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Photo Storage and Serving Infrastructure

55 posts · Oct 2008 – Mar 2026

What this trail covers

Meta's infrastructure for storing and serving user content has evolved significantly, starting with photos and expanding to encompass immersive media like 360 video and VR. This evolution includes developing dedicated mobile applications, enhancing user experience for capturing and sharing, and tackling complex technical challenges in processing and delivering new media formats. Key developments include the creation of custom video filters for transforming equirectangular layouts to cube maps, and the unification of video delivery systems across products like Watch and Reels to simplify infrastructure and improve developer velocity. This includes advancements in ranking, server-side brokering, and mobile client optimizations like client-side ranking.

All Meta trails
  1. FFmpeg at Meta: Media Processing at ScaleThis post details Meta's extensive use and significant contributions to FFmpeg for media processing at scale. It highlights how Meta moved from an internal fork to upstreaming critical features like threaded multi-lane transcoding and real-time quality metrics, enabling more efficient video encoding for VOD and livestreaming. It also discusses the integration of custom hardware like the Meta Scalable Video Processor (MSVP) through standardized FFmpeg APIs, demonstrating a commitment to both open-source collaboration and internal innovation for media handling.
  2. Enhancing HDR on Instagram for iOS With Dolby VisionThis post details the technical implementation of Dolby Vision and ambient viewing environment (amve) metadata support for HDR videos on Instagram for iOS. It describes the challenges of preserving and processing this metadata through client and server stages, including modifications to FFmpeg and Shaka packager to support Dolby Vision Profiles 8 and 10. The post also covers the implementation of manual Dolby Vision metadata extraction for AVSampleBufferDisplayLayer and the iterative A/B testing process to optimize for performance, including the adoption of compressed metadata formats and client-side decompression to reduce load times and improve user engagement.
  3. Video Invisible Watermarking at ScaleThis post introduces the implementation of invisible watermarking at scale for video content. It details the challenges and solutions for embedding imperceptible signals for content provenance, including detecting AI-generated videos, verifying upload order, and identifying creation tools. A key contribution is the development of a CPU-based watermarking solution that achieves comparable performance to GPUs with improved operational efficiency, by optimizing threading and addressing data transfer, inference latency, and model loading bottlenecks. The post also highlights the importance of managing BD-Rate impact and addressing visual quality regressions through custom post-processing and manual inspections, noting the insufficiency of traditional quality metrics.
  4. Video Streaming With the AV1 Video Codec in Mobile DevicesThis post details Meta's collaboration with Vodafone and Google to promote the adoption of the AV1 video codec for mobile devices. It highlights AV1's superior compression capabilities (up to 30% better than H.264 and VP9) and its importance for improving video streaming quality and reducing network load, especially on lower-end devices. The post advocates for hardware and software AV1 decoding solutions and emphasizes the benefits for mobile operators and end-users.
  5. Inside Facebook’s video delivery systemThis post details the unification of Facebook's video delivery system, consolidating separate infrastructure for Watch and Reels into a single, streamlined system. It describes the three main components: Ranking (RecSys) for content recommendation, Server (WWW) for brokering and feature logic, and Mobile (FB4A/FBiOS) for optimized user experience including client-side ranking. The unification process involved significant technical challenges in merging data models, architectures, and features across billions of users and tens of thousands of lines of code, while maintaining engagement and performance metrics. It also covers the migration of Watch users to a Reels surface and the unification of ranking layers to handle mixed inventory of short-form and long-form video. Key principles for video delivery include prioritizing fresh content, letting ranking decide order, vending content only when needed, deterministic fetching, and delivering content based on clear user signals. The lifecycle of a video feed network request is explained, from mobile client triggers (prefetch, head load, tail load) to server-side processing through GraphQL and the generalized video delivery stack.
  7. Bringing HDR photo support to Instagram and ThreadsThis post details the integration of High Dynamic Range (HDR) photo support into Instagram and Threads. It highlights the Media Platform Team's work in optimizing the entire image serving pipeline, from CDN to device display, to handle the increased color and range of HDR images. The post also mentions collaboration with major phone manufacturers like Google and Samsung to ensure a smooth rollout.
  8. Bringing HDR video to ReelsThis post details the technical challenges and solutions for enabling High Dynamic Range (HDR) video uploads and playback in Meta's Reels feature. It describes the implementation of client-side tone mapping on iOS using native APIs and custom shaders on Android to convert HDR to SDR for compatibility. It also outlines the development of server-side tone mapping to generate both HDR and SDR representations, ensuring optimal playback across devices with and without HDR display support. The post highlights the need to manage brightness levels for consistent UI elements over HDR video and the use of the Meta Scalable Video Processor (MSVP) for efficient HDR processing.
  9. Why xHE-AAC is being embraced at MetaThis post details the adoption and implementation of the xHE-AAC audio codec across Meta's platforms, specifically Facebook and Instagram, for features like Reels and Stories. It highlights the benefits of xHE-AAC, including integrated loudness management and adaptive bit rate audio, and explains the deployment process using encoder SDKs and shaka-packager. This contributes to the broader theme of enhancing media delivery and user experience by focusing on audio quality and adaptability.
  10. Reducing Instagram’s basic video compute time by 94 percentThis post details a specific optimization within Instagram's video processing pipeline. By repurposing progressive encoding frames to create basic ABR encodings, Instagram reduced the compute cost of basic video encodings by 94%. This freed up resources, allowing for increased production of advanced encodings, which improved overall video quality and playback smoothness for users. A testing framework was developed to validate the net positive impact of this change by comparing traffic across control and test pools.
  11. Building text animations for Instagram StoriesThis post details the technical implementation of new text animations and font styles for Instagram Stories and Reels. It addresses challenges related to font packaging and APK size on Android, leading to the development of a custom solution using Everstore. It also describes the engineering approaches for dynamically evolving text designs, including drawing arcs and creating Bezier paths, and the linguistic processing required for word-by-word reveal animations. Finally, it covers debugging native Android crashes related to font caching and the complexities of rendering various text features like ligatures, RTL text, emojis, and custom spans.
  12. How Facebook encodes your videosThis post details the evolution of Facebook's video encoding system, moving from a rule-based prioritization to a benefit-cost model combined with an ML model to predict watch time. This new approach optimizes the use of advanced codecs (like VP9) and expensive encoding recipes for videos predicted to be highly watched, thereby improving visual quality, reducing buffering, and managing compute resources more efficiently. The benefit-cost model defines benefit as (compression efficiency) * (predicted watch time) and cost as the compute required for missing encodings, with priority being Benefit/Cost. The ML model predicts watch time based on uploader history, video metadata, and past popularity trajectories.
  13. Data Transfer Project: Enabling portability of photos and videos between servicesThis post details Meta's participation in and implementation of the Data Transfer Project, an open-source framework for service-to-service data portability. It describes the project's architecture (shared data models, adapters, task management framework), deployment considerations (security, reliability, rate limiting, retries), and Meta's custom extensions integrating with internal systems like TAO, Scribe, Tupperware, and Configerator to enable direct photo and video transfers from Facebook to Google Photos.
  14. Facebook open-sources Spectrum 1.0.0 for better mobile image productionIntroduced Spectrum, an open-source image processing library for Android and iOS, to improve mobile image upload efficiency and reliability. Spectrum's declarative API and C++ core enable consistent, high-quality image transcoding by optimizing operations like cropping, rotating, and resizing, and integrating with advanced compression libraries like MozJpeg for significant file size reduction.
  15. React 360 replaces React VR for streamlined development focusThis post announces the rebranding of React VR to React 360, reflecting its broader use cases beyond VR headsets to include non-VR users and a wider range of immersive content. It highlights improvements in 2D UI integration, enhanced media support (including 180° video), and better performance, particularly on mobile devices. This signifies an evolution in Meta's approach to immersive content development, making it more accessible and versatile.
  16. Facebook video adds AV1 supportThis post details the implementation of trial support for the AV1 video format on Facebook video. It describes the benefits of AV1 (improved compression efficiency, reduced buffering) and the challenges (slower encoding times). The implementation strategy involved a segment-based encoding approach to parallelize encoding tasks across a cluster, followed by stitching and muxing. The post also outlines steps for users to view AV1 content using Chrome Canary and mentions the ongoing development of browser support and the eventual switch to the official AV1 release version.
  17. AV1 beats x264 and libvpx-vp9 in practical use caseThis post details Meta's practical evaluation of the AV1 video compression codec, demonstrating its superior compression efficiency (30-50% better than VP9 and x264) compared to existing open-source encoders like x264 and libvpx-vp9. It highlights AV1's potential for significant gains in delivering digital video more quickly, with higher quality, and reduced bandwidth/storage, especially for common Facebook video formats (SD/HD) and with promise for higher resolutions. The post also acknowledges AV1's longer encoding times due to increased complexity, a key consideration for production deployment.
  18. Quality assessment of 360 video view sessionsIntroduced SSIM360 and 360QVM, new quality assessment standards for 360 video streaming. SSIM360 addresses the warping problem by weighting per-sample SSIM scores based on stretching in the equirectangular representation, making it computationally efficient and more accurate than conventional SSIM. 360QVM addresses the field-of-view problem by mapping view direction to pixel density and then to a scaling factor applied to the SSIM360 score, allowing for view-dependent optimizations and more accurate quality assessment of streaming sessions.
  19. 2017 Year in review: Building immersive experiencesThis post details advancements in building immersive experiences, specifically focusing on the evolution of 360 video and VR technologies. It highlights the application of machine translation to break down language barriers, computer vision for understanding visual content and generating alt text, and the use of AI for AR experiences and style transfer on 4K 360 video. The post also covers significant end-to-end optimizations for 360 video streaming, including resolution switching and view prediction models, as well as the development of a spatial and head-locked audio system for immersive audio.
  20. Optimizing 360 photos at scaleThis post details the re-engineering of Facebook's photo infrastructure to support high-resolution 360 photos through tiled storage and serving. It also describes the application of a modified AlexNet deep neural network to automatically correct the tilt and roll of 360 photos, improving realism. The DNN was trained on monochrome equirectangular images and cast as a regression problem to predict tilt and roll values, achieving an average accuracy of 0.1 degrees.
  21. Under the hood: Photo SearchThis post details the application of machine learning, specifically deep neural networks, to enhance photo search capabilities on Facebook. It describes the image understanding engine that extracts semantic meaning from billions of photos, producing high-dimensional feature vectors. These vectors are compressed using iterative quantization and locality-sensitive hashing into compact embeddings. The post explains how these embeddings, along with object tags, are used to populate Unicorn, an in-memory and flash storage indexing system, for efficient search. It also elaborates on the relevance modeling, which combines concept relevance and embedding relevance using multimodal learning and ranking loss to provide low-latency, accurate search results. Furthermore, it outlines query understanding techniques like intent recognition, syntactic analysis, entity linking, and query embedding, and describes the multi-stage ranking process (first-stage and second-stage re-ranking) to deliver relevant photos.
  22. Surround 360: Beyond Stereo 360 CamerasThis post introduces the Surround 360 camera system, a significant advancement in capturing high-quality immersive content beyond stereo 360. It details the hardware and software innovations, including the camera's design, stitching, and rendering capabilities, which directly contribute to Meta's efforts in handling and serving new media formats like VR video.
  23. Announcing 360 Capture SDKIntroduced the 360 Capture SDK, which utilizes cube mapping instead of traditional stitching for capturing 360 photos and videos from PC VR experiences. This approach significantly improves accessibility by working on baseline VR hardware, maintains high quality (1080p for News Feed, 4K for VR), and drastically reduces capture time to under one second while maintaining 90 fps performance on VR systems. The SDK supports Unity, Unreal, and native game engines, and captures content directly as cube maps or cube strips, which can be converted to equirectangular format.
  24. Enhancing high-resolution 360 streaming with view predictionIntroduced a gravitational view-prediction model using physics and heatmaps to improve resolution on VR devices by up to 39 percent. Developed an AI model to generate saliency maps for videos, enabling prediction even without heatmap data. Introduced content-dependent streaming, a new encoding technique that improves 360 video resolution on non-VR devices by up to 51 percent, even in low bandwidth, by transforming frames based on saliency data into a single stream.
  25. End-to-end optimizations for dynamic streamingThis post details significant optimizations to Meta's dynamic streaming technology for 360 video. It introduces variable offset cubemaps for encoding, which allows for more efficient pixel allocation towards the viewport, reducing overall resolution by up to 92% while maintaining viewport quality. It also describes improvements in stream selection by considering six factors instead of four, leading to an 80% reduction in interruptions. Further enhancements include optimizing bandwidth estimation, chunk handling, prefetching initialization and index chunks, and boosting player performance.
  26. Spatial audio — bringing realistic sound to 360 videoIntroduced new 360 audio encoding and rendering technology, including a hybrid higher-order ambisonics system (8-channel) with rendering optimizations for large-scale consumption. Developed the Spatial Workstation tool for creators to design spatial audio for 360 video and VR, and the FB360 Encoder tool for exporting to multiple platforms. The rendering SDK is integrated into Facebook and Oculus Video. The system supports simultaneous spatial and head-locked audio with sub-millisecond latency. The audio engine (Audio360) is cross-platform, written in C++ with asm.js for web, and is lightweight (approx. 100KB binary). Encoding uses MP4 with H.264 video and AAC audio, employing a three-track configuration for spatial and head-locked audio, with metadata stored in an XML box within the MP4 file.
  27. Keyframes: Delivering scalable, high-quality animations to mobile clientsIntroduced Keyframes, a new library for exporting and playing back After Effects animations on mobile clients. Keyframes addresses challenges with resizability, quality, and file size by extracting minimal, necessary vector and transformation data from After Effects compositions. It renders animations using Core Animation on iOS and Paths/Matrices on Android, enabling real-time playback at any size with fine control over animation progression and speed. The library was initially developed for the Reactions product and has since been open-sourced.
  28. Lessons from building social experiences in VRThis post details Meta's early explorations into building social experiences within Virtual Reality. It highlights the development of more expressive avatars, the ability to share and experience 2D and 360 photos, videos, and Facebook posts together in VR, and the seamless integration of VR experiences with non-VR users through features like Messenger video calling. The post emphasizes the importance of social presence and the potential of VR as a people-first platform for connection.
  29. 360 video stabilization: A new algorithm for smoother 360 video viewingIntroduced a new stabilization algorithm for 360 video using a hybrid 3D-2D approach with a novel 'deformed-rotation' motion model. Developed a 360-hyperlapse algorithm as an extension. Optimized the stabilization algorithm for CPU-only execution, achieving sub-frame stabilization times and improving bit rate efficiency. The algorithm uses KLT tracking on cube map representations and a robust least-squares solver (Ceres) for optimization.
  30. Surround 360 is now open sourceThis post details the open-sourcing of the Surround 360 system, including its hardware design and stitching software. It explains the challenges of rendering stereo 360 video, such as massive data volumes and the need for near-perfect stereo accuracy. The post outlines methods for constructing stereo panoramas, including view interpolation and optical flow, and describes the rendering pipeline, which involves ISP conversion, lens distortion correction, rotational/translational misalignment correction, and seamless stitching of top/bottom cameras to remove the tripod pole. The goal is to achieve fast, high-quality, and comfortable stereo 360 video capture and processing.
  31. Introducing Facebook Surround 360: An open, high-quality 3D-360 video capture systemIntroduced Facebook Surround 360, an open, high-quality 3D-360 video capture system. This includes open-sourcing camera hardware designs and stitching software. The stitching software utilizes an optical flow algorithm for superior stereo disparity computation, reducing stitching time from weeks to overnight. The system exports 4K, 6K, and 8K video, with 8K playback supported on Gear VR via custom Dynamic Streaming technology. The system addresses challenges of proprietary systems, overheating, rig instability, and long stitching times by providing a production-ready, end-to-end solution.
  32. Gear VR to get Dynamic Streaming for 360 videoThis post announces the integration of dynamic streaming technology for 360 video into Gear VR, aiming to deliver clearer and faster playback of immersive content. It highlights the growth of Gear VR and the increasing consumption of 360 videos on Facebook, underscoring the need for efficient delivery mechanisms for VR experiences.
  33. Under the hood: Shipping #friendsday videosThis post details the engineering challenges and solutions for generating and shipping hundreds of millions of personalized #friendsday videos. Key contributions include: load testing and optimization of the video generation pipeline (achieving a 69% reduction in processing time via multithreading), capacity planning considering CPU, memory, storage IOPS, network bandwidth, and power availability, implementing an automated system for safe infrastructure utilization by dynamically allocating machines and blocklisting problematic ones, developing a ranking system for photo selection using signals like break-up checkup, blocks, and user interactions, and leveraging AI image understanding to assess photo quality and relevance. It also describes a staggered rollout plan to manage CDN load across different continents and time zones, and highlights the rapid development of Android and iOS UIs using React Native.
  34. Next-generation video encoding techniques for 360 video and VRThis post details advancements in video encoding techniques for 360 video and VR. It introduces two key methods: remapping equirectangular layouts to cube maps, reducing file size by 25% and improving encoding efficiency due to reduced geometry distortion and better pixel distribution. It also describes pyramid encoding for VR, which reduces file size by 80% and enables view-dependent adaptive bit rate streaming by only transmitting the visible field of view and pre-generating multiple resolutions for different viewports. The source code for the cube map transformation filter is also made available.
  35. Why we chose to move to HTML5 videoThis post details the migration of Facebook's video player from Flash to HTML5. Key technical contributions include: implementing a test suite to validate log equivalence between Flash and HTML5 players, mitigating Chrome SPDY protocol bugs by reducing concurrent video loading and canceling unnecessary loads, addressing performance regressions in older browsers by phased rollout and continuous improvement, and optimizing page load times through micro-optimizations to counteract initial regressions. The post also highlights the benefits of HTML5 for development velocity and accessibility.
  36. Under the hood: Building 360 videoIntroduced a custom video filter to remap equirectangular layouts to cube maps for 360 video, addressing issues of redundant information and geometric distortion in equirectangular projections. This transformation improves video encoding efficiency and pixel distribution. Also detailed the challenges and solutions for handling large file sizes and bit rates of 360 video, including the use of distributed encoding and optimized hardware for CPU and memory-intensive processing jobs.
  37. Under the hood: Place Tips at LollapaloozaThis post details the development of a server-controlled client rendering framework for dynamic content display, specifically for Place Tips. The framework uses 'styles' as contracts between client and server to map arbitrary data to pre-existing templates on clients, enabling flexible and consistent rendering across different app versions. This allowed for rapid iteration and deployment of features like Lollapalooza-specific Place Tips, leveraging existing client templates and server-side logic.
  38. The technology behind preview photosIntroduced a technology to deliver low-resolution image previews within 200 bytes by embedding a compressed JPEG payload with a custom header. This involved creating a fixed JPEG header (version, width, height) and a Huffman table optimized for the target byte count, allowing clients to reconstruct a blurred image preview. This reduced profile and page load times by 30% on slow connections and ensured immediate preview display on all connections.
  39. Improving Facebook’s performance on Android with FlatBuffersIntroduced FlatBuffers as a replacement for JSON for storing and serving data on Facebook's Android app. This change significantly improved performance by reducing story load time from disk cache (35ms to 4ms), decreasing transient memory allocations by 75%, improving cold start time by 10-15%, and reducing storage size by 15%. Developed mutation extensions for FlatBuffers to handle local state and server data updates efficiently, leading to a 'Flat Models' architecture that simplifies data flow between UI and storage.
  40. Under the hood: Building MomentsThis post details the technical architecture and development process behind Moments, a photo-sharing app. Key contributions include: 1. Leveraging Facebook's facial recognition technology for accurate photo suggestions. 2. Implementing a client-driven development model with optimistic UI updates to shorten the development-run-test cycle. 3. Designing a data model where the client describes data and the server stores generic blobs, with generated code for type safety. 4. Utilizing a Flux-like architecture for data flow from client cache to UI. 5. Implementing a delta-syncing protocol via GraphQL for efficient data fetching based on user subscriptions to queues. 6. Sharing business logic between iOS and Android apps using C++ with Djinni for Android bindings and lightweight Objective-C wrappers for iOS, optimizing for fast iteration, performance, and native look and feel.
  41. Under the hood: Facebook’s cold storage systemThis post details the development of Meta's cold storage system, a new capability focused on efficiently and durably storing less frequently accessed photos. It describes the full-stack approach, including custom data center design, low-power servers based on Open Vault specifications with modified drive power management, and optimized rack configurations. The software design emphasizes durability, with principles like eliminating single points of failure and self-describing data. Key technical innovations include the use of Reed-Solomon erasure coding for data redundancy and an 'anti-entropy' background process for detecting and correcting 'bit rot'. The system is designed to scale and improve with capacity additions through data rebalancing.
  42. Introducing Fresco: A new image library for AndroidIntroduced Fresco, a new image library for Android to address memory management issues and prevent crashes. Fresco leverages native heaps (ashmem) for purgeable bitmaps, avoiding Java heap garbage collection pauses on the UI thread. It implements a pipeline architecture using Producer/Consumer patterns and DataSource for progressive image loading. It also supports animated GIFs and WebPs with memory-optimized AnimatedDrawables.
  43. Shimmer for AndroidThis post introduces Shimmer for Android, a new component that provides a shimmering loading effect for Android views. It is implemented as a layout (`ShimmerFrameLayout`) that can wrap complex views and animate an alpha mask across them to create the effect. The implementation details include rendering the view twice with an alpha mask translation and allowing configuration via XML or programmatically. Shimmer is used in several Facebook applications for loading status indicators.
  44. Faster Photos in Facebook for iOSIntroduced Progressive JPEG (PJPEG) rendering for photos in Facebook for iOS. This involved building custom progressive rendering support as iOS lacked native out-of-the-box functionality. The implementation reduced data consumption by ~10% and improved perceived image loading speed by 15% by allowing the rendering of an image as soon as the first scan is available, with subsequent scans increasing detail. This also reduced the number of network connections per image and decreased disk storage by eliminating the need to download multiple image sizes.
  45. The art and science behind auto-enhance for iOSThis post details the development of an auto-enhance filter for iOS, which uses computational imaging techniques (Global Tone Mapping, Local Tone Mapping, and Noise Suppression) to improve image quality by mimicking human visual perception and darkroom photography techniques. It explains the challenges of dynamic range, image memory, and noise in digital photography and how these algorithms address them.
  46. Looking back on “Look Back” videosThis post details the engineering challenges and solutions for building the 'Look Back' video feature within a month. It highlights the rapid scaling of infrastructure, including estimating and provisioning 25 petabytes of storage and planning for 187 Gbps of outgoing bandwidth. The post describes the division of the pipeline into parallelizable tasks, the decision to isolate video storage and reduce replication factors, and the provisioning of additional hardware to handle the compute and network load. It also touches on managing data center power efficiency and the collaborative, cross-team effort required to meet the tight deadline.
  47. An analysis of Facebook photo cachingThis post details an analysis of Facebook's multi-layered photo-serving caching stack, including browser caches, edge caches, and origin caches. It quantifies the effectiveness of each layer, analyzes the shifting popularity distribution of photos across these layers, and evaluates the impact of increasing cache sizes and advanced caching algorithms (FIFO, LRU, LFU, S4LRU, Clairvoyant) on hit ratios. The findings highlight opportunities for performance improvements through optimized caching strategies.
  48. An analysis of Facebook photo cachingThis post details an analysis of Facebook's photo caching stack, including its effectiveness, photo popularity distribution across cache layers, the impact of increasing cache sizes, and the effect of more advanced caching algorithms. It references an academic study published in SOSP and highlights the use of Haystack machines for photo storage.
  49. McDipper: A key-value cache for Flash storageIntroduced McDipper, a Memcache protocol-compatible key-value cache server optimized for flash storage. McDipper offers improved capacity and performance over RAM-based Memcached for large working sets with moderate to low request rates. Key features include checksums, configurable replacement policies (FIFO, LRU), optional bloom filters, compression, encryption, and arenas. Addressed race conditions in Memcached operations with delete hold-off and read-modify-write primitives. Deployed McDipper in Facebook's photo infrastructure, reducing server counts by up to 90% and serving over 150 Gb/s from CDN forward caches.
  50. Optimizing for the MacBook Pro with Retina displayIntroduced a server-side and client-side cookie-based mechanism to dynamically serve images optimized for device pixel ratio, specifically addressing the challenges presented by the MacBook Pro with Retina display. This involved making an educated guess about the device pixel ratio, allowing for self-correction via refresh, maintaining consistency within a window, and minimizing unnecessary data transfer.
  51. Under the Hood: Building Facebook CameraThe Facebook Camera app for iPhone was developed with a focus on merging photo capture and consumption into a single interface. The team iterated on UI design for displaying multi-photo posts, captions, and tags. A key decision point was whether to start in camera mode or feed mode, influenced by iOS lock screen camera updates. A prototype was developed that places a mini camera roll at the top of the feed, providing easy access to the camera and recent photo thumbnails, serving as a prompt to share and working well with the lock-screen camera.
  52. Under the Hood: Improving Facebook PhotosIntroduced TINYsRGB, a significantly smaller sRGB color profile (524 bytes) by removing redundant data and optimizing the tone response curve sampling from 1024 points to 26. This optimization allows color profiles to be attached to thumbnails without significantly impacting page load times, improving color accuracy for all photos on Facebook.
  53. Hackathon 22: Redesigning PixelcloudThis post details the redesign of Pixelcloud, an internal image upload and commenting tool, as a hackathon project. The redesign involved a complete rebuild from scratch using XHP and other frameworks, migrating the backend to DFS3 storage from NFS, and optimizing database queries. The new design features a grid layout for images, improved search functionality (tag/user search), and enhanced image pages with view counts and trending post algorithms. These changes resulted in a significant increase in image uploads and comments.
  54. Developing Facebook’s New Photo ViewerThis post details the redesign of Facebook's Photo Viewer to improve reliability, reduce load times, and enhance user engagement. Key technical contributions include: a pop-up/lightbox overlay design to maintain user context, a two-tone layout with a dedicated space for captions and comments, CSS-based layout for positioning elements and scaling images to fit screen resolutions, separation of image and data fetches for optimized AJAX calls, prefetching of next images for faster loading, and using HTML5 data attributes to store full-size image URLs for immediate display.
  55. Building a Better Photo UploaderIntroduced a new, custom-built photo uploader that replaces a third-party ActiveX control and Java applet. The new uploader relies on a headless browser plug-in providing JavaScript APIs for filesystem browsing, background uploading threads, and thumbnailing. Uploads are asynchronous, allowing users to browse Facebook while uploads are in progress. The UI is built entirely in HTML and CSS for easier iteration. Security measures include binding the local web server to 127.0.0.1, using a hashing mechanism for secure URIs, preventing the plug-in from running on non-Facebook domains, implementing mechanisms to prevent unauthorized access to trusted functionality, securely signing and verifying all code downloads, and a remote "kill switch" to deactivate the plug-in if necessary.
  56. Needle in a haystack: efficient storage of billions of photosThis post details the transition from a metadata-heavy NFS-based photo storage infrastructure to a new system called Haystack. Haystack merges the photo serving and storage tiers into a single physical tier, utilizing an HTTP-based photo server that stores photos in a generic object store. The primary goal was to eliminate unnecessary metadata overhead for read operations. Haystack is built on commodity storage blades with RAID-6, uses XFS as an extent-based filesystem, and implements an append-only object store with an index file for efficient lookups. The Photo Store server maintains an in-memory index of photo offsets for rapid retrieval. The post also describes Haystack's write, read, and delete operations, as well as the compaction process for reclaiming space. This new infrastructure significantly improved performance by reducing I/O operations per photo.
  57. Behind the scenes with the new Facebook features in Apple’s iPhoto ’09This post details the integration of Facebook features into Apple's iPhoto '09, specifically focusing on the bi-directional syncing of photo name tags and privacy controls. It highlights the enhancement of Facebook's photo APIs to support this integration, demonstrating an expansion of how user photos can be shared and managed across platforms.
  58. 10 billion photosThis post marks a significant milestone in Meta's photo infrastructure by announcing the upload of over 10 billion photos. It quantifies the scale of this achievement by detailing that over 40 billion files are stored (4 sizes per photo), with daily uploads of 2-3 Terabytes, a total storage of over 1 petabyte, and serving over 15 billion photo images per day, peaking at over 300,000 images served per second. This data highlights the immense scale of photo storage and serving infrastructure at Meta.