AWS Lambda 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: nodejs14.x
  runtimeManagement: auto # optional, set how Lambda controls all functions runtime. AWS default is auto; this can either be 'auto' or 'onFunctionUpdate'. For 'manual', see example in hello function below (syntax for both is identical)
  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: ${sls:stage}-lambdaName # optional, Deployed Lambda name
    description: Description of what the lambda function does # optional, Description to publish to AWS
    runtime: python3.11 # optional overwrite, default is provider runtime
    runtimeManagement:
      mode: manual # syntax required for manual, mode property also supports 'auto' or 'onFunctionUpdate' (see provider.runtimeManagement)
      arn: <aws runtime arn> # required when mode is manual
    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: nodejs14.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: nodejs14.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: nodejs14.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.iam.role.statements property.

# serverless.yml
service: myService

provider:
  name: aws
  runtime: nodejs14.x
  iam:
    role:
      statements: # 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
  iam:
    role:
      statements:
        - 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 iam.role property. For example:

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

See the documentation about IAM for function level IAM roles.

Lambda Function URLs

A Lambda Function URL is a simple solution to create HTTP endpoints with AWS Lambda. Function URLs are ideal for getting started with AWS Lambda, or for single-function applications like webhooks or APIs built with web frameworks.

You can create a function URL via the url property in the function configuration in serverless.yml. By setting url to true, as shown below, the URL will be public without CORS configuration.

functions:
  func:
    handler: index.handler
    url: true

Alternatively, you can configure it as an object, and provide values for authorizer, cors and invokeMode options.

The authorizer property can be set to aws_iam to enable AWS IAM authorization on your function URL.

functions:
  func:
    handler: index.handler
    url:
      authorizer: aws_iam

When using IAM authorization, the URL will only accept HTTP requests with AWS credentials allowing lambda:InvokeFunctionUrl (similar to API Gateway IAM authentication).

You can also configure CORS headers so that your function URL can be called from other domains in browsers. Setting cors to true will allow all domains via the following CORS headers:

functions:
  func:
    handler: index.handler
    url:
      cors: true

Header	Value
Access-Control-Allow-Origin	*
Access-Control-Allow-Headers	Content-Type, X-Amz-Date, Authorization, X-Api-Key, X-Amz-Security-Token
Access-Control-Allow-Methods	*

You can also additionally adjust your CORS configuration by setting allowedOrigins, allowedHeaders, allowedMethods, allowCredentials, exposedResponseHeaders, and maxAge properties as shown in example below.

functions:
  func:
    handler: index.handler
    url:
      cors:
        allowedOrigins:
          - https://url1.com
          - https://url2.com
        allowedHeaders:
          - Content-Type
          - Authorization
        allowedMethods:
          - GET
        allowCredentials: true
        exposedResponseHeaders:
          - Special-Response-Header
        maxAge: 6000 # In seconds

In the table below you can find how the cors properties map to CORS headers

Configuration property	CORS Header
allowedOrigins	Access-Control-Allow-Origin
allowedHeaders	Access-Control-Allow-Headers
allowedMethods	Access-Control-Allow-Methods
allowCredentials	Access-Control-Allow-Credentials
exposedResponseHeaders	Access-Control-Expose-Headers
maxAge	Access-Control-Max-Age

It is also possible to remove the values in CORS configuration that are set by default by setting them to null instead.

functions:
  func:
    handler: index.handler
    url:
      cors:
        allowedHeaders: null

The invokeMode property can be set to RESPONSE_STREAM to enable streaming response. If not specified, BUFFERED invoke mode is assumed.

functions:
  func:
    handler: index.handler
    url:
      invokeMode: RESPONSE_STREAM

Referencing container image as a target

Alternatively lambda environment can be configured through docker images. Image published to AWS ECR registry can be referenced as lambda source (check AWS Lambda – Container Image Support). In addition, you can also define your own images that will be built locally and uploaded to AWS ECR registry.

Serverless will create an ECR repository for your image, but it currently does not manage updates to it. An ECR repository is created only for new services or the first time that a function configured with an image is deployed. In service configuration, you can configure the ECR repository to scan for CVEs via the provider.ecr.scanOnPush property, which is false by default. (See documentation)

In service configuration, images can be configured via provider.ecr.images. To define an image that will be built locally, you need to specify path property, which should point to valid docker context directory. Optionally, you can also set file to specify Dockerfile that should be used when building an image. It is also possible to define images that already exist in AWS ECR repository. In order to do that, you need to define uri property, which should follow <account>.dkr.ecr.<region>.amazonaws.com/<repository>@<digest> or <account>.dkr.ecr.<region>.amazonaws.com/<repository>:<tag> format.

Additionally, you can define arguments that will be passed to the docker build command via the following properties:

buildArgs: With the buildArgs property, you can define arguments that will be passed to docker build command with --build-arg flag. They might be later referenced via ARG within your Dockerfile. (See Documentation)
cacheFrom: The cacheFrom property can be used to specify which images to use as a source for layer caching in the docker build command with --cache-from flag. (See Documentation)
platform: The platform property can be used to specify the architecture target in the docker build command with the --platform flag. If not specified, Docker will build for your computer's architecture by default. AWS Lambda typically uses x86 architecture unless otherwise specified in the Lambda's runtime settings. In order to avoid runtime errors when building on an ARM-based machine (e.g. Apple M1 Mac), linux/amd64 must be used here. The options for this flag are linux/amd64 (x86-based Lambdas), linux/arm64 (arm-based Lambdas), or windows/amd64. (See Documentation)

When uri is defined for an image, buildArgs, cacheFrom, and platform cannot be defined.

Example configuration

service: service-name
provider:
  name: aws
  ecr:
    scanOnPush: true
    images:
      baseimage:
        path: ./path/to/context
        file: Dockerfile.dev
        buildArgs:
          STAGE: ${opt:stage}
        cacheFrom:
          - my-image:latest
        platform: linux/amd64
      anotherimage:
        uri: 000000000000.dkr.ecr.sa-east-1.amazonaws.com/test-lambda-docker@sha256:6bb600b4d6e1d7cf521097177dd0c4e9ea373edb91984a505333be8ac9455d38

When configuring functions, images should be referenced via image property, which can point to an image already defined in provider.ecr.images or directly to an existing AWS ECR image, following the same format as uri above. Both handler and runtime properties are not supported when image is used.

Example configuration:

service: service-name
provider:
  name: aws
  ecr:
    images:
      baseimage:
        path: ./path/to/context

functions:
  hello:
    image: 000000000000.dkr.ecr.sa-east-1.amazonaws.com/test-lambda-docker@sha256:6bb600b4d6e1d7cf521097177dd0c4e9ea373edb91984a505333be8ac9455d38
  world:
    image: baseimage

It is also possible to provide additional image configuration via workingDirectory, entryPoint and command properties of to functions[].image. The workingDirectory accepts path in form of string, where both entryPoint and command needs to be defined as a list of strings, following "exec form" format. In order to provide additional image config properties, functions[].image has to be defined as an object, and needs to define either uri pointing to an existing AWS ECR image or name property, which references image already defined in provider.ecr.images.

Example configuration:

service: service-name
provider:
  name: aws
  ecr:
    images:
      baseimage:
        path: ./path/to/context

functions:
  hello:
    image:
      uri: 000000000000.dkr.ecr.sa-east-1.amazonaws.com/test-lambda-docker@sha256:6bb600b4d6e1d7cf521097177dd0c4e9ea373edb91984a505333be8ac9455d38
      workingDirectory: /workdir
      command:
        - executable
        - flag
      entryPoint:
        - executable
        - flag
  world:
    image:
      name: baseimage
      command:
        - command
      entryPoint:
        - executable
        - flag

During the first deployment when locally built images are used, Framework will automatically create a dedicated ECR repository to store these images, with name serverless-<service>-<stage>. Currently, the Framework will not remove older versions of images uploaded to ECR as they still might be in use by versioned functions. During sls remove, the created ECR repository will be removed. During deployment, Framework will attempt to docker login to ECR if needed. Depending on your local configuration, docker authorization token might be stored unencrypted. Please refer to documentation for more details: https://docs.docker.com/engine/reference/commandline/login/#credentials-store

Instruction set architecture

By default, Lambda functions are run by 64-bit x86 architecture CPUs. However, using arm64 architecture (AWS Graviton2 processor) may result in better pricing and performance.

To switch all functions to AWS Graviton2 processor, configure architecture at provider level as follows:

provider:
  ...
  architecture: arm64

To toggle instruction set architecture per function individually, set it directly at functions[] context:

functions:
  hello:
    ...
    architecture: arm64

Runtime Management

Runtime Management allows for fine-grained control of the runtime being used for a lambda function in the rare event of compatibility issues with a function.

If you wish to keep runtimeManagement set to auto, that's the default so you don't need to specify it explicitly. If you wish for the runtime to only be updated when the function is redeployed, set it to onFunctionUpdate.

To configure runtime management for all functions, configure runtimeManagement at provider level as follows:

provider:
  ...
  runtimeManagement: onFunctionUpdate

To toggle instruction set architecture per function individually, set it directly at functions[] context:

functions:
  hello:
    ...
    runtimeManagement:
      mode: manual
      arn: <aws runtime arn>

Finally, auto and onFunctionUpdate can be set as the mode property as well for completeness (and to allow for the scenario where this value comes from another variable source, for example).

SnapStart

Lambda SnapStart for Java can improve startup performance for latency-sensitive applications.

To enable SnapStart for your lambda function you can add the snapStart object property in the function configuration which can be put to true and will result in the value PublishedVersions for this function.

functions:
  hello:
    ...
    runtime: java11
    snapStart: true

Note: Lambda SnapStart only supports the Java 11, Java 17 and Java 21 runtimes and does not support provisioned concurrency, the arm64 architecture, the Lambda Extensions API, Amazon Elastic File System (Amazon EFS), AWS X-Ray, or ephemeral storage greater than 512 MB.

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.

If you have a provider VPC set but wish to have specific functions with no VPC, you can set the vpc value for these functions to ~ (null). For example:

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

functions:
  hello: # this function will have no vpc configured
    handler: handler.hello
    vpc: ~
  users: # this function will inherit the service level vpc config above
    handler: handler.users

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.

You can opt out of the default behavior by setting disableLogs: true

You can also specify the duration for CloudWatch log retention by setting logRetentionInDays.

You can specify the DataProtectionPolicy for the LogGroup by setting logDataProtectionPolicy. On how to define the policy consult the aws docs.

functions:
  hello:
    handler: handler.hello
    disableLogs: true
  goodBye:
    handler: handler.goodBye
    logRetentionInDays: 14
    logDataProtectionPolicy:
      Name: data-protection-policy

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.

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.

To turn off function versioning, set the provider-level option versionFunctions.

provider:
  versionFunctions: false

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: nodejs14.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 kmsKeyArn config variable enables you a way to define your own KMS key which should be used for encryption.

service:
  name: service-name

provider:
  name: aws
  kmsKeyArn: arn:aws:kms:us-east-1:XXXXXX:key/some-hash
  environment:
    TABLE_NAME: tableName1

functions:
  hello: # this function will OVERWRITE the service level environment config above
    handler: handler.hello
    kmsKeyArn: 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: nodejs14.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

Asynchronous invocation

When intention is to invoke function asynchronously you may want to configure following additional settings:

Destinations

destination targets

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 or reference

functions:
  asyncHello:
    handler: handler.asyncHello
    destinations:
      onSuccess: otherFunctionInService
      onFailure: arn:aws:sns:us-east-1:xxxx:some-topic-name
  asyncGoodBye:
    handler: handler.asyncGoodBye
    destinations:
      onFailure:
        # For the case using CF intrinsic function for `arn`, to ensure target execution permission exactly, you have to specify `type` from 'sns', 'sqs', 'eventBus', 'function'.
        type: sns
        arn:
          Ref: SomeTopicName

Maximum Event Age and Maximum Retry Attempts

maximumEventAge accepts values between 60 seconds and 6 hours, provided in seconds. maximumRetryAttempts accepts values between 0 and 2.

functions:
  asyncHello:
    handler: handler.asyncHello
    maximumEventAge: 7200
    maximumRetryAttempts: 1

EFS Configuration

You can use Amazon EFS with Lambda by adding a fileSystemConfig property in the function configuration in serverless.yml. fileSystemConfig should be an object that contains the arn and localMountPath properties. The arn property should reference an existing EFS Access Point, where the localMountPath should specify the absolute path under which the file system will be mounted. Here's an example configuration:

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

functions:
  hello:
    handler: handler.hello
    fileSystemConfig:
      localMountPath: /mnt/example
      arn: arn:aws:elasticfilesystem:us-east-1:111111111111:access-point/fsap-0d0d0d0d0d0d0d0d0
    vpc:
      securityGroupIds:
        - securityGroupId1
      subnetIds:
        - subnetId1

Ephemeral storage

By default, Lambda allocates 512 MB of ephemeral storage in functions under the /tmp directory.

You can increase its size via the ephemeralStorageSize property. It should be a numerical value in MBs, between 512 and 10240.

functions:
  helloEphemeral:
    handler: handler.handler
    ephemeralStorageSize: 1024

Lambda Hashing Algorithm migration

Note Below migration guide is intended to be used if you are already using v3 version of the Framework and you have provider.lambdaHashingVersion property set to 20200924 in your configuration file. If you are still on v2 and want to upgrade to v3, please refer to V3 Upgrade docs.

In v3, Lambda version hashes are generated using an improved algorithm that fixes determinism issues. If you are still using the old hashing algorithm, you can follow the guide below to migrate to new default version.

Please keep in mind that these changes require two deployments with manual configuration adjustment between them. It also creates two additional versions and temporarily overrides descriptions of your functions. Migration will need to be done separately for each of your environments/stages.

Run sls deploy with additional --enforce-hash-update flag: that flag will override the description for Lambda functions, which will force the creation of new versions.
Remove provider.lambdaHashingVersion setting from your configuration: your service will now always deploy with the new Lambda version hashes (which is the new default in v3).
Run sls deploy, this time without additional --enforce-hash-update flag: that will restore the original descriptions on all Lambda functions.

Now your whole service is fully migrated to the new Lambda Hashing Algorithm.

If you do not want to temporarily override descriptions of your functions or would like to avoid creating unnecessary versions of your functions, you might want to use one of the following approaches:

Ensure that code for all your functions will change during deployment, remove provider.lambdaHashingVersion from your configuration, and run sls deploy. Due to the fact that all functions have code changed, all your functions will be migrated to new hashing algorithm. Please note that the change can be caused by e.g. upgrading a dependency used by all your functions so you can pair it with regular chores.
Add a dummy file that will be included in deployment artifacts for all your functions, remove provider.lambdaHashingVersion from your configuration, and run sls deploy. Due to the fact that all functions have code changed, all your functions will be migrated to new hashing algorithm.
If it is safe in your case (e.g. it's only development sandbox), you can also tear down the whole service by sls remove, remove provider.lambdaHashingVersion from your configuration, and run sls deploy. Newly recreated environment will be using new hashing algorithm.

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functions.md

functions.md

AWS Lambda Functions

Configuration

Permissions

Lambda Function URLs

Referencing container image as a target

Instruction set architecture

Runtime Management

SnapStart

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

Asynchronous invocation

Destinations

Maximum Event Age and Maximum Retry Attempts

EFS Configuration

Ephemeral storage

Lambda Hashing Algorithm migration

Files

functions.md

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functions.md

File metadata and controls

AWS Lambda Functions

Configuration

Permissions

Lambda Function URLs

Referencing container image as a target

Instruction set architecture

Runtime Management

SnapStart

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

Asynchronous invocation

Destinations

Maximum Event Age and Maximum Retry Attempts

EFS Configuration

Ephemeral storage

Lambda Hashing Algorithm migration