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🚀 System Design: Scalability & Performance Guide

Building scalable and performant systems requires careful decisions across architecture, infrastructure, and code. This guide covers key strategies and considerations.

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⚖️ 1. What Is Scalability?

• Scalability is the ability of a system to handle increased load without compromising performance.
• Performance is how fast the system responds under a given load.

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🏗️ 2. Types of Scalability

🧱 Vertical Scaling (Scaling Up)

• Add more resources (CPU, RAM) to existing machines
• Simpler but has hardware limits

🌐 Horizontal Scaling (Scaling Out)

• Add more machines (servers, nodes) to the system
• Requires distributed system design
• Needs load balancing, replication, stateless components

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📊 3. Key Metrics

Metric	Description
Latency	Time to respond to a single request
Throughput	Requests processed per unit of time
Error Rate	Failed requests ratio
Resource Usage	CPU, memory, disk, and network consumption

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🧠 4. Strategies to Improve Performance

💡 Caching

• Reduce expensive computations or DB lookups
• Types:
  • In-Memory: Redis, Memcached
  • Read-Through, Write-Through, Write-Behind
  • CDNs for static content

🏎️ Load Balancing

• Distribute traffic across multiple nodes
• Algorithms: Round Robin, Least Connections, IP Hashing
• Use HAProxy, NGINX, or cloud load balancers

✳️ Asynchronous Processing

• Offload heavy tasks to background jobs
• Use queues: RabbitMQ, Kafka, SQS, Sidekiq
• Improves user-facing responsiveness

🧵 Concurrency & Parallelism

• Use multi-threading and async IO
• Exploit CPU cores and reduce blocking calls

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🧱 5. Scalability Patterns

🔁 Replication

• Clone data/services across nodes
• Improves read scalability and fault tolerance

🍰 Sharding (Partitioning)

• Divide data across multiple databases or tables
• Avoids single-node bottlenecks

🧩 Microservices

• Break system into independently scalable services
• Enables scaling hot spots independently

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⚙️ 6. System Design Techniques

🗃️ Data Modeling

• Denormalize data for performance
• Use time-series DBs for metrics
• Index wisely

🧹 Queueing

• Decouple producers and consumers
• Smooth out traffic spikes

🧪 Load Testing

• Tools: Apache JMeter, k6, Locust
• Identify bottlenecks before they hit production

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🔐 7. Design Trade-Offs

Trade-Off	Consideration
Latency vs. Consistency	Eventual consistency may boost perf.
Freshness vs. Caching	Cached data may be slightly outdated
Cost vs. Throughput	More infra = more cost
Complexity vs. Flexibility	Scalability often adds complexity

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📋 8. Scalability Review Checklist

• [ ] Are critical components stateless?
• [ ] Can the system scale horizontally?
• [ ] Is caching implemented for hot paths?
• [ ] Is load balanced properly?
• [ ] Are async jobs used for slow tasks?
• [ ] Have we load tested at expected traffic levels?
• [ ] Have we defined SLAs for latency/throughput?

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