Tag Archives: EC2 Container Service

AWS Hot Startups – September 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/aws-hot-startups-september-2017/

As consumers continue to demand faster, simpler, and more on-the-go services, FinTech companies are responding with ever more innovative solutions to fit everyone’s needs and to improve customer experience. This month, we are excited to feature the following startups—all of whom are disrupting traditional financial services in unique ways:

  • Acorns – allowing customers to invest spare change automatically.
  • Bondlinc – improving the bond trading experience for clients, financial institutions, and private banks.
  • Lenda – reimagining homeownership with a secure and streamlined online service.

Acorns (Irvine, CA)

Driven by the belief that anyone can grow wealth, Acorns is relentlessly pursuing ways to help make that happen. Currently the fastest-growing micro-investing app in the U.S., Acorns takes mere minutes to get started and is currently helping over 2.2 million people grow their wealth. And unlike other FinTech apps, Acorns is focused on helping America’s middle class – namely the 182 million citizens who make less than $100,000 per year – and looking after their financial best interests.

Acorns is able to help their customers effortlessly invest their money, little by little, by offering ETF portfolios put together by Dr. Harry Markowitz, a Nobel Laureate in economic sciences. They also offer a range of services, including “Round-Ups,” whereby customers can automatically invest spare change from every day purchases, and “Recurring Investments,” through which customers can set up automatic transfers of just $5 per week into their portfolio. Additionally, Found Money, Acorns’ earning platform, can help anyone spend smarter as the company connects customers to brands like Lyft, Airbnb, and Skillshare, who then automatically invest in customers’ Acorns account.

The Acorns platform runs entirely on AWS, allowing them to deliver a secure and scalable cloud-based experience. By utilizing AWS, Acorns is able to offer an exceptional customer experience and fulfill its core mission. Acorns uses Terraform to manage services such as Amazon EC2 Container Service, Amazon CloudFront, and Amazon S3. They also use Amazon RDS and Amazon Redshift for data storage, and Amazon Glacier to manage document retention.

Acorns is hiring! Be sure to check out their careers page if you are interested.

Bondlinc (Singapore)

Eng Keong, Founder and CEO of Bondlinc, has long wanted to standardize, improve, and automate the traditional workflows that revolve around bond trading. As a former trader at BNP Paribas and Jefferies & Company, E.K. – as Keong is known – had personally seen how manual processes led to information bottlenecks in over-the-counter practices. This drove him, along with future Bondlinc CTO Vincent Caldeira, to start a new service that maximizes efficiency, information distribution, and accessibility for both clients and bankers in the bond market.

Currently, bond trading requires banks to spend a significant amount of resources retrieving data from expensive and restricted institutional sources, performing suitability checks, and attaching required documentation before presenting all relevant information to clients – usually by email. Bankers are often overwhelmed by these time-consuming tasks, which means clients don’t always get proper access to time-sensitive bond information and pricing. Bondlinc bridges this gap between banks and clients by providing a variety of solutions, including easy access to basic bond information and analytics, updates of new issues and relevant news, consolidated management of your portfolio, and a chat function between banker and client. By making the bond market much more accessible to clients, Bondlinc is taking private banking to the next level, while improving efficiency of the banks as well.

As a startup running on AWS since inception, Bondlinc has built and operated its SaaS product by leveraging Amazon EC2, Amazon S3, Elastic Load Balancing, and Amazon RDS across multiple Availability Zones to provide its customers (namely, financial institutions) a highly available and seamlessly scalable product distribution platform. Bondlinc also makes extensive use of Amazon CloudWatch, AWS CloudTrail, and Amazon SNS to meet the stringent operational monitoring, auditing, compliance, and governance requirements of its customers. Bondlinc is currently experimenting with Amazon Lex to build a conversational interface into its mobile application via a chat-bot that provides trading assistance services.

To see how Bondlinc works, request a demo at Bondlinc.com.

Lenda (San Francisco, CA)

Lenda is a digital mortgage company founded by seasoned FinTech entrepreneur Jason van den Brand. Jason wanted to create a smarter, simpler, and more streamlined system for people to either get a mortgage or refinance their homes. With Lenda, customers can find out if they are pre-approved for loans, and receive accurate, real-time mortgage rate quotes from industry-experienced home loan advisors. Lenda’s advisors support customers through the loan process by providing financial advice and guidance for a seamless experience.

Lenda’s innovative platform allows borrowers to complete their home loans online from start to finish. Through a savvy combination of being a direct lender with proprietary technology, Lenda has simplified the mortgage application process to save customers time and money. With an interactive dashboard, customers know exactly where they are in the mortgage process and can manage all of their documents in one place. The company recently received its Series A funding of $5.25 million, and van den Brand shared that most of the capital investment will be used to improve Lenda’s technology and fulfill the company’s mission, which is to reimagine homeownership, starting with home loans.

AWS allows Lenda to scale its business while providing a secure, easy-to-use system for a faster home loan approval process. Currently, Lenda uses Amazon S3, Amazon EC2, Amazon CloudFront, Amazon Redshift, and Amazon WorkSpaces.

Visit Lenda.com to find out more.

Thanks for reading and see you in October for another round of hot startups!

-Tina

Creating a Cost-Efficient Amazon ECS Cluster for Scheduled Tasks

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/creating-a-cost-efficient-amazon-ecs-cluster-for-scheduled-tasks/

Madhuri Peri
Sr. DevOps Consultant

When you use Amazon Relational Database Service (Amazon RDS), depending on the logging levels on the RDS instances and the volume of transactions, you could generate a lot of log data. To ensure that everything is running smoothly, many customers search for log error patterns using different log aggregation and visualization systems, such as Amazon Elasticsearch Service, Splunk, or other tool of their choice. A module needs to periodically retrieve the RDS logs using the SDK, and then send them to Amazon S3. From there, you can stream them to your log aggregation tool.

One option is writing an AWS Lambda function to retrieve the log files. However, because of the time that this function needs to execute, depending on the volume of log files retrieved and transferred, it is possible that Lambda could time out on many instances.  Another approach is launching an Amazon EC2 instance that runs this job periodically. However, this would require you to run an EC2 instance continuously, not an optimal use of time or money.

Using the new Amazon CloudWatch integration with Amazon EC2 Container Service, you can trigger this job to run in a container on an existing Amazon ECS cluster. Additionally, this would allow you to improve costs by running containers on a fleet of Spot Instances.

In this post, I will show you how to use the new scheduled tasks (cron) feature in Amazon ECS and launch tasks using CloudWatch events, while leveraging Spot Fleet to maximize availability and cost optimization for containerized workloads.

Architecture

The following diagram shows how the various components described schedule a task that retrieves log files from Amazon RDS database instances, and deposits the logs into an S3 bucket.

Amazon ECS cluster container instances are using Spot Fleet, which is a perfect match for the workload that needs to run when it can. This improves cluster costs.

The task definition defines which Docker image to retrieve from the Amazon EC2 Container Registry (Amazon ECR) repository and run on the Amazon ECS cluster.

The container image has Python code functions to make AWS API calls using boto3. It iterates over the RDS database instances, retrieves the logs, and deposits them in the S3 bucket. Many customers choose these logs to be delivered to their centralized log-store. CloudWatch Events defines the schedule for when the container task has to be launched.

Walkthrough

To provide the basic framework, we have built an AWS CloudFormation template that creates the following resources:

  • Amazon ECR repository for storing the Docker image to be used in the task definition
  • S3 bucket that holds the transferred logs
  • Task definition, with image name and S3 bucket as environment variables provided via input parameter
  • CloudWatch Events rule
  • Amazon ECS cluster
  • Amazon ECS container instances using Spot Fleet
  • IAM roles required for the container instance profiles

Before you begin

Ensure that Git, Docker, and the AWS CLI are installed on your computer.

In your AWS account, instantiate one Amazon Aurora instance using the console. For more information, see Creating an Amazon Aurora DB Cluster.

Implementation Steps

  1. Clone the code from GitHub that performs RDS API calls to retrieve the log files.
    git clone https://github.com/awslabs/aws-ecs-scheduled-tasks.git
  2. Build and tag the image.
    cd aws-ecs-scheduled-tasks/container-code/src && ls

    Dockerfile		rdslogsshipper.py	requirements.txt

    docker build -t rdslogsshipper .

    Sending build context to Docker daemon 9.728 kB
    Step 1 : FROM python:3
     ---> 41397f4f2887
    Step 2 : WORKDIR /usr/src/app
     ---> Using cache
     ---> 59299c020e7e
    Step 3 : COPY requirements.txt ./
     ---> 8c017e931c3b
    Removing intermediate container df09e1bed9f2
    Step 4 : COPY rdslogsshipper.py /usr/src/app
     ---> 099a49ca4325
    Removing intermediate container 1b1da24a6699
    Step 5 : RUN pip install --no-cache-dir -r requirements.txt
     ---> Running in 3ed98b30901d
    Collecting boto3 (from -r requirements.txt (line 1))
      Downloading boto3-1.4.6-py2.py3-none-any.whl (128kB)
    Collecting botocore (from -r requirements.txt (line 2))
      Downloading botocore-1.6.7-py2.py3-none-any.whl (3.6MB)
    Collecting s3transfer<0.2.0,>=0.1.10 (from boto3->-r requirements.txt (line 1))
      Downloading s3transfer-0.1.10-py2.py3-none-any.whl (54kB)
    Collecting jmespath<1.0.0,>=0.7.1 (from boto3->-r requirements.txt (line 1))
      Downloading jmespath-0.9.3-py2.py3-none-any.whl
    Collecting python-dateutil<3.0.0,>=2.1 (from botocore->-r requirements.txt (line 2))
      Downloading python_dateutil-2.6.1-py2.py3-none-any.whl (194kB)
    Collecting docutils>=0.10 (from botocore->-r requirements.txt (line 2))
      Downloading docutils-0.14-py3-none-any.whl (543kB)
    Collecting six>=1.5 (from python-dateutil<3.0.0,>=2.1->botocore->-r requirements.txt (line 2))
      Downloading six-1.10.0-py2.py3-none-any.whl
    Installing collected packages: six, python-dateutil, docutils, jmespath, botocore, s3transfer, boto3
    Successfully installed boto3-1.4.6 botocore-1.6.7 docutils-0.14 jmespath-0.9.3 python-dateutil-2.6.1 s3transfer-0.1.10 six-1.10.0
     ---> f892d3cb7383
    Removing intermediate container 3ed98b30901d
    Step 6 : COPY . .
     ---> ea7550c04fea
    Removing intermediate container b558b3ebd406
    Successfully built ea7550c04fea
  3. Run the CloudFormation stack and get the names for the Amazon ECR repo and S3 bucket. In the stack, choose Outputs.
  4. Open the ECS console and choose Repositories. The rdslogs repo has been created. Choose View Push Commands and follow the instructions to connect to the repository and push the image for the code that you built in Step 2. The screenshot shows the final result:
  5. Associate the CloudWatch scheduled task with the created Amazon ECS Task Definition, using a new CloudWatch event rule that is scheduled to run at intervals. The following rule is scheduled to run every 15 minutes:
    aws --profile default --region us-west-2 events put-rule --name demo-ecs-task-rule  --schedule-expression "rate(15 minutes)"

    {
        "RuleArn": "arn:aws:events:us-west-2:12345678901:rule/demo-ecs-task-rule"
    }
  6. CloudWatch requires IAM permissions to place a task on the Amazon ECS cluster when the CloudWatch event rule is executed, in addition to an IAM role that can be assumed by CloudWatch Events. This is done in three steps:
    1. Create the IAM role to be assumed by CloudWatch.
      aws --profile default --region us-west-2 iam create-role --role-name Test-Role --assume-role-policy-document file://event-role.json

      {
          "Role": {
              "AssumeRolePolicyDocument": {
                  "Version": "2012-10-17", 
                  "Statement": [
                      {
                          "Action": "sts:AssumeRole", 
                          "Effect": "Allow", 
                          "Principal": {
                              "Service": "events.amazonaws.com"
                          }
                      }
                  ]
              }, 
              "RoleId": "AROAIRYYLDCVZCUACT7FS", 
              "CreateDate": "2017-07-14T22:44:52.627Z", 
              "RoleName": "Test-Role", 
              "Path": "/", 
              "Arn": "arn:aws:iam::12345678901:role/Test-Role"
          }
      }

      The following is an example of the event-role.json file used earlier:

      {
          "Version": "2012-10-17",
          "Statement": [
              {
                  "Effect": "Allow",
                  "Principal": {
                    "Service": "events.amazonaws.com"
                  },
                  "Action": "sts:AssumeRole"
              }
          ]
      }
    2. Create the IAM policy defining the ECS cluster and task definition. You need to get these values from the CloudFormation outputs and resources.
      aws --profile default --region us-west-2 iam create-policy --policy-name test-policy --policy-document file://event-policy.json

      {
          "Policy": {
              "PolicyName": "test-policy", 
              "CreateDate": "2017-07-14T22:51:20.293Z", 
              "AttachmentCount": 0, 
              "IsAttachable": true, 
              "PolicyId": "ANPAI7XDIQOLTBUMDWGJW", 
              "DefaultVersionId": "v1", 
              "Path": "/", 
              "Arn": "arn:aws:iam::123455678901:policy/test-policy", 
              "UpdateDate": "2017-07-14T22:51:20.293Z"
          }
      }

      The following is an example of the event-policy.json file used earlier:

      {
          "Version": "2012-10-17",
          "Statement": [
            {
                "Effect": "Allow",
                "Action": [
                    "ecs:RunTask"
                ],
                "Resource": [
                    "arn:aws:ecs:*::task-definition/"
                ],
                "Condition": {
                    "ArnLike": {
                        "ecs:cluster": "arn:aws:ecs:*::cluster/"
                    }
                }
            }
          ]
      }
    3. Attach the IAM policy to the role.
      aws --profile default --region us-west-2 iam attach-role-policy --role-name Test-Role --policy-arn arn:aws:iam::1234567890:policy/test-policy
  7. Associate the CloudWatch rule created earlier to place the task on the ECS cluster. The following command shows an example. Replace the AWS account ID and region with your settings.
    aws events put-targets --rule demo-ecs-task-rule --targets "Id"="1","Arn"="arn:aws:ecs:us-west-2:12345678901:cluster/test-cwe-blog-ecsCluster-15HJFWCH4SP67","EcsParameters"={"TaskDefinitionArn"="arn:aws:ecs:us-west-2:12345678901:task-definition/test-cwe-blog-taskdef:8"},"RoleArn"="arn:aws:iam::12345678901:role/Test-Role"

    {
        "FailedEntries": [], 
        "FailedEntryCount": 0
    }

That’s it. The logs now run based on the defined schedule.

To test this, open the Amazon ECS console, select the Amazon ECS cluster that you created, and then choose Tasks, Run New Task. Select the task definition created by the CloudFormation template, and the cluster should be selected automatically. As this runs, the S3 bucket should be populated with the RDS logs for the instance.

Conclusion

In this post, you’ve seen that the choices for workloads that need to run at a scheduled time include Lambda with CloudWatch events or EC2 with cron. However, sometimes the job could run outside of Lambda execution time limits or be not cost-effective for an EC2 instance.

In such cases, you can schedule the tasks on an ECS cluster using CloudWatch rules. In addition, you can use a Spot Fleet cluster with Amazon ECS for cost-conscious workloads that do not have hard requirements on execution time or instance availability in the Spot Fleet. For more information, see Powering your Amazon ECS Cluster with Amazon EC2 Spot Instances and Scheduled Events.

If you have questions or suggestions, please comment below.

Skill up on how to perform CI/CD with AWS Developer tools

Post Syndicated from Chirag Dhull original https://aws.amazon.com/blogs/devops/skill-up-on-how-to-perform-cicd-with-aws-devops-tools/

This is a guest post from Paul Duvall, CTO of Stelligent, a division of HOSTING.

I co-founded Stelligent, a technology services company that provides DevOps Automation on AWS as a result of my own frustration in implementing all the “behind the scenes” infrastructure (including builds, tests, deployments, etc.) on software projects on which I was developing software. At Stelligent, we have worked with numerous customers looking to get software delivered to users quicker and with greater confidence. This sounds simple but it often consists of properly configuring and integrating myriad tools including, but not limited to, version control, build, static analysis, testing, security, deployment, and software release orchestration. What some might not realize is that there’s a new breed of build, deploy, test, and release tools that help reduce much of the undifferentiated heavy lifting of deploying and releasing software to users.

 
I’ve been using AWS since 2009 and I, along with many at Stelligent – have worked with the AWS Service Teams as part of the AWS Developer Tools betas that are now generally available (including AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy). I’ve combined the experience we’ve had with customers along with this specialized knowledge of the AWS Developer and Management Tools to provide a unique course that shows multiple ways to use these services to deliver software to users quicker and with confidence.

 
In DevOps Essentials on AWS, you’ll learn how to accelerate software delivery and speed up feedback loops by learning how to use AWS Developer Tools to automate infrastructure and deployment pipelines for applications running on AWS. The course demonstrates solutions for various DevOps use cases for Amazon EC2, AWS OpsWorks, AWS Elastic Beanstalk, AWS Lambda (Serverless), Amazon ECS (Containers), while defining infrastructure as code and learning more about AWS Developer Tools including AWS CodeStar, AWS CodeCommit, AWS CodeBuild, AWS CodePipeline, and AWS CodeDeploy.

 
In this course, you see me use the AWS Developer and Management Tools to create comprehensive continuous delivery solutions for a sample application using many types of AWS service platforms. You can run the exact same sample and/or fork the GitHub repository (https://github.com/stelligent/devops-essentials) and extend or modify the solutions. I’m excited to share how you can use AWS Developer Tools to create these solutions for your customers as well. There’s also an accompanying website for the course (http://www.devopsessentialsaws.com/) that I use in the video to walk through the course examples which link to resources located in GitHub or Amazon S3. In this course, you will learn how to:

  • Use AWS Developer and Management Tools to create a full-lifecycle software delivery solution
  • Use AWS CloudFormation to automate the provisioning of all AWS resources
  • Use AWS CodePipeline to orchestrate the deployments of all applications
  • Use AWS CodeCommit while deploying an application onto EC2 instances using AWS CodeBuild and AWS CodeDeploy
  • Deploy applications using AWS OpsWorks and AWS Elastic Beanstalk
  • Deploy an application using Amazon EC2 Container Service (ECS) along with AWS CloudFormation
  • Deploy serverless applications that use AWS Lambda and API Gateway
  • Integrate all AWS Developer Tools into an end-to-end solution with AWS CodeStar

To learn more, see DevOps Essentials on AWS video course on Udemy. For a limited time, you can enroll in this course for $40 and save 80%, a $160 saving. Simply use the code AWSDEV17.

 
Stelligent, an AWS Partner Network Advanced Consulting Partner holds the AWS DevOps Competency and over 100 AWS technical certifications. To stay updated on DevOps best practices, visit www.stelligent.com.

Simplify Your Jenkins Builds with AWS CodeBuild

Post Syndicated from Paul Roberts original https://aws.amazon.com/blogs/devops/simplify-your-jenkins-builds-with-aws-codebuild/

Jeff Bezos famously said, “There’s a lot of undifferentiated heavy lifting that stands between your idea and that success.” He went on to say, “…70% of your time, energy, and dollars go into the undifferentiated heavy lifting and only 30% of your energy, time, and dollars gets to go into the core kernel of your idea.”

If you subscribe to this maxim, you should not be spending valuable time focusing on operational issues related to maintaining the Jenkins build infrastructure. Companies such as Riot Games have over 1.25 million builds per year and have written several lengthy blog posts about their experiences designing a complex, custom Docker-powered Jenkins build farm. Dealing with Jenkins slaves at scale is a job in itself and Riot has engineers focused on managing the build infrastructure.

Typical Jenkins Build Farm

 

As with all technology, the Jenkins build farm architectures have evolved. Today, instead of manually building your own container infrastructure, there are Jenkins Docker plugins available to help reduce the operational burden of maintaining these environments. There is also a community-contributed Amazon EC2 Container Service (Amazon ECS) plugin that helps remove some of the overhead, but you still need to configure and manage the overall Amazon ECS environment.

There are various ways to create and manage your Jenkins build farm, but there has to be a way that significantly reduces your operational overhead.

Introducing AWS CodeBuild

AWS CodeBuild is a fully managed build service that removes the undifferentiated heavy lifting of provisioning, managing, and scaling your own build servers. With CodeBuild, there is no software to install, patch, or update. CodeBuild scales up automatically to meet the needs of your development teams. In addition, CodeBuild is an on-demand service where you pay as you go. You are charged based only on the number of minutes it takes to complete your build.

One AWS customer, Recruiterbox, helps companies hire simply and predictably through their software platform. Two years ago, they began feeling the operational pain of maintaining their own Jenkins build farms. They briefly considered moving to Amazon ECS, but chose an even easier path forward instead. Recuiterbox transitioned to using Jenkins with CodeBuild and are very happy with the results. You can read more about their journey here.

Solution Overview: Jenkins and CodeBuild

To remove the heavy lifting from managing your Jenkins build farm, AWS has developed a Jenkins AWS CodeBuild plugin. After the plugin has been enabled, a developer can configure a Jenkins project to pick up new commits from their chosen source code repository and automatically run the associated builds. After the build is successful, it will create an artifact that is stored inside an S3 bucket that you have configured. If an error is detected somewhere, CodeBuild will capture the output and send it to Amazon CloudWatch logs. In addition to storing the logs on CloudWatch, Jenkins also captures the error so you do not have to go hunting for log files for your build.

 

AWS CodeBuild with Jenkins Plugin

 

The following example uses AWS CodeCommit (Git) as the source control management (SCM) and Amazon S3 for build artifact storage. Logs are stored in CloudWatch. A development pipeline that uses Jenkins with CodeBuild plugin architecture looks something like this:

 

AWS CodeBuild Diagram

Initial Solution Setup

To keep this blog post succinct, I assume that you are using the following components on AWS already and have applied the appropriate IAM policies:

·         AWS CodeCommit repo.

·         Amazon S3 bucket for CodeBuild artifacts.

·         SNS notification for text messaging of the Jenkins admin password.

·         IAM user’s key and secret.

·         A role that has a policy with these permissions. Be sure to edit the ARNs with your region, account, and resource name. Use this role in the AWS CloudFormation template referred to later in this post.

 

Jenkins Installation with CodeBuild Plugin Enabled

To make the integration with Jenkins as frictionless as possible, I have created an AWS CloudFormation template here: https://s3.amazonaws.com/proberts-public/jenkins.yaml. Download the template, sign in the AWS CloudFormation console, and then use the template to create a stack.

 

CloudFormation Inputs

Jenkins Project Configuration

After the stack is complete, log in to the Jenkins EC2 instance using the user name “admin” and the password sent to your mobile device. Now that you have logged in to Jenkins, you need to create your first project. Start with a Freestyle project and configure the parameters based on your CodeBuild and CodeCommit settings.

 

AWS CodeBuild Plugin Configuration in Jenkins

 

Additional Jenkins AWS CodeBuild Plugin Configuration

 

After you have configured the Jenkins project appropriately you should be able to check your build status on the Jenkins polling log under your project settings:

 

Jenkins Polling Log

 

Now that Jenkins is polling CodeCommit, you can check the CodeBuild dashboard under your Jenkins project to confirm your build was successful:

Jenkins AWS CodeBuild Dashboard

Wrapping Up

In a matter of minutes, you have been able to provision Jenkins with the AWS CodeBuild plugin. This will greatly simplify your build infrastructure management. Now kick back and relax while CodeBuild does all the heavy lifting!


About the Author

Paul Roberts is a Strategic Solutions Architect for Amazon Web Services. When he is not working on Serverless, DevOps, or Artificial Intelligence, he is often found in Lake Tahoe exploring the various mountain ranges with his family.

Deploying an NGINX Reverse Proxy Sidecar Container on Amazon ECS

Post Syndicated from Nathan Peck original https://aws.amazon.com/blogs/compute/nginx-reverse-proxy-sidecar-container-on-amazon-ecs/

Reverse proxies are a powerful software architecture primitive for fetching resources from a server on behalf of a client. They serve a number of purposes, from protecting servers from unwanted traffic to offloading some of the heavy lifting of HTTP traffic processing.

This post explains the benefits of a reverse proxy, and explains how to use NGINX and Amazon EC2 Container Service (Amazon ECS) to easily implement and deploy a reverse proxy for your containerized application.

Components

NGINX is a high performance HTTP server that has achieved significant adoption because of its asynchronous event driven architecture. It can serve thousands of concurrent requests with a low memory footprint. This efficiency also makes it ideal as a reverse proxy.

Amazon ECS is a highly scalable, high performance container management service that supports Docker containers. It allows you to run applications easily on a managed cluster of Amazon EC2 instances. Amazon ECS helps you get your application components running on instances according to a specified configuration. It also helps scale out these components across an entire fleet of instances.

Sidecar containers are a common software pattern that has been embraced by engineering organizations. It’s a way to keep server side architecture easier to understand by building with smaller, modular containers that each serve a simple purpose. Just like an application can be powered by multiple microservices, each microservice can also be powered by multiple containers that work together. A sidecar container is simply a way to move part of the core responsibility of a service out into a containerized module that is deployed alongside a core application container.

The following diagram shows how an NGINX reverse proxy sidecar container operates alongside an application server container:

In this architecture, Amazon ECS has deployed two copies of an application stack that is made up of an NGINX reverse proxy side container and an application container. Web traffic from the public goes to an Application Load Balancer, which then distributes the traffic to one of the NGINX reverse proxy sidecars. The NGINX reverse proxy then forwards the request to the application server and returns its response to the client via the load balancer.

Reverse proxy for security

Security is one reason for using a reverse proxy in front of an application container. Any web server that serves resources to the public can expect to receive lots of unwanted traffic every day. Some of this traffic is relatively benign scans by researchers and tools, such as Shodan or nmap:

[18/May/2017:15:10:10 +0000] "GET /YesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScanningForResearchPurposePleaseHaveALookAtTheUserAgentTHXYesThisIsAReallyLongRequestURLbutWeAreDoingItOnPurposeWeAreScann HTTP/1.1" 404 1389 - Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.86 Safari/537.36
[18/May/2017:18:19:51 +0000] "GET /clientaccesspolicy.xml HTTP/1.1" 404 322 - Cloud mapping experiment. Contact [email protected]

But other traffic is much more malicious. For example, here is what a web server sees while being scanned by the hacking tool ZmEu, which scans web servers trying to find PHPMyAdmin installations to exploit:

[18/May/2017:16:27:39 +0000] "GET /mysqladmin/scripts/setup.php HTTP/1.1" 404 391 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /web/phpMyAdmin/scripts/setup.php HTTP/1.1" 404 394 - ZmEu
[18/May/2017:16:27:39 +0000] "GET /xampp/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /apache-default/phpmyadmin/scripts/setup.php HTTP/1.1" 404 405 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /phpMyAdmin-2.10.0.0/scripts/setup.php HTTP/1.1" 404 397 - ZmEu
[18/May/2017:16:27:40 +0000] "GET /mysql/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /admin/scripts/setup.php HTTP/1.1" 404 386 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /forum/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:41 +0000] "GET /typo3/phpmyadmin/scripts/setup.php HTTP/1.1" 404 396 - ZmEu
[18/May/2017:16:27:42 +0000] "GET /phpMyAdmin-2.10.0.1/scripts/setup.php HTTP/1.1" 404 399 - ZmEu
[18/May/2017:16:27:44 +0000] "GET /administrator/components/com_joommyadmin/phpmyadmin/scripts/setup.php HTTP/1.1" 404 418 - ZmEu
[18/May/2017:18:34:45 +0000] "GET /phpmyadmin/scripts/setup.php HTTP/1.1" 404 390 - ZmEu
[18/May/2017:16:27:45 +0000] "GET /w00tw00t.at.blackhats.romanian.anti-sec:) HTTP/1.1" 404 401 - ZmEu

In addition, servers can also end up receiving unwanted web traffic that is intended for another server. In a cloud environment, an application may end up reusing an IP address that was formerly connected to another service. It’s common for misconfigured or misbehaving DNS servers to send traffic intended for a different host to an IP address now connected to your server.

It’s the responsibility of anyone running a web server to handle and reject potentially malicious traffic or unwanted traffic. Ideally, the web server can reject this traffic as early as possible, before it actually reaches the core application code. A reverse proxy is one way to provide this layer of protection for an application server. It can be configured to reject these requests before they reach the application server.

Reverse proxy for performance

Another advantage of using a reverse proxy such as NGINX is that it can be configured to offload some heavy lifting from your application container. For example, every HTTP server should support gzip. Whenever a client requests gzip encoding, the server compresses the response before sending it back to the client. This compression saves network bandwidth, which also improves speed for clients who now don’t have to wait as long for a response to fully download.

NGINX can be configured to accept a plaintext response from your application container and gzip encode it before sending it down to the client. This allows your application container to focus 100% of its CPU allotment on running business logic, while NGINX handles the encoding with its efficient gzip implementation.

An application may have security concerns that require SSL termination at the instance level instead of at the load balancer. NGINX can also be configured to terminate SSL before proxying the request to a local application container. Again, this also removes some CPU load from the application container, allowing it to focus on running business logic. It also gives you a cleaner way to patch any SSL vulnerabilities or update SSL certificates by updating the NGINX container without needing to change the application container.

NGINX configuration

Configuring NGINX for both traffic filtering and gzip encoding is shown below:

http {
  # NGINX will handle gzip compression of responses from the app server
  gzip on;
  gzip_proxied any;
  gzip_types text/plain application/json;
  gzip_min_length 1000;
 
  server {
    listen 80;
 
    # NGINX will reject anything not matching /api
    location /api {
      # Reject requests with unsupported HTTP method
      if ($request_method !~ ^(GET|POST|HEAD|OPTIONS|PUT|DELETE)$) {
        return 405;
      }
 
      # Only requests matching the whitelist expectations will
      # get sent to the application server
      proxy_pass http://app:3000;
      proxy_http_version 1.1;
      proxy_set_header Upgrade $http_upgrade;
      proxy_set_header Connection 'upgrade';
      proxy_set_header Host $host;
      proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
      proxy_cache_bypass $http_upgrade;
    }
  }
}

The above configuration only accepts traffic that matches the expression /api and has a recognized HTTP method. If the traffic matches, it is forwarded to a local application container accessible at the local hostname app. If the client requested gzip encoding, the plaintext response from that application container is gzip-encoded.

Amazon ECS configuration

Configuring ECS to run this NGINX container as a sidecar is also simple. ECS uses a core primitive called the task definition. Each task definition can include one or more containers, which can be linked to each other:

 {
  "containerDefinitions": [
     {
       "name": "nginx",
       "image": "<NGINX reverse proxy image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true,
       "portMappings": [
         {
           "containerPort": "80",
           "protocol": "tcp"
         }
       ],
       "links": [
         "app"
       ]
     },
     {
       "name": "app",
       "image": "<app image URL here>",
       "memory": "256",
       "cpu": "256",
       "essential": true
     }
   ],
   "networkMode": "bridge",
   "family": "application-stack"
}

This task definition causes ECS to start both an NGINX container and an application container on the same instance. Then, the NGINX container is linked to the application container. This allows the NGINX container to send traffic to the application container using the hostname app.

The NGINX container has a port mapping that exposes port 80 on a publically accessible port but the application container does not. This means that the application container is not directly addressable. The only way to send it traffic is to send traffic to the NGINX container, which filters that traffic down. It only forwards to the application container if the traffic passes the whitelisted rules.

Conclusion

Running a sidecar container such as NGINX can bring significant benefits by making it easier to provide protection for application containers. Sidecar containers also improve performance by freeing your application container from various CPU intensive tasks. Amazon ECS makes it easy to run sidecar containers, and automate their deployment across your cluster.

To see the full code for this NGINX sidecar reference, or to try it out yourself, you can check out the open source NGINX reverse proxy reference architecture on GitHub.

– Nathan
 @nathankpeck

Now Available: Three New AWS Specialty Training Courses

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-three-new-aws-specialty-training-courses/

AWS Training allows you to learn from the experts so you can advance your knowledge with practical skills and get more out of the AWS Cloud. Today I am happy to announce that three of our most popular training bootcamps (a staple at AWS re:Invent and AWS Global Summits) are becoming part of our permanent instructor-led training portfolio:

These one-day courses are intended for individuals who would like to dive deeper into a specialized topic with an expert trainer.

You can explore our complete course catalog, and you can search for a public class near you within the AWS Training and Certification Portal. You can also request a private onsite training session for your team by contacting us.

Jeff;

 

 

Deploying Java Microservices on Amazon EC2 Container Service

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/deploying-java-microservices-on-amazon-ec2-container-service/

This post and accompanying code graciously contributed by:

Huy Huynh
Sr. Solutions Architect
Magnus Bjorkman
Solutions Architect

Java is a popular language used by many enterprises today. To simplify and accelerate Java application development, many companies are moving from a monolithic to microservices architecture. For some, it has become a strategic imperative. Containerization technology, such as Docker, lets enterprises build scalable, robust microservice architectures without major code rewrites.

In this post, I cover how to containerize a monolithic Java application to run on Docker. Then, I show how to deploy it on AWS using Amazon EC2 Container Service (Amazon ECS), a high-performance container management service. Finally, I show how to break the monolith into multiple services, all running in containers on Amazon ECS.

Application Architecture

For this example, I use the Spring Pet Clinic, a monolithic Java application for managing a veterinary practice. It is a simple REST API, which allows the client to manage and view Owners, Pets, Vets, and Visits.

It is a simple three-tier architecture:

  • Client
    You simulate this by using curl commands.
  • Web/app server
    This is the Java and Spring-based application that you run using the embedded Tomcat. As part of this post, you run this within Docker containers.
  • Database server
    This is the relational database for your application that stores information about owners, pets, vets, and visits. For this post, use MySQL RDS.

I decided to not put the database inside a container as containers were designed for applications and are transient in nature. The choice was made even easier because you have a fully managed database service available with Amazon RDS.

RDS manages the work involved in setting up a relational database, from provisioning the infrastructure capacity that you request to installing the database software. After your database is up and running, RDS automates common administrative tasks, such as performing backups and patching the software that powers your database. With optional Multi-AZ deployments, Amazon RDS also manages synchronous data replication across Availability Zones with automatic failover.

Walkthrough

You can find the code for the example covered in this post at amazon-ecs-java-microservices on GitHub.

Prerequisites

You need the following to walk through this solution:

  • An AWS account
  • An access key and secret key for a user in the account
  • The AWS CLI installed

Also, install the latest versions of the following:

  • Java
  • Maven
  • Python
  • Docker

Step 1: Move the existing Java Spring application to a container deployed using Amazon ECS

First, move the existing monolith application to a container and deploy it using Amazon ECS. This is a great first step before breaking the monolith apart because you still get some benefits before breaking apart the monolith:

  • An improved pipeline. The container also allows an engineering organization to create a standard pipeline for the application lifecycle.
  • No mutations to machines.

You can find the monolith example at 1_ECS_Java_Spring_PetClinic.

Container deployment overview

The following diagram is an overview of what the setup looks like for Amazon ECS and related services:

This setup consists of the following resources:

  • The client application that makes a request to the load balancer.
  • The load balancer that distributes requests across all available ports and instances registered in the application’s target group using round-robin.
  • The target group that is updated by Amazon ECS to always have an up-to-date list of all the service containers in the cluster. This includes the port on which they are accessible.
  • One Amazon ECS cluster that hosts the container for the application.
  • A VPC network to host the Amazon ECS cluster and associated security groups.

Each container has a single application process that is bound to port 8080 within its namespace. In reality, all the containers are exposed on a different, randomly assigned port on the host.

The architecture is containerized but still monolithic because each container has all the same features of the rest of the containers

The following is also part of the solution but not depicted in the above diagram:

  • One Amazon EC2 Container Registry (Amazon ECR) repository for the application.
  • A service/task definition that spins up containers on the instances of the Amazon ECS cluster.
  • A MySQL RDS instance that hosts the applications schema. The information about the MySQL RDS instance is sent in through environment variables to the containers, so that the application can connect to the MySQL RDS instance.

I have automated setup with the 1_ECS_Java_Spring_PetClinic/ecs-cluster.cf AWS CloudFormation template and a Python script.

The Python script calls the CloudFormation template for the initial setup of the VPC, Amazon ECS cluster, and RDS instance. It then extracts the outputs from the template and uses those for API calls to create Amazon ECR repositories, tasks, services, Application Load Balancer, and target groups.

Environment variables and Spring properties binding

As part of the Python script, you pass in a number of environment variables to the container as part of the task/container definition:

'environment': [
{
'name': 'SPRING_PROFILES_ACTIVE',
'value': 'mysql'
},
{
'name': 'SPRING_DATASOURCE_URL',
'value': my_sql_options['dns_name']
},
{
'name': 'SPRING_DATASOURCE_USERNAME',
'value': my_sql_options['username']
},
{
'name': 'SPRING_DATASOURCE_PASSWORD',
'value': my_sql_options['password']
}
],

The preceding environment variables work in concert with the Spring property system. The value in the variable SPRING_PROFILES_ACTIVE, makes Spring use the MySQL version of the application property file. The other environment files override the following properties in that file:

  • spring.datasource.url
  • spring.datasource.username
  • spring.datasource.password

Optionally, you can also encrypt sensitive values by using Amazon EC2 Systems Manager Parameter Store. Instead of handing in the password, you pass in a reference to the parameter and fetch the value as part of the container startup. For more information, see Managing Secrets for Amazon ECS Applications Using Parameter Store and IAM Roles for Tasks.

Spotify Docker Maven plugin

Use the Spotify Docker Maven plugin to create the image and push it directly to Amazon ECR. This allows you to do this as part of the regular Maven build. It also integrates the image generation as part of the overall build process. Use an explicit Dockerfile as input to the plugin.

FROM frolvlad/alpine-oraclejdk8:slim
VOLUME /tmp
ADD spring-petclinic-rest-1.7.jar app.jar
RUN sh -c 'touch /app.jar'
ENV JAVA_OPTS=""
ENTRYPOINT [ "sh", "-c", "java $JAVA_OPTS -Djava.security.egd=file:/dev/./urandom -jar /app.jar" ]

The Python script discussed earlier uses the AWS CLI to authenticate you with AWS. The script places the token in the appropriate location so that the plugin can work directly against the Amazon ECR repository.

Test setup

You can test the setup by running the Python script:
python setup.py -m setup -r <your region>

After the script has successfully run, you can test by querying an endpoint:
curl <your endpoint from output above>/owner

You can clean this up before going to the next section:
python setup.py -m cleanup -r <your region>

Step 2: Converting the monolith into microservices running on Amazon ECS

The second step is to convert the monolith into microservices. For a real application, you would likely not do this as one step, but re-architect an application piece by piece. You would continue to run your monolith but it would keep getting smaller for each piece that you are breaking apart.

By migrating microservices, you would get four benefits associated with microservices:

  • Isolation of crashes
    If one microservice in your application is crashing, then only that part of your application goes down. The rest of your application continues to work properly.
  • Isolation of security
    When microservice best practices are followed, the result is that if an attacker compromises one service, they only gain access to the resources of that service. They can’t horizontally access other resources from other services without breaking into those services as well.
  • Independent scaling
    When features are broken out into microservices, then the amount of infrastructure and number of instances of each microservice class can be scaled up and down independently.
  • Development velocity
    In a monolith, adding a new feature can potentially impact every other feature that the monolith contains. On the other hand, a proper microservice architecture has new code for a new feature going into a new service. You can be confident that any code you write won’t impact the existing code at all, unless you explicitly write a connection between two microservices.

Find the monolith example at 2_ECS_Java_Spring_PetClinic_Microservices.
You break apart the Spring Pet Clinic application by creating a microservice for each REST API operation, as well as creating one for the system services.

Java code changes

Comparing the project structure between the monolith and the microservices version, you can see that each service is now its own separate build.
First, the monolith version:

You can clearly see how each API operation is its own subpackage under the org.springframework.samples.petclinic package, all part of the same monolithic application.
This changes as you break it apart in the microservices version:

Now, each API operation is its own separate build, which you can build independently and deploy. You have also duplicated some code across the different microservices, such as the classes under the model subpackage. This is intentional as you don’t want to introduce artificial dependencies among the microservices and allow these to evolve differently for each microservice.

Also, make the dependencies among the API operations more loosely coupled. In the monolithic version, the components are tightly coupled and use object-based invocation.

Here is an example of this from the OwnerController operation, where the class is directly calling PetRepository to get information about pets. PetRepository is the Repository class (Spring data access layer) to the Pet table in the RDS instance for the Pet API:

@RestController
class OwnerController {

    @Inject
    private PetRepository pets;
    @Inject
    private OwnerRepository owners;
    private static final Logger logger = LoggerFactory.getLogger(OwnerController.class);

    @RequestMapping(value = "/owner/{ownerId}/getVisits", method = RequestMethod.GET)
    public ResponseEntity<List<Visit>> getOwnerVisits(@PathVariable int ownerId){
        List<Pet> petList = this.owners.findById(ownerId).getPets();
        List<Visit> visitList = new ArrayList<Visit>();
        petList.forEach(pet -> visitList.addAll(pet.getVisits()));
        return new ResponseEntity<List<Visit>>(visitList, HttpStatus.OK);
    }
}

In the microservice version, call the Pet API operation and not PetRepository directly. Decouple the components by using interprocess communication; in this case, the Rest API. This provides for fault tolerance and disposability.

@RestController
class OwnerController {

    @Value("#{environment['SERVICE_ENDPOINT'] ?: 'localhost:8080'}")
    private String serviceEndpoint;

    @Inject
    private OwnerRepository owners;
    private static final Logger logger = LoggerFactory.getLogger(OwnerController.class);

    @RequestMapping(value = "/owner/{ownerId}/getVisits", method = RequestMethod.GET)
    public ResponseEntity<List<Visit>> getOwnerVisits(@PathVariable int ownerId){
        List<Pet> petList = this.owners.findById(ownerId).getPets();
        List<Visit> visitList = new ArrayList<Visit>();
        petList.forEach(pet -> {
            logger.info(getPetVisits(pet.getId()).toString());
            visitList.addAll(getPetVisits(pet.getId()));
        });
        return new ResponseEntity<List<Visit>>(visitList, HttpStatus.OK);
    }

    private List<Visit> getPetVisits(int petId){
        List<Visit> visitList = new ArrayList<Visit>();
        RestTemplate restTemplate = new RestTemplate();
        Pet pet = restTemplate.getForObject("http://"+serviceEndpoint+"/pet/"+petId, Pet.class);
        logger.info(pet.getVisits().toString());
        return pet.getVisits();
    }
}

You now have an additional method that calls the API. You are also handing in the service endpoint that should be called, so that you can easily inject dynamic endpoints based on the current deployment.

Container deployment overview

Here is an overview of what the setup looks like for Amazon ECS and the related services:

This setup consists of the following resources:

  • The client application that makes a request to the load balancer.
  • The Application Load Balancer that inspects the client request. Based on routing rules, it directs the request to an instance and port from the target group that matches the rule.
  • The Application Load Balancer that has a target group for each microservice. The target groups are used by the corresponding services to register available container instances. Each target group has a path, so when you call the path for a particular microservice, it is mapped to the correct target group. This allows you to use one Application Load Balancer to serve all the different microservices, accessed by the path. For example, https:///owner/* would be mapped and directed to the Owner microservice.
  • One Amazon ECS cluster that hosts the containers for each microservice of the application.
  • A VPC network to host the Amazon ECS cluster and associated security groups.

Because you are running multiple containers on the same instances, use dynamic port mapping to avoid port clashing. By using dynamic port mapping, the container is allocated an anonymous port on the host to which the container port (8080) is mapped. The anonymous port is registered with the Application Load Balancer and target group so that traffic is routed correctly.

The following is also part of the solution but not depicted in the above diagram:

  • One Amazon ECR repository for each microservice.
  • A service/task definition per microservice that spins up containers on the instances of the Amazon ECS cluster.
  • A MySQL RDS instance that hosts the applications schema. The information about the MySQL RDS instance is sent in through environment variables to the containers. That way, the application can connect to the MySQL RDS instance.

I have again automated setup with the 2_ECS_Java_Spring_PetClinic_Microservices/ecs-cluster.cf CloudFormation template and a Python script.

The CloudFormation template remains the same as in the previous section. In the Python script, you are now building five different Java applications, one for each microservice (also includes a system application). There is a separate Maven POM file for each one. The resulting Docker image gets pushed to its own Amazon ECR repository, and is deployed separately using its own service/task definition. This is critical to get the benefits described earlier for microservices.

Here is an example of the POM file for the Owner microservice:

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>org.springframework.samples</groupId>
    <artifactId>spring-petclinic-rest</artifactId>
    <version>1.7</version>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>1.5.2.RELEASE</version>
    </parent>
    <properties>
        <!-- Generic properties -->
        <java.version>1.8</java.version>
        <docker.registry.host>${env.docker_registry_host}</docker.registry.host>
    </properties>
    <dependencies>
        <dependency>
            <groupId>javax.inject</groupId>
            <artifactId>javax.inject</artifactId>
            <version>1</version>
        </dependency>
        <!-- Spring and Spring Boot dependencies -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-actuator</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-rest</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-cache</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-data-jpa</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-web</artifactId>
        </dependency>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>
        <!-- Databases - Uses HSQL by default -->
        <dependency>
            <groupId>org.hsqldb</groupId>
            <artifactId>hsqldb</artifactId>
            <scope>runtime</scope>
        </dependency>
        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <scope>runtime</scope>
        </dependency>
        <!-- caching -->
        <dependency>
            <groupId>javax.cache</groupId>
            <artifactId>cache-api</artifactId>
        </dependency>
        <dependency>
            <groupId>org.ehcache</groupId>
            <artifactId>ehcache</artifactId>
        </dependency>
        <!-- end of webjars -->
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-devtools</artifactId>
            <scope>runtime</scope>
        </dependency>
    </dependencies>
    <build>
        <plugins>
            <plugin>
                <groupId>org.springframework.boot</groupId>
                <artifactId>spring-boot-maven-plugin</artifactId>
            </plugin>
            <plugin>
                <groupId>com.spotify</groupId>
                <artifactId>docker-maven-plugin</artifactId>
                <version>0.4.13</version>
                <configuration>
                    <imageName>${env.docker_registry_host}/${project.artifactId}</imageName>
                    <dockerDirectory>src/main/docker</dockerDirectory>
                    <useConfigFile>true</useConfigFile>
                    <registryUrl>${env.docker_registry_host}</registryUrl>
                    <!--dockerHost>https://${docker.registry.host}</dockerHost-->
                    <resources>
                        <resource>
                            <targetPath>/</targetPath>
                            <directory>${project.build.directory}</directory>
                            <include>${project.build.finalName}.jar</include>
                        </resource>
                    </resources>
                    <forceTags>false</forceTags>
                    <imageTags>
                        <imageTag>${project.version}</imageTag>
                    </imageTags>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

Test setup

You can test this by running the Python script:

python setup.py -m setup -r <your region>

After the script has successfully run, you can test by querying an endpoint:

curl <your endpoint from output above>/owner

Conclusion

Migrating a monolithic application to a containerized set of microservices can seem like a daunting task. Following the steps outlined in this post, you can begin to containerize monolithic Java apps, taking advantage of the container runtime environment, and beginning the process of re-architecting into microservices. On the whole, containerized microservices are faster to develop, easier to iterate on, and more cost effective to maintain and secure.

This post focused on the first steps of microservice migration. You can learn more about optimizing and scaling your microservices with components such as service discovery, blue/green deployment, circuit breakers, and configuration servers at http://aws.amazon.com/containers.

If you have questions or suggestions, please comment below.

Blue/Green Deployments with Amazon EC2 Container Service

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/bluegreen-deployments-with-amazon-ecs/

This post and accompanying code was generously contributed by:

Jeremy Cowan
Solutions Architect
Anuj Sharma
DevOps Cloud Architect
Peter Dalbhanjan
Solutions Architect

Deploying software updates in traditional non-containerized environments is hard and fraught with risk. When you write your deployment package or script, you have to assume that the target machine is in a particular state. If your staging environment is not an exact mirror image of your production environment, your deployment could fail. These failures frequently cause outages that persist until you re-deploy the last known good version of your application. If you are an Operations Manager, this is what keeps you up at night.

Increasingly, customers want to do testing in production environments without exposing customers to the new version until the release has been vetted. Others want to expose a small percentage of their customers to the new release to gather feedback about a feature before it’s released to the broader population. This is often referred to as canary analysis or canary testing. In this post, I introduce patterns to implement blue/green and canary deployments using Application Load Balancers and target groups.

If you’d like to try this approach to blue/green deployments, we have open sourced the code and AWS CloudFormation templates in the ecs-blue-green-deployment GitHub repo. The workflow builds an automated CI/CD pipeline that deploys your service onto an ECS cluster and offers a controlled process to swap target groups when you’re ready to promote the latest version of your code to production. You can quickly set up the environment in three steps and see the blue/green swap in action. We’d love for you to try it and send us your feedback!

Benefits of blue/green

Blue/green deployments are a type of immutable deployment that help you deploy software updates with less risk. The risk is reduced by creating separate environments for the current running or “blue” version of your application, and the new or “green” version of your application.

This type of deployment gives you an opportunity to test features in the green environment without impacting the current running version of your application. When you’re satisfied that the green version is working properly, you can gradually reroute the traffic from the old blue environment to the new green environment by modifying DNS. By following this method, you can update and roll back features with near zero downtime.

A typical blue/green deployment involves shifting traffic between 2 distinct environments.

This ability to quickly roll traffic back to the still-operating blue environment is one of the key benefits of blue/green deployments. With blue/green, you should be able to roll back to the blue environment at any time during the deployment process. This limits downtime to the time it takes to realize there’s an issue in the green environment and shift the traffic back to the blue environment. Furthermore, the impact of the outage is limited to the portion of traffic going to the green environment, not all traffic. If the blast radius of deployment errors is reduced, so is the overall deployment risk.

Containers make it simpler

Historically, blue/green deployments were not often used to deploy software on-premises because of the cost and complexity associated with provisioning and managing multiple environments. Instead, applications were upgraded in place.

Although this approach worked, it had several flaws, including the ability to roll back quickly from failures. Rollbacks typically involved re-deploying a previous version of the application, which could affect the length of an outage caused by a bad release. Fixing the issue took precedence over the need to debug, so there were fewer opportunities to learn from your mistakes.

Containers can ease the adoption of blue/green deployments because they’re easily packaged and behave consistently as they’re moved between environments. This consistency comes partly from their immutability. To change the configuration of a container, update its Dockerfile and rebuild and re-deploy the container rather than updating the software in place.

Containers also provide process and namespace isolation for your applications, which allows you to run multiple versions of them side by side on the same Docker host without conflicts. Given their small sizes relative to virtual machines, you can binpack more containers per host than VMs. This lets you make more efficient use of your computing resources, reducing the cost of blue/green deployments.

Fully Managed Updates with Amazon ECS

Amazon EC2 Container Service (ECS) performs rolling updates when you update an existing Amazon ECS service. A rolling update involves replacing the current running version of the container with the latest version. The number of containers Amazon ECS adds or removes from service during a rolling update is controlled by adjusting the minimum and maximum number of healthy tasks allowed during service deployments.

When you update your service’s task definition with the latest version of your container image, Amazon ECS automatically starts replacing the old version of your container with the latest version. During a deployment, Amazon ECS drains connections from the current running version and registers your new containers with the Application Load Balancer as they come online.

Target groups

A target group is a logical construct that allows you to run multiple services behind the same Application Load Balancer. This is possible because each target group has its own listener.

When you create an Amazon ECS service that’s fronted by an Application Load Balancer, you have to designate a target group for your service. Ordinarily, you would create a target group for each of your Amazon ECS services. However, the approach we’re going to explore here involves creating two target groups: one for the blue version of your service, and one for the green version of your service. We’re also using a different listener port for each target group so that you can test the green version of your service using the same path as the blue service.

With this configuration, you can run both environments in parallel until you’re ready to cut over to the green version of your service. You can also do things such as restricting access to the green version to testers on your internal network, using security group rules and placement constraints. For example, you can target the green version of your service to only run on instances that are accessible from your corporate network.

Swapping Over

When you’re ready to replace the old blue service with the new green service, call the ModifyListener API operation to swap the listener’s rules for the target group rules. The change happens instantaneously. Afterward, the green service is running in the target group with the port 80 listener and the blue service is running in the target group with the port 8080 listener. The diagram below is an illustration of the approach described.

Scenario

Two services are defined, each with their own target group registered to the same Application Load Balancer but listening on different ports. Deployment is completed by swapping the listener rules between the two target groups.

The second service is deployed with a new target group listening on a different port but registered to the same Application Load Balancer.

By using 2 listeners, requests to blue services are directed to the target group with the port 80 listener, while requests to the green services are directed to target group with the port 8080 listener.

After automated or manual testing, the deployment can be completed by swapping the listener rules on the Application Load Balancer and sending traffic to the green service.

Caveats

There are a few caveats to be mindful of when using this approach. This method:

  • Assumes that your application code is completely stateless. Store state outside of the container.
  • Doesn’t gracefully drain connections. The swapping of target groups is sudden and abrupt. Therefore, be cautious about using this approach if your service has long-running transactions.
  • Doesn’t allow you to perform canary deployments. While the method gives you the ability to quickly switch between different versions of your service, it does not allow you to divert a portion of the production traffic to a canary or control the rate at which your service is deployed across the cluster.

Canary testing

While this type of deployment automates much of the heavy lifting associated with rolling deployments, it doesn’t allow you to interrupt the deployment if you discover an issue midstream. Rollbacks using the standard Amazon ECS deployment require updating the service’s task definition with the last known good version of the container. Then, you wait for Amazon ECS to schedule and deploy it across the cluster. If the latest version introduces a breaking change that went undiscovered during testing, this might be too slow.

With canary testing, if you discover the green environment is not operating as expected, there is no impact on the blue environment. You can route traffic back to it, minimizing impaired operation or downtime, and limiting the blast radius of impact.

This type of deployment is particularly useful for A/B testing where you want to expose a new feature to a subset of users to get their feedback before making it broadly available.

For canary style deployments, you can use a variation of the blue/green swap that involves deploying the blue and the green service to the same target group. Although this method is not as fast as the swap, it allows you to control the rate at which your containers are replaced by adjusting the task count for each service. Furthermore, it gives you the ability to roll back by adjusting the number of tasks for the blue and green services respectively. Unlike the swap approach described above, connections to your containers are drained gracefully. We plan to address canary style deployments for Amazon ECS in a future post.

Conclusion

With AWS, you can operationalize your blue/green deployments using Amazon ECS, an Application Load Balancer, and target groups. I encourage you to adapt the code published to the ecs-blue-green-deployment GitHub repo for your use cases and look forward to reading your feedback.

If you’re interested in learning more, I encourage you to read the Blue/Green Deployments on AWS and Practicing Continuous Integration and Continuous Delivery on AWS whitepapers.

If you have questions or suggestions, please comment below.

AWS Hot Startups – June 2017

Post Syndicated from Tina Barr original https://aws.amazon.com/blogs/aws/aws-hot-startups-june-2017/

Thanks for stopping by for another round of AWS Hot Startups! This month we are featuring:

  • CloudRanger – helping companies understand the cloud with visual representation.
  • quintly – providing social media analytics for brands on a single dashboard.
  • Tango Card – reinventing rewards programs for businesses and their customers worldwide.

Don’t forget to check out May’s Hot Startups in case you missed them.

CloudRanger (Letterkenny, Ireland)   

The idea for CloudRanger started where most great ideas do – at a bar in Las Vegas. During a late-night conversation with his friends at re:Invent 2014, Dave Gildea (Founder and CEO) used cocktail napkins and drink coasters to visually illustrate servers and backups, and the light on his phone to represent scheduling. By the end of the night, the idea for automated visual server management was born. With CloudRanger, companies can easily create backup and retention policies, visual scheduling, and simple restoration of snapshots and AMIs. The team behind CloudRanger believes that when servers and cloud resources are represented visually, they are easier to manage and understand. Users are able to see their servers, which turns them into a tangible and important piece of business inventory.

CloudRanger is an excellent platform for MSPs who manage many different AWS accounts, and need a quick method to display many servers and audit certain attributes. The company’s goal is to give anyone the ability to create backup policies in multiple regions, apply them using a tag-based methodology, and manage backups. Servers can be scheduled from one simple dashboard, and restoration is easy and step-by-step. With CloudRanger’s visual representation of resources, customers are encouraged to fully understand their backup policies, schedules, and servers.

As an AWS Partner, CloudRanger has built a globally redundant system after going all-in with AWS. They are using over 25 AWS services for everything including enterprise-level security, automation and 24/7 runtimes, and an emphasis on Machine Learning for efficiency in the sales process. CloudRanger continues to rely more on AWS as new services and features are released, and are replacing current services with AWS CodePipeline and AWS CodeBuild. CloudRanger was also named Startup Company of the Year at a recent Irish tech event!

To learn more about CloudRanger, visit their website.

quintly (Cologne, Germany)

In 2010, brothers Alexander Peiniger and Frederik Peiniger started a journey to help companies track their social media profiles and improve their strategies against competitors. The startup began under the name “Social.Media.Tracking” and then “AllFacebook Stats” before officially becoming quintly in 2013. With quintly, brands and agencies can analyze, benchmark, and optimize their social media activities on a global scale. The innovative dashboarding system gives clients an overview across all social media profiles on the most important networks (Facebook, Twitter, YouTube, Google+, LinkedIn, Instagram, etc.) and then derives an optimal social media strategy from those profiles. Today, quintly has users in over 180 countries and paying clients in over 65 countries including major agency networks and Fortune 500 companies.

Getting an overview of a brand’s social media activities can be time-consuming, and turning insights into actions is a challenge that not all brands master. Quintly offers a variety of features designed to help clients improve their social media reach. With their web-based SaaS product, brands and agencies can compare their social media performance against competitors and their best practices. Not only can clients learn from their own historic performance, but they can leverage data from any other brand around the world.

Since the company’s founding, quintly built and operates its SaaS offering on top of AWS services, leveraging Amazon EC2, Amazon ECS, Elastic Load Balancing, and Amazon Route53 to host their Docker-based environment. Large amounts of data are stored in Amazon DynamoDB and Amazon RDS, and they use Amazon CloudWatch to monitor and seamlessly scale to the current needs. In addition, quintly is using Amazon Machine Learning to add additional attributes to the data and to drive better decisions for their clients. With the help of AWS, quintly has been able to focus on their core business while having a scalable and well-performing solution to solve their technical needs.

For more on quintly, check out their Social Media Analytics blog.

Tango Card (Seattle, Washington)

Based in the heart of West Seattle, Tango Card is revolutionizing rewards programs for companies around the world. Too often customers redeem points in a loyalty or rebate program only to wait weeks for their prize to arrive. Companies generously give their employees appreciation gifts, but the gifts can be generic and impersonal. With Tango Card, companies can choose from a variety of rewards that fit the needs of their specific program, event, or business incentive. The extensive Rewards Catalog includes options for e-gift cards that are sure to excite any recipient. There are plenty of options for everyone from traditional e-gift cards to nonprofit donations to cash equivalent rewards.

Tango Card uses a combination of desired rewards, modern technology, and expert service to change the rewards and incentive experience. The Reward Delivery Platform offers solutions including Blast Rewards, Reward Link, and Rewards as a Service API (RaaS). Blast Rewards enables companies to purchase and send e-gift cards in bulk in just one business day. Reward Link lets recipients choose from an assortment of e-gift cards, prepaid cards, digital checks, and donations and is delivered instantly. Finally, Rewards as a Service is a robust digital gift card API that is built to support apps and platforms. With RaaS, Tango Card can send out e-gift cards on company-branded email templates or deliver them directly within a user interface.

The entire Tango Card Reward Delivery Platform leverages many AWS services. They use Amazon EC2 Container Service (ECS) for rapid deployment of containerized micro services, and Amazon Relational Database Service (RDS) for low overhead managed databases. Tango Card is also leveraging Amazon Virtual Private Cloud (VPC), AWS Key Management Service (KMS), and AWS Identity and Access Management (IMS).

To learn more about Tango Card, check out their blog!

I would also like to thank Alexander Moss-Bolanos for helping with the Hot Startups posts this year.

Thanks for reading and we’ll see you next month!

-Tina Barr

Amazon EC2 Container Service – Launch Recap, Customer Stories, and Code

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-ec2-container-service-launch-recap-customer-stories-and-code/

Today seems like a good time to recap some of the features that we have added to Amazon EC2 Container Service over the last year or so, and to share some customer success stories and code with you! The service makes it easy for you to run any number of Docker containers across a managed cluster of EC2 instances, with full console, API, CloudFormation, CLI, and PowerShell support. You can store your Linux and Windows Docker images in the EC2 Container Registry for easy access.

Launch Recap
Let’s start by taking a look at some of the newest ECS features and some helpful how-to blog posts that will show you how to use them:

Application Load Balancing – We added support for the application load balancer last year. This high-performance load balancing option runs at the application level and allows you to define content-based routing rules. It provides support for dynamic ports and can be shared across multiple services, making it easier for you to run microservices in containers. To learn more, read about Service Load Balancing.

IAM Roles for Tasks – You can secure your infrastructure by assigning IAM roles to ECS tasks. This allows you to grant permissions on a fine-grained, per-task basis, customizing the permissions to the needs of each task. Read IAM Roles for Tasks to learn more.

Service Auto Scaling – You can define scaling policies that scale your services (tasks) up and down in response to changes in demand. You set the desired minimum and maximum number of tasks, create one or more scaling policies, and Service Auto Scaling will take care of the rest. The documentation for Service Auto Scaling will help you to make use of this feature.

Blox – Scheduling, in a container-based environment, is the process of assigning tasks to instances. ECS gives you three options: automated (via the built-in Service Scheduler), manual (via the RunTask function), and custom (via a scheduler that you provide). Blox is an open source scheduler that supports a one-task-per-host model, with room to accommodate other models in the future. It monitors the state of the cluster and is well-suited to running monitoring agents, log collectors, and other daemon-style tasks.

Windows – We launched ECS with support for Linux containers and followed up with support for running Windows Server 2016 Base with Containers.

Container Instance Draining – From time to time you may need to remove an instance from a running cluster in order to scale the cluster down or to perform a system update. Earlier this year we added a set of lifecycle hooks that allow you to better manage the state of the instances. Read the blog post How to Automate Container Instance Draining in Amazon ECS to see how to use the lifecycle hooks and a Lambda function to automate the process of draining existing work from an instance while preventing new work from being scheduled for it.

CI/CD Pipeline with Code* – Containers simplify software deployment and are an ideal target for a CI/CD (Continuous Integration / Continuous Deployment) pipeline. The post Continuous Deployment to Amazon ECS using AWS CodePipeline, AWS CodeBuild, Amazon ECR, and AWS CloudFormation shows you how to build and operate a CI/CD pipeline using multiple AWS services.

CloudWatch Logs Integration – This launch gave you the ability to configure the containers that run your tasks to send log information to CloudWatch Logs for centralized storage and analysis. You simply install the Amazon ECS Container Agent and enable the awslogs log driver.

CloudWatch Events – ECS generates CloudWatch Events when the state of a task or a container instance changes. These events allow you to monitor the state of the cluster using a Lambda function. To learn how to capture the events and store them in an Elasticsearch cluster, read Monitor Cluster State with Amazon ECS Event Stream.

Task Placement Policies – This launch provided you with fine-grained control over the placement of tasks on container instances within clusters. It allows you to construct policies that include cluster constraints, custom constraints (location, instance type, AMI, and attribute), placement strategies (spread or bin pack) and to use them without writing any code. Read Introducing Amazon ECS Task Placement Policies to see how to do this!

EC2 Container Service in Action
Many of our customers from large enterprises to hot startups and across all industries, such as financial services, hospitality, and consumer electronics, are using Amazon ECS to run their microservices applications in production. Companies such as Capital One, Expedia, Okta, Riot Games, and Viacom rely on Amazon ECS.

Mapbox is a platform for designing and publishing custom maps. The company uses ECS to power their entire batch processing architecture to collect and process over 100 million miles of sensor data per day that they use for powering their maps. They also optimize their batch processing architecture on ECS using Spot Instances. The Mapbox platform powers over 5,000 apps and reaches more than 200 million users each month. Its backend runs on ECS allowing it to serve more than 1.3 billion requests per day. To learn more about their recent migration to ECS, read their recent blog post, We Switched to Amazon ECS, and You Won’t Believe What Happened Next.

Travel company Expedia designed their backends with a microservices architecture. With the popularization of Docker, they decided they would like to adopt Docker for its faster deployments and environment portability. They chose to use ECS to orchestrate all their containers because it had great integration with the AWS platform, everything from ALB to IAM roles to VPC integration. This made ECS very easy to use with their existing AWS infrastructure. ECS really reduced the heavy lifting of deploying and running containerized applications. Expedia runs 75% of all apps on AWS in ECS allowing it to process 4 billion requests per hour. Read Kuldeep Chowhan‘s blog post, How Expedia Runs Hundreds of Applications in Production Using Amazon ECS to learn more.

Realtor.com provides home buyers and sellers with a comprehensive database of properties that are currently for sale. Their move to AWS and ECS has helped them to support business growth that now numbers 50 million unique monthly users who drive up to 250,000 requests per second at peak times. ECS has helped them to deploy their code more quickly while increasing utilization of their cloud infrastructure. Read the Realtor.com Case Study to learn more about how they use ECS, Kinesis, and other AWS services.

Instacart talks about how they use ECS to power their same-day grocery delivery service:

Capital One talks about how they use ECS to automate their operations and their infrastructure management:

Code
Clever developers are using ECS as a base for their own work. For example:

Rack is an open source PaaS (Platform as a Service). It focuses on infrastructure automation, runs in an isolated VPC, and uses a single-tenant build service for security.

Empire is also an open source PaaS. It provides a Heroku-like workflow and is targeted at small and medium sized startups, with an emphasis on microservices.

Cloud Container Cluster Visualizer (c3vis) helps to visualize resource utilization within ECS clusters:

Stay Tuned
We have plenty of new features in the works for ECS, so stay tuned!

Jeff;

 

AWS Enables Consortium Science to Accelerate Discovery

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/aws-enables-consortium-science-to-accelerate-discovery/

My colleague Mia Champion is a scientist (check out her publications), an AWS Certified Solutions Architect, and an AWS Certified Developer. The time that she spent doing research on large-data datasets gave her an appreciation for the value of cloud computing in the bioinformatics space, which she summarizes and explains in the guest post below!

Jeff;


Technological advances in scientific research continue to enable the collection of exponentially growing datasets that are also increasing in the complexity of their content. The global pace of innovation is now also fueled by the recent cloud-computing revolution, which provides researchers with a seemingly boundless scalable and agile infrastructure. Now, researchers can remove the hindrances of having to own and maintain their own sequencers, microscopes, compute clusters, and more. Using the cloud, scientists can easily store, manage, process and share datasets for millions of patient samples with gigabytes and more of data for each individual. As American physicist, John Bardeen once said: “Science is a collaborative effort. The combined results of several people working together is much more effective than could be that of an individual scientist working alone”.

Prioritizing Reproducible Innovation, Democratization, and Data Protection
Today, we have many individual researchers and organizations leveraging secure cloud enabled data sharing on an unprecedented scale and producing innovative, customized analytical solutions using the AWS cloud.  But, can secure data sharing and analytics be done on such a collaborative scale as to revolutionize the way science is done across a domain of interest or even across discipline/s of science? Can building a cloud-enabled consortium of resources remove the analytical variability that leads to diminished reproducibility, which has long plagued the interpretability and impact of research discoveries? The answers to these questions are ‘yes’ and initiatives such as the Neuro Cloud Consortium, The Global Alliance for Genomics and Health (GA4GH), and The Sage Bionetworks Synapse platform, which powers many research consortiums including the DREAM challenges, are starting to put into practice model cloud-initiatives that will not only provide impactful discoveries in the areas of neuroscience, infectious disease, and cancer, but are also revolutionizing the way in which scientific research is done.

Bringing Crowd Developed Models, Algorithms, and Functions to the Data
Collaborative projects have traditionally allowed investigators to download datasets such as those used for comparative sequence analysis or for training a deep learning algorithm on medical imaging data. Investigators were then able to develop and execute their analysis using institutional clusters, local workstations, or even laptops:

This method of collaboration is problematic for many reasons. The first concern is data security, since dataset download essentially permits “chain-data-sharing” with any number of recipients. Second, analytics done using compute environments that are not templated at some level introduces the risk of variable analytics that itself is not reproducible by a different investigator, or even the same investigator using a different compute environment. Third, the required data dump, processing, and then re-upload or distribution to the collaborative group is highly inefficient and dependent upon each individual’s networking and compute capabilities. Overall, traditional methods of scientific collaboration have introduced methods in which security is compromised and time to discovery is hampered.

Using the AWS cloud, collaborative researchers can share datasets easily and securely by taking advantage of Identity and Access Management (IAM) policy restrictions for user bucket access as well as S3 bucket policies or Access Control Lists (ACLs). To streamline analysis and ensure data security, many researchers are eliminating the necessity to download datasets entirely by leveraging resources that facilitate moving the analytics to the data source and/or taking advantage of remote API requests to access a shared database or data lake. One way our customers are accomplishing this is to leverage container based Docker technology to provide collaborators with a way to submit algorithms or models for execution on the system hosting the shared datasets:

Docker container images have all of the application’s dependencies bundled together, and therefore provide a high degree of versatility and portability, which is a significant advantage over using other executable-based approaches. In the case of collaborative machine learning projects, each docker container will contain applications, language runtime, packages and libraries, as well as any of the more popular deep learning frameworks commonly used by researchers including: MXNet, Caffe, TensorFlow, and Theano.

A common feature in these frameworks is the ability to leverage a host machine’s Graphical Processing Units (GPUs) for significant acceleration of the matrix and vector operations involved in the machine learning computations. As such, researchers with these objectives can leverage EC2’s new P2 instance types in order to power execution of submitted machine learning models. In addition, GPUs can be mounted directly to containers using the NVIDIA Docker tool and appear at the system level as additional devices. By leveraging Amazon EC2 Container Service and the EC2 Container Registry, collaborators are able to execute analytical solutions submitted to the project repository by their colleagues in a reproducible fashion as well as continue to build on their existing environment.  Researchers can also architect a continuous deployment pipeline to run their docker-enabled workflows.

In conclusion, emerging cloud-enabled consortium initiatives serve as models for the broader research community for how cloud-enabled community science can expedite discoveries in Precision Medicine while also providing a platform where data security and discovery reproducibility is inherent to the project execution.

Mia D. Champion, Ph.D.

 

New – Host-Based Routing Support for AWS Application Load Balancers

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-host-based-routing-support-for-aws-application-load-balancers/

Last year I told you about the new AWS Application Load Balancer (an important part of Elastic Load Balancing) and showed you how to set it up to route incoming HTTP and HTTPS traffic based on the path element of the URL in the request. This path-based routing allows you to route requests to, for example, /api to one set of servers (also known as target groups) and /mobile to another set. Segmenting your traffic in this way gives you the ability to control the processing environment for each category of requests. Perhaps /api requests are best processed on Compute Optimized instances, while /mobile requests are best handled by Memory Optimized instances.

Host-Based Routing & More Rules
Today we are giving you another routing option. You can now create Application Load Balancer rules that route incoming traffic based on the domain name specified in the Host header. Requests to api.example.com can be sent to one target group, requests to mobile.example.com to another, and all others (by way of a default rule) can be sent to a third. You can also create rules that combine host-based routing and path-based routing. This would allow you to route requests to api.example.com/production and api.example.com/sandbox to distinct target groups.

In the past, some of our customers set up and ran a fleet of proxy servers and used them for host-based routing. With today’s launch, the proxy server fleet is no longer needed since the routing can be done using Application Load Balancer rules. Getting rid of this layer of processing will simplify your architecture and reduce operational overhead.

Application Load Balancer already provides several features that support container-based applications including port mapping, health checks, and service discovery. The ability to route on both host and path allows you to build and efficiently scale applications that are comprised of multiple microservices running in individual Amazon EC2 Container Service containers. You can use host-based routing to further simplify your service discovery mechanism by aligning your service names and your container names.

As part of today’s launch we are raising the maximum number of rules per Application Load Balancer from 10 to 75, and also introducing a new rule editor. I’ll start with the following target groups:

The Load Balancing Console shows the listeners that are associated with my Application Load Balancer: From there I simply click on View/edit rules to access the new rule editor:

I already have a default rule that forwards all requests to my web-target-production target:

I click on the Insert icon (the “+” sign) and then select a location. Rules are processed in the order that they are displayed:

I click on Insert Rule and define my new rule. Rules can reference a host, a path, or both. I’ll start with just a host:

I add two rules for host-based routing and the editor now looks like this:

If I want to route production and sandbox traffic to distinct targets, I can create new rules that reference the path. Here’s the first one:

With a few more clicks and a little typing I can create a powerful set of rules:

Rules that match the Host header can include up to three “*” (match 0 or more characters) or “?” (match 1 character) wildcards. Let’s say that I give each of my large customers a unique host name for tracking purposes. I can write rules that route all of the requests to the same target group, regardless of the final portion of the host name. Here’s a simple example:

The pencil icon in the rule editor allows me to make changes to the rule sequence. I select rules, move them to a new position, and then save the updated sequence:

I can also edit existing rules or delete unneeded ones.

Available Now
This feature is available today in all 15 AWS public AWS regions.

There is no extra charge for the first 10 rules (host-based, path-based, or both) evaluated by each load balancer. After that you will be charged based on the number of rule evaluations (this is a new dimension added to the Load Balancer Capacity units (LCU) model that I described in an earlier post). Each LCU supports up to 1000 rule evaluations. We measure on all four dimensions of the LCU, but you are charged only for the dimension with the highest usage in the given hour. Rules that are configured, but not processed will not be charged.

Jeff;

 

AWS Hot Startups – March 2017

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/aws-hot-startups-march-2017/

As the madness of March rounds up, take a break from all the basketball and check out the cool startups Tina Barr brings you for this month!

-Ana


The arrival of spring brings five new startups this month:

  • Amino Apps – providing social networks for hundreds of thousands of communities.
  • Appboy – empowering brands to strengthen customer relationships.
  • Arterys – revolutionizing the medical imaging industry.
  • Protenus – protecting patient data for healthcare organizations.
  • Syapse – improving targeted cancer care with shared data from across the country.

In case you missed them, check out February’s hot startups here.

Amino Apps (New York, NY)
Amino Logo
Amino Apps was founded on the belief that interest-based communities were underdeveloped and outdated, particularly when it came to mobile. CEO Ben Anderson and CTO Yin Wang created the app to give users access to hundreds of thousands of communities, each of them a complete social network dedicated to a single topic. Some of the largest communities have over 1 million members and are built around topics like popular TV shows, video games, sports, and an endless number of hobbies and other interests. Amino hosts communities from around the world and is currently available in six languages with many more on the way.

Navigating the Amino app is easy. Simply download the app (iOS or Android), sign up with a valid email address, choose a profile picture, and start exploring. Users can search for communities and join any that fit their interests. Each community has chatrooms, multimedia content, quizzes, and a seamless commenting system. If a community doesn’t exist yet, users can create it in minutes using the Amino Creator and Manager app (ACM). The largest user-generated communities are turned into their own apps, which gives communities their own piece of real estate on members’ phones, as well as in app stores.

Amino’s vast global network of hundreds of thousands of communities is run on AWS services. Every day users generate, share, and engage with an enormous amount of content across hundreds of mobile applications. By leveraging AWS services including Amazon EC2, Amazon RDS, Amazon S3, Amazon SQS, and Amazon CloudFront, Amino can continue to provide new features to their users while scaling their service capacity to keep up with user growth.

Interested in joining Amino? Check out their jobs page here.

Appboy (New York, NY)
In 2011, Bill Magnuson, Jon Hyman, and Mark Ghermezian saw a unique opportunity to strengthen and humanize relationships between brands and their customers through technology. The trio created Appboy to empower brands to build long-term relationships with their customers and today they are the leading lifecycle engagement platform for marketing, growth, and engagement teams. The team recognized that as rapid mobile growth became undeniable, many brands were becoming frustrated with the lack of compelling and seamless cross-channel experiences offered by existing marketing clouds. Many of today’s top mobile apps and enterprise companies trust Appboy to take their marketing to the next level. Appboy manages user profiles for nearly 700 million monthly active users, and is used to power more than 10 billion personalized messages monthly across a multitude of channels and devices.

Appboy creates a holistic user profile that offers a single view of each customer. That user profile in turn powers contextual cross-channel messaging, lifecycle engagement automation, and robust campaign insights and optimization opportunities. Appboy offers solutions that allow brands to create push notifications, targeted emails, in-app and in-browser messages, news feed cards, and webhooks to enhance the user experience and increase customer engagement. The company prides itself on its interoperability, connecting to a variety of complimentary marketing tools and technologies so brands can build the perfect stack to enable their strategies and experiments in real time.

AWS makes it easy for Appboy to dynamically size all of their service components and automatically scale up and down as needed. They use an array of services including Elastic Load Balancing, AWS Lambda, Amazon CloudWatch, Auto Scaling groups, and Amazon S3 to help scale capacity and better deal with unpredictable customer loads.

To keep up with the latest marketing trends and tactics, visit the Appboy digital magazine, Relate. Appboy was also recently featured in the #StartupsOnAir video series where they gave insight into their AWS usage.

Arterys (San Francisco, CA)
Getting test results back from a physician can often be a time consuming and tedious process. Clinicians typically employ a variety of techniques to manually measure medical images and then make their assessments. Arterys founders Fabien Beckers, John Axerio-Cilies, Albert Hsiao, and Shreyas Vasanawala realized that much more computation and advanced analytics were needed to harness all of the valuable information in medical images, especially those generated by MRI and CT scanners. Clinicians were often skipping measurements and making assessments based mostly on qualitative data. Their solution was to start a cloud/AI software company focused on accelerating data-driven medicine with advanced software products for post-processing of medical images.

Arterys’ products provide timely, accurate, and consistent quantification of images, improve speed to results, and improve the quality of the information offered to the treating physician. This allows for much better tracking of a patient’s condition, and thus better decisions about their care. Advanced analytics, such as deep learning and distributed cloud computing, are used to process images. The first Arterys product can contour cardiac anatomy as accurately as experts, but takes only 15-20 seconds instead of the 45-60 minutes required to do it manually. Their computing cloud platform is also fully HIPAA compliant.

Arterys relies on a variety of AWS services to process their medical images. Using deep learning and other advanced analytic tools, Arterys is able to render images without latency over a web browser using AWS G2 instances. They use Amazon EC2 extensively for all of their compute needs, including inference and rendering, and Amazon S3 is used to archive images that aren’t needed immediately, as well as manage costs. Arterys also employs Amazon Route 53, AWS CloudTrail, and Amazon EC2 Container Service.

Check out this quick video about the technology that Arterys is creating. They were also recently featured in the #StartupsOnAir video series and offered a quick demo of their product.

Protenus (Baltimore, MD)
Protenus Logo
Protenus founders Nick Culbertson and Robert Lord were medical students at Johns Hopkins Medical School when they saw first-hand how Electronic Health Record (EHR) systems could be used to improve patient care and share clinical data more efficiently. With increased efficiency came a huge issue – an onslaught of serious security and privacy concerns. Over the past two years, 140 million medical records have been breached, meaning that approximately 1 in 3 Americans have had their health data compromised. Health records contain a repository of sensitive information and a breach of that data can cause major havoc in a patient’s life – namely identity theft, prescription fraud, Medicare/Medicaid fraud, and improper performance of medical procedures. Using their experience and knowledge from former careers in the intelligence community and involvement in a leading hedge fund, Nick and Robert developed the prototype and algorithms that launched Protenus.

Today, Protenus offers a number of solutions that detect breaches and misuse of patient data for healthcare organizations nationwide. Using advanced analytics and AI, Protenus’ health data insights platform understands appropriate vs. inappropriate use of patient data in the EHR. It also protects privacy, aids compliance with HIPAA regulations, and ensures trust for patients and providers alike.

Protenus built and operates its SaaS offering atop Amazon EC2, where Dedicated Hosts and encrypted Amazon EBS volume are used to ensure compliance with HIPAA regulation for the storage of Protected Health Information. They use Elastic Load Balancing and Amazon Route 53 for DNS, enabling unique, secure client specific access points to their Protenus instance.

To learn more about threats to patient data, read Hospitals’ Biggest Threat to Patient Data is Hiding in Plain Sight on the Protenus blog. Also be sure to check out their recent video in the #StartupsOnAir series for more insight into their product.

Syapse (Palo Alto, CA)
Syapse provides a comprehensive software solution that enables clinicians to treat patients with precision medicine for targeted cancer therapies — treatments that are designed and chosen using genetic or molecular profiling. Existing hospital IT doesn’t support the robust infrastructure and clinical workflows required to treat patients with precision medicine at scale, but Syapse centralizes and organizes patient data to clinicians at the point of care. Syapse offers a variety of solutions for oncologists that allow them to access the full scope of patient data longitudinally, view recommended treatments or clinical trials for similar patients, and track outcomes over time. These solutions are helping health systems across the country to improve patient outcomes by offering the most innovative care to cancer patients.

Leading health systems such as Stanford Health Care, Providence St. Joseph Health, and Intermountain Healthcare are using Syapse to improve patient outcomes, streamline clinical workflows, and scale their precision medicine programs. A group of experts known as the Molecular Tumor Board (MTB) reviews complex cases and evaluates patient data, documents notes, and disseminates treatment recommendations to the treating physician. Syapse also provides reports that give health system staff insight into their institution’s oncology care, which can be used toward quality improvement, business goals, and understanding variables in the oncology service line.

Syapse uses Amazon Virtual Private Cloud, Amazon EC2 Dedicated Instances, and Amazon Elastic Block Store to build a high-performance, scalable, and HIPAA-compliant data platform that enables health systems to make precision medicine part of routine cancer care for patients throughout the country.

Be sure to check out the Syapse blog to learn more and also their recent video on the #StartupsOnAir video series where they discuss their product, HIPAA compliance, and more about how they are using AWS.

Thank you for checking out another month of awesome hot startups!

-Tina Barr

 

Creating a Simple “Fetch & Run” AWS Batch Job

Post Syndicated from Bryan Liston original https://aws.amazon.com/blogs/compute/creating-a-simple-fetch-and-run-aws-batch-job/

Dougal Ballantyne
Dougal Ballantyne, Principal Product Manager – AWS Batch

Docker enables you to create highly customized images that are used to execute your jobs. These images allow you to easily share complex applications between teams and even organizations. However, sometimes you might just need to run a script!

This post details the steps to create and run a simple “fetch & run” job in AWS Batch. AWS Batch executes jobs as Docker containers using Amazon ECS. You build a simple Docker image containing a helper application that can download your script or even a zip file from Amazon S3. AWS Batch then launches an instance of your container image to retrieve your script and run your job.

AWS Batch overview

AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted.

With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 Spot Instances.

“Fetch & run” walkthrough

The following steps get everything working:

  • Build a Docker image with the fetch & run script
  • Create an Amazon ECR repository for the image
  • Push the built image to ECR
  • Create a simple job script and upload it to S3
  • Create an IAM role to be used by jobs to access S3
  • Create a job definition that uses the built image
  • Submit and run a job that execute the job script from S3

Prerequisites

Before you get started, there a few things to prepare. If this is the first time you have used AWS Batch, you should follow the Getting Started Guide and ensure you have a valid job queue and compute environment.

After you are up and running with AWS Batch, the next thing is to have an environment to build and register the Docker image to be used. For this post, register this image in an ECR repository. This is a private repository by default and can easily be used by AWS Batch jobs

You also need a working Docker environment to complete the walkthrough. For the examples, I used Docker for Mac. Alternatively, you could easily launch an EC2 instance running Amazon Linux and install Docker.

You need the AWS CLI installed. For more information, see Installing the AWS Command Line Interface.

Building the fetch & run Docker image

The fetch & run Docker image is based on Amazon Linux. It includes a simple script that reads some environment variables and then uses the AWS CLI to download the job script (or zip file) to be executed.

To get started, download the source code from the aws-batch-helpers GitHub repository. The following link pulls the latest version: https://github.com/awslabs/aws-batch-helpers/archive/master.zip. Unzip the downloaded file and navigate to the “fetch-and-run” folder. Inside this folder are two files:

  • Dockerfile
  • fetchandrun.sh

Dockerfile is used by Docker to build an image. Look at the contents; you should see something like the following:

FROM amazonlinux:latest

RUN yum -y install unzip aws-cli
ADD fetch_and_run.sh /usr/local/bin/fetch_and_run.sh
WORKDIR /tmp
USER nobody

ENTRYPOINT ["/usr/local/bin/fetch_and_run.sh"]
  • The FROM line instructs Docker to pull the base image from the amazonlinux repository, using the latest tag.
  • The RUN line executes a shell command as part of the image build process.
  • The ADD line, copies the fetchandrun.sh script into the /usr/local/bin directory inside the image.
  • The WORKDIR line, sets the default directory to /tmp when the image is used to start a container.
  • The USER line sets the default user that the container executes as.
  • Finally, the ENTRYPOINT line instructs Docker to call the /usr/local/bin/fetchandrun.sh script when it starts the container. When running as an AWS Batch job, it is passed the contents of the command parameter.

Now, build the Docker image! Assuming that the docker command is in your PATH and you don’t need sudo to access it, you can build the image with the following command (note the dot at the end of the command):

docker build -t awsbatch/fetch_and_run .   

This command should produce an output similar to the following:

Sending build context to Docker daemon 373.8 kB

Step 1/6 : FROM amazonlinux:latest
latest: Pulling from library/amazonlinux
c9141092a50d: Pull complete
Digest: sha256:2010c88ac1e7c118d61793eec71dcfe0e276d72b38dd86bd3e49da1f8c48bf54
Status: Downloaded newer image for amazonlinux:latest
 ---> 8ae6f52035b5
Step 2/6 : RUN yum -y install unzip aws-cli
 ---> Running in e49cba995ea6
Loaded plugins: ovl, priorities
Resolving Dependencies
--> Running transaction check
---> Package aws-cli.noarch 0:1.11.29-1.45.amzn1 will be installed

  << removed for brevity >>

Complete!
 ---> b30dfc9b1b0e
Removing intermediate container e49cba995ea6
Step 3/6 : ADD fetch_and_run.sh /usr/local/bin/fetch_and_run.sh
 ---> 256343139922
Removing intermediate container 326092094ede
Step 4/6 : WORKDIR /tmp
 ---> 5a8660e40d85
Removing intermediate container b48a7b9c7b74
Step 5/6 : USER nobody
 ---> Running in 72c2be3af547
 ---> fb17633a64fe
Removing intermediate container 72c2be3af547
Step 6/6 : ENTRYPOINT /usr/local/bin/fetch_and_run.sh
 ---> Running in aa454b301d37
 ---> fe753d94c372

Removing intermediate container aa454b301d37
Successfully built 9aa226c28efc

In addition, you should see a new local repository called fetchandrun, when you run the following command:

docker images
REPOSITORY               TAG              IMAGE ID            CREATED             SIZE
awsbatch/fetch_and_run   latest           9aa226c28efc        19 seconds ago      374 MB
amazonlinux              latest           8ae6f52035b5        5 weeks ago         292 MB

To add more packages to the image, you could update the RUN line or add a second one, right after it.

Creating an ECR repository

The next step is to create an ECR repository to store the Docker image, so that it can be retrieved by AWS Batch when running jobs.

  1. In the ECR console, choose Get Started or Create repository.
  2. Enter a name for the repository, for example: awsbatch/fetchandrun.
  3. Choose Next step and follow the instructions.

    fetchAndRunBatch_1.png

You can keep the console open, as the tips can be helpful.

Push the built image to ECR

Now that you have a Docker image and an ECR repository, it is time to push the image to the repository. Use the following AWS CLI commands, if you have used the previous example names. Replace the AWS account number in red with your own account.

aws ecr get-login --region us-east-1

docker tag awsbatch/fetch_and_run:latest 012345678901.dkr.ecr.us-east-1.amazonaws.com/awsbatch/fetch_and_run:latest

docker push 012345678901.dkr.ecr.us-east-1.amazonaws.com/awsbatch/fetch_and_run:latest

Create a simple job script and upload to S3

Next, create and upload a simple job script that is executed using the fetchandrun image that you just built and registered in ECR. Start by creating a file called myjob.sh with the example content below:

!/bin/bash

date
echo "Args: [email protected]"
env
echo "This is my simple test job!."
echo "jobId: $AWS_BATCH_JOB_ID"
echo "jobQueue: $AWS_BATCH_JQ_NAME"
echo "computeEnvironment: $AWS_BATCH_CE_NAME"
sleep $1
date
echo "bye bye!!"

Upload the script to an S3 bucket.

aws s3 cp myjob.sh s3://<bucket>/myjob.sh

Create an IAM role

When the fetchandrun image runs as an AWS Batch job, it fetches the job script from Amazon S3. You need an IAM role that the AWS Batch job can use to access S3.

  1. In the IAM console, choose Roles, Create New Role.
  2. Enter a name for your new role, for example: batchJobRole, and choose Next Step.
  3. For Role Type, under AWS Service Roles, choose Select next to “Amazon EC2 Container Service Task Role” and then choose Next Step.

    fetchAndRunBatch_2.png

  4. On the Attach Policy page, type “AmazonS3ReadOnlyAccess” into the Filter field and then select the check box for that policy.

    fetchAndRunBatch_3.png

  5. Choose Next Step, Create Role. You see the details of the new role.

    fetchAndRunBatch_4.png

Create a job definition

Now that you’ve have created all the resources needed, pull everything together and build a job definition that you can use to run one or many AWS Batch jobs.

  1. In the AWS Batch console, choose Job Definitions, Create.
  2. For the Job Definition, enter a name, for example, fetchandrun.
  3. For IAM Role, choose the role that you created earlier, batchJobRole.
  4. For ECR Repository URI, enter the URI where the fetchandrun image was pushed, for example: 012345678901.dkr.ecr.us-east-1.amazonaws.com/awsbatch/fetchandrun.
  5. Leave the Command field blank.
  6. For vCPUs, enter 1. For Memory, enter 500.

    fetchAndRunBatch_5.png

  7. For User, enter “nobody”.

  8. Choose Create job definition.

Submit and run a job

Now, submit and run a job that uses the fetchandrun image to download the job script and execute it.

  1. In the AWS Batch console, choose Jobs, Submit Job.
  2. Enter a name for the job, for example: script_test.
  3. Choose the latest fetchandrun job definition.
  4. For Job Queue, choose a queue, for example: first-run-job-queue.
  5. For Command, enter myjob.sh,60.
  6. Choose Validate Command.

    fetchAndRunBatch_6.png

  7. Enter the following environment variables and then choose Submit job.

    • Key=BATCHFILETYPE, Value=script
    • Key=BATCHFILES3_URL, Value=s3:///myjob.sh. Don’t forget to use the correct URL for your file.

    fetchAndRunBatch_7.png

  8. After the job is completed, check the final status in the console.

    fetchAndRunBatch_8.png

  9. In the job details page, you can also choose View logs for this job in CloudWatch console to see your job log.

    fetchAndRunBatch_9.png

How the fetch and run image works

The fetchandrun image works as a combination of the Docker ENTRYPOINT and COMMAND feature, and a shell script that reads environment variables set as part of the AWS Batch job. When building the Docker image, it starts with a base image from Amazon Linux and installs a few packages from the yum repository. This becomes the execution environment for the job.

If the script you planned to run needed more packages, you would add them using the RUN parameter in the Dockerfile. You could even change it to a different base image such as Ubuntu, by updating the FROM parameter.

Next, the fetchandrun.sh script is added to the image and set as the container ENTRYPOINT. The script simply reads some environment variables and then downloads and runs the script/zip file from S3. It is looking for the following environment variables BATCHFILETYPE and BATCHFILES3URL. If you run fetchand_run.sh, with no environment variables, you get the following usage message:

  • BATCHFILETYPE not set, unable to determine type (zip/script) of

Usage:

export BATCH_FILE_TYPE="script"

export BATCH_FILE_S3_URL="s3://my-bucket/my-script"

fetch_and_run.sh script-from-s3 [ <script arguments> ]

– or –

export BATCH_FILE_TYPE="zip"

export BATCH_FILE_S3_URL="s3://my-bucket/my-zip"

fetch_and_run.sh script-from-zip [ <script arguments> ]

This shows that it supports two values for BATCHFILETYPE, either “script” or “zip”. When you set “script”, it causes fetchandrun.sh to download a single file and then execute it, in addition to passing in any further arguments to the script. If you set it to “zip”, this causes fetchandrun.sh to download a zip file, then unpack it and execute the script name passed and any further arguments. You can use the “zip” option to pass more complex jobs with all the applications dependencies in one file.

Finally, the ENTRYPOINT parameter tells Docker to execute the /usr/local/bin/fetchandrun.sh script when creating a container. In addition, it passes the contents of the COMMAND parameter as arguments to the script. This is what enables you to pass the script and arguments to be executed by the fetchandrun image with the Command field in the SubmitJob API action call.

Summary

In this post, I detailed the steps to create and run a simple “fetch & run” job in AWS Batch. You can now easily use the same job definition to run as many jobs as you need by uploading a job script to Amazon S3 and calling SubmitJob with the appropriate environment variables.

If you have questions or suggestions, please comment below.

Managing Secrets for Amazon ECS Applications Using Parameter Store and IAM Roles for Tasks

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/compute/managing-secrets-for-amazon-ecs-applications-using-parameter-store-and-iam-roles-for-tasks/

Thanks to my colleague Stas Vonholsky  for a great blog on managing secrets with Amazon ECS applications.

—–

As containerized applications and microservice-oriented architectures become more popular, managing secrets, such as a password to access an application database, becomes more challenging and critical.

Some examples of the challenges include:

  • Support for various access patterns across container environments such as dev, test, and prod
  • Isolated access to secrets on a container/application level rather than at the host level
  • Multiple decoupled services with their own needs for access, both as services and as clients of other services

This post focuses on newly released features that support further improvements to secret management for containerized applications running on Amazon ECS. My colleague, Matthew McClean, also published an excellent post on the AWS Security Blog, How to Manage Secrets for Amazon EC2 Container Service–Based Applications by Using Amazon S3 and Docker, which discusses some of the limitations of passing and storing secrets with container parameter variables.

Most secret management tools provide the following functionality:

  • Highly secured storage system
  • Central management capabilities
  • Secure authorization and authentication mechanisms
  • Integration with key management and encryption providers
  • Secure introduction mechanisms for access
  • Auditing
  • Secret rotation and revocation

Amazon EC2 Systems Manager Parameter Store

Parameter Store is a feature of Amazon EC2 Systems Manager. It provides a centralized, encrypted store for sensitive information and has many advantages when combined with other capabilities of Systems Manager, such as Run Command and State Manager. The service is fully managed, highly available, and highly secured.

Because Parameter Store is accessible using the Systems Manager API, AWS CLI, and AWS SDKs, you can also use it as a generic secret management store. Secrets can be easily rotated and revoked. Parameter Store is integrated with AWS KMS so that specific parameters can be encrypted at rest with the default or custom KMS key. Importing KMS keys enables you to use your own keys to encrypt sensitive data.

Access to Parameter Store is enabled by IAM policies and supports resource level permissions for access. An IAM policy that grants permissions to specific parameters or a namespace can be used to limit access to these parameters. CloudTrail logs, if enabled for the service, record any attempt to access a parameter.

While Amazon S3 has many of the above features and can also be used to implement a central secret store, Parameter Store has the following added advantages:

  • Easy creation of namespaces to support different stages of the application lifecycle.
  • KMS integration that abstracts parameter encryption from the application while requiring the instance or container to have access to the KMS key and for the decryption to take place locally in memory.
  • Stored history about parameter changes.
  • A service that can be controlled separately from S3, which is likely used for many other applications.
  • A configuration data store, reducing overhead from implementing multiple systems.
  • No usage costs.

Note: At the time of publication, Systems Manager doesn’t support VPC private endpoint functionality. To enforce stricter access to a Parameter Store endpoint from a private VPC, use a NAT gateway with a set Elastic IP address together with IAM policy conditions that restrict parameter access to a limited set of IP addresses.

IAM roles for tasks

With IAM roles for Amazon ECS tasks, you can specify an IAM role to be used by the containers in a task. Applications interacting with AWS services must sign their API requests with AWS credentials. This feature provides a strategy for managing credentials for your applications to use, similar to the way that Amazon EC2 instance profiles provide credentials to EC2 instances.

Instead of creating and distributing your AWS credentials to the containers or using the EC2 instance role, you can associate an IAM role with an ECS task definition or the RunTask API operation. For more information, see IAM Roles for Tasks.

You can use IAM roles for tasks to securely introduce and authenticate the application or container with the centralized Parameter Store. Access to the secret manager should include features such as:

  • Limited TTL for credentials used
  • Granular authorization policies
  • An ID to track the requests in the logs of the central secret manager
  • Integration support with the scheduler that could map between the container or task deployed and the relevant access privileges

IAM roles for tasks support this use case well, as the role credentials can be accessed only from within the container for which the role is defined. The role exposes temporary credentials and these are rotated automatically. Granular IAM policies are supported with optional conditions about source instances, source IP addresses, time of day, and other options.

The source IAM role can be identified in the CloudTrail logs based on a unique Amazon Resource Name and the access permissions can be revoked immediately at any time with the IAM API or console. As Parameter Store supports resource level permissions, a policy can be created to restrict access to specific keys and namespaces.

Dynamic environment association

In many cases, the container image does not change when moving between environments, which supports immutable deployments and ensures that the results are reproducible. What does change is the configuration: in this context, specifically the secrets. For example, a database and its password might be different in the staging and production environments. There’s still the question of how do you point the application to retrieve the correct secret? Should it retrieve prod.app1.secret, test.app1.secret or something else?

One option can be to pass the environment type as an environment variable to the container. The application then concatenates the environment type (prod, test, etc.) with the relative key path and retrieves the relevant secret. In most cases, this leads to a number of separate ECS task definitions.

When you describe the task definition in a CloudFormation template, you could base the entry in the IAM role that provides access to Parameter Store, KMS key, and environment property on a single CloudFormation parameter, such as “environment type.” This approach could support a single task definition type that is based on a generic CloudFormation template.

Walkthrough: Securely access Parameter Store resources with IAM roles for tasks

This walkthrough is configured for the North Virginia region (us-east-1). I recommend using the same region.

Step 1: Create the keys and parameters

First, create the following KMS keys with the default security policy to be used to encrypt various parameters:

  • prod-app1 –used to encrypt any secrets for app1.
  • license-key –used to encrypt license-related secrets.
aws kms create-key --description prod-app1 --region us-east-1
aws kms create-key --description license-code --region us-east-1

Note the KeyId property in the output of both commands. You use it throughout the walkthrough to identify the KMS keys.

The following commands create three parameters in Parameter Store:

  • prod.app1.db-pass (encrypted with the prod-app1 KMS key)
  • general.license-code (encrypted with the license-key KMS key)
  • prod.app2.user-name (stored as a standard string without encryption)
aws ssm put-parameter --name prod.app1.db-pass --value "AAAAAAAAAAA" --type SecureString --key-id "<key-id-for-prod-app1-key>" --region us-east-1
aws ssm put-parameter --name general.license-code --value "CCCCCCCCCCC" --type SecureString --key-id "<key-id-for-license-code-key>" --region us-east-1
aws ssm put-parameter --name prod.app2.user-name --value "BBBBBBBBBBB" --type String --region us-east-1

Step 2: Create the IAM role and policies

Now, create a role and an IAM policy to be associated later with the ECS task that you create later on.
The trust policy for the IAM role needs to allow the ecs-tasks entity to assume the role.

{
   "Version": "2012-10-17",
   "Statement": [
     {
       "Sid": "",
       "Effect": "Allow",
       "Principal": {
         "Service": "ecs-tasks.amazonaws.com"
       },
       "Action": "sts:AssumeRole"
     }
   ]
 }

Save the above policy as a file in the local directory with the name ecs-tasks-trust-policy.json.

aws iam create-role --role-name prod-app1 --assume-role-policy-document file://ecs-tasks-trust-policy.json

The following policy is attached to the role and later associated with the app1 container. Access is granted to the prod.app1.* namespace parameters, the encryption key required to decrypt the prod.app1.db-pass parameter and the license code parameter. The namespace resource permission structure is useful for building various hierarchies (based on environments, applications, etc.).

Make sure to replace <key-id-for-prod-app1-key> with the key ID for the relevant KMS key and <account-id> with your account ID in the following policy.

{
     "Version": "2012-10-17",
     "Statement": [
         {
             "Effect": "Allow",
             "Action": [
                 "ssm:DescribeParameters"
             ],
             "Resource": "*"
         },
         {
             "Sid": "Stmt1482841904000",
             "Effect": "Allow",
             "Action": [
                 "ssm:GetParameters"
             ],
             "Resource": [
                 "arn:aws:ssm:us-east-1:<account-id>:parameter/prod.app1.*",
                 "arn:aws:ssm:us-east-1:<account-id>:parameter/general.license-code"
             ]
         },
         {
             "Sid": "Stmt1482841948000",
             "Effect": "Allow",
             "Action": [
                 "kms:Decrypt"
             ],
             "Resource": [
                 "arn:aws:kms:us-east-1:<account-id>:key/<key-id-for-prod-app1-key>"
             ]
         }
     ]
 }

Save the above policy as a file in the local directory with the name app1-secret-access.json:

aws iam create-policy --policy-name prod-app1 --policy-document file://app1-secret-access.json

Replace <account-id> with your account ID in the following command:

aws iam attach-role-policy --role-name prod-app1 --policy-arn "arn:aws:iam::<account-id>:policy/prod-app1"

Step 3: Add the testing script to an S3 bucket

Create a file with the script below, name it access-test.sh and add it to an S3 bucket in your account. Make sure the object is publicly accessible and note down the object link, for example https://s3-eu-west-1.amazonaws.com/my-new-blog-bucket/access-test.sh

#!/bin/bash
#This is simple bash script that is used to test access to the EC2 Parameter store.
# Install the AWS CLI
apt-get -y install python2.7 curl
curl -O https://bootstrap.pypa.io/get-pip.py
python2.7 get-pip.py
pip install awscli
# Getting region
EC2_AVAIL_ZONE=`curl -s http://169.254.169.254/latest/meta-data/placement/availability-zone`
EC2_REGION="`echo \"$EC2_AVAIL_ZONE\" | sed -e 's:\([0-9][0-9]*\)[a-z]*\$:\\1:'`"
# Trying to retrieve parameters from the EC2 Parameter Store
APP1_WITH_ENCRYPTION=`aws ssm get-parameters --names prod.app1.db-pass --with-decryption --region $EC2_REGION --output text 2>&1`
APP1_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption --region $EC2_REGION --output text 2>&1`
LICENSE_WITH_ENCRYPTION=`aws ssm get-parameters --names general.license-code --with-decryption --region $EC2_REGION --output text 2>&1`
LICENSE_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names general.license-code --no-with-decryption --region $EC2_REGION --output text 2>&1`
APP2_WITHOUT_ENCRYPTION=`aws ssm get-parameters --names prod.app2.user-name --no-with-decryption --region $EC2_REGION --output text 2>&1`
# The nginx server is started after the script is invoked, preparing folder for HTML.
if [ ! -d /usr/share/nginx/html/ ]; then
mkdir -p /usr/share/nginx/html/;
fi
chmod 755 /usr/share/nginx/html/

# Creating an HTML file to be accessed at http://<public-instance-DNS-name>/ecs.html
cat > /usr/share/nginx/html/ecs.html <<EOF
<!DOCTYPE html>
<html>
<head>
<title>App1</title>
<style>
body {padding: 20px;margin: 0 auto;font-family: Tahoma, Verdana, Arial, sans-serif;}
code {white-space: pre-wrap;}
result {background: hsl(220, 80%, 90%);}
</style>
</head>
<body>
<h1>Hi there!</h1>
<p style="padding-bottom: 0.8cm;">Following are the results of different access attempts as expirienced by "App1".</p>

<p><b>Access to prod.app1.db-pass:</b><br/>
<pre><code>aws ssm get-parameters --names prod.app1.db-pass --with-decryption</code><br/>
<code><result>$APP1_WITH_ENCRYPTION</result></code><br/>
<code>aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption</code><br/>
<code><result>$APP1_WITHOUT_ENCRYPTION</result></code></pre><br/>
</p>

<p><b>Access to general.license-code:</b><br/>
<pre><code>aws ssm get-parameters --names general.license-code --with-decryption</code><br/>
<code><result>$LICENSE_WITH_ENCRYPTION</result></code><br/>
<code>aws ssm get-parameters --names general.license-code --no-with-decryption</code><br/>
<code><result>$LICENSE_WITHOUT_ENCRYPTION</result></code></pre><br/>
</p>

<p><b>Access to prod.app2.user-name:</b><br/>
<pre><code>aws ssm get-parameters --names prod.app2.user-name --no-with-decryption</code><br/>
<code><result>$APP2_WITHOUT_ENCRYPTION</result></code><br/>
</p>

<p><em>Thanks for visiting</em></p>
</body>
</html>
EOF

Step 4: Create a test cluster

I recommend creating a new ECS test cluster with the latest ECS AMI and ECS agent on the instance. Use the following field values:

  • Cluster name: access-test
  • EC2 instance type: t2.micro
  • Number of instances: 1
  • Key pair: No EC2 key pair is required, unless you’d like to SSH to the instance and explore the running container.
  • VPC: Choose the default VPC. If unsure, you can find the VPC ID with the IP range 172.31.0.0/16 in the Amazon VPC console.
  • Subnets: Pick a subnet in the default VPC.
  • Security group: Create a new security group with CIDR block 0.0.0.0/0 and port 80 for inbound access.

Leave other fields with the default settings.

Create a simple task definition that relies on the public NGINX container and the role that you created for app1. Specify the properties such as the available container resources and port mappings. Note the command option is used to download and invoke a test script that installs the AWS CLI on the container, runs a number of get-parameter commands, and creates an HTML file with the results.

Replace <account-id> with your account ID, <your-S3-URI> with a link to the S3 object created in step 3 in the following commands:

aws ecs register-task-definition --family access-test --task-role-arn "arn:aws:iam::<account-id>:role/prod-app1" --container-definitions name="access-test",image="nginx",portMappings="[{containerPort=80,hostPort=80,protocol=tcp}]",readonlyRootFilesystem=false,cpu=512,memory=490,essential=true,entryPoint="sh,-c",command="\"/bin/sh -c \\\"apt-get update ; apt-get -y install curl ; curl -O <your-S3-URI> ; chmod +x access-test.sh ; ./access-test.sh ; nginx -g 'daemon off;'\\\"\"" --region us-east-1

aws ecs run-task --cluster access-test --task-definition access-test --count 1 --region us-east-1

Verifying access

After the task is in a running state, check the public DNS name of the instance and navigate to the following page:

http://<ec2-instance-public-DNS-name>/ecs.html

You should see the results of running different access tests from the container after a short duration.

If the test results don’t appear immediately, wait a few seconds and refresh the page.
Make sure that inbound traffic for port 80 is allowed on the security group attached to the instance.

The results you see in the static results HTML page should be the same as running the following commands from the container.

prod.app1.key1

aws ssm get-parameters --names prod.app1.db-pass --with-decryption --region us-east-1
aws ssm get-parameters --names prod.app1.db-pass --no-with-decryption --region us-east-1

Both commands should work, as the policy provides access to both the parameter and the required KMS key.

general.license-code

aws ssm get-parameters --names general.license-code --no-with-decryption --region us-east-1
aws ssm get-parameters --names general.license-code --with-decryption --region us-east-1

Only the first command with the “no-with-decryption” parameter should work. The policy allows access to the parameter in Parameter Store but there’s no access to the KMS key. The second command should fail with an access denied error.

prod.app2.user-name

aws ssm get-parameters --names prod.app2.user-name –no-with-decryption --region us-east-1

The command should fail with an access denied error, as there are no permissions associated with the namespace for prod.app2.

Finishing up

Remember to delete all resources (such as the KMS keys and EC2 instance), so that you don’t incur charges.

Conclusion

Central secret management is an important aspect of securing containerized environments. By using Parameter Store and task IAM roles, customers can create a central secret management store and a well-integrated access layer that allows applications to access only the keys they need, to restrict access on a container basis, and to further encrypt secrets with custom keys with KMS.

Whether the secret management layer is implemented with Parameter Store, Amazon S3, Amazon DynamoDB, or a solution such as Vault or KeyWhiz, it’s a vital part to the process of managing and accessing secrets.

From Raspberry Pi to Supercomputers to the Cloud: The Linux Operating System

Post Syndicated from Ana Visneski original https://aws.amazon.com/blogs/aws/from-raspberry-pi-to-supercomputers-to-the-cloud-the-linux-operating-system/

Matthew Freeman and Luis Daniel Soto are back talking about the use of Linux through the AWS Marketplace.
– Ana


Linux is widely used in corporations now as the basis for everything from file servers to web servers to network security servers. The no-cost as well as commercial availability of distributions makes it an obvious choice in many scenarios. Distributions of Linux now power machines as small as the tiny Raspberry Pi to the largest supercomputers in the world. There is a wide variety of minimal and security hardened distributions, some of them designed for GPU workloads.

Even more compelling is the use of Linux in cloud-based infrastructures. Its comparatively lightweight architecture, flexibility, and options for customizing it make Linux ideal as a choice for permanent network infrastructures in the cloud, as well as specialized uses such as temporary high-performance server farms that handle computational loads for scientific research. As a demonstration of their own commitment to the Linux platform, AWS developed and continues to maintain their own version of Linux that is tightly coupled with AWS services.

AWS has been a partner to the Linux and Open Source Communities through AWS Marketplace:

  • It is a managed software catalog that makes it easy for customers to discover, purchase, and deploy the software and services they need to build solutions and run their businesses.
  • It simplifies software licensing and procurement by enabling customers to accept user agreements, choose pricing options, and automate the deployment of software and associated AWS resources with just a few clicks.
  • It can be searched and filtered to help you select the Linux distribution – independently or in combination with other components – that best suits your business needs.

Selecting a Linux Distribution for Your Company
If you’re new to Linux, the dizzying array of distributions can be overwhelming. Deciding which distribution to use depends on a lot of different factors, and customers tell us that the following considerations are important to them:

  • Existing investment in Linux, if any. Is this your first foray into Linux? If so, then you’re in a position to weight all options pretty equally.
  • Existing platforms in use (such as on-premises networks). Are you adding a cloud infrastructure that must connect to your in-house network? If so, you need to consider which of the Linux distributions has the networking and application connectors you require.
  • Intention to use more than one cloud platform. Are you already using another cloud provider? Will it need to interconnect with AWS? Your choice of Linux distribution may be affected by what’s available for those connections.
  • Available applications, libraries, and components. Your choice of Linux distribution should take into consideration future requirements, and ongoing software and technical support.
  • Specialized uses, such as scientific or technical requirements. Certain applications only run on specific, customized Linux distributions.

By examining your responses to each of these areas, you can narrow the list of possible Linux distributions to suit your business needs.

Linux in AWS Marketplace
AWS Marketplace is a great place to locate and begin using Linux distributions along with the top applications that run on them. You can deploy different versions of the distributions from this online store, and AWS scans the catalog daily for security, if we found an issue we notify you — this increases your speed. Scans are run continuously to identify vulnerabilities. AWS notifies customers of any issues found and works with experts to find work-arounds and updates. In addition to support provided by the sellers, the AWS Forums are a great place to ask questions about using Linux on AWS by setting up a free account on the forum. You can also get further details about Linux on AWS from the AWS Documentation.

Applications from AWS Marketplace Running on Linux
Here is a sampling of the featured Linux distributions and applications that run on them, which customers launch from AWS Marketplace.

CentOS Versions 7, 6.5, and 6
The CentOS Project is a community-driven, free software effort focused on delivering a robust open source ecosystem. CentOS is derived from the sources of Red Hat Enterprise Linux (RHEL), and it aims to be functionally compatible with RHEL. CentOS Linux is no-cost to use, and free to redistribute. For users, CentOS offers a consistent, manageable platform that suits a wide variety of deployments. For open source communities, it offers a solid, predictable base to build upon, along with extensive resources to build, test, release, and maintain their code. AWS has several CentOS AMIs that you can launch to take advantage of the stability and widespread use of this distribution.

Debian GNU Linux
Debian GNU/Linux, which includes the GNU OS tools and Linux kernel, is a popular and influential Linux distribution. Users have access to repositories containing thousands of software packages ready for installation and use. Debian is known for relatively strict adherence to the philosophies of Unix and free software as well as using collaborative software development and testing processes. It is popular as a web server operating system. Debian officially contains only free software, but non-free software can be downloaded from the Debian repositories and installed. Debian focuses on stability and security, and is used as a base for many other distributions. AWS has AMIs for Debian available for launch immediately.

Amazon Linux AMI
Amazon Linux is a supported and maintained Linux image provided by AWS. Amazon EC2 Container Service makes it easy to manage Docker containers at scale by providing a centralized service that includes programmatic access to the complete state of the containers and Amazon EC2 instances in the cluster, schedules containers in the proper location, and uses familiar Amazon EC2 features like security groups, Amazon EBS volumes, and IAM roles. Amazon ECS allows you to make containers a foundational building block for your applications by eliminating the need to run a cluster manager, and by providing programmatic access to the full state of your cluster.

Other popular distributions available in AWS Marketplace include Ubuntu, SUSE, Red Hat, Oracle Linux, Kali Linux and more.

Getting Started with Linux on AWS Marketplace
You can view a list hundreds of Linux offerings by simply selecting the Operating System category from the Shop All Categories link on the AWS Marketplace home screen.

From there you can select your preferred distribution and browse the available offerings:

Most offerings include the ability to launch using 1-Click, so your Linux server can be up and running in minutes.

Flexibility with Pay-As-You-Go Pricing
You pay Amazon EC2 usage costs plus per hour (or per month or annual) and, if applicable, commercial Linux cost for certain distributions directly through your AWS account. You can see in advance what your costs will be, depending on the instance type you select. As a result, using AWS Marketplace is one of the fastest and easiest ways to launch your Linux solution.

Visit http://aws.amazon.com/mp/linux to learn more about Linux on AWS Marketplace.

Matthew Freeman and Luis Daniel Soto