We should design a system that is inherently capable of meeting these performance goals.
This is a production-grade scalability checklist covering:
- Database scalability
- Application-layer scalability
- Infrastructure auto-scaling
- Architecture-level scalability patterns
- Failure isolation & load control mechanisms
Legend
- (Architecture) → Advanced design patterns, usually requiring infra or system-level changes.
- Priority: High → quick wins / immediate impact, Medium → medium effort, Low → micro-optimizations.
- Start with High Priority items in Identify & fix DB issues and Reduce unnecessary load.
- Move on to improve resilience & scalability items for system stability.
- Consider (Architecture) points when planning larger design changes.
- Each row links to a deep dive in this repo or an external reference.
| Principle | Priority | Tag | Remarks |
|---|---|---|---|
| Ensure Observability | ⭐ High | Pre-tuning requirement | Observability, Structured Logging, profiling in Go etc. |
| Profile Slow Query logs | ⭐ High | Identify & fix DB issues (SQL-focused) | Observe Slow Query logs to find poorly performing queries before making other changes. |
| Understand query planner | ⭐ High | Identify & fix DB issues (SQL-focused) | Learn to read and interpret execution plans to pinpoint query bottlenecks. And Optimize those to improve execution time. |
| Indexing | ⭐ High | Identify & fix DB issues (SQL-focused) | Index the right columns (used in WHERE, JOIN, HAVING, ORDER BY, GROUP BY) to improve reads. |
| Avoid N+1 Query pattern | ⭐ High | Identify & fix DB issues (SQL-focused) | Replace multiple small queries with batched or joined queries to reduce DB round trips. |
| NoSQL-specific query tuning tips | ⭐ High | Identify & fix DB issues (NoSQL-focused) | Optimize NoSQL queries using vendor-specific techniques (e.g. compound indexes in MongoDB, query filters in DynamoDB, partition keys in Cassandra). |
| Caching | ⭐ High | Reduce unnecessary load | Use redis to cache frequently accessed read data and reduce DB hits. - Avoid caching large datasets that can degrade performance. |
| Pagination | ⭐ High | Reduce unnecessary load | Break large API response into pages using limit & offset (relay based) or cursors to prevent massive payloads. |
| Tune Service tasks count & auto-scale | ⭐ High | Improve resilience & scalability | Right-size the number of service tasks (i.e. ECS Fargate tasks) to handle expected throughput without over-provisioning. - Use CPU/memory utilization or queue depth for auto-scaling. |
| DB Connection Pooling | ⭐ High | Improve resilience & scalability | Maintain a pool of connections (with timeouts, max idle connections), instead of opening a new connection for every API request. - This would prevent connection storms or resource exhaustion. |
| Use concurrency & async processing | ⭐ High | Improve resilience & scalability | Offload long-running or non-blocking tasks using goroutines, worker pools, or async job execution. - For inter-service async communication, use message brokers (Kafka, RabbitMQ, SQS). |
| Handle timeout | ⭐ High | Improve resilience & scalability | Use proper timeouts (e.g. Go Contexts) to prevent cascading failures when upstream services fail or close connections early. |
| Graceful degradation / feature toggles | ⚡ Medium | Improve resilience & scalability | Temporarily disable non-essential or heavy features during peak load to keep core functionality responsive. |
| Compression (payload-level) | 🟢 Low | Reduce network cost | Apply compression for large payloads to reduce bandwidth - avoid overusing on small payloads to save CPU. |
| Asynchronous logging | 🟢 Low | Reduce blocking operations | Buffer logs in memory and flush asynchronously to avoid blocking request processing with I/O operations. |
| JSON Serialization | 🟢 Low | Reduce CPU cost | Consider faster JSON serialization library for JSON-heavy APIs to reduce CPU time spent on encoding/decoding. |
| Use CDN to cache static resources | ⚡ Medium | Reduce unnecessary load (Architecture) | Use a CDN to cache and serve static assets (images, CSS, JS) close to users, reducing server load and improving response times. |
| Data archiving (hot vs cold storages) | ⚡ Medium | Reduce unnecessary load / storage cost (Architecture) | Move old/infrequently accessed data to cold storage (e.g. S3, Glacier) to reduce hot DB size and improve query performance. |
| Backpressure handling | ⭐ High | Improve resilience & scalability (Architecture) | Implement load-shedding or rate-limiting to protect services under overload (e.g. HTTP 429, queue throttling). |
| Read Replicas | ⚡ Medium | Improve resilience & scalability (Architecture) | Use read replicas to offload read traffic from the primary database. |
| Sharding | ⚡ Medium | Improve resilience & scalability (Architecture) | Distribute data across multiple shards to improve horizontal scalability - Consider complexity and operational cost before implementing. |
| Use appropriate databases based on query patterns | ⚡ Medium | Improve resilience & scalability (Architecture) | Choose the right DB engine for your access patterns - SQL for relational joins. - Elasticsearch for search. - MongoDB/DynamoDB for document or key-value access. |
| Choose appropriate architecture style | ⚡ Medium | Improve resilience & scalability (Architecture) | Decide between monolith and microservices based on expected scale, team structure, and latency tolerance. |
| Async batch processing for heavy workloads | ⚡ Medium | Improve resilience & scalability (Architecture) | Move heavy aggregation/analytics tasks to asynchronous jobs instead of real-time APIs to keep request latency low. |
