It is available as Open Source on GitHub under the Apache license, v2 (see LICENSE.txt).
Access to serialized data without parsing/unpacking - What sets FlatBuffers apart is that it represents hierarchical data in a flat binary buffer in such a way that it can still be accessed directly without parsing/unpacking, while also still supporting data structure evolution (forwards/backwards compatibility).
Memory efficiency and speed - The only memory needed to access your data is that of the buffer. It requires 0 additional allocations (in C++, other languages may vary). FlatBuffers is also very suitable for use with mmap (or streaming), requiring only part of the buffer to be in memory. Access is close to the speed of raw struct access with only one extra indirection (a kind of vtable) to allow for format evolution and optional fields. It is aimed at projects where spending time and space (many memory allocations) to be able to access or construct serialized data is undesirable, such as in games or any other performance sensitive applications. See the [benchmarks](@ref flatbuffers_benchmarks) for details.
Flexible - Optional fields means not only do you get great forwards and backwards compatibility (increasingly important for long-lived games: don't have to update all data with each new version!). It also means you have a lot of choice in what data you write and what data you don't, and how you design data structures.
Tiny code footprint - Small amounts of generated code, and just a single small header as the minimum dependency, which is very easy to integrate. Again, see the benchmark section for details.
Strongly typed - Errors happen at compile time rather than manually having to write repetitive and error prone run-time checks. Useful code can be generated for you.
Convenient to use - Generated C++ code allows for terse access & construction code. Then there's optional functionality for parsing schemas and JSON-like text representations at runtime efficiently if needed (faster and more memory efficient than other JSON parsers).
Java, Kotlin and Go code supports object-reuse. C# has efficient struct based accessors.
Cross platform code with no dependencies - C++ code will work with any recent gcc/clang and VS2010. Comes with build files for the tests & samples (Android .mk files, and cmake for all other platforms).
Protocol Buffers is indeed relatively similar to FlatBuffers, with the primary difference being that FlatBuffers does not need a parsing/ unpacking step to a secondary representation before you can access data, often coupled with per-object memory allocation. The code is an order of magnitude bigger, too. Protocol Buffers has no optional text import/export.
If you do need to store data that doesn't fit a schema, FlatBuffers also offers a schema-less (self-describing) version!
Read more about the "why" of FlatBuffers in the [white paper](@ref flatbuffers_white_paper).
This section is a quick rundown of how to use this system. Subsequent sections provide a more in-depth usage guide.
Write a schema file that allows you to define the data structures you may want to serialize. Fields can have a scalar type (ints/floats of all sizes), or they can be a: string; array of any type; reference to yet another object; or, a set of possible objects (unions). Fields are optional and have defaults, so they don't need to be present for every object instance.
flatc (the FlatBuffer compiler) to generate a
C++ header (or Java/Kotlin/C#/Go/Python.. classes) with helper classes
to access and construct serialized data. This header (say
mydata_generated.h) only depends on
flatbuffers.h, which defines the core
FlatBufferBuilder class to construct a flat
binary buffer. The generated functions allow you to add objects to this
buffer recursively, often as simply as making a single function
Store or send your buffer somewhere!
When reading it back, you can obtain the pointer to the root
object from the binary buffer, and from there traverse it conveniently
flatcc](@ref flatbuffers_guide_use_c) in your own programs.