56 docs tagged with "performance"

Reduce network and system overhead by batching operations intelligently.

Optimize for limited resources on mobile devices

Isolate failures and prevent cascading outages using proven reliability patterns.

Write-through, write-behind, cache-aside, and TTL strategies for reducing database load

Master cache-aside, write-through, write-behind, and read-through patterns to optimize latency, consistency, and durability trade-offs in distributed systems.

Essential optimization patterns: caching for latency, batching for throughput, queueing for resilience.

Content delivery networks, edge caching, and geographically distributed compute

Services making excessive network calls for operations that could be done locally or batched.

Practical distinctions, decision flows, and patterns to combine concurrency, parallelism, and synchronization safely.

Separate read and write models to optimize for different access patterns and enable flexible data transformation.

Scale data storage horizontally by distributing data across multiple databases and replicas.

Services that call each other synchronously in sequence, creating tight coupling and monolith behavior.

Automatically adjust capacity based on demand while maintaining performance.

Scale APIs to handle large datasets with efficient query parameters

TL;DR

Predict growth, measure resource needs, and provision infrastructure to meet demand with margin.

Ground yourself in core architecture, systems thinking, paradigms, and data basics to make sound design decisions.

Component design, state management, rendering strategies, and performance optimization

Master Core Web Vitals, bundle optimization, lazy loading, and image optimization strategies to deliver fast, responsive user experiences at scale.

TL;DR

Maintain partial functionality and shed load during overload instead of failing completely.

Design flexible, client-driven APIs with GraphQL schemas and resolvers

Build high-performance binary RPC systems with HTTP/2 streaming

Implement defensive patterns that gracefully handle failures and slowdowns in distributed systems.

Optimize critical code paths through strategic caching and computation reuse.

Design indexes for query performance while avoiding write penalties and fragmentation

Ultra-fast simple get/set operations with sub-millisecond latency and distributed caching

Define end-to-end latency targets, track SLOs, and communicate availability guarantees via SLAs.

Clear definitions, interactions, and practical tuning to hit latency SLOs without sacrificing throughput.

Validate system behavior under various load conditions to ensure performance and reliability targets.

Use log levels strategically, enforce consistency across teams, and optimize storage costs while maintaining debuggability.

Pre-compute and cache query results for instant access to complex aggregations

Measure system behavior with metrics using RED and USE methods to identify performance issues.

TL;DR

Load balancing, network policies, mTLS, and CDN/edge patterns

Balance data consistency and query performance through normalization and strategic denormalization

TL;DR

Service workers, local caching, and progressive web app patterns

Key metrics for fast, responsive user experience

Design systems that respond quickly and scale horizontally to handle increasing load while maintaining latency budgets.

Scale data systems for growth: caching, replication, sharding, and materialized views

Define and enforce performance targets that align with user experience and business goals.

Validate latency, throughput, and scalability under various load conditions to ensure production readiness.

Optimizing code before understanding where bottlenecks actually are.

Simplify asynchronous I/O by letting the framework manage event notifications and completion handlers.

Identify performance bottlenecks using profilers, distributed tracing, and flamegraphs

TL;DR

Master the ISO/IEC 25010 model and implement performance, reliability, maintainability, testability, usability, and cost-efficiency.

Efficient data retrieval patterns for large result sets with filtering and sorting

Decouple producers and consumers using queues to smooth out demand spikes.

Multiplex I/O events across many connections using a single-threaded event-driven architecture.

Deep-dive into architecture patterns for real-time systems, streaming, IoT, ML, compliance, gaming, embedded, fintech, e-commerce, and social platforms

Server-side rendering, static generation, and incremental static regeneration

Reuse threads efficiently by maintaining a pool of pre-allocated workers to process tasks.

Master Big O notation, complexity classes, and amortized analysis to evaluate algorithm efficiency and make informed optimization decisions.

Understand scaling strategies and their trade-offs in distributed systems.