Gamal0909
439a31ba93
- Created a GitHub Actions workflow for CI/CD to deploy to Ghyamah, including testing, building, and pushing Docker images. - Added architecture design document for handling 15,000 requests per second, detailing system components, capacity planning, and cold start strategies. - Introduced a Python-based uptime/latency/SSL monitor with a static dashboard, utilizing standard libraries only. - Included Dockerfile and entrypoint script for the monitoring application, ensuring it runs as a non-root user and handles process management. - Added a .dockerignore file to exclude unnecessary files from the Docker build context. - Created an HTML dashboard for visualizing monitoring metrics, including uptime, latency, and SSL certificate status.
307 أسطر
7.1 KiB
Markdown
307 أسطر
7.1 KiB
Markdown
# High-Traffic Architecture Design (15,000 Requests/Second)
|
||
|
||
This document describes the proposed architecture for handling **15,000 requests per second (RPS)** on the Ghaymah platform. It covers the system architecture, container capacity planning, cold start mitigation strategies, and the use of Ghaymah Block Storage for stateful workloads.
|
||
|
||
> **Note**
|
||
>
|
||
> The architecture diagram below is a placeholder. Replace it with your architecture image.
|
||
|
||
---
|
||
|
||
# Table of Contents
|
||
|
||
- [Architecture Overview](#architecture-overview)
|
||
- [Architecture Diagram](#architecture-diagram)
|
||
- [Request Flow](#request-flow)
|
||
- [Container Capacity Planning](#container-capacity-planning)
|
||
- [Cold Start Strategy](#cold-start-strategy)
|
||
- [Ghaymah Block Storage for Stateful Workloads](#ghaymah-block-storage-for-stateful-workloads)
|
||
- [Key Design Decisions](#key-design-decisions)
|
||
|
||
---
|
||
|
||
# Architecture Overview
|
||
|
||
The system is designed to process **15,000 requests per second** while maintaining high availability, scalability, and fault tolerance.
|
||
|
||
The architecture consists of:
|
||
|
||
- WAF / CDN
|
||
- Ghaymah Load Balancer
|
||
- Auto-scaling application containers
|
||
- Redis cache cluster
|
||
- Primary database
|
||
- Read replicas
|
||
- Ghaymah Block Storage
|
||
|
||
The application containers remain **stateless**, while all persistent data is stored on external block storage.
|
||
|
||
---
|
||
|
||
# Architecture Diagram
|
||
|
||
The architecture diagram below shows the proposed high-traffic deployment.
|
||
|
||
|
||
|
||
---
|
||
|
||
# Request Flow
|
||
|
||
The following sequence illustrates how requests are processed.
|
||
|
||
1. Clients send requests to the application.
|
||
2. The **WAF/CDN** filters malicious traffic and caches static assets.
|
||
3. Requests are forwarded to the **Ghaymah Load Balancer**.
|
||
4. The load balancer distributes traffic across healthy application containers.
|
||
5. Containers first attempt to retrieve data from the **Redis cache**.
|
||
6. Cache misses are forwarded to the database.
|
||
7. Read operations are served by database replicas whenever possible.
|
||
8. Write operations are handled by the primary database.
|
||
9. All database data is stored on **Ghaymah Block Storage**, ensuring persistence.
|
||
|
||
---
|
||
|
||
# System Components
|
||
|
||
| Component | Responsibility |
|
||
|-----------|----------------|
|
||
| **Clients** | Generate incoming traffic (15,000 RPS) |
|
||
| **WAF / CDN** | Security filtering, DDoS protection, static content caching |
|
||
| **Ghaymah Load Balancer** | Evenly distributes requests across healthy containers |
|
||
| **Application Containers** | Stateless application processing |
|
||
| **Redis Cluster** | High-speed caching layer |
|
||
| **Primary Database** | Handles write operations |
|
||
| **Read Replicas** | Offload read traffic from the primary database |
|
||
| **Ghaymah Block Storage** | Persistent storage for stateful workloads |
|
||
|
||
---
|
||
|
||
# Container Capacity Planning
|
||
|
||
The infrastructure must support **15,000 requests per second**.
|
||
|
||
## Assumptions
|
||
|
||
| Metric | Value |
|
||
|---------|------:|
|
||
| Expected Traffic | 15,000 req/s |
|
||
| Capacity per Container | 500 req/s |
|
||
|
||
---
|
||
|
||
## Base Capacity
|
||
|
||
```
|
||
15,000 ÷ 500 = 30 Containers
|
||
```
|
||
|
||
---
|
||
|
||
## Safety Buffer
|
||
|
||
To absorb unexpected traffic spikes:
|
||
|
||
```
|
||
30 × 30% = 9 Containers
|
||
```
|
||
|
||
---
|
||
|
||
## Total Required Containers
|
||
|
||
```
|
||
30 + 9 = 39 Containers
|
||
```
|
||
|
||
| Calculation | Result |
|
||
|-------------|-------:|
|
||
| Base Containers | 30 |
|
||
| Safety Margin | 9 |
|
||
| **Recommended Total** | **39 Containers** |
|
||
|
||
---
|
||
|
||
## Recommendation
|
||
|
||
During peak traffic periods:
|
||
|
||
- Maintain approximately **39 running containers**.
|
||
- Configure the auto-scaling policy to keep the minimum replica count close to this value.
|
||
- Scale beyond this threshold during sustained traffic increases.
|
||
|
||
---
|
||
|
||
# Cold Start Strategy
|
||
|
||
## Problem
|
||
|
||
When a new container starts, it requires time to:
|
||
|
||
- Pull the container image
|
||
- Initialize the runtime
|
||
- Establish database connections
|
||
- Load application dependencies
|
||
|
||
If traffic is routed before initialization completes, users may experience increased latency or request failures.
|
||
|
||
---
|
||
|
||
## Recommended Mitigations
|
||
|
||
### Readiness Probes
|
||
|
||
Configure readiness probes so that traffic is routed only after the application is fully initialized.
|
||
|
||
Example endpoint:
|
||
|
||
```
|
||
/health/ready
|
||
```
|
||
|
||
Only containers returning **HTTP 200 OK** should receive production traffic.
|
||
|
||
---
|
||
|
||
### Pre-Warming
|
||
|
||
Initialize expensive resources during startup:
|
||
|
||
- Database connections
|
||
- Redis connections
|
||
- Configuration loading
|
||
- Dependency injection
|
||
- Frequently used libraries
|
||
|
||
Avoid performing heavy initialization during the first user request.
|
||
|
||
---
|
||
|
||
### Image Optimization
|
||
|
||
Reduce startup time by:
|
||
|
||
- Using lightweight base images
|
||
- Removing unnecessary packages
|
||
- Minimizing image layers
|
||
- Keeping image sizes as small as possible
|
||
|
||
Examples:
|
||
|
||
- Alpine Linux
|
||
- Distroless Images
|
||
|
||
---
|
||
|
||
### Capacity Buffer
|
||
|
||
Maintain spare capacity (30% safety margin) so existing containers can absorb traffic while new containers complete startup.
|
||
|
||
Benefits:
|
||
|
||
- Reduced request latency
|
||
- Smoother auto-scaling
|
||
- Improved user experience
|
||
|
||
---
|
||
|
||
# Ghaymah Block Storage for Stateful Workloads
|
||
|
||
Containers are **ephemeral** by design.
|
||
|
||
If a container is deleted or restarted, its local filesystem is also removed.
|
||
|
||
Persistent workloads such as:
|
||
|
||
- PostgreSQL
|
||
- MySQL
|
||
- MariaDB
|
||
- MongoDB
|
||
- Message Brokers
|
||
|
||
must store data externally.
|
||
|
||
---
|
||
|
||
## How It Works
|
||
|
||
1. Provision a **Ghaymah Block Storage** volume.
|
||
2. Attach the volume to the database container.
|
||
3. Store all database files on the mounted volume.
|
||
4. If the container fails, the storage remains intact.
|
||
5. A replacement container automatically reattaches the existing volume.
|
||
|
||
---
|
||
|
||
## Benefits
|
||
|
||
### Persistent Data
|
||
|
||
Application data survives:
|
||
|
||
- Container restarts
|
||
- Platform upgrades
|
||
- Node failures
|
||
- Container replacements
|
||
|
||
---
|
||
|
||
### Compute and Storage Separation
|
||
|
||
Containers remain disposable while storage persists independently.
|
||
|
||
This allows infrastructure updates without risking data loss.
|
||
|
||
---
|
||
|
||
### Faster Recovery
|
||
|
||
If the database container crashes:
|
||
|
||
1. A replacement container starts.
|
||
2. The existing block storage volume is attached.
|
||
3. The database resumes operation with its original data.
|
||
|
||
---
|
||
|
||
### High Performance
|
||
|
||
Ghaymah Block Storage provides dedicated storage performance for demanding workloads.
|
||
|
||
Benefits include:
|
||
|
||
- High IOPS
|
||
- Low latency
|
||
- Reliable throughput
|
||
- Consistent database performance
|
||
|
||
---
|
||
|
||
# Key Design Decisions
|
||
|
||
| Area | Decision |
|
||
|------|----------|
|
||
| Compute | Stateless application containers |
|
||
| Scaling | Horizontal auto-scaling |
|
||
| Load Distribution | Ghaymah Load Balancer |
|
||
| Caching | Redis Cluster |
|
||
| Database | Primary + Read Replica architecture |
|
||
| Storage | Ghaymah Block Storage |
|
||
| Availability | Multi-container deployment |
|
||
| Cold Start Mitigation | Readiness probes, pre-warming, optimized images |
|
||
| Capacity Planning | 39 containers (30 base + 30% buffer) |
|
||
|
||
---
|
||
|
||
# Summary
|
||
|
||
This architecture is designed to provide:
|
||
|
||
- High availability
|
||
- Horizontal scalability
|
||
- Fault tolerance
|
||
- Persistent storage
|
||
- Fast recovery from failures
|
||
- Efficient handling of **15,000 requests per second**
|
||
|
||
By combining stateless application containers, intelligent load balancing, Redis caching, database replication, and Ghaymah Block Storage, the platform can maintain stable performance during normal operations as well as sudden traffic spikes. |