167 أسطر
3.6 KiB
Markdown
167 أسطر
3.6 KiB
Markdown
# Scalability and Load Balancing Design
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## Scenario
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Design an architecture capable of handling:
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- **15,000 requests per second (req/s)**
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Each application container can process:
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- **500 req/s**
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A **30% capacity buffer** is required to handle traffic spikes and unexpected load.
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---
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# 1. High-Level Architecture
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The proposed architecture consists of the following components:
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- DNS
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- Load Balancer
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- Multiple API containers
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- Auto Scaling service
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- Container Orchestrator
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- Database
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- Ghaymah Block Storage
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- Monitoring and Alerting
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## Architecture Diagram
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The following diagram illustrates the proposed scalable architecture for handling 15,000 requests per second using load balancing, auto scaling, centralized monitoring, and persistent storage.
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---
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## Request Flow
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```
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Users
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│
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▼
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DNS
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│
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▼
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Load Balancer
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│
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▼
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API Containers
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│
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▼
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Database
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```
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Monitoring components (Prometheus, Grafana, and centralized logging) collect metrics and logs from the Load Balancer, API containers, and database.
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---
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# 2. Container Capacity Calculation
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## Step 1: Calculate Required Containers
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Traffic:
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```
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15,000 req/s
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```
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Container capacity:
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```
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500 req/s
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```
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Required containers:
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```
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15000 / 500 = 30 containers
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```
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---
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## Step 2: Add 30% Safety Buffer
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```
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30 × 1.30 = 39 containers
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```
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---
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## Recommended Deployment Size
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Minimum recommended deployment:
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**39 containers**
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This provides additional capacity for traffic spikes and reduces the risk of resource saturation during unexpected load increases.
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---
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# 3. Cold Start Strategy
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To minimize startup delays during scaling events, the following strategies are recommended.
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## Warm Pool
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Maintain 2–3 idle containers ready to receive traffic immediately.
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## Container Image Optimization
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- Use lightweight base images.
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- Remove unnecessary packages and dependencies.
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- Keep image size as small as possible.
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## Image Pre-Pulling
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Pre-pull container images on worker nodes to reduce deployment time and avoid downloading images during scale-out events.
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## Health Checks
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New containers must pass readiness and liveness checks before receiving production traffic.
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## Gradual Traffic Shift
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The Load Balancer should gradually route traffic to newly started containers after successful health verification.
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## Predictive Scaling
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Scale proactively based on CPU utilization, memory usage, and request-rate trends instead of waiting until the system reaches full capacity.
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---
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# 4. Using Ghaymah Block Storage for Stateful Workloads
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Ghaymah Block Storage provides persistent storage volumes that remain available even if containers are restarted, replaced, or rescheduled.
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This makes it suitable for workloads that require durable data storage.
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Examples include:
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- Databases
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- Message queues
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- File storage
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- Application uploads
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- Persistent logs
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## Benefits
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- Persistent data survives container restarts.
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- Volumes can be attached to replacement containers.
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- Improves reliability for stateful applications.
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- Simplifies backup and disaster recovery.
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- Enhances data durability during scaling events.
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---
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# Best Practices
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- Deploy multiple replicas behind a Load Balancer.
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- Enable Horizontal Auto Scaling based on CPU, memory, and request rate.
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- Configure readiness and liveness probes.
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- Use lightweight container images to reduce cold-start time.
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- Store persistent data using Ghaymah Block Storage.
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- Continuously monitor latency, error rate, CPU, and memory utilization.
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- Configure alerts for abnormal response times and infrastructure failures.
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- Regularly perform load testing to validate scalability assumptions. |