aws-solutions-constructs.aws-lambda-kinesisfirehose 2.70.0

Creator: bradpython12

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Description:

awssolutionsconstructs.awslambdakinesisfirehose 2.70.0

aws-lambda-kinesisfirehose module
---






Reference Documentation:
https://docs.aws.amazon.com/solutions/latest/constructs/







Language
Package




Python
aws_solutions_constructs.aws_lambda_kinesisfirehose


Typescript
@aws-solutions-constructs/aws-lambda-kinesisfirehose


Java
software.amazon.awsconstructs.services.lambdakinesisfirehose



Overview
This AWS Solutions Construct implements an AWS Lambda function connected to an existing Amazon Kinesis Firehose Delivery Stream.
Here is a minimal deployable pattern definition :
Typescript
import { Construct } from 'constructs';
import { Stack, StackProps } from 'aws-cdk-lib';
import { LambdaToS3 } from '@aws-solutions-constructs/aws-lambda-kinesisfirehose';
import * as lambda from 'aws-cdk-lib/aws-lambda';

// The construct requires an existing Firehose Delivery Stream, this can be created in raw CDK or extracted
// from a previously instantiated construct that created an Firehose Delivery Stream
const existingFirehoseDeliveryStream = previouslyCreatedKinesisFirehoseToS3Construct.kinesisFirehose;

new LambdaToKinesisFirehose(this, 'LambdaToFirehosePattern', {
lambdaFunctionProps: {
runtime: lambda.Runtime.NODEJS_16_X,
handler: 'index.handler',
code: lambda.Code.fromAsset(`lambda`)
},
existingKinesisFirehose: existingFirehoseDeliveryStream
});

Python
from aws_solutions_constructs.aws_lambda_kinesisfirehose import LambdaToKinesisFirehose
from aws_cdk import (
aws_lambda as _lambda,
Stack
)
from constructs import Construct

# The construct requires an existing Firehose Delivery Stream, this can be created in raw CDK or extracted
# from a previously instantiated construct that created an Firehose Delivery Stream
existingFirehoseDeliveryStream = previouslyCreatedKinesisFirehoseToS3Construct.kinesisFirehose;

LambdaToKinesisFirehose(self, 'LambdaToFirehosePattern',
existingKinesisFirehose=existingFirehoseDeliveryStream,
lambda_function_props=_lambda.FunctionProps(
code=_lambda.Code.from_asset('lambda'),
runtime=_lambda.Runtime.PYTHON_3_9,
handler='index.handler'
)
)

Java
import software.constructs.Construct;

import software.amazon.awscdk.Stack;
import software.amazon.awscdk.StackProps;
import software.amazon.awscdk.services.lambda.*;
import software.amazon.awscdk.services.lambda.Runtime;
import software.amazon.awsconstructs.services.lambdakinesisfirehose.*;

// The construct requires an existing Firehose Delivery Stream, this can be created in raw CDK or extracted
// from a previously instantiated construct that created an Firehose Delivery Stream
existingFirehoseDeliveryStream = previouslyCreatedKinesisFirehoseToS3Construct.kinesisFirehose;

new LambdaToKinesisFirehose(this, "LambdaToFirehosePattern", new LambdaToKinesisFirehoseProps.Builder()
.existingKinesisFirehose(existingFirehoseDeliveryStream)
.lambdaFunctionProps(new FunctionProps.Builder()
.runtime(Runtime.NODEJS_16_X)
.code(Code.fromAsset("lambda"))
.handler("index.handler")
.build())
.build());

Pattern Construct Props



Name
Type
Description




existingLambdaObj?
lambda.Function
Existing instance of Lambda Function object, providing both this and lambdaFunctionProps will cause an error.


lambdaFunctionProps?
lambda.FunctionProps
Optional user provided props to override the default props for the Lambda function.


existingKinesisFirehose
kinesisfirehose.CfnDeliveryStream
An existing Kinesis Firehose Delivery Stream to which the Lambda function can put data. Note - the delivery stream construct must have already been created and have the deliveryStreamName set. This construct will not create a new Delivery Stream.


existingVpc?
ec2.IVpc
An optional, existing VPC into which this pattern should be deployed. When deployed in a VPC, the Lambda function will use ENIs in the VPC to access network resources and an Interface Endpoint will be created in the VPC for Amazon Kinesis Data Firehose. If an existing VPC is provided, the deployVpc property cannot be true. This uses ec2.IVpc to allow clients to supply VPCs that exist outside the stack using the ec2.Vpc.fromLookup() method.


vpcProps?
ec2.VpcProps
Optional user provided properties to override the default properties for the new VPC. enableDnsHostnames, enableDnsSupport, natGateways and subnetConfiguration are set by the pattern, so any values for those properties supplied here will be overridden. If deployVpc is not true then this property will be ignored.


deployVpc?
boolean
Whether to create a new VPC based on vpcProps into which to deploy this pattern. Setting this to true will deploy the minimal, most private VPC to run the pattern: One isolated subnet in each Availability Zone used by the CDK programenableDnsHostnames and enableDnsSupport will both be set to trueIf this property is true then existingVpc cannot be specified. Defaults to false.


firehoseEnvironmentVariableName?
string
Optional Name for the Lambda function environment variable set to the name of the delivery stream. Default: FIREHOSE_DELIVERYSTREAM_NAME



Pattern Properties



Name
Type
Description




lambdaFunction
lambda.Function
Returns an instance of the Lambda function created by the pattern.


kinesisFirehose
kinesisfirehose.CfnDeliveryStream
The Kinesis Firehose Delivery Stream used by the construct.


vpc?
ec2.IVpc
Returns an interface on the VPC used by the pattern (if any). This may be a VPC created by the pattern or the VPC supplied to the pattern constructor.



Default settings
Out of the box implementation of the Construct without any override will set the following defaults:
AWS Lambda Function


Configure limited privilege access IAM role for Lambda function


Enable reusing connections with Keep-Alive for NodeJs Lambda function


Enable X-Ray Tracing


Set Environment Variables

(default) FIREHOSE_DELIVERYSTREAM_NAME
AWS_NODEJS_CONNECTION_REUSE_ENABLED



Amazon Kinesis Firehose Delivery Stream

This construct must be provided a configured Stream construct, it does not change this Stream.

Architecture


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License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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