43 releases (5 stable)

2.0.0-beta.2 Jun 25, 2021
2.0.0-beta May 14, 2021
1.2.2 Apr 7, 2021
1.2.0 Feb 12, 2021
0.1.2 Jun 24, 2015

#7 in Encoding

Download history 20155/week @ 2021-04-08 16846/week @ 2021-04-15 15306/week @ 2021-04-22 16880/week @ 2021-04-29 18369/week @ 2021-05-06 19116/week @ 2021-05-13 18608/week @ 2021-05-20 18988/week @ 2021-05-27 19104/week @ 2021-06-03 19057/week @ 2021-06-10 21878/week @ 2021-06-17 20214/week @ 2021-06-24 19015/week @ 2021-07-01 19000/week @ 2021-07-08 18322/week @ 2021-07-15 16252/week @ 2021-07-22

81,265 downloads per month
Used in 109 crates (68 directly)

MIT license



crates.io crates.io

Encoding and decoding support for BSON in Rust


Useful links


This crate works with Cargo and can be found on crates.io with a Cargo.toml like:

bson = "2.0.0-beta.2"

Overview of BSON Format

BSON, short for Binary JSON, is a binary-encoded serialization of JSON-like documents. Like JSON, BSON supports the embedding of documents and arrays within other documents and arrays. BSON also contains extensions that allow representation of data types that are not part of the JSON spec. For example, BSON has a datetime type and a binary data type.

// JSON equivalent
{"hello": "world"}

// BSON encoding
\x16\x00\x00\x00                   // total document size
\x02                               // 0x02 = type String
hello\x00                          // field name
\x06\x00\x00\x00world\x00          // field value
\x00                               // 0x00 = type EOO ('end of object')

BSON is the primary data representation for MongoDB, and this crate is used in the mongodb driver crate in its API and implementation.

For more information about BSON itself, see bsonspec.org.


BSON values

Many different types can be represented as a BSON value, including 32-bit and 64-bit signed integers, 64 bit floating point numbers, strings, datetimes, embedded documents, and more. To see a full list of possible BSON values, see the BSON specification. The various possible BSON values are modeled in this crate by the Bson enum.

Creating Bson instances

Bson values can be instantiated directly or via the bson! macro:

let string = Bson::String("hello world".to_string());
let int = Bson::Int32(5);
let array = Bson::Array(vec![Bson::Int32(5), Bson::Boolean(false)]);

let string: Bson = "hello world".into();
let int: Bson = 5i32.into();

let string = bson!("hello world");
let int = bson!(5);
let array = bson!([5, false]);

bson! supports both array and object literals, and it automatically converts any values specified to Bson, provided they are Into<Bson>.

Bson value unwrapping

Bson has a number of helper methods for accessing the underlying native Rust types. These helpers can be useful in circumstances in which the specific type of a BSON value is known ahead of time.


let value = Bson::Int32(5);
let int = value.as_i32(); // Some(5)
let bool = value.as_bool(); // None

let value = bson!([true]);
let array = value.as_array(); // Some(&Vec<Bson>)

BSON documents

BSON documents are ordered maps of UTF-8 encoded strings to BSON values. They are logically similar to JSON objects in that they can contain subdocuments, arrays, and values of several different types. This crate models BSON documents via the Document struct.

Creating Documents

Documents can be created directly either from a byte reader containing BSON data or via the doc! macro:

let mut bytes = hex::decode("0C0000001069000100000000").unwrap();
let doc = Document::from_reader(&mut bytes.as_slice()).unwrap(); // { "i": 1 }

let doc = doc! {
   "hello": "world",
   "int": 5,
   "subdoc": { "cat": true },

doc! works similarly to bson!, except that it always returns a Document rather than a Bson.

Document member access

Document has a number of methods on it to facilitate member access:

let doc = doc! {
   "string": "string",
   "bool": true,
   "i32": 5,
   "doc": { "x": true },

// attempt get values as untyped Bson
let none = doc.get("asdfadsf"); // None
let value = doc.get("string"); // Some(&Bson::String("string"))

// attempt to get values with explicit typing
let string = doc.get_str("string"); // Ok("string")
let subdoc = doc.get_document("doc"); // Some(Document({ "x": true }))
let error = doc.get_i64("i32"); // Err(...)

Modeling BSON with strongly typed data structures

While it is possible to work with documents and BSON values directly, it will often introduce a lot of boilerplate for verifying the necessary keys are present and their values are the correct types. serde provides a powerful way of mapping BSON data into Rust data structures largely automatically, removing the need for all that boilerplate.


#[derive(Serialize, Deserialize)]
struct Person {
    name: String,
    age: i32,
    phones: Vec<String>,

// Some BSON input data as a `Bson`.
let bson_data: Bson = bson!({
    "name": "John Doe",
    "age": 43,
    "phones": [
        "+44 1234567",
        "+44 2345678"

// Deserialize the Person struct from the BSON data, automatically
// verifying that the necessary keys are present and that they are of
// the correct types.
let mut person: Person = bson::from_bson(bson_data).unwrap();

// Do things just like with any other Rust data structure.
println!("Redacting {}'s record.", person.name);
person.name = "REDACTED".to_string();

// Get a serialized version of the input data as a `Bson`.
let redacted_bson = bson::to_bson(&person).unwrap();

Any types that implement Serialize and Deserialize can be used in this way. Doing so helps separate the "business logic" that operates over the data from the (de)serialization logic that translates the data to/from its serialized form. This can lead to more clear and concise code that is also less error prone.


We encourage and would happily accept contributions in the form of GitHub pull requests. Before opening one, be sure to run the tests locally; check out the testing section for information on how to do that. Once you open a pull request, your branch will be run against the same testing matrix that we use for our continuous integration system, so it is usually sufficient to only run the integration tests locally against a standalone. Remember to always run the linter tests before opening a pull request.

Running the tests

Integration and unit tests

To actually run the tests, you can use cargo like you would in any other crate:

cargo test --verbose # runs against localhost:27017

Linter Tests

Our linter tests use the nightly version of rustfmt to verify that the source is formatted properly and the stable version of clippy to statically detect any common mistakes. You can use rustup to install them both:

rustup component add clippy --toolchain stable
rustup component add rustfmt --toolchain nightly

To run the linter tests, run the check-clippy.sh and check-rustfmt.sh scripts in the .evergreen directory:

bash .evergreen/check-clippy.sh && bash .evergreen/check-rustfmt.sh

Continuous Integration

Commits to master are run automatically on evergreen.


~77K SLoC