flatbuffers.dev

To get started with FlatBuffers, write a schema in a .fbs file, compile it with the flatc compiler to generate code for your target language, then build and use that generated code in your application. This path applies if you are working in one of the supported languages (C++, C#, C, Go, Java, Kotlin, JavaScript, Lobster, Lua, TypeScript, PHP, Python, Rust, or Swift) and want direct access to serialized data without a parsing step. The official docs organize this as a Quick Start, a Tutorial, the flatc compiler reference, schema (.fbs) documentation, and per-language guides.

Step 1: Confirm FlatBuffers fits your use case

FlatBuffers is a cross-platform serialization library originally created at Google for game development and other performance-critical applications. It is open source on GitHub under the Apache license v2.0. The docs list four reasons to use it:

• No parsing/unpacking: you access serialized data directly.
• Memory efficiency and speed: the only memory needed to access your data is the buffer itself; no heap is required.
• Backwards and forwards compatibility: the schema can evolve over time while remaining compatible with old buffers.
• Small footprint: minimal dependencies and a small code footprint.

If your priority is human-readable data or you work mainly in dynamically typed languages with little advance knowledge of the data shape, the docs note JSON is convenient and readable, and FlatBuffers uses JSON as its optional text format. Protocol Buffers is described as relatively similar, with the primary difference being that FlatBuffers skips the parsing/unpacking step to a secondary representation, often coupled with per-object memory allocation.

Step 2: Write a schema (.fbs)

The schema defines your data types and is the input to the compiler. The docs provide a Schema (.fbs) section covering the overview, schema evolution, and grammar. Start with the Quick Start or Tutorial to see a working schema before designing your own; the evolution rules matter if you expect to change the schema after shipping buffers.

Step 3: Compile with flatc

The flatc compiler turns your .fbs schema into generated code for your chosen language. The docs include a dedicated Compiler (flatc) section, including how to build it. The expected result is generated source files you add to your project; the exact flags and output depend on your target language, so follow the compiler reference rather than guessing options.

Step 4: Build and use the generated code

Pick your language guide from the docs: C, C++, C#, Dart, Go, Java, JavaScript, Kotlin, Lobster, Lua, PHP, Python, Rust, Swift, or TypeScript. Each guide covers the language-specific API for reading and writing buffers. There is also a Supported Configurations page for build environment details.

Verify your setup

A working setup produces generated code from flatc without errors, and your application can read data from a buffer without an unpacking step. If generation fails, check the schema against the grammar section and confirm you are using the compiler version that matches your language guide.

Common sticking points

• Skipping the schema step: the compiler needs a valid .fbs file; the grammar reference is the place to check syntax.
• Assuming schema changes are free: compatibility is a designed feature, but the evolution rules in the schema section define what is safe.
• Expecting JSON-like readability: FlatBuffers is binary by default; JSON is only the optional text format.

Where to go next

Beyond the basics, the docs cover FlatBuffers internals, the intermediate representation, annotating buffers (.afb), benchmarks, and FlexBuffers, a schema-less version. These are useful once your first schema and generated code are working.

Use FlatBuffers when you need to read serialized data without a parsing or unpacking step, want to avoid per-object heap allocation, and expect your schema to evolve over time. It is a strong fit for performance-critical, cross-platform systems such as games. Choose Protocol Buffers or JSON instead when you need maximum ecosystem familiarity, human-readable payloads, or very small generated code.

What FlatBuffers actually gives you

FlatBuffers is an open-source, cross-platform serialization library (Apache license v2.0, originally created at Google for game development). Its documented advantages are:

• No parsing/unpacking step. You access serialized data directly, without unpacking or parsing it into a secondary representation first.
• Memory efficiency and speed. The only memory needed to access your data is the buffer itself; no heap is required.
• Backwards and forwards compatibility. The schema can evolve over time while remaining compatible with old flatbuffers.
• Small footprint. Minimal dependencies and a small code footprint.

It supports C++, C#, C, Go, Java, Kotlin, JavaScript, Lobster, Lua, TypeScript, PHP, Python, Rust, and Swift.

How it compares with Protocol Buffers and JSON

The docs are explicit that JSON remains a better choice for systems that know very little in advance about what data must be stored, and that JSON pairs well with dynamically typed languages such as JavaScript. For statically typed languages, JSON forces you to write more code to access data and adds runtime inefficiency.

A concrete way to decide

Take a task like a game client that receives position and state updates many times per second and must read a few fields from each message. With FlatBuffers, the received buffer is the data: you read fields directly with no unpacking pass and no per-object allocation. With a parse-then-access format, every message pays a conversion cost before the first field is read. If your messages are instead configuration files read once at startup, or payloads you want humans to inspect and edit, JSON's readability is likely worth more than the access speed.

Before you commit

• Schema first. FlatBuffers is schema-driven (.fbs files compiled with flatc), so plan for a build step and a schema evolution policy; the compatibility guarantee depends on following the documented evolution rules.
• Check your language. Confirm your target language appears in the supported list above; the docs also publish per-language guides and supported configurations.
• Measure your case. The docs include benchmarks and a white paper; use them as background, then benchmark your own access pattern, since the benefit comes from skipping the unpacking step.

If those conditions hold, FlatBuffers is the better default; if readability or minimal tooling matters more, stay with JSON or Protocol Buffers.

FlatBuffers supports C++, C#, C, Go, Java, Kotlin, JavaScript, Lobster, Lua, TypeScript, PHP, Python, Rust, and Swift. The official docs list a language-specific guide for each of these, so you can generate and read FlatBuffers data from any of them while keeping the same schema and binary format across platforms.

The full list of supported languages

According to the FlatBuffers documentation, the library is an efficient cross-platform serialization library for:

• C++
• C#
• C
• Go
• Java
• Kotlin
• JavaScript
• Lobster
• Lua
• TypeScript
• PHP
• Python
• Rust
• Swift

The docs also provide a dedicated language guide for each one, covering how to use the generated code in that language.

What "supported" means in practice

Support is not just a claim on a list. The workflow is:

1. Define a schema in a .fbs file.
2. Run the flatc compiler to generate code for your target language(s).
3. Use the generated code in your application to build and read buffers.

Because the same schema and binary format are used everywhere, a buffer written by, say, a C++ service can be read by a Go, Java, or Python client without a conversion step. That is the cross-platform serialization capability the docs describe.

Language guides in the docs

The documentation organizes language material under "Language Guides," with a page per language (C, C++, C#, Dart, Go, Java, JavaScript, Kotlin, Lobster, Lua, PHP, Python, Rust, Swift, TypeScript). Each guide is the place to check for the exact generated-code API and any language-specific setup.

Choosing a language for your project

The right choice depends on where the data is produced and consumed:

If your language is not on the list, the docs do not claim support for it, so plan on one of the listed languages for reading and writing buffers.

Why the language list matters for your design

FlatBuffers' main advantage is that you access serialized data without parsing or unpacking, and the only memory needed to access your data is that of the buffer itself, with no heap required. That property holds across all supported languages, which is why the language list is broad rather than tied to one ecosystem. The schema can also evolve over time while maintaining forwards and backwards compatibility with old buffers, so adding a language to your stack later does not require rewriting existing data.

For setup steps in a specific language, start from that language's guide in the FlatBuffers docs, then use the flatc compiler page for generation options.

FlatBuffers supports schema evolution by letting you change a schema over time while keeping both backwards and forwards compatibility with old flatbuffers. The official docs state this directly as a core feature: "FlatBuffers enables the schema to evolve over time while still maintaining forwards and backwards compatibility with old flatbuffers." The practical condition is that you follow the schema-evolution rules in the Schema (.fbs) section of the docs — compatibility is a property of how you edit the schema, not something the library enforces for you.

What backwards and forwards compatibility mean here

• Backwards compatibility: new code (built from the updated schema) can still read data written with the old schema.
• Forwards compatibility: old code (built from the old schema) can still read data written with the new schema.

Because FlatBuffers data is accessed directly from the buffer without a parsing/unpacking step, readers rely on the schema to locate fields. Evolution rules exist so that a reader looking for a field it knows about still finds it, and a reader that doesn't know about a newer field can safely skip it.

The mechanism, in plain terms

A FlatBuffers buffer is a binary layout described by a schema. When you add a field to a table in the schema, the generated accessors know how to check whether that field is present in a given buffer. Data written before the field existed simply won't contain it, and the accessor reports it as absent rather than reading garbage. That is what makes adding fields safe for old data.

The reverse direction — old code reading new data — works because unknown fields are ignored by readers that don't have accessors for them.

For example, suppose version 1 of a schema defines a Monster table with name and hp. You ship data files. Later you add mana to the table and rebuild. New code reading old files sees mana as absent; old code reading new files ignores mana entirely. Both keep working.

What the docs point you to

The Overview only summarizes the guarantee. The detailed rules — which changes are safe, which require care, and how to handle defaults and removed fields — live in the Schema (.fbs) section, specifically its Evolution page. That is the page to read before making a schema change you intend to keep compatible. The docs list it alongside the schema grammar and language guides, so you can go from the rule to the generated-code behavior in each supported language.

Practical guidance

• Treat the schema as a contract. Decide early which changes you will allow, and check each change against the Evolution guidance before shipping.
• Prefer additive changes when compatibility matters; the Overview's compatibility claim is the baseline, and the Evolution page tells you where the edges are.
• Rebuild generated code with flatc after schema changes, and test with data written by the previous schema version — that is the concrete way to verify backwards compatibility rather than assume it.
• If you need schema-less flexibility instead, the docs cover FlexBuffers separately; that is a different trade-off, not a way to evolve a typed schema.

Where this fits among the stated benefits

Schema evolution is one of four reasons the docs give for using FlatBuffers, alongside direct access without parsing/unpacking, memory efficiency and speed (only the buffer's memory is needed, no heap), and a small footprint with minimal dependencies. The docs also compare FlatBuffers with Protocol Buffers and JSON: Protocol Buffers is described as relatively similar but requiring a parsing/unpacking step to a secondary representation, often with per-object allocation, and with code an order of magnitude bigger. JSON is noted as readable and convenient with dynamically typed languages, but runtime-inefficient when serializing from statically typed languages and requiring more access code. Those comparisons are about access model and efficiency; the compatibility guarantee above is the specific claim to rely on when planning schema changes.

FlatBuffers Docs (flatbuffers.dev) is the official documentation site for FlatBuffers, an open-source, cross-platform serialization library originally created at Google for game development and other performance-critical applications. The site is the reference point for learning how the library works, getting started with it, and using its compiler and language-specific APIs. FlatBuffers is available on GitHub under the Apache license v2.0.

What the Site Covers

The documentation is organized into sections that follow the typical path from first use to advanced work:

• Overview — what FlatBuffers is, why you would use it, and how it compares to alternatives.
• Quick Start Tutorial — a guided first project.
• Compiler (flatc) — building and using the schema compiler.
• Schema (.fbs) — schema overview, evolution, grammar, and language rules.
• Language Guides — per-language usage for C, C++, C#, Dart, Go, Java, JavaScript, Kotlin, Lobster, Lua, PHP, Python, Rust, Swift, and TypeScript.
• Supported Configurations — platform and build configurations.
• White Paper — the design rationale behind the format.
• Advanced — internals, intermediate representation, buffer annotation (.afb), benchmarks, and FlexBuffers (the schema-less variant).
• Contributing — how to take part in the project.

Why Use FlatBuffers

The documentation states four core benefits:

1. Access without parsing or unpacking. You read serialized data directly, with no unpacking step.
2. Memory efficiency and speed. The only memory needed to access your data is the buffer itself; no heap allocation is required.
3. Backwards and forwards compatibility. The schema can evolve over time while remaining compatible with old flatbuffers.
4. Small footprint. Minimal dependencies and a small code footprint.

When FlatBuffers May Not Be the Right Choice

The docs are explicit about alternatives:

• Protocol Buffers is relatively similar, but FlatBuffers avoids the parsing/unpacking step into a secondary representation, which is often coupled with per-object memory allocation. The docs also note that generated code for Protocol Buffers is an order of magnitude bigger.
• JSON is very readable and convenient with dynamically typed languages such as JavaScript. When serializing from statically typed languages, however, JSON has runtime inefficiency and forces you to write more code to access data because of its dynamic-typing serialization system. The docs conclude JSON is only a better choice for systems that have very little to no information ahead of time about what data needs to be stored.

Who the Site Is For

Use flatbuffers.dev if you are evaluating a serialization format for a performance-sensitive system, or if you have already chosen FlatBuffers and need the schema grammar, compiler usage, or the guide for your language. If your priority is human-readable data or maximum flexibility with unknown data shapes, the docs themselves point you toward JSON or FlexBuffers instead.