SAM CLI (Beta)

Join the SAM developers channel (#samdev) on Slack to collaborate with fellow community members and the AWS SAM team.

``sam`` is the AWS CLI tool for managing Serverless applications written with AWS Serverless Application Model (SAM). SAM CLI can be used to test functions locally, start a local API Gateway from a SAM template, validate a SAM template, generate sample payloads for various event sources, and generate a SAM project in your favorite Lambda Runtime.

SAM CLI (Beta)

Main features

Develop and test your Lambda functions locally with sam local and Docker
Invoke functions from known event sources such as Amazon S3, Amazon DynamoDB, Amazon Kinesis, etc.
Start local API Gateway from a SAM template, and quickly iterate over your functions with hot-reloading
Validate SAM templates
Get started with boilerplate Serverless Service in your chosen Lambda Runtime sam init

Installation

Prerequisites

Docker
Python2.7 (Python3+ not yet supported)

Running Serverless projects and functions locally with SAM CLI requires Docker to be installed and running. SAM CLI will use the DOCKER_HOST environment variable to contact the docker daemon.

macOS: Docker for Mac
Windows: Docker Toolbox
Linux: Check your distro’s package manager (e.g. yum install docker)

For macOS and Windows users: SAM CLI requires that the project directory (or any parent directory) is listed in Docker file sharing options.

Verify that docker is working, and that you can run docker commands from the CLI (e.g. ‘docker ps’). You do not need to install/fetch/pull any containers – SAM CLI will do it automatically as required.

Upgrade from Version 0.2.11 or Below

Uninstall previous CLI Version

npm uninstall -g aws-sam-local

To upgrade to Version 0.3.0 or above, follow instructions below:

Windows, Linux, macOS with PIP [Recommended]

Verify Python Version is 2.7.

The easiest way to install ``sam`` is to use PIP.

pip install --user aws-sam-cli

Verify the installation worked:

sam --version

Upgrading via pip

To update ``sam`` once installed via pip:

pip install --user --upgrade aws-sam-cli

Build From Source

First, install Python(2.7) on your machine, then run the following:

# Clone the repository
$ git clone git@github.com/awslabs/aws-sam-cli.git

# cd into the git
$ cd aws-sam-cli

# pip install the repository
$ pip install --user -e .

Install with PyEnv

# Install PyEnv (https://github.com/pyenv/pyenv#installation)
$ brew update
$ brew install pyenv

# Initialize pyenv using bash_profile
$ echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi\nexport PATH="~/.pyenv/bin:$PATH"' >> ~/.bash_profile
# or using zshrc
$ echo -e 'if command -v pyenv 1>/dev/null 2>&1; then\n  eval "$(pyenv init -)"\nfi\nexport PATH="~/.pyenv/bin:$PATH"' >> ~/.zshrc

# restart the shell
$ exec "$SHELL"

# Install Python 2.7
$ pyenv install 2.7.14
$ pyenv local 2.7.14

# Install the CLI
$ pip install --user aws-sam-cli

# Verify your installation worked
$ sam –version

Troubleshooting

General Issues

If you are seeing sam command not found, this is likely due to the installation using the --user and adding sam to a path that is not in your $PATH.

# Find your path Python User Base path (where Python --user will install packages/scripts)
USER_BASE_PATH="$(python -m site --user-base)"

# Add this path to your $PATH
export PATH=$USER_BASE_PATH:$PATH

You can also try an installing aws-sam-cli without --user

# Uninstall aws-sam-cli from the --user path
pip uninstall --user aws-sam-cli

pip install aws-sam-cli

Mac Issues

[Errno 13] Permission denied If you had installed Python using Homebrew, you might need to use sudo to install SAM CLI:

sudo pip install aws-sam-cli

TLSV1_ALERT_PROTOCOL_VERSION:

If you get an error something similar to:

Could not fetch URL https://pypi.python.org/simple/click/: There was a problem confirming the ssl certificate: [SSL: TLSV1_ALERT_PROTOCOL_VERSION] tlsv1 alert protocol version (_ssl.c:590) - skipping

then you are probably using the default version of Python that came with your Mac. This is outdated. So make sure you install Python again using homebrew and try again:

brew install python@2

Followed by:

pip install --user aws-sam-cli

Usage

``sam`` requires a SAM template in order to know how to invoke your function locally, and it’s also true for spawning API Gateway locally - If no template is specified template.yaml will be used instead.

You can find sample SAM templates either under ``samples`` located in this repo or by visiting SAM official repository.

Invoke functions locally

SAM CLI Invoke Sample

You can invoke your function locally by passing its SAM logical ID and an event file. Alternatively, sam local invoke accepts stdin as an event too.

Resources:
  Ratings:  # <-- Logical ID
    Type: 'AWS::Serverless::Function'
  ...

Syntax

# Invoking function with event file
$ sam local invoke "Ratings" -e event.json

# Invoking function with event via stdin
$ echo '{"message": "Hey, are you there?" }' | sam local invoke "Ratings"

# For more options
$ sam local invoke --help

Generate sample event source payloads

To make local development and testing of Lambda functions easier, you can generate mock/sample event payloads for the following services:

S3
Kinesis
DynamoDB
Cloudwatch Scheduled Event
Cloudtrail
API Gateway

Syntax

sam local generate-event <service>

Also, you can invoke an individual lambda function locally from a sample event payload - Here’s an example using S3:

sam local generate-event s3 --bucket <bucket> --key <key> | sam local invoke <function logical id>

For more options, see sam local generate-event --help.

Run API Gateway locally

sam local start-api spawns a local API Gateway to test HTTP request/response functionality. Features hot-reloading to allow you to quickly develop, and iterate over your functions.

SAM CLI Start API

Syntax

sam local start-api

``sam`` will automatically find any functions within your SAM template that have Api event sources defined, and mount them at the defined HTTP paths.

In the example below, the Ratings function would mount ratings.py:handler() at /ratings for GET requests.

Ratings:
  Type: AWS::Serverless::Function
  Properties:
    Handler: ratings.handler
    Runtime: python3.6
    Events:
      Api:
        Type: Api
        Properties:
          Path: /ratings
          Method: get

By default, SAM uses Proxy Integration and expects the response from your Lambda function to include one or more of the following: statusCode, headers and/or body.

For example:

// Example of a Proxy Integration response
exports.handler = (event, context, callback) => {
    callback(null, {
        statusCode: 200,
        headers: { "x-custom-header" : "my custom header value" },
        body: "hello world"
    });
}

For examples in other AWS Lambda languages, see this page.

If your function does not return a valid Proxy Integration response then you will get a HTTP 500 (Internal Server Error) when accessing your function. SAM CLI will also print the following error log message to help you diagnose the problem:

ERROR: Function ExampleFunction returned an invalid response (must include one of: body, headers or statusCode in the response object)

Debugging Applications

Both sam local invoke and sam local start-api support local debugging of your functions.

To run SAM Local with debugging support enabled, just specify --debug-port or -d on the command line.

# Invoke a function locally in debug mode on port 5858
$ sam local invoke -d 5858 <function logical id>

# Start local API Gateway in debug mode on port 5858
$ sam local start-api -d 5858

Note: If using sam local start-api, the local API Gateway will expose all of your Lambda functions but, since you can specify a single debug port, you can only debug one function at a time. You will need to hit your API before SAM CLI binds to the port allowing the debugger to connect.

Here is an example showing how to debug a NodeJS function with Microsoft Visual Studio Code:

SAM Local debugging example

In order to setup Visual Studio Code for debugging with AWS SAM CLI, use the following launch configuration:

{
    "version": "0.2.0",
    "configurations": [
        {
            "name": "Attach to SAM Local",
            "type": "node",
            "request": "attach",
            "address": "localhost",
            "port": 5858,
            "localRoot": "${workspaceRoot}",
            "remoteRoot": "/var/task",
            "protocol": "legacy"
        }
    ]
}

Note: Node.js versions below 7 (e.g. Node.js 4.3 and Node.js 6.10) use the legacy protocol, while Node.js versions including and above 7 (e.g. Node.js 8.10) use the inspector protocol. Be sure to specify the corresponding protocol in the protocol entry of your launch configuration.

Debugging Python functions

Unlike Node.JS and Java, Python requires you to enable remote debugging in your Lambda function code. If you enable debugging with --debug-port or -d for a function that uses one of the Python runtimes, SAM CLI will just map through that port from your host machine through to the Lambda runtime container. You will need to enable remote debugging in your function code. To do this, use a python package such as remote-pdb. When configuring the host the debugger listens on in your code, make sure to use 0.0.0.0 not 127.0.0.1 to allow Docker to map through the port to your host machine.

Please note, due to a open bug with Visual Studio Code, you may get a Debug adapter process has terminated unexpectedly error when attempting to debug Python applications with this IDE. Please track the GitHub issue for updates.

Passing Additional Runtime Debug Arguments

To pass additional runtime arguments when debugging your function, use the environment variable DEBUGGER_ARGS. This will pass a string of arguments directly into the run command SAM CLI uses to start your function.

For example, if you want to load a debugger like iKPdb at runtime of your Python function, you could pass the following as DEBUGGER_ARGS: -m ikpdb --ikpdb-port=5858 --ikpdb-working-directory=/var/task/ --ikpdb-client-working-directory=/myApp --ikpdb-address=0.0.0.0. This would load iKPdb at runtime with the other arguments you’ve specified. In this case, your full SAM CLI command would be:

$ DEBUGGER_ARGS="-m ikpdb --ikpdb-port=5858 --ikpdb-working-directory=/var/task/ --ikpdb-client-working-directory=/myApp --ikpdb-address=0.0.0.0" echo {} | sam local invoke -d 5858 myFunction

You may pass debugger arguments to functions of all runtimes.

Connecting to docker network

Both sam local invoke and sam local start-api support connecting the create lambda docker containers to an existing docker network.

To connect the containers to an existing docker network, you can use the --docker-network command-line argument or the SAM_DOCKER_NETWORK environment variable along with the name or id of the docker network you wish to connect to.

# Invoke a function locally and connect to a docker network
$ sam local invoke --docker-network my-custom-network <function logical id>

# Start local API Gateway and connect all containers to a docker network
$ sam local start-api --docker-network b91847306671 -d 5858

Validate SAM templates

Validate your templates with $ sam validate. Currently this command will validate that the template provided is valid JSON / YAML. As with most SAM CLI commands, it will look for a template.[yaml|yml] file in your current working directory by default. You can specify a different template file/location with the -t or --template option.

Syntax

$ sam validate
<path-to-file>/template.yml is a valid SAM Template

Note: The validate command requires AWS credentials to be configured. See IAMCreds_.

Package and Deploy to Lambda

Once you have developed and tested your Serverless application locally, you can deploy to Lambda using sam package and sam deploy command. package command will zip your code artifacts, upload to S3 and produce a SAM file that is ready to be deployed to Lambda using AWS CloudFormation. deploy command will deploy the packaged SAM template to CloudFormation. Both sam package and sam deploy are identical to their AWS CLI equivalents commands `aws cloudformation package <http://docs.aws.amazon.com/cli/latest/reference/cloudformation/package.html>`__ and `aws cloudformation deploy <http://docs.aws.amazon.com/cli/latest/reference/cloudformation/deploy/index.html>`__ respectively. Please consult the AWS CLI command documentation for usage.

Example:

# Package SAM template
$ sam package --template-file sam.yaml --s3-bucket mybucket --output-template-file packaged.yaml

# Deploy packaged SAM template
$ sam deploy --template-file ./packaged.yaml --stack-name mystack --capabilities CAPABILITY_IAM

Getting started

Check out our Getting Started Guide for more details

Advanced

Compiled Languages

Java

To use SAM CLI with compiled languages, such as Java that require a packaged artifact (e.g. a JAR, or ZIP), you can specify the location of the artifact with the AWS::Serverless::Function CodeUri property in your SAM template.

For example:

AWSTemplateFormatVersion: 2010-09-09
Transform: AWS::Serverless-2016-10-31

Resources:
  ExampleJavaFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: com.example.HelloWorldHandler
      CodeUri: ./target/HelloWorld-1.0.jar
      Runtime: java8

You should then build your JAR file using your normal build process. Please note that JAR files used with AWS Lambda should be a shaded JAR file (or uber jar) containing all of the function dependencies.

// Build the JAR file
$ mvn package shade:shade

// Invoke with SAM Local
$ echo '{ "some": "input" }' | sam local invoke

// Or start local API Gateway simulator
$ sam local start-api

You can find a full Java example in the samples/java folder

.NET Core

To use SAM Local with compiled languages, such as .NET Core that require a packaged artifact (e.g. a ZIP), you can specify the location of the artifact with the AWS::Serverless::Function CodeUri property in your SAM template.

For example:

AWSTemplateFormatVersion: 2010-09-09
Transform: AWS::Serverless-2016-10-31

Resources:
  ExampleDotNetFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: HelloWorld::HelloWorld.Function::Handler
      CodeUri: ./artifacts/HelloWorld.zip
      Runtime: dotnetcore2.0

You should then build your ZIP file using your normal build process.

You can generate a .NET Core example by using the sam init --runtime dotnetcore command.

IAM Credentials

SAM CLI will invoke functions with your locally configured IAM credentials.

As with the AWS CLI and SDKs, SAM CLI will look for credentials in the following order:

Environment Variables (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY).
The AWS credentials file (located at ~/.aws/credentials on Linux, macOS, or Unix, or at C:\Users\USERNAME \.aws\credentials on Windows).
Instance profile credentials (if running on Amazon EC2 with an assigned instance role).

In order to test API Gateway with a non-default profile from your AWS credentials file append --profile <profile name> to the start-api command:

// Test API Gateway locally with a credential profile.
$ sam local start-api --profile some_profile

See this Configuring the AWS CLI for more details.

Lambda Environment Variables

If your Lambda function uses environment variables, you can provide values for them will passed to the Docker container. Here is how you would do it:

For example, consider the SAM template snippet:

Resources:
  MyFunction1:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: nodejs4.3
      Environment:
        Variables:
          TABLE_NAME: prodtable
          BUCKET_NAME: prodbucket

  MyFunction2:
    Type: AWS::Serverless::Function
    Properties:
      Handler: app.handler
      Runtime: nodejs4.3
      Environment:
        Variables:
          STAGE: prod
          TABLE_NAME: prodtable

Environment Variable file

Use --env-vars argument of invoke or start-api commands to provide a JSON file that contains values for environment variables defined in your function. The file should be structured as follows:

{
  "MyFunction1": {
    "TABLE_NAME": "localtable",
    "BUCKET_NAME": "testBucket"
  },
  "MyFunction2": {
    "TABLE_NAME": "localtable",
    "STAGE": "dev"
  },
}

$ sam local start-api --env-vars env.json

Shell environment

Variables defined in your Shell’s environment will be passed to the Docker container, if they map to a Variable in your Lambda function. Shell variables are globally applicable to functions ie. If two functions have a variable called TABLE_NAME, then the value for TABLE_NAME provided through Shell’s environment will be availabe to both functions.

Following command will make value of mytable available to both MyFunction1 and MyFunction2

$ TABLE_NAME=mytable sam local start-api

Combination of Shell and Environment Variable file

For greater control, you can use a combination shell variables and external environment variable file. If a variable is defined in both places, the one from the file will override the shell. Here is the order of priority, highest to lowest. Higher priority ones will override the lower.

Environment Variable file
Shell’s environment
Hard-coded values from the template

Identifying local execution from Lambda function code

When your Lambda function is invoked using SAM CLI, it sets an environment variable AWS_SAM_LOCAL=true in the Docker container. Your Lambda function can use this property to enable or disable functionality that would not make sense in local development. For example: Disable emitting metrics to CloudWatch (or) Enable verbose logging etc.

Static Assets

Often, it’s useful to serve up static assets (e.g CSS/HTML/Javascript etc) when developing a Serverless application. On AWS, this would normally be done with CloudFront/S3. SAM CLI by default looks for a ./public/ directory in your SAM project directory and will serve up all files from it at the root of the HTTP server when using sam local start-api. You can override the default static asset directory by using the -s or --static-dir command line flag. You can also disable this behaviour completely by setting --static-dir "".

Local Logging

Both invoke and start-api command allow you to pipe logs from the function’s invocation into a file. This will be useful if you are running automated tests against SAM CLI and want to capture logs for analysis.

Example:

$ sam local invoke --log-file ./output.log

Remote Docker

Sam CLI loads function code by mounting filesystem to a Docker Volume. As a result, The project directory must be pre-mounted on the remote host where the Docker is running.

If mounted, you can use the remote docker normally using --docker-volume-basedir or environment variable SAM_DOCKER_VOLUME_BASEDIR.

Example - Docker Toolbox (Windows):

When you install and run Docker Toolbox, the Linux VM with Docker is automatically installed in the virtual box.

The /c/ path for this Linux VM is automatically shared with C: on the host machine.

sam local invoke --docker-volume-basedir /c/Users/shlee322/projects/test "Ratings"

Project Status

[ ] Python Versions support
- [x] Python 2.7
- [ ] Python 3.6
[ ] Supported AWS Lambda Runtimes
- [x] nodejs
- [x] nodejs4.3
- [x] nodejs6.10
- [x] nodejs8.10
- [x] java8
- [x] python2.7
- [x] python3.6
- [ ] dotnetcore1.0
- [x] dotnetcore2.0
[x] AWS credential support
[x] Debugging support
[x] Inline Swagger support within SAM templates
[x] Validating SAM templates locally
[x] Generating boilerplate templates
- [x] nodejs
- [x] nodejs4.3
- [x] nodejs6.10
- [x] nodejs8.10
- [x] java8
- [x] python2.7
- [x] python3.6
- [x] dotnetcore1.0
- [x] dotnetcore2.0

Contributing

Contributions and feedback are welcome! Proposals and pull requests will be considered and responded to. For more information, see the CONTRIBUTING file.

A special thank you

SAM CLI uses the open source docker-lambda Docker images created by [@mhart](https://github.com/mhart).

Examples

You can find sample functions code and a SAM template used in this README under the samples folder within this repo.

mohisen/aws-sam-cli

SAM CLI (Beta)

Main features

Installation

Prerequisites

Upgrade from Version 0.2.11 or Below

Windows, Linux, macOS with PIP [Recommended]

Upgrading via pip

Build From Source

Install with PyEnv

Troubleshooting

General Issues

Mac Issues

Usage

Invoke functions locally

Generate sample event source payloads

Run API Gateway locally

Debugging Applications

Debugging Python functions

Passing Additional Runtime Debug Arguments

Connecting to docker network

Validate SAM templates

Package and Deploy to Lambda

Getting started

Advanced

Compiled Languages

IAM Credentials

Lambda Environment Variables

Environment Variable file

Shell environment

Combination of Shell and Environment Variable file

Identifying local execution from Lambda function code

Static Assets

Local Logging

Remote Docker

Project Status

Contributing

A special thank you

Examples