Facebook (now Meta) is the birthplace of many industry-standard technologies. By 2026, its architecture has shifted from a classic web stack to an AI-first distributed system that prioritizes massive-scale compute (through the "Meta Compute" initiative) and extreme developer velocity.
Below is the comprehensive project structure and tech stack of Facebook as of 2026.
Facebook’s frontend is famous for being almost entirely "homegrown." Every major library used was built to solve a specific scaling problem for their billions of users.
Core Framework: React (utilizing React Server Components to minimize client-side JavaScript).
Data Fetching: Relay + GraphQL. Relay is their specialized framework for fetching and managing data specifically optimized for high-performance React apps.
Styling: StyleX. A type-safe, utility-first CSS-in-JS library that generates atomic CSS to keep bundles small even as the UI grows.
Mobile Architecture:
React Native for cross-platform feature consistency.
ComponentKit (iOS) and Litho (Android) for high-performance declarative UI on native platforms.
Performance: BigPipe. A dynamic page-serving system that breaks pages into "pagelets" and serves them concurrently.
The backend has evolved from traditional PHP into a strictly-typed, high-performance ecosystem.
Primary Language: Hack. A typed dialect of PHP that runs on the HHVM (HipHop Virtual Machine), offering the speed of C++ with the flexibility of PHP.
Service Communication: Apache Thrift. A cross-language RPC framework that allows Hack, C++, Java, and Python services to talk to each other.
Build System: Buck2. A massive-scale build system written in Rust that supports multiple languages in a single monorepo.
Version Control: Sapling. A custom source control system built to handle codebases too large for traditional Git.
Orchestration: Twine (formerly Tupperware). Meta's proprietary container orchestrator (their equivalent of Kubernetes) that manages millions of containers globally.
Facebook manages one of the world's largest data footprints. They use a "Cache-Aside" architecture where the cache is often the primary source of truth for the application.
| Layer | Technology | Purpose |
| Social Graph | TAO | A distributed data store specifically for "objects" (people) and "associations" (friends, likes). |
| Object Storage | Everstore | Optimized for storing and retrieving billions of photos and videos. |
| Relational DB | MySQL 8.0 | Heavily sharded and used as the underlying persistent storage for TAO. |
| Caching | Memcached | Massive-scale deployment (tens of terabytes) used to shield MySQL from read traffic. |
| Big Data | Presto / Trino | For distributed SQL queries over petabytes of data in Hadoop/Hive. |
In 2026, AI is no longer a "feature"—it is the backbone of the infrastructure.
Meta Compute Initiative: A top-level unit created to build gigawatt-scale data centers specifically for AI workloads.
Llama 4 & GenAI: The Llama 4 models power the Meta AI assistant and automated content moderation systems.
Machine Learning Framework: PyTorch remains the industry and internal standard for all model training and inference.
Hardware: Meta increasingly uses custom MTIA (Meta Training and Inference Accelerator) silicon alongside NVIDIA GPUs to optimize AI performance.
Testing & Analysis: Jest (JavaScript), Infer (static analysis for Java/C++), and Sapienz (automated mobile app testing).
Observability: Scuba (real-time data analysis) and Zipkin (distributed tracing).
Package Management: Meta utilizes internal mirrors and custom toolchains managed by Buck2; they rarely rely on public registries (like npm) directly for production to ensure security and stability.
- Frontend: React, JavaScript, and HTML5
- Backend: PHP, Java, and Python
- Database: MySQL, Memcached, and Cassandra
- Package Manager: npm (Node.js)
- Frameworks: React, GraphQL, and Thrift
- Tools: Jenkins, Git, and Docker