The CDK Construct Library for AWS::Lambda
Project description
AWS Lambda Construct Library
---This construct library allows you to define AWS Lambda Functions.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
import path as path
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_asset(path.join(__dirname, "lambda-handler"))
)
Handler Code
The lambda.Code
class includes static convenience methods for various types of
runtime code.
lambda.Code.fromBucket(bucket, key[, objectVersion])
- specify an S3 object that contains the archive of your runtime code.lambda.Code.fromInline(code)
- inline the handle code as a string. This is limited to supported runtimes and the code cannot exceed 4KiB.lambda.Code.fromAsset(path)
- specify a directory or a .zip file in the local filesystem which will be zipped and uploaded to S3 before deployment. See also bundling asset code.
The following example shows how to define a Python function and deploy the code
from the local directory my-lambda-handler
to it:
# Example automatically generated. See https://github.com/aws/jsii/issues/826
lambda_.Function(self, "MyLambda",
code=lambda_.Code.from_asset(path.join(__dirname, "my-lambda-handler")),
handler="index.main",
runtime=lambda_.Runtime.PYTHON_3_6
)
When deploying a stack that contains this code, the directory will be zip archived and then uploaded to an S3 bucket, then the exact location of the S3 objects will be passed when the stack is deployed.
During synthesis, the CDK expects to find a directory on disk at the asset directory specified. Note that we are referencing the asset directory relatively to our CDK project directory. This is especially important when we want to share this construct through a library. Different programming languages will have different techniques for bundling resources into libraries.
Execution Role
Lambda functions assume an IAM role during execution. In CDK by default, Lambda functions will use an autogenerated Role if one is not provided.
The autogenerated Role is automatically given permissions to execute the Lambda function. To reference the autogenerated Role:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_asset(path.join(__dirname, "lambda-handler")),
fn=fn, =.role
)
You can also provide your own IAM role. Provided IAM roles will not automatically be given permissions to execute the Lambda function. To provide a role and grant it appropriate permissions:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_asset(path.join(__dirname, "lambda-handler")),
role=my_role
)
my_role.add_managed_policy(ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaBasicExecutionRole"))
my_role.add_managed_policy(ManagedPolicy.from_aws_managed_policy_name("service-role/AWSLambdaVPCAccessExecutionRole"))
Versions and Aliases
You can use versions to manage the deployment of your AWS Lambda functions. For example, you can publish a new version of a function for beta testing without affecting users of the stable production version.
The function version includes the following information:
- The function code and all associated dependencies.
- The Lambda runtime that executes the function.
- All of the function settings, including the environment variables.
- A unique Amazon Resource Name (ARN) to identify this version of the function.
You can define one or more aliases for your AWS Lambda function. A Lambda alias is like a pointer to a specific Lambda function version. Users can access the function version using the alias ARN.
The fn.currentVersion
property can be used to obtain a lambda.Version
resource that represents the AWS Lambda function defined in your application.
Any change to your function's code or configuration will result in the creation
of a new version resource. You can specify options for this version through the
currentVersionOptions
property.
The
currentVersion
property is only supported when your AWS Lambda function uses eitherlambda.Code.fromAsset
orlambda.Code.fromInline
. Other types of code providers (such aslambda.Code.fromBucket
) require that you define alambda.Version
resource directly since the CDK is unable to determine if their contents had changed.
The version.addAlias()
method can be used to define an AWS Lambda alias that
points to a specific version.
The following example defines an alias named live
which will always point to a
version that represents the function as defined in your CDK app. When you change
your lambda code or configuration, a new resource will be created. You can
specify options for the current version through the currentVersionOptions
property.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
fn = lambda_.Function(self, "MyFunction",
current_version_options={
"removal_policy": RemovalPolicy.RETAIN, # retain old versions
"retry_attempts": 1
}
)
fn.current_version.add_alias("live")
NOTE: The
fn.latestVersion
property returns alambda.IVersion
which represents the$LATEST
pseudo-version. Most AWS services require a specific AWS Lambda version, and won't allow you to use$LATEST
. Therefore, you would normally want to uselambda.currentVersion
.
Layers
The lambda.LayerVersion
class can be used to define Lambda layers and manage
granting permissions to other AWS accounts or organizations.
# Example automatically generated. See https://github.com/aws/jsii/issues/826
layer = lambda_.LayerVersion(stack, "MyLayer",
code=lambda_.Code.from_asset(path.join(__dirname, "layer-code")),
compatible_runtimes=[lambda_.Runtime.NODEJS_10_X],
license="Apache-2.0",
description="A layer to test the L2 construct"
)
# To grant usage by other AWS accounts
layer.add_permission("remote-account-grant", account_id=aws_account_id)
# To grant usage to all accounts in some AWS Ogranization
# layer.grantUsage({ accountId: '*', organizationId });
lambda_.Function(stack, "MyLayeredLambda",
code=lambda_.InlineCode("foo"),
handler="index.handler",
runtime=lambda_.Runtime.NODEJS_10_X,
layers=[layer]
)
Event Rule Target
You can use an AWS Lambda function as a target for an Amazon CloudWatch event rule:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_events_targets as targets
rule.add_target(targets.LambdaFunction(my_function))
Event Sources
AWS Lambda supports a variety of event sources.
In most cases, it is possible to trigger a function as a result of an event by
using one of the add<Event>Notification
methods on the source construct. For
example, the s3.Bucket
construct has an onEvent
method which can be used to
trigger a Lambda when an event, such as PutObject occurs on an S3 bucket.
An alternative way to add event sources to a function is to use function.addEventSource(source)
.
This method accepts an IEventSource
object. The module @aws-cdk/aws-lambda-event-sources
includes classes for the various event sources supported by AWS Lambda.
For example, the following code adds an SQS queue as an event source for a function:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
from aws_cdk.aws_lambda_event_sources import SqsEventSource
fn.add_event_source(SqsEventSource(queue))
The following code adds an S3 bucket notification as an event source:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
from aws_cdk.aws_lambda_event_sources import S3EventSource
fn.add_event_source(S3EventSource(bucket,
events=[s3.EventType.OBJECT_CREATED, s3.EventType.OBJECT_DELETED],
filters=[NotificationKeyFilter(prefix="subdir/")]
))
See the documentation for the @aws-cdk/aws-lambda-event-sources module for more details.
Lambda with DLQ
A dead-letter queue can be automatically created for a Lambda function by
setting the deadLetterQueueEnabled: true
configuration.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_inline("exports.handler = function(event, ctx, cb) { return cb(null, \"hi\"); }"),
dead_letter_queue_enabled=True
)
It is also possible to provide a dead-letter queue instead of getting a new queue created:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
import aws_cdk.aws_sqs as sqs
dlq = sqs.Queue(self, "DLQ")
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_inline("exports.handler = function(event, ctx, cb) { return cb(null, \"hi\"); }"),
dead_letter_queue=dlq
)
See the AWS documentation to learn more about AWS Lambdas and DLQs.
Lambda with X-Ray Tracing
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_inline("exports.handler = function(event, ctx, cb) { return cb(null, \"hi\"); }"),
tracing=lambda_.Tracing.ACTIVE
)
See the AWS documentation to learn more about AWS Lambda's X-Ray support.
Lambda with Profiling
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_inline("exports.handler = function(event, ctx, cb) { return cb(null, \"hi\"); }"),
profiling=True
)
See the AWS documentation to learn more about AWS Lambda's Profiling support.
Lambda with Reserved Concurrent Executions
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_lambda as lambda_
fn = lambda_.Function(self, "MyFunction",
runtime=lambda_.Runtime.NODEJS_10_X,
handler="index.handler",
code=lambda_.Code.from_inline("exports.handler = function(event, ctx, cb) { return cb(null, \"hi\"); }"),
reserved_concurrent_executions=100
)
See the AWS documentation managing concurrency.
AutoScaling
You can use Application AutoScaling to automatically configure the provisioned concurrency for your functions. AutoScaling can be set to track utilization or be based on a schedule. To configure AutoScaling on a function alias:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
alias = lambda_.Alias(stack, "Alias",
alias_name="prod",
version=version
)
# Create AutoScaling target
as = alias.add_auto_scaling(max_capacity=50)
# Configure Target Tracking
as.scale_on_utilization(
utilization_target=0.5
)
# Configure Scheduled Scaling
as.scale_on_schedule("ScaleUpInTheMorning",
schedule=appscaling.Schedule.cron(hour="8", minute="0"),
min_capacity=20
)
# Example automatically generated. See https://github.com/aws/jsii/issues/826
import aws_cdk.aws_applicationautoscaling as appscaling
import aws_cdk.core as cdk
import ...lib as lambda_
#
# Stack verification steps:
# aws application-autoscaling describe-scalable-targets --service-namespace lambda --resource-ids function:<function name>:prod
# has a minCapacity of 3 and maxCapacity of 50
#
class TestStack(cdk.Stack):
def __init__(self, scope, id):
super().__init__(scope, id)
fn = lambda_.Function(self, "MyLambda",
code=lambda_.InlineCode("exports.handler = async () => {\nconsole.log('hello world');\n};"),
handler="index.handler",
runtime=lambda_.Runtime.NODEJS_10_X
)
version = fn.add_version("1", undefined, "integ-test")
alias = lambda_.Alias(self, "Alias",
alias_name="prod",
version=version
)
scaling_target = alias.add_auto_scaling(min_capacity=3, max_capacity=50)
scaling_target.scale_on_utilization(
utilization_target=0.5
)
scaling_target.scale_on_schedule("ScaleUpInTheMorning",
schedule=appscaling.Schedule.cron(hour="8", minute="0"),
min_capacity=20
)
scaling_target.scale_on_schedule("ScaleDownAtNight",
schedule=appscaling.Schedule.cron(hour="20", minute="0"),
max_capacity=20
)
cdk.CfnOutput(self, "FunctionName",
value=fn.function_name
)
app = cdk.App()
TestStack(app, "aws-lambda-autoscaling")
app.synth()
See the AWS documentation on autoscaling lambda functions.
Log Group
Lambda functions automatically create a log group with the name /aws/lambda/<function-name>
upon first execution with
log data set to never expire.
The logRetention
property can be used to set a different expiration period.
It is possible to obtain the function's log group as a logs.ILogGroup
by calling the logGroup
property of the
Function
construct.
By default, CDK uses the AWS SDK retry options when creating a log group. The logRetentionRetryOptions
property
allows you to customize the maximum number of retries and base backoff duration.
Note that, if either logRetention
is set or logGroup
property is called, a CloudFormation custom
resource is added
to the stack that pre-creates the log group as part of the stack deployment, if it already doesn't exist, and sets the
correct log retention period (never expire, by default).
Further note that, if the log group already exists and the logRetention
is not set, the custom resource will reset
the log retention to never expire even if it was configured with a different value.
FileSystem Access
You can configure a function to mount an Amazon Elastic File System (Amazon EFS) to a
directory in your runtime environment with the filesystem
property. To access Amazon EFS
from lambda function, the Amazon EFS access point will be required.
The following sample allows the lambda function to mount the Amazon EFS access point to /mnt/msg
in the runtime environment and access the filesystem with the POSIX identity defined in posixUser
.
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
# create a new Amazon EFS filesystem
file_system = efs.FileSystem(stack, "Efs", vpc=vpc)
# create a new access point from the filesystem
access_point = file_system.add_access_point("AccessPoint",
# set /export/lambda as the root of the access point
path="/export/lambda",
# as /export/lambda does not exist in a new efs filesystem, the efs will create the directory with the following createAcl
create_acl={
"owner_uid": "1001",
"owner_gid": "1001",
"permissions": "750"
},
# enforce the POSIX identity so lambda function will access with this identity
posix_user={
"uid": "1001",
"gid": "1001"
}
)
fn = lambda_.Function(stack, "MyLambda",
code=code,
handler=handler,
runtime=runtime,
vpc=vpc,
# mount the access point to /mnt/msg in the lambda runtime environment
filesystem=lambda_.FileSystem.from_efs_access_point(access_point, "/mnt/msg")
)
Singleton Function
The SingletonFunction
construct is a way to guarantee that a lambda function will be guaranteed to be part of the stack,
once and only once, irrespective of how many times the construct is declared to be part of the stack. This is guaranteed
as long as the uuid
property and the optional lambdaPurpose
property stay the same whenever they're declared into the
stack.
A typical use case of this function is when a higher level construct needs to declare a Lambda function as part of it but
needs to guarantee that the function is declared once. However, a user of this higher level construct can declare it any
number of times and with different properties. Using SingletonFunction
here with a fixed uuid
will guarantee this.
For example, the LogRetention
construct requires only one single lambda function for all different log groups whose
retention it seeks to manage.
Bundling Asset Code
When using lambda.Code.fromAsset(path)
it is possible to bundle the code by running a
command in a Docker container. The asset path will be mounted at /asset-input
. The
Docker container is responsible for putting content at /asset-output
. The content at
/asset-output
will be zipped and used as Lambda code.
Example with Python:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
lambda_.Function(self, "Function",
code=lambda_.Code.from_asset(path.join(__dirname, "my-python-handler"),
bundling={
"image": lambda_.Runtime.PYTHON_3_6.bundling_docker_image,
"command": ["bash", "-c", "\n pip install -r requirements.txt -t /asset-output &&\n cp -au . /asset-output\n "
]
}
),
runtime=lambda_.Runtime.PYTHON_3_6,
handler="index.handler"
)
Runtimes expose a bundlingDockerImage
property that points to the AWS SAM build image.
Use cdk.BundlingDockerImage.fromRegistry(image)
to use an existing image or
cdk.BundlingDockerImage.fromAsset(path)
to build a specific image:
# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826
import aws_cdk.core as cdk
lambda_.Function(self, "Function",
code=lambda_.Code.from_asset("/path/to/handler",
bundling={
"image": cdk.BundlingDockerImage.from_asset("/path/to/dir/with/DockerFile",
build_args={
"ARG1": "value1"
}
),
"command": ["my", "cool", "command"]
}
)
)
Language-specific APIs
Language-specific higher level constructs are provided in separate modules:
- Node.js:
@aws-cdk/aws-lambda-nodejs
- Python:
@aws-cdk/aws-lambda-python
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