Tag Archives: Developer Tools

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Use Slack ChatOps to Deploy Your Code – How to Integrate Your Pipeline in AWS CodePipeline with Your Slack Channel

Post Syndicated from Rumi Olsen original https://aws.amazon.com/blogs/devops/use-slack-chatops-to-deploy-your-code-how-to-integrate-your-pipeline-in-aws-codepipeline-with-your-slack-channel/

Slack is widely used by DevOps and development teams to communicate status. Typically, when a build has been tested and is ready to be promoted to a staging environment, a QA engineer or DevOps engineer kicks off the deployment. Using Slack in a ChatOps collaboration model, the promotion can be done in a single click from a Slack channel. And because the promotion happens through a Slack channel, the whole development team knows what’s happening without checking email.

In this blog post, I will show you how to integrate AWS services with a Slack application. I use an interactive message button and incoming webhook to promote a stage with a single click.

To follow along with the steps in this post, you’ll need a pipeline in AWS CodePipeline. If you don’t have a pipeline, the fastest way to create one for this use case is to use AWS CodeStar. Go to the AWS CodeStar console and select the Static Website template (shown in the screenshot). AWS CodeStar will create a pipeline with an AWS CodeCommit repository and an AWS CodeDeploy deployment for you. After the pipeline is created, you will need to add a manual approval stage.

You’ll also need to build a Slack app with webhooks and interactive components, write two Lambda functions, and create an API Gateway API and a SNS topic.

As you’ll see in the following diagram, when I make a change and merge a new feature into the master branch in AWS CodeCommit, the check-in kicks off my CI/CD pipeline in AWS CodePipeline. When CodePipeline reaches the approval stage, it sends a notification to Amazon SNS, which triggers an AWS Lambda function (ApprovalRequester).

The Slack channel receives a prompt that looks like the following screenshot. When I click Yes to approve the build promotion, the approval result is sent to CodePipeline through API Gateway and Lambda (ApprovalHandler). The pipeline continues on to deploy the build to the next environment.

Create a Slack app

For App Name, type a name for your app. For Development Slack Workspace, choose the name of your workspace. You’ll see in the following screenshot that my workspace is AWS ChatOps.

After the Slack application has been created, you will see the Basic Information page, where you can create incoming webhooks and enable interactive components.

To add incoming webhooks:

  1. Under Add features and functionality, choose Incoming Webhooks. Turn the feature on by selecting Off, as shown in the following screenshot.
  2. Now that the feature is turned on, choose Add New Webhook to Workspace. In the process of creating the webhook, Slack lets you choose the channel where messages will be posted.
  3. After the webhook has been created, you’ll see its URL. You will use this URL when you create the Lambda function.

If you followed the steps in the post, the pipeline should look like the following.

Write the Lambda function for approval requests

This Lambda function is invoked by the SNS notification. It sends a request that consists of an interactive message button to the incoming webhook you created earlier.  The following sample code sends the request to the incoming webhook. WEBHOOK_URL and SLACK_CHANNEL are the environment variables that hold values of the webhook URL that you created and the Slack channel where you want the interactive message button to appear.

# This function is invoked via SNS when the CodePipeline manual approval action starts.
# It will take the details from this approval notification and sent an interactive message to Slack that allows users to approve or cancel the deployment.

import os
import json
import logging
import urllib.parse

from base64 import b64decode
from urllib.request import Request, urlopen
from urllib.error import URLError, HTTPError

# This is passed as a plain-text environment variable for ease of demonstration.
# Consider encrypting the value with KMS or use an encrypted parameter in Parameter Store for production deployments.
SLACK_WEBHOOK_URL = os.environ['SLACK_WEBHOOK_URL']
SLACK_CHANNEL = os.environ['SLACK_CHANNEL']

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    message = event["Records"][0]["Sns"]["Message"]
    
    data = json.loads(message) 
    token = data["approval"]["token"]
    codepipeline_name = data["approval"]["pipelineName"]
    
    slack_message = {
        "channel": SLACK_CHANNEL,
        "text": "Would you like to promote the build to production?",
        "attachments": [
            {
                "text": "Yes to deploy your build to production",
                "fallback": "You are unable to promote a build",
                "callback_id": "wopr_game",
                "color": "#3AA3E3",
                "attachment_type": "default",
                "actions": [
                    {
                        "name": "deployment",
                        "text": "Yes",
                        "style": "danger",
                        "type": "button",
                        "value": json.dumps({"approve": True, "codePipelineToken": token, "codePipelineName": codepipeline_name}),
                        "confirm": {
                            "title": "Are you sure?",
                            "text": "This will deploy the build to production",
                            "ok_text": "Yes",
                            "dismiss_text": "No"
                        }
                    },
                    {
                        "name": "deployment",
                        "text": "No",
                        "type": "button",
                        "value": json.dumps({"approve": False, "codePipelineToken": token, "codePipelineName": codepipeline_name})
                    }  
                ]
            }
        ]
    }

    req = Request(SLACK_WEBHOOK_URL, json.dumps(slack_message).encode('utf-8'))

    response = urlopen(req)
    response.read()
    
    return None

 

Create a SNS topic

Create a topic and then create a subscription that invokes the ApprovalRequester Lambda function. You can configure the manual approval action in the pipeline to send a message to this SNS topic when an approval action is required. When the pipeline reaches the approval stage, it sends a notification to this SNS topic. SNS publishes a notification to all of the subscribed endpoints. In this case, the Lambda function is the endpoint. Therefore, it invokes and executes the Lambda function. For information about how to create a SNS topic, see Create a Topic in the Amazon SNS Developer Guide.

Write the Lambda function for handling the interactive message button

This Lambda function is invoked by API Gateway. It receives the result of the interactive message button whether or not the build promotion was approved. If approved, an API call is made to CodePipeline to promote the build to the next environment. If not approved, the pipeline stops and does not move to the next stage.

The Lambda function code might look like the following. SLACK_VERIFICATION_TOKEN is the environment variable that contains your Slack verification token. You can find your verification token under Basic Information on Slack manage app page. When you scroll down, you will see App Credential. Verification token is found under the section.

# This function is triggered via API Gateway when a user acts on the Slack interactive message sent by approval_requester.py.

from urllib.parse import parse_qs
import json
import os
import boto3

SLACK_VERIFICATION_TOKEN = os.environ['SLACK_VERIFICATION_TOKEN']

#Triggered by API Gateway
#It kicks off a particular CodePipeline project
def lambda_handler(event, context):
	#print("Received event: " + json.dumps(event, indent=2))
	body = parse_qs(event['body'])
	payload = json.loads(body['payload'][0])

	# Validate Slack token
	if SLACK_VERIFICATION_TOKEN == payload['token']:
		send_slack_message(json.loads(payload['actions'][0]['value']))
		
		# This will replace the interactive message with a simple text response.
		# You can implement a more complex message update if you would like.
		return  {
			"isBase64Encoded": "false",
			"statusCode": 200,
			"body": "{\"text\": \"The approval has been processed\"}"
		}
	else:
		return  {
			"isBase64Encoded": "false",
			"statusCode": 403,
			"body": "{\"error\": \"This request does not include a vailid verification token.\"}"
		}


def send_slack_message(action_details):
	codepipeline_status = "Approved" if action_details["approve"] else "Rejected"
	codepipeline_name = action_details["codePipelineName"]
	token = action_details["codePipelineToken"] 

	client = boto3.client('codepipeline')
	response_approval = client.put_approval_result(
							pipelineName=codepipeline_name,
							stageName='Approval',
							actionName='ApprovalOrDeny',
							result={'summary':'','status':codepipeline_status},
							token=token)
	print(response_approval)

 

Create the API Gateway API

  1. In the Amazon API Gateway console, create a resource called InteractiveMessageHandler.
  2. Create a POST method.
    • For Integration type, choose Lambda Function.
    • Select Use Lambda Proxy integration.
    • From Lambda Region, choose a region.
    • In Lambda Function, type a name for your function.
  3.  Deploy to a stage.

For more information, see Getting Started with Amazon API Gateway in the Amazon API Developer Guide.

Now go back to your Slack application and enable interactive components.

To enable interactive components for the interactive message (Yes) button:

  1. Under Features, choose Interactive Components.
  2. Choose Enable Interactive Components.
  3. Type a request URL in the text box. Use the invoke URL in Amazon API Gateway that will be called when the approval button is clicked.

Now that all the pieces have been created, run the solution by checking in a code change to your CodeCommit repo. That will release the change through CodePipeline. When the CodePipeline comes to the approval stage, it will prompt to your Slack channel to see if you want to promote the build to your staging or production environment. Choose Yes and then see if your change was deployed to the environment.

Conclusion

That is it! You have now created a Slack ChatOps solution using AWS CodeCommit, AWS CodePipeline, AWS Lambda, Amazon API Gateway, and Amazon Simple Notification Service.

Now that you know how to do this Slack and CodePipeline integration, you can use the same method to interact with other AWS services using API Gateway and Lambda. You can also use Slack’s slash command to initiate an action from a Slack channel, rather than responding in the way demonstrated in this post.

Announcing Local Build Support for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/announcing-local-build-support-for-aws-codebuild/

Today, we’re excited to announce local build support in AWS CodeBuild.

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

In this blog post, I’ll show you how to set up CodeBuild locally to build and test a sample Java application.

By building an application on a local machine you can:

  • Test the integrity and contents of a buildspec file locally.
  • Test and build an application locally before committing.
  • Identify and fix errors quickly from your local development environment.

Prerequisites

In this post, I am using AWS Cloud9 IDE as my development environment.

If you would like to use AWS Cloud9 as your IDE, follow the express setup steps in the AWS Cloud9 User Guide.

The AWS Cloud9 IDE comes with Docker and Git already installed. If you are going to use your laptop or desktop machine as your development environment, install Docker and Git before you start.

Steps to build CodeBuild image locally

Run git clone https://github.com/aws/aws-codebuild-docker-images.git to download this repository to your local machine.

$ git clone https://github.com/aws/aws-codebuild-docker-images.git

Lets build a local CodeBuild image for JDK 8 environment. The Dockerfile for JDK 8 is present in /aws-codebuild-docker-images/ubuntu/java/openjdk-8.

Edit the Dockerfile to remove the last line ENTRYPOINT [“dockerd-entrypoint.sh”] and save the file.

Run cd ubuntu/java/openjdk-8 to change the directory in your local workspace.

Run docker build -t aws/codebuild/java:openjdk-8 . to build the Docker image locally. This command will take few minutes to complete.

$ cd aws-codebuild-docker-images
$ cd ubuntu/java/openjdk-8
$ docker build -t aws/codebuild/java:openjdk-8 .

Steps to setup CodeBuild local agent

Run the following Docker pull command to download the local CodeBuild agent.

$ docker pull amazon/aws-codebuild-local:latest --disable-content-trust=false

Now you have the local agent image on your machine and can run a local build.

Run the following git command to download a sample Java project.

$ git clone https://github.com/karthiksambandam/sample-web-app.git

Steps to use the local agent to build a sample project

Let’s build the sample Java project using the local agent.

Execute the following Docker command to run the local agent and build the sample web app repository you cloned earlier.

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

Note: We need to provide three environment variables namely  IMAGE_NAME, SOURCE and ARTIFACTS.

IMAGE_NAME: The name of your build environment image.

SOURCE: The absolute path to your source code directory.

ARTIFACTS: The absolute path to your artifact output folder.

When you run the sample project, you get a runtime error that says the YAML file does not exist. This is because a buildspec.yml file is not included in the sample web project. AWS CodeBuild requires a buildspec.yml to run a build. For more information about buildspec.yml, see Build Spec Example in the AWS CodeBuild User Guide.

Let’s add a buildspec.yml file with the following content to the sample-web-app folder and then rebuild the project.

version: 0.2

phases:
  build:
    commands:
      - echo Build started on `date`
      - mvn install

artifacts:
  files:
    - target/javawebdemo.war

$ docker run -it -v /var/run/docker.sock:/var/run/docker.sock -e "IMAGE_NAME=aws/codebuild/java:openjdk-8" -e "ARTIFACTS=/home/ec2-user/environment/artifacts" -e "SOURCE=/home/ec2-user/environment/sample-web-app" amazon/aws-codebuild-local

This time your build should be successful. Upon successful execution, look in the /artifacts folder for the final built artifacts.zip file to validate.

Conclusion:

In this blog post, I showed you how to quickly set up the CodeBuild local agent to build projects right from your local desktop machine or laptop. As you see, local builds can improve developer productivity by helping you identify and fix errors quickly.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

CI/CD with Data: Enabling Data Portability in a Software Delivery Pipeline with AWS Developer Tools, Kubernetes, and Portworx

Post Syndicated from Kausalya Rani Krishna Samy original https://aws.amazon.com/blogs/devops/cicd-with-data-enabling-data-portability-in-a-software-delivery-pipeline-with-aws-developer-tools-kubernetes-and-portworx/

This post is written by Eric Han – Vice President of Product Management Portworx and Asif Khan – Solutions Architect

Data is the soul of an application. As containers make it easier to package and deploy applications faster, testing plays an even more important role in the reliable delivery of software. Given that all applications have data, development teams want a way to reliably control, move, and test using real application data or, at times, obfuscated data.

For many teams, moving application data through a CI/CD pipeline, while honoring compliance and maintaining separation of concerns, has been a manual task that doesn’t scale. At best, it is limited to a few applications, and is not portable across environments. The goal should be to make running and testing stateful containers (think databases and message buses where operations are tracked) as easy as with stateless (such as with web front ends where they are often not).

Why is state important in testing scenarios? One reason is that many bugs manifest only when code is tested against real data. For example, we might simply want to test a database schema upgrade but a small synthetic dataset does not exercise the critical, finer corner cases in complex business logic. If we want true end-to-end testing, we need to be able to easily manage our data or state.

In this blog post, we define a CI/CD pipeline reference architecture that can automate data movement between applications. We also provide the steps to follow to configure the CI/CD pipeline.

 

Stateful Pipelines: Need for Portable Volumes

As part of continuous integration, testing, and deployment, a team may need to reproduce a bug found in production against a staging setup. Here, the hosting environment is comprised of a cluster with Kubernetes as the scheduler and Portworx for persistent volumes. The testing workflow is then automated by AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild.

Portworx offers Kubernetes storage that can be used to make persistent volumes portable between AWS environments and pipelines. The addition of Portworx to the AWS Developer Tools continuous deployment for Kubernetes reference architecture adds persistent storage and storage orchestration to a Kubernetes cluster. The example uses MongoDB as the demonstration of a stateful application. In practice, the workflow applies to any containerized application such as Cassandra, MySQL, Kafka, and Elasticsearch.

Using the reference architecture, a developer calls CodePipeline to trigger a snapshot of the running production MongoDB database. Portworx then creates a block-based, writable snapshot of the MongoDB volume. Meanwhile, the production MongoDB database continues serving end users and is uninterrupted.

Without the Portworx integrations, a manual process would require an application-level backup of the database instance that is outside of the CI/CD process. For larger databases, this could take hours and impact production. The use of block-based snapshots follows best practices for resilient and non-disruptive backups.

As part of the workflow, CodePipeline deploys a new MongoDB instance for staging onto the Kubernetes cluster and mounts the second Portworx volume that has the data from production. CodePipeline triggers the snapshot of a Portworx volume through an AWS Lambda function, as shown here

 

 

 

AWS Developer Tools with Kubernetes: Integrated Workflow with Portworx

In the following workflow, a developer is testing changes to a containerized application that calls on MongoDB. The tests are performed against a staging instance of MongoDB. The same workflow applies if changes were on the server side. The original production deployment is scheduled as a Kubernetes deployment object and uses Portworx as the storage for the persistent volume.

The continuous deployment pipeline runs as follows:

  • Developers integrate bug fix changes into a main development branch that gets merged into a CodeCommit master branch.
  • Amazon CloudWatch triggers the pipeline when code is merged into a master branch of an AWS CodeCommit repository.
  • AWS CodePipeline sends the new revision to AWS CodeBuild, which builds a Docker container image with the build ID.
  • AWS CodeBuild pushes the new Docker container image tagged with the build ID to an Amazon ECR registry.
  • Kubernetes downloads the new container (for the database client) from Amazon ECR and deploys the application (as a pod) and staging MongoDB instance (as a deployment object).
  • AWS CodePipeline, through a Lambda function, calls Portworx to snapshot the production MongoDB and deploy a staging instance of MongoDB• Portworx provides a snapshot of the production instance as the persistent storage of the staging MongoDB
    • The MongoDB instance mounts the snapshot.

At this point, the staging setup mimics a production environment. Teams can run integration and full end-to-end tests, using partner tooling, without impacting production workloads. The full pipeline is shown here.

 

Summary

This reference architecture showcases how development teams can easily move data between production and staging for the purposes of testing. Instead of taking application-specific manual steps, all operations in this CodePipeline architecture are automated and tracked as part of the CI/CD process.

This integrated experience is part of making stateful containers as easy as stateless. With AWS CodePipeline for CI/CD process, developers can easily deploy stateful containers onto a Kubernetes cluster with Portworx storage and automate data movement within their process.

The reference architecture and code are available on GitHub:

● Reference architecture: https://github.com/portworx/aws-kube-codesuite
● Lambda function source code for Portworx additions: https://github.com/portworx/aws-kube-codesuite/blob/master/src/kube-lambda.py

For more information about persistent storage for containers, visit the Portworx website. For more information about Code Pipeline, see the AWS CodePipeline User Guide.

Secure Build with AWS CodeBuild and LayeredInsight

Post Syndicated from Asif Khan original https://aws.amazon.com/blogs/devops/secure-build-with-aws-codebuild-and-layeredinsight/

This post is written by Asif Awan, Chief Technology Officer of Layered InsightSubin Mathew – Software Development Manager for AWS CodeBuild, and Asif Khan – Solutions Architect

Enterprises adopt containers because they recognize the benefits: speed, agility, portability, and high compute density. They understand how accelerating application delivery and deployment pipelines makes it possible to rapidly slipstream new features to customers. Although the benefits are indisputable, this acceleration raises concerns about security and corporate compliance with software governance. In this blog post, I provide a solution that shows how Layered Insight, the pioneer and global leader in container-native application protection, can be used with seamless application build and delivery pipelines like those available in AWS CodeBuild to address these concerns.

Layered Insight solutions

Layered Insight enables organizations to unify DevOps and SecOps by providing complete visibility and control of containerized applications. Using the industry’s first embedded security approach, Layered Insight solves the challenges of container performance and protection by providing accurate insight into container images, adaptive analysis of running containers, and automated enforcement of container behavior.

 

AWS CodeBuild

AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. With CodeBuild, you don’t need to provision, manage, and scale your own build servers. CodeBuild scales continuously and processes multiple builds concurrently, so your builds are not left waiting in a queue. You can get started quickly by using prepackaged build environments, or you can create custom build environments that use your own build tools.

 

Problem Definition

Security and compliance concerns span the lifecycle of application containers. Common concerns include:

Visibility into the container images. You need to verify the software composition information of the container image to determine whether known vulnerabilities associated with any of the software packages and libraries are included in the container image.

Governance of container images is critical because only certain open source packages/libraries, of specific versions, should be included in the container images. You need support for mechanisms for blacklisting all container images that include a certain version of a software package/library, or only allowing open source software that come with a specific type of license (such as Apache, MIT, GPL, and so on). You need to be able to address challenges such as:

·       Defining the process for image compliance policies at the enterprise, department, and group levels.

·       Preventing the images that fail the compliance checks from being deployed in critical environments, such as staging, pre-prod, and production.

Visibility into running container instances is critical, including:

·       CPU and memory utilization.

·       Security of the build environment.

·       All activities (system, network, storage, and application layer) of the application code running in each container instance.

Protection of running container instances that is:

·       Zero-touch to the developers (not an SDK-based approach).

·       Zero touch to the DevOps team and doesn’t limit the portability of the containerized application.

·       This protection must retain the option to switch to a different container stack or orchestration layer, or even to a different Container as a Service (CaaS ).

·       And it must be a fully automated solution to SecOps, so that the SecOps team doesn’t have to manually analyze and define detailed blacklist and whitelist policies.

 

Solution Details

In AWS CodeCommit, we have three projects:
●     “Democode” is a simple Java application, with one buildspec to build the app into a Docker container (run by build-demo-image CodeBuild project), and another to instrument said container (instrument-image CodeBuild project). The resulting container is stored in ECR repo javatestasjavatest:20180415-layered. This instrumented container is running in AWS Fargate cluster demo-java-appand can be seen in the Layered Insight runtime console as the javatestapplication in us-east-1.
●     aws-codebuild-docker-imagesis a clone of the official aws-codebuild-docker-images repo on GitHub . This CodeCommit project is used by the build-python-builder CodeBuild project to build the python 3.3.6 codebuild image and is stored at the codebuild-python ECR repo. We then manually instructed the Layered Insight console to instrument the image.
●     scan-java-imagecontains just a buildspec.yml file. This file is used by the scan-java-image CodeBuild project to instruct Layered Assessment to perform a vulnerability scan of the javatest container image built previously, and then run the scan results through a compliance policy that states there should be no medium vulnerabilities. This build fails — but in this case that is a success: the scan completes successfully, but compliance fails as there are medium-level issues found in the scan.

This build is performed using the instrumented version of the Python 3.3.6 CodeBuild image, so the activity of the processes running within the build are recorded each time within the LI console.

Build container image

Create or use a CodeCommit project with your application. To build this image and store it in Amazon Elastic Container Registry (Amazon ECR), add a buildspec file to the project and build a container image and create a CodeBuild project.

Scan container image

Once the image is built, create a new buildspec in the same project or a new one that looks similar to below (update ECR URL as necessary):

version: 0.2
phases:
  pre_build:
    commands:
      - echo Pulling down LI Scan API client scripts
      - git clone https://github.com/LayeredInsight/scan-api-example-python.git
      - echo Setting up LI Scan API client
      - cd scan-api-example-python
      - pip install layint_scan_api
      - pip install -r requirements.txt
  build:
    commands:
      - echo Scanning container started on `date`
      - IMAGEID=$(./li_add_image --name <aws-region>.amazonaws.com/javatest:20180415)
      - ./li_wait_for_scan -v --imageid $IMAGEID
      - ./li_run_image_compliance -v --imageid $IMAGEID --policyid PB15260f1acb6b2aa5b597e9d22feffb538256a01fbb4e5a95

Add the buildspec file to the git repo, push it, and then build a CodeBuild project using with the instrumented Python 3.3.6 CodeBuild image at <aws-region>.amazonaws.com/codebuild-python:3.3.6-layered. Set the following environment variables in the CodeBuild project:
●     LI_APPLICATIONNAME – name of the build to display
●     LI_LOCATION – location of the build project to display
●     LI_API_KEY – ApiKey:<key-name>:<api-key>
●     LI_API_HOST – location of the Layered Insight API service

Instrument container image

Next, to instrument the new container image:

  1. In the Layered Insight runtime console, ensure that the ECR registry and credentials are defined (click the Setup icon and the ‘+’ sign on the top right of the screen to add a new container registry). Note the name given to the registry in the console, as this needs to be referenced in the li_add_imagecommand in the script, below.
  2. Next, add a new buildspec (with a new name) to the CodeCommit project, such as the one shown below. This code will download the Layered Insight runtime client, and use it to instruct the Layered Insight service to instrument the image that was just built:
    version: 0.2
    phases:
    pre_build:
    commands:
    echo Pulling down LI API Runtime client scripts
    git clone https://github.com/LayeredInsight/runtime-api-example-python
    echo Setting up LI API client
    cd runtime-api-example-python
    pip install layint-runtime-api
    pip install -r requirements.txt
    build:
    commands:
    echo Instrumentation started on `date`
    ./li_add_image --registry "Javatest ECR" --name IMAGE_NAME:TAG --description "IMAGE DESCRIPTION" --policy "Default Policy" --instrument --wait --verbose
  3. Commit and push the new buildspec file.
  4. Going back to CodeBuild, create a new project, with the same CodeCommit repo, but this time select the new buildspec file. Use a Python 3.3.6 builder – either the AWS or LI Instrumented version.
  5. Click Continue
  6. Click Save
  7. Run the build, again on the master branch.
  8. If everything runs successfully, a new image should appear in the ECR registry with a -layered suffix. This is the instrumented image.

Run instrumented container image

When the instrumented container is now run — in ECS, Fargate, or elsewhere — it will log data back to the Layered Insight runtime console. It’s appearance in the console can be modified by setting the LI_APPLICATIONNAME and LI_LOCATION environment variables when running the container.

Conclusion

In the above blog we have provided you steps needed to embed governance and runtime security in your build pipelines running on AWS CodeBuild using Layered Insight.

 

 

 

Implement continuous integration and delivery of serverless AWS Glue ETL applications using AWS Developer Tools

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/implement-continuous-integration-and-delivery-of-serverless-aws-glue-etl-applications-using-aws-developer-tools/

AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.

AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.

When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:

  • Iterative development with unit tests
  • Continuous integration and build
  • Pushing the ETL pipeline to a test environment
  • Pushing the ETL pipeline to a production environment
  • Testing ETL applications using real data (live test)
  • Exploring and validating data

In this post, I walk you through a solution that implements a CI/CD pipeline for serverless AWS Glue ETL applications supported by AWS Developer Tools (including AWS CodePipeline, AWS CodeCommit, and AWS CodeBuild) and AWS CloudFormation.

Solution overview

The following diagram shows the pipeline workflow:

This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:

1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.

To get the ETL job source code and AWS CloudFormation template, download the gluedemoetl.zip file. This solution is developed based on a previous post, Build a Data Lake Foundation with AWS Glue and Amazon S3.

2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.

The LiveTest stage includes the following actions:

  • Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
  • AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
  • LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
  • LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval

3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.

Try it out

This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).

To get started with this solution, choose Launch Stack:

This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.

In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.

After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.

Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.

In the CodeCommit console, choose Code in the navigation pane to view the solution files.

Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.

At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.

At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.

Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:

Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.

SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";

The following shows the raw data:

The following shows the transformed data:

The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.

Add comments as needed, and choose Approve.

The LiveTestApproval stage now appears as Approved on the console.

After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.

Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.

After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.

Conclusion

In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.

If you have questions or suggestions, please comment below.

 


Additional Reading

If you found this post useful, be sure to check out Implement Continuous Integration and Delivery of Apache Spark Applications using AWS and Build a Data Lake Foundation with AWS Glue and Amazon S3.

 


About the Authors

Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.

 
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.

 

 

 

Performing Unit Testing in an AWS CodeStar Project

Post Syndicated from Jerry Mathen Jacob original https://aws.amazon.com/blogs/devops/performing-unit-testing-in-an-aws-codestar-project/

In this blog post, I will show how you can perform unit testing as a part of your AWS CodeStar project. AWS CodeStar helps you quickly develop, build, and deploy applications on AWS. With AWS CodeStar, you can set up your continuous delivery (CD) toolchain and manage your software development from one place.

Because unit testing tests individual units of application code, it is helpful for quickly identifying and isolating issues. As a part of an automated CI/CD process, it can also be used to prevent bad code from being deployed into production.

Many of the AWS CodeStar project templates come preconfigured with a unit testing framework so that you can start deploying your code with more confidence. The unit testing is configured to run in the provided build stage so that, if the unit tests do not pass, the code is not deployed. For a list of AWS CodeStar project templates that include unit testing, see AWS CodeStar Project Templates in the AWS CodeStar User Guide.

The scenario

As a big fan of superhero movies, I decided to list my favorites and ask my friends to vote on theirs by using a WebService endpoint I created. The example I use is a Python web service running on AWS Lambda with AWS CodeCommit as the code repository. CodeCommit is a fully managed source control system that hosts Git repositories and works with all Git-based tools.

Here’s how you can create the WebService endpoint:

Sign in to the AWS CodeStar console. Choose Start a project, which will take you to the list of project templates.

create project

For code edits I will choose AWS Cloud9, which is a cloud-based integrated development environment (IDE) that you use to write, run, and debug code.

choose cloud9

Here are the other tasks required by my scenario:

  • Create a database table where the votes can be stored and retrieved as needed.
  • Update the logic in the Lambda function that was created for posting and getting the votes.
  • Update the unit tests (of course!) to verify that the logic works as expected.

For a database table, I’ve chosen Amazon DynamoDB, which offers a fast and flexible NoSQL database.

Getting set up on AWS Cloud9

From the AWS CodeStar console, go to the AWS Cloud9 console, which should take you to your project code. I will open up a terminal at the top-level folder under which I will set up my environment and required libraries.

Use the following command to set the PYTHONPATH environment variable on the terminal.

export PYTHONPATH=/home/ec2-user/environment/vote-your-movie

You should now be able to use the following command to execute the unit tests in your project.

python -m unittest discover vote-your-movie/tests

cloud9 setup

Start coding

Now that you have set up your local environment and have a copy of your code, add a DynamoDB table to the project by defining it through a template file. Open template.yml, which is the Serverless Application Model (SAM) template file. This template extends AWS CloudFormation to provide a simplified way of defining the Amazon API Gateway APIs, AWS Lambda functions, and Amazon DynamoDB tables required by your serverless application.

AWSTemplateFormatVersion: 2010-09-09
Transform:
- AWS::Serverless-2016-10-31
- AWS::CodeStar

Parameters:
  ProjectId:
    Type: String
    Description: CodeStar projectId used to associate new resources to team members

Resources:
  # The DB table to store the votes.
  MovieVoteTable:
    Type: AWS::Serverless::SimpleTable
    Properties:
      PrimaryKey:
        # Name of the "Candidate" is the partition key of the table.
        Name: Candidate
        Type: String
  # Creating a new lambda function for retrieving and storing votes.
  MovieVoteLambda:
    Type: AWS::Serverless::Function
    Properties:
      Handler: index.handler
      Runtime: python3.6
      Environment:
        # Setting environment variables for your lambda function.
        Variables:
          TABLE_NAME: !Ref "MovieVoteTable"
          TABLE_REGION: !Ref "AWS::Region"
      Role:
        Fn::ImportValue:
          !Join ['-', [!Ref 'ProjectId', !Ref 'AWS::Region', 'LambdaTrustRole']]
      Events:
        GetEvent:
          Type: Api
          Properties:
            Path: /
            Method: get
        PostEvent:
          Type: Api
          Properties:
            Path: /
            Method: post

We’ll use Python’s boto3 library to connect to AWS services. And we’ll use Python’s mock library to mock AWS service calls for our unit tests.
Use the following command to install these libraries:

pip install --upgrade boto3 mock -t .

install dependencies

Add these libraries to the buildspec.yml, which is the YAML file that is required for CodeBuild to execute.

version: 0.2

phases:
  install:
    commands:

      # Upgrade AWS CLI to the latest version
      - pip install --upgrade awscli boto3 mock

  pre_build:
    commands:

      # Discover and run unit tests in the 'tests' directory. For more information, see <https://docs.python.org/3/library/unittest.html#test-discovery>
      - python -m unittest discover tests

  build:
    commands:

      # Use AWS SAM to package the application by using AWS CloudFormation
      - aws cloudformation package --template template.yml --s3-bucket $S3_BUCKET --output-template template-export.yml

artifacts:
  type: zip
  files:
    - template-export.yml

Open the index.py where we can write the simple voting logic for our Lambda function.

import json
import datetime
import boto3
import os

table_name = os.environ['TABLE_NAME']
table_region = os.environ['TABLE_REGION']

VOTES_TABLE = boto3.resource('dynamodb', region_name=table_region).Table(table_name)
CANDIDATES = {"A": "Black Panther", "B": "Captain America: Civil War", "C": "Guardians of the Galaxy", "D": "Thor: Ragnarok"}

def handler(event, context):
    if event['httpMethod'] == 'GET':
        resp = VOTES_TABLE.scan()
        return {'statusCode': 200,
                'body': json.dumps({item['Candidate']: int(item['Votes']) for item in resp['Items']}),
                'headers': {'Content-Type': 'application/json'}}

    elif event['httpMethod'] == 'POST':
        try:
            body = json.loads(event['body'])
        except:
            return {'statusCode': 400,
                    'body': 'Invalid input! Expecting a JSON.',
                    'headers': {'Content-Type': 'application/json'}}
        if 'candidate' not in body:
            return {'statusCode': 400,
                    'body': 'Missing "candidate" in request.',
                    'headers': {'Content-Type': 'application/json'}}
        if body['candidate'] not in CANDIDATES.keys():
            return {'statusCode': 400,
                    'body': 'You must vote for one of the following candidates - {}.'.format(get_allowed_candidates()),
                    'headers': {'Content-Type': 'application/json'}}

        resp = VOTES_TABLE.update_item(
            Key={'Candidate': CANDIDATES.get(body['candidate'])},
            UpdateExpression='ADD Votes :incr',
            ExpressionAttributeValues={':incr': 1},
            ReturnValues='ALL_NEW'
        )
        return {'statusCode': 200,
                'body': "{} now has {} votes".format(CANDIDATES.get(body['candidate']), resp['Attributes']['Votes']),
                'headers': {'Content-Type': 'application/json'}}

def get_allowed_candidates():
    l = []
    for key in CANDIDATES:
        l.append("'{}' for '{}'".format(key, CANDIDATES.get(key)))
    return ", ".join(l)

What our code basically does is take in the HTTPS request call as an event. If it is an HTTP GET request, it gets the votes result from the table. If it is an HTTP POST request, it sets a vote for the candidate of choice. We also validate the inputs in the POST request to filter out requests that seem malicious. That way, only valid calls are stored in the table.

In the example code provided, we use a CANDIDATES variable to store our candidates, but you can store the candidates in a JSON file and use Python’s json library instead.

Let’s update the tests now. Under the tests folder, open the test_handler.py and modify it to verify the logic.

import os
# Some mock environment variables that would be used by the mock for DynamoDB
os.environ['TABLE_NAME'] = "MockHelloWorldTable"
os.environ['TABLE_REGION'] = "us-east-1"

# The library containing our logic.
import index

# Boto3's core library
import botocore
# For handling JSON.
import json
# Unit test library
import unittest
## Getting StringIO based on your setup.
try:
    from StringIO import StringIO
except ImportError:
    from io import StringIO
## Python mock library
from mock import patch, call
from decimal import Decimal

@patch('botocore.client.BaseClient._make_api_call')
class TestCandidateVotes(unittest.TestCase):

    ## Test the HTTP GET request flow. 
    ## We expect to get back a successful response with results of votes from the table (mocked).
    def test_get_votes(self, boto_mock):
        # Input event to our method to test.
        expected_event = {'httpMethod': 'GET'}
        # The mocked values in our DynamoDB table.
        items_in_db = [{'Candidate': 'Black Panther', 'Votes': Decimal('3')},
                        {'Candidate': 'Captain America: Civil War', 'Votes': Decimal('8')},
                        {'Candidate': 'Guardians of the Galaxy', 'Votes': Decimal('8')},
                        {'Candidate': "Thor: Ragnarok", 'Votes': Decimal('1')}
                    ]
        # The mocked DynamoDB response.
        expected_ddb_response = {'Items': items_in_db}
        # The mocked response we expect back by calling DynamoDB through boto.
        response_body = botocore.response.StreamingBody(StringIO(str(expected_ddb_response)),
                                                        len(str(expected_ddb_response)))
        # Setting the expected value in the mock.
        boto_mock.side_effect = [expected_ddb_response]
        # Expecting that there would be a call to DynamoDB Scan function during execution with these parameters.
        expected_calls = [call('Scan', {'TableName': os.environ['TABLE_NAME']})]

        # Call the function to test.
        result = index.handler(expected_event, {})

        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 200

        result_body = json.loads(result.get('body'))
        # Verifying that the results match to that from the table.
        assert len(result_body) == len(items_in_db)
        for i in range(len(result_body)):
            assert result_body.get(items_in_db[i].get("Candidate")) == int(items_in_db[i].get("Votes"))

        assert boto_mock.call_count == 1
        boto_mock.assert_has_calls(expected_calls)

    ## Test the HTTP POST request flow that places a vote for a selected candidate.
    ## We expect to get back a successful response with a confirmation message.
    def test_place_valid_candidate_vote(self, boto_mock):
        # Input event to our method to test.
        expected_event = {'httpMethod': 'POST', 'body': "{\"candidate\": \"D\"}"}
        # The mocked response in our DynamoDB table.
        expected_ddb_response = {'Attributes': {'Candidate': "Thor: Ragnarok", 'Votes': Decimal('2')}}
        # The mocked response we expect back by calling DynamoDB through boto.
        response_body = botocore.response.StreamingBody(StringIO(str(expected_ddb_response)),
                                                        len(str(expected_ddb_response)))
        # Setting the expected value in the mock.
        boto_mock.side_effect = [expected_ddb_response]
        # Expecting that there would be a call to DynamoDB UpdateItem function during execution with these parameters.
        expected_calls = [call('UpdateItem', {
                                                'TableName': os.environ['TABLE_NAME'], 
                                                'Key': {'Candidate': 'Thor: Ragnarok'},
                                                'UpdateExpression': 'ADD Votes :incr',
                                                'ExpressionAttributeValues': {':incr': 1},
                                                'ReturnValues': 'ALL_NEW'
                                            })]
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 200

        assert result.get('body') == "{} now has {} votes".format(
            expected_ddb_response['Attributes']['Candidate'], 
            expected_ddb_response['Attributes']['Votes'])

        assert boto_mock.call_count == 1
        boto_mock.assert_has_calls(expected_calls)

    ## Test the HTTP POST request flow that places a vote for an non-existant candidate.
    ## We expect to get back a successful response with a confirmation message.
    def test_place_invalid_candidate_vote(self, boto_mock):
        # Input event to our method to test.
        # The valid IDs for the candidates are A, B, C, and D
        expected_event = {'httpMethod': 'POST', 'body': "{\"candidate\": \"E\"}"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'You must vote for one of the following candidates - {}.'.format(index.get_allowed_candidates())

    ## Test the HTTP POST request flow that places a vote for a selected candidate but associated with an invalid key in the POST body.
    ## We expect to get back a failed (400) response with an appropriate error message.
    def test_place_invalid_data_vote(self, boto_mock):
        # Input event to our method to test.
        # "name" is not the expected input key.
        expected_event = {'httpMethod': 'POST', 'body': "{\"name\": \"D\"}"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'Missing "candidate" in request.'

    ## Test the HTTP POST request flow that places a vote for a selected candidate but not as a JSON string which the body of the request expects.
    ## We expect to get back a failed (400) response with an appropriate error message.
    def test_place_malformed_json_vote(self, boto_mock):
        # Input event to our method to test.
        # "body" receives a string rather than a JSON string.
        expected_event = {'httpMethod': 'POST', 'body': "Thor: Ragnarok"}
        # Call the function to test.
        result = index.handler(expected_event, {})
        # Run unit test assertions to verify the expected calls to mock have occurred and verify the response.
        assert result.get('headers').get('Content-Type') == 'application/json'
        assert result.get('statusCode') == 400
        assert result.get('body') == 'Invalid input! Expecting a JSON.'

if __name__ == '__main__':
    unittest.main()

I am keeping the code samples well commented so that it’s clear what each unit test accomplishes. It tests the success conditions and the failure paths that are handled in the logic.

In my unit tests I use the patch decorator (@patch) in the mock library. @patch helps mock the function you want to call (in this case, the botocore library’s _make_api_call function in the BaseClient class).
Before we commit our changes, let’s run the tests locally. On the terminal, run the tests again. If all the unit tests pass, you should expect to see a result like this:

You:~/environment $ python -m unittest discover vote-your-movie/tests
.....
----------------------------------------------------------------------
Ran 5 tests in 0.003s

OK
You:~/environment $

Upload to AWS

Now that the tests have passed, it’s time to commit and push the code to source repository!

Add your changes

From the terminal, go to the project’s folder and use the following command to verify the changes you are about to push.

git status

To add the modified files only, use the following command:

git add -u

Commit your changes

To commit the changes (with a message), use the following command:

git commit -m "Logic and tests for the voting webservice."

Push your changes to AWS CodeCommit

To push your committed changes to CodeCommit, use the following command:

git push

In the AWS CodeStar console, you can see your changes flowing through the pipeline and being deployed. There are also links in the AWS CodeStar console that take you to this project’s build runs so you can see your tests running on AWS CodeBuild. The latest link under the Build Runs table takes you to the logs.

unit tests at codebuild

After the deployment is complete, AWS CodeStar should now display the AWS Lambda function and DynamoDB table created and synced with this project. The Project link in the AWS CodeStar project’s navigation bar displays the AWS resources linked to this project.

codestar resources

Because this is a new database table, there should be no data in it. So, let’s put in some votes. You can download Postman to test your application endpoint for POST and GET calls. The endpoint you want to test is the URL displayed under Application endpoints in the AWS CodeStar console.

Now let’s open Postman and look at the results. Let’s create some votes through POST requests. Based on this example, a valid vote has a value of A, B, C, or D.
Here’s what a successful POST request looks like:

POST success

Here’s what it looks like if I use some value other than A, B, C, or D:

 

POST Fail

Now I am going to use a GET request to fetch the results of the votes from the database.

GET success

And that’s it! You have now created a simple voting web service using AWS Lambda, Amazon API Gateway, and DynamoDB and used unit tests to verify your logic so that you ship good code.
Happy coding!

Continuous Deployment to Kubernetes using AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, Amazon ECR and AWS Lambda

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/continuous-deployment-to-kubernetes-using-aws-codepipeline-aws-codecommit-aws-codebuild-amazon-ecr-and-aws-lambda/

Thank you to my colleague Omar Lari for this blog on how to create a continuous deployment pipeline for Kubernetes!


You can use Kubernetes and AWS together to create a fully managed, continuous deployment pipeline for container based applications. This approach takes advantage of Kubernetes’ open-source system to manage your containerized applications, and the AWS developer tools to manage your source code, builds, and pipelines.

This post describes how to create a continuous deployment architecture for containerized applications. It uses AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS Lambda to deploy containerized applications into a Kubernetes cluster. In this environment, developers can remain focused on developing code without worrying about how it will be deployed, and development managers can be satisfied that the latest changes are always deployed.

What is Continuous Deployment?

There are many articles, posts and even conferences dedicated to the practice of continuous deployment. For the purposes of this post, I will summarize continuous delivery into the following points:

  • Code is more frequently released into production environments
  • More frequent releases allow for smaller, incremental changes reducing risk and enabling simplified roll backs if needed
  • Deployment is automated and requires minimal user intervention

For a more information, see “Practicing Continuous Integration and Continuous Delivery on AWS”.

How can you use continuous deployment with AWS and Kubernetes?

You can leverage AWS services that support continuous deployment to automatically take your code from a source code repository to production in a Kubernetes cluster with minimal user intervention. To do this, you can create a pipeline that will build and deploy committed code changes as long as they meet the requirements of each stage of the pipeline.

To create the pipeline, you will use the following services:

  • AWS CodePipeline. AWS CodePipeline is a continuous delivery service that models, visualizes, and automates the steps required to release software. You define stages in a pipeline to retrieve code from a source code repository, build that source code into a releasable artifact, test the artifact, and deploy it to production. Only code that successfully passes through all these stages will be deployed. In addition, you can optionally add other requirements to your pipeline, such as manual approvals, to help ensure that only approved changes are deployed to production.
  • AWS CodeCommit. AWS CodeCommit is a secure, scalable, and managed source control service that hosts private Git repositories. You can privately store and manage assets such as your source code in the cloud and configure your pipeline to automatically retrieve and process changes committed to your repository.
  • AWS CodeBuild. AWS CodeBuild is a fully managed build service that compiles source code, runs tests, and produces artifacts that are ready to deploy. You can use AWS CodeBuild to both build your artifacts, and to test those artifacts before they are deployed.
  • AWS Lambda. AWS Lambda is a compute service that lets you run code without provisioning or managing servers. You can invoke a Lambda function in your pipeline to prepare the built and tested artifact for deployment by Kubernetes to the Kubernetes cluster.
  • Kubernetes. Kubernetes is an open-source system for automating deployment, scaling, and management of containerized applications. It provides a platform for running, deploying, and managing containers at scale.

An Example of Continuous Deployment to Kubernetes:

The following example illustrates leveraging AWS developer tools to continuously deploy to a Kubernetes cluster:

  1. Developers commit code to an AWS CodeCommit repository and create pull requests to review proposed changes to the production code. When the pull request is merged into the master branch in the AWS CodeCommit repository, AWS CodePipeline automatically detects the changes to the branch and starts processing the code changes through the pipeline.
  2. AWS CodeBuild packages the code changes as well as any dependencies and builds a Docker image. Optionally, another pipeline stage tests the code and the package, also using AWS CodeBuild.
  3. The Docker image is pushed to Amazon ECR after a successful build and/or test stage.
  4. AWS CodePipeline invokes an AWS Lambda function that includes the Kubernetes Python client as part of the function’s resources. The Lambda function performs a string replacement on the tag used for the Docker image in the Kubernetes deployment file to match the Docker image tag applied in the build, one that matches the image in Amazon ECR.
  5. After the deployment manifest update is completed, AWS Lambda invokes the Kubernetes API to update the image in the Kubernetes application deployment.
  6. Kubernetes performs a rolling update of the pods in the application deployment to match the docker image specified in Amazon ECR.
    The pipeline is now live and responds to changes to the master branch of the CodeCommit repository. This pipeline is also fully extensible, you can add steps for performing testing or adding a step to deploy into a staging environment before the code ships into the production cluster.

An example pipeline in AWS CodePipeline that supports this architecture can be seen below:

Conclusion

We are excited to see how you leverage this pipeline to help ease your developer experience as you develop applications in Kubernetes.

You’ll find an AWS CloudFormation template with everything necessary to spin up your own continuous deployment pipeline at the CodeSuite – Continuous Deployment Reference Architecture for Kubernetes repo on GitHub. The repository details exactly how the pipeline is provisioned and how you can use it to deploy your own applications. If you have any questions, feedback, or suggestions, please let us know!

Instrumenting Web Apps Using AWS X-Ray

Post Syndicated from Bharath Kumar original https://aws.amazon.com/blogs/devops/instrumenting-web-apps-using-aws-x-ray/

This post was written by James Bowman, Software Development Engineer, AWS X-Ray

AWS X-Ray helps developers analyze and debug distributed applications and underlying services in production. You can identify and analyze root-causes of performance issues and errors, understand customer impact, and extract statistical aggregations (such as histograms) for optimization.

In this blog post, I will provide a step-by-step walkthrough for enabling X-Ray tracing in the Go programming language. You can use these steps to add X-Ray tracing to any distributed application.

Revel: A web framework for the Go language

This section will assist you with designing a guestbook application. Skip to “Instrumenting with AWS X-Ray” section below if you already have a Go language application.

Revel is a web framework for the Go language. It facilitates the rapid development of web applications by providing a predefined framework for controllers, views, routes, filters, and more.

To get started with Revel, run revel new github.com/jamesdbowman/guestbook. A project base is then copied to $GOPATH/src/github.com/jamesdbowman/guestbook.

$ tree -L 2
.
├── README.md
├── app
│ ├── controllers
│ ├── init.go
│ ├── routes
│ ├── tmp
│ └── views
├── conf
│ ├── app.conf
│ └── routes
├── messages
│ └── sample.en
├── public
│ ├── css
│ ├── fonts
│ ├── img
│ └── js
└── tests
└── apptest.go

Writing a guestbook application

A basic guestbook application can consist of just two routes: one to sign the guestbook and another to list all entries.
Let’s set up these routes by adding a Book controller, which can be routed to by modifying ./conf/routes.

./app/controllers/book.go:
package controllers

import (
    "math/rand"
    "time"

    "github.com/aws/aws-sdk-go/aws"
    "github.com/aws/aws-sdk-go/aws/endpoints"
    "github.com/aws/aws-sdk-go/aws/session"
    "github.com/aws/aws-sdk-go/service/dynamodb"
    "github.com/aws/aws-sdk-go/service/dynamodb/dynamodbattribute"
    "github.com/revel/revel"
)

const TABLE_NAME = "guestbook"
const SUCCESS = "Success.\n"
const DAY = 86400

var letters = []rune("ABCDEFGHIJKLMNOPQRSTUVWXYZ")

func init() {
    rand.Seed(time.Now().UnixNano())
}

// randString returns a random string of len n, used for DynamoDB Hash key.
func randString(n int) string {
    b := make([]rune, n)
    for i := range b {
        b[i] = letters[rand.Intn(len(letters))]
    }
    return string(b)
}

// Book controls interactions with the guestbook.
type Book struct {
    *revel.Controller
    ddbClient *dynamodb.DynamoDB
}

// Signature represents a user's signature.
type Signature struct {
    Message string
    Epoch   int64
    ID      string
}

// ddb returns the controller's DynamoDB client, instatiating a new client if necessary.
func (c Book) ddb() *dynamodb.DynamoDB {
    if c.ddbClient == nil {
        sess := session.Must(session.NewSession(&aws.Config{
            Region: aws.String(endpoints.UsWest2RegionID),
        }))
        c.ddbClient = dynamodb.New(sess)
    }
    return c.ddbClient
}

// Sign allows users to sign the book.
// The message is to be passed as application/json typed content, listed under the "message" top level key.
func (c Book) Sign() revel.Result {
    var s Signature

    err := c.Params.BindJSON(&s)
    if err != nil {
        return c.RenderError(err)
    }
    now := time.Now()
    s.Epoch = now.Unix()
    s.ID = randString(20)

    item, err := dynamodbattribute.MarshalMap(s)
    if err != nil {
        return c.RenderError(err)
    }

    putItemInput := &dynamodb.PutItemInput{
        TableName: aws.String(TABLE_NAME),
        Item:      item,
    }
    _, err = c.ddb().PutItem(putItemInput)
    if err != nil {
        return c.RenderError(err)
    }

    return c.RenderText(SUCCESS)
}

// List allows users to list all signatures in the book.
func (c Book) List() revel.Result {
    scanInput := &dynamodb.ScanInput{
        TableName: aws.String(TABLE_NAME),
        Limit:     aws.Int64(100),
    }
    res, err := c.ddb().Scan(scanInput)
    if err != nil {
        return c.RenderError(err)
    }

    messages := make([]string, 0)
    for _, v := range res.Items {
        messages = append(messages, *(v["Message"].S))
    }
    return c.RenderJSON(messages)
}

./conf/routes:
POST /sign Book.Sign
GET /list Book.List

Creating the resources and testing

For the purposes of this blog post, the application will be run and tested locally. We will store and retrieve messages from an Amazon DynamoDB table. Use the following AWS CLI command to create the guestbook table:

aws dynamodb create-table --region us-west-2 --table-name "guestbook" --attribute-definitions AttributeName=ID,AttributeType=S AttributeName=Epoch,AttributeType=N --key-schema AttributeName=ID,KeyType=HASH AttributeName=Epoch,KeyType=RANGE --provisioned-throughput ReadCapacityUnits=5,WriteCapacityUnits=5

Now, let’s test our sign and list routes. If everything is working correctly, the following result appears:

$ curl -d '{"message":"Hello from cURL!"}' -H "Content-Type: application/json" http://localhost:9000/book/sign
Success.
$ curl http://localhost:9000/book/list
[
  "Hello from cURL!"
]%

Integrating with AWS X-Ray

Download and run the AWS X-Ray daemon

The AWS SDKs emit trace segments over UDP on port 2000. (This port can be configured.) In order for the trace segments to make it to the X-Ray service, the daemon must listen on this port and batch the segments in calls to the PutTraceSegments API.
For information about downloading and running the X-Ray daemon, see the AWS X-Ray Developer Guide.

Installing the AWS X-Ray SDK for Go

To download the SDK from GitHub, run go get -u github.com/aws/aws-xray-sdk-go/... The SDK will appear in the $GOPATH.

Enabling the incoming request filter

The first step to instrumenting an application with AWS X-Ray is to enable the generation of trace segments on incoming requests. The SDK conveniently provides an implementation of http.Handler which does exactly that. To ensure incoming web requests travel through this handler, we can modify app/init.go, adding a custom function to be run on application start.

import (
    "github.com/aws/aws-xray-sdk-go/xray"
    "github.com/revel/revel"
)

...

func init() {
  ...
    revel.OnAppStart(installXRayHandler)
}

func installXRayHandler() {
    revel.Server.Handler = xray.Handler(xray.NewFixedSegmentNamer("GuestbookApp"), revel.Server.Handler)
}

The application will now emit a segment for each incoming web request. The service graph appears:

You can customize the name of the segment to make it more descriptive by providing an alternate implementation of SegmentNamer to xray.Handler. For example, you can use xray.NewDynamicSegmentNamer(fallback, pattern) in place of the fixed namer. This namer will use the host name from the incoming web request (if it matches pattern) as the segment name. This is often useful when you are trying to separate different instances of the same application.

In addition, HTTP-centric information such as method and URL is collected in the segment’s http subsection:

"http": {
    "request": {
        "url": "/book/list",
        "method": "GET",
        "user_agent": "curl/7.54.0",
        "client_ip": "::1"
    },
    "response": {
        "status": 200
    }
},

Instrumenting outbound calls

To provide detailed performance metrics for distributed applications, the AWS X-Ray SDK needs to measure the time it takes to make outbound requests. Trace context is passed to downstream services using the X-Amzn-Trace-Id header. To draw a detailed and accurate representation of a distributed application, outbound call instrumentation is required.

AWS SDK calls

The AWS X-Ray SDK for Go provides a one-line AWS client wrapper that enables the collection of detailed per-call metrics for any AWS client. We can modify the DynamoDB client instantiation to include this line:

// ddb returns the controller's DynamoDB client, instatiating a new client if necessary.
func (c Book) ddb() *dynamodb.DynamoDB {
    if c.ddbClient == nil {
        sess := session.Must(session.NewSession(&aws.Config{
            Region: aws.String(endpoints.UsWest2RegionID),
        }))
        c.ddbClient = dynamodb.New(sess)
        xray.AWS(c.ddbClient.Client) // add subsegment-generating X-Ray handlers to this client
    }
    return c.ddbClient
}

We also need to ensure that the segment generated by our xray.Handler is passed to these AWS calls so that the X-Ray SDK knows to which segment these generated subsegments belong. In Go, the context.Context object is passed throughout the call path to achieve this goal. (In most other languages, some variant of ThreadLocal is used.) AWS clients provide a *WithContext method variant for each AWS operation, which we need to switch to:

_, err = c.ddb().PutItemWithContext(c.Request.Context(), putItemInput)
    res, err := c.ddb().ScanWithContext(c.Request.Context(), scanInput)

We now see much more detail in the Timeline view of the trace for the sign and list operations:

We can use this detail to help diagnose throttling on our DynamoDB table. In the following screenshot, the purple in the DynamoDB service graph node indicates that our table is underprovisioned. The red in the GuestbookApp node indicates that the application is throwing faults due to this throttling.

HTTP calls

Although the guestbook application does not make any non-AWS outbound HTTP calls in its current state, there is a similar one-liner to wrap HTTP clients that make outbound requests. xray.Client(c *http.Client) wraps an existing http.Client (or nil if you want to use a default HTTP client). For example:

resp, err := ctxhttp.Get(ctx, xray.Client(nil), "https://aws.amazon.com/")

Instrumenting local operations

X-Ray can also assist in measuring the performance of local compute operations. To see this in action, let’s create a custom subsegment inside the randString method:


// randString returns a random string of len n, used for DynamoDB Hash key.
func randString(ctx context.Context, n int) string {
    xray.Capture(ctx, "randString", func(innerCtx context.Context) {
        b := make([]rune, n)
        for i := range b {
            b[i] = letters[rand.Intn(len(letters))]
        }
        s := string(b)
    })
    return s
}

// we'll also need to change the callsite

s.ID = randString(c.Request.Context(), 20)

Summary

By now, you are an expert on how to instrument X-Ray for your Go applications. Instrumenting X-Ray with your applications is an easy way to analyze and debug performance issues and understand customer impact. Please feel free to give any feedback or comments below.

For more information about advanced configuration of the AWS X-Ray SDK for Go, see the AWS X-Ray SDK for Go in the AWS X-Ray Developer Guide and the aws/aws-xray-sdk-go GitHub repository.

For more information about some of the advanced X-Ray features such as histograms, annotations, and filter expressions, see the Analyzing Performance for Amazon Rekognition Apps Written on AWS Lambda Using AWS X-Ray blog post.

Using Amazon CloudWatch and Amazon SNS to Notify when AWS X-Ray Detects Elevated Levels of Latency, Errors, and Faults in Your Application

Post Syndicated from Bharath Kumar original https://aws.amazon.com/blogs/devops/using-amazon-cloudwatch-and-amazon-sns-to-notify-when-aws-x-ray-detects-elevated-levels-of-latency-errors-and-faults-in-your-application/

AWS X-Ray helps developers analyze and debug production applications built using microservices or serverless architectures and quantify customer impact. With X-Ray, you can understand how your application and its underlying services are performing and identify and troubleshoot the root cause of performance issues and errors. You can use these insights to identify issues and opportunities for optimization.

In this blog post, I will show you how you can use Amazon CloudWatch and Amazon SNS to get notified when X-Ray detects high latency, errors, and faults in your application. Specifically, I will show you how to use this sample app to get notified through an email or SMS message when your end users observe high latencies or server-side errors when they use your application. You can customize the alarms and events by updating the sample app code.

Sample App Overview

The sample app uses the X-Ray GetServiceGraph API to get the following information:

  • Aggregated response time.
  • Requests that failed with 4xx status code (errors).
  • 429 status code (throttle).
  • 5xx status code (faults).
Sample app architecture

Overview of sample app architecture

Getting started

The sample app uses AWS CloudFormation to deploy the required resources.
To install the sample app:

  1. Run git clone to get the sample app.
  2. Update the JSON file in the Setup folder with threshold limits and notification details.
  3. Run the install.py script to install the sample app.

For more information about the installation steps, see the readme file on GitHub.

You can update the app configuration to include your phone number or email to get notified when your application in X-Ray breaches the latency, error, and fault limits you set in the configuration. If you prefer to not provide your phone number and email, then you can use the CloudWatch alarm deployed by the sample app to monitor your application in X-Ray.

The sample app deploys resources with the sample app namespace you provided during setup. This enables you to have multiple sample apps in the same region.

CloudWatch rules

The sample app uses two CloudWatch rules:

  1. SCHEDULEDLAMBDAFOR-sample_app_name to trigger at regular intervals the AWS Lambda function that queries the GetServiceGraph API.
  2. XRAYALERTSFOR-sample_app_name to look for published CloudWatch events that match the pattern defined in this rule.
CloudWatch Rules for sample app

CloudWatch rules created for the sample app

CloudWatch alarms

If you did not provide your phone number or email in the JSON file, the sample app uses a CloudWatch alarm named XRayCloudWatchAlarm-sample_app_name in combination with the CloudWatch event that you can use for monitoring.

CloudWatch Alarm for sample app

CloudWatch alarm created for the sample app

Amazon SNS messages

The sample app creates two SNS topics:

  • sample_app_name-cloudwatcheventsnstopic to send out an SMS message when the CloudWatch event matches a pattern published from the Lambda function.
  • sample_app_name-cloudwatchalarmsnstopic to send out an email message when the CloudWatch alarm goes into an ALARM state.
Amazon SNS for sample app

Amazon SNS created for the sample app

Getting notifications

The CloudWatch event looks for the following matching pattern:

{
  "detail-type": [
    "XCW Notification for Alerts"
  ],
  "source": [
    "<sample_app_name>-xcw.alerts"
  ]
}

The event then invokes an SNS topic that sends out an SMS message.

SMS in sample app

SMS that is sent when CloudWatch Event invokes Amazon SNS topic

The CloudWatch alarm looks for the TriggeredRules metric that is published whenever the CloudWatch event matches the event pattern. It goes into the ALARM state whenever TriggeredRules > 0 for the specified evaluation period and invokes an SNS topic that sends an email message.

Email sent in sample app

Email that is sent when CloudWatch Alarm goes to ALARM state

Stopping notifications

If you provided your phone number or email address, but would like to stop getting notified, change the SUBSCRIBE_TO_EMAIL_SMS environment variable in the Lambda function to No. Then, go to the Amazon SNS console and delete the subscriptions. You can still monitor your application for elevated levels of latency, errors, and faults by using the CloudWatch console.

Lambda environment variable in sample app

Change environment variable in Lambda

 

Delete subscription in SNS for sample app

Delete subscriptions to stop getting notified

Uninstalling the sample app

To uninstall the sample app, run the uninstall.py script in the Setup folder.

Extending the sample app

The sample app notifes you when when X-Ray detects high latency, errors, and faults in your application. You can extend it to provide more value for your use cases (for example, to perform an action on a resource when the state of a CloudWatch alarm changes).

To summarize, after this set up you will be able to get notified through Amazon SNS when X-Ray detects high latency, errors and faults in your application.

I hope you found this information about setting up alarms and alerts for your application in AWS X-Ray helpful. Feel free to leave questions or other feedback in the comments. Feel free to learn more about AWS X-Ray, Amazon SNS and Amazon CloudWatch

About the Author

Bharath Kumar is a Sr.Product Manager with AWS X-Ray. He has developed and launched mobile games, web applications on microservices and serverless architecture.

How to Enable Caching for AWS CodeBuild

Post Syndicated from Karthik Thirugnanasambandam original https://aws.amazon.com/blogs/devops/how-to-enable-caching-for-aws-codebuild/

AWS CodeBuild is a fully managed build service. There are no servers to provision and scale, or software to install, configure, and operate. You just specify the location of your source code, choose your build settings, and CodeBuild runs build scripts for compiling, testing, and packaging your code.

A typical application build process includes phases like preparing the environment, updating the configuration, downloading dependencies, running unit tests, and finally, packaging the built artifact.

Downloading dependencies is a critical phase in the build process. These dependent files can range in size from a few KBs to multiple MBs. Because most of the dependent files do not change frequently between builds, you can noticeably reduce your build time by caching dependencies.

In this post, I will show you how to enable caching for AWS CodeBuild.

Requirements

  • Create an Amazon S3 bucket for storing cache archives (You can use existing s3 bucket as well).
  • Create a GitHub account (if you don’t have one).

Create a sample build project:

1. Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/.

2. If a welcome page is displayed, choose Get started.

If a welcome page is not displayed, on the navigation pane, choose Build projects, and then choose Create project.

3. On the Configure your project page, for Project name, type a name for this build project. Build project names must be unique across each AWS account.

4. In Source: What to build, for Source provider, choose GitHub.

5. In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild.

  • For Operating system, choose Ubuntu.
  • For Runtime, choose Java.
  • For Version,  choose aws/codebuild/java:openjdk-8.
  • For Build specification, select Insert build commands.

Note: The build specification file (buildspec.yml) can be configured in two ways. You can package it along with your source root directory, or you can override it by using a project environment configuration. In this example, I will use the override option and will use the console editor to specify the build specification.

6. Under Build commands, click Switch to editor to enter the build specification.

Copy the following text.

version: 0.2

phases:
  build:
    commands:
      - mvn install
      
cache:
  paths:
    - '/root/.m2/**/*'

Note: The cache section in the build specification instructs AWS CodeBuild about the paths to be cached. Like the artifacts section, the cache paths are relative to $CODEBUILD_SRC_DIR and specify the directories to be cached. In this example, Maven stores the downloaded dependencies to the /root/.m2/ folder, but other tools use different folders. For example, pip uses the /root/.cache/pip folder, and Gradle uses the /root/.gradle/caches folder. You might need to configure the cache paths based on your language platform.

7. In Artifacts: Where to put the artifacts from this build project:

  • For Type, choose No artifacts.

8. In Cache:

  • For Type, choose Amazon S3.
  • For Bucket, choose your S3 bucket.
  • For Path prefix, type cache/archives/

9. In Service role, the Create a service role in your account option will display a default role name.  You can accept the default name or type your own.

If you already have an AWS CodeBuild service role, choose Choose an existing service role from your account.

10. Choose Continue.

11. On the Review page, to run a build, choose Save and build.

Review build and cache behavior:

Let us review our first build for the project.

In the first run, where no cache exists, overall build time would look something like below (notice the time for DOWNLOAD_SOURCE, BUILD and POST_BUILD):

If you check the build logs, you will see log entries for dependency downloads. The dependencies are downloaded directly from configured external repositories. At the end of the log, you will see an entry for the cache uploaded to your S3 bucket.

Let’s review the S3 bucket for the cached archive. You’ll see the cache from our first successful build is uploaded to the configured S3 path.

Let’s try another build with the same CodeBuild project. This time the build should pick up the dependencies from the cache.

In the second run, there was a cache hit (cache was generated from the first run):

You’ll notice a few things:

  1. DOWNLOAD_SOURCE took slightly longer. Because, in addition to the source code, this time the build also downloaded the cache from user’s s3 bucket.
  2. BUILD time was faster. As the dependencies didn’t need to get downloaded, but were reused from cache.
  3. POST_BUILD took slightly longer, but was relatively the same.

Overall, build duration was improved with cache.

Best practices for cache

  • By default, the cache archive is encrypted on the server side with the customer’s artifact KMS key.
  • You can expire the cache by manually removing the cache archive from S3. Alternatively, you can expire the cache by using an S3 lifecycle policy.
  • You can override cache behavior by updating the project. You can use the AWS CodeBuild the AWS CodeBuild console, AWS CLI, or AWS SDKs to update the project. You can also invalidate cache setting by using the new InvalidateProjectCache API. This API forces a new InvalidationKey to be generated, ensuring that future builds receive an empty cache. This API does not remove the existing cache, because this could cause inconsistencies with builds currently in flight.
  • The cache can be enabled for any folders in the build environment, but we recommend you only cache dependencies/files that will not change frequently between builds. Also, to avoid unexpected application behavior, don’t cache configuration and sensitive information.

Conclusion

In this blog post, I showed you how to enable and configure cache setting for AWS CodeBuild. As you see, this can save considerable build time. It also improves resiliency by avoiding external network connections to an artifact repository.

I hope you found this post useful. Feel free to leave your feedback or suggestions in the comments.

Access Resources in a VPC from AWS CodeBuild Builds

Post Syndicated from John Pignata original https://aws.amazon.com/blogs/devops/access-resources-in-a-vpc-from-aws-codebuild-builds/

John Pignata, Startup Solutions Architect, Amazon Web Services

In this blog post we’re going to discuss a new AWS CodeBuild feature that is available starting today. CodeBuild builds can now access resources in a VPC directly without these resources being exposed to the public internet. These resources include Amazon Relational Database Service (Amazon RDS) databases, Amazon ElastiCache clusters, internal services running on Amazon Elastic Compute Cloud (Amazon EC2), and Amazon EC2 Container Service (Amazon ECS), or any service endpoints that are only reachable from within a specific VPC.

CodeBuild is a fully managed build service that compiles source code, runs tests, and produces software packages that are ready to deploy. As part of the build process, developers often require access to resources that should be isolated from the public Internet. Now CodeBuild builds can be optionally configured to have VPC connectivity and access these resources directly.

Accessing Resources in a VPC

You can configure builds to have access to a VPC when you create a CodeBuild project or you can update an existing CodeBuild project with VPC configuration attributes. Here’s how it looks in the console:

 

To configure VPC connectivity: select a VPC, one or more subnets within that VPC, and one or more VPC security groups that CodeBuild should apply when attaching to your VPC. Once configured, commands running as part of your build will be able to access resources in your VPC without transiting across the public Internet.

Use Cases

The availability of VPC connectivity from CodeBuild builds unlocks many potential uses. For example, you can:

  • Run integration tests from your build against data in an Amazon RDS instance that’s isolated on a private subnet.
  • Query data in an ElastiCache cluster directly from tests.
  • Interact with internal web services hosted on Amazon EC2, Amazon ECS, or services that use internal Elastic Load Balancing.
  • Retrieve dependencies from self-hosted, internal artifact repositories such as PyPI for Python, Maven for Java, npm for Node.js, and so on.
  • Access objects in an Amazon S3 bucket configured to allow access only through a VPC endpoint.
  • Query external web services that require fixed IP addresses through the Elastic IP address of the NAT gateway associated with your subnet(s).

… and more! Your builds can now access any resource that’s hosted in your VPC without any compromise on network isolation.

Internet Connectivity

CodeBuild requires access to resources on the public Internet to successfully execute builds. At a minimum, it must be able to reach your source repository system (such as AWS CodeCommit, GitHub, Bitbucket), Amazon Simple Storage Service (Amazon S3) to deliver build artifacts, and Amazon CloudWatch Logs to stream logs from the build process. The interface attached to your VPC will not be assigned a public IP address so to enable Internet access from your builds, you will need to set up a managed NAT Gateway or NAT instance for the subnets you configure. You must also ensure your security groups allow outbound access to these services.

IP Address Space

Each running build will be assigned an IP address from one of the subnets in your VPC that you designate for CodeBuild to use. As CodeBuild scales to meet your build volume, ensure that you select subnets with enough address space to accommodate your expected number of concurrent builds.

Service Role Permissions

CodeBuild requires new permissions in order to manage network interfaces on your VPCs. If you create a service role for your new projects, these permissions will be included in that role’s policy automatically. For existing service roles, you can edit the policy document to include the additional actions. For the full policy document to apply to your service role, see Advanced Setup in the CodeBuild documentation.

For more information, see VPC Support in the CodeBuild documentation. We hope you find the ability to access internal resources on a VPC useful in your build processes! If you have any questions or feedback, feel free to reach out to us through the AWS CodeBuild forum or leave a comment!

Say Hello To Our Newest AWS Community Heroes (Fall 2017 Edition)

Post Syndicated from Sara Rodas original https://aws.amazon.com/blogs/aws/say-hello-to-our-newest-aws-community-heroes-fall-2017-edition/

The AWS Community Heroes program helps shine a spotlight on some of the innovative work being done by rockstar AWS developers around the globe. Marrying cloud expertise with a passion for community building and education, these heroes share their time and knowledge across social media and through in-person events. Heroes also actively help drive community-led tracks at conferences. At this year’s re:Invent, many Heroes will be speaking during the Monday Community Day track.

This November, we are thrilled to have four Heroes joining our network of cloud innovators. Without further ado, meet to our newest AWS Community Heroes!

 

Anh Ho Viet

Anh Ho Viet is the founder of AWS Vietnam User Group, Co-founder & CEO of OSAM, an AWS Consulting Partner in Vietnam, an AWS Certified Solutions Architect, and a cloud lover.

At OSAM, Anh and his enthusiastic team have helped many companies, from SMBs to Enterprises, move to the cloud with AWS. They offer a wide range of services, including migration, consultation, architecture, and solution design on AWS. Anh’s vision for OSAM is beyond a cloud service provider; the company will take part in building a complete AWS ecosystem in Vietnam, where other companies are encouraged to become AWS partners through training and collaboration activities.

In 2016, Anh founded the AWS Vietnam User Group as a channel to share knowledge and hands-on experience among cloud practitioners. Since then, the community has reached more than 4,800 members and is still expanding. The group holds monthly meetups, connects many SMEs to AWS experts, and provides real-time, free-of-charge consultancy to startups. In August 2017, Anh joined as lead content creator of a program called “Cloud Computing Lectures for Universities” which includes translating AWS documentation & news into Vietnamese, providing students with fundamental, up-to-date knowledge of AWS cloud computing, and supporting students’ career paths.

 

Thorsten Höger

Thorsten Höger is CEO and Cloud consultant at Taimos, where he is advising customers on how to use AWS. Being a developer, he focuses on improving development processes and automating everything to build efficient deployment pipelines for customers of all sizes.

Before being self-employed, Thorsten worked as a developer and CTO of Germany’s first private bank running on AWS. With his colleagues, he migrated the core banking system to the AWS platform in 2013. Since then he organizes the AWS user group in Stuttgart and is a frequent speaker at Meetups, BarCamps, and other community events.

As a supporter of open source software, Thorsten is maintaining or contributing to several projects on Github, like test frameworks for AWS Lambda, Amazon Alexa, or developer tools for CloudFormation. He is also the maintainer of the Jenkins AWS Pipeline plugin.

In his spare time, he enjoys indoor climbing and cooking.

 

Becky Zhang

Yu Zhang (Becky Zhang) is COO of BootDev, which focuses on Big Data solutions on AWS and high concurrency web architecture. Before she helped run BootDev, she was working at Yubis IT Solutions as an operations manager.

Becky plays a key role in the AWS User Group Shanghai (AWSUGSH), regularly organizing AWS UG events including AWS Tech Meetups and happy hours, gathering AWS talent together to communicate the latest technology and AWS services. As a female in technology industry, Becky is keen on promoting Women in Tech and encourages more woman to get involved in the community.

Becky also connects the China AWS User Group with user groups in other regions, including Korea, Japan, and Thailand. She was invited as a panelist at AWS re:Invent 2016 and spoke at the Seoul AWS Summit this April to introduce AWS User Group Shanghai and communicate with other AWS User Groups around the world.

Besides events, Becky also promotes the Shanghai AWS User Group by posting AWS-related tech articles, event forecasts, and event reports to Weibo, Twitter, Meetup.com, and WeChat (which now has over 2000 official account followers).

 

Nilesh Vaghela

Nilesh Vaghela is the founder of ElectroMech Corporation, an AWS Cloud and open source focused company (the company started as an open source motto). Nilesh has been very active in the Linux community since 1998. He started working with AWS Cloud technologies in 2013 and in 2014 he trained a dedicated cloud team and started full support of AWS cloud services as an AWS Standard Consulting Partner. He always works to establish and encourage cloud and open source communities.

He started the AWS Meetup community in Ahmedabad in 2014 and as of now 12 Meetups have been conducted, focusing on various AWS technologies. The Meetup has quickly grown to include over 2000 members. Nilesh also created a Facebook group for AWS enthusiasts in Ahmedabad, with over 1500 members.

Apart from the AWS Meetup, Nilesh has delivered a number of seminars, workshops, and talks around AWS introduction and awareness, at various organizations, as well as at colleges and universities. He has also been active in working with startups, presenting AWS services overviews and discussing how startups can benefit the most from using AWS services.

Nilesh is Red Hat Linux Technologies and AWS Cloud Technologies trainer as well.

 

To learn more about the AWS Community Heroes Program and how to get involved with your local AWS community, click here.

Bringing Datacenter-Scale Hardware-Software Co-design to the Cloud with FireSim and Amazon EC2 F1 Instances

Post Syndicated from Mia Champion original https://aws.amazon.com/blogs/compute/bringing-datacenter-scale-hardware-software-co-design-to-the-cloud-with-firesim-and-amazon-ec2-f1-instances/

The recent addition of Xilinx FPGAs to AWS Cloud compute offerings is one way that AWS is enabling global growth in the areas of advanced analytics, deep learning and AI. The customized F1 servers use pooled accelerators, enabling interconnectivity of up to 8 FPGAs, each one including 64 GiB DDR4 ECC protected memory, with a dedicated PCIe x16 connection. That makes this a powerful engine with the capacity to process advanced analytical applications at scale, at a significantly faster rate. For example, AWS commercial partner Edico Genome is able to achieve an approximately 30X speedup in analyzing whole genome sequencing datasets using their DRAGEN platform powered with F1 instances.

While the availability of FPGA F1 compute on-demand provides clear accessibility and cost advantages, many mainstream users are still finding that the “threshold to entry” in developing or running FPGA-accelerated simulations is too high. Researchers at the UC Berkeley RISE Lab have developed “FireSim”, powered by Amazon FPGA F1 instances as an open-source resource, FireSim lowers that entry bar and makes it easier for everyone to leverage the power of an FPGA-accelerated compute environment. Whether you are part of a small start-up development team or working at a large datacenter scale, hardware-software co-design enables faster time-to-deployment, lower costs, and more predictable performance. We are excited to feature FireSim in this post from Sagar Karandikar and his colleagues at UC-Berkeley.

―Mia Champion, Sr. Data Scientist, AWS

Mapping an 8-node FireSim cluster simulation to Amazon EC2 F1

As traditional hardware scaling nears its end, the data centers of tomorrow are trending towards heterogeneity, employing custom hardware accelerators and increasingly high-performance interconnects. Prototyping new hardware at scale has traditionally been either extremely expensive, or very slow. In this post, I introduce FireSim, a new hardware simulation platform under development in the computer architecture research group at UC Berkeley that enables fast, scalable hardware simulation using Amazon EC2 F1 instances.

FireSim benefits both hardware and software developers working on new rack-scale systems: software developers can use the simulated nodes with new hardware features as they would use a real machine, while hardware developers have full control over the hardware being simulated and can run real software stacks while hardware is still under development. In conjunction with this post, we’re releasing the first public demo of FireSim, which lets you deploy your own 8-node simulated cluster on an F1 Instance and run benchmarks against it. This demo simulates a pre-built “vanilla” cluster, but demonstrates FireSim’s high performance and usability.

Why FireSim + F1?

FPGA-accelerated hardware simulation is by no means a new concept. However, previous attempts to use FPGAs for simulation have been fraught with usability, scalability, and cost issues. FireSim takes advantage of EC2 F1 and open-source hardware to address the traditional problems with FPGA-accelerated simulation:
Problem #1: FPGA-based simulations have traditionally been expensive, difficult to deploy, and difficult to reproduce.
FireSim uses public-cloud infrastructure like F1, which means no upfront cost to purchase and deploy FPGAs. Developers and researchers can distribute pre-built AMIs and AFIs, as in this public demo (more details later in this post), to make experiments easy to reproduce. FireSim also automates most of the work involved in deploying an FPGA simulation, essentially enabling one-click conversion from new RTL to deploying on an FPGA cluster.

Problem #2: FPGA-based simulations have traditionally been difficult (and expensive) to scale.
Because FireSim uses F1, users can scale out experiments by spinning up additional EC2 instances, rather than spending hundreds of thousands of dollars on large FPGA clusters.

Problem #3: Finding open hardware to simulate has traditionally been difficult. Finding open hardware that can run real software stacks is even harder.
FireSim simulates RocketChip, an open, silicon-proven, RISC-V-based processor platform, and adds peripherals like a NIC and disk device to build up a realistic system. Processors that implement RISC-V automatically support real operating systems (such as Linux) and even support applications like Apache and Memcached. We provide a custom Buildroot-based FireSim Linux distribution that runs on our simulated nodes and includes many popular developer tools.

Problem #4: Writing hardware in traditional HDLs is time-consuming.
Both FireSim and RocketChip use the Chisel HDL, which brings modern programming paradigms to hardware description languages. Chisel greatly simplifies the process of building large, highly parameterized hardware components.

How to use FireSim for hardware/software co-design

FireSim drastically improves the process of co-designing hardware and software by acting as a push-button interface for collaboration between hardware developers and systems software developers. The following diagram describes the workflows that hardware and software developers use when working with FireSim.

Figure 2. The FireSim custom hardware development workflow.

The hardware developer’s view:

  1. Write custom RTL for your accelerator, peripheral, or processor modification in a productive language like Chisel.
  2. Run a software simulation of your hardware design in standard gate-level simulation tools for early-stage debugging.
  3. Run FireSim build scripts, which automatically build your simulation, run it through the Vivado toolchain/AWS shell scripts, and publish an AFI.
  4. Deploy your simulation on EC2 F1 using the generated simulation driver and AFI
  5. Run real software builds released by software developers to benchmark your hardware

The software developer’s view:

  1. Deploy the AMI/AFI generated by the hardware developer on an F1 instance to simulate a cluster of nodes (or scale out to many F1 nodes for larger simulated core-counts).
  2. Connect using SSH into the simulated nodes in the cluster and boot the Linux distribution included with FireSim. This distribution is easy to customize, and already supports many standard software packages.
  3. Directly prototype your software using the same exact interfaces that the software will see when deployed on the real future system you’re prototyping, with the same performance characteristics as observed from software, even at scale.

FireSim demo v1.0

Figure 3. Cluster topology simulated by FireSim demo v1.0.

This first public demo of FireSim focuses on the aforementioned “software-developer’s view” of the custom hardware development cycle. The demo simulates a cluster of 1 to 8 RocketChip-based nodes, interconnected by a functional network simulation. The simulated nodes work just like “real” machines:  they boot Linux, you can connect to them using SSH, and you can run real applications on top. The nodes can see each other (and the EC2 F1 instance on which they’re deployed) on the network and communicate with one another. While the demo currently simulates a pre-built “vanilla” cluster, the entire hardware configuration of these simulated nodes can be modified after FireSim is open-sourced.

In this post, I walk through bringing up a single-node FireSim simulation for experienced EC2 F1 users. For more detailed instructions for new users and instructions for running a larger 8-node simulation, see FireSim Demo v1.0 on Amazon EC2 F1. Both demos walk you through setting up an instance from a demo AMI/AFI and booting Linux on the simulated nodes. The full demo instructions also walk you through an example workload, running Memcached on the simulated nodes, with YCSB as a load generator to demonstrate network functionality.

Deploying the demo on F1

In this release, we provide pre-built binaries for driving simulation from the host and a pre-built AFI that contains the FPGA infrastructure necessary to simulate a RocketChip-based node.

Starting your F1 instances

First, launch an instance using the free FireSim Demo v1.0 product available on the AWS Marketplace on an f1.2xlarge instance. After your instance has booted, log in using the user name centos. On the first login, you should see the message “FireSim network config completed.” This sets up the necessary tap interfaces and bridge on the EC2 instance to enable communicating with the simulated nodes.

AMI contents

The AMI contains a variety of tools to help you run simulations and build software for RISC-V systems, including the riscv64 toolchain, a Buildroot-based Linux distribution that runs on the simulated nodes, and the simulation driver program. For more details, see the AMI Contents section on the FireSim website.

Single-node demo

First, you need to flash the FPGA with the FireSim AFI. To do so, run:

[[email protected]_ADDR ~]$ sudo fpga-load-local-image -S 0 -I agfi-00a74c2d615134b21

To start a simulation, run the following at the command line:

[[email protected]_ADDR ~]$ boot-firesim-singlenode

This automatically calls the simulation driver, telling it to load the Linux kernel image and root filesystem for the Linux distro. This produces output similar to the following:

Simulations Started. You can use the UART console of each simulated node by attaching to the following screens:

There is a screen on:

2492.fsim0      (Detached)

1 Socket in /var/run/screen/S-centos.

You could connect to the simulated UART console by connecting to this screen, but instead opt to use SSH to access the node instead.

First, ping the node to make sure it has come online. This is currently required because nodes may get stuck at Linux boot if the NIC does not receive any network traffic. For more information, see Troubleshooting/Errata. The node is always assigned the IP address 192.168.1.10:

[[email protected]_ADDR ~]$ ping 192.168.1.10

This should eventually produce the following output:

PING 192.168.1.10 (192.168.1.10) 56(84) bytes of data.

From 192.168.1.1 icmp_seq=1 Destination Host Unreachable

64 bytes from 192.168.1.10: icmp_seq=1 ttl=64 time=2017 ms

64 bytes from 192.168.1.10: icmp_seq=2 ttl=64 time=1018 ms

64 bytes from 192.168.1.10: icmp_seq=3 ttl=64 time=19.0 ms

At this point, you know that the simulated node is online. You can connect to it using SSH with the user name root and password firesim. It is also convenient to make sure that your TERM variable is set correctly. In this case, the simulation expects TERM=linux, so provide that:

[[email protected]_ADDR ~]$ TERM=linux ssh [email protected]

The authenticity of host ‘192.168.1.10 (192.168.1.10)’ can’t be established.

ECDSA key fingerprint is 63:e9:66:d0:5c:06:2c:1d:5c:95:33:c8:36:92:30:49.

Are you sure you want to continue connecting (yes/no)? yes

Warning: Permanently added ‘192.168.1.10’ (ECDSA) to the list of known hosts.

[email protected]’s password:

#

At this point, you’re connected to the simulated node. Run uname -a as an example. You should see the following output, indicating that you’re connected to a RISC-V system:

# uname -a

Linux buildroot 4.12.0-rc2 #1 Fri Aug 4 03:44:55 UTC 2017 riscv64 GNU/Linux

Now you can run programs on the simulated node, as you would with a real machine. For an example workload (running YCSB against Memcached on the simulated node) or to run a larger 8-node simulation, see the full FireSim Demo v1.0 on Amazon EC2 F1 demo instructions.

Finally, when you are finished, you can shut down the simulated node by running the following command from within the simulated node:

# poweroff

You can confirm that the simulation has ended by running screen -ls, which should now report that there are no detached screens.

Future plans

At Berkeley, we’re planning to keep improving the FireSim platform to enable our own research in future data center architectures, like FireBox. The FireSim platform will eventually support more sophisticated processors, custom accelerators (such as Hwacha), network models, and peripherals, in addition to scaling to larger numbers of FPGAs. In the future, we’ll open source the entire platform, including Midas, the tool used to transform RTL into FPGA simulators, allowing users to modify any part of the hardware/software stack. Follow @firesimproject on Twitter to stay tuned to future FireSim updates.

Acknowledgements

FireSim is the joint work of many students and faculty at Berkeley: Sagar Karandikar, Donggyu Kim, Howard Mao, David Biancolin, Jack Koenig, Jonathan Bachrach, and Krste Asanović. This work is partially funded by AWS through the RISE Lab, by the Intel Science and Technology Center for Agile HW Design, and by ASPIRE Lab sponsors and affiliates Intel, Google, HPE, Huawei, NVIDIA, and SK hynix.