lando

🚧 maintenance mode ahead 🚧

As of this announcement AWS not officialy supports Rust through this project. As mentioned below this projects goal was to work with and not against the strong lambda ecosystem. As a result I'm merging efforts with the official AWS Rust lambda project. Thank you for your interest and support with lando! We've still got a great road ahead.

Run Rustlang http applications on AWS Lambda with API Gateway

#[macro_use] extern crate lando;

gateway!(|_, _| {
  Ok("👋 Hello, what have we here?")
});

🤔 about

Lando is a crate for building serverless Rustlang HTTP applications.

The rustlang ecosystem has some really great HTTP server crates. Most of them bundle servers that parse HTTP messages, listen on ports and manage network connections leaving it up to you to manage hosting, scaling, monitoring and operations in addition to your application code.

Lando is different. Lando's sole focus is on writing applications. It shifts the undifferentiated heavy lifting and responsibility of managing servers to AWS. Put more directly, AWS lambda free's you up to run code without thinking about servers.

Lando's embraces the Rust community's http crate as it's core interface for API Gateway. Lando extends the existing work of the crowbar crate which provides needed lower level machinery for easily deploying Rust applications with one of lamdba's lowest overhead runtimes, Python 3.6. Lando targets API Gateway triggered lambdas. Checkout crowbar if you're building applications for one of Lambda's many other triggers.

A large and mature ecosystem of tooling for AWS lambda exists and works well, including workflow tools like the serverless toolkit. Because these tools are likely to already exist within organizations, the barrier of introducing Rustlang into their arsenel will be much lower. Lando does not intend to replace these tools but instead to work well with them 👫🏾.

👍 What makes Rust a good choice for Lambda applications

The AWS cost model for lambda is largely based on two factors: memory size and speed. The CPU provided to applications is proportional to memory size requested. Lambda has a pay per usage cost model billing favoring applications that are both fast and have low memory overheads.

As a systems language, Rust is designed specifically for these kinds of needs. Rust has a very tiny runtime, manages memory very effciently, and is extremely fast.

As a highly embeddable language, its interop story for runtimes like python's is 💖 . Be mindful that lando assumes you're exposing these applications through AWS API gateway which has its own generous pricing model.

📦 install

Add the following to your cargo project's Cargo.toml file.

[lib]
crate-type = ["cdylib"]

[dependencies]
lando = "0.2"

💡 The crate-type property links and produces a shared object ( *.so ) file allowing your rustlang application to compiled to linux native binary that can be invoked from the AWS python 3.6 lambda runtime

👩‍🏭 create

Lando exports a macro named gateway! which in turn, exports a Rust function or closure to a cpython native binary extention making it ready for use within an AWS Lambda.

#[macro_use] extern crate lando;

gateway!(|request, _context| {
  println!("{:?}", request);
  Ok("hello lambda")
});

This closure accepts an http::Request with a lando::Body. This Body type can be dereferenced as a slice of bytes if needed.

For more more in-depth details see this project's crate documentation.

Lando also supports a function attribute method for exporting a function as a lambda ready fn.

#[macro_use] extern crate lando;

use lando::{Request, LambdaContext, IntoResponse, Result};

#[lando]
fn example(
  _: Request,
  _: LambdaContext
) -> Result<impl IntoResponse> {
   Ok("hello lambda")
}

🔬 testing

Since these functions are just Rust you can test your application with the built in unit testing framework

In addition you can also integration test your functions by invoking them locally

🐳 Lambda CI

The lambda CI docker project contains docker images that mirror the AWS lambda runtimes. This enables you to build and test your lambda projects locally environments that match with AWS's.

Build

In order to invoke your function in a Lambda compatible environment you must first build it in one.

$ docker run --rm \
  -v ${PWD}:/code \
  -v ${HOME}/.cargo/registry:/root/.cargo/registry \
  -v ${HOME}/.cargo/git:/root/.cargo/git \
  -e CARGO_FLAGS="--features lando/python3-sys" \
  softprops/lambda-rust

This results in a native linux binary .so file under thetarget/lambda/release directory

Invoke

You can use the lambci/lambda:python3.6 docker images to invoke your lambda locally

This example provides the lambda's event though std in by piping a file, in this example a file called example_request.json. Feel free to create your own mock inputs.

cat example_request.json
{
  "path": "/test/hello",
  "headers": {
    "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8",
    "Accept-Encoding": "gzip, deflate, lzma, sdch, br",
    "Accept-Language": "en-US,en;q=0.8",
    "CloudFront-Forwarded-Proto": "https",
    "CloudFront-Is-Desktop-Viewer": "true",
    "CloudFront-Is-Mobile-Viewer": "false",
    "CloudFront-Is-SmartTV-Viewer": "false",
    "CloudFront-Is-Tablet-Viewer": "false",
    "CloudFront-Viewer-Country": "US",
    "Host": "wt6mne2s9k.execute-api.us-west-2.amazonaws.com",
    "Upgrade-Insecure-Requests": "1",
    "User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36 OPR/39.0.2256.48",
    "Via": "1.1 fb7cca60f0ecd82ce07790c9c5eef16c.cloudfront.net (CloudFront)",
    "X-Amz-Cf-Id": "nBsWBOrSHMgnaROZJK1wGCZ9PcRcSpq_oSXZNQwQ10OTZL4cimZo3g==",
    "X-Forwarded-For": "192.168.100.1, 192.168.1.1",
    "X-Forwarded-Port": "443",
    "X-Forwarded-Proto": "https"
  },
  "pathParameters": {
    "proxy": "hello"
  },
  "requestContext": {
    "accountId": "123456789012",
    "resourceId": "us4z18",
    "stage": "test",
    "requestId": "41b45ea3-70b5-11e6-b7bd-69b5aaebc7d9",
    "identity": {
      "cognitoIdentityPoolId": "",
      "accountId": "",
      "cognitoIdentityId": "",
      "caller": "",
      "apiKey": "",
      "sourceIp": "192.168.100.1",
      "cognitoAuthenticationType": "",
      "cognitoAuthenticationProvider": "",
      "userArn": "",
      "userAgent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/52.0.2743.82 Safari/537.36 OPR/39.0.2256.48",
      "user": ""
    },
    "resourcePath": "/{proxy+}",
    "httpMethod": "GET",
    "apiId": "wt6mne2s9k"
  },
  "resource": "/{proxy+}",
  "httpMethod": "GET",
  "queryStringParameters": {
    "name": "me"
  },
  "stageVariables": {
    "stageVarName": "stageVarValue"
  }
}

Invoke the resulting function using docker providing the contexts of the mock event as stdin.

$ docker run \
  -i -e DOCKER_LAMBDA_USE_STDIN=1 \
  --rm \
  -v \
  "$PWD/target/lambda/release":/var/task lambci/lambda:python3.6 \
  liblambda.handler < example_request.json

🚀 deploy

In order to deploy your app you will need to build it within a runtime compatible with the lambda python 3.6 env.

⚡ serverless framework

The recommended way to get started is with the serverless framework. A serverless framework plugin exists to facilitate rapid development/deployment cycles.

You can bootstrap a new deploy ready lando application by using this serverless project template

$ serverless install \
  --url https://github.com/softprops/serverless-lando \
  --name my-new-service

🐳 docker

A docker image is provided for convenience which replicates the AWS python3.6 env with rustlang build tooling.

It's focus is on applications targeting stable versions of Rust.

$ docker run --rm \
  -v ${PWD}:/code \
  -v ${HOME}/.cargo/registry:/root/.cargo/registry \
  -v ${HOME}/.cargo/git:/root/.cargo/git \
  -e CARGO_FLAGS="--features lando/python3-sys" \
  softprops/lambda-rust

This will result in a deployable .so build artifact under a target/lambda directory

This file can then be zipped up for AWS lambda deployment.

🏃 performance

Performance analysis for lambda applications, or any application, varies based on your usecase. In the specific case of lando, factors include

• your use of api gateway (the HTTP loadbalancing that AWS runs that invokes your functions)
• your lambda configuration (allocation of memory and attachment to resources like VPC's)
• lambda translation layer (translating between python and rust)
• your application (that's you!)

The serverless mindset is an explicit tradeoff of control runtime for focus on application.

Your application is very capable of running in double digit milliseconds.

Lando's goal is to provide a minimally invasive translation layer between the native python events to native rustlang http types and back

A benchmark test exists to measure that translation time with a typical gateway event which reports a typical (8.65 μ (micro) second results +/- 4 μ seconds) This is not likely going to be the bottleneck of your application

test gateway_conversion ... bench:       8,652 ns/iter (+/- 4,193)

💱 Concurrency

Consideration for concurency should be noted when approaching performance with AWS lamda.

AWS Lamda is expressly horizontal scaled. You scale not by spawning more threads in a running process ( scaling up ↕️ ) but by spawning more lambdas ( scaling out ↔️ ).

A key benefit of AWS lambda is that the platform handles concurrency by spawning more instances of your function for you. This results in some economical advantages in they way you only pay for what you use. Bear in mind you are billed at intervals of 100 milliseconds, so the usefulness optimizing for cost is lost once you've dipped below that point

Examples in the wild

• slack standup slack command webhook for automating standups
• jirabars github webhook that fills in jira placeholder info based on branch names
• barbershop github webhook that deletes branches after pr

🚧 planned changes

(none)

Doug Tangren (softprops) 2018