I don't know whther its possible to deploy to Lambda Function using Code Deploy in CodePipeline as I don't see any Target there.
if its possible How we can set it in aws console?
I don't know whther its possible to deploy to Lambda Function using Code Deploy in CodePipeline
Yes it is.
There are various ways you can achieve this. The most straight forward way is to use CodePipeline that is triggered by a CodeCommit repository for a SAM package.
CodeDeploy will deploy the SAM package in a pipeline stage which will include your Lambda function.
Here is a tutorial from AWS Docs.
Related
Is this possible to deploy AWS Lambda Containers without using SAM.
Every article I found on internet is suggesting to use SAM to deploy.
As SAM is a wrapper on AWS cloud formation I want to use only cloud formation YAML to deploy lambda containers.
As you already know, you need to create a docker image of your lambda application and then push to AWS docker registry which is ECR, now there are several ways to deploy your lambda:
1- use AWS console, go into lambda in GUI choose "container image" in the options and provide ECR link (No cloudformation will be used this way)
2- create a SAM template and then use AWS CLI or AWS Console again to setup a cloudformation and SAM will be compiled to cloudformation later in the process.
3- directly create a cloudformation template and then use CLI or AWS console to deploy your lambda
4- use CDK to do your deployment
And maybe many other choices and methods. now depending on what exactly you want to do, ask for more specific detail.
I'm trying to figure out a way to come up with a CI/CD pipeline for CloudFormation. We use Cloudformation Console directly to deploy our infrastructure and app to the cloud.
Does anyone have any examples of how they have created a CI/CD pipeline using Jenkins or other types of CI tools to do some type of linting, CI, version control, and artifact deployment to Artifactory (or similar toolset)? I'd like to execute a pipeline once a new version of the cloud formation templates is uploaded to Artifactory.
You can always use CodePipeline.
see docs:
CodePipeline
CI:
I am using GitHub, so before i can merge a pull request, my code must pass 3 tests.
Those tests are 3 Codebuilds containers that run tests.
CD:
After my code merged it invoke a CodePipeline that use mainly CodeDeploy and CodeBuild.
About your goal:
I'd like to execute a pipeline once a new version of the cloud formation templates is uploaded to Artifactory.
I don't really think you need a pipeline for this.
Let assume your artifacts uploaded to s3 bucket called artifact-bucket.
You can create a CloudWatch rule that will execute StepFunctions state machine when file added to
artifact-bucket.
see docs:
Trigger StepFunctions from S3
You can easily deploy stack with StepFunctions.
Is it possible to create a new REST api gateway using codepipeline? I already have a terraform script to create the pipeline, but I want to know if there is a way to create a pipeline that will take my script and propogate it from a dev environment api gateway to a test environment? I am trying to automate the pipeline to possibly run the script for me once the code is updated in a code commit stash
Any suggestions would be greatly appreciated.
To run a script from AWS CodePipeline you can use AWS CodeBuild action in one of your CodePipeline stages.
With CodeBuild you can specify the list of commands you want to run, like installing and running terraform.
I have use case in amazon cloud, i'm using fargate cluster and cloudformation.
I want to do continuous deployment i.e on new image upload trigger i want to update the cloudformation stack with this new image, also run this automated deployment when client wants using manual trigger.
What should i use for continuous deployment, aws code deploy or aws lambda.
aws CodeDeploy has a provider CloudFormation with limited option and less control i believe.
aws lambda has a great control over CloudFormation client through its boto api.
I also read somewhere that when you get some limitations in CodeDeploy or CodePipeline you can integrate lambda to get rid of this limitation. So why not use lambda in the first place for continuous deployment only.
I'm very convinced about aws lambda over aws CodeDeploy after doing some research, However, i'm open for comments and suggestions.
You can use both of them to achieve perfect CI-CD implementation
If image gets uploaded the Lambda will be triggered and Lambda will be having your configurations and parameters
Using that it will call CodeDeploy to build your ECR images and It will get deployed to your Farget cluster
You can also achieve your second need using this implementation, manual trigger when client wants
In lambda you can trigger manualy passing parameters runtime
I hope this helps you
Is there any way to automate cloudformation templates deployment? I mean it would be awesome if I just push the changes in the code and somebody looking for those changes in the code and once they appear - deploy the updated template.
Yes, with AWS Code Pipeline !
http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/continuous-delivery-codepipeline.html
With AWS CloudFormation and AWS CodePipeline, you can use continuous delivery to automatically build and test changes to your AWS CloudFormation templates [...]
AWS CodePipeline has built-in integration with AWS CloudFormation, so you can specify AWS CloudFormation-specific actions, such as creating, updating, or deleting a stack, within a pipeline.
CodePipeline is a great way to do what you're looking for.
At Giftbit we do this is by having a Github Repo that has our CloudFormation template in it.
When we want to make a change, we make the changes on a branch in the repo, and create a pull request into the staging branch. CodePipeline monitors the staging branch then automates a CodeBuild to validate the templates, package any SubStacks, then creates a Change Set and Executes it.
Below are have some examples to help Quick Start anyone interested:
Continuous Integration CloudFormation Template Example
Serverless Application Model (SAM) that gets deployed