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FlatBuffers

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This is an old revision of this page, as edited by Demonkratie (talk | contribs) at 20:05, 3 February 2021 (Added some specifics about the used format to better distinguish from e.g. Protocol Buffers). The present address (URL) is a permanent link to this revision, which may differ significantly from the current revision.

FlatBuffers
Original author(s)Wouter van Oortmerssen
Initial releaseJune 17, 2014; 10 years ago (2014-06-17)[1]
Stable release
1.12.0 / March 12, 2020; 4 years ago (2020-03-12)[2]
Repository
Written inC++
Operating systemAndroid, Microsoft Windows, Mac OS X, Linux
Typeserialization format and library, IDL compiler
LicenseApache License 2.0
Websitegoogle.github.io/flatbuffers/ Edit this on Wikidata

FlatBuffers is a free software library implementing a serialization format similar to Protocol Buffers, Thrift, Apache Avro, SBE, and Cap'n Proto, primarily written by Wouter van Oortmerssen and open-sourced by Google. Like Cap'n Proto and SBE, it supports “zero-copy” deserialization, so that accessing the serialized data does not require first copying it into a separate part of memory, which makes accessing data in these formats much faster than data in formats requiring more extensive processing, such as JSON, CSV, and in many cases Protocol Buffers. Compared to other serialization formats however, the handling of FlatBuffers requires usually more code, and some operations are not possible (like some mutation operations).

The serialized format allows random access to specific data elements (e.g. individual string or integer properties) without parsing all data. Unlike Protocol Buffers, which uses variable length integers, FlatBuffers encodes integers in their native size, which favors performance but leads to longer encoded representations.

FlatBuffers is a popular project on GitHub, with 15,461 stars, 442 contributors, 2,383 forks, and 645 watchers on GitHub as of 10 December 2020.[3]

FlatBuffers can be used in software written in C++, C#, C, Go, Java, JavaScript, Kotlin, Lobster, Lua, PHP, Python, Rust, Swift, and TypeScript. The schema compiler runs on Android, Microsoft Windows, Mac OS X, and Linux,[3] but games and other programs using FlatBuffers for serialization work on many other operating systems as well, including iOS, Amazon's Fire OS, and Windows Phone.[4]

Van Oortmerssen originally developed FlatBuffers for game development and similar applications.[5][1]

Although FlatBuffers has its own interface definition language to define the data to be serialized with it, it also supports schemas defined in the Protocol Buffers .proto format.[6]

Users

Some notable users of FlatBuffers:

  • Cocos2d-x, the popular free-software 2-D game programming library, uses FlatBuffers to serialize all of its game data.[7]
  • Facebook Android Client uses FlatBuffers for disk storage and communication with Facebook servers. The previously used JSON format was performing poorly.[8]


See also

References

  1. ^ a b Wouter van Oortmerssen (2014-06-17). "FlatBuffers: A Memory-Efficient Serialization Library". Retrieved 2017-06-15.
  2. ^ "Releases - google/flatbuffers". Retrieved 19 May 2020 – via GitHub.
  3. ^ a b "GitHub — google/flatbuffers: Memory Efficient Serialization Library". GitHub. Retrieved 2020-12-10.
  4. ^ "FlatBuffers for Unity". eXiin. 2015-09-21. Retrieved 2017-06-15. We tested flatbuffers [sic] on all major mobile platforms (iOS, Android, Amazon Os [sic], Windows Phone) we're building on[,] and it works pretty well.
  5. ^ "FlatBuffers Documentation". Retrieved 2017-06-21. FlatBuffers is an efficient cross platform serialization library for C++, C#, C, Go, Java, JavaScript, PHP, and Python. It was originally created at Google for game development and other performance-critical applications.
  6. ^ Kenton Varda (2014-06-17). "Cap'n Proto, FlatBuffers, and SBE". Retrieved 2017-06-15.
  7. ^ http://www.cocos2d-x.org/reference/native-cpp/V3.5/d7/d2d/namespaceflatbuffers.html
  8. ^ George Xie (2015-07-31). "Improving Facebook's performance on Android with FlatBuffers". Retrieved 2017-06-15. Story load time from disk cache is reduced from 35 ms to 4 ms per story. Transient memory allocations are reduced by 75 percent. Cold start time is improved by 10-15 percent. We have reduced storage size by 15 percent.