🧠 System Design Playbook: Common Issues and Proven Solutions
This guide offers a structured reference for addressing common challenges in system design with battle-tested solutions. Ideal for architects, engineers, and developers working on building scalable, reliable systems.
1. ⚡ Read-Heavy Systems
🔍 Problem:
Frequent reads on the same data degrade performance and increase latency.
✅ Solutions:
- Use Caching: Store frequently accessed data in fast-access memory (e.g., Redis, Memcached).
- Read Replicas: Offload reads to database replicas.
- CDNs: Serve static content from edge servers close to the user.
2. 📝 Write-Heavy Systems
🔍 Problem:
Frequent writes can overwhelm the database and increase latency.
✅ Solutions:
- Async Write Queue: Offload heavy writes to background workers using queues like Kafka or RabbitMQ.
- LSM Tree Databases: Use databases optimized for heavy writes (e.g., Cassandra, RocksDB).
- Batched Writes: Aggregate updates and write in bulk.
3. 🧨 Single Point of Failure
🔍 Problem:
Failure in one component takes down the entire system.
✅ Solutions:
- Redundancy: Deploy multiple instances of critical components.
- Failover Mechanisms: Auto-switch to backup resources.
- HAProxies and Load Balancers: Automatically reroute traffic to healthy services.
4. 🟢 High Availability
🔍 Problem:
System downtime is unacceptable.
✅ Solutions:
- Load Balancers: Distribute requests across multiple healthy instances.
- Database Replication: Enable failover and distribute read traffic.
- Health Checks: Use probes to monitor instance health.
- Stateless Services: Make services replaceable and scalable.
5. 🐢 High Latency
🔍 Problem:
Slow response times frustrate users.
✅ Solutions:
- CDNs: Distribute static content geographically.
- Caching: Serve precomputed or frequently accessed data.
- Edge Computing: Push computation closer to the user.
6. 📦 Handling Large Files
🔍 Problem:
Large files and media assets can overload servers or databases.
✅ Solutions:
- Object Storage: Use S3, MinIO, or GCS to store files.
- Block Storage: Use high-performance disks for file systems.
- Presigned URLs: Allow clients to upload/download directly from storage services.
7. 🛠️ Monitoring and Alerting
🔍 Problem:
No visibility into system failures or anomalies.
✅ Solutions:
- Centralized Logging: Use the ELK stack (Elasticsearch, Logstash, Kibana) or Loki.
- Alerting Systems: Use Prometheus + Alertmanager or PagerDuty.
- Dashboards: Visualize metrics with Grafana or Datadog.
8. 🐌 Slow Database Queries
🔍 Problem:
Database queries take too long, causing bottlenecks.
✅ Solutions:
- Indexing: Add indexes on commonly queried columns.
- Query Optimization: Rewrite queries for efficiency.
- Read Replicas: Distribute load.
- Sharding: Distribute data across multiple DB instances.
9. 📶 Handling Sudden Traffic Spikes
🔍 Problem:
System crashes under unexpected load.
✅ Solutions:
- Auto-Scaling: Scale horizontally using Kubernetes or cloud autoscalers.
- Rate Limiting: Protect backends from abuse.
- Load Shedding: Reject non-essential traffic when overloaded.
10. 🔁 Stateful vs Stateless Services
🔍 Problem:
Stateful services are hard to scale and recover.
✅ Solutions:
- Make Services Stateless: Store session/state in external systems like Redis.
- Sticky Sessions: Route user requests to the same instance if needed.
11. 🔓 Security Concerns
🔍 Problem:
Sensitive data is at risk, or services are vulnerable.
✅ Solutions:
- HTTPS Everywhere: Encrypt all traffic.
- JWT and OAuth2: Secure API access.
- RBAC: Implement role-based access control.
- Vulnerability Scanning: Use tools like Trivy or Snyk.
12. 🌐 Geographic Distribution
🔍 Problem:
Users across regions experience varying latency.
✅ Solutions:
- Global CDNs: Push static content to edge locations.
- Multi-region Deployments: Use cloud infrastructure to run replicas across continents.
- Geo-aware Load Balancing: Route users to the nearest region.
13. ⚙️ Data Consistency vs Availability
🔍 Problem:
You can’t always get strong consistency and high availability (CAP Theorem).
✅ Solutions:
- Choose per need:
- Use CP (Consistency/Partition Tolerance) for financial systems.
- Use AP (Availability/Partition Tolerance) for social feeds or analytics.
- Eventual Consistency: Accept delays for performance and availability.
14. 🪵 Event-Driven Architectures
🔍 Problem:
Monolithic workflows are slow and inflexible.
✅ Solutions:
- Use Pub-Sub: Decouple producers and consumers (Kafka, NATS, RabbitMQ).
- Event Sourcing: Maintain a log of all changes to state.
- CQRS: Separate reads and writes for performance.
15. 🧠 Smart Retry & Circuit Breakers
🔍 Problem:
Cascading failures due to retries and unresponsive services.
✅ Solutions:
- Retry Strategies: Use exponential backoff with jitter.
- Circuit Breakers: Stop repeated calls to failing services.
- Timeouts: Never wait forever; use sensible defaults.
✅ Final Thoughts
Designing resilient systems means anticipating failure and planning for scalability. The patterns in this playbook are commonly applied in real-world architectures from companies like Netflix, Uber, and Google.
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