Web application performance is affected by network latency, bandwidth, database queries, page size and many other factors.
Load Testing with Locust.io & Docker Swarm shows you how to set up load tests using Docker containers along with AWS for scaling up the tests.
HTTP Load Testing with Vegeta (and a dash of Python) covers getting started with the Vegeta load tester and uses Python to analyze the tool's results.
Four reasons developers should write their own load tests and four load testing mistakes developers love to make are opinionated pieces on how developer should use load testing to ensure their applications work properly under heavy usage.
Building a PostgreSQL load tester explains how the pgreplay-go tool works and how to obtain performance metrics for a PostgreSQL database.
Web Performance 101 introduces web application loading performance. There is a ton of great information on JavaScript, CSS and HTTP optimization as well as tools to use.
Idle until urgent explains an issue the author found when measuring First Input Delay (FID) on his site and what techniques he used to fix the problem.
How to measure web app performance is a 20 minute code-first demo that shows how to get a realistic estimate for how many requests per second your web application will be able to handle.
Practical scaling techniques for websites examines how to improve your website performance with asynchronous task queues, database optimization and caching.
The [Performance Testing Guidance for Web Applications](https://docs.microsoft.com/en-us/previous-versions/msp-n-p/bb924375(v%3dpandp.10) book from Microsoft is a gem. There are chapters on foundations of performance testing, modeling application usage and many other topics that are critical to working on web app performance.
awesome-scalability provides a list with a crazy number of scaling and performance optimization resources and tools by category.
Every Web Performance Test Tool provides a nice list of tools and provides short summaries of what each one can help with in identifying performance problems.
The Infrastructure Behind Twitter: Scale examines the evolution from having to buy your own hardware from vendors to run a service to the current days of being able to rely on cloud providers for some or all workloads regardless of scale.
Scaling to 100k users covers the architecture scaling techniques commonly used to move up in serving users by orders of magnitude, for example from 100 to 1000.
Web Performance Recipes with Puppeteer digs into tracing through page rendering to measure performance and how to extract performance metrics from the Lighthouse tool for further analysis.