If you have multiple Amazon Web Services (AWS) accounts, and you have AWS Identity and Access Management (IAM) roles among those multiple accounts that are supposed to be similar, those roles can deviate over time from your intended baseline due to manual actions performed directly out-of-band called drift. As part of regular compliance checks, you should confirm that these roles have no deviations. In this post, we present a tool called IAMCTL that you can use to extract the IAM roles and policies from two accounts, compare them, and report out the differences and statistics. We will explain how to use the tool, and will describe the key concepts.
Prerequisites
Before you install IAMCTL and start using it, here are a few prerequisites that need to be in place on the computer where you will run it:
Set up AWS CLI profiles for two accounts you want to compare, as described in Named Profiles.
To follow along in your environment, clone the files from the GitHub repository, and run the steps in order. You won’t incur any charges to run this tool.
Install IAMCTL
This section describes how to install and run the IAMCTL tool.
To confirm that your installation was successful, enter the following command.
iamctl –h
You will see results similar to those in figure 2.
Figure 2: IAMCTL help message
Now that you’ve successfully installed the IAMCTL tool, the next section will show you how to use the IAMCTL commands.
Example use scenario
Here is an example of how IAMCTL can be used to find differences in IAM roles between two AWS accounts.
A system administrator for a product team is trying to accelerate a product launch in the middle of testing cycles. Developers have found that the same version of their application behaves differently in the development environment as compared to the QA environment, and they suspect this behavior is due to differences in IAM roles and policies.
The application called “app1” primarily reads from an Amazon Simple Storage Service (Amazon S3) bucket, and runs on an Amazon Elastic Compute Cloud (Amazon EC2) instance. In the development (DEV) account, the application uses an IAM role called “app1_dev” to access the S3 bucket “app1-dev”. In the QA account, the application uses an IAM role called “app1_qa” to access the S3 bucket “app1-qa”. This is depicted in figure 3.
Figure 3: Showing the “app1” application in the development and QA accounts
Setting up the scenario
To simulate this setup for the purpose of this walkthrough, you don’t have to create the EC2 instance or the S3 bucket, but just focus on the IAM role, inline policy, and trust policy.
As noted in the prerequisites, you will switch between the two AWS accounts by using the AWS CLI named profiles “dev-profile” and “qa-profile”, which are configured to point to the DEV and QA accounts respectively.
The command creates a directory structure that looks like this: Iamctl_test |– qa |– dev
Now, switch to the dev folder to run all the following example commands against the DEV account, by using this command:
cd iamctl_test/dev
To create the required policies, first create a file named “app1_s3_access_policy.json” and add the following policy to it. You will use this file’s content as your role’s inline policy.
Second, create a file called “app1_trust_policy.json” and add the following policy to it. You will use this file’s content as your role’s trust policy.
Now use the two files to create an IAM role with the name “app1_dev” in the account by using these command(s), run in the same order as listed here:
#create role with trust policy
aws --profile dev-profile iam create-role --role-name app1_dev --assume-role-policy-document file://app1_trust_policy.json
#put inline policy to the role created above
aws --profile dev-profile iam put-role-policy --role-name app1_dev --policy-name s3_inline_policy --policy-document file://app1_s3_access_policy.json
In the QA account, the IAM role is named “app1_qa” and the S3 bucket is named “app1-qa”.
Repeat the steps from the prior example against the QA account by changing dev to qa where shown in bold in the following code samples. Change the directory to qa by using this command:
cd ../qa
To create the required policies, first create a file called “app1_s3_access_policy.json” and add the following policy to it.
Now, use the two files created so far to create an IAM role with the name “app1_qa” in your QA account by using these command(s), run in the same order as listed here:
#create role with trust policy
aws --profile qa-profile iam create-role --role-name app1_qa --assume-role-policy-document file://app1_trust_policy.json
#put inline policy to the role create above
aws --profile qa-profile iam put-role-policy --role-name app1_qa --policy-name s3_inline_policy --policy-document file://app1_s3_access_policy.json
So far, you have two accounts with an IAM role created in each of them for your application. In terms of permissions, there are no differences other than the name of the S3 bucket resource the permission is granted against.
You can expect IAMCTL to generate a minimal set of differences between the DEV and QA accounts, assuming all other IAM roles and policies are the same, but to be sure about the current state of both accounts, in IAMCTL you can run a process called baselining.
Through the process of baselining, you will generate an equivalency dictionary that represents all the known string patterns that reduce the noise in the generated deviations list, and then you will introduce a change into one of the IAM roles in your QA account, followed by a final IAMCTL diff to show the deviations.
Baselining
Baselining is the process of bringing two accounts to an “equivalence” state for IAM roles and policies by establishing a baseline, which future diff operations can leverage. The process is as simple as:
Run the iamctl diff command.
Capture all string substitutions into an equivalence dictionary to remove or reduce noise.
Save the generated detailed files as a snapshot.
Now you can go through these steps for your baseline.
Go ahead and run the iamctl diff command against these two accounts by using the following commands.
#change directory from qa to iamctl-test
cd ..
#run iamctl init
iamctl init
The results of running the init command are shown in figure 4.
Figure 4: Output of the iamctl init command
If you look at the iamctl_test directory now, shown in figure 5, you can see that the init command created two files in the iamctl_test directory.
Figure 5: The directory structure after running the init command
These two files are as follows:
iam.jsonA reference file that has all AWS services and actions listed, among other things. IAMCTL uses this to map the resource listed in an IAM policy to its corresponding AWS resource, based on Amazon Resource Name (ARN) regular expression.
equivalency_list.jsonThe default sample dictionary that IAMCTL uses to suppress false alarms when it compares two accounts. This is where the known string patterns that need to be substituted are added.
Note: A best practice is to make the directory where you store the equivalency dictionary and from which you run IAMCTL to be a Git repository. Doing this will let you capture any additions or modifications for the equivalency dictionary by using Git commits. This will not only give you an audit trail of your historical baselines but also gives context to any additions or modifications to the equivalency dictionary. However, doing this is not necessary for the regular functioning of IAMCTL.
Next, run the iamctl diff command:
#run iamctl diff
iamctl diff dev-profile dev qa-profile qa
Figure 6: Result of diff command
Figure 6 shows the results of running the diff command. You can see that IAMCTL considers the app1_qa and app1_dev roles as unique to the DEV and QA accounts, respectively. This is because IAMCTL uses role names to decide whether to compare the role or tag the role as unique.
You will add the strings “dev” and “qa” to the equivalency dictionary to instruct IAMCTL to substitute occurrences of these two strings with “accountname” by adding the follow JSON to the equivalency_list.json file. You will also clean up some defaults already present in there.
Figure 7 shows the equivalency dictionary before you take these actions, and figure 8 shows the dictionary after these actions.
Figure 7: Equivalency dictionary before
Figure 8: Equivalency dictionary after
There’s another thing to notice here. In this example, one common role was flagged as having a difference. To know which role this is and what the difference is, go to the detail reports folder listed at the bottom of the summary report. The directory structure of this folder is shown in figure 9.
Notice that the reports are created under your home directory with a folder structure that mimics the time stamp down to the second. IAMCTL does this to maintain uniqueness for each run.
tree /Users/<username>/aws-idt/output/2020/08/24/08/38/49/
Figure 9: Files written to the output reports directory
You can see there is a file called common_roles_in_dev_with_differences.csv, and it lists a role called “AwsSecurity***Audit”.
You can see there is another file called dev_to_qa_common_role_difference_items.csv, and it lists the granular IAM items from the DEV account that belong to the “AwsSecurity***Audit” role as compared to QA, but which have differences. You can see that all entries in the file have the DEV account number in the resource ARN, whereas in the qa_to_dev_common_role_difference_items.csv file, all entries have the QA account number for the same role “AwsSecurity***Audit”.
Add both of the account numbers to the equivalency dictionary to substitute them with a placeholder number, because you don’t want this role to get flagged as having differences.
#run iamctl diff
iamctl diff dev-profile dev qa-profile qa
As you can see in figure 10, you get back the result of the diff command that shows that the DEV account doesn’t have any differences in IAM roles as compared to the QA account.
Figure 10: Output showing no differences after completion of baselining
This concludes the baselining for your DEV and QA accounts. Now you will introduce a change.
Introducing drift
Drift occurs when there is a difference in actual vs expected values in the definition or configuration of a resource. There are several reasons why drift occurs, but for this scenario you will use “intentional need to respond to a time-sensitive operational event” as a reason to mimic and introduce drift into what you have built so far.
To simulate this change, add “s3:PutObject” to the qa app1_s3_access_policy.json file as shown in the following example.
The following table represents the new drift in the accounts.
Action
Account-DEV Role name: app1_dev
Account-QA Role name: app1_qa
s3:Get*
Yes
Yes
s3:List*
Yes
Yes
s3: PutObject
No
Yes
Next, run the iamctl diff command to see how it picks up the drift from your previously baselined accounts.
#change directory from qa to iamctL-test
cd ..
iamctl diff dev-profile dev qa-profile qa
Figure 11: Output showing the one deviation that was introduced
You can see that IAMCTL now shows that the QA account has one difference as compared to DEV, which is what we expect based on the deviation you’ve introduced.
Open up the file qa_to_dev_common_role_difference_items.csv to look at the one difference. Again, adjust the following path example with the output from the iamctl diff command at the bottom of the summary report in Figure 11.
As shown in figure 12, you can see that the file lists the specific S3 action “PutObject” with the role name and other relevant details.
Figure 12: Content of file qa_to_dev_common_role_difference_items.csv showing the one deviation that was introduced
You can use this information to remediate the deviation by performing corrective actions in either your DEV account or QA account. You can confirm the effectiveness of the corrective action by re-baselining to make sure that zero deviations appear.
Conclusion
In this post, you learned how to use the IAMCTL tool to compare IAM roles between two accounts, to arrive at a granular list of meaningful differences that can be used for compliance audits or for further remediation actions. If you’ve created your IAM roles by using an AWS CloudFormation stack, you can turn on drift detection and easily capture the drift because of changes done outside of AWS CloudFormation to those IAM resources. For more information about drift detection, see Detecting unmanaged configuration changes to stacks and resources. Lastly, see the GitHub repository where the tool is maintained with documentation describing each of the subcommand concepts. We welcome any pull requests for issues and enhancements.
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS IAM forum or contact AWS Support.
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AWS Identity and Access Management (IAM)roles are a significant component in the way customers operate in Amazon Web Service (AWS). In this post, I’ll dive into the details on how Cloud security architects and account administrators can protect IAM roles from misuse by using trust policies. By the end of this post, you’ll know how to use IAM roles to build trust policies that work at scale, providing guardrails to control access to resources in your organization.
In general, there are four different scenarios where you might use IAM roles in AWS:
One AWS service accesses another AWS service – When an AWS service needs access to other AWS services or functions, you can create a role that will grant that access.
One AWS account accesses another AWS account – This use case is commonly referred to as a cross-account role pattern. This allows human or machine IAM principals from other AWS accounts to assume this role and act on resources in this account.
A third-party web identity needs access – This use case allows users with identities in third-party systems like Google and Facebook, or Amazon Cognito, to use a role to access resources in the account.
Authentication using SAML2.0 federation – This is commonly used by enterprises with Active Directory that want to connect using an IAM role so that their users can use single sign-on workflows to access AWS accounts.
In all cases, the makeup of an IAM role is the same as that of an IAM user and is only differentiated by the following qualities:
An IAM role does not have long term credentials associated with it; rather, a principal (an IAM user, machine, or other authenticated identity) assumes the IAM role and inherits the permissions assigned to that role.
The tokens issued when a principal assumes an IAM role are temporary. Their expiration reduces the risks associated with credentials leaking and being reused.
An IAM role has a trust policy that defines which conditions must be met to allow other principals to assume it. This trust policy reduces the risks associated with privilege escalation.
While the list of users having access to your AWS accounts can change over time, the roles used to manage your AWS account probably won’t. The use of IAM roles essentially decouples your enterprise identity system (SAML 2.0) from your permission system (AWS IAM policies), simplifying management of each.
Managing access to IAM roles
Let’s dive into how you can create relationships between your enterprise identity system and your permissions system by looking at the policy types you can apply to an IAM role.
An IAM role has three places where it uses policies:
Permission policies (inline and attached) – These policies define the permissions that a principal assuming the role is able (or restricted) to perform, and on which resources.
Permissions boundary – A permissions boundary is an advanced feature for using a managed policy to set the maximum permissions that an identity-based policy can grant to an IAM entity. An entity’s permissions boundary allows it to perform only the actions that are allowed by both its identity-based permission policies and its permissions boundaries.
Trust relationship – This policy defines which principals can assume the role, and under which conditions. This is sometimes referred to as a resource-based policy for the IAM role. We’ll refer to this policy simply as the ‘trust policy’.
A role can be assumed by a human user or a machine principal, such as an Amazon Elastic Computer Cloud (Amazon EC2) instance or an AWS Lambda function. Over the rest of this post, you’ll see how you’re able to reduce the conditions for principals to use roles by configuring their trust policies.
An example of a simple trust policy
A common use case is when you need to provide security audit access to your account, allowing a third party to review the configuration of that account. After attaching the relevant permission policies to an IAM role, you need to add a cross-account trust policy to allow the third-party auditor to make the sts:AssumeRole API call to elevate their access in the audited account. The following trust policy shows an example policy created through the AWS Management Console:
As you can see, it has the same structure as other IAM policies with Effect, Action, and Condition components. It also has the Principal parameter, but no Resource attribute. This is because the resource, in the context of the trust policy, is the IAM role itself. For the same reason, the Action parameter will only ever be set to one of the following values: sts:AssumeRole, sts:AssumeRoleWithSAML, or sts:AssumeRoleWithWebIdentity.
Note: The suffix root in the policy’s Principal attribute equates to “authenticated and authorized principals in the account,” not the special and all-powerful root user principal that is created when an AWS account is created.
Using the Principal attribute to reduce scope
In a trust policy, the Principal attribute indicates which other principals can assume the IAM role. In the example above, 111122223333 represents the AWS account number for the auditor’s AWS account. In effect, this allows any principal in the 111122223333 AWS account with sts:AssumeRole permissions to assume this role.
To restrict access to a specific IAM user account, you can define the trust policy like the following example, which would allow only the IAM user LiJuan in the 111122223333 account to assume this role. LiJuan would also need to have sts:AssumeRole permissions attached to their IAM user for this to work:
The principals set in the Principal attribute can be any principal defined by the IAM documentation, and can refer to an AWS or a federated principal. You cannot use a wildcard (“*” or “?”) within a Principal for a trust policy, other than one special condition, which I’ll come back to in a moment: You must define precisely which principal you are referring to because there is a translation that occurs when you submit your trust policy that ties it to each principal’s hidden principal ID, and it can’t do that if there are wildcards in the principal.
The only scenario where you can use a wildcard in the Principal parameter is where the parameter value is only the “*” wildcard. Use of the global wildcard “*” for the Principal isn’t recommended unless you have clearly defined Conditional attributes in the policy statement to restrict use of the IAM role, since doing so without Conditional attributes permits assumption of the role by any principal in any AWS account, regardless of who that is.
Using identity federation on AWS
Federated users from SAML 2.0 compliant enterprise identity services are given permissions to access AWS accounts through the use of IAM roles. While the user-to-role configuration of this connection is established within the SAML 2.0 identity provider, you should also put controls in the trust policy in IAM to reduce any abuse.
Because the Principal attribute contains configuration information about the SAML mapping, in the case of Active Directory, you need to use the Condition attribute in the trust policy to restrict use of the role from the AWS account management perspective. This can be done by restricting the SourceIp address, as demonstrated later, or by using one or more of the SAML-specific Condition keys available. My recommendation here is to be as specific as you can in reducing the set of principals that can use the role as is practical. This is best achieved by adding qualifiers into the Condition attribute of your trust policy.
Using the Condition attribute in a trust policy to reduce scope
The Condition statement in your trust policy sets additional requirements for the Principal trying to assume the role. If you don’t set a Condition attribute, the IAM engine will rely solely on the Principal attribute of this policy to authorize role assumption. Given that it isn’t possible to use wildcards within the Principal attribute, the Condition attribute is a really flexible way to reduce the set of users that are able to assume the role without necessarily specifying the principals.
Limiting role use based on an identifier
Occasionally teams managing multiple roles can become confused as to which role achieves what and can inadvertently assume the wrong role. This is referred to as the Confused Deputy problem. This next section shows you a way to quickly reduce this risk.
The following trust policy requires that principals from the 111122223333 AWS account have provided a special phrase when making their request to assume the role. Adding this condition reduces the risk that someone from the 111122223333 account will assume this role by mistake. This phrase is configured by specifying an ExternalID conditional context key.
In the example trust policy above, the value ExampleSpecialPhrase isn’t a secret or a password. Adding the ExternalID condition limits this role from being assumed using the console. The only way to add this ExternalID argument into the role assumption API call is to use the AWS Command Line Interface (AWS CLI) or a programming interface. Having this condition doesn’t prevent a user who knows about this relationship and the ExternalId from assuming what might be a privileged set of permissions, but does help manage risks like the Confused Deputy problem. I see customers using an ExternalID that matches the name of the AWS account, which works to ensure that an operator is working on the account they believe they’re working on.
Limiting role use based on multi-factor authentication
By using the Condition attribute, you can also require that the principal assuming this role has passed a multi-factor authentication (MFA) check before they’re permitted to use this role. This again limits the risk associated with mistaken use of the role and adds some assurances about the principal’s identity.
In the example trust policy above, I also introduced the MultiFactorAuthPresent conditional context key. Per the AWS global condition context keys documentation, the MultiFactorAuthPresent conditional context key does not apply to sts:AssumeRole requests in the following contexts:
When authentication has taken place via federation
In the example above, the use of the BoolIfExists qualifier to the MultiFactorAuthPresent conditional context key evaluates the condition as true if:
The principal type can have an MFA attached, and does. or
The principal type cannot have an MFA attached.
This is a subtle difference but makes the use of this conditional key in trust policies much more flexible across all principal types.
Limiting role use based on time
During activities like security audits, it’s quite common for the activity to be time-bound and temporary. There’s a risk that the IAM role could be assumed even after the audit activity concludes, which might be undesirable. You can manage this risk by adding a time condition to the Condition attribute of the trust policy. This means that rather than being concerned with disabling the IAM role created immediately following the activity, customers can build the date restriction into the trust policy. You can do this by using policy attribute statements, like so:
Limiting role use based on IP addresses or CIDR ranges
If the auditor for a security audit is using a known fixed IP address, you can build that information into the trust policy, further reducing the opportunity for the role to be assumed by unauthorized actors calling the assumeRole API function from another IP address or CIDR range:
IAM tagging capabilities can also help to build flexible and adaptive trust policies, too, so that they create an attribute-based access control (ABAC) model for IAM management. You can build trust policies that only permit principals that have already been tagged with a specific key and value to assume a specific role. The following example requires that IAM principals in the AWS account 111122223333 be tagged with department = OperationsTeam for them to assume the IAM role.
tagged with department = OperationsTeam for them to assume the IAM role.
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"AWS": "arn:aws:iam::111122223333:root"
},
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": {
"aws:PrincipalTag/department": "OperationsTeam"
}
}
}
]
}
If you want to create this effect, I highly recommend the use of the PrincipalTag pattern above, but you must also be cautious about which principals are then also given iam:TagUser, iam:TagRole, iam:UnTagUser, and iam:UnTagRole permissions, perhaps even using the aws:PrincipalTag condition within the permissions boundary policy to restrict their ability to retag their own IAM principal or that of another IAM role they can assume.
Limiting or extending access to a role based on AWS Organizations
Since its announcement in 2016, almost every enterprise customer I work with uses AWS Organizations. This AWS service allows customers to create an organizational structure for their accounts by creating hard boundaries to manage blast-radius risks, among other advantages. You can use the PrincipalOrgID condition to limit assumption of an organization-wide core IAM role.
Caution: As you’ll see in the example below, you need to set the Principal attribute to “*” to do this, which would, without the conditional restriction, allow all role assumption requests to be accepted for this role, irrespective of the source of that assumption request. For that reason, be especially careful about the use of this pattern.
It isn’t practical to write out all the AWS account identifiers into a trust policy, and because of the way policies like this are evaluated, you can’t include wildcard characters for the account number in the principal’s account number field. The use of the PrincipalOrgID global condition context key provides us with a neat and dynamic mechanism to create a short policy statement.
Role chaining
There are instances where a third party might themselves be using IAM roles, or where an AWS service resource that has already assumed a role needs to assume another role (perhaps in another account), and customers might need to allow only specific IAM roles in that remote account to assume the IAM role you create in your account. You can use role chaining to build permitted role escalation routes using role assumption from within the same account or AWS organization, or from third-party AWS accounts.
Consider the following trust policy example where I use a combination of the Principal attribute to scope down to an AWS account, and the aws:UserId global conditional context key to scope down to a specific role using its RoleId. To capture the RoleId for the role you want to be able to assume, you can run the following command using the AWS CLI:
If you’re using an IAM user and have assumed the CrossAccountAuditor IAM role, the policy above will work through the AWS CLI with a call to aws sts assume-role and through the console.
This type of trust policy also works for services like Amazon EC2, allowing those instances using their assigned instance profile role to assume a role in another account to perform actions. We’ll touch on this use case later in the post.
Putting it all together
AWS customers can use combinations of all the above Principal and Condition attributes to hone the trust they’re extending out to any third party, or even within their own organization. They might create an accumulated trust policy for an IAM role which achieves the following effect:
Allows only a user named PauloSantos, in AWS account number 111122223333, to assume the role if they have also authenticated with an MFA, are logging in from an IP address in the 203.0.113.0 to 203.0.113.24 CIDR range, and the date is between noon of September 1, 2020, and noon of September 7, 2020.
I’ve seen customers use this to create IAM users who have no permissions attached besides sts:AssumeRole. Trust relationships are then configured between the IAM users and the IAM roles, creating ultimate flexibility in defining who has access to what roles without needing to update the IAM user identity pool at all.
A word on Effect: Deny and NotPrincipal in IAM role trust policies
I have seen some customers make use of an “Effect”: “Deny” clause in their trust policies. This pattern can help manage a wildcard statement in another “Effect”: “Allow” clause of the same trust policy. However, this isn’t the best approach for most scenarios. You will typically be able to define each principal in your policy as being allowed access. An example of where this might not be true is where you have a clause that uses the global wildcard “*” as a principal, in which case it will be necessary to add Deny statements to further filter the access.
Putting a wildcard into the Principal attribute of an Allow policy statement, particularly in relation to trust policies, can be dangerous if you haven’t done a robust job of managing the Condition attribute in the same statement. Be as specific as possible in your Allow statement, and use Principal attributes first, rather than then relying on Deny statements to manage potential security gaps created by your use of wildcards.
The following trust policy allows all IAM principals within the o-abcd12efg1 organization to assume the IAM role, but only if it’s before September 7, 2020:
You can also build into your trust policies a NotPrincipal condition. Again, this is rarely the best choice, because you can introduce unnecessary complexity and confusion into your policies. Instead, you can avoid that problem by using fairly simple and prescriptive Principal statements.
Statements with NotPrincipal can also use a Deny statement as well, so it can create quite baffling policy logic, which if misunderstood could create unintended opportunities for misuse or abuse.
Here’s an example where you might think to use Deny and NotPrincipal in a trust policy—but notice this has the same effect as adding arn:aws:iam::123456789012:role/CoreAccess in a single Allow statement. In general, Deny with NotPrincipal statements in trust policies create unnecessary complexity, and should be avoided.
Remember, your Principal attribute should be very specific, to reduce the set of those able to assume the role, and an IAM role trust policy won’t permit access if a corresponding Allow statement isn’t explicitly present in the trust policy. It’s better to rely on the default deny policy evaluation logic where you’re able, rather than introducing unnecessary complexity into your policy logic.
Creating trust policies for AWS services that assume roles
There are two types of contexts where AWS services need access to IAM roles to function:
Resources managed by an AWS service (like Amazon EC2 or Lambda, for example) need access to an IAM role to execute functions on other AWS resources, and need permissions to do so.
In both contexts, you have the service itself as an actor. The service is assuming your IAM role so it can provide your credentials to your Lambda function (the first context) or use those credentials to do things (the second context). In the same way that IAM roles are used by human operators to provide an escalation mechanism for users operating with specific functions in the examples above, so, too, do AWS resources, such as Lambda functions, Amazon EC2 instances, and even AWS CloudFormation, require the same mechanism. You can find more information about how to create IAM Roles for AWS Services here.
An IAM role for a human operator and for an AWS service are exactly the same, even though they have a different principal defined in the trust policy. The policy’s Principal will define the AWS service that is permitted to assume the role for its function.
Here’s an example trust policy for a role designed for an Amazon EC2 instance to assume. You can see that the principal provided is the ec2.amazonaws.com service:
Every configuration of an AWS resource should be passed a specific role unique to its function. So, if you have two Amazon EC2 launch configurations, you should design two separate IAM roles, even if the permissions they require are currently the same. This allows each configuration to grow or shrink the permissions it requires over time, without needing to reattach IAM roles to configurations, which might create a privilege escalation risk. Instead, you update the permissions attached to each IAM role independently, knowing that it will only be used by that one service resource. This helps reduce the potential impact of risks. Automating your management of roles will help here, too.
Several customers have asked if it’s possible to design a trust policy for an IAM role such that it can only be passed to a specific Amazon EC2 instance. This isn’t directly possible. You cannot place the Amazon Resource Name (ARN) for an EC2 instance into the Principal of a trust policy, nor can you use tag-based condition statements in the trust policy to limit the ability for the role to be used by a specific resource.
The only option is to manage access to the iam:PassRole action within the permission policy for those IAM principals you expect to be attaching IAM roles to AWS resources. This special Action is evaluated when a principal tries to attach another IAM role to an AWS service or AWS resource.
You should use restrictions on access to the iam:PassRole action with permission policies and permission boundaries. This means that the ability to attach roles to instance profiles for Amazon EC2 is limited, rather than using the trust policy on the role assumed by the EC2 instance to achieve this. This approach makes it much easier to manage scaling for both those principals attaching roles to EC2 instances, and the instances themselves.
You could use a permission policy to limit the ability for the associated role to attach other roles to Amazon EC2 instances with the following permission policy, unless the role name is prefixed with EC2-Webserver-:
You now have all the tools you need to build robust and effective trust policies that work at scale, providing guardrails for your users and those who might want to access resources in your account from outside your organization.
Policy logic isn’t always simple, and I encourage you to use sandbox accounts to try out your ideas. In general, simplicity should win over cleverness. IAM policies and statements that might well be frugal in their use of policy language might also be difficult to read, interpret, and update by other IAM administrators in the future. Keeping your trust policies simple helps to build IAM relationships everyone understands and can manage, and use, effectively.
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As you build on AWS, you create AWS Identity and Access Management (IAM) roles to enable teams and applications to use AWS services. As those teams and applications evolve, you might only rely on a sub-set of your original roles to meet your needs. This can leave unused roles in your AWS account. To help you identify these unused roles, IAM now reports the last-used timestamp that represents when a role was last used to make an AWS request. You or your security team can use this information to identify, analyze, and then confidently remove unused roles. This helps you improve the security posture of your AWS environments. Additionally, by removing unused roles, you can simplify your monitoring and auditing efforts by focusing only on roles that are in use. You can review when a role was last used to access your AWS environment in the IAM console, using the AWS Command Line Interface (AWS CLI), or AWS SDK.
In this post, I demonstrate how to identify and remove roles that your team or applications don’t use by viewing the last-used timestamp in the IAM console. Before I share an example, I’ll describe the existing IAM APIs where we now also report the last-used timestamp:
Get-role: Returns role details, including the path, ARN (Amazon resource number), and trust policy. You can now use this API to retrieve the last-used timestamp.
Get-account-authorization-details: Retrieves information about all the IAM users, groups, roles, and policies in your AWS account. You can now view the last-used timestamp along with the other role details.
How to use the AWS Management Console to view last-used information for roles
Imagine you’re a system administrator for Example Inc. and your development team is working on a new application. To enable them to get started with AWS quickly, you create roles for the team and their application. As the application goes through final review, you learn the team and application now rely on a smaller set of roles to access AWS services. This leaves unused roles in your AWS accounts that you might want to remove. You’re going to check the last time each role made a request to AWS and use this information to determine whether the team is using the role. If they aren’t, you plan to remove it knowing the team doesn’t need it for the application.
To view role-last-used information in the IAM Console, select Roles in the IAM navigation pane, then look for the Last activity column (see Figure 1 below). This displays the number of days that have passed since each role made an AWS service request. AWS records last-used information for the trailing 400 days. This is referred to as the tracking period. You can sort the column to identify the roles your team has not used recently.
In the case of Example Inc., let’s say you want to get rid of any roles that have been inactive for 90 days or more. From the information in Figure 1, you see that your team is using ApplicationEC2Access, TestRole, and CodeDeployRole. You also see they haven’t used AdminAccess, EC2FullAccess, and InfraSetupRole in the last 90 days. You can now delete these roles confidently. (Last activity “None”, as seen for the AdminAccess role, means that the role was not used within the trailing 400-day tracking period to make any service request.)
Figure 1: “Last activity” column in IAM console
While analyzing the last-used timestamp for each role, you notice that the MigrationRole role was last active two months ago. You want to gather more information about the role’s access patterns to determine whether you ought to delete it. To do this, select the name of the role. From the role detail page, navigate to the Access Advisor tab and investigate the list of accessed services and verify what the role was used for. Access advisor provides a report that displays a list of services and timestamps that indicate when the selected IAM principal last accessed each of the services that it has permissions to. Based on this report, you can decide to follow up with the development team to see if they still need this role. Thus, you have reduced the number of roles in your account from 9 to 6, making it easier to monitor active roles and restrict access to your AWS environments.
Figure 2: Access Advisor report
Summary
In this post, I showed you how to use role-last-used information to identify and remove unused roles. By removing unused roles, you can simplify monitoring and improve your security posture. To learn more about deleting roles, visit the deleting roles or instance profiles documentation.
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Amazon Neptune is now Generally Available in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland). Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. At the core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with millisecond latencies. Neptune supports two popular graph models, Property Graph and RDF, through Apache TinkerPop Gremlin and SPARQL, allowing you to easily build queries that efficiently navigate highly connected datasets. Neptune can be used to power everything from recommendation engines and knowledge graphs to drug discovery and network security. Neptune is fully-managed with automatic minor version upgrades, backups, encryption, and fail-over. I wrote about Neptune in detail for AWS re:Invent last year and customers have been using the preview and providing great feedback that the team has used to prepare the service for GA.
Now that Amazon Neptune is generally available there are a few changes from the preview:
A large number of performance enhancements and updates
Launching a Neptune cluster is as easy as navigating to the AWS Management Console and clicking create cluster. Of course you can also launch with CloudFormation, the CLI, or the SDKs.
You can monitor your cluster health and the health of individual instances through Amazon CloudWatch and the console.
Additional Resources
We’ve created two repos with some additional tools and examples here. You can expect continuous development on these repos as we add additional tools and examples.
Amazon Neptune Tools Repo This repo has a useful tool for converting GraphML files into Neptune compatible CSVs for bulk loading from S3.
Amazon Neptune Samples Repo This repo has a really cool example of building a collaborative filtering recommendation engine for video game preferences.
Purpose Built Databases
There’s an industry trend where we’re moving more and more onto purpose-built databases. Developers and businesses want to access their data in the format that makes the most sense for their applications. As cloud resources make transforming large datasets easier with tools like AWS Glue, we have a lot more options than we used to for accessing our data. With tools like Amazon Redshift, Amazon Athena, Amazon Aurora, Amazon DynamoDB, and more we get to choose the best database for the job or even enable entirely new use-cases. Amazon Neptune is perfect for workloads where the data is highly connected across data rich edges.
I’m really excited about graph databases and I see a huge number of applications. Looking for ideas of cool things to build? I’d love to build a web crawler in AWS Lambda that uses Neptune as the backing store. You could further enrich it by running Amazon Comprehend or Amazon Rekognition on the text and images found and creating a search engine on top of Neptune.
As always, feel free to reach out in the comments or on twitter to provide any feedback!
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.
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.
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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The EU’s General Data Protection Regulation (GDPR) describes data processor and data controller roles, and some customers and AWS Partner Network (APN) partners are asking how this affects the long-established AWS Shared Responsibility Model. I wanted to take some time to help folks understand shared responsibilities for us and for our customers in context of the GDPR.
How does the AWS Shared Responsibility Model change under GDPR? The short answer – it doesn’t. AWS is responsible for securing the underlying infrastructure that supports the cloud and the services provided; while customers and APN partners, acting either as data controllers or data processors, are responsible for any personal data they put in the cloud. The shared responsibility model illustrates the various responsibilities of AWS and our customers and APN partners, and the same separation of responsibility applies under the GDPR.
AWS responsibilities as a data processor
The GDPR does introduce specific regulation and responsibilities regarding data controllers and processors. When any AWS customer uses our services to process personal data, the controller is usually the AWS customer (and sometimes it is the AWS customer’s customer). However, in all of these cases, AWS is always the data processor in relation to this activity. This is because the customer is directing the processing of data through its interaction with the AWS service controls, and AWS is only executing customer directions. As a data processor, AWS is responsible for protecting the global infrastructure that runs all of our services. Controllers using AWS maintain control over data hosted on this infrastructure, including the security configuration controls for handling end-user content and personal data. Protecting this infrastructure, is our number one priority, and we invest heavily in third-party auditors to test our security controls and make any issues they find available to our customer base through AWS Artifact. Our ISO 27018 report is a good example, as it tests security controls that focus on protection of personal data in particular.
AWS has an increased responsibility for our managed services. Examples of managed services include Amazon DynamoDB, Amazon RDS, Amazon Redshift, Amazon Elastic MapReduce, and Amazon WorkSpaces. These services provide the scalability and flexibility of cloud-based resources with less operational overhead because we handle basic security tasks like guest operating system (OS) and database patching, firewall configuration, and disaster recovery. For most managed services, you only configure logical access controls and protect account credentials, while maintaining control and responsibility of any personal data.
Customer and APN partner responsibilities as data controllers — and how AWS Services can help
Our customers can act as data controllers or data processors within their AWS environment. As a data controller, the services you use may determine how you configure those services to help meet your GDPR compliance needs. For example, AWS Services that are classified as Infrastructure as a Service (IaaS), such as Amazon EC2, Amazon VPC, and Amazon S3, are under your control and require you to perform all routine security configuration and management that would be necessary no matter where the servers were located. With Amazon EC2 instances, you are responsible for managing: guest OS (including updates and security patches), application software or utilities installed on the instances, and the configuration of the AWS-provided firewall (called a security group).
To help you realize data protection by design principles under the GDPR when using our infrastructure, we recommend you protect AWS account credentials and set up individual user accounts with Amazon Identity and Access Management (IAM) so that each user is only given the permissions necessary to fulfill their job duties. We also recommend using multi-factor authentication (MFA) with each account, requiring the use of SSL/TLS to communicate with AWS resources, setting up API/user activity logging with AWS CloudTrail, and using AWS encryption solutions, along with all default security controls within AWS Services. You can also use advanced managed security services, such as Amazon Macie, which assists in discovering and securing personal data stored in Amazon S3.
For more information, you can download the AWS Security Best Practices whitepaper or visit the AWS Security Resources or GDPR Center webpages. In addition to our solutions and services, AWS APN partners can provide hundreds of tools and features to help you meet your security objectives, ranging from network security and configuration management to access control and data encryption.
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easier to prepare and load your data for analytics. You can create and run an ETL job with a few clicks on the AWS Management Console. Just point AWS Glue to your data store. AWS Glue discovers your data and stores the associated metadata (for example, a table definition and schema) in the AWS Glue Data Catalog.
AWS Glue has native connectors to data sources using JDBC drivers, either on AWS or elsewhere, as long as there is IP connectivity. In this post, we demonstrate how to connect to data sources that are not natively supported in AWS Glue today. We walk through connecting to and running ETL jobs against two such data sources, IBM DB2 and SAP Sybase. However, you can use the same process with any other JDBC-accessible database.
AWS Glue data sources
AWS Glue natively supports the following data stores by using the JDBC protocol:
One of the fastest growing architectures deployed on AWS is the data lake. The ETL processes that are used to ingest, clean, transform, and structure data are critically important for this architecture. Having the flexibility to interoperate with a broader range of database engines allows for a quicker adoption of the data lake architecture.
For data sources that AWS Glue doesn’t natively support, such as IBM DB2, Pivotal Greenplum, SAP Sybase, or any other relational database management system (RDBMS), you can import custom database connectors from Amazon S3 into AWS Glue jobs. In this case, the connection to the data source must be made from the AWS Glue script to extract the data, rather than using AWS Glue connections. To learn more, see Providing Your Own Custom Scripts in the AWS Glue Developer Guide.
Setting up an ETL job for an IBM DB2 data source
The first example demonstrates how to connect the AWS Glue ETL job to an IBM DB2 instance, transform the data from the source, and store it in Apache Parquet format in Amazon S3. To successfully create the ETL job using an external JDBC driver, you must define the following:
The S3 location of the job script
The S3 location of the temporary directory
The S3 location of the JDBC driver
The S3 location of the Parquet data (output)
The IAM role for the job
By default, AWS Glue suggests bucket names for the scripts and the temporary directory using the following format:
Keep in mind that having the AWS Glue job and S3 buckets in the same AWS Region helps save on cross-Region data transfer fees. For this post, we will work in the US East (Ohio) Region (us-east-2).
Creating the IAM role
The next step is to set up the IAM role that the ETL job will use:
Sign in to the AWS Management Console, and search for IAM:
On the IAM console, choose Roles in the left navigation pane.
Choose Create role. The role type of trusted entity must be an AWS service, specifically AWS Glue.
Choose Next: Permissions.
Search for the AWSGlueServiceRole policy, and select it.
Search again, now for the SecretsManagerReadWrite This policy allows the AWS Glue job to access database credentials that are stored in AWS Secrets Manager.
CAUTION: This policy is open and is being used for testing purposes only. You should create a custom policy to narrow the access just to the secrets that you want to use in the ETL job.
Select this policy, and choose Next: Review.
Give your role a name, for example, GluePermissions, and confirm that both policies were selected.
Choose Create role.
Now that you have created the IAM role, it’s time to upload the JDBC driver to the defined location in Amazon S3. For this example, we will use the DB2 driver, which is available on the IBM Support site.
Storing database credentials
It is a best practice to store database credentials in a safe store. In this case, we use AWS Secrets Manager to securely store credentials. Follow these steps to create those credentials:
Open the console, and search for Secrets Manager.
In the AWS Secrets Manager console, choose Store a new secret.
Under Select a secret type, choose Other type of secrets.
In the Secret key/value, set one row for each of the following parameters:
db_username
db_password
db_url (for example, jdbc:db2://10.10.12.12:50000/SAMPLE)
db_table
driver_name (ibm.db2.jcc.DB2Driver)
output_bucket: (for example, aws-glue-data-output-1234567890-us-east-2/User)
Choose Next.
For Secret name, use DB2_Database_Connection_Info.
Choose Next.
Keep the Disable automatic rotation check box selected.
Choose Next.
Choose Store.
Adding a job in AWS Glue
The next step is to author the AWS Glue job, following these steps:
In the AWS Management Console, search for AWS Glue.
In the navigation pane on the left, choose Jobs under the ETL
Choose Add job.
Fill in the basic Job properties:
Give the job a name (for example, db2-job).
Choose the IAM role that you created previously (GluePermissions).
For This job runs, choose A new script to be authored by you.
For ETL language, choose Python.
In the Script libraries and job parameters section, choose the location of your JDBC driver for Dependent jars path.
Choose Next.
On the Connections page, choose Next
On the summary page, choose Save job and edit script. This creates the job and opens the script editor.
In the editor, replace the existing code with the following script. Important: Line 47 of the script corresponds to the mapping of the fields in the source table to the destination, dropping of the null fields to save space in the Parquet destination, and finally writing to Amazon S3 in Parquet format.
Choose the black X on the right side of the screen to close the editor.
Running the ETL job
Now that you have created the job, the next step is to execute it as follows:
On the Jobs page, select your new job. On the Action menu, choose Run job, and confirm that you want to run the job. Wait a few moments as it finishes the execution.
After the job shows as Succeeded, choose Logs to read the output of the job.
In the output of the job, you will find the result of executing the df.printSchema() and the message with the df.count().
Also, if you go to your output bucket in S3, you will find the Parquet result of the ETL job.
Using AWS Glue, you have created an ETL job that connects to an existing database using an external JDBC driver. It enables you to execute any transformation that you need.
Setting up an ETL job for an SAP Sybase data source
In this section, we describe how to create an AWS Glue ETL job against an SAP Sybase data source. The process mentioned in the previous section works for a Sybase data source with a few changes required in the job:
While creating the job, choose the correct jar for the JDBC dependency.
In the script, change the reference to the secret to be used from AWS Secrets Manager:
After you successfully execute the new ETL job, the output contains the same type of information that was generated with the DB2 data source.
Note that each of these JDBC drivers has its own nuances and different licensing terms that you should be aware of before using them.
Maximizing JDBC read parallelism
Something to keep in mind while working with big data sources is the memory consumption. In some cases, “Out of Memory” errors are generated when all the data is read into a single executor. One approach to optimize this is to rely on the parallelism on read that you can implement with Apache Spark and AWS Glue. To learn more, see the Apache Spark SQL module.
You can use the following options:
partitionColumn: The name of an integer column that is used for partitioning.
lowerBound: The minimum value of partitionColumn that is used to decide partition stride.
upperBound: The maximum value of partitionColumn that is used to decide partition stride.
numPartitions: The number of partitions. This, along with lowerBound (inclusive) and upperBound (exclusive), form partition strides for generated WHERE clause expressions used to split the partitionColumn When unset, this defaults to SparkContext.defaultParallelism.
Those options specify the parallelism of the table read. lowerBound and upperBound decide the partition stride, but they don’t filter the rows in the table. Therefore, Spark partitions and returns all rows in the table. For example:
It’s important to be careful with the number of partitions because too many partitions could also result in Spark crashing your external database systems.
Conclusion
Using the process described in this post, you can connect to and run AWS Glue ETL jobs against any data source that can be reached using a JDBC driver. This includes new generations of common analytical databases like Greenplum and others.
You can improve the query efficiency of these datasets by using partitioning and pushdown predicates. For more information, see Managing Partitions for ETL Output in AWS Glue. This technique opens the door to moving data and feeding data lakes in hybrid environments.
Kapil Shardha is a Technical Account Manager and supports enterprise customers with their AWS adoption. He has background in infrastructure automation and DevOps.
William Torrealba is an AWS Solutions Architect supporting customers with their AWS adoption. He has background in Application Development, High Available Distributed Systems, Automation, and DevOps.
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
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.
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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Last week, Oracle announced the general availability of MySQL 8.0. This is good news for database users, as it means Oracle is still developing MySQL.
I decide to celebrate the event by doing a quick test of MySQL 8.0. Here follows a step-by-step description of my first experience with MySQL 8.0. Note that I did the following without reading the release notes, as is what I have done with every MySQL / MariaDB release up to date; In this case it was not the right thing to do.
I pulled MySQL 8.0 from [email protected]:mysql/mysql-server.git I was pleasantly surprised that ‘cmake . ; make‘ worked without without any compiler warnings! I even checked the used compiler options and noticed that MySQL was compiled with -Wall + several other warning flags. Good job MySQL team!
I did have a little trouble finding the mysqld binary as Oracle had moved it to ‘runtime_output_directory’; Unexpected, but no big thing.
Now it’s was time to install MySQL 8.0.
I did know that MySQL 8.0 has removed mysql_install_db, so I had to use the mysqld binary directly to install the default databases: (I have specified datadir=/my/data3 in the /tmp/my.cnf file)
> cd runtime_output_directory > mkdir /my/data3 > ./mysqld –defaults-file=/tmp/my.cnf –install
2018-04-22T12:38:18.332967Z 1 [ERROR] [MY-011011] [Server] Failed to find valid data directory. 2018-04-22T12:38:18.333109Z 0 [ERROR] [MY-010020] [Server] Data Dictionary initialization failed. 2018-04-22T12:38:18.333135Z 0 [ERROR] [MY-010119] [Server] Aborting
A quick look in mysqld –help –verbose output showed that the right command option is –-initialize. My bad, lets try again,
> ./mysqld –defaults-file=/tmp/my.cnf –initialize
2018-04-22T12:39:31.910509Z 0 [ERROR] [MY-010457] [Server] –initialize specified but the data directory has files in it. Aborting. 2018-04-22T12:39:31.910578Z 0 [ERROR] [MY-010119] [Server] Aborting
Now I used the right options, but still didn’t work. I took a quick look around:
> ls /my/data3/ binlog.index
So even if the mysqld noticed that the data3 directory was wrong, it still wrote things into it. This even if I didn’t have –log-binlog enabled in the my.cnf file. Strange, but easy to fix:
> rm /my/data3/binlog.index > ./mysqld –defaults-file=/tmp/my.cnf –initialize 2018-04-22T12:40:45.633637Z 0 [ERROR] [MY-011071] [Server] unknown variable ‘max-tmp-tables=100’ 2018-04-22T12:40:45.633657Z 0 [Warning] [MY-010952] [Server] The privilege system failed to initialize correctly. If you have upgraded your server, make sure you’re executing mysql_upgrade to correct the issue. 2018-04-22T12:40:45.633663Z 0 [ERROR] [MY-010119] [Server] Aborting
The warning about the privilege system confused me a bit, but I ignored it for the time being and removed from my configuration files the variables that MySQL 8.0 doesn’t support anymore. I couldn’t find a list of the removed variables anywhere so this was done with the trial and error method.
> ./mysqld –defaults-file=/tmp/my.cnf
2018-04-22T12:42:56.626583Z 0 [ERROR] [MY-010735] [Server] Can’t open the mysql.plugin table. Please run mysql_upgrade to create it. 2018-04-22T12:42:56.827685Z 0 [Warning] [MY-010015] [Repl] Gtid table is not ready to be used. Table ‘mysql.gtid_executed’ cannot be opened. 2018-04-22T12:42:56.838501Z 0 [Warning] [MY-010068] [Server] CA certificate ca.pem is self signed. 2018-04-22T12:42:56.848375Z 0 [Warning] [MY-010441] [Server] Failed to open optimizer cost constant tables 2018-04-22T12:42:56.848863Z 0 [ERROR] [MY-013129] [Server] A message intended for a client cannot be sent there as no client-session is attached. Therefore, we’re sending the information to the error-log instead: MY-001146 – Table ‘mysql.component’ doesn’t exist 2018-04-22T12:42:56.848916Z 0 [Warning] [MY-013129] [Server] A message intended for a client cannot be sent there as no client-session is attached. Therefore, we’re sending the information to the error-log instead: MY-003543 – The mysql.component table is missing or has an incorrect definition. …. 2018-04-22T12:42:56.854141Z 0 [System] [MY-010931] [Server] /home/my/mysql-8.0/runtime_output_directory/mysqld: ready for connections. Version: ‘8.0.11’ socket: ‘/tmp/mysql.sock’ port: 3306 Source distribution.
I figured out that if there is a single wrong variable in the configuration file, running mysqld –initialize will leave the database in an inconsistent state. NOT GOOD! I am happy I didn’t try this in a production system!
2018-04-22T12:44:45.548960Z 5 [Note] [MY-010454] [Server] A temporary password is generated for [email protected]: px)NaaSp?6um 2018-04-22T12:44:51.221751Z 0 [System] [MY-013170] [Server] /home/my/mysql-8.0/runtime_output_directory/mysqld (mysqld 8.0.11) initializing of server has completed
Success!
I wonder why the temporary password is so complex; It could easily have been something that one could easily remember without decreasing security, it’s temporary after all. No big deal, one can always paste it from the logs. (Side note: MariaDB uses socket authentication on many system and thus doesn’t need temporary installation passwords).
Now lets start the MySQL server for real to do some testing:
> ./client/mysql –socket=/tmp/mysql.sock –user=root –password=”px)NaaSp?6um” ERROR 2059 (HY000): Plugin caching_sha2_password could not be loaded: /usr/local/mysql/lib/plugin/caching_sha2_password.so: cannot open shared object file: No such file or directory
Apparently MySQL 8.0 doesn’t work with old MySQL / MariaDB clients by default 🙁
I was testing this in a system with MariaDB installed, like all modern Linux system today, and didn’t want to use the MySQL clients or libraries.
I decided to try to fix this by changing the authentication to the native (original) MySQL authentication method.
> mysqld –skip-grant-tables
> ./client/mysql –socket=/tmp/mysql.sock –user=root ERROR 1045 (28000): Access denied for user ‘root’@’localhost’ (using password: NO)
Apparently –skip-grant-tables is not good enough anymore. Let’s try again with:
> ./client/mysql –socket=/tmp/mysql.sock –user=root mysql Welcome to the MariaDB monitor. Commands end with ; or \g. Your MySQL connection id is 7 Server version: 8.0.11 Source distribution
Great, we are getting somewhere, now lets fix “root” to work with the old authenticaion:
MySQL [mysql]> update mysql.user set plugin=”mysql_native_password”,authentication_string=password(“test”) where user=”root”; ERROR 1064 (42000): You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ‘(“test”) where user=”root”‘ at line 1
A quick look in the MySQL 8.0 release notes told me that the PASSWORD() function is removed in 8.0. Why???? I don’t know how one in MySQL 8.0 is supposed to generate passwords compatible with old installations of MySQL. One could of course start an old MySQL or MariaDB version, execute the password() function and copy the result.
I decided to fix this the easy way and use an empty password:
(Update:: I later discovered that the right way would have been to use: FLUSH PRIVILEGES; ALTER USER’ root’@’localhost’ identified by ‘test’ ; I however dislike this syntax as it has the password in clear text which is easy to grab and the command can’t be used to easily update the mysql.user table. One must also disable the –skip-grant mode to do use this)
MySQL [mysql]> update mysql.user set plugin=”mysql_native_password”,authentication_string=”” where user=”root”; Query OK, 1 row affected (0.077 sec) Rows matched: 1 Changed: 1 Warnings: 0 I restarted mysqld: > mysqld –default_authentication_plugin=mysql_native_password
> ./client/mysql –user=root –password=”” mysql ERROR 1862 (HY000): Your password has expired. To log in you must change it using a client that supports expired passwords.
2018-04-22T13:18:06.629548Z 5 [Warning] [MY-010453] [Server] [email protected] is created with an empty password ! Please consider switching off the –initialize-insecure option.
Hm. Don’t understand the warning as–initialize-insecure is not an option that one would use more than one time and thus nothing one would ‘switch off’.
> ./mysqld –defaults-file=/tmp/my.cnf
> ./client/mysql –user=root –password=”” mysql ERROR 2059 (HY000): Plugin caching_sha2_password could not be loaded: /usr/local/mysql/lib/plugin/caching_sha2_password.so: cannot open shared object file: No such file or directory
Back to the beginning 🙁
To get things to work with old clients, one has to initialize the database with: > ./mysqld –defaults-file=/tmp/my.cnf –initialize-insecure –default_authentication_plugin=mysql_native_password
Now I finally had MySQL 8.0 up and running and thought I would take it up for a spin by running the “standard” MySQL/MariaDB sql-bench test suite. This was removed in MySQL 5.7, but as I happened to have MariaDB 10.3 installed, I decided to run it from there.
sql-bench is a single threaded benchmark that measures the “raw” speed for some common operations. It gives you the ‘maximum’ performance for a single query. Its different from other benchmarks that measures the maximum throughput when you have a lot of users, but sql-bench still tells you a lot about what kind of performance to expect from the database.
I tried first to be clever and create the “test” database, that I needed for sql-bench, with > mkdir /my/data3/test
but when I tried to run the benchmark, MySQL 8.0 complained that the test database didn’t exist.
MySQL 8.0 has gone away from the original concept of MySQL where the user can easily create directories and copy databases into the database directory. This may have serious implication for anyone doing backup of databases and/or trying to restore a backup with normal OS commands.
I created the ‘test’ database with mysqladmin and then tried to run sql-bench:
> ./run-all-tests –user=root
The first run failed in test-ATIS:
Can’t execute command ‘create table class_of_service (class_code char(2) NOT NULL,rank tinyint(2) NOT NULL,class_description char(80) NOT NULL,PRIMARY KEY (class_code))’ Error: You have an error in your SQL syntax; check the manual that corresponds to your MySQL server version for the right syntax to use near ‘rank tinyint(2) NOT NULL,class_description char(80) NOT NULL,PRIMARY KEY (class_’ at line 1
This happened because ‘rank‘ is now a reserved word in MySQL 8.0. This is also reserved in ANSI SQL, but I don’t know of any other database that has failed to run test-ATIS before. I have in the past run it against Oracle, PostgreSQL, Mimer, MSSQL etc without any problems.
MariaDB also has ‘rank’ as a keyword in 10.2 and 10.3 but one can still use it as an identifier.
I fixed test-ATIS and then managed to run all tests on MySQL 8.0.
I did run the test both with MySQL 8.0 and MariaDB 10.3 with the InnoDB storage engine and by having identical values for all InnoDB variables, table-definition-cache and table-open-cache. I turned off performance schema for both databases. All test are run with a user with an empty password (to keep things comparable and because it’s was too complex to generate a password in MySQL 8.0)
The result are as follows Results per test in seconds: Operation |MariaDB|MySQL-8|
This is of course just a first view of the performance of MySQL 8.0 in a single user environment. Some reflections about the results:
Alter-table test is slower (as expected) in 8.0 as some of the alter tests benefits of the instant add column in MariaDB 10.3.
connect test is also better for MariaDB as we put a lot of efforts to speed this up in MariaDB 10.2
table-elimination shows an optimization in MariaDB for the Anchor table model, which MySQL doesn’t have.
CREATE and DROP TABLE is almost 8 times slower in MySQL 8.0 than in MariaDB 10.3. I assume this is the cost of ‘atomic DDL’. This may also cause performance problems for any thread using the data dictionary when another thread is creating/dropping tables.
When looking at the individual test results, MySQL 8.0 was slower in almost every test, in many significantly slower.
The only test where MySQL was faster was “update_with_key_prefix”. I checked this and noticed that there was a bug in the test and the columns was updated to it’s original value (which should be instant with any storage engine). This is an old bug that MySQL has found and fixed and that we have not been aware of in the test or in MariaDB.
While writing this, I noticed that MySQL 8.0 is now using utf8mb4 as the default character set instead of latin1. This may affect some of the benchmarks slightly (not much as most tests works with numbers and Oracle claims that utf8mb4 is only 20% slower than latin1), but needs to be verified.
Oracle claims that MySQL 8.0 is much faster on multi user benchmarks. The above test indicates that they may have done this by sacrificing single user performance.
We need to do more and many different benchmarks to better understand exactly what is going on. Stay tuned!
Short summary of my first run with MySQL 8.0:
Using the new caching_sha2_password authentication as default for new installation is likely to cause a lot of problems for users. No old application will be able to use MySQL 8.0, installed with default options, without moving to MySQL’s client libraries. While working on this blog I saw MySQL users complain on IRC that not even MySQL Workbench can authenticate with MySQL 8.0. This is the first time in MySQL’s history where such an incompatible change has ever been done!
Atomic DDL is a good thing (We plan to have this in MariaDB 10.4), but it should not have such a drastic impact on performance. I am also a bit skeptical of MySQL 8.0 having just one copy of the data dictionary as if this gets corrupted you will lose all your data. (Single point of failure)
MySQL 8.0 has several new reserved words and hasremoved a lot of variables, which makes upgrades hard. Before upgrading to MySQL 8.0 one has to check all one’s databases and applications to ensure that there are no conflicts.
As my test above shows, if you have a single deprecated variable in your configuration files, the installation of MySQL will abort and can leave the database in inconsistent state. I did of course my tests by installing into an empty data dictionary, but one can assume that some of the problems may also happen when upgrading an old installation.
Conclusions: In many ways, MySQL 8.0 has caught up with some earlier versions of MariaDB. For instance, in MariaDB 10.0, we introduced roles (four years ago). In MariaDB 10.1, we introduced encrypted redo/undo logs (three years ago). In MariaDB 10.2, we introduced window functions and CTEs (a year ago). However, some catch-up of MariaDB Server 10.2 features still remains for MySQL (such as check constraints, binlog compression, and log-based rollback).
MySQL 8.0 has a few new interesting features (mostly Atomic DDL and JSON TABLE functions), but at the same time MySQL has strayed away from some of the fundamental corner stone principles of MySQL:
From the start of the first version of MySQL in 1995, all development has been focused around 3 core principles:
Ease of use
Performance
Stability
With MySQL 8.0, Oracle has sacrifices 2 of 3 of these.
In addition (as part of ease of use), while I was working on MySQL, we did our best to ensure that the following should hold:
Upgrades should be trivial
Things should be kept compatible, if possible (don’t remove features/options/functions that are used)
Minimize reserved words, don’t remove server variables
One should be able to use normal OS commands to create and drop databases, copy and move tables around within the same system or between different systems. With 8.0 and data dictionary taking backups of specific tables will be hard, even if the server is not running.
mysqldump should always be usable backups and to move to new releases
Old clients and application should be able to use ‘any’ MySQL server version unchanged. (Some Oracle client libraries, like C++, by default only supports the new X protocol and can thus not be used with older MySQL or any MariaDB version)
We plan to add a data dictionary to MariaDB 10.4 or MariaDB 10.5, but in a way to not sacrifice any of the above principles!
The competition between MySQL and MariaDB is not just about a tactical arms race on features. It’s about design philosophy, or strategic vision, if you will.
This shows in two main ways: our respective view of the Storage Engine structure, and of the top-level direction of the roadmap.
On the Storage Engine side, MySQL is converging on InnoDB, even for clustering and partitioning. In doing so, they are abandoning the advantages of multiple ways of storing data. By contrast, MariaDB sees lots of value in the Storage Engine architecture: MariaDB Server 10.3 will see the general availability of MyRocks (for write-intensive workloads) and Spider (for scalable workloads). On top of that, we have ColumnStore for analytical workloads. One can use the CONNECT engine to join with other databases. The use of different storage engines for different workloads and different hardware is a competitive differentiator, now more than ever.
On the roadmap side, MySQL is carefully steering clear of features that close the gap between MySQL and Oracle. MariaDB has no such constraints. With MariaDB 10.3, we are introducing PL/SQL compatibility (Oracle’s stored procedures) and AS OF (built-in system versioned tables with point-in-time querying). For both of those features, MariaDB is the first Open Source database doing so. I don’t except Oracle to provide any of the above features in MySQL!
Also on the roadmap side, MySQL is not working with the ecosystem in extending the functionality. In 2017, MariaDB accepted more code contributions in one year, than MySQL has done during its entire lifetime, and the rate is increasing!
I am sure that the experience I had with testing MySQL 8.0 would have been significantly better if MySQL would have an open development model where the community could easily participate in developing and testing MySQL continuously. Most of the confusing error messages and strange behavior would have been found and fixed long before the GA release.
Before upgrading to MySQL 8.0 please readhttps://dev.mysql.com/doc/refman/8.0/en/upgrading-from-previous-series.html to see what problems you can run into! Don’t expect that old installations or applications will work out of the box without testing as a lot of features and options has been removed (query cache, partition of myisam tables etc)! You probably also have to revise your backup methods, especially if you want to ever restore just a few tables. (With 8.0, I don’t know how this can be easily done).
According to the MySQL 8.0 release notes, one can’t use mysqldump to copy a database to MySQL 8.0. One has to first to move to a MySQL 5.7 GA version (with mysqldump, as recommended by Oracle) and then to MySQL 8.0 with in-place update. I assume this means that all old mysqldump backups are useless for MySQL 8.0?
MySQL 8.0 seams to be a one way street to an unknown future. Up to MySQL 5.7 it has been trivial to move to MariaDB and one could always move back to MySQL with mysqldump. All MySQL client libraries has worked with MariaDB and all MariaDB client libraries has worked with MySQL. With MySQL 8.0 this has changed in the wrong direction.
As long as you are using MySQL 5.7 and below you have choices for your future, after MySQL 8.0 you have very little choice. But don’t despair, as MariaDB will always be able to load a mysqldump file and it’s very easy to upgrade your old MySQL installation to MariaDB 🙂
I wish you good luck to try MySQL 8.0 (and also the upcoming MariaDB 10.3)!
This post courtesy of Massimiliano Angelino, AWS Solutions Architect
Different enterprise systems—ERP, CRM, BI, HR, etc.—need to exchange information but normally cannot do that natively because they are from different vendors. Enterprises have tried multiple ways to integrate heterogeneous systems, generally referred to as enterprise application integration (EAI).
Modern EAI systems are based on a message-oriented middleware (MoM), also known as enterprise service bus (ESB). An ESB provides data communication via a message bus, on top of which it also provides components to orchestrate, route, translate, and monitor the data exchange. Communication with the ESB is done via adapters or connectors provided by the ESB. In this way, the different applications do not have to have specific knowledge of the technology used to provide the integration.
Amazon MQ used with Apache Camel is an open-source alternative to commercial ESBs. With the launch of Amazon MQ, integration between on-premises applications and cloud services becomes much simpler. Amazon MQ provides a managed message broker service currently supporting ApacheMQ 5.15.0.
In this post, I show how a simple integration between Amazon MQ and other AWS services can be achieved by using Apache Camel.
Apache Camel provides built-in connectors for integration with a wide variety of AWS services such as Amazon MQ, Amazon SQS, Amazon SNS, Amazon SWF, Amazon S3, AWS Lambda, Amazon DynamoDB, AWS Elastic Beanstalk, and Amazon Kinesis Streams. It also provides a broad range of other connectors including Cassandra, JDBC, Spark, and even Facebook and Slack.
EAI system architecture
Different applications use different data formats, hence the need for a translation/transformation service. Such services can be provided to or from a common “normalized” format, or specifically between two applications.
The use of normalized formats simplifies the integration process when multiple applications need to share the same data, as the number of conversions to be realized is N (number of applications). This is at the cost of a more complex adaptation to a common format, which is required to cover all needs from the different applications, current and future.
Another characteristic of an EAI system is the support of distributed transactions to ensure data consistency across multiple applications.
EAI system architecture is normally composed of the following components:
A centralized broker that handles security, access control, and data communications. Amazon MQ provides these features through the support of multiple transport protocols (AMQP, Openwire, MQTT, WebSocket), security (all communications are encrypted via SSL), and per destination granular access control.
An independent data model, also known as the canonical data model. XML is the de facto standard for the data representation.
Connectors/agents that allow the applications to communicate with the broker.
A system model to allow a standardized way for all components to interface with the EAI. Java Message Service (JMS) and Windows Communication Foundation (WCF) are standard APIs to interact with constructs such as queues and topics to implement the different messaging patterns.
Walkthrough
This solution walks you through the following steps:
Creating the broker
Writing a simple application
Adding the dependencies
Triaging files into S3
Writing the Camel route
Sending files to the AMQP queue
Setting up AMQP
Testing the code
Creating the broker
To create a new broker, log in to your AWS account and choose Amazon MQ. Amazon MQ is currently available in six AWS Regions:
US East (N. Virginia)
US East (Ohio)
US West (Oregon)
EU (Ireland)
EU (Frankfurt)
Asia Pacific (Sydney) regions.
Make sure that you have selected one of these Regions.
The master user name and password are used to access the monitoring console of the broker and can be also used to authenticate when connecting the clients to the broker. I recommend creating separate users, without console access, to authenticate the clients to the broker, after the broker has been created.
For this example, create a single broker without failover. If your application requires a higher availability level, check the Create standby in a different zone check box. In case the principal broker instance would fail, the standby takes over in seconds. To make the client aware of the standby, use the failover:// protocol in the connection configuration pointing to both broker endpoints.
Leave the other settings as is. The broker takes few minutes to be created. After it’s done, you can see the list of endpoints available for the different protocols.
After the broker has been created, modify the security group to add the allowed ports and sources for access.
For this example, you need access to the ActiveMQ admin page and to AMQP. Open up ports 8162 and 5671 to the public address of your laptop.
You can also create a new user for programmatic access to the broker. In the Users section, choose Create User and add a new user named sdk.
Writing a simple application
The complete code for this walkthrough is available from the aws-amazonmq-apachecamel-sample GitHub repo. Clone the repository on your local machine to have the fully functional example. The rest of this post offers step-by-step instructions to build this solution.
To write the application, use Apache Maven and the Camel archetypes provided by Maven. If you do not have Apache Maven installed on your machine, you can follow the instructions at Installing Apache Maven.
From a terminal, run the following command:
mvn archetype:generate
You get a list of archetypes. Type camel to get only the one related to camel. In this case, use the java8 example and type the following:
Maven now generates the skeleton code in a folder named as the artifactId. In this case:
camel-aws-simple
Next, test that the environment is configured correctly to run Camel. At the prompt, run the following commands:
cd camel-aws-simple
mvn install
mvn exec:java
You should see a log appearing in the console, printing the following:
[INFO] --- exec-maven-plugin:1.6.0:java (default-cli) @ camel-aws-test ---
[ com.angmas.MainApp.main()] DefaultCamelContext INFO Apache Camel 2.20.1 (CamelContext: camel-1) is starting
[ com.angmas.MainApp.main()] ManagedManagementStrategy INFO JMX is enabled
[ com.angmas.MainApp.main()] DefaultTypeConverter INFO Type converters loaded (core: 192, classpath: 0)
[ com.angmas.MainApp.main()] DefaultCamelContext INFO StreamCaching is not in use. If using streams then its recommended to enable stream caching. See more details at http://camel.apache.org/stream-caching.html
[ com.angmas.MainApp.main()] DefaultCamelContext INFO Route: route1 started and consuming from: timer://simple?period=1000
[ com.angmas.MainApp.main()] DefaultCamelContext INFO Total 1 routes, of which 1 are started
[ com.angmas.MainApp.main()] DefaultCamelContext INFO Apache Camel 2.20.1 (CamelContext: camel-1) started in 0.419 seconds
[-1) thread #2 - timer://simple] route1 INFO Got a String body
[-1) thread #2 - timer://simple] route1 INFO Got an Integer body
[-1) thread #2 - timer://simple] route1 INFO Got a Double body
[-1) thread #2 - timer://simple] route1 INFO Got a String body
[-1) thread #2 - timer://simple] route1 INFO Got an Integer body
[-1) thread #2 - timer://simple] route1 INFO Got a Double body
[-1) thread #2 - timer://simple] route1 INFO Got a String body
[-1) thread #2 - timer://simple] route1 INFO Got an Integer body
[-1) thread #2 - timer://simple] route1 INFO Got a Double body
Adding the dependencies
Now that you have verified that the sample works, modify it to add the dependencies to interface to Amazon MQ/ActiveMQ and AWS.
For the following steps, you can use a normal text editor, such as vi, Sublime Text, or Visual Studio Code. Or, open the maven project in an IDE such as Eclipse or IntelliJ IDEA.
Open pom.xml and add the following lines inside the <dependencies> tag:
The camel-aws component is taking care of the interface with the supported AWS services without requiring any in-depth knowledge of the AWS Java SDK. For more information, see Camel Components for Amazon Web Services.
Triaging files into S3
Write a Camel component that receives files as a payload to messages in a queue and write them to an S3 bucket with different prefixes depending on the extension.
Because the broker that you created is exposed via a public IP address, you can execute the code from anywhere that there is an internet connection that allows communication on the specific ports. In this example, run the code from your own laptop. A broker can also be created without public IP address, in which case it is only accessible from inside the VPC in which it has been created, or by any peered VPC or network connected via a virtual gateway (VPN or AWS Direct Connect).
First, look at the code created by Maven. The archetype chosen created a standalone Camel context run via the helper org.apache.camel.main.Main class. This provides an easy way to run Camel routes from an IDE or the command line without needing to deploy it inside a container. Apache Camel can be also run as an OSGi module, or Spring and SpringBoot bean.
package com.angmas;
import org.apache.camel.main.Main;
/**
* A Camel Application
*/
public class MainApp {
/**
* A main() so you can easily run these routing rules in your IDE
*/
public static void main(String... args) throws Exception {
Main main = new Main();
main.addRouteBuilder(new MyRouteBuilder());
main.run(args);
}
}
The main method instantiates the Camel Main helper class and the routes, and runs the Camel application. The MyRouteBuilder class creates a route using Java DSL. It is also possible to define routes in Spring XML and load them dynamically in the code.
public void configure() {
// this sample sets a random body then performs content-based
// routing on the message using method references
from("timer:simple?period=1000")
.process()
.message(m -> m.setHeader("index", index++ % 3))
.transform()
.message(this::randomBody)
.choice()
.when()
.body(String.class::isInstance)
.log("Got a String body")
.when()
.body(Integer.class::isInstance)
.log("Got an Integer body")
.when()
.body(Double.class::isInstance)
.log("Got a Double body")
.otherwise()
.log("Other type message");
}
Writing the Camel route
Replace the existing route with one that fetches messages from Amazon MQ over AMQP, and routes the content to different S3 buckets depending on the file name extension.
Reads messages from the AMQP queue named filequeue.
Processes the message and sets a new ext header using the setExtensionHeader method (see below).
Checks the value of the ext header and write the body of the message as an object in an S3 bucket using different key prefixes, retaining the original name of the file.
The Amazon S3 component is configured with the bucket name, and a reference to an S3 client (amazonS3client=#s3Client) that you added to the Camel registry in the Main method of the app. Adding the object to the Camel registry allows Camel to find the object at runtime. Even though you could pass the region, accessKey, and secretKey parameters directly in the component URI, this way is more secure. It can make use of EC2 instance roles, so that you never need to pass the secrets.
Sending files to the AMQP queue
To send the files to the AMQP queue for testing, add another Camel route. In a real scenario, the messages to the AMQP queue are generated by another client. You are going to create a new route builder, but you could also add this route inside the existing MyRouteBuilder.
package com.angmas;
import org.apache.camel.builder.RouteBuilder;
/**
* A Camel Java8 DSL Router
*/
public class MessageProducerBuilder extends RouteBuilder {
/**
* Configure the Camel routing rules using Java code...
*/
public void configure() {
from("file://input?delete=false&noop=true")
.log("Content ${body} ${headers.CamelFileName}")
.to("amqp:filequeue");
}
}
The code reads files from the input folder in the work directory and publishes it to the queue. The route builder is added in the main class:
By default, Camel tries to connect to a local AMQP broker. Configure it to connect to your Amazon MQ broker.
Create an AMQPConnectionDetails object that is configured to connect to Amazon MQ broker with SSL and pass the user name and password that you set on the broker. Adding the object to the Camel registry allows Camel to find the object at runtime and use it as the default connection to AMQP.
public class MainApp {
public static String BROKER_URL = System.getenv("BROKER_URL");
public static String AMQP_URL = "amqps://"+BROKER_URL+":5671";
public static String BROKER_USERNAME = System.getenv("BROKER_USERNAME");
public static String BROKER_PASSWORD = System.getenv("BROKER_PASSWORD");
/**
* A main() so you can easily run these routing rules in your IDE
*/
public static void main(String... args) throws Exception {
Main main = new Main();
main.bind("amqp", getAMQPconnection());
main.bind("s3Client", AmazonS3ClientBuilder.standard().withRegion(Regions.US_EAST_1).build());
main.addRouteBuilder(new MyRouteBuilder());
main.addRouteBuilder(new MessageProducerBuilder());
main.run(args);
}
public static AMQPConnectionDetails getAMQPconnection() {
return new AMQPConnectionDetails(AMQP_URL, BROKER_USERNAME, BROKER_PASSWORD);
}
}
The AMQP_URL uses the amqps schema that indicates that you are using SSL. You then add the component to the registry. Camel finds it by matching the class type. main.bind("amqp-ssl", getAMQPConnection());
Testing the code
Create an input folder in the project root, and create few files with different extensions, such as txt, html, and csv.
Set the different environment variables required by the code, either in the shell or in your IDE as execution configuration.
If you are running the example from an EC2 instance, ensure that the EC2 instance role has read permission on the S3 bucket.
If you are running this on your laptop, ensure that you have configured the AWS credentials in the environment, for example, by using the aws configure command.
From the command line, execute the code:
mvn exec:java
If you are using an IDE, execute the main class. Camel outputs logging information and you should see messages listing the content and names of the files in the input folder.
Keep adding some more files to the input folder. You see that they are triaged in S3 a few seconds later. You can open the S3 console to check that they have been created.
To stop Camel, press CTRL+C in the shell.
Conclusion
In this post, I showed you how to create a publicly accessible Amazon MQ broker, and how to use Apache Camel to easily integrate AWS services with the broker. In the example, you created a Camel route that reads messages containing files from the AMQP queue and triages them by file extension into an S3 bucket.
Camel supports several components and provides blueprints for several enterprise integration patterns. Used in combination with the Amazon MQ, it provides a powerful and flexible solution to extend traditional enterprise solutions to the AWS Cloud, and integrate them seamlessly with cloud-native services, such as Amazon S3, Amazon SNS, Amazon SQS, Amazon CloudWatch, and AWS Lambda.
To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.
AWS Glue is an increasingly popular way to develop serverless ETL (extract, transform, and load) applications for big data and data lake workloads. Organizations that transform their ETL applications to cloud-based, serverless ETL architectures need a seamless, end-to-end continuous integration and continuous delivery (CI/CD) pipeline: from source code, to build, to deployment, to product delivery. Having a good CI/CD pipeline can help your organization discover bugs before they reach production and deliver updates more frequently. It can also help developers write quality code and automate the ETL job release management process, mitigate risk, and more.
AWS Glue is a fully managed data catalog and ETL service. It simplifies and automates the difficult and time-consuming tasks of data discovery, conversion, and job scheduling. AWS Glue crawls your data sources and constructs a data catalog using pre-built classifiers for popular data formats and data types, including CSV, Apache Parquet, JSON, and more.
When you are developing ETL applications using AWS Glue, you might come across some of the following CI/CD challenges:
Iterative development with unit tests
Continuous integration and build
Pushing the ETL pipeline to a test environment
Pushing the ETL pipeline to a production environment
Testing ETL applications using real data (live test)
The following diagram shows the pipeline workflow:
This solution uses AWS CodePipeline, which lets you orchestrate and automate the test and deploy stages for ETL application source code. The solution consists of a pipeline that contains the following stages:
1.) Source Control: In this stage, the AWS Glue ETL job source code and the AWS CloudFormation template file for deploying the ETL jobs are both committed to version control. I chose to use AWS CodeCommit for version control.
2.) LiveTest: In this stage, all resources—including AWS Glue crawlers, jobs, S3 buckets, roles, and other resources that are required for the solution—are provisioned, deployed, live tested, and cleaned up.
The LiveTest stage includes the following actions:
Deploy: In this action, all the resources that are required for this solution (crawlers, jobs, buckets, roles, and so on) are provisioned and deployed using an AWS CloudFormation template.
AutomatedLiveTest: In this action, all the AWS Glue crawlers and jobs are executed and data exploration and validation tests are performed. These validation tests include, but are not limited to, record counts in both raw tables and transformed tables in the data lake and any other business validations. I used AWS CodeBuild for this action.
LiveTestApproval: This action is included for the cases in which a pipeline administrator approval is required to deploy/promote the ETL applications to the next stage. The pipeline pauses in this action until an administrator manually approves the release.
LiveTestCleanup: In this action, all the LiveTest stage resources, including test crawlers, jobs, roles, and so on, are deleted using the AWS CloudFormation template. This action helps minimize cost by ensuring that the test resources exist only for the duration of the AutomatedLiveTest and LiveTestApproval
3.) DeployToProduction: In this stage, all the resources are deployed using the AWS CloudFormation template to the production environment.
Try it out
This code pipeline takes approximately 20 minutes to complete the LiveTest test stage (up to the LiveTest approval stage, in which manual approval is required).
To get started with this solution, choose Launch Stack:
This creates the CI/CD pipeline with all of its stages, as described earlier. It performs an initial commit of the sample AWS Glue ETL job source code to trigger the first release change.
In the AWS CloudFormation console, choose Create. After the template finishes creating resources, you see the pipeline name on the stack Outputs tab.
After that, open the CodePipeline console and select the newly created pipeline. Initially, your pipeline’s CodeCommit stage shows that the source action failed.
Allow a few minutes for your new pipeline to detect the initial commit applied by the CloudFormation stack creation. As soon as the commit is detected, your pipeline starts. You will see the successful stage completion status as soon as the CodeCommit source stage runs.
In the CodeCommit console, choose Code in the navigation pane to view the solution files.
Next, you can watch how the pipeline goes through the LiveTest stage of the deploy and AutomatedLiveTest actions, until it finally reaches the LiveTestApproval action.
At this point, if you check the AWS CloudFormation console, you can see that a new template has been deployed as part of the LiveTest deploy action.
At this point, make sure that the AWS Glue crawlers and the AWS Glue job ran successfully. Also check whether the corresponding databases and external tables have been created in the AWS Glue Data Catalog. Then verify that the data is validated using Amazon Athena, as shown following.
Open the AWS Glue console, and choose Databases in the navigation pane. You will see the following databases in the Data Catalog:
Open the Amazon Athena console, and run the following queries. Verify that the record counts are matching.
SELECT count(*) FROM "nycitytaxi_gluedemocicdtest"."data";
SELECT count(*) FROM "nytaxiparquet_gluedemocicdtest"."datalake";
The following shows the raw data:
The following shows the transformed data:
The pipeline pauses the action until the release is approved. After validating the data, manually approve the revision on the LiveTestApproval action on the CodePipeline console.
Add comments as needed, and choose Approve.
The LiveTestApproval stage now appears as Approved on the console.
After the revision is approved, the pipeline proceeds to use the AWS CloudFormation template to destroy the resources that were deployed in the LiveTest deploy action. This helps reduce cost and ensures a clean test environment on every deployment.
Production deployment is the final stage. In this stage, all the resources—AWS Glue crawlers, AWS Glue jobs, Amazon S3 buckets, roles, and so on—are provisioned and deployed to the production environment using the AWS CloudFormation template.
After successfully running the whole pipeline, feel free to experiment with it by changing the source code stored on AWS CodeCommit. For example, if you modify the AWS Glue ETL job to generate an error, it should make the AutomatedLiveTest action fail. Or if you change the AWS CloudFormation template to make its creation fail, it should affect the LiveTest deploy action. The objective of the pipeline is to guarantee that all changes that are deployed to production are guaranteed to work as expected.
Conclusion
In this post, you learned how easy it is to implement CI/CD for serverless AWS Glue ETL solutions with AWS developer tools like AWS CodePipeline and AWS CodeBuild at scale. Implementing such solutions can help you accelerate ETL development and testing at your organization.
If you have questions or suggestions, please comment below.
Prasad Alle is a Senior Big Data Consultant with AWS Professional Services. He spends his time leading and building scalable, reliable Big data, Machine learning, Artificial Intelligence and IoT solutions for AWS Enterprise and Strategic customers. His interests extend to various technologies such as Advanced Edge Computing, Machine learning at Edge. In his spare time, he enjoys spending time with his family.
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.
Amazon Redshift is a data warehouse service that logs the history of the system in STL log tables. The STL log tables manage disk space by retaining only two to five days of log history, depending on log usage and available disk space.
To retain STL tables’ data for an extended period, you usually have to create a replica table for every system table. Then, for each you load the data from the system table into the replica at regular intervals. By maintaining replica tables for STL tables, you can run diagnostic queries on historical data from the STL tables. You then can derive insights from query execution times, query plans, and disk-spill patterns, and make better cluster-sizing decisions. However, refreshing replica tables with live data from STL tables at regular intervals requires schedulers such as Cron or AWS Data Pipeline. Also, these tables are specific to one cluster and they are not accessible after the cluster is terminated. This is especially true for transient Amazon Redshift clusters that last for only a finite period of ad hoc query execution.
In this blog post, I present a solution that exports system tables from multiple Amazon Redshift clusters into an Amazon S3 bucket. This solution is serverless, and you can schedule it as frequently as every five minutes. The AWS CloudFormation deployment template that I provide automates the solution setup in your environment. The system tables’ data in the Amazon S3 bucket is partitioned by cluster name and query execution date to enable efficient joins in cross-cluster diagnostic queries.
I also provide another CloudFormation template later in this post. This second template helps to automate the creation of tables in the AWS Glue Data Catalog for the system tables’ data stored in Amazon S3. After the system tables are exported to Amazon S3, you can run cross-cluster diagnostic queries on the system tables’ data and derive insights about query executions in each Amazon Redshift cluster. You can do this using Amazon QuickSight, Amazon Athena, Amazon EMR, or Amazon Redshift Spectrum.
You can find all the code examples in this post, including the CloudFormation templates, AWS Glue extract, transform, and load (ETL) scripts, and the resolution steps for common errors you might encounter in this GitHub repository.
Solution overview
The solution in this post uses AWS Glue to export system tables’ log data from Amazon Redshift clusters into Amazon S3. The AWS Glue ETL jobs are invoked at a scheduled interval by AWS Lambda. AWS Systems Manager, which provides secure, hierarchical storage for configuration data management and secrets management, maintains the details of Amazon Redshift clusters for which the solution is enabled. The last-fetched time stamp values for the respective cluster-table combination are maintained in an Amazon DynamoDB table.
The following diagram covers the key steps involved in this solution.
The solution as illustrated in the preceding diagram flows like this:
The Lambda function, invoke_rs_stl_export_etl, is triggered at regular intervals, as controlled by Amazon CloudWatch. It’s triggered to look up the AWS Systems Manager parameter store to get the details of the Amazon Redshift clusters for which the system table export is enabled.
The same Lambda function, based on the Amazon Redshift cluster details obtained in step 1, invokes the AWS Glue ETL job designated for the Amazon Redshift cluster. If an ETL job for the cluster is not found, the Lambda function creates one.
The ETL job invoked for the Amazon Redshift cluster gets the cluster credentials from the parameter store. It gets from the DynamoDB table the last exported time stamp of when each of the system tables was exported from the respective Amazon Redshift cluster.
The ETL job unloads the system tables’ data from the Amazon Redshift cluster into an Amazon S3 bucket.
The ETL job updates the DynamoDB table with the last exported time stamp value for each system table exported from the Amazon Redshift cluster.
The Amazon Redshift cluster system tables’ data is available in Amazon S3 and is partitioned by cluster name and date for running cross-cluster diagnostic queries.
Understanding the configuration data
This solution uses AWS Systems Manager parameter store to store the Amazon Redshift cluster credentials securely. The parameter store also securely stores other configuration information that the AWS Glue ETL job needs for extracting and storing system tables’ data in Amazon S3. Systems Manager comes with a default AWS Key Management Service (AWS KMS) key that it uses to encrypt the password component of the Amazon Redshift cluster credentials.
The following table explains the global parameters and cluster-specific parameters required in this solution. The global parameters are defined once and applicable at the overall solution level. The cluster-specific parameters are specific to an Amazon Redshift cluster and repeat for each cluster for which you enable this post’s solution. The CloudFormation template explained later in this post creates these parameters as part of the deployment process.
Parameter name
Type
Description
Global parameters—defined once and applied to all jobs
redshift_query_logs.global.s3_prefix
String
The Amazon S3 path where the query logs are exported. Under this path, each exported table is partitioned by cluster name and date.
redshift_query_logs.global.tempdir
String
The Amazon S3 path that AWS Glue ETL jobs use for temporarily staging the data.
redshift_query_logs.global.role>
String
The name of the role that the AWS Glue ETL jobs assume. Just the role name is sufficient. The complete Amazon Resource Name (ARN) is not required.
redshift_query_logs.global.enabled_cluster_list
StringList
A comma-separated list of cluster names for which system tables’ data export is enabled. This gives flexibility for a user to exclude certain clusters.
Cluster-specific parameters—for each cluster specified in the enabled_cluster_list parameter
redshift_query_logs.<<cluster_name>>.connection
String
The name of the AWS Glue Data Catalog connection to the Amazon Redshift cluster. For example, if the cluster name is product_warehouse, the entry is redshift_query_logs.product_warehouse.connection.
redshift_query_logs.<<cluster_name>>.user
String
The user name that AWS Glue uses to connect to the Amazon Redshift cluster.
redshift_query_logs.<<cluster_name>>.password
Secure String
The password that AWS Glue uses to connect the Amazon Redshift cluster’s encrypted-by key that is managed in AWS KMS.
For example, suppose that you have two Amazon Redshift clusters, product-warehouse and category-management, for which the solution described in this post is enabled. In this case, the parameters shown in the following screenshot are created by the solution deployment CloudFormation template in the AWS Systems Manager parameter store.
Solution deployment
To make it easier for you to get started, I created a CloudFormation template that automatically configures and deploys the solution—only one step is required after deployment.
Prerequisites
To deploy the solution, you must have one or more Amazon Redshift clusters in a private subnet. This subnet must have a network address translation (NAT) gateway or a NAT instance configured, and also a security group with a self-referencing inbound rule for all TCP ports. For more information about why AWS Glue ETL needs the configuration it does, described previously, see Connecting to a JDBC Data Store in a VPC in the AWS Glue documentation.
To start the deployment, launch the CloudFormation template:
CloudFormation stack parameters
The following table lists and describes the parameters for deploying the solution to export query logs from multiple Amazon Redshift clusters.
Property
Default
Description
S3Bucket
mybucket
The bucket this solution uses to store the exported query logs, stage code artifacts, and perform unloads from Amazon Redshift. For example, the mybucket/extract_rs_logs/data bucket is used for storing all the exported query logs for each system table partitioned by the cluster. The mybucket/extract_rs_logs/temp/ bucket is used for temporarily staging the unloaded data from Amazon Redshift. The mybucket/extract_rs_logs/code bucket is used for storing all the code artifacts required for Lambda and the AWS Glue ETL jobs.
ExportEnabledRedshiftClusters
Requires Input
A comma-separated list of cluster names from which the system table logs need to be exported.
DataStoreSecurityGroups
Requires Input
A list of security groups with an inbound rule to the Amazon Redshift clusters provided in the parameter, ExportEnabledClusters. These security groups should also have a self-referencing inbound rule on all TCP ports, as explained on Connecting to a JDBC Data Store in a VPC.
After you launch the template and create the stack, you see that the following resources have been created:
AWS Glue connections for each Amazon Redshift cluster you provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
All parameters required for this solution created in the parameter store.
The Lambda function that invokes the AWS Glue ETL jobs for each configured Amazon Redshift cluster at a regular interval of five minutes.
The DynamoDB table that captures the last exported time stamps for each exported cluster-table combination.
The AWS Glue ETL jobs to export query logs from each Amazon Redshift cluster provided in the CloudFormation stack parameter, ExportEnabledRedshiftClusters.
The IAM roles and policies required for the Lambda function and AWS Glue ETL jobs.
After the deployment
For each Amazon Redshift cluster for which you enabled the solution through the CloudFormation stack parameter, ExportEnabledRedshiftClusters, the automated deployment includes temporary credentials that you must update after the deployment:
Note the parameters <<cluster_name>>.user and redshift_query_logs.<<cluster_name>>.password that correspond to each Amazon Redshift cluster for which you enabled this solution. Edit these parameters to replace the placeholder values with the right credentials.
For example, if product-warehouse is one of the clusters for which you enabled system table export, you edit these two parameters with the right user name and password and choose Save parameter.
Querying the exported system tables
Within a few minutes after the solution deployment, you should see Amazon Redshift query logs being exported to the Amazon S3 location, <<S3Bucket_you_provided>>/extract_redshift_query_logs/data/. In that bucket, you should see the eight system tables partitioned by customer name and date: stl_alert_event_log, stl_dlltext, stl_explain, stl_query, stl_querytext, stl_scan, stl_utilitytext, and stl_wlm_query.
To run cross-cluster diagnostic queries on the exported system tables, create external tables in the AWS Glue Data Catalog. To make it easier for you to get started, I provide a CloudFormation template that creates an AWS Glue crawler, which crawls the exported system tables stored in Amazon S3 and builds the external tables in the AWS Glue Data Catalog.
Launch this CloudFormation template to create external tables that correspond to the Amazon Redshift system tables. S3Bucket is the only input parameter required for this stack deployment. Provide the same Amazon S3 bucket name where the system tables’ data is being exported. After you successfully create the stack, you can see the eight tables in the database, redshift_query_logs_db, as shown in the following screenshot.
Now, navigate to the Athena console to run cross-cluster diagnostic queries. The following screenshot shows a diagnostic query executed in Athena that retrieves query alerts logged across multiple Amazon Redshift clusters.
You can build the following example Amazon QuickSight dashboard by running cross-cluster diagnostic queries on Athena to identify the hourly query count and the key query alert events across multiple Amazon Redshift clusters.
How to extend the solution
You can extend this post’s solution in two ways:
Add any new Amazon Redshift clusters that you spin up after you deploy the solution.
Add other system tables or custom query results to the list of exports from an Amazon Redshift cluster.
Extend the solution to other Amazon Redshift clusters
To extend the solution to more Amazon Redshift clusters, add the three cluster-specific parameters in the AWS Systems Manager parameter store following the guidelines earlier in this post. Modify the redshift_query_logs.global.enabled_cluster_list parameter to append the new cluster to the comma-separated string.
Extend the solution to add other tables or custom queries to an Amazon Redshift cluster
The current solution ships with the export functionality for the following Amazon Redshift system tables:
stl_alert_event_log
stl_dlltext
stl_explain
stl_query
stl_querytext
stl_scan
stl_utilitytext
stl_wlm_query
You can easily add another system table or custom query by adding a few lines of code to the AWS Glue ETL job, <<cluster-name>_extract_rs_query_logs. For example, suppose that from the product-warehouse Amazon Redshift cluster you want to export orders greater than $2,000. To do so, add the following five lines of code to the AWS Glue ETL job product-warehouse_extract_rs_query_logs, where product-warehouse is your cluster name:
Get the last-processed time-stamp value. The function creates a value if it doesn’t already exist.
returnDF=functions.runQuery(query="select * from sales s join order o where o.order_amnt > 2000 and sale_timestamp > '{}'".format (salesLastProcessTSValue) ,tableName="mydb.sales_2000",job_configs=job_configs)
In this post, I demonstrate a serverless solution to retain the system tables’ log data across multiple Amazon Redshift clusters. By using this solution, you can incrementally export the data from system tables into Amazon S3. By performing this export, you can build cross-cluster diagnostic queries, build audit dashboards, and derive insights into capacity planning by using services such as Athena. I also demonstrate how you can extend this solution to other ad hoc query use cases or tables other than system tables by adding a few lines of code.
Karthik Sonti is a senior big data architect at Amazon Web Services. He helps AWS customers build big data and analytical solutions and provides guidance on architecture and best practices.
Thanks to Raja Mani, AWS Solutions Architect, for this great blog.
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In this blog post, I’ll walk you through the steps for setting up continuous replication of an AWS CodeCommit repository from one AWS region to another AWS region using a serverless architecture. CodeCommit is a fully-managed, highly scalable source control service that stores anything from source code to binaries. It works seamlessly with your existing Git tools and eliminates the need to operate your own source control system. Replicating an AWS CodeCommit repository from one AWS region to another AWS region enables you to achieve lower latency pulls for global developers. This same approach can also be used to automatically back up repositories currently hosted on other services (for example, GitHub or BitBucket) to AWS CodeCommit.
This solution uses AWS Lambda and AWS Fargate for continuous replication. Benefits of this approach include:
The replication process can be easily setup to trigger based on events, such as commits made to the repository.
Setting up a serverless architecture means you don’t need to provision, maintain, or administer servers.
Note: AWS Fargate has a limitation of 10 GB for storage and is available in US East (N. Virginia) region. A similar solution that uses Amazon EC2 instances to replicate the repositories on a schedule was published in a previous blog and can be used if your repository does not meet these conditions.
Replication using Fargate
As you follow this blog post, you’ll set up an architecture that looks like this:
Any change in the AWS CodeCommit repository will trigger a Lambda function. The Lambda function will call the Fargate task that replicates the repository using a Git command line tool.
Let us assume a user wants to replicate a repository (Source) from US East (N. Virginia/us-east-1) region to a repository (Destination) in US West (Oregon/us-west-2) region. I’ll walk you through the steps for it:
Prerequisites
Create an AWS Service IAM role for Amazon EC2 that has permission for both source and destination repositories, IAM CreateRole, AttachRolePolicy and Amazon ECR privileges. Here is the EC2 role policy I used:
You need a Docker environment to build this solution. You can launch an EC2 instance and install Docker (or) you can use AWS Cloud9 that comes with Docker and Git preinstalled. I used an EC2 instance and installed Docker in it. Use the IAM role created in the previous step when creating the EC2 instance. I am going to refer this environment as “Docker Environment” in the following steps.
You need to install the AWS CLI on the Docker environment. For AWS CLI installation, refer this page.
You need to install Git, including a Git command line on the Docker environment.
Step 1: Create the Docker image
To create the Docker image, first it needs a Dockerfile. A Dockerfile is a manifest that describes the base image to use for your Docker image and what you want installed and running on it. For more information about Dockerfiles, go to the Dockerfile Reference.
1. Choose a directory in the Docker environment and perform the following steps in that directory. I used /home/ec2-user directory to perform the following steps.
2. Clone the AWS CodeCommit repository in the Docker environment. Open the terminal to the Docker environment and run the following commands to clone your source AWS CodeCommit repository (I ran the commands from /home/ec2-user directory):
Note: Change the URL marked in red to your source and destination repository URL.
3. Create a file called Dockerfile (case sensitive) with the following content (I created it in /home/ec2-user directory):
# Pull the Amazon Linux latest base image
FROM amazonlinux:latest
#Install aws-cli and git command line tools
RUN yum -y install unzip aws-cli
RUN yum -y install git
WORKDIR /home/ec2-user
RUN mkdir LocalRepository
WORKDIR /home/ec2-user/LocalRepository
#Copy Cloned CodeCommit repository to Docker container
COPY ./LocalRepository /home/ec2-user/LocalRepository
#Copy shell script that does the replication
COPY ./repl_repository.bash /home/ec2-user/LocalRepository
RUN chmod ugo+rwx /home/ec2-user/LocalRepository/repl_repository.bash
WORKDIR /home/ec2-user/LocalRepository
#Call this script when Docker starts the container
ENTRYPOINT ["/home/ec2-user/LocalRepository/repl_repository.bash"]
4. Copy the following shell script into a file called repl_repository.bash to the DockerFile directory location in the Docker environment (I created it in /home/ec2-user directory)
6. Verify whether the replication is working by running the repl_repository.bash script from the LocalRepository directory. Go to LocalRepository directory and run this command: . ../repl_repository.bash If it is successful, you will get the “Everything up-to-date” at the last line of the result like this:
$ . ../repl_repository.bash
Everything up-to-date
Step 2: Build the Docker Image
1. Build the Docker image by running this command from the directory where you created the DockerFile in the Docker environment in the previous step (I ran it from /home/ec2-user directory):
$ docker build . –t ccrepl
Output: It installs various packages and set environment variables as part of steps 1 to 3 from the Dockerfile. The steps 4 to 11 from the Dockerfile should produce an output similar to the following:
2. Run the following command to verify that the image was created successfully. It will display “Everything up-to-date” at the end if it is successful.
[[email protected] LocalRepository]$ docker run ccrepl
Everything up-to-date
Step 3: Push the Docker Image to Amazon Elastic Container Registry (ECR)
Perform the following steps in the Docker Environment.
1. Run the AWS CLI configure command and set default region as your source repository region (I used us-east-1).
$ aws configure set default.region <Source Repository Region>
2. Create an Amazon ECR repository using this command to store your ccrepl image (Note the repositoryUri in the output):
2. Create a role called AccessRoleForCCfromFG using the following command in the DockerEnvironment:
$ aws iam create-role --role-name AccessRoleForCCfromFG --assume-role-policy-document file://trustpolicyforecs.json
3. Assign CodeCommit service full access to the above role using the following command in the DockerEnvironment:
$ aws iam attach-role-policy --policy-arn arn:aws:iam::aws:policy/AWSCodeCommitFullAccess --role-name AccessRoleForCCfromFG
4. In the Amazon ECS Console, choose Repositories and select the ccrepl repository that was created in the previous step. Copy the Repository URI.
5. In the Amazon ECS Console, choose Task Definitions and click Create New Task Definition.
6. Select launch type compatibility as FARGATE and click Next Step.
7. In the create task definition screen, do the following:
In Task Definition Name, type ccrepl
In Task Role, choose AccessRoleForCCfromFG
In Task Memory, choose 2GB
In Task CPU, choose 1 vCPU
Click Add Container under Container Definitions in the same screen. In the Add Container screen, do the following:
Enter Container name as ccreplcont
Enter Image URL copied from step 4
Enter Memory Limits as 128 and click Add.
Note: Select TaskExecutionRole as “ecsTaskExecutionRole” if it already exists. If not, select create new role and it will create “ecsTaskExecutionRole” for you.
8. Click the Create button in the task definition screen to create the task. It will successfully create the task, execution role and AWS CloudWatch Log groups.
9. In the Amazon ECS Console, click Clusters and create cluster. Select template as “Networking only, Powered by AWS Fargate” and click next step.
10. Enter cluster name as ccreplcluster and click create.
Step 5: Create the Lambda Function
In this section, I used Amazon Elastic Container Service (ECS) run task API from Lambda to invoke the Fargate task.
1. In the IAM Console, create a new role called ECSLambdaRole with the permissions to AWS CodeCommit, Amazon ECS as well as pass roles privileges needed to run the ECS task. Your statement should look similar to the following (replace <your account id>):
2. In AWS management console, select VPC service and click subnets in the left navigation screen. Note down the Subnet IDs that you want to run the Fargate task in.
3. Create a new Lambda Node.js function called FargateTaskExecutionFunc and assign the role ECSLambdaRole with the following content:
Note: Replace subnets values (marked in red color) with the subnet IDs you identified as the subnets you wanted to run the Fargate task on in Step 2 of this section.
1. In the Lambda Console, click FargateTaskExecutionFunc under functions.
2. Under Add triggers in the Designer, select CodeCommit
3. In the Configure triggers screen, do the following:
Enter Repository name as Source (your source repository name)
Enter trigger name as LambdaTrigger
Leave the Events as “All repository events”
Leave the Branch names as “All branches”
Click Add button
Click Save button to save the changes
Step 6: Verification
To test the application, make a commit and push the changes to the source repository in AWS CodeCommit. That should automatically trigger the Lambda function and replicate the changes in the destination repository. You can verify this by checking CloudWatch Logs for Lambda and ECS, or simply going to the destination repository and verifying the change appears.
Conclusion
Congratulations! You have successfully configured repository replication of an AWS CodeCommit repository using AWS Lambda and AWS Fargate. You can use this technique in a deployment pipeline. You can also tweak the trigger configuration in AWS CodeCommit to call the Lambda function in response to any supported trigger event in AWS CodeCommit.
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.
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!
In this post, I’ll show you how to create a sample dataset for Amazon Macie, and how you can use Amazon Macie to implement data-centric compliance and security analytics in your Amazon S3 environment. I’ll also dive into the different kinds of credentials, document types, and PII detections supported by Macie. First, I’ll walk through creating a “getting started” sample set of artificial, generated data that you can use to test Macie capabilities and start building your own policies and alerts.
Create a realistic data sample set in S3
I’ll use amazon-macie-activity-generator, which we call “AMG” for short, a sample application developed by AWS that generates realistic content and accesses your test account to create the data. AMG uses AWS CloudFormation, AWS Lambda, and Python’s excellent Faker library to create a data set with artificial—but realistic—data classifications and access patterns to help test some of the features and capabilities of Macie. AMG is released under Amazon Software License 1.0, and we’ll accept pull requests on our GitHub repository and monitor any issues that are opened so we can try to fix bugs and consider new feature requests.
The following diagram shows a high level architecture overview of the components that will be created in your AWS account for AMG. For additional detail about these components and their relationships, review the CloudFormation setup script.
Depending on the data types specified in your JSON configuration template (details below), AMG will periodically generate artificial documents for the specified S3 target with a PutObject action. By default, the CloudFormation stack uses a configuration file that instructs AMG to create a new, private S3 bucket that can only be accessed by authorized AWS users/roles in the same account as the bucket. All the S3 objects with fake data in this bucket have a private ACL and inherit the bucket’s access control configuration. All generated objects feature the header in the example below, and AMG supports all fake data providers offered by https://faker.readthedocs.io/en/latest/index.html, as well as a few of AMG‘s own custom fake data providers requested by our customers: aws_creds, slack_creds, github_creds, facebook_creds, linux_shadow, rsa, linux_passwd, dsa, ec, pgp, cert, itin, swift_code, and cve.
# Sample Report - No identification of actual persons or places is # intended or should be inferred
74323 Julie Field Lake Joshuamouth, OR 30055-3905 1-196-191-4438x974 53001 Paul Union New John, HI 94740 Mastercard Amanda Wells 5135725008183484 09/26 CVV: 550
354-70-6172 242 George Plaza East Lawrencefurt, VA 37287-7620 GB73WAUS0628038988364 587 Silva Village Pearsonburgh, NM 11616-7231 LDNM1948227117807 American Express Brett Garza 347965534580275 05/20 CID: 4758
599.335.2742 JCB 15 digit Michael Arias 210069190253121 03/27 CVC: 861
Create your amazon-macie-activity-generator CloudFormation stack
You can deploy AMG in your AWS account by using either these methods:
Log in to the AWS Console in a region supported by Amazon Macie, which currently includes US East (N. Virginia), US West (Oregon).
Select the One-click CloudFormation launch stack, or launch CloudFormation using the template above.
Read our terms, select the Acknowledgement box, and then select Create.
Creating the data takes a few minutes, and you can periodically refresh CloudWatch to track progress.
Add the new sample data to Macie
Now, I’ll log into the Macie console and add the newly created sample data buckets for analysis by Macie.
Note: If you don’t explicitly specify a bucket for S3 targets in CloudFormation, AMG will use the S3 bucket that’s created by default for the stack, which will be printed out in the CloudFormation stack’s output.
To add buckets for data classification, follow these steps:
Log in to Amazon Macie.
Select Integrations, and then select Services.
Select your account, and then select Details from the Amazon S3 card.
Select your newly created buckets for Full classification, including existing data.
For additional details on configuring Macie, refer to our getting started documentation.
Macie classifies all historical and newly created data in the buckets created by AMG, and the data will be available in the Macie console as it’s classified. Typically, you can expect the data in the sample set to be classified within 60 minutes of the time it was selected for analysis.
Classifying objects with Macie
To see the objects in your test sample set, in Macie, open the Research tab, and then select the S3 Objects index. We’ll use the regular expression search capability in Macie to find any objects written to buckets that start with “amazon-macie-activity-generator-defaults3bucket”. To search for this, type the following text into the Macie search box and select the magnifying glass icon.
From here, you can see a nice breakdown of the kinds of objects that have been classified by Macie, as well as the object-specific details. Create an advanced search using Lucene Query Syntax, and save it as an alert to be matched against any newly created data.
Analyzing accesses to your test data
In addition to classifying data, Macie tracks all control plane and data plane accesses to your content using CloudTrail. To see accesses to your generated environment (created periodically by AMG to mimic user activity), on the Macie navigation bar, select Research, select the CloudTrail data index, and then use the following search to identify our generated role activity:
From this search, you can dive into the user activity (IAM users, assumed roles, federated users, and so on), which is summarized in 5-minute aggregations (user sessions). For example, in the screen shot you can see that one of our AMG-generated users listed objects one time (ListObjects) and wrote 56 objects to S3 (PutObject) during a 5-minute period.
Macie alerts
Macie features both predictive (machine learning-based) and basic (rule-based) alerts, including alerts on unencrypted credentials being uploaded to S3 (because this activity might not follow compliance best practices), risky activity such as data exfiltration, and user-defined alerts that are based on saved searches. To see alerts that have been generated based on AMG‘s activity, on the Macie navigation bar, select Alerts.
AMG will continue to run, periodically uploading content to the specified S3 buckets. To stop AMG, delete the AMG CloudFormation stack and associated resources here.
What are the costs?
Macie has a free tier enabling up to 1GB of content to be analyzed per month at no cost to you. By default, AMG will write approximately 10MB of objects to Amazon S3 per day, and you will incur charges for data classification after crossing the 1GB monthly free tier. Running continuously, AMG will generate about 310MB of content per month (10MB/day x 31 days), which will stay below the free tier. Any data use above 1GB will be billed at the Macie public price of $5/GB. For more detail, see the Macie pricing documentation.
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 Macie forum or contact AWS Support.
With AWS Organizations, you can centrally manage policies across multiple AWS accounts without having to use custom scripts and manual processes. For example, you can apply service control policies (SCPs) across multiple AWS accounts that are members of an organization. SCPs allow you to define which AWS service APIs can and cannot be executed by AWS Identity and Access Management (IAM) entities (such as IAM users and roles) in your organization’s member AWS accounts. SCPs are created and applied from the master account, which is the AWS account that you used when you created your organization.
In a previous post, How to Use Service Control Policies in AWS Organizations to Enforce Healthcare Compliance in Your AWS Account, we reviewed how to create and manage SCPs and Organizational Units (OU) within an organization. In this post, I show how to use SCPs for access control in Organizations, with a specific focus on evaluating SCPs when an IAM entity calls an API in a member AWS account. I first cover some key Organizations concepts, and then I show how an SCP attached to an organization impacts which AWS service APIs are available to member accounts. Finally, I demonstrate these concepts with an example.
Organizational structure in Organizations
OUs give you a way to logically group and structure member AWS accounts in your organization. The screenshot shows the tree view of an example organizational structure in my organization with several OUs. Currently, I have selected OrgUnit01, and this is the current view I see in my main window. You can see here that within the OrgUnit01 OU, I have nested two additional OUs (OrgUnit01ChildA and OrgUnit01ChildB) and an AWS account is also contained within OrgUnit01, named “Developer Sandbox Account”.
The parts of the example organizational structure in the screenshot are:
Tree view — The hierarchy of your organization’s root and any OUs you have created
Tree view toggle — Enable and disable tree view
Organizational Units — Any child OUs of the selected root or OU in tree view
Accounts — Any AWS accounts (members or master) in the current OU
In the next section, I explain why at least one SCP must be attached to your root and OUs and introduce SCP evaluation.
How Service Control Policy evaluation logic works
To allow an AWS service API at the member account level, you must allow the API at every level between the member account and the root of your organization. This means you must attach an SCP at every level between your organization’s root and the member account that allows the given AWS service API (such as ec2:RunInstances). For more information, see About Service Control Policies.
Let’s say you want to allow the ec2:RunInstances API in the Developer Sandbox Account in the example structure in the preceding screenshot. To allow this AWS service API, you must allow the API in at least one SCP attached at each of these levels:
The organization’s root
The OU named OrgUnit01
If you don’t allow the AWS service in an SCP attached at each of these two levels, neither IAM entities nor the root user in the Developer Sandbox Account will be able to call ec2:RunInstances, even if an administrator has given them permission to do so (for IAM entities). In terms of policy evaluation, SCPs follow exactly the same policy evaluation logic as IAM does: by default, all requests are denied, an explicit allow overrides this default, and an explicit deny overrides any explicit allows.
What does this look like in practice? In the next section, I share a practical example to demonstrate how this works in Organizations.
An example structure with nested OUs and SCPs
In the previous section, I introduced design aspects of AWS Organizations that help prevent administrators from breaking structures in their Organizations. But because AWS Organizations is flexible enough to address multiple use cases, administrators can make changes that have unintended consequences, such as breaking organizational structures when moving an AWS account from one OU to another. In this section, I show an example with broken OU and SCP structures and explain how you can fix them.
I’ll take a blacklisting approach. That is, I’ll use the FullAWSAccess SCP, which doesn’t filter out any AWS service APIs. Then, I will filter out specific APIs by blacklisting them in subsequent SCPs attached to OUs at various points in my organization’s structure. For further reading on blacklisting and whitelisting with AWS Organizations, review AWS Organizations Terminology and Concepts.
Let’s say I have developed the OU and SCP structure shown in the diagram below. Before taking a close look at that diagram, I’ll briefly outline the goals I’m trying to achieve. Broadly speaking, there’s a small subset of APIs that I want to filter out using SCPs. This means that IAM entities in some AWS accounts in my organization will not have access to particular AWS service APIs, such as those related to Amazon EC2, while other accounts will not have access to APIs associated with Amazon CloudWatch, Amazon S3, and so on. Apart from these special cases, I do want the accounts in my organization to have access to all other APIs. More specifically, my goals are as follows:
Any AWS accounts in the Root should not have any API filtered out.
Any AWS accounts in OU 001 should have APIs for CloudWatch filtered out, but all other APIs will be accessible.
Any AWS accounts in OU 002 should have APIs for both CloudWatch and EC2 filtered out, but all other APIs will be accessible.
Any AWS accounts in OU 003 should have APIs for S3 filtered out, but all other APIs will be accessible.
To that end, let’s now look at my initial SCP and OU configuration in the image below that shows the example OU and SCP structure. The arrow shows the direction of inheritance: the root and the OUs below it (children) inherit SCPs from the OUs above them (parents). This example structure contains the following SCPs:
FullAWSAccess — Allows all AWS service APIs
Deny_CW — Denies all CloudWatch APIs
Deny_EC2 — Denies all Amazon EC2 APIs
Deny_S3 — Denies all Amazon S3 APIs
Now that I’ve outlined my intent, and shown you the OU / SCP structure that I’ve created to meet that set of goals, you can probably already see that the structure provided in the image above will not work correctly for my stated goals. In fact, AWS accounts in the Root container and OU 001 will have the intended access, as per my goals (1) and (2). I will not, however, meet my goals (3) and (4) with the above structure: entities in member accounts directly under OU 002 cannot perform any actions, even if they’re granted permissions by IAM access policies. This is because the FullAWSAccess SCP isn’t attached directly to this OU (it’s only inherited).
Why is this important? For an AWS service API to be available to IAM entities in a member account, the API must be specified in an SCP attached at every level all the way down the hierarchy to the relevant member account. Similarly, even though OU 003 does have the FullAWSAccess SCP attached directly to it, the fact that it’s not attached to the parent OU (OU 002) means that IAM entities in member accounts under OU 003 also aren’t able to access any service APIs. This doesn’t happen by default—I have deliberately taken this action to organize my structure in this way, to show both the flexibility and the kind of problems you can encounter when working with OUs and SCPs.
So I now need to fix the problems that I’ve inadvertently created. To start with, I’m going to make one change to OU 002 by attaching the FullAWSAccess SCP directly to that OU. After I do that, OU 002 has the attached and inherited policies that are shown in the following image.
With the FullAWSAccess policy attached to OU 002, member accounts in both OU 002 and OU 003 can access the other non-restricted AWS service APIs (keeping in mind that the FullAWSAccess policy was already applied to OU 003).
I have one final issue to address in this example: OU 003 has an SCP attached that blocks access to the Amazon S3 APIs. However, in this OU, the intent is to allow IAM entities in member accounts to access the EC2 APIs. EC2 API access is blocked because in the parent OU (OU 002), an SCP is attached that denies access to that API (the Deny_EC2 SCP), which means that any actions listed in Deny_EC2 have already been filtered out. An explicit deny always trumps an allow, so to meet goal (4) and have an OU in which EC2 APIs are allowed but access to CloudWatch APIs and S3 APIs is filtered out, I will move OU 003 up one level, placing it directly under OU 001. This change gives me a working OU and policy structure, as shown in the following image.
I recommend that at each level of your organization’s hierarchy, you directly apply the relevant SCPs. By doing this, you’re less likely to forget to apply an SCP to a particular OU, which can break your permission structure. By directly applying SCPs, you also make your policy structure easier to read.
If you have a group of accounts in your organization that are for testing purposes, I recommend that you experiment with OUs and SCPs. Applying SCPs to OUs and then moving an AWS account around within that structure can show you how SCPs affect IAM entities. For example, if you have an IAM user with the AdministratorAccess policy attached, you should see how SCPs can filter out certain AWS service APIs from specified member accounts.
Conclusion
I showed you how you can effectively apply SCPs to OUs in your organization and avoid some of the common issues that you might experience. I demonstrated an approach to designing a working organizational structure that I hope will help smooth your deployment of your organization and enables you to better centrally secure and manage your AWS accounts.
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 Organizations forum.
This post courtesy of Michael Edge, Sr. Cloud Architect – AWS Professional Services
Applications built using a microservices architecture typically result in a number of independent, loosely coupled microservices communicating with each other, synchronously via their APIs and asynchronously via events. These microservices are often owned by different product teams, and these teams may segregate their resources into different AWS accounts for reasons that include security, billing, and resource isolation. This can sometimes result in the following challenges:
Cross-account deployment: A single pipeline must deploy a microservice into multiple accounts; for example, a microservice must be deployed to environments such as DEV, QA, and PROD, all in separate accounts.
Cross-account lookup: During deployment, a resource deployed into one AWS account may need to refer to a resource deployed in another AWS account.
Cross-account communication: Microservices executing in one AWS account may need to communicate with microservices executing in another AWS account.
In this post, I look at ways to address these challenges using a sample application composed of a web application supported by two serverless microservices. The microservices are owned by different product teams and deployed into different accounts using AWS CodePipeline, AWS CloudFormation, and the Serverless Application Model (SAM). At runtime, the microservices communicate using an event-driven architecture that requires asynchronous, cross-account communication via an Amazon Simple Notification Service (Amazon SNS) topic.
Sample application
First, look at the sample application I use to demonstrate these concepts. In the following overview diagram, you can see the following:
The entire application consists of three main services:
A Booking microservice, owned by the Booking account.
An Airmiles microservice, owned by the Airmiles account.
A web application that uses the services exposed by both microservices, owned by the Web Channel account.
The Booking microservice creates flight bookings and publishes booking events to an SNS topic.
The Airmiles microservice consumes booking events from the SNS topic and uses the booking event to calculate the airmiles associated with the flight booking. It also supports querying airmiles for a specific flight booking.
The web application allows an end user to make flight bookings, view flights bookings, and view the airmiles associated with a flight booking.
In the sample application, the Booking and Airmiles microservices are implemented using AWS Lambda. Together with Amazon API Gateway, Amazon DynamoDB, and SNS, the sample application is completely serverless.
Sample Serverless microservices application
The typical booking flow would be triggered by an end user making a flight booking using the web application, which invokes the Booking microservice via its REST API. The Booking microservice persists the flight booking and publishes the booking event to an SNS topic to enable sharing of the booking with other interested consumers. In this sample application, the Airmiles microservice subscribes to the SNS topic and consumes the booking event, using the booking information to calculate the airmiles. In line with microservices best practices, both the Booking and Airmiles microservices store their information in their own DynamoDB tables, and expose an API (via API Gateway) that is used by the web application.
Setup
Before you delve into the details of the sample application, get the source code and deploy it.
Cross-account deployment of Lambda functions using CodePipeline has been previously discussed by my colleague Anuj Sharma in his post, Building a Secure Cross-Account Continuous Delivery Pipeline. This sample application builds upon the solution proposed by Anuj, using some of the same scripts and a similar account structure. To make it feasible for you to deploy the sample application, I’ve reduced the number of accounts needed down to three accounts by consolidating some of the services. In the following diagram, you can see the services used by the sample application require three accounts:
Tools: A central location for the continuous delivery/deployment services such as CodePipeline and AWS CodeBuild. To reduce the number of accounts required by the sample application, also deploy the AWS CodeCommit repositories here, though typically they may belong in a separate Dev account.
Booking: Account for the Booking microservice.
Airmiles: Account for the Airmiles microservice.
Without consolidation, the sample application may require up to 10 accounts: one for Tools, and three accounts each for Booking, Airmiles and Web Application (to support the DEV, QA, and PROD environments).
Account structure for sample application
To follow the rest of this post, clone the repository in step 1 below. To deploy the application on AWS, follow steps 2 and 3:
Follow the instructions in the repository README to build the CodePipeline pipelines and deploy the microservices and web application.
Challenge 1: Cross-account deployment using CodePipeline
Though the Booking pipeline executes in the Tools account, it deploys the Booking Lambda functions into the Booking account.
In the sample application code repository, open the ToolsAcct/code-pipeline.yaml CloudFormation template.
Scroll down to the Pipeline resource and look for the DeployToTest pipeline stage (shown below). There are two AWS Identity and Access Management (IAM) service roles used in this stage that allow cross-account activity. Both of these roles exist in the Booking account:
Under Actions.RoleArn, find the service role assumed by CodePipeline to execute this pipeline stage in the Booking account. The role referred to by the parameter NonProdCodePipelineActionServiceRole allows access to the CodePipeline artifacts in the S3 bucket in the Tools account, and also access to the AWS KMS key needed to encrypt/decrypt the artifacts.
Under Actions.Configuration.RoleArn, find the service role assumed by CloudFormation when it carries out the CHANGE_SET_REPLACE action in the Booking account.
These roles are created in the CloudFormation template NonProdAccount/toolsacct-codepipeline-cloudformation-deployer.yaml.
Challenge 2: Cross-account stack lookup using custom resources
Asynchronous communication is a fairly common pattern in microservices architectures. A publishing microservice publishes an event that consumers may be interested in, without any concern for who those consumers may be.
In the case of the sample application, the publisher is the Booking microservice, publishing a flight booking event onto an SNS topic that exists in the Booking account. The consumer is the Airmiles microservice in the Airmiles account. To enable the two microservices to communicate, the Airmiles microservice must look up the ARN of the Booking SNS topic at deployment time in order to set up a subscription to it.
To enable CloudFormation templates to be reused, you are not hardcoding resource names in the templates. Because you allow CloudFormation to generate a resource name for the Booking SNS topic, the Airmiles microservice CloudFormation template must look up the SNS topic name at stack creation time. It’s not possible to use cross-stack references, as these can’t be used across different accounts.
However, you can use a CloudFormation custom resource to achieve the same outcome. This is discussed in the next section. Using a custom resource to look up stack exports in another stack does not create a dependency between the two stacks, unlike cross-stack references. A stack dependency would prevent one stack being deleted if another stack depended upon it. For more information, see Fn::ImportValue.
Using a Lambda function as a custom resource to look up the booking SNS topic
The sample application uses a Lambda function as a custom resource. This approach is discussed in AWS Lambda-backed Custom Resources. Walk through the sample application and see how the custom resource is used to list the stack export variables in another account and return these values to the calling AWS CloudFormation stack. Examine each of the following aspects of the custom resource:
Deploying the custom resource.
Custom resource IAM role.
A calling CloudFormation stack uses the custom resource.
The custom resource assumes a role in another account.
The custom resource obtains the stack exports from all stacks in the other account.
The custom resource returns the stack exports to the calling CloudFormation stack.
The custom resource handles all the event types sent by the calling CloudFormation stack.
Step1: Deploying the custom resource
The custom Lambda function is deployed using SAM, as are the Lambda functions for the Booking and Airmiles microservices. See the CustomLookupExports resource in Custom/custom-lookup-exports.yml.
Step2: Custom resource IAM role
The CustomLookupExports resource in Custom/custom-lookup-exports.yml executes using the CustomLookupLambdaRole IAM role. This role allows the custom Lambda function to assume a cross account role that is created along with the other cross account roles in the NonProdAccount/toolsacct-codepipeline-cloudformation-deployer.yml. See the resource CustomCrossAccountServiceRole.
Step3: The CloudFormation stack uses the custom resource
The Airmiles microservice is created by the Airmiles CloudFormation template Airmiles/sam-airmile.yml, a SAM template that uses the custom Lambda resource to look up the ARN of the Booking SNS topic. The custom resource is specified by the CUSTOMLOOKUP resource, and the PostAirmileFunction resource uses the custom resource to look up the ARN of the SNS topic and create a subscription to it.
Because the Airmiles Lambda function is going to subscribe to an SNS topic in another account, it must grant the SNS topic in the Booking account permissions to invoke the Airmiles Lambda function in the Airmiles account whenever a new event is published. Permissions are granted by the LambdaResourcePolicy resource.
Step4: The custom resource assumes a role in another account
When the Lambda custom resource is invoked by the Airmiles CloudFormation template, it must assume a role in the Booking account (see Step 2) in order to query the stack exports in that account.
This can be seen in Custom/custom-lookup-exports.py, where the AWS Simple Token Service (AWS STS) is used to obtain a temporary access key to allow access to resources in the account referred to by the environment variable: ‘CUSTOM_CROSS_ACCOUNT_ROLE_ARN’. This environment variable is defined in the Custom/custom-lookup-exports.yml CloudFormation template, and refers to the role created in Step 2.
Step5: The custom resource obtains the stack exports from all stacks in the other account
The function get_exports() in Custom/custom-lookup-exports.py uses the AWS SDK to list all the stack exports in the Booking account.
Step 6: The custom resource returns the stack exports to the calling CloudFormation stack
The calling CloudFormation template, Airmiles/sam-airmile.yml, uses the custom resource to look up a stack export with the name of booking-lambda-BookingTopicArn. This is exported by the CloudFormation template Booking/sam-booking.yml.
Step7: The custom resource handles all the event types sent by the calling CloudFormation stack
Custom resources used in a stack are called whenever the stack is created, updated, or deleted. They must therefore handle CREATE, UPDATE, and DELETE stack events. If your custom resource fails to send a SUCCESS or FAILED notification for each of these stack events, your stack may become stuck in a state such as CREATE_IN_PROGRESS or UPDATE_ROLLBACK_IN_PROGRESS. To handle this cleanly, use the crhelper custom resource helper. This strongly encourages you to handle the CREATE, UPDATE, and DELETE CloudFormation stack events.
Challenge 3: Cross-account SNS subscription
The SNS topic is specified as a resource in the Booking/sam-booking.yml CloudFormation template. To allow an event published to this topic to trigger the Airmiles Lambda function, permissions must be granted on both the Booking SNS topic and the Airmiles Lambda function. This is discussed in the next section.
Permissions on booking SNS topic
SNS topics support resource-based policies, which allow a policy to be attached directly to a resource specifying who can access the resource. This policy can specify which accounts can access a resource, and what actions those accounts are allowed to perform. This is the same approach as used by a small number of AWS services, such as Amazon S3, KMS, Amazon SQS, and Lambda. In Booking/sam-booking.yml, the SNS topic policy allows resources in the Airmiles account (referenced by the parameter NonProdAccount in the following snippet) to subscribe to the Booking SNS topic:
The Airmiles microservice is created by the Airmiles/sam-airmile.yml CloudFormation template. The template uses SAM to specify the Lambda functions together with their associated API Gateway configurations. SAM hides much of the complexity of deploying Lambda functions from you.
For instance, by adding an ‘Events’ resource to the PostAirmileFunction in the CloudFormation template, the Airmiles Lambda function is triggered by an event published to the Booking SNS topic. SAM creates the SNS subscription, as well as the permissions necessary for the SNS subscription to trigger the Lambda function.
However, the Lambda permissions generated automatically by SAM are not sufficient for cross-account SNS subscription, which means you must specify an additional Lambda permissions resource in the CloudFormation template. LambdaResourcePolicy, in the following snippet, specifies that the Booking SNS topic is allowed to invoke the Lambda function PostAirmileFunction in the Airmiles account.
In this post, I showed you how product teams can use CodePipeline to deploy microservices into different AWS accounts and different environments such as DEV, QA, and PROD. I also showed how, at runtime, microservices in different accounts can communicate securely with each other using an asynchronous, event-driven architecture. This allows product teams to maintain their own AWS accounts for billing and security purposes, while still supporting communication with microservices in other accounts owned by other product teams.
Acknowledgments
Thanks are due to the following people for their help in preparing this post:
Kevin Yung for the great web application used in the sample application
Jay McConnell for crhelper, the custom resource helper used with the CloudFormation custom resources
Now, your applications and federated users can complete longer running workloads in a single session by increasing the maximum session duration up to 12 hours for an IAM role. Users and applications still retrieve temporary credentials by assuming roles using AWS Security Token Service (AWS STS), but these credentials can now be valid for up to 12 hours when using the AWS SDK or CLI. This change allows your users and applications to perform longer running workloads, such as a batch upload to S3 or a CloudFormation template, using a single session. You can extend the maximum session duration using the IAM console or CLI. Once you increase the maximum session duration, users and applications assuming the IAM role can request temporary credentials that expire when the IAM role session expires.
In this post, I show you how to configure the maximum session duration for an existing IAM role to 4 hours (maximum allowed duration is 12 hours) using the IAM console. I’ll use 4 hours because AWS recommends configuring the session duration for a role to the shortest duration that your federated users would require to access your AWS resources. I’ll then show how existing federated users can use the AWS SDK or CLI to request temporary security credentials that are valid until the role session expires.
Configure the maximum session duration for an existing IAM role to 4 hours
Let’s assume you have an existing IAM role called ADFS-Production that allows your federated users to upload objects to an S3 bucket in your AWS account. You want to extend the maximum session duration for this role to 4 hours. By default, IAM roles in your AWS accounts have a maximum session duration of one hour. To extend a role’s maximum session duration to 4 hours, follow the steps below:
In the left navigation pane, select Roles and then select the role for which you want to increase the maximum session duration. For this example, I select ADFS-Production and verify the maximum session duration for this role. This value is set to 1 hour (3,600 seconds) by default.
Select Edit, and then define the maximum session duration.
Select one of the predefined durations or provide a custom duration. For this example, I set the maximum session duration to be 4 hours.
Select Save changes.
Alternatively, you can use the latest AWS CLI and call Update-Role to set the maximum session duration for the role ADFS-Production. Here’s an example to set the maximum session duration to 14,400 seconds (4 hours).
$ aws iam update-role -–role-name ADFS-Production -–MaxSessionDuration 14400
Now that you’ve successfully extended the maximum session for your IAM role, ADFS-Production, your federated users can use AWS STS to retrieve temporary credentials that are valid for 4 hours to access your S3 buckets.
Access AWS resources with temporary security credentials using AWS CLI/SDK
To enable federated SDK and CLI access for your users who use temporary security credentials, you might have implemented the solution described in the blog post on How to Implement Federated API and CLI Access Using SAML 2.0 and AD FS. That blog post demonstrates how to use the AWS Python SDK and some additional client-side integration code provided in the post to implement federated SDK and CLI access for your users. To enable your users to request longer temporary security credentials, you can make the following changes suggested in this blog to the solution provided in that post.
When calling AssumeRoleWithSAML API to request AWS temporary security credentials, you need to include the DurationSeconds parameter. The value of this parameter is the duration the user requests and, therefore, the duration their temporary security credentials are valid. In this example, I am using boto to request the maximum length of 14,400 seconds (4 hours) using code from the How to Implement Federated API and CLI Access Using SAML 2.0 and AD FS post that I have updated:
# Use the assertion to get an AWS STS token using Assume Role with SAML conn = boto.sts.connect_to_region(region) token = conn.assume_role_with_saml(role_arn, principal_arn, assertion, 14400)
By adding a value for the DurationSeconds parameter in the AssumeRoleWithSAML call, your federated user can retrieve temporary security credentials that are valid for up to 14,400 seconds (4 hours). If you don’t provide this value, the default session duration is 1 hour. If you provide a value of 5 hours for your temporary security credentials, AWS STS will throw an error since this is longer than the role session duration of 4 hours.
Conclusion
I demonstrated how you can configure the maximum session duration for a role from 1 hour (default) up to 12 hours. Then, I showed you how your federated users can retrieve temporary security credentials that are valid for longer durations to access AWS resources using AWS CLI/SDK for up to 12 hours.
Similarly, you can also increase the maximum role session duration for your applications and users who use Web Identity or OpenID Connect Federation or Cross-Account Access with Assume Role. If you have comments about this blog, submit them in the Comments section below. If you have questions or suggestions, please start a new thread on the IAM forum.
Applying technology to healthcare data has the potential to produce many exciting and important outcomes. The analysis produced from healthcare data can empower clinicians to improve the health of individuals and populations by enabling them to make better decisions that enhance the care they provide.
The Observational Health Data Sciences and Informatics (OHDSI, pronounced “Odyssey”) program and community is working toward this goal by producing data standards and open-source solutions to store and analyze observational health data. Using the OHDSI tools, you can visualize the health of your entire population. You can build cohorts of patients, analyze incidence rates for various conditions, and estimate the effect of treatments on patients with certain conditions. You can also model health outcome predictions using machine learning algorithms.
One of the challenges often faced when working with big data tools is the expense of the infrastructure required to run them. Another challenge is the learning curve to implement and begin using these tools. Amazon Web Services has enabled us to address many of the classic IT challenges by making enterprise class infrastructure and technology available in an affordable, elastic, and automated way. This blog post demonstrates how to combine some of the OHDSI projects (Atlas, Achilles, WebAPI, and the OMOP Common Data Model) with AWS technologies. By doing so, you can quickly and inexpensively implement a health data science and informatics environment.
Shown following is just one example of the population health analysis that is possible with the OHDSI tools. This visualization shows the prevalence of various drugs within the given population of people. This information helps researchers and clinicians discover trends and make better informed decisions about patient health.
OHDSI application architecture on AWS
Before deploying an application on AWS that transmits, processes, or stores protected health information (PHI) or personally identifiable information (PII), address your organization’s compliance concerns. Make sure that you have worked with your internal compliance and legal team to ensure compliance with the laws and regulations that govern your organization. To understand how you can use AWS services as a part of your overall compliance program, see the AWS HIPAA Compliance whitepaper. With that said, we paid careful attention to the HIPAA control set during the design of this solution.
This blog post presents a complete OHDSI application environment, including a data warehouse with sample data. It has the following features:
Following, you can see a block diagram of how the OHDSI tools map to the services provided by AWS.
Atlas is the web application that researchers interact with to perform analysis. Atlas interacts with the underlying databases through a web services application named WebAPI. In this example, both Atlas and WebAPI are deployed and managed by AWS Elastic Beanstalk. Elastic Beanstalk is an easy-to-use service for deploying and scaling web applications. Simply upload the Atlas and WebAPI code and Elastic Beanstalk automatically handles the deployment. It covers everything from capacity provisioning, load balancing, autoscaling, and high availability, to application health monitoring. Using a feature of Elastic Beanstalk called ebextensions, the Atlas and WebAPI servers are customized to use an encrypted storage volume for the middleware application logs.
Atlas stores the state of the various patient cohorts that are analyzed in a dedicated database separate from your observational health data. This database is provided by Amazon Aurora with PostgreSQL compatibility.
Amazon Aurora is a relational database built for the cloud that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open-source databases. It provides cost-efficient and resizable capacity while automating time-consuming administration tasks such as hardware provisioning, database setup, patching, and backups. It is configured for high availability and uses encryption at rest for the database and backups, and encryption in flight for the JDBC connections.
All of your observational health data is stored inside the OHDSI Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). This model also stores useful vocabulary tables that help to translate values from various data sources (like EHR systems and claims data).
The OMOP CDM schema is deployed onto Amazon Redshift. Amazon Redshift is a fast, fully managed data warehouse that allows you to run complex analytic queries against petabytes of structured data. It uses using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. You can also resize an Amazon Redshift cluster as your requirements for it change.
The solution in this blog post automatically loads de-identified sample data of 1,000 people from the CMS 2008–2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF). The data has helpful formatting from LTS Computing LLC. Vocabulary data from the OHDSI Athena project is also loaded into the OMOP CDM, and a results set is computed by OHDSI Achilles.
Following is a detailed technical diagram showing the configuration of the architecture to be deployed.
Deploying OHDSI on AWS
Everything just described is automatically deployed by using an AWS CloudFormation template. Using this template, you can quickly get started with the OHDSI project. The CloudFormation templates for this deployment as well as all of the supporting scripts and source code can be found in the AWS Labs GitHub repo.
From your AWS account, open the CloudFormation Management Console and choose Create Stack. From there, copy and paste the following URL in the Specify an Amazon S3 template URL box, and choose Next.
On the next screen, you provide a Stack Name (this can be anything you like) and a few other parameters for your OHDSI environment.
You use the DatabasePassword parameter to set the password for the master user account of the Amazon Redshift and Aurora databases.
You use the EBEndpoint name to generate a unique URL for Atlas to access the OHDSI environment. It is http://EBEndpoint.AWS-Region.elasticbeanstalk.com, where EBEndpoint.AWS-Region indicates the Elastic Beanstalk endpoint and AWS Region. You can configure this URL through Elastic Beanstalk if you want to change it in the future.
You use the KPair option to choose one of your existing Amazon EC2 key pairs to use with the instances that Elastic Beanstalk deploys. By doing this, you can gain administrative access to these instances in the future if you need to. If you don’t already have an Amazon EC2 key pair, you can generate one for free. You do this by going to the Key Pairs section of the EC2 console and choosing Generate Key Pair.
Finally, you use the UserIPRange parameter to specify a CIDR IP address range from which to access your OHDSI environment. By default, your OHDSI environment is accessible over the public internet. Use UserIPRange to limit access over the Internet to a single IP address or a range of IP addresses that represent users you want to have access. Through additional configuration, you can also make your OHDSI environment completely private and accessible only through a VPN or AWS Direct Connect private circuit.
When you’ve provided all Parameters, choose Next.
On the next screen, you can provide some other optional information like tags at your discretion, or just choose Next.
On the next screen, you can review what will be deployed. At the bottom of the screen, there is a check box for you to acknowledge that AWS CloudFormation might create IAM resources with custom names. This is correct; the template being deployed creates four custom roles that give permission for the AWS services involved to communicate with each other. Details of these permissions are inside the CloudFormation template referenced in the URL given in the first step. Check the box acknowledging this and choose Next.
You can watch as CloudFormation builds out your OHDSI architecture. A CloudFormation deployment is called a stack. The parent stack creates two child stacks, one containing the VPC and IAM roles and another created by Elastic Beanstalk with the Atlas and WebAPI servers. When all three stacks have reached the green CREATE_COMPLETE status, as shown in the screenshot following, then the OHDSI architecture has been deployed.
There is still some work going on behind the scenes, though. To watch the progress, browse to the Amazon Redshift section of your AWS Management Console and choose the Amazon Redshift cluster that was created for your OHDSI architecture. After you do so, you can observe the Loads and Queries tabs.
First, on the Loads tab, you can see the CMS De-SynPUF sample data and Athena vocabulary data being loaded into the OMOP Common Data Model. After you see the VOCABULARY table reach the COMPLETED status (as shown following), all of the sample and vocabulary data has been loaded.
After the data loads, the Achilles computation starts. On the Queries tab, you can watch Achilles running queries against your database to build out the Results schema. Achilles runs a large number of queries, and the entire process can take quite some time (about 20 minutes for the sample data we’ve loaded). Eventually, no new queries show up in the Queries tab, which shows that the Achilles computation is completed. The entire process from the time you executed the CloudFormation template until the Achilles computation is completed usually takes about an hour and 15 minutes.
At this point, you can browse to the Elastic Beanstalk section of the AWS Management Console. There, you can choose the OHDSI Application and Environment (green box) that was deployed by the CloudFormation template. At the top of the dashboard, as shown following, you see a link to a URL. This URL matches the name you provided in the EBEndpoint parameter of the CloudFormation template. Choose this URL, and you can start using Atlas to explore the CMS DE-SynPUF sample data!
Cost of deploying this environment
It used to be common to see healthcare data analytics environments deployed in an on-premises data center with expensive data warehouse appliances and virtualized environments. The cloud era has democratized the availability of the infrastructure required to do this type of data analysis, so that now it is within reach of even small organizations. This environment can expand to analyze petabyte-scale health data, and you only pay for what you need. See an estimated breakdown of the monthly cost components for this environment as deployed on the AWS Solution Calculator.
It’s also worth noting that this environment does not have to be run all of the time. If you are only performing analyses periodically, you can terminate the environment when you are finished and restore it from the database backups when you want to continue working. This would reduce the cost of operation even further.
Summary
Now that you have a fully functional OHDSI environment with sample data, you can use this to explore and learn the toolset and its capabilities. After learning with the sample data, you can begin gaining insights by analyzing your own organization’s health data. You can do this using an extract, transform, load (ETL) process from one or more of your health data sources.
James Wiggins is a senior healthcare solutions architect at AWS. He is passionate about using technology to help organizations positively impact world health. He also loves spending time with his wife and three children.
AWS Key Management Service (KMS) now uses FIPS 140-2 validated hardware security modules (HSM) and supports FIPS 140-2 validated endpoints, which provide independent assurances about the confidentiality and integrity of your keys. Having additional third-party assurances about the keys you manage in AWS KMS can make it easier to use the service for regulated workloads.
AWS KMS HSMs are designed so that no one, not even AWS employees, can retrieve your plaintext keys. The service uses the FIPS 140-2 validated HSMs to protect your keys when you request the service to create keys on your behalf or when you import them. Your plaintext keys are never written to disk and are only used in volatile memory of the HSMs while performing your requested cryptographic operation. Furthermore, AWS KMS keys are never transmitted outside the AWS Regions they were created. And HSM firmware updates are controlled by multi-party access that is audited and reviewed by an independent group within AWS.
AWS KMS HSMs are validated at level 2 overall and at level 3 in the following areas:
Cryptographic Module Specification
Roles, Services, and Authentication
Physical Security
Design Assurance
You can also make AWS KMS requests to API endpoints that terminate TLS sessions using a FIPS 140-2 validated cryptographic software module. To do so, connect to the unique FIPS 140-2 validated HTTPS endpoints in the AWS KMS requests made from your applications. AWS KMS FIPS 140-2 validated HTTPS endpoints are powered by the OpenSSL FIPS Object Module. FIPS 140-2 validated API endpoints are available in all commercial regions where AWS KMS is available.
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Kapil Shardha is a Technical Account Manager and supports enterprise customers with their AWS adoption. He has background in infrastructure automation and DevOps.
William Torrealba is an AWS Solutions Architect supporting customers with their AWS adoption. He has background in Application Development, High Available Distributed Systems, Automation, and DevOps.
Karthik Sonti is a senior big data architect at Amazon Web Services. He helps AWS customers build big data and analytical solutions and provides guidance on architecture and best practices.
