Facebook uses a variety of factors to calculate and optimize response times for content on both desktop and mobile devices. Some of the key elements that impact response time include page weight, number of server requests, caching, compression, content distribution networks, and code efficiency.
Page Weight
Heavier pages with more content, images, videos, ads, and other elements take longer to load. Facebook engineers aim to keep individual page weight as low as possible without compromising function. Some techniques they use include lazy loading (loading non-essential content only as needed), optimizing images, and pruning unnecessary code.
Number of Server Requests
Each element on a Facebook page requires one or more requests to Facebook servers to load. The more requests required, the longer it takes for the full page to render. Reducing the number of requests improves response time. Strategies like Concatenate CSS and JS files, using image sprites, and caching/CDNs help cut down on requests.
Caching
Caching stores Facebook page elements and data closer to end users so that repeat visits do not require full re-loads from Facebook’s distant data centers. Facebook has an extensive global network of cache servers. They also make heavy use of browser caching through cache control headers so browsers store local copies of page resources.
Compression
Compressing page resources before sending them to browsers reduces file sizes, saving bandwidth and speeding up load times. Facebook uses a variety of compression methods such as Gzip, Brotli, and Zstandard across its servers, CDNs, and browsers to shrink resources like HTML, CSS, JS, and images.
Content Delivery Networks
Facebook has deployed a massive private CDN called Facebook Edge Network with hundreds of points of presence globally. This distributes and caches page resources in many geographic locations nearer to end users. Rather than distance to one central server, users connect to nearby CDN nodes, reducing latency and round trip times.
Code Efficiency
Optimized code runs faster, resulting in improved response times. Facebook engineers constantly refactor and enhance their code to make it more efficient. Examples include lazy loading non-essential JS, eliminating unnecessary re-renders, using more performant frameworks like React, optimizing algorithms, and upgrading to faster languages.
Measuring Response Time
Facebook closely tracks a number of internal metrics to measure real-world response time experienced by users and identify areas for improvement. Some key metrics include:
- Time to First Byte – Time from request sent to first byte received from server
- DOMContentLoaded – Time until browser finishes loading/parsing HTML and constructs DOM tree
- Load Time – Time until full page including all resources like CSS, JS, and images loads
- TTI (Time to Interact) – Time until page becomes interactive and responds to input
- FRP (First Paint/First Contentful Paint) – Time until browser renders first pixels
Facebook also uses lab testing, synthetic monitoring, user timing APIs, and real user data like percent of slow loads to measure performance.
Optimizing Response Time
Facebook is constantly tweaking and improving their web architecture to optimize response times. Some of their core optimization strategies include:
- Scaling infrastructure – Expanding networks, data centers, CDNs, and capacity to handle growing demand.
- Caching and CDNs – Expanding caching layers and points of presence geographically.
- Code splitting – Lazy loading non-essential JS only when needed.
- Async resources – Loading scripts and assets asynchronously without blocking rendering.
- Compression – Intelligent compression across the stack from servers to CDNs to browsers.
- Image optimization – Converting, resizing, lazy loading images.
- Reducing server requests – Combining files, removing duplicate requests.
- Minifying and pruning code – Eliminating whitespace, comments, unused code to slim down payloads.
Common Causes of Slow Response Times
When Facebook experiences degraded response times, some common culprits tend to be:
- Code changes that unintentionally add bloat
- New features requiring more requests
- Increased media assets like images and videos
- Failures or congestion in CDN nodes
- Scaling limits reached on infrastructure capacity
- Regional outages or intermediate network issues
- Cascading failures from one system overload impacting others
- Browser bugs or changes affecting caching or rendering
- 3rd party tag performance regressions
- A/B testing code variants with subpar performance
Prioritizing User Experience
Facebook heavily prioritizes speed as a core component of user experience. Their developers follow a philosophy called Move Fast With Stable Infra, optimizing for both innovation velocity and reliability/performance. Slow response times lead to poor user engagement, retention, and satisfaction so response time factors heavily into development of new features and fixes for performance regressions.
Facebook is also very proactive about eliminating “bad latency” – inconsistent response times driven by backend bottlenecks. They aim for predictably fast performance at scale even during traffic spikes by scaling capacity, monitoring key infrastructure metrics, and optimizing backend efficiency.
Evolution of Response Time Targets
As internet speeds have increased over the years, Facebook has continued raising the bar on their response time standards. Some of their historical page load time targets include:
| Year | Time Goal |
|---|---|
| 2012 | 1 second |
| 2015 | 700 ms |
| 2019 | 500 ms median |
| 2021 | 100 ms Time to Interactive |
Going forward they aim to achieve 10 ms app to glass latency from end to end comprising:
- 1 ms app processing
- 1 ms encoding/decoding
- 8 ms transport
This sub 100 ms target reflects their continued efforts to optimize every millisecond of response time even on modern high-speed internet.
Troubleshooting Slow Response Times
When slow response times are reported, Facebook uses a variety of debugging techniques to identify the bottlenecks. These include:
- Examining internal metrics like Time to First Byte and load waterfalls to pinpoint slow steps
- Using network profiling tools in browsers to analyze resource load times
- Enabling debug modes like React’s dev tools profiler to isolate expensive components
- Drilling into backend logs and traces to find API call latency issues
- Throttling CPU/memory/network in simulators to model slow resource scenarios
- Comparing A/B test variant performance to detect code regressions
- Inspecting CDN node health, caching efficiency, and regional performance
Addressing a wide variety of potential front-end and back-end factors is key to diagnosing the myriad of possible performance bottlenecks.
Limits on Optimization
While response time is a top priority, Facebook does have limits on how much they will optimize solely for performance. At a certain point, excessive optimizations start to significantly increase complexity or compromise functionality and rapid innovation. Their guidelines around optimization consider:
- Value of developer productivity and velocity
- Avoiding premature optimization
- User-centered performance budgets
- Robustness principles – be conservative in sending behavior
- Security, privacy, and compliance constraints
By keeping these other priorities in balance, they try to avoid getting stuck in endless micro-optimization cycles or over-engineering for hypothetical issues prematurely when simpler solutions exist.
Conclusion
Optimizing response time is a complex, multifaceted endeavor for Facebook. They employ a diverse toolkit of performance strategies across the client, server, and network. Facebook’s performance targets have become increasingly aggressive as internet speeds have improved. While new features and growth pose optimization challenges, Facebook remains committed to providing the fastest possible experience to its billions of users worldwide.