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Typical: data interchange with algebraic data types

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Typical is a data serialization framework. You define data types in a file called a schema, then Typical generates efficient serialization and deserialization code for various languages. The generated code can be used for marshalling messages between services, storing structured data on disk, etc. The compact binary encoding supports forward and backward compatibility between different versions of your schema to accommodate evolving requirements.

Typical can be compared to Protocol Buffers and Apache Thrift. The main difference is that Typical has a more modern type system based on algebraic data types, emphasizing a safer programming style with non-nullable types and exhaustive pattern matching. You'll feel at home if you have experience with languages that embrace this style, such as Rust and Haskell. Typical offers a new solution ("asymmetric" fields) to the classic problem of how to safely add or remove fields in record types without breaking compatibility. The concept of asymmetric fields also solves the dual problem of how to preserve compatibility when adding or removing cases in sum types.

In short, Typical offers two important features that are conventionally thought to be at odds:

  1. Uncompromising type safety
  2. Binary compatibility between schema versions

Typical's design was informed by experience using Protocol Buffers at Google and Apache Thrift at Airbnb. This is not an officially supported product of either company. If you want to support Typical, you can do so here.

Supported programming languages

The following languages are currently supported:

  • Rust
  • TypeScript
  • JavaScript (via TypeScript)


To understand what this is all about, let's walk through an example scenario. Suppose you want to build a simple API for sending emails, and you need to decide how requests and responses will be serialized over the wire. You could use a self-describing format like JSON or XML, but you may want better type safety and performance. Typical has a great story to tell about those things.

Although our example scenario involves a client talking to a server, Typical has no notion of clients or servers. It only deals with serialization and deserialization. Other concerns like networking, encryption, and authentication are outside Typical's purview.

Step 1: Write a schema

You can start by creating a schema file called types.t (or any other name you prefer) with some types for your API:

struct SendEmailRequest {
    to: String = 0
    subject: String = 1
    body: String = 2

choice SendEmailResponse {
    success = 0
    error: String = 1

This schema defines two types: SendEmailRequest and SendEmailResponse. The first type is a struct, which means it describes messages containing a fixed set of fields (in this case, to, subject, and body). The second type is a choice, which means it describes messages containing exactly one field from a fixed set of possibilities (in this case, success and error). Structs and choices are called algebraic data types since they can be understood abstractly as multiplication and addition of types, respectively, but you don't need to know anything about that to use Typical.

Each field has both a name (e.g., body) and an integer index (e.g., 2). The name is just for humans, as only the index is used to identify fields in the binary encoding. You can freely rename fields without worrying about binary incompatibility, as long as you don't change the indices.

Each field also has a type (e.g., String). If the type is missing, as it is for the success field above, then it defaults to a built-in type called Unit. The Unit type holds no information and takes zero bytes to encode.

Once you've written your schema, Typical can format it to ensure consistent orthography such as indentation, letter case, etc. The following command will do it, though it won't have any effect on our example since it's already formatted properly:

typical format types.t

Step 2: Generate code for serialization and deserialization

Now that we've defined some types, we can use Typical to generate the code for serialization and deserialization. For example, you can generate Rust and TypeScript code with the following:

typical generate types.t --rust types.rs --typescript types.ts

Refer to the example projects for how to automate this. In summary:

  • For Rust, you can use a Cargo build script that is executed when you invoke cargo build.
  • For TypeScript, you can use the scripts property of your package.json.

It's not necessary to set up an automated build system to use Typical, but we recommend doing so for convenience.

Step 3: Serialize and deserialize messages

With the code generated in the previous section, let's write a simple Rust program to serialize a message. We can write the message to an in-memory buffer, a socket, or anything that implements std::io::Write. For this example, we'll stream the data to a file.

let message = SendEmailRequestOut {
    to: "typical@example.com".to_owned(),
    subject: "I love Typical!".to_owned(),
    body: "It makes serialization easy and safe.".to_owned(),

let file = BufWriter::new(File::create(FILE_PATH)?);

Another program could read the file and deserialize the message as follows:

let file = BufReader::new(File::open(FILE_PATH)?);
let message = SendEmailRequestIn::deserialize(file)?;

println!("to: {}", message.to);
println!("subject: {}", message.subject);
println!("body: {}", message.body);

The full code for this example can be found here. The TypeScript version is here.

We'll see in the next section why our SendEmailRequest type turned into SendEmailRequestOut and SendEmailRequestIn.

Required, optional, and asymmetric fields

Fields are required by default. This is an unusual design decision, since required fields are often thought to cause trouble for backward and forward compatibility between schema versions. Let's explore this topic in detail and see how Typical deals with it.

Adding or removing required fields is risky

Experience has taught us that it can be difficult to introduce a required field to a type that is already being used. For example, suppose your email API is up and running, and you want to add a new from field to the request type:

struct SendEmailRequest {
    to: String = 0

    # A new required field
    from: String = 3

    subject: String = 1
    body: String = 2

The only safe way to roll out this change (as written) is to finish updating all clients before beginning to update any servers. Otherwise, a client still running the old code might send a request to an updated server, which promptly rejects the request because it lacks the new field.

That kind of rollout may not be feasible. You might not be in control of the order in which clients and servers are updated. Or, perhaps the clients and servers are updated together, but not atomically. The client and the server might even be part of the same replicated service, so it wouldn't be possible to update one before the other no matter how careful you are.

Removing a required field can present analogous difficulties. Suppose, despite the aforementioned challenges, you were able to successfully introduce from as a required field. Now, an unrelated issue is forcing you to roll it back. That's just as dangerous as adding it was in the first place: if a client gets updated before a server, that client may then send the server a message without the from field, which the server will reject since it still expects that field to be present.

Promoting optional fields to required or vice versa is risky

A somewhat safer way to introduce a required field is to first introduce it as optional, and later promote it to required. For example, you can safely introduce this change:

struct SendEmailRequest {
    to: String = 0

    # A new optional field
    optional from: String = 3

    subject: String = 1
    body: String = 2

You would then update clients to set the new field. Once you're confident that the new field is always being set, you can promote it to required.

The trouble is that, as long as the field is optional, you can't rely on the type system to ensure the new field is always being set. Even if you're confident you've updated the client code appropriately, a teammate unaware of your efforts might introduce a new instance of the field being unset before you have the chance to promote it to required.

You can run into similar trouble when demoting a required field to optional. Once the field has been demoted, clients might stop setting the field before the servers can handle its absence, unless you can be sure the servers are updated first.

Making every field optional isn't ergonomic or safe

Due to the trouble associated with required fields, the conventional wisdom is simply to never use them; all fields should be declared optional. For example:

struct SendEmailRequest {
    optional to: String = 0
    optional subject: String = 1
    optional body: String = 2

However, this advice ignores the reality that some things really are semantically required, even if they aren't required according to the schema. An API cannot be expected to work if it doesn't have the data it needs. Having semantically required fields declared optional places extra burden on both writers and readers: writers can't rely on the type system to prevent them from accidentally forgetting to set the fields, and readers must address the case of the fields being missing to satisfy the type checker even though those fields are always supposed to be set.

Asymmetric fields can safely be promoted to required and vice versa

To help you safely add and remove required fields, Typical offers an intermediate state between optional and required: asymmetric. An asymmetric field in a struct is considered required for the writer, but optional for the reader. Unlike optional fields, an asymmetric field can safely be promoted to required and vice versa.

Let's make that more concrete with our email API example. Instead of directly introducing the from field as required, we first introduce it as asymmetric:

struct SendEmailRequest {
    to: String = 0

    # A new asymmetric field
    asymmetric from: String = 3

    subject: String = 1
    body: String = 2

Let's take a look at the generated code for this schema; we'll use Rust for this example. The generated code has two flavors of our SendEmailRequest type, one for serialization and another for deserialization:

pub struct SendEmailRequestOut {
    pub to: String,
    pub from: String,
    pub subject: String,
    pub body: String,

pub struct SendEmailRequestIn {
    pub to: String,
    pub from: Option<String>,
    pub subject: String,
    pub body: String,

impl Serialize for SendEmailRequestOut {
    // Serialization code omitted.

impl Deserialize for SendEmailRequestIn {
    // Deserialization code omitted.

We can see the effect of from being an asymmetric field: its type is String in SendEmailRequestOut, but its type is Option<String> in SendEmailRequestIn. That means clients (which use SendEmailRequestOut) are now required to set the new field, but servers (which use SendEmailRequestIn) aren't yet allowed to rely on it. Once this change has been rolled out (at least to clients), we can safely promote the field to required in a subsequent change.

It works in reverse too. Suppose we now want to remove a required field. It may be unsafe to delete the field directly, since then clients might stop setting it before servers can handle its absence. But we can demote it to asymmetric, which forces servers to consider it optional and handle its potential absence, even though clients are still required to set it. Once that change has been rolled out (at least to servers), we can confidently delete the field (or demote it to optional), as the servers no longer rely on it.

In some situations, a field might stay in the asymmetric state for months, say, if you're waiting for a sufficient fraction of your users to update your mobile app. Typical can help immensely in those situations by preventing new code which uses the field inappropriately from being introduced during that period.

Choices can have optional and asymmetric fields too

Our discussion so far has been framed around structs, since they are familiar to most programmers. Now we turn our attention to the other side of the coin: choices.

The code generated for choices supports case analysis, so clients can take different actions depending on which field was set. Happily, this is done in a way that ensures you've handled all the cases. This is called exhaustive pattern matching, and it's a great feature to help you write correct code. For example, in Rust we might pattern match on a response from our email API as follows:

fn handle_response(response: SendEmailResponseIn) {
    match response {
        Success => println!("The email was sent!"),
        Error(message) => println!("An error occurred: {}", message),

If we add a new field to the SendEmailResponse choice, then the Rust compiler will force us to acknowledge the new case here. That's a good thing! But when serializing and deserializing data, the rigor of exhaustive pattern matching can be a double-edged sword: readers will fail to deserialize a choice if they don't recognize the field that was set.

That means it's unsafe, in general, to add or remove required fields in a choice—just like with structs. If you add a required field, updated writers may start setting it before non-updated readers know how to handle it. Conversely, if you remove a required field, updated readers will no longer be able to handle it even though non-updated writers may still be setting it.

Not to worry—choices can have optional and asymmetric fields, just like structs!

Optional and asymmetric fields in choices must be constructed with a fallback field, which is used as a backup in case the reader doesn't recognize or doesn't want to handle the original field. Readers aren't required to handle optional fields; hence, optional. Note that the fallback itself might be optional or asymmetric, in which case the fallback must have a fallback, etc. Eventually, the fallback chain ends with a required field. Readers will scan the fallback chain for the first field they recognize.

Note: An optional field in a choice isn't simply a field with an option type/nullable type. The word "optional" here means that readers can ignore it and use a fallback instead, not that its payload might be missing. It's tempting to assume things work the same way for structs and choices, but in reality things work in dual ways: optionality for a struct relaxes the burden on writers (they don't have to set the field), whereas for a choice the burden is relaxed on readers (they don't have to handle the field).

Although asymmetric fields in choices must be constructed with a fallback, the fallback isn't exposed to readers; they must be able to handle the asymmetric field itself. Thus, asymmetric fields in choices behave like optional fields for writers and like required fields for readers—the opposite of their behavior in structs. Duality strikes again! As with structs, asymmetric fields in choices can safely be promoted to required and vice versa. To emphasize: that's the sole purpose of asymmetric fields.

Consider a more elaborate version of our API response type:

choice SendEmailResponse {
    success = 0
    error: String = 1

    # A more specific type of error for curious clients
    optional authentication_error: String = 2

    # To be promoted to required in the future
    asymmetric please_try_again = 3

Let's inspect the generated code. As with structs, we end up with separate types for serialization and deserialization:

pub enum SendEmailResponseOut {
    AuthenticationError(String, Box<SendEmailResponseOut>),

pub enum SendEmailResponseIn {
    AuthenticationError(String, Box<SendEmailResponseIn>),

impl Serialize for SendEmailResponseOut {
    // Serialization code omitted.

impl Deserialize for SendEmailResponseIn {
    // Deserialization code omitted.

The required cases (Success and Error) are as you would expect in both types.

The optional case, AuthenticationError, has a String for the error message and a second payload for the fallback. A writer might set the less specific Error case as the fallback. Readers can use the fallback if they don't wish to handle the optional case, and readers which don't even know about the optional case will use the fallback automatically.

The asymmetric case, PleaseTryAgain, also requires writers to provide a fallback. However, readers don't get to use it. This is a safe intermediate state to use before changing the field to required (which will stop requiring writers to provide a fallback) or changing the field from required to optional/deleting it (which will stop readers from having to handle it).

What about default values?

Typical has no notion of a "default" value for each type. This means, for example, if a reader sees the value 0 for a field, it can be confident that this value was explicitly set by a writer, and that the writer didn't just accidentally forget to set it. Zeroes, empty strings, empty arrays, and so on aren't semantically special in any way.

Summary of what kinds of schema changes are safe

Assuming each schema change takes finite time to propagate, any user-defined type can safely be migrated to any other user-defined type through a series of backward and forward compatible changes. Here are the rules for what is allowed in a single change:

  • You can safely rename and reorder fields, as long as you don't change their indices.
  • You can safely add and remove optional and asymmetric fields.
  • You can safely convert asymmetric fields to optional or required and vice versa.
  • You can safely convert a struct with exactly one field, which must be required, into a choice with just that field and vice versa. This type of change is rare, but is needed to guarantee that any user-defined type can be eventually migrated to any other user-defined type.
  • No other changes are guaranteed to be safe.

In mathematical terms, these rules define a homogeneous compatibility relation over schemas which is reflexive (every schema is compatible with itself) and symmetric (forward compatibility and backward compatibility imply each other), but not transitive (two individually safe schema changes aren't necessarily safe as a single change). In particular, symmetry is the crucial property that makes Typical safer than other frameworks.

Schema reference

A schema contains only two kinds of things: imports and user-defined types. Any imports must come before user-defined types. Whitespace is ignored.


You don't need to fit all your type definitions in one schema file. You can organize your types into separate schema files at your leisure, and then import schemas from other schemas. For example, suppose you have a schema called email_util.t with the following contents:

struct Address {
    local_part: String = 0
    domain: String = 1

Then you can import it from another file, such as our types.t file:

import 'email_util.t'

struct SendEmailRequest {
    to: email_util.Address = 0
    subject: String = 1
    body: String = 2

You only need to run Typical on types.t. The generated code will include types from both types.t and email_util.t, since the former imports the latter.

Import paths are considered relative to the directory containing the schema doing the importing. Typical has no notion of a "top-level" directory on which all paths are based.

A useful convention is to create a types.t schema that imports all the other schemas, directly or indirectly. Then it's clear which schema to give to Typical for code generation. Alternatively, in a large organization, you might have a separate top-level schema per project that imports only the types needed by that project. These are merely conventions, as Typical has no intrinsic notion of "project".

If you import two schemas with the same name from different directories, you'll need to disambiguate usages of those schemas. Suppose, for example, you attempted the following:

import 'apis/email.t'
import 'util/email.t'

struct Employee {
    name: String = 0

    # Uh oh! Which schema is this type from?
    email: email.Address = 1

Fortunately, Typical will tell you about this problem and ask you to clarify what you mean. You can do so with import aliases as follows:

import 'apis/email.t' as email_api
import 'util/email.t' as email_util

struct Employee {
    name: String = 0
    email: email_util.Address = 1

User-defined types

Every user-defined type is either a struct or a choice, and they have the same abstract syntax: a name, a list of fields, and an optional list of indices of deleted fields. Here's are some examples of user-defined types:

import 'apis/email.t'
import 'net/ip.t'

choice DeviceIpAddress {
    static_v4: ip.V4Address = 0
    static_v6: ip.V6Address = 1
    dynamic = 2

struct Device {
    hostname: String = 0
    asymmetric ip_address: DeviceIpAddress = 1
    optional owner: email.Address = 2


A field consists of an optional rule, a human-readable name, an optional type, and an index.

The rule, if present, is either optional or asymmetric. The absence of a rule indicates that the field is required.

The name is a human-readable identifier for the field. It's used to refer to the field in code, but it's never encoded on the wire and can safely be renamed at will. The size of the name doesn't affect the size of the encoded messages, so be as descriptive as you want.

The type, if present, is either a built-in type (e.g., String), the name of a user-defined type in the same schema (e.g., DeviceIpAddress), or the name of an import and the name of a type from the schema corresponding to that import (e.g., email.Address). If the type is missing, it defaults to Unit. This can be used to create traditional enumerated types:

choice Weekday {
    monday = 0
    tuesday = 1
    wednesday = 2
    thursday = 3
    friday = 4

The index is a non-negative integer which is required to be unique within the type. The largest possible index is 4,611,686,018,427,387,903 (i.e., 2^62 - 1). The indices aren't required to be consecutive or in any particular order, but starting with consecutive indices is a good convention.

Deleted fields

If you delete a field, you must be careful not to reuse that field's index for any new fields as long as there are messages still containing the deleted field. Otherwise, the old field would be decoded as the new field, which is likely to result in deserialization errors and is almost certainly not what you want. To avoid this, you can reserve the indices of deleted fields to prevent them from being reused. For example, if we delete the ip_address and owner fields from the Device struct above, we can reserve their indices as follows:

struct Device {
    hostname: String = 0

    deleted 1 2

Typical will then prevent us from introducing new fields with those indices.

Built-in types

The following built-in types are supported:

  • Unit is a type which holds no information. It's mainly used for the fields of choices that represent enumerated types.
  • F64 is the type of double-precision floating-point numbers as defined by IEEE 754.
  • U64 is the type of integers in the range [0, 2^64).
  • S64 is the type of integers in the range [-2^63, 2^63).
  • Bool is the type of Booleans.
    • You could define your own Boolean type as a choice with two fields, and it would use the exact same space on the wire. However, the built-in Bool type is often more convenient to use, since it corresponds to the native Boolean type in the generated code.
  • Bytes is the type of binary blobs.
  • String is the type of Unicode text.
  • Arrays (e.g., [String]) are the types of sequences of some other type. Arrays can be nested (e.g., [[String]]).


Comments can be used to add helpful context to your schemas. A comment begins with a # and continues to the end of the line, as with Python, Ruby, Perl, etc.

Unlike with most programming languages, comments in Typical schemas are associated with specific items. Specifically, comments are attached to structs, choices, individual fields, or entire schema files. The following schema demonstrates all the contexts in which comments may be used:

# This file contains types relating to a hypothetical email sending API.

# A request to send an email
struct SendEmailRequest {
    # To whom the email is addressed
    to: String = 0

    # The subject line of the email
    subject: String = 1

    # The contents of the email
    body: String = 2

# The result of attempting to send an email
choice SendEmailResponse {
    # The email was delivered
    success = 0

    # There was a problem sending the email
    error: String = 1


An identifier (the name of a type, field, or import) must start with a letter, and every subsequent character must be a letter, an underscore, or a digit. If you want to use a keyword (e.g., choice) as an identifier, you can do so by prefixing it with a $ (e.g., $choice). The $ isn't included in the generated code.


The generated deserialization code is designed to be safe from malicious inputs in the sense that it protects against unsafe memory accesses like buffer over-reading, buffer overflowing, and arbitrary code execution.

To mitigate memory-based denial-of-service attacks, it's good practice to reject implausibly large messages rather than attempting to deserialize them. In general, you can expect the size of a deserialized message in memory to be within the same order of magnitude as the size of the corresponding serialized message on the wire. However, there is one exception: for values of type [Unit] (array of units), only the number of elements is encoded, since the Unit values themselves take up zero bytes on the wire. If a field with that type is expected, an attacker can force the deserialization logic to reconstruct arbitrarily large arrays of units (see billion laughs attack). For this reason, we strongly recommend avoiding the use of [Unit] in your schema if you intend to consume untrusted inputs. This isn't a major loss, however, since that type is generally useless anyway. It's only supported for the uniformity of the type system; arrays can contain anything, even if certain types of arrays have no practical purpose.

Please report any security issues to typical-security@googlegroups.com.

Code generation

Each code generator produces a single self-contained source file regardless of the number of schema files. The example projects demonstrate how to use the code generated for each language. The sections below contain some language-specific remarks.


  • Typical's type system maps straightforwardly to Rust's structs and enums, but with slightly different naming conventions. All Typical types are written in UpperCamelCase (e.g., String), whereas Rust uses a combination of that and lower_snake_case (e.g., u64). Note that Typical's integer types are called S64 and U64 ("S" for signed, "U" for unsigned), but the respective types in Rust are i64 and u64 ("i" for integer, "u" for unsigned).

JavaScript and TypeScript

  • The generated code runs in Node.js and modern web browsers. Older browsers can be targeted with tools like Babel. For web applications, it's sensible to minify the generated code along with your other application code.

  • The generated code never uses reflection or dynamic code evaluation, so it works in Content Security Policy-restricted environments.

  • Typical's integer types map to bigint rather than number. It's safe to use integers to represent money or other quantities that shouldn't be rounded. Typical's F64 type maps to number, as one would expect.

  • The generated functions never throw exceptions when given well-typed arguments. The deserialize functions can return an Error to signal failure, and TypeScript requires callers to acknowledge that possibility.

  • The generated code exports a function called unreachable which can be used to perform exhaustive pattern matching. For example, suppose you have the following schema:

    struct Square {
        side_length: F64 = 0
    struct Rectangle {
        width: F64 = 0
        height: F64 = 1
    struct Circle {
        radius: F64 = 0
    choice Shape {
        square: Square = 0
        rectangle: Rectangle = 1
        circle: Circle = 2

    Then you might pattern match on a Shape as follows:

    import { Types, unreachable } from '../generated/types';
    function area(shape: Types.ShapeIn): number {
      switch (shape.$field) {
        case 'square':
          return shape.square.sideLength * shape.square.sideLength;
        case 'rectangle':
          return shape.rectangle.width * shape.rectangle.height;
        case 'circle':
          return Math.PI * shape.circle.radius * shape.circle.radius;
          return unreachable(shape);

    If a new field is added to the choice, TypeScript will force you to add the appropriate case to that switch statement.

Binary encoding

The following sections describe how Typical serializes your data. In most cases, Typical's encoding scheme is more compact than that of Protocol Buffers and Apache Thrift thanks to smaller field headers, a more efficient variable-width integer encoding, and a trick that allows some information to be inferred from the size of a field rather than being encoded explicitly.

Variable-width integers

Many situations require Typical to serialize integer values, e.g., for encoding field indices, buffer sizes, and user-provided integer data. Where appropriate, Typical uses a variable-width encoding that allows smaller integers to use fewer bytes. With the distributions that occur in practice, most integers end up consuming only a single byte.

Unsigned variable-width integers

The valid range for unsigned variable-width integers is [0, 2^64).

Let n be the integer to be encoded. The encoding scheme described below is little-endian, so the last byte contains the most significant bits.

  • If 0 <= n < 128:
    • Embed the 7 bits of n in 1 byte as follows: xxxxxxx1.
  • If 128 <= n < 16,512
    • Embed the 14 bits of n - 128 in 2 bytes as follows: xxxxxx10 xxxxxxxx.
  • If 16,512 <= n < 2,113,664
    • Embed the 21 bits of n - 16,512 in 3 bytes as follows: xxxxx100 xxxxxxxx xxxxxxxx.
  • If 2,113,664 <= n < 270,549,120
    • Embed the 28 bits of n - 2,113,664 in 4 bytes as follows: xxxx1000 xxxxxxxx xxxxxxxx xxxxxxxx.
  • If 270,549,120 <= n < 34,630,287,488
    • Embed the 35 bits of n - 270,549,120 in 5 bytes as follows: xxx10000 xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx.
  • If 34,630,287,488 <= n < 4,432,676,798,592
    • Embed the 42 bits of n - 34,630,287,488 in 6 bytes as follows: xx100000 xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx.
  • If 4,432,676,798,592 <= n < 567,382,630,219,904
    • Embed the 49 bits of n - 4,432,676,798,592 in 7 bytes as follows: x1000000 xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx.
  • If 567,382,630,219,904 <= n < 72,624,976,668,147,840
    • Embed the 56 bits of n - 567,382,630,219,904 in 8 bytes as follows: 10000000 xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx.
  • If 72,624,976,668,147,840 <= n < 18,446,744,073,709,551,616
    • Embed the 64 bits of n - 72,624,976,668,147,840 in 9 bytes as follows: 00000000 xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx xxxxxxxx.

The number of trailing zero bits in the first byte indicates how many subsequent bytes there are. This allows the number of bytes in an encoded integer to be efficiently determined with a single instruction (e.g., BSF or TZCNT) on most modern processors.

This variable-width integer encoding is similar to the "base 128 varints" used by Protocol Buffers and Thrift's compact protocol. However, Typical's encoding differs in two ways:

  1. For time efficiency reasons, Typical moves all the continuation bits to the first byte. This allows the continuation bits to be counted with a single CPU instruction on most processors, as mentioned above.
  2. For space efficiency reasons, Typical uses a technique called bijective numeration, which uses fewer bytes in some cases and never uses more bytes than the aforementioned base 128 varint encoding. For example, the number 16,500 uses 2 bytes in Typical's encoding, but 3 bytes in the encoding used by Protocol Buffers and Thrift's compact protocol.

Signed variable-width integers

The valid range for signed variable-width integers is [-2^63, 2^63).

Typical converts signed integers into an unsigned "ZigZag" representation, and then encodes the unsigned result as described above. The ZigZag representation converts signed integers with small magnitudes into unsigned integers with small magnitudes, and signed integers with large magnitudes into unsigned integers with large magnitudes. This allows signed integers with small magnitudes to be encoded using fewer bytes.

Specifically, the ZigZag representation of a two's complement 64-bit integer n is (n >> 63) ^ (n << 1), where >> is an arithmetic shift. The inverse operation is (n >> 1) ^ -(n & 1), where >> is a logical shift.

To give you a sense of how it works, the ZigZag representations of the numbers (0, -1, 1, -2, 2) are (0, 1, 2, 3, 4), respectively.

User-defined types


A struct is encoded as the contiguous arrangement of (header, value) pairs, one pair per field, where the value is encoded according to its type and the header is encoded as one or two parts:

  • The first part of the header is an unsigned integer tag, which is encoded as a variable-width integer. The meaning of the tag is as follows:
    • The two least significant bits of the tag (not its variable-width encoding) are called the size mode and indicate how to compute the size of the value:
      • 0: The size of the value is 0 bytes.
      • 1: The size of the value is 8 bytes.
      • 2: The value is encoded as a variable-width integer, so its size can be determined from its first byte. The size of the value is neither 0 nor 8 bytes, since otherwise the size mode would be 0 or 1, respectively.
      • 3: The size of the value is given by the second part of the header (below). It's neither 0 nor 8 bytes, since otherwise the size mode would be 0 or 1, respectively. The value isn't encoded as a variable-width integer, since otherwise the size mode would be 2.
    • The remaining bits of the tag (not its variable-width encoding) represent the index of the field as an unsigned integer.
  • The second part of the header, if applicable, is the size of the value encoded as a variable-width integer. It's only present if the size mode is 3.

For fields of type Unit, F64, U64, S64, or Bool for which the index is less than 32, the header is encoded as a single byte.

A struct must follow these rules:

  • Encoding rules:
    • Optional fields may be missing, but required and asymmetric fields must be present.
  • Decoding rules:
    • All required fields must be present, but optional and asymmetric fields may be missing.
    • Unrecognized fields are ignored.


A choice is encoded in the same way as a struct, but with different rules:

  • Encoding rules:
    • At least one required field must be present.
  • Decoding rules:
    • At least one required or asymmetric field must be present.
    • The first field recognized by the reader is used.

For a simple enumerated type (such as Weekday above), a field with an index less than 32 takes up a single byte.

Built-in types

  • Unit takes 0 bytes to encode.
  • F64 is normally encoded in the little-endian double-precision floating-point format defined by IEEE 754. Thus, it normally takes 8 bytes to encode. However, for field values (rather than, say, elements of an array), positive zero is encoded as 0 bytes.
  • U64 is normally encoded as a variable-width integer. Thus, it normally takes 1-9 bytes to encode, depending on the value. However, for field values (rather than, say, elements of an array), 0 is encoded as 0 bytes, and values greater than or equal to 567,382,630,219,904 are encoded as fixed-width 8-byte little-endian integers.
  • S64 is first converted into the unsigned ZigZag representation, which is then encoded in the same way as a U64, including the special behavior for field values if applicable.
  • Bool is first converted into an integer with 0 representing false and 1 representing true. The value is then encoded in the same way as a U64, including the special behavior for field values if applicable.
  • Bytes is encoded verbatim.
  • String is encoded as UTF-8. The original code point sequence is preserved; no normalization is performed.
  • Arrays (e.g., [U64]) are encoded in one of three ways, depending on the element type:
    • Arrays of Unit are represented by the number of elements encoded the same way as a U64, including the special behavior for field values if applicable. Since the elements (of type Unit) take 0 bytes to encode, there's no way to infer the number of elements from the size of the buffer. Thus, it's encoded explicitly.
    • Arrays of F64, U64, S64, or Bool are represented as the contiguous arrangement of the respective encodings of the elements. The number of elements isn't explicitly encoded.
    • Arrays of any other type (Bytes, String, nested arrays, or nested messages) are encoded as the contiguous arrangement of (size, element) pairs, where size is the number of bytes of the encoded element and is encoded as a variable-width integer. The element is encoded according to its type. The number of elements isn't explicitly encoded.

Notice that several types can take advantage of a more compact representation when they are used for the values of fields. For example, a variable-width integer takes 1-9 bytes to encode, but U64 and S64 fields only take 0-8 bytes to encode, not including the field header. This may seem impossible—the resolution to this paradox is that the extra information comes from the size mode of the field header.


We have coarse-grained benchmarks here for each code generator. The data below were averaged over 3 runs on a 2022 MacBook Air with the Apple M2 chip and 8 GB of RAM. The Rust benchmark was compiled by Rust 1.69.0 with --release. The TypeScript benchmark was transpiled to JavaScript by TypeScript 4.5.5 and run with Node.js 18.16.0.

One benchmark serializes and deserializes a large message containing several hundred megabytes of text:

Rust TypeScript
Per-thread serialization rate 7.258 GiB/s 3.345 GiB/s
Per-thread deserialization rate 2.141 GiB/s 2.408 GiB/s

Another benchmark repeatedly serializes and deserializes a pathological message containing many small and deeply nested values:

Rust TypeScript
Per-thread serialization rate 632.890 MiB/s 42.953 MiB/s
Per-thread deserialization rate 205.773 MiB/s 2.061 MiB/s

These benchmarks represent two extremes. Real-world performance will be somewhere in the middle.


Once Typical is installed, you can use it to generate code for a schema called types.t with the following:

typical generate types.t --rust types.rs --typescript types.ts

Here are the supported command-line options:

    typical <SUBCOMMAND>

    -h, --help
            Prints help information

    -v, --version
            Prints version information

            Formats a schema and its transitive dependencies

            Generates code for a schema and its transitive dependencies

            Prints this message or the help of the given subcommand(s)

            Prints a shell completion script. Supports Zsh, Fish, Zsh, PowerShell, and Elvish.

In particular, the generate subcommand has the following options:

    typical generate [FLAGS] [OPTIONS] <SCHEMA_PATH>

    -h, --help            Prints help information
        --list-schemas    Lists the schemas imported by the given schema (and the given schema

        --rust <PATH>          Sets the path of the Rust file to emit
        --typescript <PATH>    Sets the path of the TypeScript file to emit

    <SCHEMA_PATH>    Sets the path of the schema

Installation instructions

Installation on macOS or Linux (AArch64 or x86-64)

If you're running macOS or Linux (AArch64 or x86-64), you can install Typical with this command:

curl https://raw.githubusercontent.com/stepchowfun/typical/main/install.sh -LSfs | sh

The same command can be used again to update to the latest version.

The installation script supports the following optional environment variables:

  • VERSION=x.y.z (defaults to the latest version)
  • PREFIX=/path/to/install (defaults to /usr/local/bin)

For example, the following will install Typical into the working directory:

curl https://raw.githubusercontent.com/stepchowfun/typical/main/install.sh -LSfs | PREFIX=. sh

If you prefer not to use this installation method, you can download the binary from the releases page, make it executable (e.g., with chmod), and place it in some directory in your PATH (e.g., /usr/local/bin).

Installation on Windows (AArch64 or x86-64)

If you're running Windows (AArch64 or x86-64), download the latest binary from the releases page and rename it to typical (or typical.exe if you have file extensions visible). Create a directory called Typical in your %PROGRAMFILES% directory (e.g., C:\Program Files\Typical), and place the renamed binary in there. Then, in the "Advanced" tab of the "System Properties" section of Control Panel, click on "Environment Variables..." and add the full path to the new Typical directory to the PATH variable under "System variables". Note that the Program Files directory might have a different name if Windows is configured for a language other than English.

To update an existing installation, simply replace the existing binary.

Installation with Homebrew

If you have Homebrew, you can install Typical as follows:

brew install typical

You can update an existing installation with brew upgrade typical.

Installation with Cargo

If you have Cargo, you can install Typical as follows:

cargo install typical

You can run that command with --force to update an existing installation.


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