Tag Archives: IAM policies

Delegate permission management to developers by using IAM permissions boundaries

2018-07-13 Apurv Awasthi

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/delegate-permission-management-to-developers-using-iam-permissions-boundaries/

Today, AWS released a new IAM feature that makes it easier for you to delegate permissions management to trusted employees. As your organization grows, you might want to allow trusted employees to configure and manage IAM permissions to help your organization scale permission management and move workloads to AWS faster. For example, you might want to grant a developer the ability to create and manage permissions for an IAM role required to run an application on Amazon EC2. This ability is powerful and might be used inappropriately or accidentally to attach an administrator access policy to obtain full access to all resources in an account. Now, you can set a permissions boundary to control the maximum permissions employees can grant to the IAM principals (that is, users and roles) that they create and manage.

A permissions boundary is an advanced feature that allows you to limit the maximum permissions that a principal can have. Before we walk you through a specific example, here is an overview of how permissions boundaries work. As the IAM administrator, you can define one or more permissions boundaries using managed policies and allow your employee to create a principal with this boundary. The employee can then attach a permissions policy to this principal. However, the effective permissions of the principal are the intersection of the permissions boundary and permissions policy. As a result, the new principal cannot exceed the boundary that you defined. See the following diagram for a visual representation.

Figure 1: The intersection of permission boundaries and policies

Figure 1: The intersection of permissions boundary and permissions policy

In this post, we’ll walk through an example that shows how to grant an employee permission to create IAM roles and assign permissions. We’ll also show how to ensure that these IAM roles can only access Amazon DynamoDB actions and resources in the AWS EU (Frankfurt) region. This solution requires the following steps.

IAM administrator tasks

Define the permissions boundary by creating a customer-managed policy.
Create and attach a permissions policy to allow an employee to create roles, but only with a permissions boundary and a name that meets a specific convention.
Create and attach a permissions policy to allow an employee to pass this role to Amazon EC2.

Employee tasks

Create a role with the required permissions boundary.
Attach a permissions policy to the role.

Administrator step 1: Define the permissions boundary

As an IAM administrator, we’ll create a customer managed policy that grants permissions to put, update, and delete items on all DynamoDB tables in the AWS EU (Frankfurt) region. We’ll require employees to set this policy as the permissions boundary for the roles they create. To follow along, paste the following JSON policy in a file with the name DynamoDB_Boundary_Frankfurt_Text.json.


{
  "Version" : "2012-10-17",
  "Statement" : [
  {
    "Effect": "Allow",
    "Action": [
                "dynamodb:PutItem",
                "dynamodb:UpdateItem",
                "dynamodb:DeleteItem"
   ],
    "Resource": "*",
    "Condition": {
        "StringEquals": {
            "aws:RequestedRegion": "eu-central-1"
        }
    }
  }
]
}

Next, use the create-policy AWS CLI command to create the policy, DynamoDB_Boundary_Frankfurt.

$aws iam create-policy –policy-name DynamoDB_Boundary_Frankfurt –policy-document file://DynamoDB_Boundary_Frankfurt_Text.json

Note: You can also use an AWS managed policy as a permissions boundary.

Administrator step 2: Create and attach the permissions policy

Create a policy that grants permissions to create IAM roles with the DynamoDB_Boundary_Frankfurt permissions boundary, and a name that begins with the prefix MyTestApp. This policy also grants permissions to create and attach IAM policies to roles with this boundary and naming convention. The permissions boundary controls the maximum permissions these roles can have. The naming convention enables administrators to more effectively grant access to manage and use these roles, without updating the employee’s permissions when they create a role. The naming convention also makes it easier to audit and identify roles created by an employee. To create this policy, paste the following JSON policy document in a file with the name Permissions_Policy_For_Employee_Text.json. Make sure to replace the variable <ACCOUNT NUMBER> with your own AWS account number. You can update the policy to grant additional permissions, such as launching EC2 instances in a specific subnet or allowing read-only access on items in a DynmoDB table.


{
  "Version" : "2012-10-17",
  "Statement" : [
     {
"Sid": "SetPermissionsBoundary",
"Effect": "Allow",
"Action": [
"iam:CreateRole",
"iam:AttachRolePolicy",
"iam:DetachRolePolicy"
],
"Resource": "arn:aws:iam::<ACCOUNT_NUMBER>:role/MyTestApp*",
"Condition": {
     "StringEquals": {
     "iam:PermissionsBoundary":     
     "arn:aws:iam::<ACCOUNT_NUMBER>:policy/DynamoDB_Boundary_Frankfurt"}}
      },
     {
      "Sid": "CreateAndEditPermissionsPolicy",
"Effect": "Allow",
"Action": [
"iam:CreatePolicy",
      "iam:CreatePolicyVersion"
],
"Resource": "*"
     }
]
}

Next, use the create-policy command to create the customer managed policy, Permissions_Policy_For_Employee, and use the attach-role-policy command to attach this policy to the principal, MyEmployeeRole, used by your employee.

$aws iam create-policy –policy-name Permissions_Policy_For_Employee –policy-document file://Permissions_Policy_For_Employee_Text.json

$aws iam attach-role-policy –policy-arn arn:aws:iam::<ACCOUNT_NUMBER>:policy/Permissions_Policy_For_Employee –role-name MyEmployeeRole

Administrator step 3: Create and attach the permissions policy for granting permissions to pass the role

Create a policy to allow the employee to pass the roles they created to AWS services, such as Amazon EC2, enabling these services to assume the roles and perform actions on the employee’s behalf. To do this, paste the following JSON policy document in a file with the name Pass_Role_Policy_Text.json.


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": "iam:PassRole",
            "Resource": "arn:aws:iam::<ACCOUNT_NUMBER>:role/MyTestApp*"
        }
    ]
}

Then, use the create-policycreate-policy command to create the policy, Pass_Role_Policy, and the attach-role-policy command to attach this policy to the principal, MyEmployeeRole.

$aws iam create-policy –policy-name Pass_Role_Policy –policy-document file://Pass_Role_Policy_Text.json

$aws iam attach-role-policy –policy-arn arn:aws:iam::<ACCOUNT_NUMBER>:policy/Pass_Role_Policy –role-name MyEmployeeRole

As the IAM administrator, we’ve successfully defined a permissions boundary. We’ve also granted our employee the ability to create IAM roles and attach permissions policies, while ensuring the permissions of the roles don’t exceed the boundary that we set.

Managing Permissions Boundaries

Changing and modifying a permissions boundary is a powerful permission. You should reserve this permission for full administrators in an account. You can do this by ensuring that policies you use as permissions boundaries don’t include the DeleteUserPermissionsBoundary and DeleteRolePermissionsBoundary actions. Or, if you allow “iam:*” actions, then you must explicitly deny those actions.

Employee step 1: Create a role by providing the permissions boundary

Your employee can now use the create-role command to create a new IAM role with the DynamoDB_Boundary_Frankfurt permissions boundary and the attach-role-policy command to attach permissions policies to this role.

For this post, we assume that your employee operates an application, MyTestApp, on Amazon EC2 that requires access to the Amazon DynamoDB table, MyTestApp_DDB_Table. The employee can paste the following JSON policy document and save it as Role_Trust_Policy_Text.json to define the trust policy.


{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "ec2.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Then, the employee can use the create-role command to create the IAM role, MyTestAppRole, and define the permissions boundary as DynamoDB_Boundary_Frankfurt. The create-role command will fail if the employee doesn’t provide the appropriate permissions boundary. Make sure to the <ACCOUNT NUMBER> variable is replaced with the employee’s in the policy below.

$aws iam create-role –role-name MyTestAppRole
–assume-role-policy-document file://Role_Trust_Policy_Text.json
–permissions-boundary arn:aws:iam::<ACCOUNT_NUMBER>:policy/DynamoDB_Boundary_Frankfurt

Next, the employee grants permissions to this role by using the attach-role-policy command to attach the following policy, MyTestApp_DDB_Permissions. This policy grants the ability to perform all actions on the DynamoDB table, MyTestApp_DDB_Table.


{
    "Version": "2012-10-17",
    "Statement": [
{
"Effect": "Allow",
"Action": "dynamodb:*",
"Resource": [
"arn:aws:dynamodb:eu-central-1:<ACCOUNT_NUMBER>:table/MyTestApp_DDB_Table"]
}
]
}

$aws iam attach-role-policy –policy-arn arn:aws:iam::<ACCOUNT_NUMBER>:policy/MyTestApp_DDB_Permissions
–role-name MyTestAppRole

Although the employee granted full DynamoDB access, the effective permissions for this IAM role are the intersection of the permissions boundary, DynamoDB_Boundary_Frankfurt, and the permissions policy, MyTestApp_DDB_Permissions. This means the role only has access to put, update, and delete items on the MyTestApp_DDB_Table in the AWS EU (Frankfurt) region. See the following diagram for a visual representation.

Figure 2: Effective permissions for the IAM role

Summary

We demonstrated how to use permissions boundaries to delegate IAM permission management. Using permissions boundaries can help you scale permission management in your organization and move workloads to AWS faster. To learn more, see the IAM documentation for permissions boundaries.

If you have comments about this post, submit them in the Comments section below. If you have questions or suggestions, please start a new thread on the IAM forum.

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An easier way to control access to AWS resources by using the AWS organization of IAM principals

2018-05-17 Sulay Shah

Post Syndicated from Sulay Shah original https://aws.amazon.com/blogs/security/control-access-to-aws-resources-by-using-the-aws-organization-of-iam-principals/

AWS Identity and Access Management (IAM) now makes it easier for you to control access to your AWS resources by using the AWS organization of IAM principals (users and roles). For some services, you grant permissions using resource-based policies to specify the accounts and principals that can access the resource and what actions they can perform on it. Now, you can use a new condition key, aws:PrincipalOrgID, in these policies to require all principals accessing the resource to be from an account in the organization. For example, let’s say you have an Amazon S3 bucket policy and you want to restrict access to only principals from AWS accounts inside of your organization. To accomplish this, you can define the aws:PrincipalOrgID condition and set the value to your organization ID in the bucket policy. Your organization ID is what sets the access control on the S3 bucket. Additionally, when you use this condition, policy permissions apply when you add new accounts to this organization without requiring an update to the policy.

In this post, I walk through the details of the new condition and show you how to restrict access to only principals in your organization using S3.

Condition concepts

Before I introduce the new condition, let’s review the condition element of an IAM policy. A condition is an optional IAM policy element you can use to specify special circumstances under which the policy grants or denies permission. A condition includes a condition key, operator, and value for the condition. There are two types of conditions: service-specific conditions and global conditions. Service-specific conditions are specific to certain actions in an AWS service. For example, the condition key ec2:InstanceType supports specific EC2 actions. Global conditions support all actions across all AWS services.

Now that I’ve reviewed the condition element in an IAM policy, let me introduce the new condition.

AWS:PrincipalOrgID Condition Key

You can use this condition key to apply a filter to the Principal element of a resource-based policy. You can use any string operator, such as StringLike, with this condition and specify the AWS organization ID for as its value.

Condition key	Description	Operator(s)	Value
aws:PrincipalOrgID	Validates if the principal accessing the resource belongs to an account in your organization.	All String operators	Any AWS organization ID

Example: Restrict access to only principals from my organization

Let’s consider an example where I want to give specific IAM principals in my organization direct access to my S3 bucket, 2018-Financial-Data, that contains sensitive financial information. I have two accounts in my AWS organization with multiple account IDs, and only some IAM users from these accounts need access to this financial report.

To grant this access, I author a resource-based policy for my S3 bucket as shown below. In this policy, I list the individuals who I want to grant access. For the sake of this example, let’s say that while doing so, I accidentally specify an incorrect account ID. This means a user named Steve, who is not in an account in my organization, can now access my financial report. To require the principal account to be in my organization, I add a condition to my policy using the global condition key aws:PrincipalOrgID. This condition requires that only principals from accounts in my organization can access the S3 bucket. This means that although Steve is one of the principals in the policy, he can’t access the financial report because the account that he is a member of doesn’t belong to my organization.




{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "AllowPutObject",
            "Effect": "Allow",
            "Principal": ["arn:aws:iam::094697565664:user/Casey",
				"arn:aws:iam::094697565664:user/David",
				"arn:aws:iam::094697565664:user/Tom",
				"arn:aws:iam::094697565664:user/Michael",
				"arn:aws:iam::094697565664:user/Brenda",
				"arn:aws:iam::094697565664:user/Lisa",
				"arn:aws:iam::094697565664:user/Norman",
				"arn:aws:iam::094697565646:user/Steve",
				"arn:aws:iam::087695765465:user/Douglas",
				"arn:aws:iam::087695765465:user/Michelle"],
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::2018-Financial-Data/*",
            "Condition": {"ForAnyValue:StringLike": 
                             {"aws:PrincipalOrgID": [ "o-xxxxxxxxxx" ]}
                         }
        }
    ]
}

In the policy above, I specify the principals that I grant access to using the principal element of the statement. Next, I add s3:GetObject as the action and 2018-Financial-Data/* as the resource to grant read access to my S3 bucket. Finally, I add the new condition key aws:PrincipalOrgID and specify my organization ID in the condition element of the statement to make sure only the principals from the accounts in my organization can access this bucket.

Summary

You can now use the aws:PrincipalOrgID condition key in your resource-based policies to more easily restrict access to IAM principals from accounts in your AWS organization. For more information about this global condition key and policy examples using aws:PrincipalOrgID, read the IAM documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about or suggestions for this solution, start a new thread on the IAM forum or contact AWS Support.

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Refining Access to Branches in AWS CodeCommit

2018-05-16 Chris Barclay

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/refining-access-to-branches-in-aws-codecommit/

Thanks to Susan Ferrell, Senior Technical Writer, for a great blog post on how to use CodeCommit branch-level permissions.
—-

AWS CodeCommit users have been asking for a way to restrict commits to some repository branches to just a few people. In this blog post, we’re going to show you how to do that by creating and applying a conditional policy, an AWS Identity and Access Management (IAM) policy that contains a context key.

Why would I do this?

When you create a branch in an AWS CodeCommit repository, the branch is available, by default, to all repository users. Here are some scenarios in which refining access might help you:

You maintain a branch in a repository for production-ready code, and you don’t want to allow changes to this branch except from a select group of people.
You want to limit the number of people who can make changes to the default branch in a repository.
You want to ensure that pull requests cannot be merged to a branch except by an approved group of developers.

We’ll show you how to create a policy in IAM that prevents users from pushing commits to and merging pull requests to a branch named master. You’ll attach that policy to one group or role in IAM, and then test how users in that group are affected when that policy is applied. We’ll explain how it works, so you can create custom policies for your repositories.

What you need to get started

You’ll need to sign in to AWS with sufficient permissions to:
- - Create and apply policies in IAM.
  - Create groups in IAM.
  - Add users to those groups.
  - Apply policies to those groups.
You can use existing IAM groups, but because you’re going to be changing permissions, you might want to first test this out on groups and users you’ve created specifically for this purpose.
You’ll need a repository in AWS CodeCommit with at least two branches: master and test-branch. For information about how to create repositories, see Create a Repository. For information about how to create branches, see Create a Branch. In this blog post, we’ve named the repository MyDemoRepo. You can use an existing repository with branches of another name, if you prefer.

Let’s get started!

Create two groups in IAM

We’re going to set up two groups in IAM: Developers and Senior_Developers. To start, both groups will have the same managed policy, AWSCodeCommitPowerUsers, applied. Users in each group will have exactly the same permissions to perform actions in IAM.

Figure 1: Two example groups in IAM, with distinct users but the same managed policy applied to each group

First, create the Developers group.

Sign in to the AWS Management Console and open the IAM console at https://console.aws.amazon.com/iam/.
In the navigation pane, choose Groups, and then choose Create New Group.
In the Group Name box, type Developers, and then choose Next Step.
In the list of policies, select the check box for AWSCodeCommitPowerUsers, then choose Next Step.
Choose Create Group.

Now, follow these steps to create the Senior_Developers group and attach the AWSCodeCommitPowerUsers managed policy. You now have two empty groups with the same policy attached.

Create users in IAM

Next, add at least one unique user to each group. You can use existing IAM users, but because you’ll be affecting their access to AWS CodeCommit, you might want to create two users just for testing purposes. Let’s go ahead and create Arnav and Mary.

In the navigation pane, choose Users, and then choose Add user.
For the new user, type Arnav_Desai.
Choose Add another user, and then type Mary_Major.
Select the type of access (programmatic access, access to the AWS Management Console, or both). In this blog post, we’ll be testing everything from the console, but if you want to test AWS CodeCommit using the AWS CLI, make sure you include programmatic access and console access.
For Console password type, choose Custom password. Each user is assigned the password that you type in the box. Write these down so you don’t forget them. You’ll need to sign in to the console using each of these accounts.
Choose Next: Permissions.
On the Set permissions page, choose Add user to group. Add Arnav to the Developers group. Add Mary to the Senior_Developers group.
Choose Next: Review to see all of the choices you made up to this point. When you are ready to proceed, choose Create user.

Sign in as Arnav, and then follow these steps to go to the master branch and add a file. Then sign in as Mary and follow the same steps.

Open the AWS CodeCommit console at https://console.aws.amazon.com/codecommit.
On the Dashboard page, from the list of repositories, choose MyDemoRepo.
In the Code view, choose the branch named master.
Choose Add file, and then choose Create file. Type some text or code in the editor.
Provide information to other users about who added this file to the repository and why.
1. In Author name, type the name of the user (Arnav or Mary).
2. In Email address, type an email address so that other repository users can contact you about this change.
3. In Commit message, type a brief description to help you remember why you added this file or any other details you might find helpful.
4. Type a name for the file.
Choose Commit file.

Now follow the same steps to add a file in a different branch. (In our example repository, that’s the branch named test-branch.) You should be able to add a file to both branches regardless of whether you’re signed in as Arnav or Mary.

Let’s change that.

Create a conditional policy in IAM

You’re going to create a policy in IAM that will deny API actions if certain conditions are met. We want to prevent users with this policy applied from updating a branch named master, but we don’t want to prevent them from viewing the branch, cloning the repository, or creating pull requests that will merge to that branch. For this reason, we want to pick and choose our APIs carefully. Looking at the Permissions Reference, the logical permissions for this are:

GitPush
PutFile
MergePullRequestByFastForward

Now’s the time to think about what else you might want this policy to do. For example, because we don’t want users with this policy to make changes to this branch, we probably don’t want them to be able to delete it either, right? So let’s add one more permission:

DeleteBranch

The branch in which we want to deny these actions is master. The repository in which the branch resides is MyDemoRepo. We’re going to need more than just the repository name, though. We need the repository ARN. Fortunately, that’s easy to find. Just go to the AWS CodeCommit console, choose the repository, and choose Settings. The repository ARN is displayed on the General tab.

Now we’re ready to create a policy.
1. Open the IAM console at https://console.aws.amazon.com/iam/. Make sure you’re signed in with the account that has sufficient permissions to create policies, and not as Arnav or Mary.
2. In the navigation pane, choose Policies, and then choose Create policy.
3. Choose JSON, and then paste in the following:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Deny",
            "Action": [
                "codecommit:GitPush",
                "codecommit:DeleteBranch",
                "codecommit:PutFile",
                "codecommit:MergePullRequestByFastForward"
            ],
            "Resource": "arn:aws:codecommit:us-east-2:80398EXAMPLE:MyDemoRepo",
            "Condition": {
                "StringEqualsIfExists": {
                    "codecommit:References": [
                        "refs/heads/master"   
                    ]
                },
                "Null": {
                    "codecommit:References": false
                }
            }
        }
    ]
}

You’ll notice a few things here. First, change the repository ARN to the ARN for your repository and include the repository name. Second, if you want to restrict access to a branch with a name different from our example, master, change that reference too.

Now let’s talk about this policy and what it does. You might be wondering why we’re using a Git reference (refs/heads) value instead of just the branch name. The answer lies in how Git references things, and how AWS CodeCommit, as a Git-based repository service, implements its APIs. A branch in Git is a simple pointer (reference) to the SHA-1 value of the head commit for that branch.

You might also be wondering about the second part of the condition, the nullification language. This is necessary because of the way git push and git-receive-pack work. Without going into too many technical details, when you attempt to push a change from a local repo to AWS CodeCommit, an initial reference call is made to AWS CodeCommit without any branch information. AWS CodeCommit evaluates that initial call to ensure that:

a) You’re authorized to make calls.

b) A repository exists with the name specified in the initial call. If you left that null out of the policy, users with that policy would be unable to complete any pushes from their local repos to the AWS CodeCommit remote repository at all, regardless of which branch they were trying to push their commits to.

Could you write a policy in such a way that the null is not required? Of course. IAM policy language is flexible. There’s an example of how to do this in the AWS CodeCommit User Guide, if you’re curious. But for the purposes of this blog post, let’s continue with this policy as written.

So what have we essentially said in this policy? We’ve asked IAM to deny the relevant CodeCommit permissions if the request is made to the resource MyDemoRepo and it meets the following condition: the reference is to refs/heads/master. Otherwise, the deny does not apply.

I’m sure you’re wondering if this policy has to be constrained to a specific repository resource like MyDemoRepo. After all, it would be awfully convenient if a single policy could apply to all branches in any repository in an AWS account, particularly since the default branch in any repository is initially the master branch. Good news! Simply replace the ARN with an *, and your policy will affect ALL branches named master in every AWS CodeCommit repository in your AWS account. Make sure that this is really what you want, though. We suggest you start by limiting the scope to just one repository, and then changing things when you’ve tested it and are happy with how it works.

When you’re sure you’ve modified the policy for your environment, choose Review policy to validate it. Give this policy a name, such as DenyChangesToMaster, provide a description of its purpose, and then choose Create policy.

Now that you have a policy, it’s time to apply and test it.

Apply the policy to a group

In theory, you could apply the policy you just created directly to any IAM user, but that really doesn’t scale well. You should apply this policy to a group, if you use IAM groups to manage users, or to a role, if your users assume a role when interacting with AWS resources.

In the IAM console, choose Groups, and then choose Developers.
On the Permissions tab, choose Attach Policy.
Choose DenyChangesToMaster, and then choose Attach policy.

Your groups now have a critical difference: users in the Developers group have an additional policy applied that restricts their actions in the master branch. In other words, Mary can continue to add files, push commits, and merge pull requests in the master branch, but Arnav cannot.

Figure 2: Two example groups in IAM, one with an additional policy applied that will prevent users in this group from making changes to the master branch

Test it out. Sign in as Arnav, and do the following:

Open the AWS CodeCommit console at https://console.aws.amazon.com/codecommit.
On the Dashboard page, from the list of repositories, choose MyDemoRepo.
In the Code view, choose the branch named master.
Choose Add file, and then choose Create file, just as you did before. Provide some text, and then add the file name and your user information.
Choose Commit file.

This time you’ll see an error after choosing Commit file. It’s not a pretty message, but at the very end, you’ll see a telling phrase: “explicit deny”. That’s the policy in action. You, as Arnav, are explicitly denied PutFile, which prevents you from adding a file to the master branch. You’ll see similar results if you try other actions denied by that policy, such as deleting the master branch.

Stay signed in as Arnav, but this time add a file to test-branch. You should be able to add a file without seeing any errors. You can create a branch based on the master branch, add a file to it, and create a pull request that will merge to the master branch, all just as before. However, you cannot perform denied actions on that master branch.

Sign out as Arnav and sign in as Mary. You’ll see that as that IAM user, you can add and edit files in the master branch, merge pull requests to it, and even, although we don’t recommend this, delete it.

Conclusion

You can use conditional statements in policies in IAM to refine how users interact with your AWS CodeCommit repositories. This blog post showed how to use such a policy to prevent users from making changes to a branch named master. There are many other options. We hope this blog post will encourage you to experiment with AWS CodeCommit, IAM policies, and permissions. If you have any questions or suggestions, we’d love to hear from you.

How to Eliminate the Need for Hardcoded AWS Credentials in Devices by Using the AWS IoT Credentials Provider

2018-04-30 Ramkishore Bhattacharyya

Post Syndicated from Ramkishore Bhattacharyya original https://aws.amazon.com/blogs/security/how-to-eliminate-the-need-for-hardcoded-aws-credentials-in-devices-by-using-the-aws-iot-credentials-provider/

The Internet of Things (IoT) has precipitated to an influx of connected devices and data that can be mined to gain useful business insights. If you own an IoT device, you might want the data to be uploaded seamlessly from your connected devices to the cloud so that you can make use of cloud storage and the processing power to perform sophisticated analysis of data. To upload the data to the AWS Cloud, devices must pass authentication and authorization checks performed by the respective AWS services. The standard way of authenticating AWS requests is the Signature Version 4 algorithm that requires the caller to have an access key ID and secret access key. Consequently, you need to hardcode the access key ID and the secret access key on your devices. Alternatively, you can use the built-in X.509 certificate as the unique device identity to authenticate AWS requests.

AWS IoT has introduced the credentials provider feature that allows a caller to authenticate AWS requests by having an X.509 certificate. The credentials provider authenticates a caller using an X.509 certificate, and vends a temporary, limited-privilege security token. The token can be used to sign and authenticate any AWS request. Thus, the credentials provider relieves you from having to manage and periodically refresh the access key ID and secret access key remotely on your devices.

In the process of retrieving a security token, you use AWS IoT to create a thing (a representation of a specific device or logical entity), register a certificate, and create AWS IoT policies. You also configure an AWS Identity and Access Management (IAM) role and attach appropriate IAM policies to the role so that the credentials provider can assume the role on your behalf. You also make an HTTP-over-Transport Layer Security (TLS) mutual authentication request to the credentials provider that uses your preconfigured thing, certificate, policies, and IAM role to authenticate and authorize the request, and obtain a security token on your behalf. You can then use the token to sign any AWS request using Signature Version 4.

In this blog post, I explain the AWS IoT credentials provider design and then demonstrate the end-to-end process of retrieving a security token from AWS IoT and using the token to write a temperature and humidity record to a specific Amazon DynamoDB table.

Note: This post assumes you are familiar with AWS IoT and IAM to perform steps using the AWS CLI and OpenSSL. Make sure you are running the latest version of the AWS CLI.

Overview of the credentials provider workflow

The following numbered diagram illustrates the credentials provider workflow. The diagram is followed by explanations of the steps.

To explain the steps of the workflow as illustrated in the preceding diagram:

The AWS IoT device uses the AWS SDK or custom client to make an HTTPS request to the credentials provider for a security token. The request includes the device X.509 certificate for authentication.
The credentials provider forwards the request to the AWS IoT authentication and authorization module to verify the certificate and the permission to request the security token.
If the certificate is valid and has permission to request a security token, the AWS IoT authentication and authorization module returns success. Otherwise, it returns failure, which goes back to the device with the appropriate exception.
On successful validation, the credentials provider invokes the AWS Security Token Service (AWS STS) to assume the preconfigured IAM role.
If assuming the role succeeds, AWS STS returns a temporary, limited-privilege security token to the credentials provider.
The credentials provider returns the security token to the device.
The AWS SDK on the device uses the security token to sign an AWS request with AWS Signature Version 4.
The requested service invokes IAM to validate the signature and authorize the request against access policies attached to the preconfigured IAM role.
If IAM validates the signature successfully and authorizes the request, the request goes through.

In another solution, you could configure an AWS Lambda rule that ingests your device data and sends it to another AWS service. However, in applications that require the uploading of large files such as videos or aggregated telemetry to the AWS Cloud, you may want your devices to be able to authenticate and send data directly to the AWS service of your choice. The credentials provider enables you to do that.

Outline of the steps to retrieve and use security token

Perform the following steps as part of this solution:

Create an AWS IoT thing: Start by creating a thing that corresponds to your home thermostat in the AWS IoT thing registry database. This allows you to authenticate the request as a thing and use thing attributes as policy variables in AWS IoT and IAM policies.
Register a certificate: Create and register a certificate with AWS IoT, and attach it to the thing for successful device authentication.
Create and configure an IAM role: Create an IAM role to be assumed by the service on behalf of your device. I illustrate how to configure a trust policy and an access policy so that AWS IoT has permission to assume the role, and the token has necessary permission to make requests to DynamoDB.
Create a role alias: Create a role alias in AWS IoT. A role alias is an alternate data model pointing to an IAM role. The credentials provider request must include a role alias name to indicate which IAM role to assume for obtaining a security token from AWS STS. You may update the role alias on the server to point to a different IAM role and thus make your device obtain a security token with different permissions.
Attach a policy: Create an authorization policy with AWS IoT and attach it to the certificate to control which device can assume which role aliases.
Request a security token: Make an HTTPS request to the credentials provider and retrieve a security token and use it to sign a DynamoDB request with Signature Version 4.
Use the security token to sign a request: Use the retrieved token to sign a request to DynamoDB and successfully write a temperature and humidity record from your home thermostat in a specific table. Thus, starting with an X.509 certificate on your home thermostat, you can successfully upload your thermostat record to DynamoDB and use it for further analysis. Before the availability of the credentials provider, you could not do this.

Deploy the solution

1. Create an AWS IoT thing

Register your home thermostat in the AWS IoT thing registry database by creating a thing type and a thing. You can use the AWS CLI with the following command to create a thing type. The thing type allows you to store description and configuration information that is common to a set of things.

aws iot create-thing-type --thing-type-name Thermostat

The following is sample output of the create-thing-type command. It contains the thingTypeName, thingTypeId, and thingTypeArn.

{
    "thingTypeName": "Thermostat", 
    "thingTypeId": "11f6d708-919e-479d-8a37-790ce48204d8",
    "thingTypeArn": "arn:aws:iot:us-east-1:<your_aws_account_id>:thingtype/Thermostat"
}

Run the following command in the AWS CLI to create a thing.

aws iot create-thing --thing-name MyHomeThermostat --thing-type-name Thermostat --attribute-payload "{\"attributes\": {\"Owner\":\"Alice\"}}"

The following is sample output of the create-thing command. It contains the thingArn, thingName, and thingId.

{
    "thingArn": "arn:aws:iot:us-east-1:<your_aws_account_id>:thing/MyHomeThermostat", 
    "thingName": "MyHomeThermostat",
    "thingId": "a9b098ac-2ee9-4ba6-aa40-163113eb18d0"
}

2. Register a certificate

Now, you need to have a Certificate Authority (CA) certificate, sign a device certificate using the CA certificate, and register both certificates with AWS IoT before your device can authenticate to AWS IoT. If you do not already have a CA certificate, you can use OpenSSL to create a CA certificate, as described in Use Your Own Certificate. To register your CA certificate with AWS IoT, follow the steps on Registering Your CA Certificate.

You then have to create a device certificate signed by the CA certificate and register it with AWS IoT, which you can do by following the steps on Creating a Device Certificate Using Your CA Certificate. Save the certificate and the corresponding key pair; you will use them when you request a security token later. Also, remember the password you provide when you create the certificate.

Run the following command in the AWS CLI to attach the device certificate to your thing so that you can use thing attributes in policy variables.

aws iot attach-thing-principal --thing-name MyHomeThermostat --principal <certificate-arn>

If the attach-thing-principal command succeeds, the output is empty.

3. Configure an IAM role

Next, configure an IAM role in your AWS account that will be assumed by the credentials provider on behalf of your device. You are required to associate two policies with the role: a trust policy that controls who can assume the role, and an access policy that controls which actions can be performed on which resources by assuming the role.

The following trust policy grants the credentials provider permission to assume the role. Put it in a text document and save the document with the name, trustpolicyforiot.json.

{
  "Version": "2012-10-17",
  "Statement": {
    "Effect": "Allow",
    "Principal": {"Service": "credentials.iot.amazonaws.com"},
    "Action": "sts:AssumeRole"
  }
}

Run the following command in the AWS CLI to create an IAM role with the preceding trust policy.

aws iam create-role --role-name dynamodb-access-role --assume-role-policy-document file://trustpolicyforiot.json

The following is sample output of the create-role command.

{
    "Role": {
        "AssumeRolePolicyDocument": {
            "Version": "2012-10-17", 
            "Statement": {
                "Action": "sts:AssumeRole", 
                "Effect": "Allow", 
                "Principal": {
                    "Service": "credentials.iot.amazonaws.com"
                }
            }
        }, 
        "RoleId": "AROAJWE6XX2DBF3PMINT6", 
        "CreateDate": "2018-01-18T08:40:03.788Z", 
        "RoleName": "dynamodb-access-role", 
        "Path": "/", 
        "Arn": "arn:aws:iam::<your_aws_account_id>:role/dynamodb-access-role"
    }
}

The following access policy allows DynamoDB operations on the table that has the same name as the thing name that you created in Step 1, MyHomeThermostat, by using credentials-iot:ThingName as a policy variable. I explain after Step 5 about using thing attributes as policy variables. Put the following policy in a text document and save the document with the name, accesspolicyfordynamodb.json.

{
  "Version": "2012-10-17",
  "Statement": {
    "Effect": "Allow",
    "Action": [
      "dynamodb:GetItem",
      "dynamodb:BatchGetItem",
      "dynamodb:PutItem",
      "dynamodb:UpdateItem",
      "dynamodb:DeleteItem"
    ],
    "Resource": "arn:aws:dynamodb:us-east-1:<your_aws_account_id>:table/${credentials-iot:ThingName}"
  }
}

Run the following command in the AWS CLI to create the access policy.

aws iam create-policy --policy-name accesspolicyfordynamodb --policy-document file://accesspolicyfordynamodb.json

The following is sample output of the create-policy command.

{
    "Policy": {
        "PolicyName": "accesspolicyfordynamodb", 
        "CreateDate": "2018-01-18T08:47:38.368Z", 
        "AttachmentCount": 0, 
        "IsAttachable": true, 
        "PolicyId": "ANPAJMRTTZH25SEHZOBYG", 
        "DefaultVersionId": "v1", 
        "Path": "/", 
        "Arn": "arn:aws:iam::<your_aws_account_id>:policy/accesspolicyfordynamodb", 
        "UpdateDate": "2018-01-18T08:47:38.368Z"
    }
}

Finally, run the following command in the AWS CLI to attach the access policy to your role.

aws iam attach-role-policy --role-name dynamodb-access-role --policy-arn arn:aws:iam::<your_aws_account_id>:policy/accesspolicyfordynamodb

If the attach-role-policy command succeeds, the output is empty.

Configure the PassRole permissions

The IAM role that you have created must be passed to AWS IoT to create a role alias, as described in Step 4. The user who performs the operation requires iam:PassRole permission to authorize this action. You also should add permission for the iam:GetRole action to allow the user to retrieve information about the specified role. Create the following policy to grant iam:PassRole and iam:GetRole permissions. Name this policy, passrolepermission.json.

{
  "Version": "2012-10-17",
  "Statement": {
    "Effect": "Allow",
    "Action": [
        "iam:GetRole",
        "iam:PassRole"
    ],
    "Resource": "arn:aws:iam::<your_aws_account_id>:role/dynamodb-access-role"
  }
}

Run the following command in the AWS CLI to create the policy in your AWS account.

aws iam create-policy --policy-name passrolepermission --policy-document file://passrolepermission.json

The following is sample output of the create-policy command.

{
    "Policy": {
        "PolicyName": "passrolepermission", 
        "CreateDate": "2018-01-18T08:53:35.016Z", 
        "AttachmentCount": 0, 
        "IsAttachable": true, 
        "PolicyId": "ANPAJ6HSQYSBLAUCR5XRC", 
        "DefaultVersionId": "v1", 
        "Path": "/", 
        "Arn": "arn:aws:iam::<your_aws_account_id>:policy/passrolepermission", 
        "UpdateDate": "2018-01-18T08:53:35.016Z"
    }
}

Now, run the following command to attach the policy to the user.

aws iam attach-user-policy --policy-arn arn:aws:iam::<your_aws_account_id>:policy/passrolepermission --user-name <user_name>

If the attach-user-policy command succeeds, the output is empty.

4. Create a role alias

Now that you have configured the IAM role, you will create a role alias with AWS IoT. You must provide the following pieces of information when creating a role alias:

RoleAlias: This is the primary key of the role alias data model and hence a mandatory attribute. It is a string; the minimum length is 1 character, and the maximum length is 128 characters.
RoleArn: This is the Amazon Resource Name (ARN) of the IAM role you have created. This is also a mandatory attribute.
CredentialDurationSeconds: This is an optional attribute specifying the validity (in seconds) of the security token. The minimum value is 900 seconds (15 minutes), and the maximum value is 3,600 seconds (60 minutes); the default value is 3,600 seconds, if not specified.

Run the following command in the AWS CLI to create a role alias. Use the credentials of the user to whom you have given the iam:PassRole permission.

aws iot create-role-alias --role-alias Thermostat-dynamodb-access-role-alias --role-arn arn:aws:iam::<your_aws_account_id>:role/dynamodb-access-role --credential-duration-seconds 3600

The following is sample output of the create-role-alias command. It contains the roleAlias as specified in the request and the roleArn.

{
    "roleAlias": "Thermostat-dynamodb-access-role-alias",
    "roleAliasArn": "arn:aws:iot:us-east-1:<your_aws_account_id>:rolealias/Thermostat-dynamodb-access-role-alias"
}

5. Attach a policy

You created and registered a certificate with AWS IoT earlier for successful authentication of your device. Now, you need to create and attach a policy to the certificate to authorize the request for the security token.

Let’s say you want to allow a thing to get credentials for the role alias, Thermostat-dynamodb-access-role-alias, with thing owner Alice, thing type thermostat, and the thing attached to a principal. The following policy, with thing attributes as policy variables, achieves these requirements. After this step, I explain more about using thing attributes as policy variables. Put the policy in a text document, and save it with the name, alicethermostatpolicy.json.

{
  "Version": "2012-10-17",
  "Statement": {
    "Effect": "Allow",
    "Action": "iot:AssumeRoleWithCertificate",
    "Resource": "arn:aws:iot:us-east-1:<your_aws_account_id>:rolealias/${iot:Connection.Thing.ThingTypeName}-dynamodb-access-role-alias",
    "Condition": {
      "StringEquals": {
        "iot:Connection.Thing.Attributes[Owner]": "Alice",
        "iot:Connection.Thing.ThingTypeName": "Thermostat"
      },
      "Bool": {
        "iot:Connection.Thing.IsAttached": "true"
      }
    }
  }
}

Run the following command in the AWS CLI to create the policy in your AWS IoT database.

aws iot create-policy --policy-name AliceThermostatPolicy --policy-document file://alicethermostatpolicy.json

The following is sample output of the create-policy command. It contains the policyName, policyArn, policyDocument, and policyVersionId.

{
    "policyName": "AliceThermostatPolicy", 
    "policyArn": "arn:aws:iot:us-east-1:<your_aws_account_id>:policy/AliceThermostatPolicy", 
    "policyDocument": "{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": {\n    \"Effect\": \"Allow\",\n    \"Action\": \"iot:AssumeRoleWithCertificate\",\n    \"Resource\": \"arn:aws:iot:us-east-1:<your_aws_account_id>:rolealias/${iot:Connection.Thing.ThingTypeName}-dynamodb-access-role-alias\",\n    \"Condition\": {\n      \"StringEquals\": {\n        \"iot:Connection.Thing.Attributes[Owner]\": \"Alice\",\n        \"iot:Connection.Thing.ThingTypeName\": \"Thermostat\"\n      },\n      \"Bool\": {\n        \"iot:Connection.Thing.IsAttached\": \"true\"\n      }\n    }\n  }\n}\n", 
    "policyVersionId": "1"
}

Use the following command to attach the policy with the certificate you registered earlier.

aws iot attach-policy --policy-name AliceThermostatPolicy --target <certificate-arn>

If the attach-policy command succeeds, the output is empty.

You have completed all the necessary steps to request an AWS security token from the credentials provider!

Using thing attributes as policy variables

Before I show how to request a security token, I want to explain more about how to use thing attributes as policy variables and the advantage of using them. As a prerequisite, a device must provide a thing name in the credentials provider request.

Thing substitution variables in AWS IoT policies

AWS IoT Simplified Permission Management allows you to associate a connection with a specific thing, and allow the thing name, thing type, and other thing attributes to be available as substitution variables in AWS IoT policies. You can write a generic AWS IoT policy as in alicethermostatpolicy.json in Step 5, attach it to multiple certificates, and authorize the connection as a thing. For example, you could attach alicethermostatpolicy.json to certificates corresponding to each of the thermostats you have that you want to assume the role alias, Thermostat-dynamodb-access-role-alias, and allow operations only on the table with the name that matches the thing name. For more information, see the full list of thing policy variables.

Thing substitution variables in IAM policies

You also can use the following three substitution variables in the IAM role’s access policy (I used credentials-iot:ThingName in accesspolicyfordynamodb.json in Step 3):

credentials-iot:ThingName
credentials-iot:ThingTypeName
credentials-iot:AwsCertificateId

When the device provides the thing name in the request, the credentials provider fetches these three variables from the database and adds them as context variables to the security token. When the device uses the token to access DynamoDB, the variables in the role’s access policy are replaced with the corresponding values in the security token. Note that you also can use credentials-iot:AwsCertificateId as a policy variable; AWS IoT returns certificateId during registration.

6. Request a security token

Make an HTTPS request to the credentials provider to fetch a security token. You have to supply the following information:

Certificate and key pair: Because this is an HTTP request over TLS mutual authentication, you have to provide the certificate and the corresponding key pair to your client while making the request. Use the same certificate and key pair that you used during certificate registration with AWS IoT.
RoleAlias: Provide the role alias (in this example, Thermostat-dynamodb-access-role-alias) to be assumed in the request.
ThingName: Provide the thing name that you created earlier in the AWS IoT thing registry database. This is passed as a header with the name, x-amzn-iot-thingname. Note that the thing name is mandatory only if you have thing attributes as policy variables in AWS IoT or IAM policies.

Run the following command in the AWS CLI to obtain your AWS account-specific endpoint for the credentials provider. See the DescribeEndpoint API documentation for further details.

aws iot describe-endpoint --endpoint-type iot:CredentialProvider

The following is sample output of the describe-endpoint command. It contains the endpointAddress.

{
    "endpointAddress": "<your_aws_account_specific_prefix>.credentials.iot.us-east-1.amazonaws.com"
}

Note that if you are on Mac OS X, you need to export your certificate to a .pfx or .p12 file before you can pass it in the https request. Use OpenSSL with the following command to convert the device certificate from .pem to .pfx format. Remember the password because you will need it subsequently in a curl command.

openssl pkcs12 -export -out deviceCert.pfx -inkey <device-certificate-key-pair> -in <device-certificate-pem>

Now, make an HTTPS request to the credentials provider to fetch a security token. You may use your preferred HTTP client for the request. I use curl in the following examples.

curl --cert deviceCert.pfx --pass <certificate-password> --key <device-certificate-key-pair> -H "x-amzn-iot-thingname: MyHomeThermostat" https://<your_credentials_provider_endpoint>/role-aliases/Thermostat-dynamodb-access-role-alias/credentials

This command returns a security token object that has an accessKeyId, a secretAccessKey, a sessionToken, and an expiration. The following is sample output of the curl command.

{"credentials":{"accessKeyId":"ASIAJAKDHYFVFZCETC4A","secretAccessKey":"6RcojWDX0uWj3hRekTu/kOhXyyfqMU+T5U81LZzf","sessionToken":"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","expiration":"2018-01-18T09:18:06Z"}}

7. Use the security token to sign a request

Create a DynamoDB table called MyHomeThermostat in your AWS account. You will have to choose the hash (partition key) and the range (sort key) while creating the table to uniquely identify a record. Make the hash the serial_number of the thermostat and the range the timestamp of the record. Create a text file with the following JSON to put a temperature and humidity record in the table. Name the file, item.json.

{
  "serial_number": {"S": "123456789"},
  "timestamp": {"S": "2017-11-20T06:00:00.000Z"},
  "current_temp": {"N": "65"},
  "target_temp": {"N": "70"},
  "humidity": {"N": "45"}
}

You can use the accessKeyId, secretAccessKey, and sessionToken retrieved from the output of the curl command to sign a request that writes the temperature and humidity record to the DynamoDB table. Use the following commands to accomplish this.

export AWS_ACCESS_KEY_ID=<access-key-id>
export AWS_SECRET_ACCESS_KEY=<secret-access-key>
export AWS_SESSION_TOKEN=<session-token>
aws dynamodb put-item --table-name MyHomeThermostat --item file://item.json --return-consumed-capacity TOTAL

The following is sample output of the put-item command.

{
    "ConsumedCapacity": {
        "CapacityUnits": 1.0, 
        "TableName": "MyHomeThermostat"
    }
}

Conclusion

In this blog post, I demonstrated how to retrieve a security token by using an X.509 certificate and then writing an item to a DynamoDB table by using the security token. Similarly, you could run applications on surveillance cameras or sensor devices that exchange the X.509 certificate for an AWS security token and use the token to upload video streams to Amazon Kinesis or telemetry data to Amazon CloudWatch.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the AWS IoT forum.

– Ramkishore

Easier way to control access to AWS regions using IAM policies

2018-04-26 Sulay Shah

Post Syndicated from Sulay Shah original https://aws.amazon.com/blogs/security/easier-way-to-control-access-to-aws-regions-using-iam-policies/

We made it easier for you to comply with regulatory standards by controlling access to AWS Regions using IAM policies. For example, if your company requires users to create resources in a specific AWS region, you can now add a new condition to the IAM policies you attach to your IAM principal (user or role) to enforce this for all AWS services. In this post, I review conditions in policies, introduce the new condition, and review a policy example to demonstrate how you can control access across multiple AWS services to a specific region.

Condition concepts

Before I introduce the new condition, let’s review the condition element of an IAM policy. A condition is an optional IAM policy element that lets you specify special circumstances under which the policy grants or denies permission. A condition includes a condition key, operator, and value for the condition. There are two types of conditions: service-specific conditions and global conditions. Service-specific conditions are specific to certain actions in an AWS service. For example, the condition key ec2:InstanceType supports specific EC2 actions. Global conditions support all actions across all AWS services.

Now that I’ve reviewed the condition element in an IAM policy, let me introduce the new condition.

AWS:RequestedRegion condition key

The new global condition key, , supports all actions across all AWS services. You can use any string operator and specify any AWS region for its value.

Condition key	Description	Operator(s)	Value
aws:RequestedRegion	Allows you to specify the region to which the IAM principal (user or role) can make API calls	All string operators (for example, StringEquals	Any AWS region (for example, us-east-1)

I’ll now demonstrate the use of the new global condition key.

Example: Policy with region-level control

Let’s say a group of software developers in my organization is working on a project using Amazon EC2 and Amazon RDS. The project requires a web server running on an EC2 instance using Amazon Linux and a MySQL database instance in RDS. The developers also want to test Amazon Lambda, an event-driven platform, to retrieve data from the MySQL DB instance in RDS for future use.

My organization requires all the AWS resources to remain in the Frankfurt, eu-central-1, region. To make sure this project follows these guidelines, I create a single IAM policy for all the AWS services that this group is going to use and apply the new global condition key aws:RequestedRegion for all the services. This way I can ensure that any new EC2 instances launched or any database instances created using RDS are in Frankfurt. This policy also ensures that any Lambda functions this group creates for testing are also in the Frankfurt region.


{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "ec2:DescribeAccountAttributes",
                "ec2:DescribeAvailabilityZones",
                "ec2:DescribeInternetGateways",
                "ec2:DescribeSecurityGroups",
                "ec2:DescribeSubnets",
                "ec2:DescribeVpcAttribute",
                "ec2:DescribeVpcs",
                "ec2:DescribeInstances",
                "ec2:DescribeImages",
                "ec2:DescribeKeyPairs",
                "rds:Describe*",
                "iam:ListRolePolicies",
                "iam:ListRoles",
                "iam:GetRole",
                "iam:ListInstanceProfiles",
                "iam:AttachRolePolicy",
                "lambda:GetAccountSettings"
            ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [
                "ec2:RunInstances",
                "rds:CreateDBInstance",
                "rds:CreateDBCluster",
                "lambda:CreateFunction",
                "lambda:InvokeFunction"
            ],
            "Resource": "*",
      "Condition": {"StringEquals": {"aws:RequestedRegion": "eu-central-1"}}

        },
        {
            "Effect": "Allow",
            "Action": [
                "iam:PassRole"
            ],
            "Resource": "arn:aws:iam::account-id:role/*"
        }
    ]
}

The first statement in the above example contains all the read-only actions that let my developers use the console for EC2, RDS, and Lambda. The permissions for IAM-related actions are required to launch EC2 instances with a role, enable enhanced monitoring in RDS, and for AWS Lambda to assume the IAM execution role to execute the Lambda function. I’ve combined all the read-only actions into a single statement for simplicity. The second statement is where I give write access to my developers for the three services and restrict the write access to the Frankfurt region using the aws:RequestedRegion condition key. You can also list multiple AWS regions with the new condition key if your developers are allowed to create resources in multiple regions. The third statement grants permissions for the IAM action iam:PassRole required by AWS Lambda. For more information on allowing users to create a Lambda function, see Using Identity-Based Policies for AWS Lambda.

Summary

You can now use the aws:RequestedRegion global condition key in your IAM policies to specify the region to which the IAM principal (user or role) can invoke an API call. This capability makes it easier for you to restrict the AWS regions your IAM principals can use to comply with regulatory standards and improve account security. For more information about this global condition key and policy examples using aws:RequestedRegion, see the IAM documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about or suggestions for this solution, start a new thread on the IAM forum.

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Store, Protect, Optimize Your Healthcare Data with AWS

2018-04-26 Stephen Jepsen

Post Syndicated from Stephen Jepsen original https://aws.amazon.com/blogs/architecture/store-protect-optimize-your-healthcare-data-with-aws/

This blog post was co-authored by Ujjwal Ratan, a senior AI/ML solutions architect on the global life sciences team.

Healthcare data is generated at an ever-increasing rate and is predicted to reach 35 zettabytes by 2020. Being able to cost-effectively and securely manage this data whether for patient care, research or legal reasons is increasingly important for healthcare providers.

Healthcare providers must have the ability to ingest, store and protect large volumes of data including clinical, genomic, device, financial, supply chain, and claims. AWS is well-suited to this data deluge with a wide variety of ingestion, storage and security services (e.g. AWS Direct Connect, Amazon Kinesis Streams, Amazon S3, Amazon Macie) for customers to handle their healthcare data. In a recent Healthcare IT News article, healthcare thought-leader, John Halamka, noted, “I predict that five years from now none of us will have datacenters. We’re going to go out to the cloud to find EHRs, clinical decision support, analytics.”

I realize simply storing this data is challenging enough. Magnifying the problem is the fact that healthcare data is increasingly attractive to cyber attackers, making security a top priority. According to Mariya Yao in her Forbes column, it is estimated that individual medical records can be worth hundreds or even thousands of dollars on the black market.

In this first of a 2-part post, I will address the value that AWS can bring to customers for ingesting, storing and protecting provider’s healthcare data. I will describe key components of any cloud-based healthcare workload and the services AWS provides to meet these requirements. In part 2 of this post we will dive deep into the AWS services used for advanced analytics, artificial intelligence and machine learning.

The data tsunami is upon us

So where is this data coming from? In addition to the ubiquitous electronic health record (EHR), the sources of this data include:

genomic sequencers
devices such as MRIs, x-rays and ultrasounds
sensors and wearables for patients
medical equipment telemetry
mobile applications

Additional sources of data come from non-clinical, operational systems such as:

human resources
finance
supply chain
claims and billing

Data from these sources can be structured (e.g., claims data) as well as unstructured (e.g., clinician notes). Some data comes across in streams such as that taken from patient monitors, while some comes in batch form. Still other data comes in near-real time such as HL7 messages. All of this data has retention policies dictating how long it must be stored. Much of this data is stored in perpetuity as many systems in use today have no purge mechanism. AWS has services to manage all these data types as well as their retention, security and access policies.

Imaging is a significant contributor to this data tsunami. Increasing demand for early-stage diagnoses along with aging populations drive increasing demand for images from CT, PET, MRI, ultrasound, digital pathology, X-ray and fluoroscopy. For example, a thin-slice CT image can be hundreds of megabytes. Increasing demand and strict retention policies make storage costly.

Due to the plummeting cost of gene sequencing, molecular diagnostics (including liquid biopsy) is a large contributor to this data deluge. Many predict that as the value of molecular testing becomes more identifiable, the reimbursement models will change and it will increasingly become the standard of care. According to the Washington Post article “Sequencing the Genome Creates so Much Data We Don’t Know What to do with It,”

“Some researchers predict that up to one billion people will have their genome sequenced by 2025 generating up to 40 exabytes of data per year.”

Although genomics is primarily used for oncology diagnostics today, it’s also used for other purposes, pharmacogenomics — used to understand how an individual will metabolize a medication.

Reference Architecture

It is increasingly challenging for the typical hospital, clinic or physician practice to securely store, process and manage this data without cloud adoption.

Amazon has a variety of ingestion techniques depending on the nature of the data including size, frequency and structure. AWS Snowball and AWS Snowmachine are appropriate for extremely-large, secure data transfers whether one time or episodic. AWS Glue is a fully-managed ETL service for securely moving data from on-premise to AWS and Amazon Kinesis can be used for ingesting streaming data.

Amazon S3, Amazon S3 IA, and Amazon Glacier are economical, data-storage services with a pay-as-you-go pricing model that expand (or shrink) with the customer’s requirements.

The above architecture has four distinct components – ingestion, storage, security, and analytics. In this post I will dive deeper into the first three components, namely ingestion, storage and security. In part 2, I will look at how to use AWS’ analytics services to draw value on, and optimize, your healthcare data.

Ingestion

A typical provider data center will consist of many systems with varied datasets. AWS provides multiple tools and services to effectively and securely connect to these data sources and ingest data in various formats. The customers can choose from a range of services and use them in accordance with the use case.

For use cases involving one-time (or periodic), very large data migrations into AWS, customers can take advantage of AWS Snowball devices. These devices come in two sizes, 50 TB and 80 TB and can be combined together to create a petabyte scale data transfer solution.

The devices are easy to connect and load and they are shipped to AWS avoiding the network bottlenecks associated with such large-scale data migrations. The devices are extremely secure supporting 256-bit encryption and come in a tamper-resistant enclosure. AWS Snowball imports data in Amazon S3 which can then interface with other AWS compute services to process that data in a scalable manner.

For use cases involving a need to store a portion of datasets on premises for active use and offload the rest on AWS, the Amazon storage gateway service can be used. The service allows you to seamlessly integrate on premises applications via standard storage protocols like iSCSI or NFS mounted on a gateway appliance. It supports a file interface, a volume interface and a tape interface which can be utilized for a range of use cases like disaster recovery, backup and archiving, cloud bursting, storage tiering and migration.

The AWS Storage Gateway appliance can use the AWS Direct Connect service to establish a dedicated network connection from the on premises data center to AWS.

Specific Industry Use Cases

By using the AWS proposed reference architecture for disaster recovery, healthcare providers can ensure their data assets are securely stored on the cloud and are easily accessible in the event of a disaster. The “AWS Disaster Recovery” whitepaper includes details on options available to customers based on their desired recovery time objective (RTO) and recovery point objective (RPO).

AWS is an ideal destination for offloading large volumes of less-frequently-accessed data. These datasets are rarely used in active compute operations but are exceedingly important to retain for reasons like compliance. By storing these datasets on AWS, customers can take advantage of the highly-durable platform to securely store their data and also retrieve them easily when they need to. For more details on how AWS enables customers to run back and archival use cases on AWS, please refer to the following set of whitepapers.

A healthcare provider may have a variety of databases spread throughout the hospital system supporting critical applications such as EHR, PACS, finance and many more. These datasets often need to be aggregated to derive information and calculate metrics to optimize business processes. AWS Glue is a fully-managed Extract, Transform and Load (ETL) service that can read data from a JDBC-enabled, on-premise database and transfer the datasets into AWS services like Amazon S3, Amazon Redshift and Amazon RDS. This allows customers to create transformation workflows that integrate smaller datasets from multiple sources and aggregates them on AWS.

Healthcare providers deal with a variety of streaming datasets which often have to be analyzed in near real time. These datasets come from a variety of sources such as sensors, messaging buses and social media, and often do not adhere to an industry standard. The Amazon Kinesis suite of services, that includes Amazon Kinesis Streams, Amazon Kinesis Firehose, and Amazon Kinesis Analytics, are the ideal set of services to accomplish the task of deriving value from streaming data.

Example: Using AWS Glue to de-identify and ingest healthcare data into S3
Let’s consider a scenario in which a provider maintains patient records in a database they want to ingest into S3. The provider also wants to de-identify the data by stripping personally- identifiable attributes and store the non-identifiable information in an S3 bucket. This bucket is different from the one that contains identifiable information. Doing this allows the healthcare provider to separate sensitive information with more restrictions set up via S3 bucket policies.

To ingest records into S3, we create a Glue job that reads from the source database using a Glue connection. The connection is also used by a Glue crawler to populate the Glue data catalog with the schema of the source database. We will use the Glue development endpoint and a zeppelin notebook server on EC2 to develop and execute the job.

Step 1: Import the necessary libraries and also set a glue context which is a wrapper on the spark context:

Step 2: Create a dataframe from the source data. I call the dataframe “readmissionsdata”. Here is what the schema would look like:

Step 3: Now select the columns that contains indentifiable information and store it in a new dataframe. Call the new dataframe “phi”.

Step 4: Non-PHI columns are stored in a separate dataframe. Call this dataframe “nonphi”.

Step 5: Write the two dataframes into two separate S3 buckets

Once successfully executed, the PHI and non-PHI attributes are stored in two separate files in two separate buckets that can be individually maintained.

Storage

In 2016, 327 healthcare providers reported a protected health information (PHI) breach, affecting 16.4m patient records[1]. There have been 342 data breaches reported in 2017 — involving 3.2 million patient records.[2]

To date, AWS has released 51 HIPAA-eligible services to help customers address security challenges and is in the process of making many more services HIPAA-eligible. These HIPAA-eligible services (along with all other AWS services) help customers build solutions that comply with HIPAA security and auditing requirements. A catalogue of HIPAA-enabled services can be found at AWS HIPAA-eligible services. It is important to note that AWS manages physical and logical access controls for the AWS boundary. However, the overall security of your workloads is a shared responsibility, where you are responsible for controlling user access to content on your AWS accounts.

AWS storage services allow you to store data efficiently while maintaining high durability and scalability. By using Amazon S3 as the central storage layer, you can take advantage of the Amazon S3 storage management features to get operational metrics on your data sets and transition them between various storage classes to save costs. By tagging objects on Amazon S3, you can build a governance layer on Amazon S3 to grant role based access to objects using Amazon IAM and Amazon S3 bucket policies.

To learn more about the Amazon S3 storage management features, see the following link.

Security

In the example above, we are storing the PHI information in a bucket named “phi.” Now, we want to protect this information to make sure its encrypted, does not have unauthorized access, and all access requests to the data are logged.

Encryption: S3 provides settings to enable default encryption on a bucket. This ensures any object in the bucket is encrypted by default.

Logging: S3 provides object level logging that can be used to capture all API calls to the object. The API calls are logged in cloudtrail for easy access and consolidation. Moreover, it also supports events to proactively alert customers of read and write operations.

Access control: Customers can use S3 bucket policies and IAM policies to restrict access to the phi bucket. It can also put a restriction to enforce multi-factor authentication on the bucket. For example, the following policy enforces multi-factor authentication on the phi bucket:

"Version": "2012-10-17",
    "Id": "123",
    "Statement": [
      {
        "Sid": "",
        "Effect": "Deny",
        "Principal": "*",
        "Action": "s3:*",
        "Resource": "arn:aws:s3:::phi/*",
        "Condition": { "Null": { "aws:MultiFactorAuthAge": true } }
      },
      {
        "Sid": "",
        "Effect": "Allow",
        "Principal": "*",
        "Action": ["s3:GetObject"],
        "Resource": "arn:aws:s3:::phi/*"
      }
    ]
 }

Conclusion

In Part 1 of this blog, we detailed the ingestion, storage, security and management of healthcare data on AWS. Stay tuned for part two where we are going to dive deep into optimizing the data for analytics and machine learning.

Please visit Healthcare Providers and Insurers in the Cloud for additional information.

[1] “Largest Healthcare Data Breaches of 2016.” HIPAA Journal, 29 Aug. 2017
[2] “Largest Healthcare Data Breaches of 2017.” HIPAA Journal, 8 Mar. 2018

About the Author

Stephen Jepsen is a Global HCLS Practice Manager in AWS Professional Services.

Rotate Amazon RDS database credentials automatically with AWS Secrets Manager

2018-04-05 Apurv Awasthi

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

Recently, we launched AWS Secrets Manager, a service that makes it easier to rotate, manage, and retrieve database credentials, API keys, and other secrets throughout their lifecycle. You can configure Secrets Manager to rotate secrets automatically, which can help you meet your security and compliance needs. Secrets Manager offers built-in integrations for MySQL, PostgreSQL, and Amazon Aurora on Amazon RDS, and can rotate credentials for these databases natively. You can control access to your secrets by using fine-grained AWS Identity and Access Management (IAM) policies. To retrieve secrets, employees replace plaintext secrets with a call to Secrets Manager APIs, eliminating the need to hard-code secrets in source code or update configuration files and redeploy code when secrets are rotated.

In this post, I introduce the key features of Secrets Manager. I then show you how to store a database credential for a MySQL database hosted on Amazon RDS and how your applications can access this secret. Finally, I show you how to configure Secrets Manager to rotate this secret automatically.

Key features of Secrets Manager

These features include the ability to:

Rotate secrets safely. You can configure Secrets Manager to rotate secrets automatically without disrupting your applications. Secrets Manager offers built-in integrations for rotating credentials for Amazon RDS databases for MySQL, PostgreSQL, and Amazon Aurora. You can extend Secrets Manager to meet your custom rotation requirements by creating an AWS Lambda function to rotate other types of secrets. For example, you can create an AWS Lambda function to rotate OAuth tokens used in a mobile application. Users and applications retrieve the secret from Secrets Manager, eliminating the need to email secrets to developers or update and redeploy applications after AWS Secrets Manager rotates a secret.
Secure and manage secrets centrally. You can store, view, and manage all your secrets. By default, Secrets Manager encrypts these secrets with encryption keys that you own and control. Using fine-grained IAM policies, you can control access to secrets. For example, you can require developers to provide a second factor of authentication when they attempt to retrieve a production database credential. You can also tag secrets to help you discover, organize, and control access to secrets used throughout your organization.
Monitor and audit easily. Secrets Manager integrates with AWS logging and monitoring services to enable you to meet your security and compliance requirements. For example, you can audit AWS CloudTrail logs to see when Secrets Manager rotated a secret or configure AWS CloudWatch Events to alert you when an administrator deletes a secret.
Pay as you go. Pay for the secrets you store in Secrets Manager and for the use of these secrets; there are no long-term contracts or licensing fees.

Get started with Secrets Manager

Now that you’re familiar with the key features, I’ll show you how to store the credential for a MySQL database hosted on Amazon RDS. To demonstrate how to retrieve and use the secret, I use a python application running on Amazon EC2 that requires this database credential to access the MySQL instance. Finally, I show how to configure Secrets Manager to rotate this database credential automatically. Let’s get started.

Phase 1: Store a secret in Secrets Manager

Open the Secrets Manager console and select Store a new secret.
I select Credentials for RDS database because I’m storing credentials for a MySQL database hosted on Amazon RDS. For this example, I store the credentials for the database superuser. I start by securing the superuser because it’s the most powerful database credential and has full access over the database.

Note: For this example, you need permissions to store secrets in Secrets Manager. To grant these permissions, you can use the AWSSecretsManagerReadWriteAccess managed policy. Read the AWS Secrets Manager Documentation for more information about the minimum IAM permissions required to store a secret.
Next, I review the encryption setting and choose to use the default encryption settings. Secrets Manager will encrypt this secret using the Secrets Manager DefaultEncryptionKeyDefaultEncryptionKey in this account. Alternatively, I can choose to encrypt using a customer master key (CMK) that I have stored in AWS KMS.
Next, I view the list of Amazon RDS instances in my account and select the database this credential accesses. For this example, I select the DB instance mysql-rds-database, and then I select Next.
In this step, I specify values for Secret Name and Description. For this example, I use Applications/MyApp/MySQL-RDS-Database as the name and enter a description of this secret, and then select Next.
For the next step, I keep the default setting Disable automatic rotation because my secret is used by my application running on Amazon EC2. I’ll enable rotation after I’ve updated my application (see Phase 2 below) to use Secrets Manager APIs to retrieve secrets. I then select Next.

Note: If you’re storing a secret that you’re not using in your application, select Enable automatic rotation. See our AWS Secrets Manager getting started guide on rotation for details.
Review the information on the next screen and, if everything looks correct, select Store. We’ve now successfully stored a secret in Secrets Manager.
Next, I select See sample code.
Take note of the code samples provided. I will use this code to update my application to retrieve the secret using Secrets Manager APIs.

Phase 2: Update an application to retrieve secret from Secrets Manager

Now that I have stored the secret in Secrets Manager, I update my application to retrieve the database credential from Secrets Manager instead of hard coding this information in a configuration file or source code. For this example, I show how to configure a python application to retrieve this secret from Secrets Manager.

I connect to my Amazon EC2 instance via Secure Shell (SSH).
Previously, I configured my application to retrieve the database user name and password from the configuration file. Below is the source code for my application.
import MySQLdb import config def no_secrets_manager_sample() # Get the user name, password, and database connection information from a config file. database = config.database user_name = config.user_name password = config.password

# Use the user name, password, and database connection information to connect to the database db = MySQLdb.connect(database.endpoint, user_name, password, database.db_name, database.port)
I use the sample code from Phase 1 above and update my application to retrieve the user name and password from Secrets Manager. This code sets up the client and retrieves and decrypts the secret Applications/MyApp/MySQL-RDS-Database. I’ve added comments to the code to make the code easier to understand.
# Use the code snippet provided by Secrets Manager. import boto3 from botocore.exceptions import ClientError def get_secret(): #Define the secret you want to retrieve secret_name = "Applications/MyApp/MySQL-RDS-Database" #Define the Secrets mManager end-point your code should use. endpoint_url = "https://secretsmanager.us-east-1.amazonaws.com" region_name = "us-east-1" #Setup the client session = boto3.session.Session() client = session.client( service_name='secretsmanager', region_name=region_name, endpoint_url=endpoint_url ) #Use the client to retrieve the secret try: get_secret_value_response = client.get_secret_value( SecretId=secret_name ) #Error handling to make it easier for your code to tolerate faults except ClientError as e: if e.response['Error']['Code'] == 'ResourceNotFoundException': print("The requested secret " + secret_name + " was not found") elif e.response['Error']['Code'] == 'InvalidRequestException': print("The request was invalid due to:", e) elif e.response['Error']['Code'] == 'InvalidParameterException': print("The request had invalid params:", e) else: # Decrypted secret using the associated KMS CMK # Depending on whether the secret was a string or binary, one of these fields will be populated if 'SecretString' in get_secret_value_response: secret = get_secret_value_response['SecretString'] else: binary_secret_data = get_secret_value_response['SecretBinary']

# Your code goes here.
Applications require permissions to access Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to obtain access to AWS services. I will attach the following policy to my IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secret from Secrets Manager. This policy also uses the resource element to limit my application to read only the Applications/MyApp/MySQL-RDS-Database secret from Secrets Manager. You can visit the AWS Secrets Manager Documentation to understand the minimum IAM permissions required to retrieve a secret.
{ "Version": "2012-10-17", "Statement": { "Sid": "RetrieveDbCredentialFromSecretsManager", "Effect": "Allow", "Action": "secretsmanager:GetSecretValue", "Resource": "arn:aws:secretsmanager:::secret:Applications/MyApp/MySQL-RDS-Database" } }

Phase 3: Enable Rotation for Your Secret

Rotating secrets periodically is a security best practice because it reduces the risk of misuse of secrets. Secrets Manager makes it easy to follow this security best practice and offers built-in integrations for rotating credentials for MySQL, PostgreSQL, and Amazon Aurora databases hosted on Amazon RDS. When you enable rotation, Secrets Manager creates a Lambda function and attaches an IAM role to this function to execute rotations on a schedule you define.

Note: Configuring rotation is a privileged action that requires several IAM permissions and you should only grant this access to trusted individuals. To grant these permissions, you can use the AWS IAMFullAccess managed policy.

Next, I show you how to configure Secrets Manager to rotate the secret Applications/MyApp/MySQL-RDS-Database automatically.

From the Secrets Manager console, I go to the list of secrets and choose the secret I created in the first step Applications/MyApp/MySQL-RDS-Database.
I scroll to Rotation configuration, and then select Edit rotation.
To enable rotation, I select Enable automatic rotation. I then choose how frequently I want Secrets Manager to rotate this secret. For this example, I set the rotation interval to 60 days.
Next, Secrets Manager requires permissions to rotate this secret on your behalf. Because I’m storing the superuser database credential, Secrets Manager can use this credential to perform rotations. Therefore, I select Use the secret that I provided in step 1, and then select Next.
The banner on the next screen confirms that I have successfully configured rotation and the first rotation is in progress, which enables you to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 60 days.

Summary

I introduced AWS Secrets Manager, explained the key benefits, and showed you how to help meet your compliance requirements by configuring AWS Secrets Manager to rotate database credentials automatically on your behalf. Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, visit the Secrets Manager console. To learn more, visit Secrets Manager documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Secrets Manager forum.

Want more AWS Security news? Follow us on Twitter.

AWS Certificate Manager Launches Private Certificate Authority

2018-04-04 Randall Hunt

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-certificate-manager-launches-private-certificate-authority/

Today we’re launching a new feature for AWS Certificate Manager (ACM), Private Certificate Authority (CA). This new service allows ACM to act as a private subordinate CA. Previously, if a customer wanted to use private certificates, they needed specialized infrastructure and security expertise that could be expensive to maintain and operate. ACM Private CA builds on ACM’s existing certificate capabilities to help you easily and securely manage the lifecycle of your private certificates with pay as you go pricing. This enables developers to provision certificates in just a few simple API calls while administrators have a central CA management console and fine grained access control through granular IAM policies. ACM Private CA keys are stored securely in AWS managed hardware security modules (HSMs) that adhere to FIPS 140-2 Level 3 security standards. ACM Private CA automatically maintains certificate revocation lists (CRLs) in Amazon Simple Storage Service (S3) and lets administrators generate audit reports of certificate creation with the API or console. This service is packed full of features so let’s jump in and provision a CA.

Provisioning a Private Certificate Authority (CA)

First, I’ll navigate to the ACM console in my region and select the new Private CAs section in the sidebar. From there I’ll click Get Started to start the CA wizard. For now, I only have the option to provision a subordinate CA so we’ll select that and use my super secure desktop as the root CA and click Next. This isn’t what I would do in a production setting but it will work for testing out our private CA.

Now, I’ll configure the CA with some common details. The most important thing here is the Common Name which I’ll set as secure.internal to represent my internal domain.

Now I need to choose my key algorithm. You should choose the best algorithm for your needs but know that ACM has a limitation today that it can only manage certificates that chain up to to RSA CAs. For now, I’ll go with RSA 2048 bit and click Next.

In this next screen, I’m able to configure my certificate revocation list (CRL). CRLs are essential for notifying clients in the case that a certificate has been compromised before certificate expiration. ACM will maintain the revocation list for me and I have the option of routing my S3 bucket to a custome domain. In this case I’ll create a new S3 bucket to store my CRL in and click Next.

Finally, I’ll review all the details to make sure I didn’t make any typos and click Confirm and create.

A few seconds later and I’m greeted with a fancy screen saying I successfully provisioned a certificate authority. Hooray! I’m not done yet though. I still need to activate my CA by creating a certificate signing request (CSR) and signing that with my root CA. I’ll click Get started to begin that process.

Now I’ll copy the CSR or download it to a server or desktop that has access to my root CA (or potentially another subordinate – so long as it chains to a trusted root for my clients).

Now I can use a tool like openssl to sign my cert and generate the certificate chain.


$openssl ca -config openssl_root.cnf -extensions v3_intermediate_ca -days 3650 -notext -md sha256 -in csr/CSR.pem -out certs/subordinate_cert.pem
Using configuration from openssl_root.cnf
Enter pass phrase for /Users/randhunt/dev/amzn/ca/private/root_private_key.pem:
Check that the request matches the signature
Signature ok
The Subject's Distinguished Name is as follows
stateOrProvinceName   :ASN.1 12:'Washington'
localityName          :ASN.1 12:'Seattle'
organizationName      :ASN.1 12:'Amazon'
organizationalUnitName:ASN.1 12:'Engineering'
commonName            :ASN.1 12:'secure.internal'
Certificate is to be certified until Mar 31 06:05:30 2028 GMT (3650 days)
Sign the certificate? [y/n]:y


1 out of 1 certificate requests certified, commit? [y/n]y
Write out database with 1 new entries
Data Base Updated

After that I’ll copy my subordinate_cert.pem and certificate chain back into the console. and click Next.

Finally, I’ll review all the information and click Confirm and import. I should see a screen like the one below that shows my CA has been activated successfully.

Now that I have a private CA we can provision private certificates by hopping back to the ACM console and creating a new certificate. After clicking create a new certificate I’ll select the radio button Request a private certificate then I’ll click Request a certificate.

From there it’s just similar to provisioning a normal certificate in ACM.

Now I have a private certificate that I can bind to my ELBs, CloudFront Distributions, API Gateways, and more. I can also export the certificate for use on embedded devices or outside of ACM managed environments.

Available Now
ACM Private CA is a service in and of itself and it is packed full of features that won’t fit into a blog post. I strongly encourage the interested readers to go through the developer guide and familiarize themselves with certificate based security. ACM Private CA is available in in US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), EU (Frankfurt) and EU (Ireland). Private CAs cost $400 per month (prorated) for each private CA. You are not charged for certificates created and maintained in ACM but you are charged for certificates where you have access to the private key (exported or created outside of ACM). The pricing per certificate is tiered starting at $0.75 per certificate for the first 1000 certificates and going down to $0.001 per certificate after 10,000 certificates.

I’m excited to see administrators and developers take advantage of this new service. As always please let us know what you think of this service on Twitter or in the comments below.

– Randall

AWS Secrets Manager: Store, Distribute, and Rotate Credentials Securely

2018-04-04 Randall Hunt

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/aws-secrets-manager-store-distribute-and-rotate-credentials-securely/

Today we’re launching AWS Secrets Manager which makes it easy to store and retrieve your secrets via API or the AWS Command Line Interface (CLI) and rotate your credentials with built-in or custom AWS Lambda functions. Managing application secrets like database credentials, passwords, or API Keys is easy when you’re working locally with one machine and one application. As you grow and scale to many distributed microservices, it becomes a daunting task to securely store, distribute, rotate, and consume secrets. Previously, customers needed to provision and maintain additional infrastructure solely for secrets management which could incur costs and introduce unneeded complexity into systems.

AWS Secrets Manager

Imagine that I have an application that takes incoming tweets from Twitter and stores them in an Amazon Aurora database. Previously, I would have had to request a username and password from my database administrator and embed those credentials in environment variables or, in my race to production, even in the application itself. I would also need to have our social media manager create the Twitter API credentials and figure out how to store those. This is a fairly manual process, involving multiple people, that I have to restart every time I want to rotate these credentials. With Secrets Manager my database administrator can provide the credentials in secrets manager once and subsequently rely on a Secrets Manager provided Lambda function to automatically update and rotate those credentials. My social media manager can put the Twitter API keys in Secrets Manager which I can then access with a simple API call and I can even rotate these programmatically with a custom lambda function calling out to the Twitter API. My secrets are encrypted with the KMS key of my choice, and each of these administrators can explicitly grant access to these secrets with with granular IAM policies for individual roles or users.

Let’s take a look at how I would store a secret using the AWS Secrets Manager console. First, I’ll click Store a new secret to get to the new secrets wizard. For my RDS Aurora instance it’s straightforward to simply select the instance and provide the initial username and password to connect to the database.

Next, I’ll fill in a quick description and a name to access my secret by. You can use whatever naming scheme you want here.

Next, we’ll configure rotation to use the Secrets Manager-provided Lambda function to rotate our password every 10 days.

Finally, we’ll review all the details and check out our sample code for storing and retrieving our secret!

Finally I can review the secrets in the console.

Now, if I needed to access these secrets I’d simply call the API.

import json
import boto3
secrets = boto3.client("secretsmanager")
rds = json.dumps(secrets.get_secrets_value("prod/TwitterApp/Database")['SecretString'])
print(rds)

Which would give me the following values:


{'engine': 'mysql',
 'host': 'twitterapp2.abcdefg.us-east-1.rds.amazonaws.com',
 'password': '-)Kw>THISISAFAKEPASSWORD:lg{&sad+Canr',
 'port': 3306,
 'username': 'ranman'}

More than passwords

AWS Secrets Manager works for more than just passwords. I can store OAuth credentials, binary data, and more. Let’s look at storing my Twitter OAuth application keys.

Now, I can define the rotation for these third-party OAuth credentials with a custom AWS Lambda function that can call out to Twitter whenever we need to rotate our credentials.

Custom Rotation

One of the niftiest features of AWS Secrets Manager is custom AWS Lambda functions for credential rotation. This allows you to define completely custom workflows for credentials. Secrets Manager will call your lambda with a payload that includes a Step which specifies which step of the rotation you’re in, a SecretId which specifies which secret the rotation is for, and importantly a ClientRequestToken which is used to ensure idempotency in any changes to the underlying secret.

When you’re rotating secrets you go through a few different steps:

createSecret
setSecret
testSecret
finishSecret

The advantage of these steps is that you can add any kind of approval steps you want for each phase of the rotation. For more details on custom rotation check out the documentation.

Available Now
AWS Secrets Manager is available today in US East (N. Virginia), US East (Ohio), US West (N. California), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), and South America (São Paulo). Secrets are priced at $0.40 per month per secret and $0.05 per 10,000 API calls. I’m looking forward to seeing more users adopt rotating credentials to secure their applications!

– Randall

Tag Amazon EBS Snapshots on Creation and Implement Stronger Security Policies

2018-04-03 Woo Kim

Post Syndicated from Woo Kim original https://aws.amazon.com/blogs/compute/tag-amazon-ebs-snapshots-on-creation-and-implement-stronger-security-policies/

This blog was contributed by Rucha Nene, Sr. Product Manager for Amazon EBS

AWS customers use tags to track ownership of resources, implement compliance protocols, control access to resources via IAM policies, and drive their cost accounting processes. Last year, we made tagging for Amazon EC2 instances and Amazon EBS volumes easier by adding the ability to tag these resources upon creation. We are now extending this capability to EBS snapshots.

Earlier, you could tag your EBS snapshots only after the resource had been created and sometimes, ended up with EBS snapshots in an untagged state if tagging failed. You also could not control the actions that users and groups could take over specific snapshots, or enforce tighter security policies.

To address these issues, we are making tagging for EBS snapshots more flexible and giving customers more control over EBS snapshots by introducing two new capabilities:

Tag on creation for EBS snapshots – You can now specify tags for EBS snapshots as part of the API call that creates the resource or via the Amazon EC2 Console when creating an EBS snapshot.
Resource-level permission and enforced tag usage – The CreateSnapshot, DeleteSnapshot, and ModifySnapshotAttrribute API actions now support IAM resource-level permissions. You can now write IAM policies that mandate the use of specific tags when taking actions on EBS snapshots.

Tag on creation

You can now specify tags for EBS snapshots as part of the API call that creates the resources. The resource creation and the tagging are performed atomically; both must succeed in order for the operation CreateSnapshot to succeed. You no longer need to build tagging scripts that run after EBS snapshots have been created.

Here’s how you specify tags when you create an EBS snapshot, using the console:

Open the Amazon EC2 console at https://console.aws.amazon.com/ec2/.
In the navigation pane, choose Snapshots, Create Snapshot.
On the Create Snapshot page, select the volume for which to create a snapshot.
(Optional) Choose Add tags to your snapshot. For each tag, provide a tag key and a tag value.
Choose Create Snapshot.

Using the AWS CLI:

aws ec2 create-snapshot --volume-id vol-0c0e757e277111f3c --description 'Prod_Backup' --tag-specifications 
'ResourceType=snapshot,Tags=[{Key=costcenter,Value=115},{Key=IsProd,Value=Yes}]'

To learn more, see Using Tags.

Resource-level permissions and enforced tag usage

CreateSnapshot, DeleteSnapshot, and ModifySnapshotAttribute now support resource-level permissions, which allow you to exercise more control over EBS snapshots. You can write IAM policies that give you precise control over access to resources and let you specify which users are able to create snapshots for a given set of volumes. You can also enforce the use of specific tags to help track resources and achieve more accurate cost allocation reporting.

For example, here’s a statement that requires that the costcenter tag (with a value of “115”) be present on the volume from which snapshots are being created. It requires that this tag be applied to all newly created snapshots. In addition, it requires that the created snapshots are tagged with User:username for the customer.

{
   "Version":"2012-10-17",
   "Statement":[
      {
         "Effect":"Allow",
         "Action":"ec2:CreateSnapshot",
         "Resource":"arn:aws:ec2:us-east-1:123456789012:volume/*",
	   "Condition": {
		"StringEquals":{
               "ec2:ResourceTag/costcenter":"115"
}
 }
	
      },
      {
         "Sid":"AllowCreateTaggedSnapshots",
         "Effect":"Allow",
         "Action":"ec2:CreateSnapshot",
         "Resource":"arn:aws:ec2:us-east-1::snapshot/*",
         "Condition":{
            "StringEquals":{
               "aws:RequestTag/costcenter":"115",
		   "aws:RequestTag/User":"${aws:username}"
            },
            "ForAllValues:StringEquals":{
               "aws:TagKeys":[
                  "costcenter",
			"User"
               ]
            }
         }
      },
      {
         "Effect":"Allow",
         "Action":"ec2:CreateTags",
         "Resource":"arn:aws:ec2:us-east-1::snapshot/*",
         "Condition":{
            "StringEquals":{
               "ec2:CreateAction":"CreateSnapshot"
            }
         }
      }
   ]
}

To implement stronger compliance and security policies, you could also restrict access to DeleteSnapshot, if the resource is not tagged with the user’s name. Here’s a statement that allows the deletion of a snapshot only if the snapshot is tagged with User:username for the customer.

{
   "Version":"2012-10-17",
   "Statement":[
      {
         "Effect":"Allow",
         "Action":"ec2:DeleteSnapshot",
         "Resource":"arn:aws:ec2:us-east-1::snapshot/*",
         "Condition":{
            "StringEquals":{
               "ec2:ResourceTag/User":"${aws:username}"
            }
         }
      }
   ]
}

To learn more and to see some sample policies, see IAM Policies for Amazon EC2 and Working with Snapshots.

Available Now

These new features are available now in all AWS Regions. You can start using it today from the Amazon EC2 Console, AWS Command Line Interface (CLI), or the AWS APIs.

Taking Advantage of Amazon EC2 Spot Instance Interruption Notices

2018-03-09 Chad Schmutzer

Post Syndicated from Chad Schmutzer original https://aws.amazon.com/blogs/compute/taking-advantage-of-amazon-ec2-spot-instance-interruption-notices/

Amazon EC2 Spot Instances are spare compute capacity in the AWS Cloud available to you at steep discounts compared to On-Demand prices. The only difference between On-Demand Instances and Spot Instances is that Spot Instances can be interrupted by Amazon EC2 with two minutes of notification when EC2 needs the capacity back.

Customers have been taking advantage of Spot Instance interruption notices available via the instance metadata service since January 2015 to orchestrate their workloads seamlessly around any potential interruptions. Examples include saving the state of a job, detaching from a load balancer, or draining containers. Needless to say, the two-minute Spot Instance interruption notice is a powerful tool when using Spot Instances.

In January 2018, the Spot Instance interruption notice also became available as an event in Amazon CloudWatch Events. This allows targets such as AWS Lambda functions or Amazon SNS topics to process Spot Instance interruption notices by creating a CloudWatch Events rule to monitor for the notice.

In this post, I walk through an example use case for taking advantage of Spot Instance interruption notices in CloudWatch Events to automatically deregister Spot Instances from an Elastic Load Balancing Application Load Balancer.

Architecture

In this reference architecture, you use an AWS CloudFormation template to deploy the following:

An Amazon Virtual Private Cloud (Amazon VPC) with subnets in two Availability Zones
An Application Load Balancer with a listener and target group
An Amazon CloudWatch Events rule
An AWS Lambda function
An Amazon Simple Notification Service (SNS) topic
Associated IAM policies and roles for all of the above

After the AWS CloudFormation stack deployment is complete, you then create an Amazon EC2 Spot Fleet request diversified across both Availability Zones and use a couple of recent Spot Fleet features: Elastic Load Balancing integration and Tagging Spot Fleet Instances.

When any of the Spot Instances receives an interruption notice, Spot Fleet sends the event to CloudWatch Events. The CloudWatch Events rule then notifies both targets, the Lambda function and SNS topic. The Lambda function detaches the Spot Instance from the Application Load Balancer target group, taking advantage of nearly a full two minutes of connection draining before the instance is interrupted. The SNS topic also receives a message, and is provided as an example for the reader to use as an exercise.

EC2 Spot Instance Interruption Notices Reference Architecture Diagram

Walkthrough

To complete this walkthrough, have the AWS CLI installed and configured, as well as the ability to launch CloudFormation stacks.

Launch the stack

Go ahead and launch the CloudFormation stack. You can check it out from GitHub, or grab the template directly. In this post, I use the stack name “spot-spin-cwe“, but feel free to use any name you like. Just remember to change it in the instructions.

$ git clone https://github.com/awslabs/ec2-spot-labs.git

$ aws cloudformation create-stack --stack-name spot-spin-cwe \
  --template-body file://ec2-spot-labs/ec2-spot-interruption-notice-cloudwatch-events/ec2-spot-interruption-notice-cloudwatch-events.yaml \
  --capabilities CAPABILITY_IAM

You should receive a StackId value in return, confirming the stack is launching.

{
  "StackId": "arn:aws:cloudformation:us-east-1:123456789012:stack/spot-spin-cwe/083e7ad0-0ade-11e8-9e36-500c219ab02a"
}

Review the details

Here are the details of the architecture being launched by the stack.

IAM permissions

Give permissions to a few components in the architecture:

The Lambda function
The CloudWatch Events rule
The Spot Fleet

The Lambda function needs basic Lambda function execution permissions so that it can write logs to CloudWatch Logs. You can use the AWS managed policy for this. It also needs to describe EC2 tags as well as deregister targets within Elastic Load Balancing. You can create a custom policy for these.

lambdaFunctionRole:
    Properties:
      AssumeRolePolicyDocument:
        Statement:
        - Action:
          - sts:AssumeRole
          Effect: Allow
          Principal:
            Service:
            - lambda.amazonaws.com
        Version: 2012-10-17
      ManagedPolicyArns:
      - arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole
      Path: /
      Policies:
      - PolicyDocument:
          Statement:
          - Action: elasticloadbalancing:DeregisterTargets
            Effect: Allow
            Resource: '*'
          - Action: ec2:DescribeTags
            Effect: Allow
            Resource: '*'
          Version: '2012-10-17'
        PolicyName:
          Fn::Join:
          - '-'
          - - Ref: AWS::StackName
            - lambdaFunctionRole
    Type: AWS::IAM::Role

Allow CloudWatch Events to call the Lambda function and publish to the SNS topic.

lambdaFunctionPermission:
    Properties:
      Action: lambda:InvokeFunction
      FunctionName:
        Fn::GetAtt:
        - lambdaFunction
        - Arn
      Principal: events.amazonaws.com
      SourceArn:
        Fn::GetAtt:
        - eventRule
        - Arn
    Type: AWS::Lambda::Permission

snsTopicPolicy:
    DependsOn:
    - snsTopic
    Properties:
      PolicyDocument:
        Id:
          Fn::GetAtt:
          - snsTopic
          - TopicName
        Statement:
        - Action: sns:Publish
          Effect: Allow
          Principal:
            Service:
            - events.amazonaws.com
          Resource:
            Ref: snsTopic
        Version: '2012-10-17'
      Topics:
      - Ref: snsTopic
    Type: AWS::SNS::TopicPolicy

Finally, Spot Fleet needs permissions to request Spot Instances, tag, and register targets in Elastic Load Balancing. You can tap into an AWS managed policy for this.

spotFleetRole:
    Properties:
      AssumeRolePolicyDocument:
        Statement:
        - Action:
          - sts:AssumeRole
          Effect: Allow
          Principal:
            Service:
            - spotfleet.amazonaws.com
        Version: 2012-10-17
      ManagedPolicyArns:
      - arn:aws:iam::aws:policy/service-role/AmazonEC2SpotFleetTaggingRole
      Path: /
    Type: AWS::IAM::Role

Elastic Load Balancing timeout delay

Because you are taking advantage of the two-minute Spot Instance notice, you can tune the Elastic Load Balancing target group deregistration timeout delay to match. When a target is deregistered from the target group, it is put into connection draining mode for the length of the timeout delay: 120 seconds to equal the two-minute notice.

loadBalancerTargetGroup:
    DependsOn:
    - vpc
    Properties:
      HealthCheckIntervalSeconds: 5
      HealthCheckPath: /
      HealthCheckTimeoutSeconds: 2
      Port: 80
      Protocol: HTTP
      TargetGroupAttributes:
      - Key: deregistration_delay.timeout_seconds
        Value: 120
      UnhealthyThresholdCount: 2
      VpcId:
        Ref: vpc
    Type: AWS::ElasticLoadBalancingV2::TargetGroup

CloudWatch Events rule

To capture the Spot Instance interruption notice being published to CloudWatch Events, create a rule with two targets: the Lambda function and the SNS topic.

eventRule:
    DependsOn:
    - snsTopic
    Properties:
      Description: Events rule for Spot Instance Interruption Notices
      EventPattern:
        detail-type:
        - EC2 Spot Instance Interruption Warning
        source:
        - aws.ec2
      State: ENABLED
      Targets:
      - Arn:
          Ref: snsTopic
        Id:
          Fn::GetAtt:
          - snsTopic
          - TopicName
      - Arn:
          Fn::GetAtt:
          - lambdaFunction
          - Arn
        Id:
          Ref: lambdaFunction
    Type: AWS::Events::Rule

Lambda function

The Lambda function does the heavy lifting for you. The details of the CloudWatch event are published to the Lambda function, which then uses boto3 to make a couple of AWS API calls. The first call is to describe the EC2 tags for the Spot Instance, filtering on a key of “TargetGroupArn”. If this tag is found, the instance is then deregistered from the target group ARN stored as the value of the tag.

import boto3
def handler(event, context):
  instanceId = event['detail']['instance-id']
  instanceAction = event['detail']['instance-action']
  try:
    ec2client = boto3.client('ec2')
    describeTags = ec2client.describe_tags(Filters=[{'Name': 'resource-id','Values':[instanceId],'Name':'key','Values':['loadBalancerTargetGroup']}])
  except:
    print("No action being taken. Unable to describe tags for instance id:", instanceId)
    return
  try:
    elbv2client = boto3.client('elbv2')
    deregisterTargets = elbv2client.deregister_targets(TargetGroupArn=describeTags['Tags'][0]['Value'],Targets=[{'Id':instanceId}])
  except:
    print("No action being taken. Unable to deregister targets for instance id:", instanceId)
    return
  print("Detaching instance from target:")
  print(instanceId, describeTags['Tags'][0]['Value'], deregisterTargets, sep=",")
  return

SNS topic

Finally, you’ve created an SNS topic as an example target. For example, you could subscribe an email address to the SNS topic in order to receive email notifications when a Spot Instance interruption notice is received.

snsTopic:
    Properties:
      DisplayName: SNS Topic for EC2 Spot Instance Interruption Notices
    Type: AWS::SNS::Topic

Create a Spot Fleet request

To proceed to creating your Spot Fleet request, use some of the resources that the CloudFormation stack created, to populate the Spot Fleet request launch configuration. You can find the values in the outputs values of the CloudFormation stack:

$ aws cloudformation describe-stacks --stack-name spot-spin-cwe

Using the output values of the CloudFormation stack, update the following values in the Spot Fleet request configuration:

%spotFleetRole%
%publicSubnet1%
%publicSubnet2%
%loadBalancerTargetGroup% (in two places)

Be sure to also replace %amiId% with the latest Amazon Linux AMI for your region and %keyName% with your environment.

{
  "AllocationStrategy": "diversified",
  "IamFleetRole": "%spotFleetRole%",
  "LaunchSpecifications": [
    {
      "ImageId": "%amiId%",
      "InstanceType": "c4.large",
      "Monitoring": {
        "Enabled": true
      },
      "KeyName": "%keyName%",
      "SubnetId": "%publicSubnet1%,%publicSubnet2%",
      "UserData": "IyEvYmluL2Jhc2gKeXVtIC15IHVwZGF0ZQp5dW0gLXkgaW5zdGFsbCBodHRwZApjaGtjb25maWcgaHR0cGQgb24KaW5zdGFuY2VpZD0kKGN1cmwgaHR0cDovLzE2OS4yNTQuMTY5LjI1NC9sYXRlc3QvbWV0YS1kYXRhL2luc3RhbmNlLWlkKQplY2hvICJoZWxsbyBmcm9tICRpbnN0YW5jZWlkIiA+IC92YXIvd3d3L2h0bWwvaW5kZXguaHRtbApzZXJ2aWNlIGh0dHBkIHN0YXJ0Cg==",
      "TagSpecifications": [
        {
          "ResourceType": "instance",
          "Tags": [
            {
              "Key": "loadBalancerTargetGroup",
              "Value": "%loadBalancerTargetGroup%"
            }
          ]
        }
      ]
    }
  ],
  "TargetCapacity": 2,
  "TerminateInstancesWithExpiration": true,
  "Type": "maintain",
  "ReplaceUnhealthyInstances": true,
  "InstanceInterruptionBehavior": "terminate",
  "LoadBalancersConfig": {
    "TargetGroupsConfig": {
      "TargetGroups": [
        {
          "Arn": "%loadBalancerTargetGroup%"
        }
      ]
    }
  }
}

Save the configuration and place the Spot Fleet request:

$ aws ec2 request-spot-fleet --spot-fleet-request-config file://sfr.json

You should receive a SpotFleetRequestId in return, confirming the request:

{
    "SpotFleetRequestId": "sfr-3cec4927-9d86-4cc5-a4f0-faa996c841b7"
}

You can confirm that the Spot Fleet request was fulfilled by checking that ActivityStatus is “fulfilled”, or by checking that FulfilledCapacity is greater than or equal to TargetCapacity, while describing the request:

$ aws ec2 describe-spot-fleet-requests --spot-fleet-request-id sfr-3cec4927-9d86-4cc5-a4f0-faa996c841b7

{
    "SpotFleetRequestConfigs": [
        {
            "ActivityStatus": "fulfilled",
            "CreateTime": "2018-02-08T01:23:16.029Z",
            "SpotFleetRequestConfig": {
                "AllocationStrategy": "diversified",
                "ExcessCapacityTerminationPolicy": "Default",
                "FulfilledCapacity": 2.0,
                …
                "TargetCapacity": 2,
                …
        }
    ]
}

Next, you can confirm that the Spot Instances have been registered with the Elastic Load Balancing target group and are in a healthy state:

$ aws elbv2 describe-target-health --target-group-arn arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/spot-loadB-1DZUVWL720VS6/26456d12cddbf23a

{
    "TargetHealthDescriptions": [
        {
            "Target": {
                "Id": "i-056c95d9dd6fde892",
                "Port": 80
            },
            "HealthCheckPort": "80",
            "TargetHealth": {
                "State": "healthy"
            }
        },
        {
            "Target": {
                "Id": "i-06c4c47228fd999b8",
                "Port": 80
            },
            "HealthCheckPort": "80",
            "TargetHealth": {
                "State": "healthy"
            }
        }
    ]
}

Test

In order to test, you can take advantage of the fact that any interruption action that Spot Fleet takes on a Spot Instance results in a Spot Instance interruption notice being provided. Therefore, you can simply decrease the target size of your Spot Fleet from 2 to 1. The instance that is interrupted receives the interruption notice:

$ aws ec2 modify-spot-fleet-request --spot-fleet-request-id sfr-3cec4927-9d86-4cc5-a4f0-faa996c841b7 --target-capacity 1

{
    "Return": true
}

As soon as the interruption notice is published to CloudWatch Events, the Lambda function triggers and detaches the instance from the target group, effectively putting the instance in a draining state.

$ aws elbv2 describe-target-health --target-group-arn arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/spot-loadB-1DZUVWL720VS6/26456d12cddbf23a

{
    "TargetHealthDescriptions": [
        {
            "Target": {
                "Id": "i-0c3dcd78efb9b7e53",
                "Port": 80
            },
            "HealthCheckPort": "80",
            "TargetHealth": {
                "State": "draining",
                "Reason": "Target.DeregistrationInProgress",
                "Description": "Target deregistration is in progress"
            }
        },
        {
            "Target": {
                "Id": "i-088c91a66078b4299",
                "Port": 80
            },
            "HealthCheckPort": "80",
            "TargetHealth": {
                "State": "healthy"
            }
        }
    ]
}

Conclusion

In conclusion, Amazon EC2 Spot Instance interruption notices are an extremely powerful tool when taking advantage of Amazon EC2 Spot Instances in your workloads, for tasks such as saving state, draining connections, and much more. I’d love to hear how you are using them in your own environment!

Chad Schmutzer
Solutions Architect

Chad Schmutzer is a Solutions Architect at Amazon Web Services based in Pasadena, CA. As an extension of the Amazon EC2 Spot Instances team, Chad helps customers significantly reduce the cost of running their applications, growing their compute capacity and throughput without increasing budget, and enabling new types of cloud computing applications.

How to Use Bucket Policies and Apply Defense-in-Depth to Help Secure Your Amazon S3 Data

2018-03-07 Rajat Ravinder Varuni

Post Syndicated from Rajat Ravinder Varuni original https://aws.amazon.com/blogs/security/how-to-use-bucket-policies-and-apply-defense-in-depth-to-help-secure-your-amazon-s3-data/

Amazon S3 provides comprehensive security and compliance capabilities that meet even the most stringent regulatory requirements. It gives you flexibility in the way you manage data for cost optimization, access control, and compliance. However, because the service is flexible, a user could accidentally configure buckets in a manner that is not secure. For example, let’s say you uploaded files to an Amazon S3 bucket with public read permissions, even though you intended only to share this file with a colleague or a partner. Although this might have accomplished your task to share the file internally, the file is now available to anyone on the internet, even without authentication.

In this blog post, we show you how to prevent your Amazon S3 buckets and objects from allowing public access. We discuss how to secure data in Amazon S3 with a defense-in-depth approach, where multiple security controls are put in place to help prevent data leakage. This approach helps prevent you from allowing public access to confidential information, such as personally identifiable information (PII) or protected health information (PHI).

Preventing your Amazon S3 buckets and objects from allowing public access

Every call to an Amazon S3 service becomes a REST API request. When your request is transformed via a REST call, the permissions are converted into parameters included in the HTTP header or as URL parameters. The Amazon S3 bucket policy allows or denies access to the Amazon S3 bucket or Amazon S3 objects based on policy statements, and then evaluates conditions based on those parameters. To learn more, see Using Bucket Policies and User Policies.

With this in mind, let’s say multiple AWS Identity and Access Management (IAM) users at Example Corp. have access to an Amazon S3 bucket and the objects in the bucket. Example Corp. wants to share the objects among its IAM users, while at the same time preventing the objects from being made available publicly.

To demonstrate how to do this, we start by creating an Amazon S3 bucket named examplebucket. After creating this bucket, we must apply the following bucket policy. This policy denies any uploaded object (PutObject) with the attribute x-amz-acl having the values public-read, public-read-write, or authenticated-read. This means authenticated users cannot upload objects to the bucket if the objects have public permissions.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "DenyPublicReadACL",
            "Effect": "Deny",
            "Principal": {
                "AWS": "*"
            },
            "Action": [
                "s3:PutObject",
                "s3:PutObjectAcl"
            ],
            "Resource": "arn:aws:s3:::examplebucket/*",
            "Condition": {
                "StringEquals": {
                    "s3:x-amz-acl": [
                        "public-read",
                        "public-read-write",
                        "authenticated-read"
                    ]
                }
            }
        },
        {
            "Sid": "DenyPublicReadGrant",
            "Effect": "Deny",
            "Principal": {
                "AWS": "*"
            },
            "Action": [
                "s3:PutObject",
                "s3:PutObjectAcl"
            ],
            "Resource": "arn:aws:s3:::examplebucket/*",
            "Condition": {
                "StringLike": {
                    "s3:x-amz-grant-read": [
                        "*http://acs.amazonaws.com/groups/global/AllUsers*",
                        "*http://acs.amazonaws.com/groups/global/AuthenticatedUsers*"
                    ]
                }
            }
        },
        {
            "Sid": "DenyPublicListACL",
            "Effect": "Deny",
            "Principal": {
                "AWS": "*"
            },
            "Action": "s3:PutBucketAcl",
            "Resource": "arn:aws:s3:::examplebucket",
            "Condition": {
                "StringEquals": {
                    "s3:x-amz-acl": [
                        "public-read",
                        "public-read-write",
                        "authenticated-read"
                    ]
                }
            }
        },
        {
            "Sid": "DenyPublicListGrant",
            "Effect": "Deny",
            "Principal": {
                "AWS": "*"
            },
            "Action": "s3:PutBucketAcl",
            "Resource": "arn:aws:s3:::examplebucket",
            "Condition": {
                "StringLike": {
                    "s3:x-amz-grant-read": [
                        "*http://acs.amazonaws.com/groups/global/AllUsers*",
                        "*http://acs.amazonaws.com/groups/global/AuthenticatedUsers*"
                    ]
                }
            }
        }
    ]
}

To better understand what is happening in this bucket policy, we’ll explain each statement. Let’s start with the first statement.


{
    "Sid": "DenyPublicReadACL",
    "Effect": "Deny",
    "Principal": {
        "AWS": "*"
    },
    "Action": [
        "s3:PutObject",
        "s3:PutObjectAcl"
    ],
    "Resource": "arn:aws:s3:::examplebucket/*",
    "Condition": {
        "StringEquals": {
            "s3:x-amz-acl": [
                "public-read",
                "public-read-write",
                "authenticated-read"
            ]
        }
    }
}

This statement accomplishes the following:

“Deny any Amazon S3 request to PutObject or PutObjectAcl in the bucket examplebucket when the request includes one of the following access control lists (ACLs): public-read, public-read-write, or authenticated-read.”

Remember that IAM policies are evaluated not in a first-match-and-exit model. Instead, IAM evaluates first if there is an explicit Deny. If there is not, IAM continues to evaluate if you have an explicit Allow and then you have an implicit Deny.

The above policy creates an explicit Deny. Even when any authenticated user tries to upload (PutObject) an object with public read or write permissions, such as public-read or public-read-write or authenticated-read, the action will be denied. To understand how S3 Access Permissions work, you must understand what Access Control Lists (ACL) and Grants are. You can find the documentation here.

Now let’s continue our bucket policy explanation by examining the next statement.

{
    "Sid": "DenyPublicReadGrant",
    "Effect": "Deny",
    "Principal": {
        "AWS": "*"
    },
    "Action": [
        "s3:PutObject",
        "s3:PutObjectAcl"
    ],
    "Resource": "arn:aws:s3:::examplebucket/*",
    "Condition": {
        "StringLike": {
            "s3:x-amz-grant-read": [
                "*http://acs.amazonaws.com/groups/global/AllUsers*",
                "*http://acs.amazonaws.com/groups/global/AuthenticatedUsers*"
            ]
        }
    }
}

This statement is very similar to the first statement, except that instead of checking the ACLs, we are checking specific user groups’ grants that represent the following groups:

AuthenticatedUsers group. Represented by http://acs.amazonaws.com/groups/global/AuthenticatedUsers, this group represents all AWS accounts. Access permissions to this group allow any AWS account to access the resource. However, all requests must be signed (authenticated).
AllUsers group. Represented by http://acs.amazonaws.com/groups/global/AllUsers, access permissions to this group allow anyone on the internet access to the resource. The requests can be signed (authenticated) or unsigned (anonymous). Unsigned requests omit the Authentication header in the request.

For more information about which parameters you can use to create bucket policies, see Using Bucket Policies and User Policies.

Now that you know how to deny object uploads with permissions that would make the object public, you just have two statement policies that prevent users from changing the bucket permissions (Denying s3:PutBucketACL from ACL and Denying s3:PutBucketACL from Grants).

Below is how we’re preventing users from changing the bucket permisssions.

{
  "Sid": "DenyPublicListACL",
  "Effect": "Deny",
  "Principal": {
    "AWS": "*"
  },
  "Action": "s3:PutBucketAcl",
  "Resource": "arn:aws:s3:::examplebucket",
  "Condition": {
    "StringEquals": {
      "s3:x-amz-acl": [
        "public-read",
        "public-read-write",
        "authenticated-read"
      ]
    }
  }
},
{
  "Sid": "DenyPublicListGrant",
  "Effect": "Deny",
  "Principal": {
    "AWS": "*"
  },
  "Action": "s3:PutBucketAcl",
  "Resource": "arn:aws:s3:::examplebucket",
  "Condition": {
    "StringLike": {
      "s3:x-amz-grant-read": [
        "*http://acs.amazonaws.com/groups/global/AllUsers*",
        "*http://acs.amazonaws.com/groups/global/AuthenticatedUsers*"
      ]
    }
  }
}

As you can see above, the statement is very similar to the Object statements, except that now we use s3:PutBucketAcl instead of s3:PutObjectAcl, the Resource is just the bucket ARN, and the objects have the “/*” in the end of the ARN.

In this section, we showed how to prevent IAM users from accidently uploading Amazon S3 objects with public permissions to buckets. In the next section, we show you how to enforce multiple layers of security controls, such as encryption of data at rest and in transit while serving traffic from Amazon S3.

Securing data on Amazon S3 with defense-in-depth

Let’s say that Example Corp. wants to serve files securely from Amazon S3 to its users with the following requirements:

The data must be encrypted at rest and during transit.
The data must be accessible only by a limited set of public IP addresses.
All requests for data should be handled only by Amazon CloudFront (which is a content delivery network) instead of being directly available from an Amazon S3 URL. If you’re using an Amazon S3 bucket as the origin for a CloudFront distribution, you can grant public permission to read the objects in your bucket. This allows anyone to access your objects either through CloudFront or the Amazon S3 URL. CloudFront doesn’t expose Amazon S3 URLs, but your users still might have access to those URLs if your application serves any objects directly from Amazon S3, or if anyone gives out direct links to specific objects in Amazon S3.
A domain name is required to consume the content. Custom SSL certificate support lets you deliver content over HTTPS by using your own domain name and your own SSL certificate. This gives visitors to your website the security benefits of CloudFront over an SSL connection that uses your own domain name, in addition to lower latency and higher reliability.

To represent defense-in-depth visually, the following diagram contains several Amazon S3 objects (A) in a single Amazon S3 bucket (B). You can encrypt these objects on the server side or the client side. You also can configure the bucket policy such that objects are accessible only through CloudFront, which you can accomplish through an origin access identity (C). You then can configure CloudFront to deliver content only over HTTPS in addition to using your own domain name (D).

Defense-in-depth requirement 1: Data must be encrypted at rest and during transit

Let’s start with the objects themselves. Amazon S3 objects—files in this case—can range from zero bytes to multiple terabytes in size (see service limits for the latest information). Each Amazon S3 bucket includes a collection of objects, and the objects can be uploaded via the Amazon S3 console, AWS CLI, or AWS API.

You can encrypt Amazon S3 objects at rest and during transit. At rest, objects in a bucket are encrypted with server-side encryption by using Amazon S3 managed keys or AWS Key Management Service (AWS KMS) managed keys or customer-provided keys through AWS KMS. You also can encrypt objects on the client side by using AWS KMS managed keys or a customer-supplied client-side master key.

If you choose to use server-side encryption, Amazon S3 encrypts your objects before saving them on disks in AWS data centers. To encrypt an object at the time of upload, you need to add the x-amz-server-side-encryption header to the request to tell Amazon S3 to encrypt the object using Amazon S3 managed keys (SSE-S3), AWS KMS managed keys (SSE-KMS), or customer-provided keys (SSE-C). There are two possible values for the x-amz-server-side-encryption header: AES256, which tells Amazon S3 to use Amazon S3 managed keys, and aws:kms, which tells Amazon S3 to use AWS KMS managed keys.

The following code example shows a Put request using SSE-S3.

PUT /example-object HTTP/1.1
Host: myBucket.s3.amazonaws.com
Date: Wed, 8 Jun 2016 17:50:00 GMT
Authorization: authorization string
Content-Type: text/plain
Content-Length: 11434
x-amz-meta-author: Janet
Expect: 100-continue
x-amz-server-side-encryption: AES256
[11434 bytes of object data]

If you choose to use client-side encryption, you can encrypt data on the client side and upload the encrypted data to Amazon S3. In this case, you manage the encryption process, the encryption keys, and related tools. You encrypt data on the client side by using AWS KMS managed keys or a customer-supplied, client-side master key.

Defense-in-depth requirement 2: Data must be accessible only by a limited set of public IP addresses

At the Amazon S3 bucket level, you can configure permissions through a bucket policy. For example, you can limit access to the objects in a bucket by IP address range or specific IP addresses. Alternatively, you can make the objects accessible only through HTTPS.

The following bucket policy allows access to Amazon S3 objects only through HTTPS (the policy was generated with the AWS Policy Generator). Here the bucket policy explicitly denies ("Effect": "Deny") all read access ("Action": "s3:GetObject") from anybody who browses ("Principal": "*") to Amazon S3 objects within an Amazon S3 bucket if they are not accessed through HTTPS ("aws:SecureTransport": "false").

{
    "Version": "2012-10-17",
    "Id": "Policy1504640911349",
    "Statement": [
        {
            "Sid": "Stmt1504640908907",
            "Effect": "Deny",
            "Principal": "*",
            "Action": "s3:GetObject",
            "Resource": "arn:aws:s3:::/*",
            "Condition": {
                "Bool": {
                    "aws:SecureTransport": "false"
                }
            }
        }
    ]
}

Defense-in-depth requirement 3: Data must not be publicly accessible directly from an Amazon S3 URL

Next, configure Amazon CloudFront to serve traffic from within the bucket. The use of CloudFront serves several purposes:

CloudFront is a content delivery network that acts as a cache to serve static files quickly to clients.
Depending on the number of requests, the cost of delivery is less than if objects were served directly via Amazon S3.
Objects served through CloudFront can be limited to specific countries.

Access to these Amazon S3 objects is available only through CloudFront. We do this by creating an origin access identity (OAI) for CloudFront and granting access to objects in the respective Amazon S3 bucket only to that OAI. As a result, access to Amazon S3 objects from the internet is possible only through CloudFront; all other means of accessing the objects—such as through an Amazon S3 URL—are denied. CloudFront acts not only as a content distribution network, but also as a host that denies access based on geographic restrictions. You apply these restrictions by updating your CloudFront web distribution and adding a whitelist that contains only a specific country’s name (let’s say Liechtenstein). Alternatively, you could add a blacklist that contains every country except that country. Learn more about how to use CloudFront geographic restriction to whitelist or blacklist a country to restrict or allow users in specific locations from accessing web content in the AWS Support Knowledge Center.

Defense-in-depth requirement 4: A domain name is required to consume the content

To serve content from CloudFront, you must use a domain name in the URLs for objects on your webpages or in your web application. The domain name can be either of the following:

The domain name that CloudFront automatically assigns when you create a distribution, such as d111111abcdef8.cloudfront.net
Your own domain name, such as example.com

For example, you might use one of the following URLs to return the file image.jpg:

http://d111111abcdef8.cloudfront.net/images/image.jpg
http://example.com/images/image.jpg

You use the same URL format whether you store the content in Amazon S3 buckets or at a custom origin, like one of your own web servers.

Instead of using the default domain name that CloudFront assigns for you when you create a distribution, you can add an alternate domain name that’s easier to work with, like example.com. By setting up your own domain name with CloudFront, you can use a URL like this for objects in your distribution: http://example.com/images/image.jpg.

Let’s say that you already have a domain name hosted on Amazon Route 53. You would like to serve traffic from the domain name, request an SSL certificate, and add this to your CloudFront web distribution. The SSL offloading occurs in CloudFront by serving traffic securely from each CloudFront location. You also can configure CloudFront to deliver your content over HTTPS by using your custom domain name and your own SSL certificate. Serving web content through CloudFront reduces response from the origin as requests are redirected to the nearest edge location. This results in faster download times than if the visitor had requested the content from a data center that is located farther away.

Summary

In this post, we demonstrated how you can apply policies to Amazon S3 buckets so that only users with appropriate permissions are allowed to access the buckets. Anonymous users (with public-read/public-read-write permissions) and authenticated users without the appropriate permissions are prevented from accessing the buckets.

We also examined how to secure access to objects in Amazon S3 buckets. The objects in Amazon S3 buckets can be encrypted at rest and during transit. Doing so helps provide end-to-end security from the source (in this case, Amazon S3) to your users.

If you have feedback about this blog post, submit comments in the “Comments” section below. If you have questions about this blog post, start a new thread on the Amazon S3 forum or contact AWS Support.

Building an Immersive VR Streaming Solution on AWS

2018-02-23 Chad Schmutzer

Post Syndicated from Chad Schmutzer original https://aws.amazon.com/blogs/compute/building-an-immersive-vr-streaming-solution-on-aws/

This post was contributed by:

Konstantin Wilms
Solutions Architect

Shawn Przybilla
Solutions Architect

Chad Schmutzer
Solutions Architect

With the explosion in virtual reality (VR) technologies over the past few years, we’ve had an increasing number of customers ask us for advice and best practices around deploying their VR-based products and service offerings on the AWS Cloud. It soon became apparent that while the VR ecosystem is large in both scope and depth of types of workloads (gaming, e-medicine, security analytics, live streaming events, etc.), many of the workloads followed repeatable patterns, with storage and delivery of live and on-demand immersive video at the top of the list.

Looking at consumer trends, the desire for live and on-demand immersive video is fairly self-explanatory. VR has ushered in convenient and low-cost access for consumers and businesses to a wide variety of options for consuming content, ranging from browser playback of live and on-demand 360º video, all the way up to positional tracking systems with a high degree of immersion. All of these scenarios contain one lowest common denominator: video.

Which brings us to the topic of this post. We set out to build a solution that could support both live and on-demand events, bring with it a high degree of scalability, be flexible enough to support transformation of video if required, run at a low cost, and use open-source software to every extent possible.

In this post, we describe the reference architecture we created to solve this challenge, using Amazon EC2 Spot Instances, Amazon S3, Elastic Load Balancing, Amazon CloudFront, AWS CloudFormation, and Amazon CloudWatch, with open-source software such as NGINX, FFMPEG, and JavaScript-based client-side playback technologies. We step you through deployment of the solution and how the components work, as well as the capture, processing, and playback of the underlying live and on-demand immersive media streams.

This GitHub repository includes the source code necessary to follow along. We’ve also provided a self-paced workshop, from AWS re:Invent 2017 that breaks down this architecture even further. If you experience any issues or would like to suggest an enhancement, please use the GitHub issue tracker.

Prerequisites

As a side note, you’ll also need a few additional components to take best advantage of the infrastructure:

A camera/capture device capable of encoding and streaming RTMP video
A browser to consume the content.

You’re going to generate HTML5-compatible video (Apple HLS to be exact), but there are many other native iOS and Android options for consuming the media that you create. It’s also worth noting that your playback device should support projection of your input stream. We’ll talk more about that in the next section.

How does immersive media work?

At its core, any flavor of media, be that audio or video, can be viewed with some level of immersion. The ability to interact passively or actively with the content brings with it a further level of immersion. When you look at VR devices with rotational and positional tracking, you naturally need more than an ability to interact with a flat plane of video. The challenge for any creative thus becomes a tradeoff between immersion features (degrees of freedom, monoscopic 2D or stereoscopic 3D, resolution, framerate) and overall complexity.

Where can you start from a simple and effective point of view, that enables you to build out a fairly modular solution and test it? There are a few areas we chose to be prescriptive with our solution.

Source capture from the Ricoh Theta S

First, monoscopic 360-degree video is currently one of the most commonly consumed formats on consumer devices. We explicitly chose to focus on this format, although the infrastructure is not limited to it. More on this later.

Second, if you look at most consumer-level cameras that provide live streaming ability, and even many professional rigs, there are at least two lenses or cameras at a minimum. The figure above illustrates a single capture from a Ricoh Theta S in monoscopic 2D. The left image captures 180 degrees of the field of view, and the right image captures the other 180 degrees.

For this post, we chose a typical midlevel camera (the Ricoh Theta S), and used a laptop with open-source software (Open Broadcaster Software) to encode and stream the content. Again, the solution infrastructure is not limited to this particular brand of camera. Any camera or encoder that outputs 360º video and encodes to H264+AAC with an RTMP transport will work.

Third, capturing and streaming multiple camera feeds brings additional requirements around stream synchronization and cost of infrastructure. There is also a requirement to stitch media in real time, which can be CPU and GPU-intensive. Many devices and platforms do this either on the device, or via outboard processing that sits close to the camera location. If you stitch and deliver a single stream, you can save the costs of infrastructure and bitrate/connectivity requirements. We chose to keep these aspects on the encoder side to save on cost and reduce infrastructure complexity.

Last, the most common delivery format that requires little to no processing on the infrastructure side is equirectangular projection, as per the above figure. By stitching and unwrapping the spherical coordinates into a flat plane, you can easily deliver the video exactly as you would with any other live or on-demand stream. The only caveat is that resolution and bit rate are of utmost importance. The higher you can push these (high bit rate @ 4K resolution), the more immersive the experience is for viewers. This is due to the increase in sharpness and reduction of compression artifacts.

Knowing that we would be transcoding potentially at 4K on the source camera, but in a format that could be transmuxed without an encoding penalty on the origin servers, we implemented a pass-through for the highest bit rate, and elected to only transcode lower bitrates. This requires some level of configuration on the source encoder, but saves on cost and infrastructure. Because you can conform the source stream, you may as well take advantage of that!

For this post, we chose not to focus on ways to optimize projection. However, the reference architecture does support this with additional open source components compiled into the FFMPEG toolchain. A number of options are available to this end, such as open source equirectangular to cubic transformation filters. There is a tradeoff, however, in that reprojection implies that all streams must be transcoded.

Processing and origination stack

To get started, we’ve provided a CloudFormation template that you can launch directly into your own AWS account. We quickly review how it works, the solution’s components, key features, processing steps, and examine the main configuration files. Following this, you launch the stack, and then proceed with camera and encoder setup.

Immersive streaming reference architecture

The event encoder publishes the RTMP source to multiple origin elastic IP addresses for packaging into the HLS adaptive bitrate.
The client requests the live stream through the CloudFront CDN.
The origin responds with the appropriate HLS stream.
The edge fleet caches media requests from clients and elastically scales across both Availability Zones to meet peak demand.
CloudFront caches media at local edge PoPs to improve performance for users and reduce the origin load.
When the live event is finished, the VOD asset is published to S3. An S3 event is then published to SQS.
The encoding fleet processes the read messages from the SQS queue, processes the VOD clips, and stores them in the S3 bucket.

How it works

A camera captures content, and with the help of a contribution encoder, publishes a live stream in equirectangular format. The stream is encoded at a high bit rate (at least 2.5 Mbps, but typically 16+ Mbps for 4K) using H264 video and AAC audio compression codecs, and delivered to a primary origin via the RTMP protocol. Streams may transit over the internet or dedicated links to the origins. Typically, for live events in the field, internet or bonded cellular are the most widely used.

The encoder is typically configured to push the live stream to a primary URI, with the ability (depending on the source encoding software/hardware) to roll over to a backup publishing point origin if the primary fails. Because you run across multiple Availability Zones, this architecture could handle an entire zone outage with minor disruption to live events. The primary and backup origins handle the ingestion of the live stream as well as transcoding to H264+AAC-based adaptive bit rate sets. After transcode, they package the streams into HLS for delivery and create a master-level manifest that references all adaptive bit rates.

The edge cache fleet pulls segments and manifests from the active origin on demand, and supports failover from primary to backup if the primary origin fails. By adding this caching tier, you effectively separate the encoding backend tier from the cache tier that responds to client or CDN requests. In addition to origin protection, this separation allows you to independently monitor, configure, and scale these components.

Viewers can use the sample HTML5 player (or compatible desktop, iOS or Android application) to view the streams. Navigation in the 360-degree view is handled either natively via device-based gyroscope, positionally via more advanced devices such as a head mount display, or via mouse drag on the desktop. Adaptive bit rate is key here, as this allows you to target multiple device types, giving the player on each device the option of selecting an optimum stream based on network conditions or device profile.

Solution components

When you deploy the CloudFormation template, all the architecture services referenced above are created and launched. This includes:

The compute tier running on Spot Instances for the corresponding components:
- the primary and backup ingest origins
- the edge cache fleet
- the transcoding fleet
- the test source
The CloudFront distribution
S3 buckets for storage of on-demand VOD assets
An Application Load Balancer for load balancing the service
An Amazon ECS cluster and container for the test source
An Amazon SQS queue

The template also provisions the underlying dependencies:

A VPC
Security groups
IAM policies and roles
Elastic network interfaces
Elastic IP addresses

The edge cache fleet instances need some way to discover the primary and backup origin locations. You use elastic network interfaces and elastic IP addresses for this purpose.

As each component of the infrastructure is provisioned, software required to transcode and process the streams across the Spot Instances is automatically deployed. This includes NGiNX-RTMP for ingest of live streams, FFMPEG for transcoding, NGINX for serving, and helper scripts to handle various tasks (potential Spot Instance interruptions, queueing, moving content to S3). Metrics and logs are available through CloudWatch and you can manage the deployment using the CloudFormation console or AWS CLI.

Key features include:

Live and video-on-demand recording

You’re supporting both live and on-demand. On-demand content is created automatically when the encoder stops publishing to the origin.

Cost-optimization and operating at scale using Spot Instances

Spot Instances are used exclusively for infrastructure to optimize cost and scale throughput.

Midtier caching

To protect the origin servers, the midtier cache fleet pulls, caches, and delivers to downstream CDNs.

Distribution via CloudFront or multi-CDN

The Application Load Balancer endpoint allows CloudFront or any third-party CDN to source content from the edge fleet and, indirectly, the origin.

FFMPEG + NGINX + NGiNX-RTMP

These three components form the core of the stream ingest, transcode, packaging, and delivery infrastructure, as well as the VOD-processing component for creating transcoded VOD content on-demand.

Simple deployment using a CloudFormation template

All infrastructure can be easily created and modified using CloudFormation.

Prototype player page

To provide an end-to-end experience right away, we’ve included a test player page hosted as a static site on S3. This page uses A-Frame, a cross-platform, open-source framework for building VR experiences in the browser. Though A-Frame provides many features, it’s used here to render a sphere that acts as a 3D canvas for your live stream.

Spot Instance considerations

At this stage, and before we discuss processing, it is important to understand how the architecture operates with Spot Instances.

Spot Instances are spare compute capacity in the AWS Cloud available to you at steep discounts compared to On-Demand prices. Spot Instances enables you to optimize your costs on the AWS Cloud and scale your application’s throughput up to 10X for the same budget. By selecting Spot Instances, you can save up-to 90% on On-Demand prices. This allows you to greatly reduce the cost of running the solution because, outside of S3 for storage and CloudFront for delivery, this solution is almost entirely dependent on Spot Instances for infrastructure requirements.

We also know that customers running events look to deploy streaming infrastructure at the lowest price point, so it makes sense to take advantage of it wherever possible. A potential challenge when using Spot Instances for live streaming and on-demand processing is that you need to proactively deal with potential Spot Instance interruptions. How can you best deal with this?

First, the origin is deployed in a primary/backup deployment. If a Spot Instance interruption happens on the primary origin, you can fail over to the backup with a brief interruption. Should a potential interruption not be acceptable, then either Reserved Instances or On-Demand options (or a combination) can be used at this tier.

Second, the edge cache fleet runs a job (started automatically at system boot) that periodically queries the local instance metadata to detect if an interruption is scheduled to occur. Spot Instance Interruption Notices provide a two-minute warning of a pending interruption. If you poll every 5 seconds, you have almost 2 full minutes to detach from the Load Balancer and drain or stop any traffic directed to your instance.

Lastly, use an SQS queue when transcoding. If a transcode for a Spot Instance is interrupted, the stale item falls back into the SQS queue and is eventually re-surfaced into the processing pipeline. Only remove items from the queue after the transcoded files have been successfully moved to the destination S3 bucket.

Processing

As discussed in the previous sections, you pass through the video for the highest bit rate to save on having to increase the instance size to transcode the 4K or similar high bit rate or resolution content.

We’ve selected a handful of bitrates for the adaptive bit rate stack. You can customize any of these to suit the requirements for your event. The default ABR stack includes:

2160p (4K)
1080p
540p
480p

These can be modified by editing the /etc/nginx/rtmp.d/rtmp.conf NGINX configuration file on the origin or the CloudFormation template.

It’s important to understand where and how streams are transcoded. When the source high bit rate stream enters the primary or backup origin at the /live RTMP application entry point, it is recorded on stop and start of publishing. On completion, it is moved to S3 by a cleanup script, and a message is placed in your SQS queue for workers to use. These workers transcode the media and push it to a playout location bucket.

This solution uses Spot Fleet with automatic scaling to drive the fleet size. You can customize it based on CloudWatch metrics, such as simple utilization metrics to drive the size of the fleet. Why use Spot Instances for the transcode option instead of Amazon Elastic Transcoder? This allows you to implement reprojection of the input stream via FFMPEG filters in the future.

The origins handle all the heavy live streaming work. Edges only store and forward the segments and manifests, and provide scaling plus reduction of burden on the origin. This lets you customize the origin to the right compute capacity without having to rely on a ‘high watermark’ for compute sizing, thus saving additional costs.

Loopback is an important concept for the live origins. The incoming stream entering /live is transcoded by FFMPEG to multiple bit rates, which are streamed back to the same host via RTMP, on a secondary publishing point /show. The secondary publishing point is transparent to the user and encoder, but handles HLS segment generation and cleanup, and keeps a sliding window of live segments and constantly updating manifests.

Configuration

Our solution provides two key points of configuration that can be used to customize the solution to accommodate ingest, recording, transcoding, and delivery, all controlled via origin and edge configuration files, which are described later. In addition, a number of job scripts run on the instances to provide hooks into Spot Instance interruption events and the VOD SQS-based processing queue.

Origin instances

The rtmp.conf excerpt below also shows additional parameters that can be customized, such as maximum recording file size in Kbytes, HLS Fragment length, and Playlist sizes. We’ve created these in accordance with general industry best practices to ensure the reliable streaming and delivery of your content.

rtmp {
  server {
    listen 1935;
    chunk_size 4000;
    application live {
      live on;
      record all;
      record_path /var/lib/nginx/rec;
      record_max_size 128000K;
      exec_record_done /usr/local/bin/record-postprocess.sh $path $basename;
      exec /usr/local/bin/ffmpeg <…parameters…>;
    }
    application show {
      live on;
      hls on;
      ...
      hls_type live;
      hls_fragment 10s;
      hls_playlist_length 60s;
      ...
    }
  }
}

This exposes a few URL endpoints for debugging and general status. In production, you would most likely turn these off:

/stat provides a statistics endpoint accessible via any standard web browser.
/control enables control of RTMP streams and publishing points.

You also control the TTLs, as previously discussed. It’s important to note here that you are setting TTLs explicitly at the origin, instead of in CloudFront’s distribution configuration. While both are valid, this approach allows you to reconfigure and restart the service on the fly without having to push changes through CloudFront. This is useful for debugging any caching or playback issues.

location /stat {
  ...
}

location /stat.xsl {
  ...
}

location /control {
  ...
}

# origin controlled ttls

location ~* /hls/.*\.ts$ {
  ...
  add_header Cache-Control "max-age=900";
  expires 900s;
}

location ~* /hls/.*\.m3u8$ {
  ...
  add_header Cache-Control "max-age=5";
  expires 5s;
}

Handler scripts

Here is a brief overview of the scripts we use to handle the events and process of the solution. We encourage you to take some time to review them.

spot-termination-handler.sh – Provides a graceful shutdown for the transcoding and edge fleet instances.
record-postprocess.sh – Ensures that recorded files on the origin are well-formed, and transfers them to S3 for processing.
ffmpeg.sh – Transcodes content on the encoding fleet, pulling source media from your S3 ingress bucket, based on SQS queue entries, and pushing transcoded adaptive bit rate segments and manifests to your VOD playout egress bucket.

For more details, see the Delivery and Playback section later in this post.

Camera source

With the processing and origination infrastructure running, you need to configure your camera and encoder.

As discussed, we chose to use a Ricoh Theta S camera and Open Broadcaster Software (OBS) to stitch and deliver a stream into the infrastructure. Ricoh provides a free ‘blender’ driver, which allows you to transform, stitch, encode, and deliver both transformed equirectangular (used for this post) video as well as spherical (two camera) video. The Theta provides an easy way to get capturing for under $300, and OBS is a free and open-source software application for capturing and live streaming on a budget. It is quick, cheap, and enjoys wide use by the gaming community. OBS lowers the barrier to getting started with immersive streaming.

While the resolution and bit rate of the Theta may not be 4K, it still provides us with a way to test the functionality of the entire pipeline end to end, without having to invest in a more expensive camera rig. One could also use this type of model to target smaller events, which may involve mobile devices with smaller display profiles, such as phones and potentially smaller sized tablets.

Looking for a more professional solution? Nokia, GoPro, Samsung, and many others have options ranging from $500 to $50,000. This solution is based around the Theta S capabilities, but we’d encourage you to extend it to meet your specific needs.

If your device can support equirectangular RTMP, then it can deliver media through the reference architecture (dependent on instance sizing for higher bit rate sources, of course). If additional features are required such as camera stitching, mixing, or device bonding, we’d recommend exploring a commercial solution such as Teradek Sphere.

Teradek Rig (Teradek)

Ricoh Theta (CNET)

All cameras have varied PC connectivity support. We chose the Ricoh Theta S due to the real-time video connectivity that it provides through software drivers on macOS and PC. If you plan to purchase a camera to use with a PC, confirm that it supports real-time capabilities as a peripheral device.

Encoding and publishing

Now that you have a camera, encoder, and AWS stack running, you can finally publish a live stream.

To start streaming with OBS, configure the source camera and set a publishing point. Use the RTMP application name /live on port 1935 to ingest into the primary origin’s Elastic IP address provided as the CloudFormation output: primaryOriginElasticIp.

You also need to choose a stream name or stream key in OBS. You can use any stream name, but keep the naming short and lowercase, and use only alphanumeric characters. This avoids any parsing issues on client-side player frameworks. There’s no publish point protection in your deployment, so any stream key works with the default NGiNX-RTMP configuration. For more information about stream keys, publishing point security, and extending the NGiNX-RTMP module, see the NGiNX-RTMP Wiki.

You should end up with a configuration similar to the following:

OBS Stream Settings

The Output settings dialog allows us to rescale the Video canvas and encode it for delivery to our AWS infrastructure. In the dialog below, we’ve set the Theta to encode at 5 Mbps in CBR mode using a preset optimized for low CPU utilization. We chose these settings in accordance with best practices for the stream pass-through at the origin for the initial incoming bit rate. You may notice that they largely match the FFMPEG encoding settings we use on the origin – namely constant bit rate, a single audio track, and x264 encoding with the ‘veryfast’ encoding profile.

OBS Output Settings

Live to On-Demand

As you may have noticed, an on-demand component is included in the solution architecture. When talking to customers, one frequent request that we see is that they would like to record the incoming stream with as little effort as possible.

NGINX-RTMP’s recording directives provide an easy way to accomplish this. We record any newly published stream on stream start at the primary or backup origins, using the incoming source stream, which also happens to be the highest bit rate. When the encoder stops broadcasting, NGINX-RTMP executes an exec_record_done script – record-postprocess.sh (described in the Configuration section earlier), which ensures that the content is well-formed, and then moves it to an S3 ingest bucket for processing.

Transcoding of content to make it ready for VOD as adaptive bit rate is a multi-step pipeline. First, Spot Instances in the transcoding cluster periodically poll the SQS queue for new jobs. Items on the queue are pulled off on demand by processing instances, and transcoded via FFMPEG into adaptive bit rate HLS. This allows you to also extend FFMPEG using filters for cubic and other bitrate-optimizing 360-specific transforms. Finally, transcoded content is moved from the ingest bucket to an egress bucket, making them ready for playback via your CloudFront distribution.

Separate ingest and egress by bucket to provide hard security boundaries between source recordings (which are highest quality and unencrypted), and destination derivatives (which may be lower quality and potentially require encryption). Bucket separation also allows you to order and archive input and output content using different taxonomies, which is common when moving content from an asset management and archival pipeline (the ingest bucket) to a consumer-facing playback pipeline (the egress bucket, and any other attached infrastructure or services, such as CMS, Mobile applications, and so forth).

Because streams are pushed over the internet, there is always the chance that an interruption could occur in the network path, or even at the origin side of the equation (primary to backup roll-over). Both of these scenarios could result in malformed or partial recordings being created. For the best level of reliability, encoding should always be recorded locally on-site as a precaution to deal with potential stream interruptions.

Delivery and playback

With the camera turned on and OBS streaming to AWS, the final step is to play the live stream. We’ve primarily tested the prototype player on the latest Chrome and Firefox browsers on macOS, so your mileage may vary on different browsers or operating systems. For those looking to try the livestream on Google Cardboard, or similar headsets, native apps for iOS (VRPlayer) and Android exist that can play back HLS streams.

The prototype player is hosted in an S3 bucket and can be found from the CloudFormation output clientWebsiteUrl. It requires a stream URL provided as a query parameter ?url=<stream_url> to begin playback. This stream URL is determined by the RTMP stream configuration in OBS. For example, if OBS is publishing to rtmp://x.x.x.x:1935/live/foo, the resulting playback URL would be:

https://<cloudFrontDistribution>/hls/foo.m3u8

The combined player URL and playback URL results in a path like this one:

https://<clientWebsiteUrl>/?url=https://<cloudFrontDistribution>/hls/foo.m3u8

To assist in setup/debugging, we’ve provided a test source as part of the CloudFormation template. A color bar pattern with timecode and audio is being generated by FFmpeg running as an ECS task. Much like OBS, FFmpeg is streaming the test pattern to the primary origin over the RTMP protocol. The prototype player and test HLS stream can be accessed by opening the clientTestPatternUrl CloudFormation output link.

Test Stream Playback

What’s next?

In this post, we walked you through the design and implementation of a full end-to-end immersive streaming solution architecture. As you may have noticed, there are a number of areas this could expand into, and we intend to do this in follow-up posts around the topic of virtual reality media workloads in the cloud. We’ve identified a number of topics such as load testing, content protection, client-side metrics and analytics, and CI/CD infrastructure for 24/7 live streams. If you have any requests, please drop us a line.

We would like to extend extra-special thanks to Scott Malkie and Chad Neal for their help and contributions to this post and reference architecture.

Now Available – AWS Serverless Application Repository

2018-02-21 Jeff Barr

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-aws-serverless-application-repository/

Last year I suggested that you Get Ready for the AWS Serverless Application Repository and gave you a sneak peek. The Repository is designed to make it as easy as possible for you to discover, configure, and deploy serverless applications and components on AWS. It is also an ideal venue for AWS partners, enterprise customers, and independent developers to share their serverless creations.

Now Available
After a well-received public preview, the AWS Serverless Application Repository is now generally available and you can start using it today!

As a consumer, you will be able to tap in to a thriving ecosystem of serverless applications and components that will be a perfect complement to your machine learning, image processing, IoT, and general-purpose work. You can configure and consume them as-is, or you can take them apart, add features, and submit pull requests to the author.

As a publisher, you can publish your contribution in the Serverless Application Repository with ease. You simply enter a name and a description, choose some labels to increase discoverability, select an appropriate open source license from a menu, and supply a README to help users get started. Then you enter a link to your existing source code repo, choose a SAM template, and designate a semantic version.

Let’s take a look at both operations…

Consuming a Serverless Application
The Serverless Application Repository is accessible from the Lambda Console. I can page through the existing applications or I can initiate a search:

A search for “todo” returns some interesting results:

I simply click on an application to learn more:

I can configure the application and deploy it right away if I am already familiar with the application:

I can expand each of the sections to learn more. The Permissions section tells me which IAM policies will be used:

And the Template section displays the SAM template that will be used to deploy the application:

I can inspect the template to learn more about the AWS resources that will be created when the template is deployed. I can also use the templates as a learning resource in preparation for creating and publishing my own application.

The License section displays the application’s license:

To deploy todo, I name the application and click Deploy:

Deployment starts immediately and is done within a minute (application deployment time will vary, depending on the number and type of resources to be created):

I can see all of my deployed applications in the Lambda Console:

There’s currently no way for a SAM template to indicate that an API Gateway function returns binary media types, so I set this up by hand and then re-deploy the API:

Following the directions in the Readme, I open the API Gateway Console and find the URL for the app in the API Gateway Dashboard:

I visit the URL and enter some items into my list:

Publishing a Serverless Application
Publishing applications is a breeze! I visit the Serverless App Repository page and click on Publish application to get started:

Then I assign a name to my application, enter my own name, and so forth:

I can choose from a long list of open-source friendly SPDX licenses:

I can create an initial version of my application at this point, or I can do it later. Either way, I simply provide a version number, a URL to a public repository containing my code, and a SAM template:

Available Now
The AWS Serverless Application Repository is available now and you can start using it today, paying only for the AWS resources consumed by the serverless applications that you deploy.

You can deploy applications in the US East (Ohio), US East (N. Virginia), US West (N. California), US West (Oregon), Asia Pacific (Tokyo), Asia Pacific (Seoul), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Canada (Central), EU (Frankfurt), EU (Ireland), EU (London), and South America (São Paulo) Regions. You can publish from the US East (N. Virginia) or US East (Ohio) Regions for global availability.

— Jeff;

Sharing Secrets with AWS Lambda Using AWS Systems Manager Parameter Store

2018-02-09 Chris Munns

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/sharing-secrets-with-aws-lambda-using-aws-systems-manager-parameter-store/

This post courtesy of Roberto Iturralde, Sr. Application Developer- AWS Professional Services

Application architects are faced with key decisions throughout the process of designing and implementing their systems. One decision common to nearly all solutions is how to manage the storage and access rights of application configuration. Shared configuration should be stored centrally and securely with each system component having access only to the properties that it needs for functioning.

With AWS Systems Manager Parameter Store, developers have access to central, secure, durable, and highly available storage for application configuration and secrets. Parameter Store also integrates with AWS Identity and Access Management (IAM), allowing fine-grained access control to individual parameters or branches of a hierarchical tree.

This post demonstrates how to create and access shared configurations in Parameter Store from AWS Lambda. Both encrypted and plaintext parameter values are stored with only the Lambda function having permissions to decrypt the secrets. You also use AWS X-Ray to profile the function.

Solution overview

This example is made up of the following components:

An AWS SAM template that defines:
- A Lambda function and its permissions
- An unencrypted Parameter Store parameter that the Lambda function loads
- A KMS key that only the Lambda function can access. You use this key to create an encrypted parameter later.
Lambda function code in Python 3.6 that demonstrates how to load values from Parameter Store at function initialization for reuse across invocations.

Launch the AWS SAM template

To create the resources shown in this post, you can download the SAM template or choose the button to launch the stack. The template requires one parameter, an IAM user name, which is the name of the IAM user to be the admin of the KMS key that you create. In order to perform the steps listed in this post, this IAM user will need permissions to execute Lambda functions, create Parameter Store parameters, administer keys in KMS, and view the X-Ray console. If you have these privileges in your IAM user account you can use your own account to complete the walkthrough. You can not use the root user to administer the KMS keys.

SAM template resources

The following sections show the code for the resources defined in the template.
Lambda function

ParameterStoreBlogFunctionDev:
    Type: 'AWS::Serverless::Function'
    Properties:
      FunctionName: 'ParameterStoreBlogFunctionDev'
      Description: 'Integrating lambda with Parameter Store'
      Handler: 'lambda_function.lambda_handler'
      Role: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
      CodeUri: './code'
      Environment:
        Variables:
          ENV: 'dev'
          APP_CONFIG_PATH: 'parameterStoreBlog'
          AWS_XRAY_TRACING_NAME: 'ParameterStoreBlogFunctionDev'
      Runtime: 'python3.6'
      Timeout: 5
      Tracing: 'Active'

  ParameterStoreBlogFunctionRoleDev:
    Type: AWS::IAM::Role
    Properties:
      AssumeRolePolicyDocument:
        Version: '2012-10-17'
        Statement:
          -
            Effect: Allow
            Principal:
              Service:
                - 'lambda.amazonaws.com'
            Action:
              - 'sts:AssumeRole'
      ManagedPolicyArns:
        - 'arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole'
      Policies:
        -
          PolicyName: 'ParameterStoreBlogDevParameterAccess'
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              -
                Effect: Allow
                Action:
                  - 'ssm:GetParameter*'
                Resource: !Sub 'arn:aws:ssm:${AWS::Region}:${AWS::AccountId}:parameter/dev/parameterStoreBlog*'
        -
          PolicyName: 'ParameterStoreBlogDevXRayAccess'
          PolicyDocument:
            Version: '2012-10-17'
            Statement:
              -
                Effect: Allow
                Action:
                  - 'xray:PutTraceSegments'
                  - 'xray:PutTelemetryRecords'
                Resource: '*'

In this YAML code, you define a Lambda function named ParameterStoreBlogFunctionDev using the SAM AWS::Serverless::Function type. The environment variables for this function include the ENV (dev) and the APP_CONFIG_PATH where you find the configuration for this app in Parameter Store. X-Ray tracing is also enabled for profiling later.

The IAM role for this function extends the AWSLambdaBasicExecutionRole by adding IAM policies that grant the function permissions to write to X-Ray and get parameters from Parameter Store, limited to paths under /dev/parameterStoreBlog*.
Parameter Store parameter

SimpleParameter:
    Type: AWS::SSM::Parameter
    Properties:
      Name: '/dev/parameterStoreBlog/appConfig'
      Description: 'Sample dev config values for my app'
      Type: String
      Value: '{"key1": "value1","key2": "value2","key3": "value3"}'

This YAML code creates a plaintext string parameter in Parameter Store in a path that your Lambda function can access.
KMS encryption key

ParameterStoreBlogDevEncryptionKeyAlias:
    Type: AWS::KMS::Alias
    Properties:
      AliasName: 'alias/ParameterStoreBlogKeyDev'
      TargetKeyId: !Ref ParameterStoreBlogDevEncryptionKey

  ParameterStoreBlogDevEncryptionKey:
    Type: AWS::KMS::Key
    Properties:
      Description: 'Encryption key for secret config values for the Parameter Store blog post'
      Enabled: True
      EnableKeyRotation: False
      KeyPolicy:
        Version: '2012-10-17'
        Id: 'key-default-1'
        Statement:
          -
            Sid: 'Allow administration of the key & encryption of new values'
            Effect: Allow
            Principal:
              AWS:
                - !Sub 'arn:aws:iam::${AWS::AccountId}:user/${IAMUsername}'
            Action:
              - 'kms:Create*'
              - 'kms:Encrypt'
              - 'kms:Describe*'
              - 'kms:Enable*'
              - 'kms:List*'
              - 'kms:Put*'
              - 'kms:Update*'
              - 'kms:Revoke*'
              - 'kms:Disable*'
              - 'kms:Get*'
              - 'kms:Delete*'
              - 'kms:ScheduleKeyDeletion'
              - 'kms:CancelKeyDeletion'
            Resource: '*'
          -
            Sid: 'Allow use of the key'
            Effect: Allow
            Principal:
              AWS: !GetAtt ParameterStoreBlogFunctionRoleDev.Arn
            Action:
              - 'kms:Encrypt'
              - 'kms:Decrypt'
              - 'kms:ReEncrypt*'
              - 'kms:GenerateDataKey*'
              - 'kms:DescribeKey'
            Resource: '*'

This YAML code creates an encryption key with a key policy with two statements.

The first statement allows a given user (${IAMUsername}) to administer the key. Importantly, this includes the ability to encrypt values using this key and disable or delete this key, but does not allow the administrator to decrypt values that were encrypted with this key.

The second statement grants your Lambda function permission to encrypt and decrypt values using this key. The alias for this key in KMS is ParameterStoreBlogKeyDev, which is how you reference it later.

Lambda function

Here I walk you through the Lambda function code.

import os, traceback, json, configparser, boto3
from aws_xray_sdk.core import patch_all
patch_all()

# Initialize boto3 client at global scope for connection reuse
client = boto3.client('ssm')
env = os.environ['ENV']
app_config_path = os.environ['APP_CONFIG_PATH']
full_config_path = '/' + env + '/' + app_config_path
# Initialize app at global scope for reuse across invocations
app = None

class MyApp:
    def __init__(self, config):
        """
        Construct new MyApp with configuration
        :param config: application configuration
        """
        self.config = config

    def get_config(self):
        return self.config

def load_config(ssm_parameter_path):
    """
    Load configparser from config stored in SSM Parameter Store
    :param ssm_parameter_path: Path to app config in SSM Parameter Store
    :return: ConfigParser holding loaded config
    """
    configuration = configparser.ConfigParser()
    try:
        # Get all parameters for this app
        param_details = client.get_parameters_by_path(
            Path=ssm_parameter_path,
            Recursive=False,
            WithDecryption=True
        )

        # Loop through the returned parameters and populate the ConfigParser
        if 'Parameters' in param_details and len(param_details.get('Parameters')) > 0:
            for param in param_details.get('Parameters'):
                param_path_array = param.get('Name').split("/")
                section_position = len(param_path_array) - 1
                section_name = param_path_array[section_position]
                config_values = json.loads(param.get('Value'))
                config_dict = {section_name: config_values}
                print("Found configuration: " + str(config_dict))
                configuration.read_dict(config_dict)

    except:
        print("Encountered an error loading config from SSM.")
        traceback.print_exc()
    finally:
        return configuration

def lambda_handler(event, context):
    global app
    # Initialize app if it doesn't yet exist
    if app is None:
        print("Loading config and creating new MyApp...")
        config = load_config(full_config_path)
        app = MyApp(config)

    return "MyApp config is " + str(app.get_config()._sections)

Beneath the import statements, you import the patch_all function from the AWS X-Ray library, which you use to patch boto3 to create X-Ray segments for all your boto3 operations.

Next, you create a boto3 SSM client at the global scope for reuse across function invocations, following Lambda best practices. Using the function environment variables, you assemble the path where you expect to find your configuration in Parameter Store. The class MyApp is meant to serve as an example of an application that would need its configuration injected at construction. In this example, you create an instance of ConfigParser, a class in Python’s standard library for handling basic configurations, to give to MyApp.

The load_config function loads the all the parameters from Parameter Store at the level immediately beneath the path provided in the Lambda function environment variables. Each parameter found is put into a new section in ConfigParser. The name of the section is the name of the parameter, less the base path. In this example, the full parameter name is /dev/parameterStoreBlog/appConfig, which is put in a section named appConfig.

Finally, the lambda_handler function initializes an instance of MyApp if it doesn’t already exist, constructing it with the loaded configuration from Parameter Store. Then it simply returns the currently loaded configuration in MyApp. The impact of this design is that the configuration is only loaded from Parameter Store the first time that the Lambda function execution environment is initialized. Subsequent invocations reuse the existing instance of MyApp, resulting in improved performance. You see this in the X-Ray traces later in this post. For more advanced use cases where configuration changes need to be received immediately, you could implement an expiry policy for your configuration entries or push notifications to your function.

To confirm that everything was created successfully, test the function in the Lambda console.

Open the Lambda console.
In the navigation pane, choose Functions.
In the Functions pane, filter to ParameterStoreBlogFunctionDev to find the function created by the SAM template earlier. Open the function name to view its details.
On the top right of the function detail page, choose Test. You may need to create a new test event. The input JSON doesn’t matter as this function ignores the input.

After running the test, you should see output similar to the following. This demonstrates that the function successfully fetched the unencrypted configuration from Parameter Store.

Create an encrypted parameter

You currently have a simple, unencrypted parameter and a Lambda function that can access it.

Next, you create an encrypted parameter that only your Lambda function has permission to use for decryption. This limits read access for this parameter to only this Lambda function.

To follow along with this section, deploy the SAM template for this post in your account and make your IAM user name the KMS key admin mentioned earlier.

In the Systems Manager console, under Shared Resources, choose Parameter Store.
Choose Create Parameter.
- For Name, enter /dev/parameterStoreBlog/appSecrets.
- For Type, select Secure String.
- For KMS Key ID, choose alias/ParameterStoreBlogKeyDev, which is the key that your SAM template created.
- For Value, enter {"secretKey": "secretValue"}.
- Choose Create Parameter.
If you now try to view the value of this parameter by choosing the name of the parameter in the parameters list and then choosing Show next to the Value field, you won’t see the value appear. This is because, even though you have permission to encrypt values using this KMS key, you do not have permissions to decrypt values.
In the Lambda console, run another test of your function. You now also see the secret parameter that you created and its decrypted value.

If you do not see the new parameter in the Lambda output, this may be because the Lambda execution environment is still warm from the previous test. Because the parameters are loaded at Lambda startup, you need a fresh execution environment to refresh the values.

Adjust the function timeout to a different value in the Advanced Settings at the bottom of the Lambda Configuration tab. Choose Save and test to trigger the creation of a new Lambda execution environment.

Profiling the impact of querying Parameter Store using AWS X-Ray

By using the AWS X-Ray SDK to patch boto3 in your Lambda function code, each invocation of the function creates traces in X-Ray. In this example, you can use these traces to validate the performance impact of your design decision to only load configuration from Parameter Store on the first invocation of the function in a new execution environment.

From the Lambda function details page where you tested the function earlier, under the function name, choose Monitoring. Choose View traces in X-Ray.

This opens the X-Ray console in a new window filtered to your function. Be aware of the time range field next to the search bar if you don’t see any search results.
In this screenshot, I’ve invoked the Lambda function twice, one time 10.3 minutes ago with a response time of 1.1 seconds and again 9.8 minutes ago with a response time of 8 milliseconds.

Looking at the details of the longer running trace by clicking the trace ID, you can see that the Lambda function spent the first ~350 ms of the full 1.1 sec routing the request through Lambda and creating a new execution environment for this function, as this was the first invocation with this code. This is the portion of time before the initialization subsegment.

Next, it took 725 ms to initialize the function, which includes executing the code at the global scope (including creating the boto3 client). This is also a one-time cost for a fresh execution environment.

Finally, the function executed for 65 ms, of which 63.5 ms was the GetParametersByPath call to Parameter Store.

Looking at the trace for the second, much faster function invocation, you see that the majority of the 8 ms execution time was Lambda routing the request to the function and returning the response. Only 1 ms of the overall execution time was attributed to the execution of the function, which makes sense given that after the first invocation you’re simply returning the config stored in MyApp.

While the Traces screen allows you to view the details of individual traces, the X-Ray Service Map screen allows you to view aggregate performance data for all traced services over a period of time.

In the X-Ray console navigation pane, choose Service map. Selecting a service node shows the metrics for node-specific requests. Selecting an edge between two nodes shows the metrics for requests that traveled that connection. Again, be aware of the time range field next to the search bar if you don’t see any search results.

After invoking your Lambda function several more times by testing it from the Lambda console, you can view some aggregate performance metrics. Look at the following:

From the client perspective, requests to the Lambda service for the function are taking an average of 50 ms to respond. The function is generating ~1 trace per minute.
The function itself is responding in an average of 3 ms. In the following screenshot, I’ve clicked on this node, which reveals a latency histogram of the traced requests showing that over 95% of requests return in under 5 ms.
Parameter Store is responding to requests in an average of 64 ms, but note the much lower trace rate in the node. This is because you only fetch data from Parameter Store on the initialization of the Lambda execution environment.

Conclusion

Deduplication, encryption, and restricted access to shared configuration and secrets is a key component to any mature architecture. Serverless architectures designed using event-driven, on-demand, compute services like Lambda are no different.

In this post, I walked you through a sample application accessing unencrypted and encrypted values in Parameter Store. These values were created in a hierarchy by application environment and component name, with the permissions to decrypt secret values restricted to only the function needing access. The techniques used here can become the foundation of secure, robust configuration management in your enterprise serverless applications.

Dynamically Create Friendly URLs for Your Amazon EMR Web Interfaces

2018-02-04 Ilya Epshteyn

Post Syndicated from Ilya Epshteyn original https://aws.amazon.com/blogs/big-data/dynamically-create-friendly-urls-for-your-amazon-emr-web-interfaces/

Amazon EMR enables data analysts and scientists to deploy a cluster running popular frameworks such as Spark, HBase, Presto, and Flink of any size in minutes. When you launch a cluster, Amazon EMR automatically configures the underlying Amazon EC2 instances with the frameworks and applications that you choose for your cluster. This can include popular web interfaces such as Hue workbench, Zeppelin notebook, and Ganglia monitoring dashboards and tools.

These web interfaces are hosted on the EMR master node and must be accessed using the public DNS name of the master node (master public DNS value). The master public DNS value is dynamically created, not very user friendly and is hard to remember— it looks something like ip-###-###-###-###.us-west-2.compute.internal. Not having a friendly URL to connect to the popular workbench or notebook interfaces may impact the workflow and hinder your gained agility.

Some customers have addressed this challenge through custom bootstrap actions, steps, or external scripts that periodically check for new clusters and register a friendlier name in DNS. These approaches either put additional burden on the data practitioners or require additional resources to execute the scripts. In addition, there is typically some lag time associated with such scripts. They often don’t do a great job cleaning up the DNS records after the cluster has terminated, potentially resulting in a security risk.

The solution in this post provides an automated, serverless approach to registering a friendly master node name for easy access to the web interfaces.

AWS services

Inspired in part by our colleague’s post on Building a Dynamic DNS for Route 53 using CloudWatch Events and Lambda, this solution leverages Amazon CloudWatch Events, AWS Lambda, and Amazon Route 53 to dynamically register a CNAME with a friendly name in a Route 53 private hosted zone.

Before I dive deeper, I review these key services and how they are part of this solution.

CloudWatch Events

CloudWatch Events delivers a near real-time stream of system events that describe changes in AWS resources. Using simple rules, you can match events and route them to one or more target functions or streams. An event can be generated in one of four ways:

From an AWS service when resources change state
From API calls that are delivered via AWS CloudTrail
From your own code that can generate application-level events
Issued on cron-style scheduling

In this solution, I cover the first type of event, which is automatically emitted by EMR when the cluster state changes. Based on the state of this event, either create or update the DNS record in Route 53 when the cluster state changes to STARTING, or delete the DNS record when the cluster is no longer needed and the state changes to TERMINATED. For more information about all EMR event details, see Monitor CloudWatch Events.

Route 53 private hosted zones

A private hosted zone is a container that holds information about how to route traffic for a domain and its subdomains within one or more VPCs. Private hosted zones enable you to use custom DNS names for your internal resources without exposing the names or IP addresses to the internet.

Route 53 supports resource record sets with a wide range of record types. In this solution, you use a CNAME record that is used to specify a domain name as an alias for another domain (the ‘canonical’ domain). You use a friendly name of the cluster as the CNAME for the EMR master public DNS value.

You are using private hosted zones because an EMR cluster is typically deployed within a private subnet and is accessed either from within the VPC or from on-premises resources over VPN or AWS Direct Connect. To resolve domain names in private hosted zones from your on-premises network, configure a DNS forwarder, as described in How can I resolve Route 53 private hosted zones from an on-premises network via an Ubuntu instance?.

Lambda

Lambda is a compute service that lets you run code without provisioning or managing servers. Lambda executes your code only when needed and scales automatically to thousands of requests per second. Lambda takes care of high availability, and server and OS maintenance and patching. You pay only for the consumed compute time. There is no charge when your code is not running.

Lambda provides the ability to invoke your code in response to events, such as when an object is put to an Amazon S3 bucket or as in this case, when a CloudWatch event is emitted. As part of this solution, you deploy a Lambda function as a target that is invoked by CloudWatch Events when the event matches your rule. You also configure the necessary permissions based on the Lambda permissions model, including a Lambda function policy and Lambda execution role.

Putting it all together

Now that you have all of the pieces, you can put together a complete solution. The following diagram illustrates how the solution works:

Start with a user activity such as launching or terminating an EMR cluster.
EMR automatically sends events to the CloudWatch Events stream.
A CloudWatch Events rule matches the specified event, and routes it to a target, which in this case is a Lambda function. In this case, you are using the EMR Cluster State Change
The Lambda function performs the following key steps:
- Get the clusterId value from the event detail and use it to call EMR. DescribeCluster API to retrieve the following data points:
  - MasterPublicDnsName – public DNS name of the master node
  - Locate the tag containing the friendly name to use as the CNAME for the cluster. The key name containing the friendly name should be The value should be specified as host.domain.com, where domain is the private hosted zone in which to update the DNS record.
- Update DNS based on the state in the event detail.
  - If the state is STARTING, the function calls the Route 53 API to create or update a resource record set in the private hosted zone specified by the domain tag. This is a CNAME record mapped to MasterPublicDnsName.
  - Conversely, if the state is TERMINATED, the function calls the Route 53 API to delete the associated resource record set from the private hosted zone.

Deploying the solution

Because all of the components of this solution are serverless, use the AWS Serverless Application Model (AWS SAM) template to deploy the solution. AWS SAM is natively supported by AWS CloudFormation and provides a simplified syntax for expressing serverless resources, resulting in fewer lines of code.

Overview of the SAM template

For this solution, the SAM template has 76 lines of text as compared to 142 lines without SAM resources (and writing the template in YAML would be even slightly smaller). The solution can be deployed using the AWS Management Console, AWS Command Line Interface (AWS CLI), or AWS SAM Local.

CloudFormation transforms help simplify template authoring by condensing a multiple-line resource declaration into a single line in your template. To inform CloudFormation that your template defines a serverless application, add a line under the template format version as follows:

"AWSTemplateFormatVersion":"2010-09-09",
"Transform":"AWS::Serverless-2016-10-31",

Before SAM, you would use the AWS::Lambda::Function resource type to define your Lambda function. You would then need a resource to define the permissions for the function (AWS::Lambda::Permission), another resource to define a Lambda execution role (AWS::IAM::Role), and finally a CloudWatch Events resource (Events::Rule) that triggers this function.

With SAM, you need to define just a single resource for your function, AWS::Serverless::Function. Using this single resource type, you can define everything that you need, including function properties such as function handler, runtime, and code URI, as well as the required IAM policies and the CloudWatch event.

"DnsSetterLambda":{
         "Type":"AWS::Serverless::Function",
         "Properties":{
            "Handler":"emr-dns-setter.lambda_handler",
            "Runtime":"python2.7",
            "CodeUri":"emr-dns-setter.py.zip",
            "Description":"Create PHZ record for EMR cluster",
            "Timeout":90,
            "Policies":[
               {
                  "Version":"2012-10-17",
                  "Statement":[
                     {
                        "Effect":"Allow",
                        "Action":"ec2:Describe*",
                        "Resource":"*"
                     },
                     {
                        "Effect":"Allow",
                        "Action":[
                           "elasticmapreduce:Describe*"
                        ],
                        "Resource":"*"
                     },
                     {
                        "Effect":"Allow",
                        "Action":[
                           "logs:CreateLogGroup",
                           "logs:CreateLogStream",
                           "logs:PutLogEvents"
                        ],
                        "Resource":"*"
                     },
                     {
                        "Effect":"Allow",
                        "Action":[
                           "route53:ChangeResourceRecordSets",
                           "route53:GetHostedZone",
                           "route53:ListHostedZones",
                           "route53:ListHostedZonesByName",
                           "route53:ListResourceRecordSets"
                        ],
                        "Resource":[
                           "*"
                        ]
                     }
                  ]
               }
            ],
            "Events":{
               "CloudWatchEventDNS":{
                  "Type":"CloudWatchEvent",
                  "Properties":{
                     "Pattern":{
                        "source":[
                           "aws.emr"
                        ],
                        "detail-type":[
                           "EMR Cluster State Change"
                        ]
                     }
                  }
               }
            }
         }
      }
   },
   "Outputs":{

   }
}

A few additional things to note in the code example:

CodeUri – Before you can deploy a SAM template, first upload your Lambda function code zip to S3. You can do this manually or use the aws cloudformation package CLI command to automate the task of uploading local artifacts to a S3 bucket, as shown later.
Lambda execution role and permissions – You are not specifying a Lambda execution role in the template. Rather, you are providing the required permissions as IAM policy documents. When the template is submitted, CloudFormation expands the AWS::Serverless::Function resource, declaring a Lambda function and an execution role. The created role has two attached policies: a default AWSLambdaBasicExecutionRole and the inline policy specified in the template.
CloudWatch Events rule – Instead of specifying a CloudWatch Events resource type, you are defining an event source object as a property of the function itself. When the template is submitted, CloudFormation expands this into a CloudWatch Events rule resource and automatically creates the Lambda resource-based permissions to allow the CloudWatch Events rule to trigger the function.

Deploying the solution using the console

1.) Log in to the CloudFormation console and choose Create stack.

2.) For Choose a template, select Specify an Amazon S3 template URL and enter the following URL:

https://s3.amazonaws.com/aws-bigdata-blog/artifacts/emr-dns-setter/emr-dns-setter-sam.json

NOTE: If you are trying this solution outside of us-east-1, then you should download the necessary files, upload them to the buckets in your region, edit the script as appropriate and then run it or use the CLI deployment method below.

3.) Choose Next.

4.) On the Specify Details page, keep or modify the stack name and choose Next.

5.) On the Options page, choose Next.

6.) On the Review page, take the following steps:

Acknowledge the two Transform access capabilities. This allows the CloudFormation transform to create the required IAM resources with custom names.

Under Transforms, choose Create Change Set.

Wait a few seconds for the change set to be created before proceeding. The change set should look as follows:

7.) Choose Execute to deploy the template.

After the template is deployed, you should see four resources created:

Deploying the solution using the AWS CLI

Download the Lambda function code emr-dns-setter.py and the SAM template emr-dns-setter-sam.json to your local machine.
Modify the CodeUri property in emr-dns-setter-sam.json template to the local path of the function code:

<file path>\emr-dns-setter.py

Use the aws cloudformation package CLI command to upload the package.

aws cloudformation package --template-file <FILE_PATH>\emr-dns-setter-sam.json --output-template-file serverless-output.template --s3-bucket <BUCKET_USED_TO_UPLOAD_YOUR_ARTIFACTS>

After the package is successfully uploaded, the output should look as follows:

Uploading to 0f6d12c7872b50b37dbfd5a60385b854  1872 / 1872.0  (100.00%)
Successfully packaged artifacts and wrote output template to file serverless-output.template.

The CodeUri property in serverless-output.template is now referencing the packaged artifacts in the S3 bucket that you specified:

s3://<bucket>/0f6d12c7872b50b37dbfd5a60385b854

Use the aws cloudformation deploy CLI command to deploy the stack:

aws cloudformation deploy --template-file <FILE PATH>\serverless-output.template --stack-name <STACK_NAME> --capabilities CAPABILITY_IAM

You should see the following output after the stack has been successfully created:

Waiting for changeset to be created...
Waiting for stack create/update to complete
Successfully created/updated stack – EmrDnsSetterCli

Validating results

To test the solution, launch an EMR cluster. The Lambda function looks for the cluster_name tag associated with the EMR cluster. Make sure to specify the friendly name of your cluster as host.domain.com where the domain is the private hosted zone in which to create the CNAME record.

Here is a sample CLI command to launch a cluster within a specific subnet in a VPC with the required tag cluster_name.

aws emr create-cluster --tags LOB="finance" cluster_name="finance-ingest.domain.com" --release-label emr-5.3.1  --use-default-roles --instance-groups InstanceGroupType=MASTER,InstanceCount=1,InstanceType=m3.xlarge InstanceGroupType=CORE,InstanceCount=2,InstanceType=m3.xlarge --ec2-attributes SubnetId=subnet-xxxxxxxx,KeyName=keyname --no-auto-terminate

After the cluster is launched, log in to the Route 53 console. In the left navigation pane, choose Hosted Zones to view the list of private and public zones currently configured in Route 53. Select the hosted zone that you specified in the ZONE tag when you launched the cluster. Verify that the resource records were created.

You can also monitor the CloudWatch Events metrics that are published to CloudWatch every minute, such as the number of TriggeredRules and Invocations.

Now that you’ve verified that the Lambda function successfully updated the Route 53 resource records in the zone file, terminate the EMR cluster and verify that the records are removed by the same function.

Conclusion

This solution provides a serverless approach to automatically assigning a friendly name for your EMR cluster for easy access to popular notebooks and other web interfaces. CloudWatch Events also supports cross-account event delivery, so if you are running EMR clusters in multiple AWS accounts, all cluster state events across accounts can be consolidated into a single account.

I hope that this solution provides a small glimpse into the power of CloudWatch Events and Lambda and how they can be leveraged with EMR and other AWS big data services. For example, by using the EMR step state change event, you can chain various pieces of your analytics pipeline. You may have a transient cluster perform data ingest and, when the task successfully completes, spin up an ETL cluster for transformation and upload to Amazon Redshift. The possibilities are truly endless.

Announcing Go Support for AWS Lambda

2018-01-16 Chris Munns

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/announcing-go-support-for-aws-lambda/

This post courtesy of Paul Maddox, Specialist Solutions Architect (Developer Technologies).

Today, we’re excited to announce Go as a supported language for AWS Lambda.

As someone who’s done their fair share of Go development (recent projects include AWS SAM Local and GoFormation), this is a release I’ve been looking forward to for a while. I’m going to take this opportunity to walk you through how it works by creating a Go serverless application, and deploying it to Lambda.

Prerequisites

This post assumes that you already have Go installed and configured on your development machine, as well as a basic understanding of Go development concepts. For more details, see https://golang.org/doc/install.

Creating an example Serverless application with Go

Lambda functions can be triggered by variety of event sources:

Asynchronous events (such as an object being put in an Amazon S3 bucket)
Streaming events (for example, new data records on an Amazon Kinesis stream)
Synchronous events (manual invocation, or HTTPS request via Amazon API Gateway)

As an example, you’re going to create an application that uses an API Gateway event source to create a simple Hello World RESTful API. The full source code for this example application can be found on GitHub at: https://github.com/aws-samples/lambda-go-samples.

After the application is published, it receives a name via the HTTPS request body, and responds with “Hello <name>.” For example:

$ curl -XPOST -d "Paul" "https://my-awesome-api.example.com/"
Hello Paul

To implement this, create a Lambda handler function in Go.

Import the github.com/aws/aws-lambda-go package, which includes helpful Go definitions for Lambda event sources, as well as the lambda.Start() method used to register your handler function.

Start by creating a new project directory in your $GOPATH, and then creating a main.go file that contains your Lambda handler function:

package main

import (
 "errors"
 "log"
 "github.com/aws/aws-lambda-go/events"
 "github.com/aws/aws-lambda-go/lambda"
)

var (
 // ErrNameNotProvided is thrown when a name is not provided
 ErrNameNotProvided = errors.New("no name was provided in the HTTP body")
)

// Handler is your Lambda function handler
// It uses Amazon API Gateway request/responses provided by the aws-lambda-go/events package,
// However you could use other event sources (S3, Kinesis etc), or JSON-decoded primitive types such as 'string'.
func Handler(request events.APIGatewayProxyRequest) (events.APIGatewayProxyResponse, error) {

 // stdout and stderr are sent to AWS CloudWatch Logs
 log.Printf("Processing Lambda request %s\n", request.RequestContext.RequestID)

 // If no name is provided in the HTTP request body, throw an error
 if len(request.Body) < 1 {
  return events.APIGatewayProxyResponse{}, ErrNameNotProvided
 }

 return events.APIGatewayProxyResponse{
  Body:       "Hello " + request.Body,
  StatusCode: 200,
 }, nil

}

func main() {
 lambda.Start(Handler)
}

The lambda.Start() method takes a handler, and talks to an internal Lambda endpoint to pass Invoke requests to the handler. If a handler does not match one of the supported types, the Lambda package responds to new invocations served by an internal endpoint with an error message such as:

json: cannot unmarshal object into Go value of type int32: UnmarshalTypeError

The lambda.Start() method blocks, and does not return after being called, meaning that it’s suitable to run in your Go application’s main entry point.

More detail on AWS Lambda function handlers with Go

A handler function passed to lambda.Start() must follow these rules:

It must be a function.
The function may take between 0 and 2 arguments.
- If there are two arguments, the first argument must implement context.Context.
The function may return between 0 and 2 values.
- If there is one return value, it must implement error.
- If there are two return values, the second value must implement error.

The github.com/aws/aws-lambda-go library automatically unmarshals the Lambda event JSON to the argument type used by your handler function. To do this, it uses Go’s standard encoding/json package, so your handler function can use any of the standard types supported for unmarshalling (or custom types containing those):

bool, for JSON booleans
float64, for JSON numbers
string, for JSON strings
[]interface{}, for JSON arrays
map[string]interface{}, for JSON objects
nil, for JSON null

For example, your Lambda function received a JSON event payload like the following:

{
  "id": 12345,
  "value": "some-value"
}

It should respond with a JSON response that looks like the following:

{
  "message": "processed request ID 12345",
  "ok": true
}

You could use a Lambda handler function that looks like the following:

package main

import (
  "fmt"
  "github.com/aws/aws-lambda-go/lambda"
)

type Request struct {
  ID        float64 `json:"id"`
  Value     string  `json:"value"`
}

type Response struct {
  Message string `json:"message"`
  Ok      bool   `json:"ok"`
}

func Handler(request Request) (Response, error) {
 return Response{
  Message: fmt.Sprintf("Processed request ID %f", request.ID),
  Ok:      true,
 }, nil
}

func main() {
 lambda.Start(Handler)
}

For convenience, the github.com/aws/aws-lambda-go package provides event sources that you can also use in your handler function arguments. It also provides return values for common sources such as S3, Kinesis, Cognito, and the API Gateway event source and response objects that you’re using in the application example.

Adding unit tests

To test that the Lambda handler works as expected, create a main_test.go file containing some basic unit tests.

package main_test

import (
 "testing"
 main "github.com/aws-samples/lambda-go-samples"
 "github.com/aws/aws-lambda-go/events"
 "github.com/stretchr/testify/assert"
)

func TestHandler(t *testing.T) {
 tests := []struct {
  request events.APIGatewayProxyRequest
  expect  string
  err     error
 }{
   {
    // Test that the handler responds with the correct response
    // when a valid name is provided in the HTTP body
    request: events.APIGatewayProxyRequest{Body: "Paul"},
    expect:  "Hello Paul",
    err:     nil,
   },
   {
    // Test that the handler responds ErrNameNotProvided
    // when no name is provided in the HTTP body
    request: events.APIGatewayProxyRequest{Body: ""},
    expect:  "",
    err:     main.ErrNameNotProvided,
   },
  }

  for _, test := range tests {
   response, err := main.Handler(test.request) 
   assert.IsType(t, test.err, err)
   assert.Equal(t, test.expect, response.Body)
  }
}

Run your tests:

$ go test
PASS
ok      github.com/awslabs/lambda-go-example    0.041s

Note: To make the unit tests more readable, this example uses a third-party library (https://github.com/stretchr/testify). This allows you to describe the test cases in a more natural format, making them more maintainable for other people who may be working in the code base.

Build and deploy

As Go is a compiled language, build the application and create a Lambda deployment package. To do this, build a binary that runs on Linux, and zip it up into a deployment package.

To do this, we need to build a binary that will run on Linux, and ZIP it up into a deployment package.

$ GOOS=linux go build -o main
$ zip deployment.zip main

The binary doesn’t need to be called main, but the name must match the Handler configuration property of the deployed Lambda function.

The deployment package is now ready to be deployed to Lambda. One deployment method is to use the AWS CLI. Provide a valid Lambda execution role for –role.

$ aws lambda create-function \
--region us-west-1 \
--function-name HelloFunction \
--zip-file fileb://./deployment.zip \
--runtime go1.x \
--tracing-config Mode=Active
--role arn:aws:iam::<account-id>:role/<role> \
--handler main

From here, configure the invoking service for your function, in this example API Gateway, to call this function and provide the HTTPS frontend for your API. For more information about how to do this in the API Gateway console, see Create an API with Lambda Proxy Integration. You could also do this in the Lambda console by assigning an API Gateway trigger.

Then, configure the trigger:

API name: lambda-go
Deployment stage: prod
Security: open

This results in an API Gateway endpoint that you can test.

Now, you can use cURL to test your API:

$ curl -XPOST -d "Paul" https://u7fe6p3v64.execute-api.us-east-1.amazonaws.com/prod/main
Hello Paul

Doing this manually is fine and works for testing and exploration. If you were doing this for real, you’d want to automate this process further. The next section shows how to add a CI/CD pipeline to this process to build, test, and deploy your serverless application as you change your code.

Automating tests and deployments

Next, configure AWS CodePipeline and AWS CodeBuild to build your application automatically and run all of the tests. If it passes, deploy your application to Lambda.

The first thing you need to do is create an AWS Serverless Application Model (AWS SAM) template in your source repository. SAM provides an easy way to deploy Serverless resources, such as Lambda functions, APIs, and other event sources, as well as all of the necessary IAM permissions, etc. You can also include any valid AWS CloudFormation resources within your SAM template, such as a Kinesis stream, or an Amazon DynamoDB table. They are deployed alongside your Serverless application.

Create a file called template.yml in your application repository with the following contents:

AWSTemplateFormatVersion: 2010-09-09
Transform: AWS::Serverless-2016-10-31
Resources:
  HelloFunction:
    Type: AWS::Serverless::Function
    Properties:
      Handler: main
      Runtime: go1.x
      Tracing: Active
      Events:
        GetEvent:
          Type: Api
          Properties:
            Path: /
            Method: post

The above template instructs SAM to deploy a Lambda function (called HelloFunction in this case), with the Go runtime (go1.x), and also an API configured to pass HTTP POST requests to your Lambda function. The Handler property defines which binary in the deployment package needs to be executed (main in this case).

You’re going to use CodeBuild to run your tests, build your Go application, and package it. You can tell CodeBuild how to do all of this by creating a buildspec.yml file in your repository containing the following:


version: 0.2
env:
  variables:
    # This S3 bucket is used to store the packaged Lambda deployment bundle.
    # Make sure to provide a valid S3 bucket name (it must exist already).
    # The CodeBuild IAM role must allow write access to it.
    S3_BUCKET: "your-s3-bucket"
    PACKAGE: "github.com/aws-samples/lambda-go-samples"

phases:
  install:
    commands:
      # AWS Codebuild Go images use /go for the $GOPATH so copy the
      # application source code into that directory structure.
      - mkdir -p "/go/src/$(dirname ${PACKAGE})"
      - ln -s "${CODEBUILD_SRC_DIR}" "/go/src/${PACKAGE}"
      # Print all environment variables (handy for AWS CodeBuild logs)
      - env
      # Install golint
      - go get -u github.com/golang/lint/golint

  pre_build:
    commands:
      # Make sure we're in the project directory within our GOPATH
      - cd "/go/src/${PACKAGE}"
      # Fetch all dependencies
      - go get ./...
      # Ensure that the code passes all lint tests
      - golint -set_exit_status
      # Check for common Go problems with 'go vet'
      - go vet .
      # Run all tests included with the application
      - go test .

  build:
    commands:
      # Build the go application
      - go build -o main
      # Package the application with AWS SAM
      - aws cloudformation package --template-file template.yml --s3-bucket ${S3_BUCKET} --output-template-file packaged.yml

artifacts:
  files:
  - packaged.yml

This buildspec file does the following:

Sets up your GOPATH, ready for building
Runs golint to make sure that any committed code matches the Go style and formatting specification
Runs any unit tests present (via go test)
Builds your application binary
Packages the binary into a Lambda deployment package and uploads it to S3

For more details about buildspec files, see the Build Specification Reference for AWS CodeBuild.

Your project directory should now contain the following files:

$ tree
.
├── buildspec.yml    (AWS CodeBuild configuration file)
├── main.go          (Our application)
├── main_test.go     (Unit tests)
└── template.yml     (AWS SAM template)
0 directories, 4 files

You’re now ready to set up your automated pipeline with CodePipeline.

Create a new pipeline

Get started by navigating to the CodePipeline console. You need to give your new pipeline a name, such as HelloService.

Next, select the source repository in which your application code is located. CodePipeline supports either AWS CodeCommit, GitHub.com, or S3. To use the example GitHub.com repository mentioned earlier in this post, fork it into your own GitHub.com account or create a new CodeCommit repository and clone it into there. Do this first before selecting a source location.

Tell CodePipeline to use CodeBuild to test, build, and package your application using the buildspec.yml file created earlier:

Important: CodeBuild needs read/write access to the S3 bucket referenced in the buildspec.yml file that you wrote. It places the packaged Lambda deployment package into S3 after the tests and build are completed. Make sure that the CodeBuild service role created or provided has the correct IAM permissions. For more information, see Writing IAM Policies: How to grant access to an Amazon S3 bucket. If you don’t do this, CodeBuild fails.

Finally, set up the deployment stage of your pipeline. Select AWS CloudFormation as the deployment method, and the Create or replace a change set mode (as required by SAM). To deploy multiple environments (for example, staging, production), add additional deployment stages to your pipeline after it has been created.

After being created, your pipeline takes a few minutes to initialize, and then automatically triggers. You can see the latest commit in your version control system make progress through the build and deploy stages of your pipeline.

You do not need to configure anything further to automatically run your pipeline on new version control commits. It already automatically triggers, builds, and deploys each time.

Make one final change to the pipeline, to configure the deployment stage to execute the CloudFormation changeset that it creates. To make this change, choose the Edit button on your pipeline, choose the pencil icon on the staging deployment stage, and add a new action:

After the action is added, save your pipeline. You can test it by making a small change to your Lambda function, and then committing it back to version control. You can see your pipeline trigger, and the changes get deployed to your staging environment.

See it in Action

After a successful run of the pipeline has completed, you can navigate to the CloudFormation console to see the deployment details.

In your case, you have a CloudFormation stack deployed. If you look at the Resources tab, you see a table of the AWS resources that have been deployed.

Choose the ServerlessRestApi item link to navigate to the API Gateway console and view the details of your deployed API, including the URL,

You can use cURL to test that your Serverless application is functioning as expected:

$ curl -XPOST -d "Paul" https://y5fjgtq6dj.execute-api.us-west-1.amazonaws.com/Stage
Hello Paul

One more thing!

We are also excited to announce that AWS X-Ray can be enabled in your Lambda runtime to analyze and debug your Go functions written for Lambda. The X-Ray SDK for Go works with the Go context of your Lambda function, providing features such as AWS SDK retry visibility and one-line error capture.

You can use annotations and metadata to capture additional information in X-Ray about your function invocations. Moreover, the SDK supports the net/http client package, enabling you to trace requests made to endpoints even if they are not X-Ray enabled.

Wrapping it up!

Support for Go has been a much-requested feature in Lambda and we are excited to be able to bring it to you. In this post, you created a basic Go-based API and then went on to create a full continuous integration and delivery pipeline that tests, builds, and deploys your application each time you make a change.

You can also get started with AWS Lambda Go support through AWS CodeStar. AWS CodeStar lets you quickly launch development projects that include a sample application, source control and release automation. With this announcement, AWS CodeStar introduced new project templates for Go running on AWS Lambda. Select one of the CodeStar Go project templates to get started. CodeStar makes it easy to begin editing your Go project code in AWS Cloud9, an online IDE, with just a few clicks.

Excited about Go in Lambda or have questions? Let us know in the comments here, in the AWS Forums for Lambda, or find us on Twitter at @awscloud.

Combine Transactional and Analytical Data Using Amazon Aurora and Amazon Redshift

2018-01-06 Re Alvarez-Parmar

Post Syndicated from Re Alvarez-Parmar original https://aws.amazon.com/blogs/big-data/combine-transactional-and-analytical-data-using-amazon-aurora-and-amazon-redshift/

A few months ago, we published a blog post about capturing data changes in an Amazon Aurora database and sending it to Amazon Athena and Amazon QuickSight for fast analysis and visualization. In this post, I want to demonstrate how easy it can be to take the data in Aurora and combine it with data in Amazon Redshift using Amazon Redshift Spectrum.

With Amazon Redshift, you can build petabyte-scale data warehouses that unify data from a variety of internal and external sources. Because Amazon Redshift is optimized for complex queries (often involving multiple joins) across large tables, it can handle large volumes of retail, inventory, and financial data without breaking a sweat.

In this post, we describe how to combine data in Aurora in Amazon Redshift. Here’s an overview of the solution:

Use AWS Lambda functions with Amazon Aurora to capture data changes in a table.
Save data in an Amazon S3
Query data using Amazon Redshift Spectrum.

We use the following services:

Amazon Aurora
AWS Lambda
Amazon Kinesis Data Firehose
Amazon S3
Amazon Redshift

Serverless architecture for capturing and analyzing Aurora data changes

Consider a scenario in which an e-commerce web application uses Amazon Aurora for a transactional database layer. The company has a sales table that captures every single sale, along with a few corresponding data items. This information is stored as immutable data in a table. Business users want to monitor the sales data and then analyze and visualize it.

In this example, you take the changes in data in an Aurora database table and save it in Amazon S3. After the data is captured in Amazon S3, you combine it with data in your existing Amazon Redshift cluster for analysis.

By the end of this post, you will understand how to capture data events in an Aurora table and push them out to other AWS services using AWS Lambda.

The following diagram shows the flow of data as it occurs in this tutorial:

The starting point in this architecture is a database insert operation in Amazon Aurora. When the insert statement is executed, a custom trigger calls a Lambda function and forwards the inserted data. Lambda writes the data that it received from Amazon Aurora to a Kinesis data delivery stream. Kinesis Data Firehose writes the data to an Amazon S3 bucket. Once the data is in an Amazon S3 bucket, it is queried in place using Amazon Redshift Spectrum.

Creating an Aurora database

First, create a database by following these steps in the Amazon RDS console:

Sign in to the AWS Management Console, and open the Amazon RDS console.
Choose Launch a DB instance, and choose Next.
For Engine, choose Amazon Aurora.
Choose a DB instance class. This example uses a small, since this is not a production database.
In Multi-AZ deployment, choose No.
Configure DB instance identifier, Master username, and Master password.
Launch the DB instance.

After you create the database, use MySQL Workbench to connect to the database using the CNAME from the console. For information about connecting to an Aurora database, see Connecting to an Amazon Aurora DB Cluster.

The following screenshot shows the MySQL Workbench configuration:

Next, create a table in the database by running the following SQL statement:

Create Table
CREATE TABLE Sales (
InvoiceID int NOT NULL AUTO_INCREMENT,
ItemID int NOT NULL,
Category varchar(255),
Price double(10,2), 
Quantity int not NULL,
OrderDate timestamp,
DestinationState varchar(2),
ShippingType varchar(255),
Referral varchar(255),
PRIMARY KEY (InvoiceID)
)

You can now populate the table with some sample data. To generate sample data in your table, copy and run the following script. Ensure that the highlighted (bold) variables are replaced with appropriate values.

#!/usr/bin/python
import MySQLdb
import random
import datetime

db = MySQLdb.connect(host="AURORA_CNAME",
                     user="DBUSER",
                     passwd="DBPASSWORD",
                     db="DB")

states = ("AL","AK","AZ","AR","CA","CO","CT","DE","FL","GA","HI","ID","IL","IN",
"IA","KS","KY","LA","ME","MD","MA","MI","MN","MS","MO","MT","NE","NV","NH","NJ",
"NM","NY","NC","ND","OH","OK","OR","PA","RI","SC","SD","TN","TX","UT","VT","VA",
"WA","WV","WI","WY")

shipping_types = ("Free", "3-Day", "2-Day")

product_categories = ("Garden", "Kitchen", "Office", "Household")
referrals = ("Other", "Friend/Colleague", "Repeat Customer", "Online Ad")

for i in range(0,10):
    item_id = random.randint(1,100)
    state = states[random.randint(0,len(states)-1)]
    shipping_type = shipping_types[random.randint(0,len(shipping_types)-1)]
    product_category = product_categories[random.randint(0,len(product_categories)-1)]
    quantity = random.randint(1,4)
    referral = referrals[random.randint(0,len(referrals)-1)]
    price = random.randint(1,100)
    order_date = datetime.date(2016,random.randint(1,12),random.randint(1,30)).isoformat()

    data_order = (item_id, product_category, price, quantity, order_date, state,
    shipping_type, referral)

    add_order = ("INSERT INTO Sales "
                   "(ItemID, Category, Price, Quantity, OrderDate, DestinationState, \
                   ShippingType, Referral) "
                   "VALUES (%s, %s, %s, %s, %s, %s, %s, %s)")

    cursor = db.cursor()
    cursor.execute(add_order, data_order)

    db.commit()

cursor.close()
db.close()

The following screenshot shows how the table appears with the sample data:

Sending data from Amazon Aurora to Amazon S3

There are two methods available to send data from Amazon Aurora to Amazon S3:

Using a Lambda function
Using SELECT INTO OUTFILE S3

To demonstrate the ease of setting up integration between multiple AWS services, we use a Lambda function to send data to Amazon S3 using Amazon Kinesis Data Firehose.

Alternatively, you can use a SELECT INTO OUTFILE S3 statement to query data from an Amazon Aurora DB cluster and save it directly in text files that are stored in an Amazon S3 bucket. However, with this method, there is a delay between the time that the database transaction occurs and the time that the data is exported to Amazon S3 because the default file size threshold is 6 GB.

Creating a Kinesis data delivery stream

The next step is to create a Kinesis data delivery stream, since it’s a dependency of the Lambda function.

To create a delivery stream:

Open the Kinesis Data Firehose console
Choose Create delivery stream.
For Delivery stream name, type AuroraChangesToS3.
For Source, choose Direct PUT.
For Record transformation, choose Disabled.
For Destination, choose Amazon S3.
In the S3 bucket drop-down list, choose an existing bucket, or create a new one.
Enter a prefix if needed, and choose Next.
For Data compression, choose GZIP.
In IAM role, choose either an existing role that has access to write to Amazon S3, or choose to generate one automatically. Choose Next.
Review all the details on the screen, and choose Create delivery stream when you’re finished.

Creating a Lambda function

Now you can create a Lambda function that is called every time there is a change that needs to be tracked in the database table. This Lambda function passes the data to the Kinesis data delivery stream that you created earlier.

To create the Lambda function:

Open the AWS Lambda console.
Ensure that you are in the AWS Region where your Amazon Aurora database is located.
If you have no Lambda functions yet, choose Get started now. Otherwise, choose Create function.
Choose Author from scratch.
Give your function a name and select Python 3.6 for Runtime
Choose and existing or create a new Role, the role would need to have access to call firehose:PutRecord
Choose Next on the trigger selection screen.
Paste the following code in the code window. Change the stream_name variable to the Kinesis data delivery stream that you created in the previous step.
Choose File -> Save in the code editor and then choose Save.

import boto3
import json

firehose = boto3.client('firehose')
stream_name = ‘AuroraChangesToS3’


def Kinesis_publish_message(event, context):
    
    firehose_data = (("%s,%s,%s,%s,%s,%s,%s,%s\n") %(event['ItemID'], 
    event['Category'], event['Price'], event['Quantity'],
    event['OrderDate'], event['DestinationState'], event['ShippingType'], 
    event['Referral']))
    
    firehose_data = {'Data': str(firehose_data)}
    print(firehose_data)
    
    firehose.put_record(DeliveryStreamName=stream_name,
    Record=firehose_data)

Note the Amazon Resource Name (ARN) of this Lambda function.

Giving Aurora permissions to invoke a Lambda function

To give Amazon Aurora permissions to invoke a Lambda function, you must attach an IAM role with appropriate permissions to the cluster. For more information, see Invoking a Lambda Function from an Amazon Aurora DB Cluster.

Once you are finished, the Amazon Aurora database has access to invoke a Lambda function.

Creating a stored procedure and a trigger in Amazon Aurora

Now, go back to MySQL Workbench, and run the following command to create a new stored procedure. When this stored procedure is called, it invokes the Lambda function you created. Change the ARN in the following code to your Lambda function’s ARN.

DROP PROCEDURE IF EXISTS CDC_TO_FIREHOSE;
DELIMITER ;;
CREATE PROCEDURE CDC_TO_FIREHOSE (IN ItemID VARCHAR(255), 
									IN Category varchar(255), 
									IN Price double(10,2),
                                    IN Quantity int(11),
                                    IN OrderDate timestamp,
                                    IN DestinationState varchar(2),
                                    IN ShippingType varchar(255),
                                    IN Referral  varchar(255)) LANGUAGE SQL 
BEGIN
  CALL mysql.lambda_async('arn:aws:lambda:us-east-1:XXXXXXXXXXXXX:function:CDCFromAuroraToKinesis', 
     CONCAT('{ "ItemID" : "', ItemID, 
            '", "Category" : "', Category,
            '", "Price" : "', Price,
            '", "Quantity" : "', Quantity, 
            '", "OrderDate" : "', OrderDate, 
            '", "DestinationState" : "', DestinationState, 
            '", "ShippingType" : "', ShippingType, 
            '", "Referral" : "', Referral, '"}')
     );
END
;;
DELIMITER ;

Create a trigger TR_Sales_CDC on the Sales table. When a new record is inserted, this trigger calls the CDC_TO_FIREHOSE stored procedure.

DROP TRIGGER IF EXISTS TR_Sales_CDC;
 
DELIMITER ;;
CREATE TRIGGER TR_Sales_CDC
  AFTER INSERT ON Sales
  FOR EACH ROW
BEGIN
  SELECT  NEW.ItemID , NEW.Category, New.Price, New.Quantity, New.OrderDate
  , New.DestinationState, New.ShippingType, New.Referral
  INTO @ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral;
  CALL  CDC_TO_FIREHOSE(@ItemID , @Category, @Price, @Quantity, @OrderDate
  , @DestinationState, @ShippingType, @Referral);
END
;;
DELIMITER ;

If a new row is inserted in the Sales table, the Lambda function that is mentioned in the stored procedure is invoked.

Verify that data is being sent from the Lambda function to Kinesis Data Firehose to Amazon S3 successfully. You might have to insert a few records, depending on the size of your data, before new records appear in Amazon S3. This is due to Kinesis Data Firehose buffering. To learn more about Kinesis Data Firehose buffering, see the “Amazon S3” section in Amazon Kinesis Data Firehose Data Delivery.

Every time a new record is inserted in the sales table, a stored procedure is called, and it updates data in Amazon S3.

Querying data in Amazon Redshift

In this section, you use the data you produced from Amazon Aurora and consume it as-is in Amazon Redshift. In order to allow you to process your data as-is, where it is, while taking advantage of the power and flexibility of Amazon Redshift, you use Amazon Redshift Spectrum. You can use Redshift Spectrum to run complex queries on data stored in Amazon S3, with no need for loading or other data prep.

Just create a data source and issue your queries to your Amazon Redshift cluster as usual. Behind the scenes, Redshift Spectrum scales to thousands of instances on a per-query basis, ensuring that you get fast, consistent performance even as your dataset grows to beyond an exabyte! Being able to query data that is stored in Amazon S3 means that you can scale your compute and your storage independently. You have the full power of the Amazon Redshift query model and all the reporting and business intelligence tools at your disposal. Your queries can reference any combination of data stored in Amazon Redshift tables and in Amazon S3.

Redshift Spectrum supports open, common data types, including CSV/TSV, Apache Parquet, SequenceFile, and RCFile. Files can be compressed using gzip or Snappy, with other data types and compression methods in the works.

First, create an Amazon Redshift cluster. Follow the steps in Launch a Sample Amazon Redshift Cluster.

Next, create an IAM role that has access to Amazon S3 and Athena. By default, Amazon Redshift Spectrum uses the Amazon Athena data catalog. Your cluster needs authorization to access your external data catalog in AWS Glue or Athena and your data files in Amazon S3.

In the demo setup, I attached AmazonS3FullAccess and AmazonAthenaFullAccess. In a production environment, the IAM roles should follow the standard security of granting least privilege. For more information, see IAM Policies for Amazon Redshift Spectrum.

Attach the newly created role to the Amazon Redshift cluster. For more information, see Associate the IAM Role with Your Cluster.

Next, connect to the Amazon Redshift cluster, and create an external schema and database:

create external schema if not exists spectrum_schema
from data catalog 
database 'spectrum_db' 
region 'us-east-1'
IAM_ROLE 'arn:aws:iam::XXXXXXXXXXXX:role/RedshiftSpectrumRole'
create external database if not exists;

Don’t forget to replace the IAM role in the statement.

Then create an external table within the database:

 CREATE EXTERNAL TABLE IF NOT EXISTS spectrum_schema.ecommerce_sales(
  ItemID int,
  Category varchar,
  Price DOUBLE PRECISION,
  Quantity int,
  OrderDate TIMESTAMP,
  DestinationState varchar,
  ShippingType varchar,
  Referral varchar)
ROW FORMAT DELIMITED
      FIELDS TERMINATED BY ','
LINES TERMINATED BY '\n'
LOCATION 's3://{BUCKET_NAME}/CDC/'

Query the table, and it should contain data. This is a fact table.

select top 10 * from spectrum_schema.ecommerce_sales

Next, create a dimension table. For this example, we create a date/time dimension table. Create the table:

CREATE TABLE date_dimension (
  d_datekey           integer       not null sortkey,
  d_dayofmonth        integer       not null,
  d_monthnum          integer       not null,
  d_dayofweek                varchar(10)   not null,
  d_prettydate        date       not null,
  d_quarter           integer       not null,
  d_half              integer       not null,
  d_year              integer       not null,
  d_season            varchar(10)   not null,
  d_fiscalyear        integer       not null)
diststyle all;

Populate the table with data:

copy date_dimension from 's3://reparmar-lab/2016dates' 
iam_role 'arn:aws:iam::XXXXXXXXXXXX:role/redshiftspectrum'
DELIMITER ','
dateformat 'auto';

The date dimension table should look like the following:

Querying data in local and external tables using Amazon Redshift

Now that you have the fact and dimension table populated with data, you can combine the two and run analysis. For example, if you want to query the total sales amount by weekday, you can run the following:

select sum(quantity*price) as total_sales, date_dimension.d_season
from spectrum_schema.ecommerce_sales 
join date_dimension on spectrum_schema.ecommerce_sales.orderdate = date_dimension.d_prettydate 
group by date_dimension.d_season

You get the following results:

Similarly, you can replace d_season with d_dayofweek to get sales figures by weekday:

With Amazon Redshift Spectrum, you pay only for the queries you run against the data that you actually scan. We encourage you to use file partitioning, columnar data formats, and data compression to significantly minimize the amount of data scanned in Amazon S3. This is important for data warehousing because it dramatically improves query performance and reduces cost.

Partitioning your data in Amazon S3 by date, time, or any other custom keys enables Amazon Redshift Spectrum to dynamically prune nonrelevant partitions to minimize the amount of data processed. If you store data in a columnar format, such as Parquet, Amazon Redshift Spectrum scans only the columns needed by your query, rather than processing entire rows. Similarly, if you compress your data using one of the supported compression algorithms in Amazon Redshift Spectrum, less data is scanned.

Analyzing and visualizing Amazon Redshift data in Amazon QuickSight

Modify the Amazon Redshift security group to allow an Amazon QuickSight connection. For more information, see Authorizing Connections from Amazon QuickSight to Amazon Redshift Clusters.

After modifying the Amazon Redshift security group, go to Amazon QuickSight. Create a new analysis, and choose Amazon Redshift as the data source.

Enter the database connection details, validate the connection, and create the data source.

Choose the schema to be analyzed. In this case, choose spectrum_schema, and then choose the ecommerce_sales table.

Next, we add a custom field for Total Sales = Price*Quantity. In the drop-down list for the ecommerce_sales table, choose Edit analysis data sets.

On the next screen, choose Edit.

In the data prep screen, choose New Field. Add a new calculated field Total Sales $, which is the product of the Price*Quantity fields. Then choose Create. Save and visualize it.

Next, to visualize total sales figures by month, create a graph with Total Sales on the x-axis and Order Data formatted as month on the y-axis.

After you’ve finished, you can use Amazon QuickSight to add different columns from your Amazon Redshift tables and perform different types of visualizations. You can build operational dashboards that continuously monitor your transactional and analytical data. You can publish these dashboards and share them with others.

Final notes

Amazon QuickSight can also read data in Amazon S3 directly. However, with the method demonstrated in this post, you have the option to manipulate, filter, and combine data from multiple sources or Amazon Redshift tables before visualizing it in Amazon QuickSight.

In this example, we dealt with data being inserted, but triggers can be activated in response to an INSERT, UPDATE, or DELETE trigger.

Keep the following in mind:

Be careful when invoking a Lambda function from triggers on tables that experience high write traffic. This would result in a large number of calls to your Lambda function. Although calls to the lambda_async procedure are asynchronous, triggers are synchronous.
A statement that results in a large number of trigger activations does not wait for the call to the AWS Lambda function to complete. But it does wait for the triggers to complete before returning control to the client.
Similarly, you must account for Amazon Kinesis Data Firehose limits. By default, Kinesis Data Firehose is limited to a maximum of 5,000 records/second. For more information, see Monitoring Amazon Kinesis Data Firehose.

In certain cases, it may be optimal to use AWS Database Migration Service (AWS DMS) to capture data changes in Aurora and use Amazon S3 as a target. For example, AWS DMS might be a good option if you don’t need to transform data from Amazon Aurora. The method used in this post gives you the flexibility to transform data from Aurora using Lambda before sending it to Amazon S3. Additionally, the architecture has the benefits of being serverless, whereas AWS DMS requires an Amazon EC2 instance for replication.

For design considerations while using Redshift Spectrum, see Using Amazon Redshift Spectrum to Query External Data.

If you have questions or suggestions, please comment below.

Additional Reading

If you found this post useful, be sure to check out Capturing Data Changes in Amazon Aurora Using AWS Lambda and 10 Best Practices for Amazon Redshift Spectrum

About the Authors

Re Alvarez-Parmar is a solutions architect for Amazon Web Services. He helps enterprises achieve success through technical guidance and thought leadership. In his spare time, he enjoys spending time with his two kids and exploring outdoors.

The Top 20 Most Viewed AWS IAM Documentation Pages in 2017

2018-01-05 Stephenie Swope

Post Syndicated from Stephenie Swope original https://aws.amazon.com/blogs/security/the-top-20-most-viewed-aws-iam-documentation-pages-in-2017/

The following 20 pages were the most viewed AWS Identity and Access Management (IAM) documentation pages in 2017. I have included a brief description with each link to explain what each page covers. Use this list to see what other AWS customers have been viewing and perhaps to pique your own interest in a topic you’ve been meaning to learn about.

What Is IAM?
Learn more about IAM, a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and how they can use resources (authorization).
Creating an IAM User in Your AWS Account
You can create one or more IAM users in your AWS account. You might create an IAM user when someone joins your organization, or when you have a new application that needs to make API calls to AWS.
Managing Access Keys for IAM Users
Users need their own access keys to make programmatic calls to AWS from the AWS Command Line Interface (AWS CLI), Tools for Windows PowerShell, the AWS SDKs, or direct HTTP calls using the APIs for individual AWS services. To fill this need, you can create, modify, view, or rotate access keys (access key IDs and secret access keys) for IAM users.
IAM JSON Policy Elements Reference
Learn more about the elements that you can use when you create a JSON policy. View additional JSON policy examples and learn about conditions, supported data types, and how they are used in various services.
IAM Best Practices
To help secure your AWS resources, follow these best practices for IAM.
Tutorial: Delegate Access to the Billing Console
Learn how to delegate access to specific IAM users who need to view or manage AWS Billing and Cost Management data for an AWS account.
Using Multi-Factor Authentication (MFA) in AWS
For an additional layer of security when signing in to your AWS account, AWS recommends that you configure MFA to help protect your AWS resources. MFA adds extra security because it requires users to enter a unique authentication code from an approved authentication device when they access AWS websites or services.
The IAM Console and the Sign-in Page
Learn about the IAM-enabled AWS Management Console sign-in page and how to sign in as an AWS account root user or as an IAM user. To help your users sign in easily, create a unique sign-in URL for your account.
Enabling a Virtual MFA Device
Learn how to enable and manage virtual MFA devices from the AWS Management Console.
How Users Sign In to Your Account
After you create IAM users and passwords for each, your users can sign in to the AWS Management Console using your account ID or alias, or from a special URL that includes your account ID.
Working with Server Certificates
Some AWS services can use server certificates that you manage with IAM or AWS Certificate Manager (ACM). ACM is the preferred tool to provision, manage, and deploy your server certificates. Use IAM as a certificate manager only when you must support HTTPS connections in a region that is not supported by ACM.
Your AWS Account ID and Its Alias
Learn how to find your AWS account ID and its alias.
IAM Roles
A role is an AWS identity with permission policies that determine what the identity can and cannot do in AWS using temporary security credentials that are created dynamically and provided to the user. A role is intended to be assumable by anyone who needs it using these temporary security credentials.
IAM Policies
Read an overview of policies, which are entities in AWS that, when attached to an identity or resource, define their permissions. Policies are stored in AWS as JSON documents attached to principals as identity-based policies or to resources as resource-based policies.
Example Policies
This collection of policies can help you define permissions for your IAM identities, such as granting access to a specific Amazon DynamoDB table or launching Amazon EC2 instances in a specific subnet.
Tutorial: Delegate Access Across AWS Accounts Using IAM Roles
Learn how to use an IAM role to delegate access to resources that are in different AWS accounts that you own.
Using an IAM Role to Grant Permissions to Applications Running on Amazon EC2 Instances
Use an IAM role to manage temporary credentials for applications that run on an EC2 instance. When you use a role, you do not have to distribute long-term credentials to an EC2 instance. Instead, the role supplies temporary permissions that applications can use when they make calls to other AWS resources.
Creating Your First IAM Admin User and Group
As a best practice, do not use the AWS account root user for any task where it’s not required. Instead, learn how to create an IAM administrator user and group for yourself.
Temporary Security Credentials
You can use the AWS Security Token Service (AWS STS) to create and provide trusted users with temporary security credentials that can control access to your AWS resources. Temporary security credentials work almost identically to the long-term access key credentials that your IAM users can use.
The AWS Account Root User
When you first create an AWS account, you begin with a single sign-in identity that has complete access to all AWS services and resources in the account. This identity is called the AWS account root user and is accessed by signing in with the email address and password that you used to create the account. To manage your root user, follow the steps on this page.

In the “Comments” section below, let us know if you would like to see anything on these or other IAM documentation pages expanded or updated to make them more useful to you.

– Stephenie

The Most Viewed AWS Security Blog Posts in 2017

2018-01-03 Craig Liebendorfer

Post Syndicated from Craig Liebendorfer original https://aws.amazon.com/blogs/security/the-most-viewed-aws-security-blog-posts-in-2017/

The following 10 posts were the most viewed AWS Security Blog posts that we published during 2017. You can use this list as a guide to catch up on your AWS Security Blog reading or read a post again that you found particularly useful.

The following 10 posts published since the blog’s inception in April 2013 were the most viewed AWS Security Blog posts in 2017.

Let us know in the comments section below if there is a specific security or compliance topic you would like us to cover on the Security Blog in 2018.

– Craig