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google/flatbuffers

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TLDR

Fast binary data format that skips conversion overhead, letting programs read structured data directly from memory without unpacking.

Mindmap

mindmap
  root((FlatBuffers))
    What it does
      Binary serialization
      Zero-copy reads
      Cross-language support
    How it works
      Schema definition
      Code generation
      Direct memory access
    Use cases
      Games and graphics
      Mobile apps
      ML pipelines
      Real-time systems
    Tech stack
      C++ core
      Multi-language codegen
      Compiler tool

Things people build with this

USE CASE 1

Build high-performance games that serialize player state and network messages without conversion overhead.

USE CASE 2

Create mobile apps that need to store and load large datasets quickly while minimizing memory usage.

USE CASE 3

Design machine learning pipelines that move tensors and structured data between services in different languages.

USE CASE 4

Develop real-time systems like chat apps or trading platforms where serialization speed directly impacts latency.

Tech stack

C++PythonJavaScriptTypeScriptJavaRustGoSwift

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

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.

Copy-paste prompts

Prompt 1
Show me how to define a FlatBuffers schema for a game character with position, health, and inventory data.
Prompt 2
How do I generate Python code from a FlatBuffers schema and use it to serialize and deserialize game events?
Prompt 3
What is the performance difference between FlatBuffers and JSON for sending 10,000 game state updates per second?
Prompt 4
How can I use FlatBuffers to share data between a C++ game engine and a Python machine learning service?
Prompt 5
Walk me through setting up FlatBuffers in a TypeScript project and reading binary data without unpacking it.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.