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AWS - Functions

If you are using AWS as a provider, all functions inside the service are AWS Lambda functions.

Configuration

All of the Lambda functions in your serverless service can be found in serverless.yml under the functions property.

# serverless.yml
service: myService

provider:
  name: aws
  runtime: nodejs12.x
  memorySize: 512 # optional, in MB, default is 1024
  timeout: 10 # optional, in seconds, default is 6
  versionFunctions: false # optional, default is true
  tracing:
    lambda: true # optional, enables tracing for all functions (can be true (true equals 'Active') 'Active' or 'PassThrough')

functions:
  hello:
    handler: handler.hello # required, handler set in AWS Lambda
    name: ${self:provider.stage}-lambdaName # optional, Deployed Lambda name
    description: Description of what the lambda function does # optional, Description to publish to AWS
    runtime: python2.7 # optional overwrite, default is provider runtime
    memorySize: 512 # optional, in MB, default is 1024
    timeout: 10 # optional, in seconds, default is 6
    provisionedConcurrency: 3 # optional, Count of provisioned lambda instances
    reservedConcurrency: 5 # optional, reserved concurrency limit for this function. By default, AWS uses account concurrency limit
    tracing: PassThrough # optional, overwrite, can be 'Active' or 'PassThrough'

The handler property points to the file and module containing the code you want to run in your function.

// handler.js
module.exports.functionOne = function(event, context, callback) {};

You can add as many functions as you want within this property.

# serverless.yml

service: myService

provider:
  name: aws
  runtime: nodejs12.x

functions:
  functionOne:
    handler: handler.functionOne
    description: optional description for your Lambda
  functionTwo:
    handler: handler.functionTwo
  functionThree:
    handler: handler.functionThree

Your functions can either inherit their settings from the provider property.

# serverless.yml
service: myService

provider:
  name: aws
  runtime: nodejs12.x
  memorySize: 512 # will be inherited by all functions

functions:
  functionOne:
    handler: handler.functionOne

Or you can specify properties at the function level.

# serverless.yml
service: myService

provider:
  name: aws
  runtime: nodejs12.x

functions:
  functionOne:
    handler: handler.functionOne
    memorySize: 512 # function specific

You can specify an array of functions, which is useful if you separate your functions in to different files:

# serverless.yml
---
functions:
  - ${file(./foo-functions.yml)}
  - ${file(./bar-functions.yml)}

# foo-functions.yml
getFoo:
  handler: handler.foo
deleteFoo:
  handler: handler.foo

Permissions

Every AWS Lambda function needs permission to interact with other AWS infrastructure resources within your account. These permissions are set via an AWS IAM Role. You can set permission policy statements within this role via the provider.iamRoleStatements property.

# serverless.yml
service: myService

provider:
  name: aws
  runtime: nodejs12.x
  iamRoleStatements: # permissions for all of your functions can be set here
    - Effect: Allow
      Action: # Gives permission to DynamoDB tables in a specific region
        - dynamodb:DescribeTable
        - dynamodb:Query
        - dynamodb:Scan
        - dynamodb:GetItem
        - dynamodb:PutItem
        - dynamodb:UpdateItem
        - dynamodb:DeleteItem
      Resource: 'arn:aws:dynamodb:us-east-1:*:*'

functions:
  functionOne:
    handler: handler.functionOne
    memorySize: 512

Another example:

# serverless.yml
service: myService
provider:
  name: aws
  iamRoleStatements:
    - Effect: 'Allow'
      Action:
        - 's3:ListBucket'
      # You can put CloudFormation syntax in here.  No one will judge you.
      # Remember, this all gets translated to CloudFormation.
      Resource: { 'Fn::Join': ['', ['arn:aws:s3:::', { 'Ref': 'ServerlessDeploymentBucket' }]] }
    - Effect: 'Allow'
      Action:
        - 's3:PutObject'
      Resource:
        Fn::Join:
          - ''
          - - 'arn:aws:s3:::'
            - 'Ref': 'ServerlessDeploymentBucket'
            - '/*'

functions:
  functionOne:
    handler: handler.functionOne
    memorySize: 512

You can also use an existing IAM role by adding your IAM Role ARN in the role property. For example:

# serverless.yml
service: new-service
provider:
  name: aws
  role: arn:aws:iam::YourAccountNumber:role/YourIamRole

See the documentation about IAM for function level IAM roles.

VPC Configuration

You can add VPC configuration to a specific function in serverless.yml by adding a vpc object property in the function configuration. This object should contain the securityGroupIds and subnetIds array properties needed to construct VPC for this function. Here's an example configuration:

# serverless.yml
service: service-name
provider: aws

functions:
  hello:
    handler: handler.hello
    vpc:
      securityGroupIds:
        - securityGroupId1
        - securityGroupId2
      subnetIds:
        - subnetId1
        - subnetId2

Or if you want to apply VPC configuration to all functions in your service, you can add the configuration to the higher level provider object, and overwrite these service level config at the function level. For example:

# serverless.yml
service: service-name
provider:
  name: aws
  vpc:
    securityGroupIds:
      - securityGroupId1
      - securityGroupId2
    subnetIds:
      - subnetId1
      - subnetId2

functions:
  hello: # this function will overwrite the service level vpc config above
    handler: handler.hello
    vpc:
      securityGroupIds:
        - securityGroupId1
        - securityGroupId2
      subnetIds:
        - subnetId1
        - subnetId2
  users: # this function will inherit the service level vpc config above
    handler: handler.users

Then, when you run serverless deploy, VPC configuration will be deployed along with your lambda function.

VPC IAM permissions

The Lambda function execution role must have permissions to create, describe and delete Elastic Network Interfaces (ENI). When VPC configuration is provided the default AWS AWSLambdaVPCAccessExecutionRole will be associated with your Lambda execution role. In case custom roles are provided be sure to include the proper ManagedPolicyArns. For more information please check configuring a Lambda Function for Amazon VPC Access

VPC Lambda Internet Access

By default, when a Lambda function is executed inside a VPC, it loses internet access and some resources inside AWS may become unavailable. In order for S3 resources and DynamoDB resources to be available for your Lambda function running inside the VPC, a VPC end point needs to be created. For more information please check VPC Endpoint for Amazon S3. In order for other services such as Kinesis streams to be made available, a NAT Gateway needs to be configured inside the subnets that are being used to run the Lambda, for the VPC used to execute the Lambda. For more information, please check Enable Outgoing Internet Access within VPC

Environment Variables

You can add environment variable configuration to a specific function in serverless.yml by adding an environment object property in the function configuration. This object should contain a key-value pairs of string to string:

# serverless.yml
service: service-name
provider: aws

functions:
  hello:
    handler: handler.hello
    environment:
      TABLE_NAME: tableName

Or if you want to apply environment variable configuration to all functions in your service, you can add the configuration to the higher level provider object. Environment variables configured at the function level are merged with those at the provider level, so your function with specific environment variables will also have access to the environment variables defined at the provider level. If an environment variable with the same key is defined at both the function and provider levels, the function-specific value overrides the provider-level default value. For example:

# serverless.yml
service: service-name
provider:
  name: aws
  environment:
    SYSTEM_NAME: mySystem
    TABLE_NAME: tableName1

functions:
  hello:
    # this function will have SYSTEM_NAME=mySystem and TABLE_NAME=tableName1 from the provider-level environment config above
    handler: handler.hello
  users:
    # this function will have SYSTEM_NAME=mySystem from the provider-level environment config above
    # but TABLE_NAME will be tableName2 because this more specific config will override the default above
    handler: handler.users
    environment:
      TABLE_NAME: tableName2

If you want your function's environment variables to have the same values from your machine's environment variables, please read the documentation about Referencing Environment Variables.

Layers

Using the layers configuration makes it possible for your function to use Lambda Layers

functions:
  hello:
    handler: handler.hello
    layers:
      - arn:aws:lambda:region:XXXXXX:layer:LayerName:Y

Layers can be used in combination with runtime: provided to implement your own custom runtime on AWS Lambda.

To publish Lambda Layers, check out the Layers documentation.

Log Group Resources

By default, the framework will create LogGroups for your Lambdas. This makes it easy to clean up your log groups in the case you remove your service, and make the lambda IAM permissions much more specific and secure.

Versioning Deployed Functions

By default, the framework creates function versions for every deploy. This behavior is optional, and can be turned off in cases where you don't invoke past versions by their qualifier. If you would like to do this, you can invoke your functions as arn:aws:lambda:....:function/myFunc:3 to invoke version 3 for example.

To turn off this feature, set the provider-level option versionFunctions.

provider:
  versionFunctions: false

These versions are not cleaned up by serverless, so make sure you use a plugin or other tool to prune sufficiently old versions. The framework can't clean up versions because it doesn't have information about whether older versions are invoked or not. This feature adds to the number of total stack outputs and resources because a function version is a separate resource from the function it refers to.

Dead Letter Queue (DLQ)

When AWS lambda functions fail, they are retried. If the retries also fail, AWS has a feature to send information about the failed request to a SNS topic or SQS queue, called the Dead Letter Queue, which you can use to track and diagnose and react to lambda failures.

You can setup a dead letter queue for your serverless functions with the help of a SNS topic and the onError config parameter.

Note: You can only provide one onError config per function.

DLQ with SNS

The SNS topic needs to be created beforehand and provided as an arn on the function level.

service: service

provider:
  name: aws
  runtime: nodejs12.x

functions:
  hello:
    handler: handler.hello
    onError: arn:aws:sns:us-east-1:XXXXXX:test # Ref, Fn::GetAtt and Fn::ImportValue are supported as well

DLQ with SQS

Although Dead Letter Queues support both SNS topics and SQS queues, the onError config currently only supports SNS topic arns due to a race condition when using SQS queue arns and updating the IAM role.

We're working on a fix so that SQS queue arns will be supported in the future.

KMS Keys

AWS Lambda uses AWS Key Management Service (KMS) to encrypt your environment variables at rest.

The awsKmsKeyArn config variable enables you a way to define your own KMS key which should be used for encryption.

service:
  name: service-name
  awsKmsKeyArn: arn:aws:kms:us-east-1:XXXXXX:key/some-hash

provider:
  name: aws
  environment:
    TABLE_NAME: tableName1

functions:
  hello: # this function will OVERWRITE the service level environment config above
    handler: handler.hello
    awsKmsKeyArn: arn:aws:kms:us-east-1:XXXXXX:key/some-hash
    environment:
      TABLE_NAME: tableName2
  goodbye: # this function will INHERIT the service level environment config above
    handler: handler.goodbye

Secrets using environment variables and KMS

When storing secrets in environment variables, AWS strongly suggests encrypting sensitive information. AWS provides a tutorial on using KMS for this purpose.

AWS X-Ray Tracing

You can enable AWS X-Ray Tracing on your Lambda functions through the optional tracing config variable:

service: myService

provider:
  name: aws
  runtime: nodejs12.x
  tracing:
    lambda: true

You can also set this variable on a per-function basis. This will override the provider level setting if present:

functions:
  hello:
    handler: handler.hello
    tracing: Active
  goodbye:
    handler: handler.goodbye
    tracing: PassThrough

Destinations

When intention is to invoke function asynchronously you may want to configure destination targets for it.

Target can be the other lambdas you also deploy with a service or other qualified target (externally managed lambda, EventBridge event bus, SQS queue or SNS topic) which you can address via its ARN

functions:
  asyncHello:
    handler: handler.asyncHello
    destinations:
      onSuccess: otherFunctionInService
      onFailure: arn:aws:sns:us-east-1:xxxx:some-topic-name

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functions.md

functions.md

Read this on the main serverless docs site

AWS - Functions

Configuration

Permissions

VPC Configuration

Environment Variables

Tags

Layers

Log Group Resources

Versioning Deployed Functions

Dead Letter Queue (DLQ)

DLQ with SNS

DLQ with SQS

KMS Keys

Secrets using environment variables and KMS

AWS X-Ray Tracing

Destinations

Files

functions.md

Latest commit

History

functions.md

File metadata and controls

Read this on the main serverless docs site

AWS - Functions

Configuration

Permissions

VPC Configuration

Environment Variables

Tags

Layers

Log Group Resources

Versioning Deployed Functions

Dead Letter Queue (DLQ)

DLQ with SNS

DLQ with SQS

KMS Keys

Secrets using environment variables and KMS

AWS X-Ray Tracing

Destinations