FastAPI Performance and Scalable Architecture: Build High-Performance, Concurrent, Cached, and Production-Ready Python APIs (FASTAPI DEVELOPMENT SERIES)
Language: English
Published by Independently published, 2026
- Softcover
- New
Seller: California Books, Miami, FL, U.S.A.California Books
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- Title
- FastAPI Performance and Scalable Architecture: Build High-Performance, Concurrent, Cached, and Production-Ready Python APIs (FASTAPI DEVELOPMENT SERIES)
- Author
- Byteforge, Liora
- Publisher
- Independently published
- Publication year
- 2026
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 13
- 9798174114524
- Series
- Book 6 of 6: FASTAPI DEVELOPMENT SERIES
FastAPI can be fast. But building an API that stays fast when traffic grows, databases become busy, dependencies slow down, and hundreds of requests arrive at the same time requires more than choosing a high-performance framework.
FastAPI Performance and Scalable Architecture is a practical guide to designing, measuring, optimizing, and scaling production FastAPI applications.
Instead of relying on performance myths or isolated benchmarks, this book teaches you how to understand where time is actually being spent, identify the resource limiting your application, make targeted improvements, and verify the results with realistic measurements.
Throughout the book, you will build and evolve ScaleStore API, a production-minded catalog and order-processing service that exposes the same performance challenges found in real backend systems. You will move from a simple FastAPI application to an architecture using PostgreSQL, SQLAlchemy, Redis, Celery, multiple application replicas, load balancing, performance testing, and production observability.
You will learn how to:
• Measure latency, throughput, concurrency, saturation, p95, and p99 performance
• Use async I/O correctly without accidentally blocking the event loop
• Control concurrency with timeouts, cancellation, connection pools, and backpressure
• Profile Python code and build realistic load, stress, spike, and soak tests with k6
• Optimize PostgreSQL queries, indexes, SQLAlchemy sessions, and connection pools
• Prevent N+1 queries and design efficient pagination and data-access patterns
• Build Redis caching with TTLs, invalidation, stampede protection, and stale-while-revalidate strategies
• Move durable and CPU-heavy work to Celery workers
• Design idempotent tasks, retries, queue limits, and worker-capacity strategies
• Scale FastAPI with Uvicorn workers, containers, stateless replicas, and load balancers
• Use autoscaling, load shedding, bulkheads, and graceful degradation
• Monitor production systems with metrics, logs, traces, SLOs, alerts, and capacity dashboards
• Diagnose real performance failures through hands-on labs, case studies, troubleshooting guides, and production runbooks
This is not a book about chasing the highest requests-per-second benchmark. It is about building APIs that remain predictable, measurable, resilient, and efficient under real production pressure.
If you already understand the fundamentals of Python and FastAPI and are ready to move beyond simply building APIs into performance engineering and scalable backend architecture, this book provides the practical next step.
Build FastAPI services that do more than run fast in development. Build systems whose limits you can measure, explain, and scale with confidence.
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California Books
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