## File: README.md FlatBuffers =========== [](https://buildkite.com/bazel/flatbuffers) [](https://bugs.chromium.org/p/oss-fuzz/issues/list?sort=-opened&can=1&q=proj:flatbuffers) [](https:///discord.gg/6qgKs3R) [](https://twitter.com/wvo) [](https://twitter.com/dbaileychess) **FlatBuffers** is a cross platform serialization library architected for maximum memory efficiency. It allows you to directly access serialized data without parsing/unpacking it first, while still having great forwards/backwards compatibility. ## Quick Start 1. Build the compiler for flatbuffers (`flatc`) Use `cmake` to create the build files for your platform and then perform the compilation (Linux example). ``` cmake -G "Unix Makefiles" make -j ``` 2. Define your flatbuffer schema (`.fbs`) Write the [schema](https://flatbuffers.dev/flatbuffers_guide_writing_schema.html) to define the data you want to serialize. See [monster.fbs](https://github.com/google/flatbuffers/blob/master/samples/monster.fbs) for an example. 3. Generate code for your language(s) Use the `flatc` compiler to take your schema and generate language-specific code: ``` ./flatc --cpp --rust monster.fbs ``` Which generates `monster_generated.h` and `monster_generated.rs` files. 4. Serialize data Use the generated code, as well as the `FlatBufferBuilder` to construct your serialized buffer. ([`C++` example](https://github.com/google/flatbuffers/blob/master/samples/sample_binary.cpp#L24-L56)) 5. Transmit/store/save Buffer Use your serialized buffer however you want. Send it to someone, save it for later, etc... 6. Read the data Use the generated accessors to read the data from the serialized buffer. It doesn't need to be the same language/schema version, FlatBuffers ensures the data is readable across languages and schema versions. See the [`Rust` example](https://github.com/google/flatbuffers/blob/master/samples/sample_binary.rs#L92-L106) reading the data written by `C++`. ## Documentation **Go to our [landing page][] to browse our documentation.** ## Supported operating systems - Windows - macOS - Linux - Android - And any others with a recent C++ compiler (C++ 11 and newer) ## Supported programming languages Code generation and runtime libraries for many popular languages. 1. C 1. C++ - [snapcraft.io](https://snapcraft.io/flatbuffers) 1. C# - [nuget.org](https://www.nuget.org/packages/Google.FlatBuffers) 1. Dart - [pub.dev](https://pub.dev/packages/flat_buffers) 1. Go - [go.dev](https://pkg.go.dev/github.com/google/flatbuffers) 1. Java - [Maven](https://search.maven.org/artifact/com.google.flatbuffers/flatbuffers-java) 1. JavaScript - [NPM](https://www.npmjs.com/package/flatbuffers) 1. Kotlin 1. Lobster 1. Lua 1. PHP 1. Python - [PyPI](https://pypi.org/project/flatbuffers/) 1. Rust - [crates.io](https://crates.io/crates/flatbuffers) 1. Swift - [swiftpackageindex](https://swiftpackageindex.com/google/flatbuffers) 1. TypeScript - [NPM](https://www.npmjs.com/package/flatbuffers) 1. Nim ## Versioning FlatBuffers does not follow traditional SemVer versioning (see [rationale](https://github.com/google/flatbuffers/wiki/Versioning)) but rather uses a format of the date of the release. ## Contribution * [FlatBuffers Issues Tracker][] to submit an issue. * [stackoverflow.com][] with [`flatbuffers` tag][] for any questions regarding FlatBuffers. *To contribute to this project,* see [CONTRIBUTING][]. ## Community * [Discord Server](https:///discord.gg/6qgKs3R) ## Security Please see our [Security Policy](SECURITY.md) for reporting vulnerabilities. ## Licensing *Flatbuffers* is licensed under the Apache License, Version 2.0. See [LICENSE][] for the full license text. [CONTRIBUTING]: http://github.com/google/flatbuffers/blob/master/CONTRIBUTING.md [`flatbuffers` tag]: https://stackoverflow.com/questions/tagged/flatbuffers [FlatBuffers Google Group]: https://groups.google.com/forum/#!forum/flatbuffers [FlatBuffers Issues Tracker]: http://github.com/google/flatbuffers/issues [stackoverflow.com]: http://stackoverflow.com/search?q=flatbuffers [landing page]: https://google.github.io/flatbuffers [LICENSE]: https://github.com/google/flatbuffers/blob/master/LICENSE --- ## File: benchmarks/swift/README.md # Benchmarks To open the benchmarks in xcode use: `open --env BENCHMARK_DISABLE_JEMALLOC=true Package.swift` or running them directly within terminal using: `swift package benchmark` --- ## File: dart/README.md # FlatBuffers for Dart This package is used to read and write [FlatBuffers](https://google.github.io/flatbuffers/). Most consumers will want to use the [`flatc` - FlatBuffer compiler](https://github.com/google/flatbuffers) binary for your platform. You can download the flatc version matching your dart package version from [GitHub releases](https://github.com/google/flatbuffers/releases). The FlatBuffer compiler `flatc` reads a FlatBuffers IDL schema and generates Dart code. The generated classes can be used to read or write binary data/files that are interoperable with other languages and platforms supported by FlatBuffers, as illustrated in the `example.dart` in the examples folder. For more details and documentation, head over to the official site and read the [Tutorial](https://google.github.io/flatbuffers/flatbuffers_guide_tutorial.html) and how to [use FlatBuffers in Dart](https://google.github.io/flatbuffers/flatbuffers_guide_use_dart.html). --- ## File: docs/source/languages/c.md Use in C {#flatbuffers_guide_use_c} ========== The C language binding exists in a separate project named [FlatCC](https://github.com/dvidelabs/flatcc). The `flatcc` C schema compiler can generate code offline as well as online via a C library. It can also generate buffer verifiers and fast JSON parsers, printers. Great care has been taken to ensure compatibility with the main `flatc` project. ## General Documention - [Tutorial](../tutorial.md) - select C as language when scrolling down - [FlatCC Guide](https://github.com/dvidelabs/flatcc#flatcc-flatbuffers-in-c-for-c) - [The C Builder Interface](https://github.com/dvidelabs/flatcc/blob/master/doc/builder.md#the-builder-interface) - [The Monster Sample in C](https://github.com/dvidelabs/flatcc/blob/master/samples/monster/monster.c) - [GitHub](https://github.com/dvidelabs/flatcc) ## Supported Platforms - Ubuntu (clang / gcc, ninja / gnu make) - OS-X (clang / gcc, ninja / gnu make) - Windows MSVC 2010, 2013, 2015 CI builds recent versions of gcc, clang and MSVC on OS-X, Ubuntu, and Windows, and occasionally older compiler versions. See main project [Status](https://github.com/dvidelabs/flatcc#status). Other platforms may well work, including Centos, but are not tested regularly. The monster sample project was specifically written for C99 in order to follow the C++ version and for that reason it will not work with MSVC 2010. ## Modular Object Creation In the tutorial we used the call `Monster_create_as_root` to create the root buffer object since this is easier in simple use cases. Sometimes we need more modularity so we can reuse a function to create nested tables and root tables the same way. For this we need the `flatcc_builder_buffer_create_call`. It is best to keep `flatcc_builder` calls isolated at the top driver level, so we get: ~~~{.c} ns(Monster_ref_t) create_orc(flatcc_builder_t *B) { // ... same as in the tutorial. return s(Monster_create(B, ...)); } void create_monster_buffer() { uint8_t *buf; size_t size; flatcc_builder_t builder, *B; // Initialize the builder object. B = &builder; flatcc_builder_init(B); // Only use `buffer_create` without `create/start/end_as_root`. flatcc_builder_buffer_create(create_orc(B)); // Allocate and copy buffer to user memory. buf = flatcc_builder_finalize_buffer(B, &size); // ... write the buffer to disk or network, or something. free(buf); flatcc_builder_clear(B); } ~~~ The same principle applies with `start/end` vs `start/end_as_root` in the top-down approach. ## Top Down Example The tutorial uses a bottom up approach. In C it is also possible to use a top-down approach by starting and ending objects nested within each other. In the tutorial there is no deep nesting, so the difference is limited, but it shows the idea: ~~~{.c} uint8_t treasure[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}; size_t treasure_count = c_vec_len(treasure); ns(Weapon_ref_t) axe; // NOTE: if we use end_as_root, we MUST also start as root. ns(Monster_start_as_root(B)); ns(Monster_pos_create(B, 1.0f, 2.0f, 3.0f)); ns(Monster_hp_add(B, 300)); ns(Monster_mana_add(B, 150)); // We use create_str instead of add because we have no existing string reference. ns(Monster_name_create_str(B, "Orc")); // Again we use create because we no existing vector object, only a C-array. ns(Monster_inventory_create(B, treasure, treasure_count)); ns(Monster_color_add(B, ns(Color_Red))); if (1) { ns(Monster_weapons_start(B)); ns(Monster_weapons_push_create(B, flatbuffers_string_create_str(B, "Sword"), 3)); // We reuse the axe object later. Note that we dereference a pointer // because push always returns a short-term pointer to the stored element. // We could also have created the axe object first and simply pushed it. axe = *ns(Monster_weapons_push_create(B, flatbuffers_string_create_str(B, "Axe"), 5)); ns(Monster_weapons_end(B)); } else { // We can have more control with the table elements added to a vector: // ns(Monster_weapons_start(B)); ns(Monster_weapons_push_start(B)); ns(Weapon_name_create_str(B, "Sword")); ns(Weapon_damage_add(B, 3)); ns(Monster_weapons_push_end(B)); ns(Monster_weapons_push_start(B)); ns(Monster_weapons_push_start(B)); ns(Weapon_name_create_str(B, "Axe")); ns(Weapon_damage_add(B, 5)); axe = *ns(Monster_weapons_push_end(B)); ns(Monster_weapons_end(B)); } // Unions can get their type by using a type-specific add/create/start method. ns(Monster_equipped_Weapon_add(B, axe)); ns(Monster_end_as_root(B)); ~~~ ## Basic Reflection The C-API does support reading binary schema (.bfbs) files via code generated from the `reflection.fbs` schema, and an [example usage](https://github.com/dvidelabs/flatcc/tree/master/samples/reflection) shows how to use this. The reflection schema files are pre-generated in the [runtime distribution](https://github.com/dvidelabs/flatcc/tree/master/include/flatcc/reflection). ## Mutations and Reflection The C-API does not support mutating reflection like C++ does, nor does the reader interface support mutating scalars (and it is generally unsafe to do so even after verification). The generated reader interface supports sorting vectors in-place after casting them to a mutating type because it is not practical to do so while building a buffer. This is covered in the builder documentation. The reflection example makes use of this feature to look up objects by name. It is possible to build new buffers using complex objects from existing buffers as source. This can be very efficient due to direct copy semantics without endian conversion or temporary stack allocation. Scalars, structs and strings can be used as source, as well vectors of these. It is currently not possible to use an existing table or vector of table as source, but it would be possible to add support for this at some point. ## Namespaces The `FLATBUFFERS_WRAP_NAMESPACE` approach used in the tutorial is convenient when each function has a very long namespace prefix. But it isn't always the best approach. If the namespace is absent, or simple and informative, we might as well use the prefix directly. The [reflection example](https://github.com/dvidelabs/flatcc/blob/master/samples/reflection/bfbs2json.c) mentioned above uses this approach. ## Checking for Present Members Not all languages support testing if a field is present, but in C we can elaborate the reader section of the tutorial with tests for this. Recall that `mana` was set to the default value `150` and therefore shouldn't be present. ~~~{.c} int hp_present = ns(Monster_hp_is_present(monster)); // 1 int mana_present = ns(Monster_mana_is_present(monster)); // 0 ~~~ ## Alternative ways to add a Union In the tutorial we used a single call to add a union. Here we show different ways to accomplish the same thing. The last form is rarely used, but is the low-level way to do it. It can be used to group small values together in the table by adding type and data at different points in time. ~~~{.c} ns(Equipment_union_ref_t) equipped = ns(Equipment_as_Weapon(axe)); ns(Monster_equipped_add(B, equipped)); // or alternatively ns(Monster_equipped_Weapon_add(B, axe); // or alternatively ns(Monster_equipped_add_type(B, ns(Equipment_Weapon)); ns(Monster_equipped_add_member(B, axe)); ~~~ ## Why not integrate with the `flatc` tool? [It was considered how the C code generator could be integrated into the `flatc` tool](https://github.com/dvidelabs/flatcc/issues/1), but it would either require that the standalone C implementation of the schema compiler was dropped, or it would lead to excessive code duplication, or a complicated intermediate representation would have to be invented. Neither of these alternatives are very attractive, and it isn't a big deal to use the `flatcc` tool instead of `flatc` given that the FlatBuffers C runtime library needs to be made available regardless. --- ## File: docs/source/languages/c_sharp.md Use in C\# {#flatbuffers_guide_use_c-sharp} ============== ## Before you get started Before diving into the FlatBuffers usage in C#, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including C#). This page is designed to cover the nuances of FlatBuffers usage, specific to C#. You should also have read the [Building](../building.md) documentation to build `flatc` and should be familiar with [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). ## FlatBuffers C# code location The code for the FlatBuffers C# library can be found at `flatbuffers/net/FlatBuffers`. You can browse the library on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/net/ FlatBuffers). ## Building the FlatBuffers C# library The `FlatBuffers.csproj` project contains multitargeting for .NET Standard 2.0, 2.1, .NET 6 and .NET 8. You can build for a specific framework target when using the cross-platform [.NET Core SDK](https://dotnet.microsoft.com/download) by adding the `-f` command line option: ~~~{.sh} dotnet build -f netstandard2.1 "FlatBuffers.csproj" ~~~ The `FlatBuffers.csproj` project also provides support for defining various conditional compilation symbols (see "Conditional compilation symbols" section below) using the `-p` command line option: ~~~{.sh} dotnet build -f netstandard2.1 -p:ENABLE_SPAN_T=true -p:UNSAFE_BYTEBUFFER=true "FlatBuffers.csproj" ~~~ ## Testing the FlatBuffers C# library The code to test the libraries can be found at `flatbuffers/tests`. The test code for C# is located in the [FlatBuffers.Test](https://github.com/ google/flatbuffers/tree/master/tests/FlatBuffers.Test) subfolder. To run the tests, open `FlatBuffers.Test.csproj` in [Visual Studio]( https://www.visualstudio.com), and compile/run the project. Optionally, you can run this using [Mono](http://www.mono-project.com/) instead. Once you have installed Mono, you can run the tests from the command line by running the following commands from inside the `FlatBuffers.Test` folder: ~~~{.sh} mcs *.cs ../MyGame/Example/*.cs ../../net/FlatBuffers/*.cs mono Assert.exe ~~~ ## Using the FlatBuffers C# library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers in C#.* FlatBuffers supports reading and writing binary FlatBuffers in C#. To use FlatBuffers in your own code, first generate C# classes from your schema with the `--csharp` option to `flatc`. Then you can include both FlatBuffers and the generated code to read or write a FlatBuffer. For example, here is how you would read a FlatBuffer binary file in C#: First, import the library and generated code. Then, you read a FlatBuffer binary file into a `byte[]`. You then turn the `byte[]` into a `ByteBuffer`, which you pass to the `GetRootAsMyRootType` function: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} using MyGame.Example; using Google.FlatBuffers; // This snippet ignores exceptions for brevity. byte[] data = File.ReadAllBytes("monsterdata_test.mon"); ByteBuffer bb = new ByteBuffer(data); Monster monster = Monster.GetRootAsMonster(bb); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Now you can access the data from the `Monster monster`: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} short hp = monster.Hp; Vec3 pos = monster.Pos; ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ C# code naming follows standard C# style with PascalCasing identifiers, e.g. `GetRootAsMyRootType`. Also, values (except vectors and unions) are available as properties instead of parameterless accessor methods. The performance-enhancing methods to which you can pass an already created object are prefixed with `Get`, e.g.: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} // property var pos = monster.Pos; // method filling a preconstructed object var preconstructedPos = new Vec3(); monster.GetPos(preconstructedPos); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Storing dictionaries in a FlatBuffer FlatBuffers doesn't support dictionaries natively, but there is support to emulate their behavior with vectors and binary search, which means you can have fast lookups directly from a FlatBuffer without having to unpack your data into a `Dictionary` or similar. To use it: - Designate one of the fields in a table as the "key" field. You do this by setting the `key` attribute on this field, e.g. `name:string (key)`. You may only have one key field, and it must be of string or scalar type. - Write out tables of this type as usual, collect their offsets in an array. - Instead of calling standard generated method, e.g.: `Monster.createTestarrayoftablesVector`, call `CreateSortedVectorOfMonster` in C# which will first sort all offsets such that the tables they refer to are sorted by the key field, then serialize it. - Now when you're accessing the FlatBuffer, you can use the `ByKey` accessor to access elements of the vector, e.g.: `monster.TestarrayoftablesByKey("Frodo")` in C#, which returns an object of the corresponding table type, or `null` if not found. `ByKey` performs a binary search, so should have a similar speed to `Dictionary`, though may be faster because of better caching. `ByKey` only works if the vector has been sorted, it will likely not find elements if it hasn't been sorted. ## Buffer verification As mentioned in [C++ Usage](cpp.md) buffer accessor functions do not verify buffer offsets at run-time. If it is necessary, you can optionally use a buffer verifier before you access the data. This verifier will check all offsets, all sizes of fields, and null termination of strings to ensure that when a buffer is accessed, all reads will end up inside the buffer. Each root type will have a verification function generated for it, e.g. `Monster.VerifyMonster`. This can be called as shown: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} var ok = Monster.VerifyMonster(buf); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if `ok` is true, the buffer is safe to read. For a more detailed control of verification `MonsterVerify.Verify` for `Monster` type can be used: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} # Sequence of calls FlatBuffers.Verifier verifier = new FlatBuffers.Verifier(buf); var ok = verifier.VerifyBuffer("MONS", false, MonsterVerify.Verify); # Or single line call var ok = new FlatBuffers.Verifier(bb).setStringCheck(true).\ VerifyBuffer("MONS", false, MonsterVerify.Verify); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ if `ok` is true, the buffer is safe to read. A second parameter of `verifyBuffer` specifies whether buffer content is size prefixed or not. In the example above, the buffer is assumed to not include size prefix (`false`). Verifier supports options that can be set using appropriate fluent methods: * SetMaxDepth - limit the nesting depth. Default: 1000000 * SetMaxTables - total amount of tables the verifier may encounter. Default: 64 * SetAlignmentCheck - check content alignment. Default: True * SetStringCheck - check if strings contain termination '0' character. Default: true ## Text parsing There currently is no support for parsing text (Schema's and JSON) directly from C#, though you could use the C++ parser through native call interfaces available to each language. Please see the C++ documentation for more on text parsing. ## Object based API FlatBuffers is all about memory efficiency, which is why its base API is written around using as little as possible of it. This does make the API clumsier (requiring pre-order construction of all data, and making mutation harder). For times when efficiency is less important a more convenient object based API can be used (through `--gen-object-api`) that is able to unpack & pack a FlatBuffer into objects and standard `System.Collections.Generic` containers, allowing for convenient construction, access and mutation. To use: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} // Deserialize from buffer into object. MonsterT monsterobj = GetMonster(flatbuffer).UnPack(); // Update object directly like a C# class instance. Console.WriteLine(monsterobj.Name); monsterobj.Name = "Bob"; // Change the name. // Serialize into new flatbuffer. FlatBufferBuilder fbb = new FlatBufferBuilder(1); fbb.Finish(Monster.Pack(fbb, monsterobj).Value); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ### Json Serialization An additional feature of the object API is the ability to allow you to serialize & deserialize a JSON text. To use Json Serialization, add `--cs-gen-json-serializer` option to `flatc` and add `Newtonsoft.Json` nuget package to csproj. This requires explicitly setting the `--gen-object-api` option as well. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.cs} // Deserialize MonsterT from json string jsonText = File.ReadAllText(@"Resources/monsterdata_test.json"); MonsterT mon = MonsterT.DeserializeFromJson(jsonText); // Serialize MonsterT to json string jsonText2 = mon.SerializeToJson(); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ * Limitation * `hash` attribute currently not supported. * NuGet package Dependency * [Newtonsoft.Json](https://github.com/JamesNK/Newtonsoft.Json) ## Conditional compilation symbols There are three conditional compilation symbols that have an impact on performance/features of the C# `ByteBuffer` implementation. * `UNSAFE_BYTEBUFFER` This will use unsafe code to manipulate the underlying byte array. This can yield a reasonable performance increase. * `BYTEBUFFER_NO_BOUNDS_CHECK` This will disable the bounds check asserts to the byte array. This can yield a small performance gain in normal code. * `ENABLE_SPAN_T` This will enable reading and writing blocks of memory with a `Span` instead of just `T[]`. You can also enable writing directly to shared memory or other types of memory by providing a custom implementation of `ByteBufferAllocator`. `ENABLE_SPAN_T` also requires `UNSAFE_BYTEBUFFER` to be defined, or .NET Standard 2.1. Using `UNSAFE_BYTEBUFFER` and `BYTEBUFFER_NO_BOUNDS_CHECK` together can yield a performance gain of ~15% for some operations, however doing so is potentially dangerous. Do so at your own risk! --- ## File: docs/source/languages/cpp.md # Language Guide: C++ ## Before you get started Before diving into the FlatBuffers usage in C++, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including C++). This page is designed to cover the nuances of FlatBuffers usage, specific to C++. #### Prerequisites This page assumes you have written a FlatBuffers schema and compiled it with the Schema Compiler. If you have not, please see [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). Assuming you wrote a schema, say `mygame.fbs` (though the extension doesn't matter), you've generated a C++ header called `mygame_generated.h` using the compiler (e.g. `flatc -c mygame.fbs`), you can now start using this in your program by including the header. As noted, this header relies on `flatbuffers/flatbuffers.h`, which should be in your include path. ## FlatBuffers C++ library code location The code for the FlatBuffers C++ library can be found at `flatbuffers/include/flatbuffers`. You can browse the library code on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/include/flatbuffers). ## Testing the FlatBuffers C++ library The code to test the C++ library can be found at `flatbuffers/tests`. The test code itself is located in [test.cpp](https://github.com/google/flatbuffers/blob/master/tests/test.cpp). This test file is built alongside `flatc`. To review how to build the project, please read the [Building](../building.md) documentation. To run the tests, execute `flattests` from the root `flatbuffers/` directory. For example, on [Linux](https://en.wikipedia.org/wiki/Linux), you would simply run: `./flattests`. ## Using the FlatBuffers C++ library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers in C++.* FlatBuffers supports both reading and writing FlatBuffers in C++. To use FlatBuffers in your code, first generate the C++ classes from your schema with the `--cpp` option to `flatc`. Then you can include both FlatBuffers and the generated code to read or write FlatBuffers. For example, here is how you would read a FlatBuffer binary file in C++: First, include the library and generated code. Then read the file into a `char *` array, which you pass to `GetMonster()`. ```cpp #include "flatbuffers/flatbuffers.h" #include "monster_test_generated.h" #include // C++ header file for printing #include // C++ header file for file access std::ifstream infile; infile.open("monsterdata_test.mon", std::ios::binary | std::ios::in); infile.seekg(0,std::ios::end); int length = infile.tellg(); infile.seekg(0,std::ios::beg); char *data = new char[length]; infile.read(data, length); infile.close(); auto monster = GetMonster(data); ``` `monster` is of type `Monster *`, and points to somewhere *inside* your buffer (root object pointers are not the same as `buffer_pointer` \!). If you look in your generated header, you'll see it has convenient accessors for all fields, e.g. `hp()`, `mana()`, etc: ```cpp std::cout << "hp : " << monster->hp() << std::endl; // '80' std::cout << "mana : " << monster->mana() << std::endl; // default value of '150' std::cout << "name : " << monster->name()->c_str() << std::endl; // "MyMonster" ``` *Note: That we never stored a `mana` value, so it will return the default.* The following attributes are supported: - `shared` (on a field): For string fields, this enables the usage of string pooling (i.e. `CreateSharedString`) as default serialization behavior. Specifically, `CreateXxxDirect` functions and `Pack` functions for object based API (see below) will use `CreateSharedString` to create strings. ## Object based API FlatBuffers is all about memory efficiency, which is why its base API is written around using as little as possible of it. This does make the API clumsier (requiring pre-order construction of all data, and making mutation harder). For times when efficiency is less important a more convenient object based API can be used (through `--gen-object-api`) that is able to unpack & pack a FlatBuffer into objects and standard STL containers, allowing for convenient construction, access and mutation. To use: ```cpp // Autogenerated class from table Monster. MonsterT monsterobj; // Deserialize from buffer into object. GetMonster(flatbuffer)->UnPackTo(&monsterobj); // Update object directly like a C++ class instance. cout << monsterobj.name; // This is now a std::string! monsterobj.name = "Bob"; // Change the name. // Serialize into new flatbuffer. FlatBufferBuilder fbb; fbb.Finish(Monster::Pack(fbb, &monsterobj)); ``` The following attributes are specific to the object-based API code generation: - `native_inline` (on a field): Because FlatBuffer tables and structs are optionally present in a given buffer, they are best represented as pointers (specifically std::unique_ptrs) in the native class since they can be null. This attribute changes the member declaration to use the type directly rather than wrapped in a unique_ptr. - `native_default("value")` (on a field): For members that are declared "native_inline", the value specified with this attribute will be included verbatim in the class constructor initializer list for this member. - `native_custom_alloc("custom_allocator")` (on a table or struct): When using the object-based API all generated NativeTables that are allocated when unpacking your flatbuffer will use "custom allocator". The allocator is also used by any std::vector that appears in a table defined with `native_custom_alloc`. This can be used to provide allocation from a pool for example, for faster unpacking when using the object-based API. Minimal Example: schema: ```cpp table mytable(native_custom_alloc:"custom_allocator") { ... } ``` with `custom_allocator` defined before `flatbuffers.h` is included, as: ```cpp template struct custom_allocator : public std::allocator { typedef T *pointer; template struct rebind { typedef custom_allocator other; }; pointer allocate(const std::size_t n) { return std::allocator::allocate(n); } void deallocate(T* ptr, std::size_t n) { return std::allocator::deallocate(ptr,n); } custom_allocator() throw() {} template custom_allocator(const custom_allocator&) throw() {} }; ``` - `native_type("type")` (on a struct): In some cases, a more optimal C++ data type exists for a given struct. For example, the following schema: ```cpp struct Vec2 { x: float; y: float; } ``` generates the following Object-Based API class: ```cpp struct Vec2T : flatbuffers::NativeTable { float x; float y; }; ``` However, it can be useful to instead use a user-defined C++ type since it can provide more functionality, eg. ```cpp struct vector2 { float x = 0, y = 0; vector2 operator+(vector2 rhs) const { ... } vector2 operator-(vector2 rhs) const { ... } float length() const { ... } // etc. }; ``` The `native_type` attribute will replace the usage of the generated class with the given type. So, continuing with the example, the generated code would use `vector2` in place of `Vec2T` for all generated code of the Object-Based API. However, because the `native_type` is unknown to flatbuffers, the user must provide the following functions to aide in the serialization process: ```cpp namespace flatbuffers { Vec2 Pack(const vector2& obj); vector2 UnPack(const Vec2& obj); } ``` - `native_type_pack_name("name")` (on a struct when `native_type` is specified, too): when you want to use the same `native_type` multiple times (e. g. with different precision) you must make the names of the Pack/UnPack functions unique, otherwise you will run into compile errors. This attribute appends a name to the expected Pack/UnPack functions. So when you specify `native_type_pack_name("Vec2")` in the above example you now need to implement these serialization functions instead: ```cpp namespace flatbuffers { Vec2 PackVec2(const vector2& obj); vector2 UnPackVec2(const Vec2& obj); } ``` - `native_type("type")` (on a table): Tables can also be represented with native types. For example, the following schema: ```cpp table Matrix (native_type: "NativeMatrix") { rows: int32; columns: int32; values: [float]; } ``` Would be represented by a user-defined C++ class: ```cpp class NativeMatrix { ... } ``` In this case, the following function declarations are generated by the compiler. The user must provide and link the matching function definitions: ```cpp struct Matrix FLATBUFFERS_FINAL_CLASS : private ::flatbuffers::Table { // ... static ::flatbuffers::Offset Pack( ::flatbuffers::FlatBufferBuilder& _fbb, const NativeMatrix* _o, const ::flatbuffers::rehasher_function_t* _rehasher = nullptr); void UnPackTo( NativeMatrix* _o, const ::flatbuffers::resolver_function_t* _resolver = nullptr) const; } ``` Finally, the following top-level attributes: - `native_include("path")` (at file level): Because the `native_type` attribute can be used to introduce types that are unknown to flatbuffers, it may be necessary to include "external" header files in the generated code. This attribute can be used to directly add an #include directive to the top of the generated code that includes the specified path directly. - `force_align`: this attribute may not be respected in the object API, depending on the aligned of the allocator used with `new`. ## External references An additional feature of the object API is the ability to allow you to load multiple independent FlatBuffers, and have them refer to eachothers objects using hashes which are then represented as typed pointers in the object API. To make this work have a field in the objects you want to referred to which is using the string hashing feature (see `hash` attribute in the [schema](../schema.md) documentation). Then you have a similar hash in the field referring to it, along with a `cpp_type` attribute specifying the C++ type this will refer to (this can be any C++ type, and will get a `*` added). Then, in JSON or however you create these buffers, make sure they use the same string (or hash). When you call `UnPack` (or `Create`), you'll need a function that maps from hash to the object (see `resolver_function_t` for details). ## Using different pointer types By default the object tree is built out of `std::unique_ptr`, but you can influence this either globally (using the `--cpp-ptr-type` argument to `flatc`) or per field (using the `cpp_ptr_type` attribute) to by any smart pointer type (`my_ptr`), or by specifying `naked` as the type to get `T *` pointers. Unlike the smart pointers, naked pointers do not manage memory for you, so you'll have to manage their lifecycles manually. To reference the pointer type specified by the `--cpp-ptr-type` argument to `flatc` from a flatbuffer field set the `cpp_ptr_type` attribute to `default_ptr_type`. ## Using different string type By default the object tree is built out of `std::string`, but you can influence this either globally (using the `--cpp-str-type` argument to `flatc`) or per field using the `cpp_str_type` attribute. The type must support `T::c_str()`, `T::length()` and `T::empty()` as member functions. Further, the type must be constructible from std::string, as by default a std::string instance is constructed and then used to initialize the custom string type. This behavior impedes efficient and zero-copy construction of custom string types; the `--cpp-str-flex-ctor` argument to `flatc` or the per field attribute `cpp_str_flex_ctor` can be used to change this behavior, so that the custom string type is constructed by passing the pointer and length of the FlatBuffers String. The custom string class will require a constructor in the following format: `custom_str_class(const char *, size_t)`. Please note that the character array is not guaranteed to be NULL terminated, you should always use the provided size to determine end of string. ## Reflection (& Resizing) There is experimental support for reflection in FlatBuffers, allowing you to read and write data even if you don't know the exact format of a buffer, and even allows you to change sizes of strings and vectors in-place. The way this works is very elegant; there is actually a FlatBuffer schema that describes schemas (\!) which you can find in `reflection/reflection.fbs`. The compiler, `flatc`, can write out any schemas it has just parsed as a binary FlatBuffer, corresponding to this meta-schema. Loading in one of these binary schemas at runtime allows you traverse any FlatBuffer data that corresponds to it without knowing the exact format. You can query what fields are present, and then read/write them after. For convenient field manipulation, you can include the header `flatbuffers/reflection.h` which includes both the generated code from the meta schema, as well as a lot of helper functions. And example of usage, for the time being, can be found in `test.cpp/ReflectionTest()`. ## Mini Reflection A more limited form of reflection is available for direct inclusion in generated code, which doesn't do any (binary) schema access at all. It was designed to keep the overhead of reflection as low as possible (on the order of 2-6 bytes per field added to your executable), but doesn't contain all the information the (binary) schema contains. You add this information to your generated code by specifying `--reflect-types` (or instead `--reflect-names` if you also want field / enum names). You can now use this information, for example to print a FlatBuffer to text: auto s = flatbuffers::FlatBufferToString(flatbuf, MonsterTypeTable()); `MonsterTypeTable()` is declared in the generated code for each type. The string produced is very similar to the JSON produced by the `Parser` based text generator. You'll need `flatbuffers/minireflect.h` for this functionality. In there is also a convenient visitor/iterator so you can write your own output / functionality based on the mini reflection tables without having to know the FlatBuffers or reflection encoding. ## Storing maps / dictionaries in a FlatBuffer FlatBuffers doesn't support maps natively, but there is support to emulate their behavior with vectors and binary search, which means you can have fast lookups directly from a FlatBuffer without having to unpack your data into a `std::map` or similar. To use it: - Designate one of the fields in a table as they "key" field. You do this by setting the `key` attribute on this field, e.g. `name:string (key)`. You may only have one key field, and it must be of string or scalar type. - Write out tables of this type as usual, collect their offsets in an array or vector. - Instead of `CreateVector`, call `CreateVectorOfSortedTables`, which will first sort all offsets such that the tables they refer to are sorted by the key field, then serialize it. - Now when you're accessing the FlatBuffer, you can use `Vector::LookupByKey` instead of just `Vector::Get` to access elements of the vector, e.g.: `myvector->LookupByKey("Fred")`, which returns a pointer to the corresponding table type, or `nullptr` if not found. `LookupByKey` performs a binary search, so should have a similar speed to `std::map`, though may be faster because of better caching. `LookupByKey` only works if the vector has been sorted, it will likely not find elements if it hasn't been sorted. ## Direct memory access As you can see from the above examples, all elements in a buffer are accessed through generated accessors. This is because everything is stored in little endian format on all platforms (the accessor performs a swap operation on big endian machines), and also because the layout of things is generally not known to the user. For structs, layout is deterministic and guaranteed to be the same across platforms (scalars are aligned to their own size, and structs themselves to their largest member), and you are allowed to access this memory directly by using `sizeof()` and `memcpy` on the pointer to a struct, or even an array of structs. To compute offsets to sub-elements of a struct, make sure they are a structs themselves, as then you can use the pointers to figure out the offset without having to hardcode it. This is handy for use of arrays of structs with calls like `glVertexAttribPointer` in OpenGL or similar APIs. It is important to note is that structs are still little endian on all machines, so only use tricks like this if you can guarantee you're not shipping on a big endian machine (an `assert(FLATBUFFERS_LITTLEENDIAN)` would be wise). ## Access of untrusted buffers The generated accessor functions access fields over offsets, which is very quick. These offsets are not verified at run-time, so a malformed buffer could cause a program to crash by accessing random memory. When you're processing large amounts of data from a source you know (e.g. your own generated data on disk), this is acceptable, but when reading data from the network that can potentially have been modified by an attacker, this is undesirable. For this reason, you can optionally use a buffer verifier before you access the data. This verifier will check all offsets, all sizes of fields, and null termination of strings to ensure that when a buffer is accessed, all reads will end up inside the buffer. Each root type will have a verification function generated for it, e.g. for `Monster`, you can call: ```cpp Verifier verifier(buf, len); bool ok = VerifyMonsterBuffer(verifier); ``` if `ok` is true, the buffer is safe to read. Besides untrusted data, this function may be useful to call in debug mode, as extra insurance against data being corrupted somewhere along the way. While verifying a buffer isn't "free", it is typically faster than a full traversal (since any scalar data is not actually touched), and since it may cause the buffer to be brought into cache before reading, the actual overhead may be even lower than expected. In specialized cases where a denial of service attack is possible, the verifier has two additional constructor arguments that allow you to limit the nesting depth and total amount of tables the verifier may encounter before declaring the buffer malformed. The default is `Verifier(buf, len, 64 /* max depth */, 1000000, /* max tables */)` which should be sufficient for most uses. ## Text & schema parsing Using binary buffers with the generated header provides a super low overhead use of FlatBuffer data. There are, however, times when you want to use text formats, for example because it interacts better with source control, or you want to give your users easy access to data. Another reason might be that you already have a lot of data in JSON format, or a tool that generates JSON, and if you can write a schema for it, this will provide you an easy way to use that data directly. (see the schema documentation for some specifics on the JSON format accepted). Schema evolution compatibility for the JSON format follows the same rules as the binary format (JSON formatted data will be forwards/backwards compatible with schemas that evolve in a compatible way). There are two ways to use text formats: #### Using the compiler as a conversion tool This is the preferred path, as it doesn't require you to add any new code to your program, and is maximally efficient since you can ship with binary data. The disadvantage is that it is an extra step for your users/developers to perform, though you might be able to automate it. flatc -b myschema.fbs mydata.json This will generate the binary file `mydata_wire.bin` which can be loaded as before. #### Making your program capable of loading text directly This gives you maximum flexibility. You could even opt to support both, i.e. check for both files, and regenerate the binary from text when required, otherwise just load the binary. This option is currently only available for C++, or Java through JNI. As mentioned in the section "Building" above, this technique requires you to link a few more files into your program, and you'll want to include `flatbuffers/idl.h`. Load text (either a schema or json) into an in-memory buffer (there is a convenient `LoadFile()` utility function in `flatbuffers/util.h` if you wish). Construct a parser: ```cpp flatbuffers::Parser parser; ``` Now you can parse any number of text files in sequence: ```cpp parser.Parse(text_file.c_str()); ``` This works similarly to how the command-line compiler works: a sequence of files parsed by the same `Parser` object allow later files to reference definitions in earlier files. Typically this means you first load a schema file (which populates `Parser` with definitions), followed by one or more JSON files. As optional argument to `Parse`, you may specify a null-terminated list of include paths. If not specified, any include statements try to resolve from the current directory. If there were any parsing errors, `Parse` will return `false`, and `Parser::error_` contains a human readable error string with a line number etc, which you should present to the creator of that file. After each JSON file, the `Parser::fbb` member variable is the `FlatBufferBuilder` that contains the binary buffer version of that file, that you can access as described above. `samples/sample_text.cpp` is a code sample showing the above operations. ## Threading Reading a FlatBuffer does not touch any memory outside the original buffer, and is entirely read-only (all const), so is safe to access from multiple threads even without synchronisation primitives. Creating a FlatBuffer is not thread safe. All state related to building a FlatBuffer is contained in a FlatBufferBuilder instance, and no memory outside of it is touched. To make this thread safe, either do not share instances of FlatBufferBuilder between threads (recommended), or manually wrap it in synchronisation primitives. There's no automatic way to accomplish this, by design, as we feel multithreaded construction of a single buffer will be rare, and synchronisation overhead would be costly. ## Advanced union features The C++ implementation currently supports vectors of unions (i.e. you can declare a field as `[T]` where `T` is a union type instead of a table type). It also supports structs and strings in unions, besides tables. For an example of these features, see `tests/union_vector`, and `UnionVectorTest` in `test.cpp`. Since these features haven't been ported to other languages yet, if you choose to use them, you won't be able to use these buffers in other languages (`flatc` will refuse to compile a schema that uses these features). These features reduce the amount of "table wrapping" that was previously needed to use unions. To use scalars, simply wrap them in a struct. ## Depth limit of nested objects and stack-overflow control The parser of Flatbuffers schema or json-files is kind of recursive parser. To avoid stack-overflow problem the parser has a built-in limiter of recursion depth. Number of nested declarations in a schema or number of nested json-objects is limited. By default, this depth limit set to `64`. It is possible to override this limit with `FLATBUFFERS_MAX_PARSING_DEPTH` definition. This definition can be helpful for testing purposes or embedded applications. For details see [build](../building.md) of CMake-based projects. ## Dependence from C-locale {#flatbuffers_locale_cpp} The Flatbuffers [grammar](../grammar.md) uses ASCII character set for identifiers, alphanumeric literals, reserved words. Internal implementation of the Flatbuffers depends from functions which depend from C-locale: `strtod()` or `strtof()`, for example. The library expects the dot `.` symbol as the separator of an integer part from the fractional part of a float number. Another separator symbols (`,` for example) will break the compatibility and may lead to an error while parsing a Flatbuffers schema or a json file. The Standard C locale is a global resource, there is only one locale for the entire application. Some modern compilers and platforms have locale-independent or locale-narrow functions `strtof_l`, `strtod_l`, `strtoll_l`, `strtoull_l` to resolve this dependency. These functions use specified locale rather than the global or per-thread locale instead. They are part of POSIX-2008 but not part of the C/C++ standard library, therefore, may be missing on some platforms. The Flatbuffers library try to detect these functions at configuration and compile time: - CMake `"CMakeLists.txt"`: - Check existence of `strtol_l` and `strtod_l` in the ``. - Compile-time `"/include/base.h"`: - `_MSC_VER >= 1900`: MSVC2012 or higher if build with MSVC. - `_XOPEN_SOURCE>=700`: POSIX-2008 if build with GCC/Clang. After detection, the definition `FLATBUFFERS_LOCALE_INDEPENDENT` will be set to `0` or `1`. To override or stop this detection use CMake `-DFLATBUFFERS_LOCALE_INDEPENDENT={0|1}` or predefine `FLATBUFFERS_LOCALE_INDEPENDENT` symbol. To test the compatibility of the Flatbuffers library with a specific locale use the environment variable `FLATBUFFERS_TEST_LOCALE`: ```sh >FLATBUFFERS_TEST_LOCALE="" ./flattests >FLATBUFFERS_TEST_LOCALE="ru_RU.CP1251" ./flattests ``` ## Support of floating-point numbers The Flatbuffers library assumes that a C++ compiler and a CPU are compatible with the `IEEE-754` floating-point standard. The schema and json parser may fail if `fast-math` or `/fp:fast` mode is active. ### Support of hexadecimal and special floating-point numbers According to the [grammar](../grammar.md) `fbs` and `json` files may use hexadecimal and special (`NaN`, `Inf`) floating-point literals. The Flatbuffers uses `strtof` and `strtod` functions to parse floating-point literals. The Flatbuffers library has a code to detect a compiler compatibility with the literals. If necessary conditions are met the preprocessor constant `FLATBUFFERS_HAS_NEW_STRTOD` will be set to `1`. The support of floating-point literals will be limited at compile time if `FLATBUFFERS_HAS_NEW_STRTOD` constant is less than `1`. In this case, schemas with hexadecimal or special literals cannot be used. ### Comparison of floating-point NaN values The floating-point `NaN` (`not a number`) is special value which representing an undefined or unrepresentable value. `NaN` may be explicitly assigned to variables, typically as a representation for missing values or may be a result of a mathematical operation. The `IEEE-754` defines two kind of `NaNs`: - Quiet NaNs, or `qNaNs`. - Signaling NaNs, or `sNaNs`. According to the `IEEE-754`, a comparison with `NaN` always returns an unordered result even when compared with itself. As a result, a whole Flatbuffers object will be not equal to itself if has one or more `NaN`. Flatbuffers scalar fields that have the default value are not actually stored in the serialized data but are generated in code (see [Writing a schema](../schema.md)). Scalar fields with `NaN` defaults break this behavior. If a schema has a lot of `NaN` defaults the Flatbuffers can override the unordered comparison by the ordered: `(NaN==NaN)->true`. This ordered comparison is enabled when compiling a program with the symbol `FLATBUFFERS_NAN_DEFAULTS` defined. Additional computations added by `FLATBUFFERS_NAN_DEFAULTS` are very cheap if GCC or Clang used. These compilers have a compile-time implementation of `isnan` checking which MSVC does not. ## gRPC ### Before you get started Before diving into the FlatBuffers gRPC usage in C++, you should already be familiar with the following: - FlatBuffers as a serialization format - [gRPC](http://www.grpc.io/docs/) usage ### Using the FlatBuffers gRPC C++ library NOTE: The examples below are also in the `grpc/samples/greeter` directory. We will illustrate usage with the following schema: ```c++ title="grpc/samples/greeter/greeter.fbs" --8<-- "https://raw.githubusercontent.com/google/flatbuffers/refs/heads/master/grpc/samples/greeter/greeter.fbs" ``` When we run `flatc`, we pass in the `--grpc` option and generage an additional `greeter.grpc.fb.h` and `greeter.grpc.fb.cc`. Example server code looks like this: ```c++ title="grpc/samples/greeter/server.cpp" --8<-- "https://raw.githubusercontent.com/google/flatbuffers/refs/heads/master/grpc/samples/greeter/server.cpp" ``` Example client code looks like this: ```c++ title="grpc/samples/greeter/client.cpp" --8<-- "https://raw.githubusercontent.com/google/flatbuffers/refs/heads/master/grpc/samples/greeter/client.cpp" ``` --- ## File: docs/source/languages/dart.md Use in Dart {#flatbuffers_guide_use_dart} =========== ## Before you get started Before diving into the FlatBuffers usage in Dart, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including Dart). This page is designed to cover the nuances of FlatBuffers usage, specific to Dart. You should also have read the [Building](../building.md) documentation to build `flatc` and should be familiar with [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). ## FlatBuffers Dart library code location The code for the FlatBuffers Dart library can be found at `flatbuffers/dart`. You can browse the library code on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/dart). ## Testing the FlatBuffers Dart library The code to test the Dart library can be found at `flatbuffers/tests`. The test code itself is located in [dart_test.dart](https://github.com/google/ flatbuffers/blob/master/tests/dart_test.dart). To run the tests, use the [DartTest.sh](https://github.com/google/flatbuffers/ blob/master/tests/DartTest.sh) shell script. *Note: The shell script requires the [Dart SDK](https://www.dartlang.org/tools/sdk) to be installed.* ## Using the FlatBuffers Dart library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers in Dart.* FlatBuffers supports reading and writing binary FlatBuffers in Dart. To use FlatBuffers in your own code, first generate Dart classes from your schema with the `--dart` option to `flatc`. Then you can include both FlatBuffers and the generated code to read or write a FlatBuffer. For example, here is how you would read a FlatBuffer binary file in Dart: First, include the library and generated code. Then read a FlatBuffer binary file into a `List`, which you pass to the factory constructor for `Monster`: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.dart} import 'dart:io' as io; import 'package:flat_buffers/flat_buffers.dart' as fb; import './monster_my_game.sample_generated.dart' as myGame; List data = await new io.File('monster.dat').readAsBytes(); var monster = new myGame.Monster(data); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Now you can access values like this: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.dart} var hp = monster.hp; var pos = monster.pos; ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Differences from the Dart SDK Front End flat_buffers The work in this repository is signfiicantly based on the implementation used internally by the Dart SDK in the front end/analyzer package. Several significant changes have been made. 1. Support for packed boolean lists has been removed. This is not standard in other implementations and is not compatible with them. Do note that, like in the JavaScript implementation, __null values in boolean lists will be treated as false__. It is also still entirely possible to pack data in a single scalar field, but that would have to be done on the application side. 2. The SDK implementation supports enums with regular Dart enums, which works if enums are always indexed at 1; however, FlatBuffers does not require that. This implementation uses specialized enum-like classes to ensure proper mapping from FlatBuffers to Dart and other platforms. 3. The SDK implementation does not appear to support FlatBuffer structs or vectors of structs - it treated everything as a built-in scalar or a table. This implementation treats structs in a way that is compatible with other non-Dart implementations, and properly handles vectors of structs. Many of the methods prefixed with 'low' have been prepurposed to support this. 4. The SDK implementation treats int64 and uint64 as float64s. This implementation does not. This may cause problems with JavaScript compatibility - however, it should be possible to use the JavaScript implementation, or to do a customized implementation that treats all 64 bit numbers as floats. Supporting the Dart VM and Flutter was a more important goal of this implementation. Support for 16 bit integers was also added. 5. The code generation in this offers an "ObjectBuilder", which generates code very similar to the SDK classes that consume FlatBuffers, as well as Builder classes, which produces code which more closely resembles the builders in other languages. The ObjectBuilder classes are easier to use, at the cost of additional references allocated. ## Text Parsing There currently is no support for parsing text (Schema's and JSON) directly from Dart, though you could use the C++ parser through Dart Native Extensions. Please see the C++ documentation for more on text parsing (note that this is not currently an option in Flutter - follow [this issue](https://github.com/flutter/flutter/issues/7053) for the latest). ## Object based API FlatBuffers is all about memory efficiency, which is why its base API is written around using as little as possible of it. This does make the API clumsier (requiring pre-order construction of all data, and making mutation harder). For times when efficiency is less important a more convenient object based API can be used (through `--gen-object-api`) that is able to unpack & pack a FlatBuffer into objects and lists, allowing for convenient construction, access and mutation. To use: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.dart} // Deserialize from buffer into object. MonsterT monster = Monster(flatbuffer).unpack(); // Update object directly like a Dart class instance. print(monster.Name); monster.Name = "Bob"; // Change the name. // Serialize into new flatbuffer. final fbb = Builder(); fbb.Finish(monster.pack(fbb)); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ --- ## File: docs/source/languages/go.md Use in Go {#flatbuffers_guide_use_go} ========= ## Before you get started Before diving into the FlatBuffers usage in Go, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including Go). This page is designed to cover the nuances of FlatBuffers usage, specific to Go. You should also have read the [Building](../building.md) documentation to build `flatc` and should be familiar with [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). ## FlatBuffers Go library code location The code for the FlatBuffers Go library can be found at `flatbuffers/go`. You can browse the library code on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/go). ## Testing the FlatBuffers Go library The code to test the Go library can be found at `flatbuffers/tests`. The test code itself is located in [go_test.go](https://github.com/google/ flatbuffers/blob/master/tests/go_test.go). To run the tests, use the [GoTest.sh](https://github.com/google/flatbuffers/ blob/master/tests/GoTest.sh) shell script. *Note: The shell script requires [Go](https://golang.org/doc/install) to be installed.* ## Using the FlatBuffers Go library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers in Go.* FlatBuffers supports reading and writing binary FlatBuffers in Go. To use FlatBuffers in your own code, first generate Go classes from your schema with the `--go` option to `flatc`. Then you can include both FlatBuffers and the generated code to read or write a FlatBuffer. For example, here is how you would read a FlatBuffer binary file in Go: First, include the library and generated code. Then read a FlatBuffer binary file into a `[]byte`, which you pass to the `GetRootAsMonster` function: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.go} import ( example "MyGame/Example" flatbuffers "github.com/google/flatbuffers/go" "os" ) buf, err := os.ReadFile("monster.dat") // handle err monster := example.GetRootAsMonster(buf, 0) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Now you can access values like this: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.go} hp := monster.Hp() pos := monster.Pos(nil) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In some cases it's necessary to modify values in an existing FlatBuffer in place (without creating a copy). For this reason, scalar fields of a Flatbuffer table or struct can be mutated. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.go} monster := example.GetRootAsMonster(buf, 0) // Set table field. if ok := monster.MutateHp(10); !ok { panic("failed to mutate Hp") } // Set struct field. monster.Pos().MutateZ(4) // This mutation will fail because the mana field is not available in // the buffer. It should be set when creating the buffer. if ok := monster.MutateMana(20); !ok { panic("failed to mutate Hp") } ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The term `mutate` is used instead of `set` to indicate that this is a special use case. All mutate functions return a boolean value which is false if the field we're trying to mutate is not available in the buffer. ## Text Parsing There currently is no support for parsing text (Schema's and JSON) directly from Go, though you could use the C++ parser through cgo. Please see the C++ documentation for more on text parsing. --- ## File: docs/source/languages/java.md Use in Java {#flatbuffers_guide_use_java} ============== ## Before you get started Before diving into the FlatBuffers usage in Java, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including Java). This page is designed to cover the nuances of FlatBuffers usage, specific to Java. You should also have read the [Building](../building.md) documentation to build `flatc` and should be familiar with [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). ## FlatBuffers Java code location The code for the FlatBuffers Java library can be found at `flatbuffers/java/com/google/flatbuffers`. You can browse the library on the [FlatBuffers GitHub page](https://github.com/google/flatbuffers/tree/master/ java/com/google/flatbuffers). ## Testing the FlatBuffers Java libraries The code to test the libraries can be found at `flatbuffers/tests`. The test code for Java is located in [JavaTest.java](https://github.com/google /flatbuffers/blob/master/tests/JavaTest.java). To run the tests, use either [JavaTest.sh](https://github.com/google/ flatbuffers/blob/master/tests/JavaTest.sh) or [JavaTest.bat](https://github.com/ google/flatbuffers/blob/master/tests/JavaTest.bat), depending on your operating system. *Note: These scripts require that [Java](https://www.oracle.com/java/index.html) is installed.* ## Using the FlatBuffers Java library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers in Java.* FlatBuffers supports reading and writing binary FlatBuffers in Java. To use FlatBuffers in your own code, first generate Java classes from your schema with the `--java` option to `flatc`. Then you can include both FlatBuffers and the generated code to read or write a FlatBuffer. For example, here is how you would read a FlatBuffer binary file in Java: First, import the library and generated code. Then, you read a FlatBuffer binary file into a `byte[]`. You then turn the `byte[]` into a `ByteBuffer`, which you pass to the `getRootAsMyRootType` function: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.java} import MyGame.Example.*; import com.google.flatbuffers.FlatBufferBuilder; // This snippet ignores exceptions for brevity. File file = new File("monsterdata_test.mon"); RandomAccessFile f = new RandomAccessFile(file, "r"); byte[] data = new byte[(int)f.length()]; f.readFully(data); f.close(); ByteBuffer bb = ByteBuffer.wrap(data); Monster monster = Monster.getRootAsMonster(bb); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Now you can access the data from the `Monster monster`: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~{.java} short hp = monster.hp(); Vec3 pos = monster.pos(); ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ## Storing dictionaries in a FlatBuffer FlatBuffers doesn't support dictionaries natively, but there is support to emulate their behavior with vectors and binary search, which means you can have fast lookups directly from a FlatBuffer without having to unpack your data into a `Dictionary` or similar. To use it: - Designate one of the fields in a table as the "key" field. You do this by setting the `key` attribute on this field, e.g. `name:string (key)`. You may only have one key field, and it must be of string or scalar type. - Write out tables of this type as usual, collect their offsets in an array. - Instead of calling standard generated method, e.g.: `Monster.createTestarrayoftablesVector`, call `createSortedVectorOfTables` (from the `FlatBufferBuilder` object). which will first sort all offsets such that the tables they refer to are sorted by the key field, then serialize it. - Now when you're accessing the FlatBuffer, you can use the `ByKey` accessor to access elements of the vector, e.g.: `monster.testarrayoftablesByKey("Frodo")`. which returns an object of the corresponding table type, or `null` if not found. `ByKey` performs a binary search, so should have a similar speed to `Dictionary`, though may be faster because of better caching. `ByKey` only works if the vector has been sorted, it will likely not find elements if it hasn't been sorted. ## Text parsing There currently is no support for parsing text (Schema's and JSON) directly from Java, though you could use the C++ parser through native call interfaces available to each language. Please see the C++ documentation for more on text parsing. --- ## File: docs/source/languages/javascript.md Use in JavaScript {#flatbuffers_guide_use_javascript} ================= ## Before you get started Before diving into the FlatBuffers usage in JavaScript, it should be noted that the [Tutorial](../tutorial.md) page has a complete guide to general FlatBuffers usage in all of the supported languages (including JavaScript). This page is specifically designed to cover the nuances of FlatBuffers usage in JavaScript. You should also have read the [Building](../building.md) documentation to build `flatc` and should be familiar with [Using the schema compiler](../flatc.md) and [Writing a schema](../schema.md). ## FlatBuffers JavaScript library code location The generated code for the FlatBuffers JavaScript library can be found at https://www.npmjs.com/package/flatbuffers. To use it from sources: 1. Run `npm run compile` from the main folder to generate JS files from TS. 1. In your project, install it as a normal dependency, using the flatbuffers folder as the source. ## Using the FlatBuffers JavaScript library *Note: See [Tutorial](../tutorial.md) for a more in-depth example of how to use FlatBuffers.* Due to the complexity related with large amounts of JS flavors and module types, native JS support has been replaced in 2.0 by transpilation from TypeScript. Please look at [TypeScript usage](typescript.md) and transpile your sources to desired JS flavor. The minimal steps to get up and running with JS are: 1. Generate TS files from `*.fbs` by using the `--ts` option. 1. Transpile resulting TS files to desired JS flavor using `tsc` (see https://www.typescriptlang.org/download for installation instructions). ~~~{.js} // Note: These require functions are an example - use your desired module flavor. var fs = require('fs'); var flatbuffers = require('../flatbuffers').flatbuffers; var MyGame = require('./monster_generated').MyGame; var data = new Uint8Array(fs.readFileSync('monster.dat')); var buf = new flatbuffers.ByteBuffer(data); var monster = MyGame.Example.Monster.getRootAsMonster(buf); //--------------------------------------------------------------------------// // Note: This code is an example of browser-based HTML/JavaScript. See above // for the code using JavaScript module loaders (e.g. Node.js). // Open the HTML file in a browser and select "monster.dat" from with the // field. ~~~ Now you can access values like this: ~~~{.js} var hp = monster.hp(); var pos = monster.pos(); ~~~ ## Text parsing FlatBuffers in JavaScript There currently is no support for parsing text (Schema's and JSON) directly from JavaScript.