Instagram Engineering
https://instagram-engineering.com/Why it matters: This migration consolidates technical insights into a single platform, making it easier for engineers to access Instagram's architectural and scaling case studies alongside other Meta technologies while promising more frequent updates.
- •Instagram Engineering is moving its blog to the Engineering at Meta platform.
- •The migration aims to streamline internal operations and improve publishing efficiency.
- •Future technical content will be hosted under a dedicated Instagram section on the Meta Engineering site.
- •The move is expected to result in more frequent updates regarding Instagram's technical innovations.
- •Readers are encouraged to follow Meta Engineering social channels for future updates.
Why it matters: Managing content quality at scale requires balancing real-time signals with static analysis. This approach shows how to operationalize quality metrics and use multi-stage ML pipelines to protect users while maintaining high-performance recommendation systems.
- •Combined manual labeling with classifier scores to create calibrated metrics for statistically significant A/B testing results.
- •Developed 'read-path' models that utilize real-time engagement signals like comments and likes to improve detection precision.
- •Maintained 'write-path' filters at the sourcing level to handle low-prevalence violations and ensure a baseline of benign content.
- •Implemented a multi-stage pipeline that balances high-precision sourcing filters with fine-tuned ranking models.
- •Established continuous model performance tracking to identify edge cases and maintain user safety standards.
Why it matters: Engineers must balance performance and resource consumption. This case study shows how optimizing data usage through prefetching and resolution controls can improve user engagement and retention in data-constrained markets, proving that efficiency and growth can go hand-in-hand.
- •Instagram launched Data Saver Mode for Android to address high data consumption and improve efficiency relative to other Meta apps.
- •The implementation focuses on three levers: disabling video prefetch, disabling video autoplay, and offering manual media resolution controls.
- •Disabling prefetch ensures video data is only downloaded when a user stops scrolling, preventing waste on unviewed content.
- •Users can configure high-resolution media settings to 'Never,' 'Wi-Fi Only,' or 'Cellular and Wi-Fi' to manage their data budgets.
- •Global A/B testing showed that reducing data usage led to unexpected increases in user interactions and content creation.
- •The custom solution provides a smoother experience than Android's native Data Saver, which often blocks media loading entirely.
Why it matters: This article provides a blueprint for building massive-scale recommendation engines. It demonstrates how custom DSLs and multi-stage filtering balance high-velocity experimentation with the extreme computational efficiency required to serve millions of users in real-time.
- •Instagram uses a three-stage ranking funnel to filter billions of media items into a personalized feed for each user in real-time.
- •Engineers developed IGQL, a C++ optimized domain-specific language, to allow for high-level algorithm design with low-latency execution.
- •The system utilizes 'ig2vec' account embeddings to identify topical similarities based on user interaction sequences, similar to word2vec.
- •Facebook’s FAISS library is used for efficient nearest-neighbor retrieval across millions of account embeddings.
- •The infrastructure supports massive scale, processing 65 billion features and making 90 million model predictions every second.
Why it matters: This interview highlights the intersection of machine learning and social responsibility, demonstrating how engineers balance technical innovation with strict privacy and legal requirements in a high-scale, data-driven environment.
- •Shupin Mao transitioned from academic coding in C to professional iOS development using Objective-C at Facebook.
- •The Instagram Well-being team utilizes machine learning models to identify and combat the sale of illegal goods like drugs and firearms.
- •Instagram's engineering culture emphasizes a data-driven approach, where projects are guided by analytical goals and user feedback.
- •Teams allocate 20% of their time to address ad-hoc issues, ensuring flexibility and responsiveness to unexpected technical challenges.
- •Engineers work closely with cross-functional partners, including legal, policy, and privacy experts, to review every product change.
- •The organization maintains a flat management structure, allowing engineers to take on large scopes of work and communicate directly with leadership.
Why it matters: Optimizing JavaScript execution and parsing is critical for web performance on low-end devices. By focusing on pre-compression size and deferring execution, engineers can significantly reduce Time to Interactive even when network speeds are not the primary bottleneck.
- •Prioritized reducing pre-compression JavaScript size over post-compression size, as parsing and execution on the CPU are often the primary bottlenecks on mobile devices.
- •Implemented inline requires using the Metro bundler to defer module execution until first use, resulting in a 12% improvement in Time to Interactive (TTI).
- •Transitioned to serving ES2017 bundles to modern browsers, reducing the overhead of transpiled code and polyfills for features like async/await.
- •Established Critical Bytes Per Route as a key metric to monitor and limit the amount of eagerly executed JavaScript on the critical path.
- •Utilized dynamic imports to move non-visible or interaction-dependent UI components out of initial page bundles to improve initial load performance.
Why it matters: Managing a multi-million line Python monolith requires addressing the risks of dynamic imports. Uncontrolled side effects and global state mutation slow down development cycles and introduce production instability, necessitating stricter module boundaries for performance and reliability.
- •Instagram's multi-million line Python monolith faces significant performance bottlenecks due to arbitrary code execution during module imports.
- •Import-time side effects like regex compilation and decorator execution prevent incremental reloading, causing server startup times of up to 60 seconds.
- •Unsafe import practices, such as fetching network configuration at the module level, lead to non-deterministic initialization failures and production risks.
- •The dynamic nature of Python allows for mutable global state, which often causes request pollution and test flakiness in large-scale environments.
- •Standard Python lacks explicit control over import order, making it difficult to prevent 'spooky action at a distance' bugs in complex dependency graphs.
Why it matters: Cache-first rendering provides immediate UI feedback but creates complex state sync challenges. This approach shows how to use Git-like rebase patterns in Redux to ensure user interactions aren't lost when merging stale cached data with fresh server responses.
- •Implemented cache-first rendering by storing a subset of the Redux store in IndexedDB to allow immediate page hydration.
- •Addressed race conditions where user interactions on cached data, such as likes or comments, could be overwritten by incoming server responses.
- •Developed a staging mechanism that treats cached state as a local branch and server data as master, performing a rebase-like operation for state updates.
- •Created a staging API using stagingAction and stagingCommit to queue dispatched actions while network requests are pending.
- •Used a Redux reducer enhancer to apply queued actions to the fresh server state before committing it to the main store.
- •Achieved significant performance gains, including a 2.5% improvement in feed display time and an 11% improvement in stories tray display time.
Why it matters: This article provides a blueprint for large-scale feature adoption across legacy codebases. It demonstrates how to leverage native platform APIs while maintaining backward compatibility through clever wrapping and conditional compilation, ensuring a seamless transition for users and developers.
- •Adopted a 'stand on the shoulders of giants' philosophy by sticking closely to Apple's UIKit APIs for ease of use and maintainability.
- •Developed thin, backwards-compatible wrappers around iOS 13 APIs to support developers still using Xcode 10 and devices on iOS 12.
- •Utilized dynamic colors and images that automatically adapt to light/dark modes, elevation levels, and accessibility settings.
- •Implemented a semantic color palette to reduce complexity for product teams and ensure consistent UI across the application.
- •Created a custom IGTraitCollection struct to bridge the gap between different iOS versions and handle interface style changes safely.
- •Addressed technical hurdles like color equality issues by ensuring dynamic color instances remained comparable within the system.
Why it matters: This interview offers a look into how Instagram uses data science and experimentation to drive product strategy. It highlights the intersection of technical leadership, user-centric culture, and the professional development skills necessary to succeed in high-scale engineering organizations.
- •Tamar Shapiro leads Instagram's analytics team, overseeing data scientists and engineers focused on experimentation and data-driven product decisions.
- •A key project discussed is the 'private like counts' test, which aimed to shift user focus from quantity to quality of interactions.
- •Instagram's engineering culture is characterized as highly collaborative with a 'people first' value system centered on the user community.
- •Effective communication and context sharing are emphasized as vital for maintaining alignment in fast-paced development environments.
- •Career growth advice for engineers includes prioritizing networking outside immediate teams and building confidence to advocate for accomplishments.