Scalability

Micro-Frontends for Scalable Web Apps Micro-frontends split a large frontend into smaller, independently deployable pieces. Each piece is owned by a separate team and evolves on its own release cycle. This makes big apps easier to maintain as features grow and teams change. Although parts are independent, users still see one smooth app. A shared design system, common tokens, and clear contracts keep the look and behavior consistent across the whole product. ...

Database Performance Tuning for Scalable Apps Databases are the backbone of scalable apps. To keep performance steady as traffic grows, tuning must be practical and measurable. Start with observability: collect latency distributions, query counts, cache hit rate, and resource use such as CPU, memory, and disk I/O. A clear baseline helps you see if a change truly helps. Understanding bottlenecks is the first step. Common culprits include slow queries, missing or poor indexes, and operations that scan many rows. Lock contention, high I/O waits, and heavy joins can also bite. Use production or staging data to identify where users feel slower. ...

API Design Principles for Scalable Systems APIs power almost every modern system. As traffic grows, a well designed API keeps integrations simple and services reliable. The goal is to make the API easy to learn, hard to break, and cheap to scale. This guide shares practical principles teams can apply from day one to support steady growth without creating chaos. Principles that scale Clear contracts and stable interfaces Define a precise contract with consistent input, output, and error formats. Use stable field names and types, and document behavior in all common cases. Treat public endpoints as stable surfaces; avoid silent renames or breaking changes. A well described contract reduces back-and-forth and accelerates adoption. ...

Database Design for Scalable Applications Designing a database for a growing app means balancing structure, performance, and flexibility. Start by naming the core data domains, the main entities, and how they relate. Keep the model simple at first, then adjust as traffic grows. A solid design reduces risk and makes future changes easier. Choose the right data model: relational databases fit strong consistency and complex queries; NoSQL options help with high write throughput or schema flexibility; hybrid or polyglot persistence can suit different parts of the app. Plan for growth from the start: think about how data will expand, how reads will be spread, and how many writes you expect per second. Partitioning and sharding are key to handling large data sets. Horizontal partitioning spreads data across many machines. Pick a shard key based on common read patterns, such as customer_id or region. Have a plan to rebalance data when growth happens to avoid hotspots. ...