# 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.

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# 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)

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# 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.

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# Architecture Diagram

The architecture diagram below shows the proposed high-traffic deployment.

![Architecture Diagram](./GhaymahAPI.png)

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# 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.

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# 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 |

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# 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 |

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## 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.

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# 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.

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## 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.

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### 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.

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### 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

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# 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.

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## 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.

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## Benefits

### Persistent Data

Application data survives:

- Container restarts
- Platform upgrades
- Node failures
- Container replacements

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### Compute and Storage Separation

Containers remain disposable while storage persists independently.

This allows infrastructure updates without risking data loss.

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### 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.

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### High Performance

Ghaymah Block Storage provides dedicated storage performance for demanding workloads.

Benefits include:

- High IOPS
- Low latency
- Reliable throughput
- Consistent database performance

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# 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) |

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# 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.