 hour/month)
✅ Fully private and runs on own VPS
---
## 🔮 Optional Future Upgrades
* Add **Prometheus + node_exporter** → for CPU/memory metrics in Grafana.
* Add **Loki** → to centralize plain text logs.
* Add **Kafka MirrorMaker** → for geographic redundancy.
* Replicate ClickHouse or back it up to S3 for disaster recovery.
---
### 🧭 In short:
Have one **Analytics VPS** running **Kafka + ClickHouse + Grafana (via Docker Compose)**,
and all your other servers will **stream their logs and database events** to it for **centralized visualization and analysis**.