flatbuffers
FlatBuffers: Memory Efficient Serialization Library
FlatBuffers is a Google library for storing and sending data between programs extremely fast, it reads data directly from memory without unpacking it, using far less time and memory than JSON.
FlatBuffers is a Google-created library for efficiently storing and transmitting data between programs or systems. When apps need to save data to disk or send data across a network, they typically have to convert it into a format like JSON or convert it back again — a process that takes processing time and memory. FlatBuffers skips most of that conversion by storing data in a way that can be read directly from memory without unpacking it first.
In practical terms, this makes it extremely fast — useful for games, mobile apps, machine learning pipelines, or any performance-sensitive system where you are moving large amounts of structured data around. It uses significantly less memory than alternatives like JSON or XML, which matters on mobile devices or embedded systems.
The workflow involves writing a schema file (a description of what your data looks like), then using a compiler tool to auto-generate code for whatever programming language you are using. It supports a wide range of languages including C++, Python, JavaScript, TypeScript, Java, Rust, Go, Swift, Dart, and more — so the same data format can be used across different parts of a system written in different languages.
For a non-technical founder: this is deep infrastructure — the kind of tool engineering teams use when building games, chat apps, or AI systems that need to handle data as fast as possible. You would not interact with FlatBuffers directly. If your product is being built by developers who mention needing fast serialization or binary data formats, this is what they might reach for. It is free, open-source, and maintained by Google.
Where it fits
- Replace JSON serialization in a mobile app to reduce memory usage and speed up data loading on low-end devices.
- Share data between a C++ game engine and a Python machine learning pipeline using a single FlatBuffers schema.
- Transmit structured game state between a server and clients over a network with minimal parsing overhead.
- Store training data or model outputs in a binary format that can be memory-mapped and read without copying.