Tag Archives: AWS Management Console

Easier Certificate Validation Using DNS with AWS Certificate Manager

Post Syndicated from Todd Cignetti original https://aws.amazon.com/blogs/security/easier-certificate-validation-using-dns-with-aws-certificate-manager/

Secure Sockets Layer/Transport Layer Security (SSL/TLS) certificates are used to secure network communications and establish the identity of websites over the internet. Before issuing a certificate for your website, Amazon must validate that you control the domain name for your site. You can now use AWS Certificate Manager (ACM) Domain Name System (DNS) validation to establish that you control a domain name when requesting SSL/TLS certificates with ACM. Previously ACM supported only email validation, which required the domain owner to receive an email for each certificate request and validate the information in the request before approving it.

With DNS validation, you write a CNAME record to your DNS configuration to establish control of your domain name. After you have configured the CNAME record, ACM can automatically renew DNS-validated certificates before they expire, as long as the DNS record has not changed. To make it even easier to validate your domain, ACM can update your DNS configuration for you if you manage your DNS records with Amazon Route 53. In this blog post, I demonstrate how to request a certificate for a website by using DNS validation. To perform the equivalent steps using the AWS CLI or AWS APIs and SDKs, see AWS Certificate Manager in the AWS CLI Reference and the ACM API Reference.

Requesting an SSL/TLS certificate by using DNS validation

In this section, I walk you through the four steps required to obtain an SSL/TLS certificate through ACM to identify your site over the internet. SSL/TLS provides encryption for sensitive data in transit and authentication by using certificates to establish the identity of your site and secure connections between browsers and applications and your site. DNS validation and SSL/TLS certificates provisioned through ACM are free.

Step 1: Request a certificate

To get started, sign in to the AWS Management Console and navigate to the ACM console. Choose Get started to request a certificate.

Screenshot of getting started in the ACM console

If you previously managed certificates in ACM, you will instead see a table with your certificates and a button to request a new certificate. Choose Request a certificate to request a new certificate.

Screenshot of choosing "Request a certificate"

Type the name of your domain in the Domain name box and choose Next. In this example, I type www.example.com. You must use a domain name that you control. Requesting certificates for domains that you don’t control violates the AWS Service Terms.

Screenshot of entering a domain name

Step 2: Select a validation method

With DNS validation, you write a CNAME record to your DNS configuration to establish control of your domain name. Choose DNS validation, and then choose Review.

Screenshot of selecting validation method

Step 3: Review your request

Review your request and choose Confirm and request to request the certificate.

Screenshot of reviewing request and confirming it

Step 4: Submit your request

After a brief delay while ACM populates your domain validation information, choose the down arrow (highlighted in the following screenshot) to display all the validation information for your domain.

Screenshot of validation information

ACM displays the CNAME record you must add to your DNS configuration to validate that you control the domain name in your certificate request. If you use a DNS provider other than Route 53 or if you use a different AWS account to manage DNS records in Route 53, copy the DNS CNAME information from the validation information, or export it to a file (choose Export DNS configuration to a file) and write it to your DNS configuration. For information about how to add or modify DNS records, check with your DNS provider. For more information about using DNS with Route 53 DNS, see the Route 53 documentation.

If you manage DNS records for your domain with Route 53 in the same AWS account, choose Create record in Route 53 to have ACM update your DNS configuration for you.

After updating your DNS configuration, choose Continue to return to the ACM table view.

ACM then displays a table that includes all your certificates. The certificate you requested is displayed so that you can see the status of your request. After you write the DNS record or have ACM write the record for you, it typically takes DNS 30 minutes to propagate the record, and it might take several hours for Amazon to validate it and issue the certificate. During this time, ACM shows the Validation status as Pending validation. After ACM validates the domain name, ACM updates the Validation status to Success. After the certificate is issued, the certificate status is updated to Issued. If ACM cannot validate your DNS record and issue the certificate after 72 hours, the request times out, and ACM displays a Timed out validation status. To recover, you must make a new request. Refer to the Troubleshooting Section of the ACM User Guide for instructions about troubleshooting validation or issuance failures.

Screenshot of a certificate issued and validation successful

You now have an ACM certificate that you can use to secure your application or website. For information about how to deploy certificates with other AWS services, see the documentation for Amazon CloudFront, Amazon API Gateway, Application Load Balancers, and Classic Load Balancers. Note that your certificate must be in the US East (N. Virginia) Region to use the certificate with CloudFront.

ACM automatically renews certificates that are deployed and in use with other AWS services as long as the CNAME record remains in your DNS configuration. To learn more about ACM DNS validation, see the ACM FAQs and the ACM documentation.

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

– Todd

The 10 Most Viewed Security-Related AWS Knowledge Center Articles and Videos for November 2017

Post Syndicated from Maggie Burke original https://aws.amazon.com/blogs/security/the-10-most-viewed-security-related-aws-knowledge-center-articles-and-videos-for-november-2017/

AWS Knowledge Center image

The AWS Knowledge Center helps answer the questions most frequently asked by AWS Support customers. The following 10 Knowledge Center security articles and videos have been the most viewed this month. It’s likely you’ve wondered about a few of these topics yourself, so here’s a chance to learn the answers!

  1. How do I create an AWS Identity and Access Management (IAM) policy to restrict access for an IAM user, group, or role to a particular Amazon Virtual Private Cloud (VPC)?
    Learn how to apply a custom IAM policy to restrict IAM user, group, or role permissions for creating and managing Amazon EC2 instances in a specified VPC.
  2. How do I use an MFA token to authenticate access to my AWS resources through the AWS CLI?
    One IAM best practice is to protect your account and its resources by using a multi-factor authentication (MFA) device. If you plan use the AWS Command Line Interface (CLI) while using an MFA device, you must create a temporary session token.
  3. Can I restrict an IAM user’s EC2 access to specific resources?
    This article demonstrates how to link multiple AWS accounts through AWS Organizations and isolate IAM user groups in their own accounts.
  4. I didn’t receive a validation email for the SSL certificate I requested through AWS Certificate Manager (ACM)—where is it?
    Can’t find your ACM validation emails? Be sure to check the email address to which you requested that ACM send validation emails.
  5. How do I create an IAM policy that has a source IP restriction but still allows users to switch roles in the AWS Management Console?
    Learn how to write an IAM policy that not only includes a source IP restriction but also lets your users switch roles in the console.
  6. How do I allow users from another account to access resources in my account through IAM?
    If you have the 12-digit account number and permissions to create and edit IAM roles and users for both accounts, you can permit specific IAM users to access resources in your account.
  7. What are the differences between a service control policy (SCP) and an IAM policy?
    Learn how to distinguish an SCP from an IAM policy.
  8. How do I share my customer master keys (CMKs) across multiple AWS accounts?
    To grant another account access to your CMKs, create an IAM policy on the secondary account that grants access to use your CMKs.
  9. How do I set up AWS Trusted Advisor notifications?
    Learn how to receive free weekly email notifications from Trusted Advisor.
  10. How do I use AWS Key Management Service (AWS KMS) encryption context to protect the integrity of encrypted data?
    Encryption context name-value pairs used with AWS KMS encryption and decryption operations provide a method for checking ciphertext authenticity. Learn how to use encryption context to help protect your encrypted data.

The AWS Security Blog will publish an updated version of this list regularly going forward. You also can subscribe to the AWS Knowledge Center Videos playlist on YouTube.

– Maggie

How to Patch, Inspect, and Protect Microsoft Windows Workloads on AWS—Part 2

Post Syndicated from Koen van Blijderveen original https://aws.amazon.com/blogs/security/how-to-patch-inspect-and-protect-microsoft-windows-workloads-on-aws-part-2/

Yesterday in Part 1 of this blog post, I showed you how to:

  1. Launch an Amazon EC2 instance with an AWS Identity and Access Management (IAM) role, an Amazon Elastic Block Store (Amazon EBS) volume, and tags that Amazon EC2 Systems Manager (Systems Manager) and Amazon Inspector use.
  2. Configure Systems Manager to install the Amazon Inspector agent and patch your EC2 instances.

Today in Steps 3 and 4, I show you how to:

  1. Take Amazon EBS snapshots using Amazon EBS Snapshot Scheduler to automate snapshots based on instance tags.
  2. Use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

To catch up on Steps 1 and 2, see yesterday’s blog post.

Step 3: Take EBS snapshots using EBS Snapshot Scheduler

In this section, I show you how to use EBS Snapshot Scheduler to take snapshots of your instances at specific intervals. To do this, I will show you how to:

  • Determine the schedule for EBS Snapshot Scheduler by providing you with best practices.
  • Deploy EBS Snapshot Scheduler by using AWS CloudFormation.
  • Tag your EC2 instances so that EBS Snapshot Scheduler backs up your instances when you want them backed up.

In addition to making sure your EC2 instances have all the available operating system patches applied on a regular schedule, you should take snapshots of the EBS storage volumes attached to your EC2 instances. Taking regular snapshots allows you to restore your data to a previous state quickly and cost effectively. With Amazon EBS snapshots, you pay only for the actual data you store, and snapshots save only the data that has changed since the previous snapshot, which minimizes your cost. You will use EBS Snapshot Scheduler to make regular snapshots of your EC2 instance. EBS Snapshot Scheduler takes advantage of other AWS services including CloudFormation, Amazon DynamoDB, and AWS Lambda to make backing up your EBS volumes simple.

Determine the schedule

As a best practice, you should back up your data frequently during the hours when your data changes the most. This reduces the amount of data you lose if you have to restore from a snapshot. For the purposes of this blog post, the data for my instances changes the most between the business hours of 9:00 A.M. to 5:00 P.M. Pacific Time. During these hours, I will make snapshots hourly to minimize data loss.

In addition to backing up frequently, another best practice is to establish a strategy for retention. This will vary based on how you need to use the snapshots. If you have compliance requirements to be able to restore for auditing, your needs may be different than if you are able to detect data corruption within three hours and simply need to restore to something that limits data loss to five hours. EBS Snapshot Scheduler enables you to specify the retention period for your snapshots. For this post, I only need to keep snapshots for recent business days. To account for weekends, I will set my retention period to three days, which is down from the default of 15 days when deploying EBS Snapshot Scheduler.

Deploy EBS Snapshot Scheduler

In Step 1 of Part 1 of this post, I showed how to configure an EC2 for Windows Server 2012 R2 instance with an EBS volume. You will use EBS Snapshot Scheduler to take eight snapshots each weekday of your EC2 instance’s EBS volumes:

  1. Navigate to the EBS Snapshot Scheduler deployment page and choose Launch Solution. This takes you to the CloudFormation console in your account. The Specify an Amazon S3 template URL option is already selected and prefilled. Choose Next on the Select Template page.
  2. On the Specify Details page, retain all default parameters except for AutoSnapshotDeletion. Set AutoSnapshotDeletion to Yes to ensure that old snapshots are periodically deleted. The default retention period is 15 days (you will specify a shorter value on your instance in the next subsection).
  3. Choose Next twice to move to the Review step, and start deployment by choosing the I acknowledge that AWS CloudFormation might create IAM resources check box and then choosing Create.

Tag your EC2 instances

EBS Snapshot Scheduler takes a few minutes to deploy. While waiting for its deployment, you can start to tag your instance to define its schedule. EBS Snapshot Scheduler reads tag values and looks for four possible custom parameters in the following order:

  • <snapshot time> – Time in 24-hour format with no colon.
  • <retention days> – The number of days (a positive integer) to retain the snapshot before deletion, if set to automatically delete snapshots.
  • <time zone> – The time zone of the times specified in <snapshot time>.
  • <active day(s)>all, weekdays, or mon, tue, wed, thu, fri, sat, and/or sun.

Because you want hourly backups on weekdays between 9:00 A.M. and 5:00 P.M. Pacific Time, you need to configure eight tags—one for each hour of the day. You will add the eight tags shown in the following table to your EC2 instance.

Tag Value
scheduler:ebs-snapshot:0900 0900;3;utc;weekdays
scheduler:ebs-snapshot:1000 1000;3;utc;weekdays
scheduler:ebs-snapshot:1100 1100;3;utc;weekdays
scheduler:ebs-snapshot:1200 1200;3;utc;weekdays
scheduler:ebs-snapshot:1300 1300;3;utc;weekdays
scheduler:ebs-snapshot:1400 1400;3;utc;weekdays
scheduler:ebs-snapshot:1500 1500;3;utc;weekdays
scheduler:ebs-snapshot:1600 1600;3;utc;weekdays

Next, you will add these tags to your instance. If you want to tag multiple instances at once, you can use Tag Editor instead. To add the tags in the preceding table to your EC2 instance:

  1. Navigate to your EC2 instance in the EC2 console and choose Tags in the navigation pane.
  2. Choose Add/Edit Tags and then choose Create Tag to add all the tags specified in the preceding table.
  3. Confirm you have added the tags by choosing Save. After adding these tags, navigate to your EC2 instance in the EC2 console. Your EC2 instance should look similar to the following screenshot.
    Screenshot of how your EC2 instance should look in the console
  4. After waiting a couple of hours, you can see snapshots beginning to populate on the Snapshots page of the EC2 console.Screenshot of snapshots beginning to populate on the Snapshots page of the EC2 console
  5. To check if EBS Snapshot Scheduler is active, you can check the CloudWatch rule that runs the Lambda function. If the clock icon shown in the following screenshot is green, the scheduler is active. If the clock icon is gray, the rule is disabled and does not run. You can enable or disable the rule by selecting it, choosing Actions, and choosing Enable or Disable. This also allows you to temporarily disable EBS Snapshot Scheduler.Screenshot of checking to see if EBS Snapshot Scheduler is active
  1. You can also monitor when EBS Snapshot Scheduler has run by choosing the name of the CloudWatch rule as shown in the previous screenshot and choosing Show metrics for the rule.Screenshot of monitoring when EBS Snapshot Scheduler has run by choosing the name of the CloudWatch rule

If you want to restore and attach an EBS volume, see Restoring an Amazon EBS Volume from a Snapshot and Attaching an Amazon EBS Volume to an Instance.

Step 4: Use Amazon Inspector

In this section, I show you how to you use Amazon Inspector to scan your EC2 instance for common vulnerabilities and exposures (CVEs) and set up Amazon SNS notifications. To do this I will show you how to:

  • Install the Amazon Inspector agent by using EC2 Run Command.
  • Set up notifications using Amazon SNS to notify you of any findings.
  • Define an Amazon Inspector target and template to define what assessment to perform on your EC2 instance.
  • Schedule Amazon Inspector assessment runs to assess your EC2 instance on a regular interval.

Amazon Inspector can help you scan your EC2 instance using prebuilt rules packages, which are built and maintained by AWS. These prebuilt rules packages tell Amazon Inspector what to scan for on the EC2 instances you select. Amazon Inspector provides the following prebuilt packages for Microsoft Windows Server 2012 R2:

  • Common Vulnerabilities and Exposures
  • Center for Internet Security Benchmarks
  • Runtime Behavior Analysis

In this post, I’m focused on how to make sure you keep your EC2 instances patched, backed up, and inspected for common vulnerabilities and exposures (CVEs). As a result, I will focus on how to use the CVE rules package and use your instance tags to identify the instances on which to run the CVE rules. If your EC2 instance is fully patched using Systems Manager, as described earlier, you should not have any findings with the CVE rules package. Regardless, as a best practice I recommend that you use Amazon Inspector as an additional layer for identifying any unexpected failures. This involves using Amazon CloudWatch to set up weekly Amazon Inspector scans, and configuring Amazon Inspector to notify you of any findings through SNS topics. By acting on the notifications you receive, you can respond quickly to any CVEs on any of your EC2 instances to help ensure that malware using known CVEs does not affect your EC2 instances. In a previous blog post, Eric Fitzgerald showed how to remediate Amazon Inspector security findings automatically.

Install the Amazon Inspector agent

To install the Amazon Inspector agent, you will use EC2 Run Command, which allows you to run any command on any of your EC2 instances that have the Systems Manager agent with an attached IAM role that allows access to Systems Manager.

  1. Choose Run Command under Systems Manager Services in the navigation pane of the EC2 console. Then choose Run a command.
    Screenshot of choosing "Run a command"
  2. To install the Amazon Inspector agent, you will use an AWS managed and provided command document that downloads and installs the agent for you on the selected EC2 instance. Choose AmazonInspector-ManageAWSAgent. To choose the target EC2 instance where this command will be run, use the tag you previously assigned to your EC2 instance, Patch Group, with a value of Windows Servers. For this example, set the concurrent installations to 1 and tell Systems Manager to stop after 5 errors.
    Screenshot of installing the Amazon Inspector agent
  3. Retain the default values for all other settings on the Run a command page and choose Run. Back on the Run Command page, you can see if the command that installed the Amazon Inspector agent executed successfully on all selected EC2 instances.
    Screenshot showing that the command that installed the Amazon Inspector agent executed successfully on all selected EC2 instances

Set up notifications using Amazon SNS

Now that you have installed the Amazon Inspector agent, you will set up an SNS topic that will notify you of any findings after an Amazon Inspector run.

To set up an SNS topic:

  1. In the AWS Management Console, choose Simple Notification Service under Messaging in the Services menu.
  2. Choose Create topic, name your topic (only alphanumeric characters, hyphens, and underscores are allowed) and give it a display name to ensure you know what this topic does (I’ve named mine Inspector). Choose Create topic.
    "Create new topic" page
  3. To allow Amazon Inspector to publish messages to your new topic, choose Other topic actions and choose Edit topic policy.
  4. For Allow these users to publish messages to this topic and Allow these users to subscribe to this topic, choose Only these AWS users. Type the following ARN for the US East (N. Virginia) Region in which you are deploying the solution in this post: arn:aws:iam::316112463485:root. This is the ARN of Amazon Inspector itself. For the ARNs of Amazon Inspector in other AWS Regions, see Setting Up an SNS Topic for Amazon Inspector Notifications (Console). Amazon Resource Names (ARNs) uniquely identify AWS resources across all of AWS.
    Screenshot of editing the topic policy
  5. To receive notifications from Amazon Inspector, subscribe to your new topic by choosing Create subscription and adding your email address. After confirming your subscription by clicking the link in the email, the topic should display your email address as a subscriber. Later, you will configure the Amazon Inspector template to publish to this topic.
    Screenshot of subscribing to the new topic

Define an Amazon Inspector target and template

Now that you have set up the notification topic by which Amazon Inspector can notify you of findings, you can create an Amazon Inspector target and template. A target defines which EC2 instances are in scope for Amazon Inspector. A template defines which packages to run, for how long, and on which target.

To create an Amazon Inspector target:

  1. Navigate to the Amazon Inspector console and choose Get started. At the time of writing this blog post, Amazon Inspector is available in the US East (N. Virginia), US West (N. California), US West (Oregon), EU (Ireland), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), and Asia Pacific (Tokyo) Regions.
  2. For Amazon Inspector to be able to collect the necessary data from your EC2 instance, you must create an IAM service role for Amazon Inspector. Amazon Inspector can create this role for you if you choose Choose or create role and confirm the role creation by choosing Allow.
    Screenshot of creating an IAM service role for Amazon Inspector
  3. Amazon Inspector also asks you to tag your EC2 instance and install the Amazon Inspector agent. You already performed these steps in Part 1 of this post, so you can proceed by choosing Next. To define the Amazon Inspector target, choose the previously used Patch Group tag with a Value of Windows Servers. This is the same tag that you used to define the targets for patching. Then choose Next.
    Screenshot of defining the Amazon Inspector target
  4. Now, define your Amazon Inspector template, and choose a name and the package you want to run. For this post, use the Common Vulnerabilities and Exposures package and choose the default duration of 1 hour. As you can see, the package has a version number, so always select the latest version of the rules package if multiple versions are available.
    Screenshot of defining an assessment template
  5. Configure Amazon Inspector to publish to your SNS topic when findings are reported. You can also choose to receive a notification of a started run, a finished run, or changes in the state of a run. For this blog post, you want to receive notifications if there are any findings. To start, choose Assessment Templates from the Amazon Inspector console and choose your newly created Amazon Inspector assessment template. Choose the icon below SNS topics (see the following screenshot).
    Screenshot of choosing an assessment template
  6. A pop-up appears in which you can choose the previously created topic and the events about which you want SNS to notify you (choose Finding reported).
    Screenshot of choosing the previously created topic and the events about which you want SNS to notify you

Schedule Amazon Inspector assessment runs

The last step in using Amazon Inspector to assess for CVEs is to schedule the Amazon Inspector template to run using Amazon CloudWatch Events. This will make sure that Amazon Inspector assesses your EC2 instance on a regular basis. To do this, you need the Amazon Inspector template ARN, which you can find under Assessment templates in the Amazon Inspector console. CloudWatch Events can run your Amazon Inspector assessment at an interval you define using a Cron-based schedule. Cron is a well-known scheduling agent that is widely used on UNIX-like operating systems and uses the following syntax for CloudWatch Events.

Image of Cron schedule

All scheduled events use a UTC time zone, and the minimum precision for schedules is one minute. For more information about scheduling CloudWatch Events, see Schedule Expressions for Rules.

To create the CloudWatch Events rule:

  1. Navigate to the CloudWatch console, choose Events, and choose Create rule.
    Screenshot of starting to create a rule in the CloudWatch Events console
  2. On the next page, specify if you want to invoke your rule based on an event pattern or a schedule. For this blog post, you will select a schedule based on a Cron expression.
  3. You can schedule the Amazon Inspector assessment any time you want using the Cron expression, or you can use the Cron expression I used in the following screenshot, which will run the Amazon Inspector assessment every Sunday at 10:00 P.M. GMT.
    Screenshot of scheduling an Amazon Inspector assessment with a Cron expression
  4. Choose Add target and choose Inspector assessment template from the drop-down menu. Paste the ARN of the Amazon Inspector template you previously created in the Amazon Inspector console in the Assessment template box and choose Create a new role for this specific resource. This new role is necessary so that CloudWatch Events has the necessary permissions to start the Amazon Inspector assessment. CloudWatch Events will automatically create the new role and grant the minimum set of permissions needed to run the Amazon Inspector assessment. To proceed, choose Configure details.
    Screenshot of adding a target
  5. Next, give your rule a name and a description. I suggest using a name that describes what the rule does, as shown in the following screenshot.
  6. Finish the wizard by choosing Create rule. The rule should appear in the Events – Rules section of the CloudWatch console.
    Screenshot of completing the creation of the rule
  7. To confirm your CloudWatch Events rule works, wait for the next time your CloudWatch Events rule is scheduled to run. For testing purposes, you can choose your CloudWatch Events rule and choose Edit to change the schedule to run it sooner than scheduled.
    Screenshot of confirming the CloudWatch Events rule works
  8. Now navigate to the Amazon Inspector console to confirm the launch of your first assessment run. The Start time column shows you the time each assessment started and the Status column the status of your assessment. In the following screenshot, you can see Amazon Inspector is busy Collecting data from the selected assessment targets.
    Screenshot of confirming the launch of the first assessment run

You have concluded the last step of this blog post by setting up a regular scan of your EC2 instance with Amazon Inspector and a notification that will let you know if your EC2 instance is vulnerable to any known CVEs. In a previous Security Blog post, Eric Fitzgerald explained How to Remediate Amazon Inspector Security Findings Automatically. Although that blog post is for Linux-based EC2 instances, the post shows that you can learn about Amazon Inspector findings in other ways than email alerts.

Conclusion

In this two-part blog post, I showed how to make sure you keep your EC2 instances up to date with patching, how to back up your instances with snapshots, and how to monitor your instances for CVEs. Collectively these measures help to protect your instances against common attack vectors that attempt to exploit known vulnerabilities. In Part 1, I showed how to configure your EC2 instances to make it easy to use Systems Manager, EBS Snapshot Scheduler, and Amazon Inspector. I also showed how to use Systems Manager to schedule automatic patches to keep your instances current in a timely fashion. In Part 2, I showed you how to take regular snapshots of your data by using EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

If you have comments about today’s or yesterday’s post, submit them in the “Comments” section below. If you have questions about or issues implementing any part of this solution, start a new thread on the Amazon EC2 forum or the Amazon Inspector forum, or contact AWS Support.

– Koen

Now You Can Use AWS Shield Advanced to Help Protect Your Amazon EC2 Instances and Network Load Balancers

Post Syndicated from Ritwik Manan original https://aws.amazon.com/blogs/security/now-you-can-use-aws-shield-advanced-to-protect-your-amazon-ec2-instances-and-network-load-balancers/

AWS Shield image

Starting today, AWS Shield Advanced can help protect your Amazon EC2 instances and Network Load Balancers against infrastructure-layer Distributed Denial of Service (DDoS) attacks. Enable AWS Shield Advanced on an AWS Elastic IP address and attach the address to an internet-facing EC2 instance or Network Load Balancer. AWS Shield Advanced automatically detects the type of AWS resource behind the Elastic IP address and mitigates DDoS attacks.

AWS Shield Advanced also ensures that all your Amazon VPC network access control lists (ACLs) are automatically executed on AWS Shield at the edge of the AWS network, giving you access to additional bandwidth and scrubbing capacity as well as mitigating large volumetric DDoS attacks. You also can customize additional mitigations on AWS Shield by engaging the AWS DDoS Response Team, which can preconfigure the mitigations or respond to incidents as they happen. For every incident detected by AWS Shield Advanced, you also get near-real-time visibility via Amazon CloudWatch metrics and details about the incident, such as the geographic origin and source IP address of the attack.

AWS Shield Advanced for Elastic IP addresses extends the coverage of DDoS cost protection, which safeguards against scaling charges as a result of a DDoS attack. DDoS cost protection now allows you to request service credits for Elastic Load Balancing, Amazon CloudFront, Amazon Route 53, and your EC2 instance hours in the event that these increase as the result of a DDoS attack.

Get started protecting EC2 instances and Network Load Balancers

To get started:

  1. Sign in to the AWS Management Console and navigate to the AWS WAF and AWS Shield console.
  2. Activate AWS Shield Advanced by choosing Activate AWS Shield Advanced and accepting the terms.
  3. Navigate to Protected Resources through the navigation pane.
  4. Choose the Elastic IP addresses that you want to protect (these can point to EC2 instances or Network Load Balancers).

If AWS Shield Advanced detects a DDoS attack, you can get details about the attack by checking CloudWatch, or the Incidents tab on the AWS WAF and AWS Shield console. To learn more about this new feature and AWS Shield Advanced, see the AWS Shield home page.

If you have comments or questions about this post, submit them in the “Comments” section below, start a new thread in the AWS Shield forum, or contact AWS Support.

– Ritwik

How to Patch, Inspect, and Protect Microsoft Windows Workloads on AWS—Part 1

Post Syndicated from Koen van Blijderveen original https://aws.amazon.com/blogs/security/how-to-patch-inspect-and-protect-microsoft-windows-workloads-on-aws-part-1/

Most malware tries to compromise your systems by using a known vulnerability that the maker of the operating system has already patched. To help prevent malware from affecting your systems, two security best practices are to apply all operating system patches to your systems and actively monitor your systems for missing patches. In case you do need to recover from a malware attack, you should make regular backups of your data.

In today’s blog post (Part 1 of a two-part post), I show how to keep your Amazon EC2 instances that run Microsoft Windows up to date with the latest security patches by using Amazon EC2 Systems Manager. Tomorrow in Part 2, I show how to take regular snapshots of your data by using Amazon EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

What you should know first

To follow along with the solution in this post, you need one or more EC2 instances. You may use existing instances or create new instances. For the blog post, I assume this is an EC2 for Microsoft Windows Server 2012 R2 instance installed from the Amazon Machine Images (AMIs). If you are not familiar with how to launch an EC2 instance, see Launching an Instance. I also assume you launched or will launch your instance in a private subnet. A private subnet is not directly accessible via the internet, and access to it requires either a VPN connection to your on-premises network or a jump host in a public subnet (a subnet with access to the internet). You must make sure that the EC2 instance can connect to the internet using a network address translation (NAT) instance or NAT gateway to communicate with Systems Manager and Amazon Inspector. The following diagram shows how you should structure your Amazon Virtual Private Cloud (VPC). You should also be familiar with Restoring an Amazon EBS Volume from a Snapshot and Attaching an Amazon EBS Volume to an Instance.

Later on, you will assign tasks to a maintenance window to patch your instances with Systems Manager. To do this, the AWS Identity and Access Management (IAM) user you are using for this post must have the iam:PassRole permission. This permission allows this IAM user to assign tasks to pass their own IAM permissions to the AWS service. In this example, when you assign a task to a maintenance window, IAM passes your credentials to Systems Manager. This safeguard ensures that the user cannot use the creation of tasks to elevate their IAM privileges because their own IAM privileges limit which tasks they can run against an EC2 instance. You should also authorize your IAM user to use EC2, Amazon Inspector, Amazon CloudWatch, and Systems Manager. You can achieve this by attaching the following AWS managed policies to the IAM user you are using for this example: AmazonInspectorFullAccess, AmazonEC2FullAccess, and AmazonSSMFullAccess.

Architectural overview

The following diagram illustrates the components of this solution’s architecture.

Diagram showing the components of this solution's architecture

For this blog post, Microsoft Windows EC2 is Amazon EC2 for Microsoft Windows Server 2012 R2 instances with attached Amazon Elastic Block Store (Amazon EBS) volumes, which are running in your VPC. These instances may be standalone Windows instances running your Windows workloads, or you may have joined them to an Active Directory domain controller. For instances joined to a domain, you can be using Active Directory running on an EC2 for Windows instance, or you can use AWS Directory Service for Microsoft Active Directory.

Amazon EC2 Systems Manager is a scalable tool for remote management of your EC2 instances. You will use the Systems Manager Run Command to install the Amazon Inspector agent. The agent enables EC2 instances to communicate with the Amazon Inspector service and run assessments, which I explain in detail later in this blog post. You also will create a Systems Manager association to keep your EC2 instances up to date with the latest security patches.

You can use the EBS Snapshot Scheduler to schedule automated snapshots at regular intervals. You will use it to set up regular snapshots of your Amazon EBS volumes. EBS Snapshot Scheduler is a prebuilt solution by AWS that you will deploy in your AWS account. With Amazon EBS snapshots, you pay only for the actual data you store. Snapshots save only the data that has changed since the previous snapshot, which minimizes your cost.

You will use Amazon Inspector to run security assessments on your EC2 for Windows Server instance. In this post, I show how to assess if your EC2 for Windows Server instance is vulnerable to any of the more than 50,000 CVEs registered with Amazon Inspector.

In today’s and tomorrow’s posts, I show you how to:

  1. Launch an EC2 instance with an IAM role, Amazon EBS volume, and tags that Systems Manager and Amazon Inspector will use.
  2. Configure Systems Manager to install the Amazon Inspector agent and patch your EC2 instances.
  3. Take EBS snapshots by using EBS Snapshot Scheduler to automate snapshots based on instance tags.
  4. Use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any common vulnerabilities and exposures (CVEs).

Step 1: Launch an EC2 instance

In this section, I show you how to launch your EC2 instances so that you can use Systems Manager with the instances and use instance tags with EBS Snapshot Scheduler to automate snapshots. This requires three things:

  • Create an IAM role for Systems Manager before launching your EC2 instance.
  • Launch your EC2 instance with Amazon EBS and the IAM role for Systems Manager.
  • Add tags to instances so that you can automate policies for which instances you take snapshots of and when.

Create an IAM role for Systems Manager

Before launching your EC2 instance, I recommend that you first create an IAM role for Systems Manager, which you will use to update the EC2 instance you will launch. AWS already provides a preconfigured policy that you can use for your new role, and it is called AmazonEC2RoleforSSM.

  1. Sign in to the IAM console and choose Roles in the navigation pane. Choose Create new role.
    Screenshot of choosing "Create role"
  2. In the role-creation workflow, choose AWS service > EC2 > EC2 to create a role for an EC2 instance.
    Screenshot of creating a role for an EC2 instance
  3. Choose the AmazonEC2RoleforSSM policy to attach it to the new role you are creating.
    Screenshot of attaching the AmazonEC2RoleforSSM policy to the new role you are creating
  4. Give the role a meaningful name (I chose EC2SSM) and description, and choose Create role.
    Screenshot of giving the role a name and description

Launch your EC2 instance

To follow along, you need an EC2 instance that is running Microsoft Windows Server 2012 R2 and that has an Amazon EBS volume attached. You can use any existing instance you may have or create a new instance.

When launching your new EC2 instance, be sure that:

  • The operating system is Microsoft Windows Server 2012 R2.
  • You attach at least one Amazon EBS volume to the EC2 instance.
  • You attach the newly created IAM role (EC2SSM).
  • The EC2 instance can connect to the internet through a network address translation (NAT) gateway or a NAT instance.
  • You create the tags shown in the following screenshot (you will use them later).

If you are using an already launched EC2 instance, you can attach the newly created role as described in Easily Replace or Attach an IAM Role to an Existing EC2 Instance by Using the EC2 Console.

Add tags

The final step of configuring your EC2 instances is to add tags. You will use these tags to configure Systems Manager in Step 2 of this blog post and to configure Amazon Inspector in Part 2. For this example, I add a tag key, Patch Group, and set the value to Windows Servers. I could have other groups of EC2 instances that I treat differently by having the same tag key but a different tag value. For example, I might have a collection of other servers with the Patch Group tag key with a value of IAS Servers.

Screenshot of adding tags

Note: You must wait a few minutes until the EC2 instance becomes available before you can proceed to the next section.

At this point, you now have at least one EC2 instance you can use to configure Systems Manager, use EBS Snapshot Scheduler, and use Amazon Inspector.

Note: If you have a large number of EC2 instances to tag, you may want to use the EC2 CreateTags API rather than manually apply tags to each instance.

Step 2: Configure Systems Manager

In this section, I show you how to use Systems Manager to apply operating system patches to your EC2 instances, and how to manage patch compliance.

To start, I will provide some background information about Systems Manager. Then, I will cover how to:

  • Create the Systems Manager IAM role so that Systems Manager is able to perform patch operations.
  • Associate a Systems Manager patch baseline with your instance to define which patches Systems Manager should apply.
  • Define a maintenance window to make sure Systems Manager patches your instance when you tell it to.
  • Monitor patch compliance to verify the patch state of your instances.

Systems Manager is a collection of capabilities that helps you automate management tasks for AWS-hosted instances on EC2 and your on-premises servers. In this post, I use Systems Manager for two purposes: to run remote commands and apply operating system patches. To learn about the full capabilities of Systems Manager, see What Is Amazon EC2 Systems Manager?

Patch management is an important measure to prevent malware from infecting your systems. Most malware attacks look for vulnerabilities that are publicly known and in most cases are already patched by the maker of the operating system. These publicly known vulnerabilities are well documented and therefore easier for an attacker to exploit than having to discover a new vulnerability.

Patches for these new vulnerabilities are available through Systems Manager within hours after Microsoft releases them. There are two prerequisites to use Systems Manager to apply operating system patches. First, you must attach the IAM role you created in the previous section, EC2SSM, to your EC2 instance. Second, you must install the Systems Manager agent on your EC2 instance. If you have used a recent Microsoft Windows Server 2012 R2 AMI published by AWS, Amazon has already installed the Systems Manager agent on your EC2 instance. You can confirm this by logging in to an EC2 instance and looking for Amazon SSM Agent under Programs and Features in Windows. To install the Systems Manager agent on an instance that does not have the agent preinstalled or if you want to use the Systems Manager agent on your on-premises servers, see the documentation about installing the Systems Manager agent. If you forgot to attach the newly created role when launching your EC2 instance or if you want to attach the role to already running EC2 instances, see Attach an AWS IAM Role to an Existing Amazon EC2 Instance by Using the AWS CLI or use the AWS Management Console.

To make sure your EC2 instance receives operating system patches from Systems Manager, you will use the default patch baseline provided and maintained by AWS, and you will define a maintenance window so that you control when your EC2 instances should receive patches. For the maintenance window to be able to run any tasks, you also must create a new role for Systems Manager. This role is a different kind of role than the one you created earlier: Systems Manager will use this role instead of EC2. Earlier we created the EC2SSM role with the AmazonEC2RoleforSSM policy, which allowed the Systems Manager agent on our instance to communicate with the Systems Manager service. Here we need a new role with the policy AmazonSSMMaintenanceWindowRole to make sure the Systems Manager service is able to execute commands on our instance.

Create the Systems Manager IAM role

To create the new IAM role for Systems Manager, follow the same procedure as in the previous section, but in Step 3, choose the AmazonSSMMaintenanceWindowRole policy instead of the previously selected AmazonEC2RoleforSSM policy.

Screenshot of creating the new IAM role for Systems Manager

Finish the wizard and give your new role a recognizable name. For example, I named my role MaintenanceWindowRole.

Screenshot of finishing the wizard and giving your new role a recognizable name

By default, only EC2 instances can assume this new role. You must update the trust policy to enable Systems Manager to assume this role.

To update the trust policy associated with this new role:

  1. Navigate to the IAM console and choose Roles in the navigation pane.
  2. Choose MaintenanceWindowRole and choose the Trust relationships tab. Then choose Edit trust relationship.
  3. Update the policy document by copying the following policy and pasting it in the Policy Document box. As you can see, I have added the ssm.amazonaws.com service to the list of allowed Principals that can assume this role. Choose Update Trust Policy.
    {
       "Version":"2012-10-17",
       "Statement":[
          {
             "Sid":"",
             "Effect":"Allow",
             "Principal":{
                "Service":[
                   "ec2.amazonaws.com",
                   "ssm.amazonaws.com"
               ]
             },
             "Action":"sts:AssumeRole"
          }
       ]
    }

Associate a Systems Manager patch baseline with your instance

Next, you are going to associate a Systems Manager patch baseline with your EC2 instance. A patch baseline defines which patches Systems Manager should apply. You will use the default patch baseline that AWS manages and maintains. Before you can associate the patch baseline with your instance, though, you must determine if Systems Manager recognizes your EC2 instance.

Navigate to the EC2 console, scroll down to Systems Manager Shared Resources in the navigation pane, and choose Managed Instances. Your new EC2 instance should be available there. If your instance is missing from the list, verify the following:

  1. Go to the EC2 console and verify your instance is running.
  2. Select your instance and confirm you attached the Systems Manager IAM role, EC2SSM.
  3. Make sure that you deployed a NAT gateway in your public subnet to ensure your VPC reflects the diagram at the start of this post so that the Systems Manager agent can connect to the Systems Manager internet endpoint.
  4. Check the Systems Manager Agent logs for any errors.

Now that you have confirmed that Systems Manager can manage your EC2 instance, it is time to associate the AWS maintained patch baseline with your EC2 instance:

  1. Choose Patch Baselines under Systems Manager Services in the navigation pane of the EC2 console.
  2. Choose the default patch baseline as highlighted in the following screenshot, and choose Modify Patch Groups in the Actions drop-down.
    Screenshot of choosing Modify Patch Groups in the Actions drop-down
  3. In the Patch group box, enter the same value you entered under the Patch Group tag of your EC2 instance in “Step 1: Configure your EC2 instance.” In this example, the value I enter is Windows Servers. Choose the check mark icon next to the patch group and choose Close.Screenshot of modifying the patch group

Define a maintenance window

Now that you have successfully set up a role and have associated a patch baseline with your EC2 instance, you will define a maintenance window so that you can control when your EC2 instances should receive patches. By creating multiple maintenance windows and assigning them to different patch groups, you can make sure your EC2 instances do not all reboot at the same time. The Patch Group resource tag you defined earlier will determine to which patch group an instance belongs.

To define a maintenance window:

  1. Navigate to the EC2 console, scroll down to Systems Manager Shared Resources in the navigation pane, and choose Maintenance Windows. Choose Create a Maintenance Window.
    Screenshot of starting to create a maintenance window in the Systems Manager console
  2. Select the Cron schedule builder to define the schedule for the maintenance window. In the example in the following screenshot, the maintenance window will start every Saturday at 10:00 P.M. UTC.
  3. To specify when your maintenance window will end, specify the duration. In this example, the four-hour maintenance window will end on the following Sunday morning at 2:00 A.M. UTC (in other words, four hours after it started).
  4. Systems manager completes all tasks that are in process, even if the maintenance window ends. In my example, I am choosing to prevent new tasks from starting within one hour of the end of my maintenance window because I estimated my patch operations might take longer than one hour to complete. Confirm the creation of the maintenance window by choosing Create maintenance window.
    Screenshot of completing all boxes in the maintenance window creation process
  5. After creating the maintenance window, you must register the EC2 instance to the maintenance window so that Systems Manager knows which EC2 instance it should patch in this maintenance window. To do so, choose Register new targets on the Targets tab of your newly created maintenance window. You can register your targets by using the same Patch Group tag you used before to associate the EC2 instance with the AWS-provided patch baseline.
    Screenshot of registering new targets
  6. Assign a task to the maintenance window that will install the operating system patches on your EC2 instance:
    1. Open Maintenance Windows in the EC2 console, select your previously created maintenance window, choose the Tasks tab, and choose Register run command task from the Register new task drop-down.
    2. Choose the AWS-RunPatchBaseline document from the list of available documents.
    3. For Parameters:
      1. For Role, choose the role you created previously (called MaintenanceWindowRole).
      2. For Execute on, specify how many EC2 instances Systems Manager should patch at the same time. If you have a large number of EC2 instances and want to patch all EC2 instances within the defined time, make sure this number is not too low. For example, if you have 1,000 EC2 instances, a maintenance window of 4 hours, and 2 hours’ time for patching, make this number at least 500.
      3. For Stop after, specify after how many errors Systems Manager should stop.
      4. For Operation, choose Install to make sure to install the patches.
        Screenshot of stipulating maintenance window parameters

Now, you must wait for the maintenance window to run at least once according to the schedule you defined earlier. Note that if you don’t want to wait, you can adjust the schedule to run sooner by choosing Edit maintenance window on the Maintenance Windows page of Systems Manager. If your maintenance window has expired, you can check the status of any maintenance tasks Systems Manager has performed on the Maintenance Windows page of Systems Manager and select your maintenance window.

Screenshot of the maintenance window successfully created

Monitor patch compliance

You also can see the overall patch compliance of all EC2 instances that are part of defined patch groups by choosing Patch Compliance under Systems Manager Services in the navigation pane of the EC2 console. You can filter by Patch Group to see how many EC2 instances within the selected patch group are up to date, how many EC2 instances are missing updates, and how many EC2 instances are in an error state.

Screenshot of monitoring patch compliance

In this section, you have set everything up for patch management on your instance. Now you know how to patch your EC2 instance in a controlled manner and how to check if your EC2 instance is compliant with the patch baseline you have defined. Of course, I recommend that you apply these steps to all EC2 instances you manage.

Summary

In Part 1 of this blog post, I have shown how to configure EC2 instances for use with Systems Manager, EBS Snapshot Scheduler, and Amazon Inspector. I also have shown how to use Systems Manager to keep your Microsoft Windows–based EC2 instances up to date. In Part 2 of this blog post tomorrow, I will show how to take regular snapshots of your data by using EBS Snapshot Scheduler and how to use Amazon Inspector to check if your EC2 instances running Microsoft Windows contain any CVEs.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about or issues implementing this solution, start a new thread on the EC2 forum or the Amazon Inspector forum, or contact AWS Support.

– Koen

Using AWS CodeCommit Pull Requests to request code reviews and discuss code

Post Syndicated from Chris Barclay original https://aws.amazon.com/blogs/devops/using-aws-codecommit-pull-requests-to-request-code-reviews-and-discuss-code/

Thank you to Michael Edge, Senior Cloud Architect, for a great blog on CodeCommit pull requests.

~~~~~~~

AWS CodeCommit is a fully managed service for securely hosting private Git repositories. CodeCommit now supports pull requests, which allows repository users to review, comment upon, and interactively iterate on code changes. Used as a collaboration tool between team members, pull requests help you to review potential changes to a CodeCommit repository before merging those changes into the repository. Each pull request goes through a simple lifecycle, as follows:

  • The new features to be merged are added as one or more commits to a feature branch. The commits are not merged into the destination branch.
  • The pull request is created, usually from the difference between two branches.
  • Team members review and comment on the pull request. The pull request might be updated with additional commits that contain changes made in response to comments, or include changes made to the destination branch.
  • Once team members are happy with the pull request, it is merged into the destination branch. The commits are applied to the destination branch in the same order they were added to the pull request.

Commenting is an integral part of the pull request process, and is used to collaborate between the developers and the reviewer. Reviewers add comments and questions to a pull request during the review process, and developers respond to these with explanations. Pull request comments can be added to the overall pull request, a file within the pull request, or a line within a file.

To make the comments more useful, sign in to the AWS Management Console as an AWS Identity and Access Management (IAM) user. The username will then be associated with the comment, indicating the owner of the comment. Pull request comments are a great quality improvement tool as they allow the entire development team visibility into what reviewers are looking for in the code. They also serve as a record of the discussion between team members at a point in time, and shouldn’t be deleted.

AWS CodeCommit is also introducing the ability to add comments to a commit, another useful collaboration feature that allows team members to discuss code changed as part of a commit. This helps you discuss changes made in a repository, including why the changes were made, whether further changes are necessary, or whether changes should be merged. As is the case with pull request comments, you can comment on an overall commit, on a file within a commit, or on a specific line or change within a file, and other repository users can respond to your comments. Comments are not restricted to commits, they can also be used to comment on the differences between two branches, or between two tags. Commit comments are separate from pull request comments, i.e. you will not see commit comments when reviewing a pull request – you will only see pull request comments.

A pull request example

Let’s get started by running through an example. We’ll take a typical pull request scenario and look at how we’d use CodeCommit and the AWS Management Console for each of the steps.

To try out this scenario, you’ll need:

  • An AWS CodeCommit repository with some sample code in the master branch. We’ve provided sample code below.
  • Two AWS Identity and Access Management (IAM) users, both with the AWSCodeCommitPowerUser managed policy applied to them.
  • Git installed on your local computer, and access configured for AWS CodeCommit.
  • A clone of the AWS CodeCommit repository on your local computer.

In the course of this example, you’ll sign in to the AWS CodeCommit console as one IAM user to create the pull request, and as the other IAM user to review the pull request. To learn more about how to set up your IAM users and how to connect to AWS CodeCommit with Git, see the following topics:

  • Information on creating an IAM user with AWS Management Console access.
  • Instructions on how to access CodeCommit using Git.
  • If you’d like to use the same ‘hello world’ application as used in this article, here is the source code:
package com.amazon.helloworld;

public class Main {
	public static void main(String[] args) {

		System.out.println("Hello, world");
	}
}

The scenario below uses the us-east-2 region.

Creating the branches

Before we jump in and create a pull request, we’ll need at least two branches. In this example, we’ll follow a branching strategy similar to the one described in GitFlow. We’ll create a new branch for our feature from the main development branch (the default branch). We’ll develop the feature in the feature branch. Once we’ve written and tested the code for the new feature in that branch, we’ll create a pull request that contains the differences between the feature branch and the main development branch. Our team lead (the second IAM user) will review the changes in the pull request. Once the changes have been reviewed, the feature branch will be merged into the development branch.

Figure 1: Pull request link

Sign in to the AWS CodeCommit console with the IAM user you want to use as the developer. You can use an existing repository or you can go ahead and create a new one. We won’t be merging any changes to the master branch of your repository, so it’s safe to use an existing repository for this example. You’ll find the Pull requests link has been added just above the Commits link (see Figure 1), and below Commits you’ll find the Branches link. Click Branches and create a new branch called ‘develop’, branched from the ‘master’ branch. Then create a new branch called ‘feature1’, branched from the ‘develop’ branch. You’ll end up with three branches, as you can see in Figure 2. (Your repository might contain other branches in addition to the three shown in the figure).

Figure 2: Create a feature branch

If you haven’t cloned your repo yet, go to the Code link in the CodeCommit console and click the Connect button. Follow the instructions to clone your repo (detailed instructions are here). Open a terminal or command line and paste the git clone command supplied in the Connect instructions for your repository. The example below shows cloning a repository named codecommit-demo:

git clone https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo

If you’ve previously cloned the repo you’ll need to update your local repo with the branches you created. Open a terminal or command line and make sure you’re in the root directory of your repo, then run the following command:

git remote update origin

You’ll see your new branches pulled down to your local repository.

$ git remote update origin
Fetching origin
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
 * [new branch]      develop    -> origin/develop
 * [new branch]      feature1   -> origin/feature1

You can also see your new branches by typing:

git branch --all

$ git branch --all
* master
  remotes/origin/develop
  remotes/origin/feature1
  remotes/origin/master

Now we’ll make a change to the ‘feature1’ branch. Open a terminal or command line and check out the feature1 branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Make code changes

Edit a file in the repo using your favorite editor and save the changes. Commit your changes to the local repository, and push your changes to CodeCommit. For example:

git commit -am 'added new feature'
git push origin feature1

$ git commit -am 'added new feature'
[feature1 8f6cb28] added new feature
1 file changed, 1 insertion(+), 1 deletion(-)

$ git push origin feature1
Counting objects: 9, done.
Delta compression using up to 4 threads.
Compressing objects: 100% (4/4), done.
Writing objects: 100% (9/9), 617 bytes | 617.00 KiB/s, done.
Total 9 (delta 2), reused 0 (delta 0)
To https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   2774a53..8f6cb28  feature1 -> feature1

Creating the pull request

Now we have a ‘feature1’ branch that differs from the ‘develop’ branch. At this point we want to merge our changes into the ‘develop’ branch. We’ll create a pull request to notify our team members to review our changes and check whether they are ready for a merge.

In the AWS CodeCommit console, click Pull requests. Click Create pull request. On the next page select ‘develop’ as the destination branch and ‘feature1’ as the source branch. Click Compare. CodeCommit will check for merge conflicts and highlight whether the branches can be automatically merged using the fast-forward option, or whether a manual merge is necessary. A pull request can be created in both situations.

Figure 3: Create a pull request

After comparing the two branches, the CodeCommit console displays the information you’ll need in order to create the pull request. In the ‘Details’ section, the ‘Title’ for the pull request is mandatory, and you may optionally provide comments to your reviewers to explain the code change you have made and what you’d like them to review. In the ‘Notifications’ section, there is an option to set up notifications to notify subscribers of changes to your pull request. Notifications will be sent on creation of the pull request as well as for any pull request updates or comments. And finally, you can review the changes that make up this pull request. This includes both the individual commits (a pull request can contain one or more commits, available in the Commits tab) as well as the changes made to each file, i.e. the diff between the two branches referenced by the pull request, available in the Changes tab. After you have reviewed this information and added a title for your pull request, click the Create button. You will see a confirmation screen, as shown in Figure 4, indicating that your pull request has been successfully created, and can be merged without conflicts into the ‘develop’ branch.

Figure 4: Pull request confirmation page

Reviewing the pull request

Now let’s view the pull request from the perspective of the team lead. If you set up notifications for this CodeCommit repository, creating the pull request would have sent an email notification to the team lead, and he/she can use the links in the email to navigate directly to the pull request. In this example, sign in to the AWS CodeCommit console as the IAM user you’re using as the team lead, and click Pull requests. You will see the same information you did during creation of the pull request, plus a record of activity related to the pull request, as you can see in Figure 5.

Figure 5: Team lead reviewing the pull request

Commenting on the pull request

You now perform a thorough review of the changes and make a number of comments using the new pull request comment feature. To gain an overall perspective on the pull request, you might first go to the Commits tab and review how many commits are included in this pull request. Next, you might visit the Changes tab to review the changes, which displays the differences between the feature branch code and the develop branch code. At this point, you can add comments to the pull request as you work through each of the changes. Let’s go ahead and review the pull request. During the review, you can add review comments at three levels:

  • The overall pull request
  • A file within the pull request
  • An individual line within a file

The overall pull request
In the Changes tab near the bottom of the page you’ll see a ‘Comments on changes’ box. We’ll add comments here related to the overall pull request. Add your comments as shown in Figure 6 and click the Save button.

Figure 6: Pull request comment

A specific file in the pull request
Hovering your mouse over a filename in the Changes tab will cause a blue ‘comments’ icon to appear to the left of the filename. Clicking the icon will allow you to enter comments specific to this file, as in the example in Figure 7. Go ahead and add comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 7: File comment

A specific line in a file in the pull request
A blue ‘comments’ icon will appear as you hover over individual lines within each file in the pull request, allowing you to create comments against lines that have been added, removed or are unchanged. In Figure 8, you add comments against a line that has been added to the source code, encouraging the developer to review the naming standards. Go ahead and add line comments for one of the files changed by the developer. Click the Save button to save your comment.

Figure 8: Line comment

A pull request that has been commented at all three levels will look similar to Figure 9. The pull request comment is shown expanded in the ‘Comments on changes’ section, while the comments at file and line level are shown collapsed. A ‘comment’ icon indicates that comments exist at file and line level. Clicking the icon will expand and show the comment. Since you are expecting the developer to make further changes based on your comments, you won’t merge the pull request at this stage, but will leave it open awaiting feedback. Each comment you made results in a notification being sent to the developer, who can respond to the comments. This is great for remote working, where developers and team lead may be in different time zones.

Figure 9: Fully commented pull request

Adding a little complexity

A typical development team is going to be creating pull requests on a regular basis. It’s highly likely that the team lead will merge other pull requests into the ‘develop’ branch while pull requests on feature branches are in the review stage. This may result in a change to the ‘Mergable’ status of a pull request. Let’s add this scenario into the mix and check out how a developer will handle this.

To test this scenario, we could create a new pull request and ask the team lead to merge this to the ‘develop’ branch. But for the sake of simplicity we’ll take a shortcut. Clone your CodeCommit repo to a new folder, switch to the ‘develop’ branch, and make a change to one of the same files that were changed in your pull request. Make sure you change a line of code that was also changed in the pull request. Commit and push this back to CodeCommit. Since you’ve just changed a line of code in the ‘develop’ branch that has also been changed in the ‘feature1’ branch, the ‘feature1’ branch cannot be cleanly merged into the ‘develop’ branch. Your developer will need to resolve this merge conflict.

A developer reviewing the pull request would see the pull request now looks similar to Figure 10, with a ‘Resolve conflicts’ status rather than the ‘Mergable’ status it had previously (see Figure 5).

Figure 10: Pull request with merge conflicts

Reviewing the review comments

Once the team lead has completed his review, the developer will review the comments and make the suggested changes. As a developer, you’ll see the list of review comments made by the team lead in the pull request Activity tab, as shown in Figure 11. The Activity tab shows the history of the pull request, including commits and comments. You can reply to the review comments directly from the Activity tab, by clicking the Reply button, or you can do this from the Changes tab. The Changes tab shows the comments for the latest commit, as comments on previous commits may be associated with lines that have changed or been removed in the current commit. Comments for previous commits are available to view and reply to in the Activity tab.

In the Activity tab, use the shortcut link (which looks like this </>) to move quickly to the source code associated with the comment. In this example, you will make further changes to the source code to address the pull request review comments, so let’s go ahead and do this now. But first, you will need to resolve the ‘Resolve conflicts’ status.

Figure 11: Pull request activity

Resolving the ‘Resolve conflicts’ status

The ‘Resolve conflicts’ status indicates there is a merge conflict between the ‘develop’ branch and the ‘feature1’ branch. This will require manual intervention to restore the pull request back to the ‘Mergable’ state. We will resolve this conflict next.

Open a terminal or command line and check out the develop branch by running the following command:

git checkout develop

$ git checkout develop
Switched to branch 'develop'
Your branch is up-to-date with 'origin/develop'.

To incorporate the changes the team lead made to the ‘develop’ branch, merge the remote ‘develop’ branch with your local copy:

git pull

$ git pull
remote: Counting objects: 9, done.
Unpacking objects: 100% (9/9), done.
From https://git-codecommit.us-east-2.amazonaws.com/v1/repos/codecommit-demo
   af13c82..7b36f52  develop    -> origin/develop
Updating af13c82..7b36f52
Fast-forward
 src/main/java/com/amazon/helloworld/Main.java | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

Then checkout the ‘feature1’ branch:

git checkout feature1

$ git checkout feature1
Switched to branch 'feature1'
Your branch is up-to-date with 'origin/feature1'.

Now merge the changes from the ‘develop’ branch into your ‘feature1’ branch:

git merge develop

$ git merge develop
Auto-merging src/main/java/com/amazon/helloworld/Main.java
CONFLICT (content): Merge conflict in src/main/java/com/amazon/helloworld/Main.java
Automatic merge failed; fix conflicts and then commit the result.

Yes, this fails. The file Main.java has been changed in both branches, resulting in a merge conflict that can’t be resolved automatically. However, Main.java will now contain markers that indicate where the conflicting code is, and you can use these to resolve the issues manually. Edit Main.java using your favorite IDE, and you’ll see it looks something like this:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

<<<<<<< HEAD
        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earthling. Today's date is: " + todaysdate);
=======
      System.out.println("Hello, earth");
>>>>>>> develop
   }
}

The code between HEAD and ‘===’ is the code the developer added in the ‘feature1’ branch (HEAD represents ‘feature1’ because this is the current checked out branch). The code between ‘===’ and ‘>>> develop’ is the code added to the ‘develop’ branch by the team lead. We’ll resolve the conflict by manually merging both changes, resulting in an updated Main.java:

package com.amazon.helloworld;

import java.util.*;

/**
 * This class prints a hello world message
 */

public class Main {
   public static void main(String[] args) {

        Date todaysdate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysdate);
   }
}

After saving the change you can add and commit it to your local repo:

git add src/
git commit -m 'fixed merge conflict by merging changes'

Fixing issues raised by the reviewer

Now you are ready to address the comments made by the team lead. If you are no longer pointing to the ‘feature1’ branch, check out the ‘feature1’ branch by running the following command:

git checkout feature1

$ git checkout feature1
Branch feature1 set up to track remote branch feature1 from origin.
Switched to a new branch 'feature1'

Edit the source code in your favorite IDE and make the changes to address the comments. In this example, the developer has updated the source code as follows:

package com.amazon.helloworld;

import java.util.*;

/**
 *  This class prints a hello world message
 *
 * @author Michael Edge
 * @see HelloEarth
 * @version 1.0
 */

public class Main {
   public static void main(String[] args) {

        Date todaysDate = Calendar.getInstance().getTime();

        System.out.println("Hello, earth. Today's date is: " + todaysDate);
   }
}

After saving the changes, commit and push to the CodeCommit ‘feature1’ branch as you did previously:

git commit -am 'updated based on review comments'
git push origin feature1

Responding to the reviewer

Now that you’ve fixed the code issues you will want to respond to the review comments. In the AWS CodeCommit console, check that your latest commit appears in the pull request Commits tab. You now have a pull request consisting of more than one commit. The pull request in Figure 12 has four commits, which originated from the following activities:

  • 8th Nov: the original commit used to initiate this pull request
  • 10th Nov, 3 hours ago: the commit by the team lead to the ‘develop’ branch, merged into our ‘feature1’ branch
  • 10th Nov, 24 minutes ago: the commit by the developer that resolved the merge conflict
  • 10th Nov, 4 minutes ago: the final commit by the developer addressing the review comments

Figure 12: Pull request with multiple commits

Let’s reply to the review comments provided by the team lead. In the Activity tab, reply to the pull request comment and save it, as shown in Figure 13.

Figure 13: Replying to a pull request comment

At this stage, your code has been committed and you’ve updated your pull request comments, so you are ready for a final review by the team lead.

Final review

The team lead reviews the code changes and comments made by the developer. As team lead, you own the ‘develop’ branch and it’s your decision on whether to merge the changes in the pull request into the ‘develop’ branch. You can close the pull request with or without merging using the Merge and Close buttons at the bottom of the pull request page (see Figure 13). Clicking Close will allow you to add comments on why you are closing the pull request without merging. Merging will perform a fast-forward merge, incorporating the commits referenced by the pull request. Let’s go ahead and click the Merge button to merge the pull request into the ‘develop’ branch.

Figure 14: Merging the pull request

After merging a pull request, development of that feature is complete and the feature branch is no longer needed. It’s common practice to delete the feature branch after merging. CodeCommit provides a check box during merge to automatically delete the associated feature branch, as seen in Figure 14. Clicking the Merge button will merge the pull request into the ‘develop’ branch, as shown in Figure 15. This will update the status of the pull request to ‘Merged’, and will close the pull request.

Conclusion

This blog has demonstrated how pull requests can be used to request a code review, and enable reviewers to get a comprehensive summary of what is changing, provide feedback to the author, and merge the code into production. For more information on pull requests, see the documentation.

Event-Driven Computing with Amazon SNS and AWS Compute, Storage, Database, and Networking Services

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/event-driven-computing-with-amazon-sns-compute-storage-database-and-networking-services/

Contributed by Otavio Ferreira, Manager, Software Development, AWS Messaging

Like other developers around the world, you may be tackling increasingly complex business problems. A key success factor, in that case, is the ability to break down a large project scope into smaller, more manageable components. A service-oriented architecture guides you toward designing systems as a collection of loosely coupled, independently scaled, and highly reusable services. Microservices take this even further. To improve performance and scalability, they promote fine-grained interfaces and lightweight protocols.

However, the communication among isolated microservices can be challenging. Services are often deployed onto independent servers and don’t share any compute or storage resources. Also, you should avoid hard dependencies among microservices, to preserve maintainability and reusability.

If you apply the pub/sub design pattern, you can effortlessly decouple and independently scale out your microservices and serverless architectures. A pub/sub messaging service, such as Amazon SNS, promotes event-driven computing that statically decouples event publishers from subscribers, while dynamically allowing for the exchange of messages between them. An event-driven architecture also introduces the responsiveness needed to deal with complex problems, which are often unpredictable and asynchronous.

What is event-driven computing?

Given the context of microservices, event-driven computing is a model in which subscriber services automatically perform work in response to events triggered by publisher services. This paradigm can be applied to automate workflows while decoupling the services that collectively and independently work to fulfil these workflows. Amazon SNS is an event-driven computing hub, in the AWS Cloud, that has native integration with several AWS publisher and subscriber services.

Which AWS services publish events to SNS natively?

Several AWS services have been integrated as SNS publishers and, therefore, can natively trigger event-driven computing for a variety of use cases. In this post, I specifically cover AWS compute, storage, database, and networking services, as depicted below.

Compute services

  • Auto Scaling: Helps you ensure that you have the correct number of Amazon EC2 instances available to handle the load for your application. You can configure Auto Scaling lifecycle hooks to trigger events, as Auto Scaling resizes your EC2 cluster.As an example, you may want to warm up the local cache store on newly launched EC2 instances, and also download log files from other EC2 instances that are about to be terminated. To make this happen, set an SNS topic as your Auto Scaling group’s notification target, then subscribe two Lambda functions to this SNS topic. The first function is responsible for handling scale-out events (to warm up cache upon provisioning), whereas the second is in charge of handling scale-in events (to download logs upon termination).

  • AWS Elastic Beanstalk: An easy-to-use service for deploying and scaling web applications and web services developed in a number of programming languages. You can configure event notifications for your Elastic Beanstalk environment so that notable events can be automatically published to an SNS topic, then pushed to topic subscribers.As an example, you may use this event-driven architecture to coordinate your continuous integration pipeline (such as Jenkins CI). That way, whenever an environment is created, Elastic Beanstalk publishes this event to an SNS topic, which triggers a subscribing Lambda function, which then kicks off a CI job against your newly created Elastic Beanstalk environment.

  • Elastic Load Balancing: Automatically distributes incoming application traffic across Amazon EC2 instances, containers, or other resources identified by IP addresses.You can configure CloudWatch alarms on Elastic Load Balancing metrics, to automate the handling of events derived from Classic Load Balancers. As an example, you may leverage this event-driven design to automate latency profiling in an Amazon ECS cluster behind a Classic Load Balancer. In this example, whenever your ECS cluster breaches your load balancer latency threshold, an event is posted by CloudWatch to an SNS topic, which then triggers a subscribing Lambda function. This function runs a task on your ECS cluster to trigger a latency profiling tool, hosted on the cluster itself. This can enhance your latency troubleshooting exercise by making it timely.

Storage services

  • Amazon S3: Object storage built to store and retrieve any amount of data.You can enable S3 event notifications, and automatically get them posted to SNS topics, to automate a variety of workflows. For instance, imagine that you have an S3 bucket to store incoming resumes from candidates, and a fleet of EC2 instances to encode these resumes from their original format (such as Word or text) into a portable format (such as PDF).In this example, whenever new files are uploaded to your input bucket, S3 publishes these events to an SNS topic, which in turn pushes these messages into subscribing SQS queues. Then, encoding workers running on EC2 instances poll these messages from the SQS queues; retrieve the original files from the input S3 bucket; encode them into PDF; and finally store them in an output S3 bucket.

  • Amazon EFS: Provides simple and scalable file storage, for use with Amazon EC2 instances, in the AWS Cloud.You can configure CloudWatch alarms on EFS metrics, to automate the management of your EFS systems. For example, consider a highly parallelized genomics analysis application that runs against an EFS system. By default, this file system is instantiated on the “General Purpose” performance mode. Although this performance mode allows for lower latency, it might eventually impose a scaling bottleneck. Therefore, you may leverage an event-driven design to handle it automatically.Basically, as soon as the EFS metric “Percent I/O Limit” breaches 95%, CloudWatch could post this event to an SNS topic, which in turn would push this message into a subscribing Lambda function. This function automatically creates a new file system, this time on the “Max I/O” performance mode, then switches the genomics analysis application to this new file system. As a result, your application starts experiencing higher I/O throughput rates.

  • Amazon Glacier: A secure, durable, and low-cost cloud storage service for data archiving and long-term backup.You can set a notification configuration on an Amazon Glacier vault so that when a job completes, a message is published to an SNS topic. Retrieving an archive from Amazon Glacier is a two-step asynchronous operation, in which you first initiate a job, and then download the output after the job completes. Therefore, SNS helps you eliminate polling your Amazon Glacier vault to check whether your job has been completed, or not. As usual, you may subscribe SQS queues, Lambda functions, and HTTP endpoints to your SNS topic, to be notified when your Amazon Glacier job is done.

  • AWS Snowball: A petabyte-scale data transport solution that uses secure appliances to transfer large amounts of data.You can leverage Snowball notifications to automate workflows related to importing data into and exporting data from AWS. More specifically, whenever your Snowball job status changes, Snowball can publish this event to an SNS topic, which in turn can broadcast the event to all its subscribers.As an example, imagine a Geographic Information System (GIS) that distributes high-resolution satellite images to users via Web browser. In this example, the GIS vendor could capture up to 80 TB of satellite images; create a Snowball job to import these files from an on-premises system to an S3 bucket; and provide an SNS topic ARN to be notified upon job status changes in Snowball. After Snowball changes the job status from “Importing” to “Completed”, Snowball publishes this event to the specified SNS topic, which delivers this message to a subscribing Lambda function, which finally creates a CloudFront web distribution for the target S3 bucket, to serve the images to end users.

Database services

  • Amazon RDS: Makes it easy to set up, operate, and scale a relational database in the cloud.RDS leverages SNS to broadcast notifications when RDS events occur. As usual, these notifications can be delivered via any protocol supported by SNS, including SQS queues, Lambda functions, and HTTP endpoints.As an example, imagine that you own a social network website that has experienced organic growth, and needs to scale its compute and database resources on demand. In this case, you could provide an SNS topic to listen to RDS DB instance events. When the “Low Storage” event is published to the topic, SNS pushes this event to a subscribing Lambda function, which in turn leverages the RDS API to increase the storage capacity allocated to your DB instance. The provisioning itself takes place within the specified DB maintenance window.

  • Amazon ElastiCache: A web service that makes it easy to deploy, operate, and scale an in-memory data store or cache in the cloud.ElastiCache can publish messages using Amazon SNS when significant events happen on your cache cluster. This feature can be used to refresh the list of servers on client machines connected to individual cache node endpoints of a cache cluster. For instance, an ecommerce website fetches product details from a cache cluster, with the goal of offloading a relational database and speeding up page load times. Ideally, you want to make sure that each web server always has an updated list of cache servers to which to connect.To automate this node discovery process, you can get your ElastiCache cluster to publish events to an SNS topic. Thus, when ElastiCache event “AddCacheNodeComplete” is published, your topic then pushes this event to all subscribing HTTP endpoints that serve your ecommerce website, so that these HTTP servers can update their list of cache nodes.

  • Amazon Redshift: A fully managed data warehouse that makes it simple to analyze data using standard SQL and BI (Business Intelligence) tools.Amazon Redshift uses SNS to broadcast relevant events so that data warehouse workflows can be automated. As an example, imagine a news website that sends clickstream data to a Kinesis Firehose stream, which then loads the data into Amazon Redshift, so that popular news and reading preferences might be surfaced on a BI tool. At some point though, this Amazon Redshift cluster might need to be resized, and the cluster enters a ready-only mode. Hence, this Amazon Redshift event is published to an SNS topic, which delivers this event to a subscribing Lambda function, which finally deletes the corresponding Kinesis Firehose delivery stream, so that clickstream data uploads can be put on hold.At a later point, after Amazon Redshift publishes the event that the maintenance window has been closed, SNS notifies a subscribing Lambda function accordingly, so that this function can re-create the Kinesis Firehose delivery stream, and resume clickstream data uploads to Amazon Redshift.

  • AWS DMS: Helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database.DMS also uses SNS to provide notifications when DMS events occur, which can automate database migration workflows. As an example, you might create data replication tasks to migrate an on-premises MS SQL database, composed of multiple tables, to MySQL. Thus, if replication tasks fail due to incompatible data encoding in the source tables, these events can be published to an SNS topic, which can push these messages into a subscribing SQS queue. Then, encoders running on EC2 can poll these messages from the SQS queue, encode the source tables into a compatible character set, and restart the corresponding replication tasks in DMS. This is an event-driven approach to a self-healing database migration process.

Networking services

  • Amazon Route 53: A highly available and scalable cloud-based DNS (Domain Name System). Route 53 health checks monitor the health and performance of your web applications, web servers, and other resources.You can set CloudWatch alarms and get automated Amazon SNS notifications when the status of your Route 53 health check changes. As an example, imagine an online payment gateway that reports the health of its platform to merchants worldwide, via a status page. This page is hosted on EC2 and fetches platform health data from DynamoDB. In this case, you could configure a CloudWatch alarm for your Route 53 health check, so that when the alarm threshold is breached, and the payment gateway is no longer considered healthy, then CloudWatch publishes this event to an SNS topic, which pushes this message to a subscribing Lambda function, which finally updates the DynamoDB table that populates the status page. This event-driven approach avoids any kind of manual update to the status page visited by merchants.

  • AWS Direct Connect (AWS DX): Makes it easy to establish a dedicated network connection from your premises to AWS, which can reduce your network costs, increase bandwidth throughput, and provide a more consistent network experience than Internet-based connections.You can monitor physical DX connections using CloudWatch alarms, and send SNS messages when alarms change their status. As an example, when a DX connection state shifts to 0 (zero), indicating that the connection is down, this event can be published to an SNS topic, which can fan out this message to impacted servers through HTTP endpoints, so that they might reroute their traffic through a different connection instead. This is an event-driven approach to connectivity resilience.

More event-driven computing on AWS

In addition to SNS, event-driven computing is also addressed by Amazon CloudWatch Events, which delivers a near real-time stream of system events that describe changes in AWS resources. With CloudWatch Events, you can route each event type to one or more targets, including:

Many AWS services publish events to CloudWatch. As an example, you can get CloudWatch Events to capture events on your ETL (Extract, Transform, Load) jobs running on AWS Glue and push failed ones to an SQS queue, so that you can retry them later.

Conclusion

Amazon SNS is a pub/sub messaging service that can be used as an event-driven computing hub to AWS customers worldwide. By capturing events natively triggered by AWS services, such as EC2, S3 and RDS, you can automate and optimize all kinds of workflows, namely scaling, testing, encoding, profiling, broadcasting, discovery, failover, and much more. Business use cases presented in this post ranged from recruiting websites, to scientific research, geographic systems, social networks, retail websites, and news portals.

Start now by visiting Amazon SNS in the AWS Management Console, or by trying the AWS 10-Minute Tutorial, Send Fan-out Event Notifications with Amazon SNS and Amazon SQS.

 

Capturing Custom, High-Resolution Metrics from Containers Using AWS Step Functions and AWS Lambda

Post Syndicated from Nathan Taber original https://aws.amazon.com/blogs/compute/capturing-custom-high-resolution-metrics-from-containers-using-aws-step-functions-and-aws-lambda/

Contributed by Trevor Sullivan, AWS Solutions Architect

When you deploy containers with Amazon ECS, are you gathering all of the key metrics so that you can correctly monitor the overall health of your ECS cluster?

By default, ECS writes metrics to Amazon CloudWatch in 5-minute increments. For complex or large services, this may not be sufficient to make scaling decisions quickly. You may want to respond immediately to changes in workload or to identify application performance problems. Last July, CloudWatch announced support for high-resolution metrics, up to a per-second basis.

These high-resolution metrics can be used to give you a clearer picture of the load and performance for your applications, containers, clusters, and hosts. In this post, I discuss how you can use AWS Step Functions, along with AWS Lambda, to cost effectively record high-resolution metrics into CloudWatch. You implement this solution using a serverless architecture, which keeps your costs low and makes it easier to troubleshoot the solution.

To show how this works, you retrieve some useful metric data from an ECS cluster running in the same AWS account and region (Oregon, us-west-2) as the Step Functions state machine and Lambda function. However, you can use this architecture to retrieve any custom application metrics from any resource in any AWS account and region.

Why Step Functions?

Step Functions enables you to orchestrate multi-step tasks in the AWS Cloud that run for any period of time, up to a year. Effectively, you’re building a blueprint for an end-to-end process. After it’s built, you can execute the process as many times as you want.

For this architecture, you gather metrics from an ECS cluster, every five seconds, and then write the metric data to CloudWatch. After your ECS cluster metrics are stored in CloudWatch, you can create CloudWatch alarms to notify you. An alarm can also trigger an automated remediation activity such as scaling ECS services, when a metric exceeds a threshold defined by you.

When you build a Step Functions state machine, you define the different states inside it as JSON objects. The bulk of the work in Step Functions is handled by the common task state, which invokes Lambda functions or Step Functions activities. There is also a built-in library of other useful states that allow you to control the execution flow of your program.

One of the most useful state types in Step Functions is the parallel state. Each parallel state in your state machine can have one or more branches, each of which is executed in parallel. Another useful state type is the wait state, which waits for a period of time before moving to the next state.

In this walkthrough, you combine these three states (parallel, wait, and task) to create a state machine that triggers a Lambda function, which then gathers metrics from your ECS cluster.

Step Functions pricing

This state machine is executed every minute, resulting in 60 executions per hour, and 1,440 executions per day. Step Functions is billed per state transition, including the Start and End state transitions, and giving you approximately 37,440 state transitions per day. To reach this number, I’m using this estimated math:

26 state transitions per-execution x 60 minutes x 24 hours

Based on current pricing, at $0.000025 per state transition, the daily cost of this metric gathering state machine would be $0.936.

Step Functions offers an indefinite 4,000 free state transitions every month. This benefit is available to all customers, not just customers who are still under the 12-month AWS Free Tier. For more information and cost example scenarios, see Step Functions pricing.

Why Lambda?

The goal is to capture metrics from an ECS cluster, and write the metric data to CloudWatch. This is a straightforward, short-running process that makes Lambda the perfect place to run your code. Lambda is one of the key services that makes up “Serverless” application architectures. It enables you to consume compute capacity only when your code is actually executing.

The process of gathering metric data from ECS and writing it to CloudWatch takes a short period of time. In fact, my average Lambda function execution time, while developing this post, is only about 250 milliseconds on average. For every five-second interval that occurs, I’m only using 1/20th of the compute time that I’d otherwise be paying for.

Lambda pricing

For billing purposes, Lambda execution time is rounded up to the nearest 100-ms interval. In general, based on the metrics that I observed during development, a 250-ms runtime would be billed at 300 ms. Here, I calculate the cost of this Lambda function executing on a daily basis.

Assuming 31 days in each month, there would be 535,680 five-second intervals (31 days x 24 hours x 60 minutes x 12 five-second intervals = 535,680). The Lambda function is invoked every five-second interval, by the Step Functions state machine, and runs for a 300-ms period. At current Lambda pricing, for a 128-MB function, you would be paying approximately the following:

Total compute

Total executions = 535,680
Total compute = total executions x (3 x $0.000000208 per 100 ms) = $0.334 per day

Total requests

Total requests = (535,680 / 1000000) * $0.20 per million requests = $0.11 per day

Total Lambda Cost

$0.11 requests + $0.334 compute time = $0.444 per day

Similar to Step Functions, Lambda offers an indefinite free tier. For more information, see Lambda Pricing.

Walkthrough

In the following sections, I step through the process of configuring the solution just discussed. If you follow along, at a high level, you will:

  • Configure an IAM role and policy
  • Create a Step Functions state machine to control metric gathering execution
  • Create a metric-gathering Lambda function
  • Configure a CloudWatch Events rule to trigger the state machine
  • Validate the solution

Prerequisites

You should already have an AWS account with a running ECS cluster. If you don’t have one running, you can easily deploy a Docker container on an ECS cluster using the AWS Management Console. In the example produced for this post, I use an ECS cluster running Windows Server (currently in beta), but either a Linux or Windows Server cluster works.

Create an IAM role and policy

First, create an IAM role and policy that enables Step Functions, Lambda, and CloudWatch to communicate with each other.

  • The CloudWatch Events rule needs permissions to trigger the Step Functions state machine.
  • The Step Functions state machine needs permissions to trigger the Lambda function.
  • The Lambda function needs permissions to query ECS and then write to CloudWatch Logs and metrics.

When you create the state machine, Lambda function, and CloudWatch Events rule, you assign this role to each of those resources. Upon execution, each of these resources assumes the specified role and executes using the role’s permissions.

  1. Open the IAM console.
  2. Choose Roles, create New Role.
  3. For Role Name, enter WriteMetricFromStepFunction.
  4. Choose Save.

Create the IAM role trust relationship
The trust relationship (also known as the assume role policy document) for your IAM role looks like the following JSON document. As you can see from the document, your IAM role needs to trust the Lambda, CloudWatch Events, and Step Functions services. By configuring your role to trust these services, they can assume this role and inherit the role permissions.

  1. Open the IAM console.
  2. Choose Roles and select the IAM role previously created.
  3. Choose Trust RelationshipsEdit Trust Relationships.
  4. Enter the following trust policy text and choose Save.
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "lambda.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    },
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "events.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    },
    {
      "Effect": "Allow",
      "Principal": {
        "Service": "states.us-west-2.amazonaws.com"
      },
      "Action": "sts:AssumeRole"
    }
  ]
}

Create an IAM policy

After you’ve finished configuring your role’s trust relationship, grant the role access to the other AWS resources that make up the solution.

The IAM policy is what gives your IAM role permissions to access various resources. You must whitelist explicitly the specific resources to which your role has access, because the default IAM behavior is to deny access to any AWS resources.

I’ve tried to keep this policy document as generic as possible, without allowing permissions to be too open. If the name of your ECS cluster is different than the one in the example policy below, make sure that you update the policy document before attaching it to your IAM role. You can attach this policy as an inline policy, instead of creating the policy separately first. However, either approach is valid.

  1. Open the IAM console.
  2. Select the IAM role, and choose Permissions.
  3. Choose Add in-line policy.
  4. Choose Custom Policy and then enter the following policy. The inline policy name does not matter.
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [ "logs:*" ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [ "cloudwatch:PutMetricData" ],
            "Resource": "*"
        },
        {
            "Effect": "Allow",
            "Action": [ "states:StartExecution" ],
            "Resource": [
                "arn:aws:states:*:*:stateMachine:WriteMetricFromStepFunction"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [ "lambda:InvokeFunction" ],
            "Resource": "arn:aws:lambda:*:*:function:WriteMetricFromStepFunction"
        },
        {
            "Effect": "Allow",
            "Action": [ "ecs:Describe*" ],
            "Resource": "arn:aws:ecs:*:*:cluster/ECSEsgaroth"
        }
    ]
}

Create a Step Functions state machine

In this section, you create a Step Functions state machine that invokes the metric-gathering Lambda function every five (5) seconds, for a one-minute period. If you divide a minute (60) seconds into equal parts of five-second intervals, you get 12. Based on this math, you create 12 branches, in a single parallel state, in the state machine. Each branch triggers the metric-gathering Lambda function at a different five-second marker, throughout the one-minute period. After all of the parallel branches finish executing, the Step Functions execution completes and another begins.

Follow these steps to create your Step Functions state machine:

  1. Open the Step Functions console.
  2. Choose DashboardCreate State Machine.
  3. For State Machine Name, enter WriteMetricFromStepFunction.
  4. Enter the state machine code below into the editor. Make sure that you insert your own AWS account ID for every instance of “676655494xxx”
  5. Choose Create State Machine.
  6. Select the WriteMetricFromStepFunction IAM role that you previously created.
{
    "Comment": "Writes ECS metrics to CloudWatch every five seconds, for a one-minute period.",
    "StartAt": "ParallelMetric",
    "States": {
      "ParallelMetric": {
        "Type": "Parallel",
        "Branches": [
          {
            "StartAt": "WriteMetricLambda",
            "States": {
             	"WriteMetricLambda": {
                  "Type": "Task",
				  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
    	  {
            "StartAt": "WaitFive",
            "States": {
            	"WaitFive": {
            		"Type": "Wait",
            		"Seconds": 5,
            		"Next": "WriteMetricLambdaFive"
          		},
             	"WriteMetricLambdaFive": {
                  "Type": "Task",
				  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
    	  {
            "StartAt": "WaitTen",
            "States": {
            	"WaitTen": {
            		"Type": "Wait",
            		"Seconds": 10,
            		"Next": "WriteMetricLambda10"
          		},
             	"WriteMetricLambda10": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
    	  {
            "StartAt": "WaitFifteen",
            "States": {
            	"WaitFifteen": {
            		"Type": "Wait",
            		"Seconds": 15,
            		"Next": "WriteMetricLambda15"
          		},
             	"WriteMetricLambda15": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait20",
            "States": {
            	"Wait20": {
            		"Type": "Wait",
            		"Seconds": 20,
            		"Next": "WriteMetricLambda20"
          		},
             	"WriteMetricLambda20": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait25",
            "States": {
            	"Wait25": {
            		"Type": "Wait",
            		"Seconds": 25,
            		"Next": "WriteMetricLambda25"
          		},
             	"WriteMetricLambda25": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait30",
            "States": {
            	"Wait30": {
            		"Type": "Wait",
            		"Seconds": 30,
            		"Next": "WriteMetricLambda30"
          		},
             	"WriteMetricLambda30": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait35",
            "States": {
            	"Wait35": {
            		"Type": "Wait",
            		"Seconds": 35,
            		"Next": "WriteMetricLambda35"
          		},
             	"WriteMetricLambda35": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait40",
            "States": {
            	"Wait40": {
            		"Type": "Wait",
            		"Seconds": 40,
            		"Next": "WriteMetricLambda40"
          		},
             	"WriteMetricLambda40": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait45",
            "States": {
            	"Wait45": {
            		"Type": "Wait",
            		"Seconds": 45,
            		"Next": "WriteMetricLambda45"
          		},
             	"WriteMetricLambda45": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait50",
            "States": {
            	"Wait50": {
            		"Type": "Wait",
            		"Seconds": 50,
            		"Next": "WriteMetricLambda50"
          		},
             	"WriteMetricLambda50": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          },
          {
            "StartAt": "Wait55",
            "States": {
            	"Wait55": {
            		"Type": "Wait",
            		"Seconds": 55,
            		"Next": "WriteMetricLambda55"
          		},
             	"WriteMetricLambda55": {
                  "Type": "Task",
                  "Resource": "arn:aws:lambda:us-west-2:676655494xxx:function:WriteMetricFromStepFunction",
                  "End": true
                } 
            }
          }
        ],
        "End": true
      }
  }
}

Now you’ve got a shiny new Step Functions state machine! However, you might ask yourself, “After the state machine has been created, how does it get executed?” Before I answer that question, create the Lambda function that writes the custom metric, and then you get the end-to-end process moving.

Create a Lambda function

The meaty part of the solution is a Lambda function, written to consume the Python 3.6 runtime, that retrieves metric values from ECS, and then writes them to CloudWatch. This Lambda function is what the Step Functions state machine is triggering every five seconds, via the Task states. Key points to remember:

The Lambda function needs permission to:

  • Write CloudWatch metrics (PutMetricData API).
  • Retrieve metrics from ECS clusters (DescribeCluster API).
  • Write StdOut to CloudWatch Logs.

Boto3, the AWS SDK for Python, is included in the Lambda execution environment for Python 2.x and 3.x.

Because Lambda includes the AWS SDK, you don’t have to worry about packaging it up and uploading it to Lambda. You can focus on writing code and automatically take a dependency on boto3.

As for permissions, you’ve already created the IAM role and attached a policy to it that enables your Lambda function to access the necessary API actions. When you create your Lambda function, make sure that you select the correct IAM role, to ensure it is invoked with the correct permissions.

The following Lambda function code is generic. So how does the Lambda function know which ECS cluster to gather metrics for? Your Step Functions state machine automatically passes in its state to the Lambda function. When you create your CloudWatch Events rule, you specify a simple JSON object that passes the desired ECS cluster name into your Step Functions state machine, which then passes it to the Lambda function.

Use the following property values as you create your Lambda function:

Function Name: WriteMetricFromStepFunction
Description: This Lambda function retrieves metric values from an ECS cluster and writes them to Amazon CloudWatch.
Runtime: Python3.6
Memory: 128 MB
IAM Role: WriteMetricFromStepFunction

import boto3

def handler(event, context):
    cw = boto3.client('cloudwatch')
    ecs = boto3.client('ecs')
    print('Got boto3 client objects')
    
    Dimension = {
        'Name': 'ClusterName',
        'Value': event['ECSClusterName']
    }

    cluster = get_ecs_cluster(ecs, Dimension['Value'])
    
    cw_args = {
       'Namespace': 'ECS',
       'MetricData': [
           {
               'MetricName': 'RunningTask',
               'Dimensions': [ Dimension ],
               'Value': cluster['runningTasksCount'],
               'Unit': 'Count',
               'StorageResolution': 1
           },
           {
               'MetricName': 'PendingTask',
               'Dimensions': [ Dimension ],
               'Value': cluster['pendingTasksCount'],
               'Unit': 'Count',
               'StorageResolution': 1
           },
           {
               'MetricName': 'ActiveServices',
               'Dimensions': [ Dimension ],
               'Value': cluster['activeServicesCount'],
               'Unit': 'Count',
               'StorageResolution': 1
           },
           {
               'MetricName': 'RegisteredContainerInstances',
               'Dimensions': [ Dimension ],
               'Value': cluster['registeredContainerInstancesCount'],
               'Unit': 'Count',
               'StorageResolution': 1
           }
        ]
    }
    cw.put_metric_data(**cw_args)
    print('Finished writing metric data')
    
def get_ecs_cluster(client, cluster_name):
    cluster = client.describe_clusters(clusters = [ cluster_name ])
    print('Retrieved cluster details from ECS')
    return cluster['clusters'][0]

Create the CloudWatch Events rule

Now you’ve created an IAM role and policy, Step Functions state machine, and Lambda function. How do these components actually start communicating with each other? The final step in this process is to set up a CloudWatch Events rule that triggers your metric-gathering Step Functions state machine every minute. You have two choices for your CloudWatch Events rule expression: rate or cron. In this example, use the cron expression.

A couple key learning points from creating the CloudWatch Events rule:

  • You can specify one or more targets, of different types (for example, Lambda function, Step Functions state machine, SNS topic, and so on).
  • You’re required to specify an IAM role with permissions to trigger your target.
    NOTE: This applies only to certain types of targets, including Step Functions state machines.
  • Each target that supports IAM roles can be triggered using a different IAM role, in the same CloudWatch Events rule.
  • Optional: You can provide custom JSON that is passed to your target Step Functions state machine as input.

Follow these steps to create the CloudWatch Events rule:

  1. Open the CloudWatch console.
  2. Choose Events, RulesCreate Rule.
  3. Select Schedule, Cron Expression, and then enter the following rule:
    0/1 * * * ? *
  4. Choose Add Target, Step Functions State MachineWriteMetricFromStepFunction.
  5. For Configure Input, select Constant (JSON Text).
  6. Enter the following JSON input, which is passed to Step Functions, while changing the cluster name accordingly:
    { "ECSClusterName": "ECSEsgaroth" }
  7. Choose Use Existing Role, WriteMetricFromStepFunction (the IAM role that you previously created).

After you’ve completed with these steps, your screen should look similar to this:

Validate the solution

Now that you have finished implementing the solution to gather high-resolution metrics from ECS, validate that it’s working properly.

  1. Open the CloudWatch console.
  2. Choose Metrics.
  3. Choose custom and select the ECS namespace.
  4. Choose the ClusterName metric dimension.

You should see your metrics listed below.

Troubleshoot configuration issues

If you aren’t receiving the expected ECS cluster metrics in CloudWatch, check for the following common configuration issues. Review the earlier procedures to make sure that the resources were properly configured.

  • The IAM role’s trust relationship is incorrectly configured.
    Make sure that the IAM role trusts Lambda, CloudWatch Events, and Step Functions in the correct region.
  • The IAM role does not have the correct policies attached to it.
    Make sure that you have copied the IAM policy correctly as an inline policy on the IAM role.
  • The CloudWatch Events rule is not triggering new Step Functions executions.
    Make sure that the target configuration on the rule has the correct Step Functions state machine and IAM role selected.
  • The Step Functions state machine is being executed, but failing part way through.
    Examine the detailed error message on the failed state within the failed Step Functions execution. It’s possible that the
  • IAM role does not have permissions to trigger the target Lambda function, that the target Lambda function may not exist, or that the Lambda function failed to complete successfully due to invalid permissions.
    Although the above list covers several different potential configuration issues, it is not comprehensive. Make sure that you understand how each service is connected to each other, how permissions are granted through IAM policies, and how IAM trust relationships work.

Conclusion

In this post, you implemented a Serverless solution to gather and record high-resolution application metrics from containers running on Amazon ECS into CloudWatch. The solution consists of a Step Functions state machine, Lambda function, CloudWatch Events rule, and an IAM role and policy. The data that you gather from this solution helps you rapidly identify issues with an ECS cluster.

To gather high-resolution metrics from any service, modify your Lambda function to gather the correct metrics from your target. If you prefer not to use Python, you can implement a Lambda function using one of the other supported runtimes, including Node.js, Java, or .NET Core. However, this post should give you the fundamental basics about capturing high-resolution metrics in CloudWatch.

If you found this post useful, or have questions, please comment below.

Cross-Account Integration with Amazon SNS

Post Syndicated from Christie Gifrin original https://aws.amazon.com/blogs/compute/cross-account-integration-with-amazon-sns/

Contributed by Zak Islam, Senior Manager, Software Development, AWS Messaging

 

Amazon Simple Notification Service (Amazon SNS) is a fully managed AWS service that makes it easy to decouple your application components and fan-out messages. SNS provides topics (similar to topics in message brokers such as RabbitMQ or ActiveMQ) that you can use to create 1:1, 1:N, or N:N producer/consumer design patterns. For more information about how to send messages from SNS to Amazon SQS, AWS Lambda, or HTTP(S) endpoints in the same account, see Sending Amazon SNS Messages to Amazon SQS Queues.

SNS can be used to send messages within a single account or to resources in different accounts to create administrative isolation. This enables administrators to grant only the minimum level of permissions required to process a workload (for example, limiting the scope of your application account to only send messages and to deny deletes). This approach is commonly known as the “principle of least privilege.” If you are interested, read more about AWS’s multi-account security strategy.

This is great from a security perspective, but why would you want to share messages between accounts? It may sound scary, but it’s a common practice to isolate application components (such as producer and consumer) to operate using different AWS accounts to lock down privileges in case credentials are exposed. In this post, I go slightly deeper and explore how to set up your SNS topic so that it can route messages to SQS queues that are owned by a separate AWS account.

Potential use cases

First, look at a common order processing design pattern:

This is a simple architecture. A web server submits an order directly to an SNS topic, which then fans out messages to two SQS queues. One SQS queue is used to track all incoming orders for audits (such as anti-entropy, comparing the data of all replicas and updating each replica to the newest version). The other is used to pass the request to the order processing systems.

Imagine now that a few years have passed, and your downstream processes no longer scale, so you are kicking around the idea of a re-architecture project. To thoroughly test your system, you need a way to replay your production messages in your development system. Sure, you can build a system to replicate and replay orders from your production environment in your development environment. Wouldn’t it be easier to subscribe your development queues to the production SNS topic so you can test your new system in real time? That’s exactly what you can do here.

Here’s another use case. As your business grows, you recognize the need for more metrics from your order processing pipeline. The analytics team at your company has built a metrics aggregation service and ingests data via a central SQS queue. Their architecture is as follows:

Again, it’s a fairly simple architecture. All data is ingested via SQS queues (master_ingest_queue, in this case). You subscribe the master_ingest_queue, running under the analytics team’s AWS account, to the topic that is in the order management team’s account.

Making it work

Now that you’ve seen a few scenarios, let’s dig into the details. There are a couple of ways to link an SQS queue to an SNS topic (subscribe a queue to a topic):

  1. The queue owner can create a subscription to the topic.
  2. The topic owner can subscribe a queue in another account to the topic.

Queue owner subscription

What happens when the queue owner subscribes to a topic? In this case, assume that the topic owner has given permission to the subscriber’s account to call the Subscribe API action using the topic ARN (Amazon Resource Name). For the examples below, also assume the following:

  •  Topic_Owner is the identifier for the account that owns the topic MainTopic
  • Queue_Owner is the identifier for the account that owns the queue subscribed to the main topic

To enable the subscriber to subscribe to a topic, the topic owner must add the sns:Subscribe and topic ARN to the topic policy via the AWS Management Console, as follows:

{
  "Version":"2012-10-17",
  "Id":"MyTopicSubscribePolicy",
  "Statement":[{
      "Sid":"Allow-other-account-to-subscribe-to-topic",
      "Effect":"Allow",
      "Principal":{
        "AWS":"Topic_Owner"
      },
      "Action":"sns:Subscribe",
      "Resource":"arn:aws:sns:us-east-1:Queue_Owner:MainTopic"
    }
  ]
}

After this has been set up, the subscriber (using account Queue_Owner) can call Subscribe to link the queue to the topic. After the queue has been successfully subscribed, SNS starts to publish notifications. In this case, neither the topic owner nor the subscriber have had to process any kind of confirmation message.

Topic owner subscription

The second way to subscribe an SQS queue to an SNS topic is to have the Topic_Owner account initiate the subscription for the queue from account Queue_Owner. In this case, SNS first sends a confirmation message to the queue. To confirm the subscription, a user who can read messages from the queue must visit the URL specified in the SubscribeURL value in the message. Until the subscription is confirmed, no notifications published to the topic are sent to the queue. To confirm a subscription, you can use the SQS console or the ReceiveMessage API action.

What’s next?

In this post, I covered a few simple use cases but the principles can be extended to complex systems as well. As you architect new systems and refactor existing ones, think about where you can leverage queues (SQS) and topics (SNS) to build a loosely coupled system that can be quickly and easily extended to meet your business need.

For step by step instructions, see Sending Amazon SNS messages to an Amazon SQS queue in a different account. You can also visit the following resources to get started working with message queues and topics:

Updated AWS SOC Reports Are Now Available with 19 Additional Services in Scope

Post Syndicated from Chad Woolf original https://aws.amazon.com/blogs/security/updated-aws-soc-reports-are-now-available-with-19-additional-services-in-scope/

AICPA SOC logo

Newly updated reports are available for AWS System and Organization Control Report 1 (SOC 1), formerly called AWS Service Organization Control Report 1, and AWS SOC 2: Security, Availability, & Confidentiality Report. You can download both reports for free and on demand in the AWS Management Console through AWS Artifact. The updated AWS SOC 3: Security, Availability, & Confidentiality Report also was just released. All three reports cover April 1, 2017, through September 30, 2017.

With the addition of the following 19 services, AWS now supports 51 SOC-compliant AWS services and is committed to increasing the number:

  • Amazon API Gateway
  • Amazon Cloud Directory
  • Amazon CloudFront
  • Amazon Cognito
  • Amazon Connect
  • AWS Directory Service for Microsoft Active Directory
  • Amazon EC2 Container Registry
  • Amazon EC2 Container Service
  • Amazon EC2 Systems Manager
  • Amazon Inspector
  • AWS IoT Platform
  • Amazon Kinesis Streams
  • AWS Lambda
  • AWS [email protected]
  • AWS Managed Services
  • Amazon S3 Transfer Acceleration
  • AWS Shield
  • AWS Step Functions
  • AWS WAF

With this release, we also are introducing a separate spreadsheet, eliminating the need to extract the information from multiple PDFs.

If you are not yet an AWS customer, contact AWS Compliance to access the SOC Reports.

– Chad

Now You Can Monitor DDoS Attack Trends with AWS Shield Advanced

Post Syndicated from Ritwik Manan original https://aws.amazon.com/blogs/security/now-you-can-monitor-ddos-attack-trends-with-aws-shield-advanced/

AWS Shield Advanced has always notified you about DDoS attacks on your applications via the AWS Management Console and API as well as Amazon CloudWatch metrics. Today, we added the global threat environment dashboard to AWS Shield Advanced to allow you to view trends and metrics about DDoS attacks across Amazon CloudFront, Elastic Load Balancing, and Amazon Route 53. This information can help you understand the DDoS target profile of the AWS services you use and, in turn, can help you create a more resilient and distributed architecture for your application.

The global threat environment dashboard shows comprehensive and easy-to-understand data about DDoS attacks. The dashboard displays a summary of the global threat environment, including the largest attacks, top vectors, and the relative number of significant attacks. You also can view the dashboard for different time durations to give you a history of DDoS attacks.

To get started with the global threat environment dashboard:

  1. Sign in to the AWS Management Console and navigate to the AWS WAF and AWS Shield console.
  2. To activate AWS Shield Advanced, choose Protected resources in the navigation pane, choose Activate AWS Shield Advanced, and then accept the terms by typing I accept.
  3. Navigate to the global threat environment dashboard through the navigation pane.
  4. Choose your desired time period from the time period drop-down menu in the top right part of the page.

You can use the information on the global threat environment dashboard to understand the threat landscape as well as to inform decisions you make that will help to better protect your AWS resources.

To learn more information, see Global Threat Environment Dashboard: View DDoS Attack Trends Across AWS.

– Ritwik

Introducing AWS Directory Service for Microsoft Active Directory (Standard Edition)

Post Syndicated from Peter Pereira original https://aws.amazon.com/blogs/security/introducing-aws-directory-service-for-microsoft-active-directory-standard-edition/

Today, AWS introduced AWS Directory Service for Microsoft Active Directory (Standard Edition), also known as AWS Microsoft AD (Standard Edition), which is managed Microsoft Active Directory (AD) that is performance optimized for small and midsize businesses. AWS Microsoft AD (Standard Edition) offers you a highly available and cost-effective primary directory in the AWS Cloud that you can use to manage users, groups, and computers. It enables you to join Amazon EC2 instances to your domain easily and supports many AWS and third-party applications and services. It also can support most of the common use cases of small and midsize businesses. When you use AWS Microsoft AD (Standard Edition) as your primary directory, you can manage access and provide single sign-on (SSO) to cloud applications such as Microsoft Office 365. If you have an existing Microsoft AD directory, you can also use AWS Microsoft AD (Standard Edition) as a resource forest that contains primarily computers and groups, allowing you to migrate your AD-aware applications to the AWS Cloud while using existing on-premises AD credentials.

In this blog post, I help you get started by answering three main questions about AWS Microsoft AD (Standard Edition):

  1. What do I get?
  2. How can I use it?
  3. What are the key features?

After answering these questions, I show how you can get started with creating and using your own AWS Microsoft AD (Standard Edition) directory.

1. What do I get?

When you create an AWS Microsoft AD (Standard Edition) directory, AWS deploys two Microsoft AD domain controllers powered by Microsoft Windows Server 2012 R2 in your Amazon Virtual Private Cloud (VPC). To help deliver high availability, the domain controllers run in different Availability Zones in the AWS Region of your choice.

As a managed service, AWS Microsoft AD (Standard Edition) configures directory replication, automates daily snapshots, and handles all patching and software updates. In addition, AWS Microsoft AD (Standard Edition) monitors and automatically recovers domain controllers in the event of a failure.

AWS Microsoft AD (Standard Edition) has been optimized as a primary directory for small and midsize businesses with the capacity to support approximately 5,000 employees. With 1 GB of directory object storage, AWS Microsoft AD (Standard Edition) has the capacity to store 30,000 or more total directory objects (users, groups, and computers). AWS Microsoft AD (Standard Edition) also gives you the option to add domain controllers to meet the specific performance demands of your applications. You also can use AWS Microsoft AD (Standard Edition) as a resource forest with a trust relationship to your on-premises directory.

2. How can I use it?

With AWS Microsoft AD (Standard Edition), you can share a single directory for multiple use cases. For example, you can share a directory to authenticate and authorize access for .NET applications, Amazon RDS for SQL Server with Windows Authentication enabled, and Amazon Chime for messaging and video conferencing.

The following diagram shows some of the use cases for your AWS Microsoft AD (Standard Edition) directory, including the ability to grant your users access to external cloud applications and allow your on-premises AD users to manage and have access to resources in the AWS Cloud. Click the diagram to see a larger version.

Diagram showing some ways you can use AWS Microsoft AD (Standard Edition)--click the diagram to see a larger version

Use case 1: Sign in to AWS applications and services with AD credentials

You can enable multiple AWS applications and services such as the AWS Management Console, Amazon WorkSpaces, and Amazon RDS for SQL Server to use your AWS Microsoft AD (Standard Edition) directory. When you enable an AWS application or service in your directory, your users can access the application or service with their AD credentials.

For example, you can enable your users to sign in to the AWS Management Console with their AD credentials. To do this, you enable the AWS Management Console as an application in your directory, and then assign your AD users and groups to IAM roles. When your users sign in to the AWS Management Console, they assume an IAM role to manage AWS resources. This makes it easy for you to grant your users access to the AWS Management Console without needing to configure and manage a separate SAML infrastructure.

Use case 2: Manage Amazon EC2 instances

Using familiar AD administration tools, you can apply AD Group Policy objects (GPOs) to centrally manage your Amazon EC2 for Windows or Linux instances by joining your instances to your AWS Microsoft AD (Standard Edition) domain.

In addition, your users can sign in to your instances with their AD credentials. This eliminates the need to use individual instance credentials or distribute private key (PEM) files. This makes it easier for you to instantly grant or revoke access to users by using AD user administration tools you already use.

Use case 3: Provide directory services to your AD-aware workloads

AWS Microsoft AD (Standard Edition) is an actual Microsoft AD that enables you to run traditional AD-aware workloads such as Remote Desktop Licensing Manager, Microsoft SharePoint, and Microsoft SQL Server Always On in the AWS Cloud. AWS Microsoft AD (Standard Edition) also helps you to simplify and improve the security of AD-integrated .NET applications by using group Managed Service Accounts (gMSAs) and Kerberos constrained delegation (KCD).

Use case 4: SSO to Office 365 and other cloud applications

You can use AWS Microsoft AD (Standard Edition) to provide SSO for cloud applications. You can use Azure AD Connect to synchronize your users into Azure AD, and then use Active Directory Federation Services (AD FS) so that your users can access Microsoft Office 365 and other SAML 2.0 cloud applications by using their AD credentials.

Use case 5: Extend your on-premises AD to the AWS Cloud

If you already have an AD infrastructure and want to use it when migrating AD-aware workloads to the AWS Cloud, AWS Microsoft AD (Standard Edition) can help. You can use AD trusts to connect AWS Microsoft AD (Standard Edition) to your existing AD. This means your users can access AD-aware and AWS applications with their on-premises AD credentials, without needing you to synchronize users, groups, or passwords.

For example, your users can sign in to the AWS Management Console and Amazon WorkSpaces by using their existing AD user names and passwords. Also, when you use AD-aware applications such as SharePoint with AWS Microsoft AD (Standard Edition), your logged-in Windows users can access these applications without needing to enter credentials again.

3. What are the key features?

AWS Microsoft AD (Standard Edition) includes the features detailed in this section.

Extend your AD schema

With AWS Microsoft AD, you can run customized AD-integrated applications that require changes to your directory schema, which defines the structures of your directory. The schema is composed of object classes such as user objects, which contain attributes such as user names. AWS Microsoft AD lets you extend the schema by adding new AD attributes or object classes that are not present in the core AD attributes and classes.

For example, if you have a human resources application that uses employee badge color to assign specific benefits, you can extend the schema to include a badge color attribute in the user object class of your directory. To learn more, see How to Move More Custom Applications to the AWS Cloud with AWS Directory Service.

Create user-specific password policies

With user-specific password policies, you can apply specific restrictions and account lockout policies to different types of users in your AWS Microsoft AD (Standard Edition) domain. For example, you can enforce strong passwords and frequent password change policies for administrators, and use less-restrictive policies with moderate account lockout policies for general users.

Add domain controllers

You can increase the performance and redundancy of your directory by adding domain controllers. This can help improve application performance by enabling directory clients to load-balance their requests across a larger number of domain controllers.

Encrypt directory traffic

You can use AWS Microsoft AD (Standard Edition) to encrypt Lightweight Directory Access Protocol (LDAP) communication between your applications and your directory. By enabling LDAP over Secure Sockets Layer (SSL)/Transport Layer Security (TLS), also called LDAPS, you encrypt your LDAP communications end to end. This helps you to protect sensitive information you keep in your directory when it is accessed over untrusted networks.

Improve the security of signing in to AWS services by using multi-factor authentication (MFA)

You can improve the security of signing in to AWS services, such as Amazon WorkSpaces and Amazon QuickSight, by enabling MFA in your AWS Microsoft AD (Standard Edition) directory. With MFA, your users must enter a one-time passcode (OTP) in addition to their AD user names and passwords to access AWS applications and services you enable in AWS Microsoft AD (Standard Edition).

Get started

To get started, use the Directory Service console to create your first directory with just a few clicks. If you have not used Directory Service before, you may be eligible for a 30-day limited free trial.

Summary

In this blog post, I explained what AWS Microsoft AD (Standard Edition) is and how you can use it. With a single directory, you can address many use cases for your business, making it easier to migrate and run your AD-aware workloads in the AWS Cloud, provide access to AWS applications and services, and connect to other cloud applications. To learn more about AWS Microsoft AD, see the Directory Service home page.

If you have comments about this post, submit them in the “Comments” section below. If you have questions about this blog post, start a new thread on the Directory Service forum.

– Peter

Federate Database User Authentication Easily with IAM and Amazon Redshift

Post Syndicated from Thiyagarajan Arumugam original https://aws.amazon.com/blogs/big-data/federate-database-user-authentication-easily-with-iam-and-amazon-redshift/

Managing database users though federation allows you to manage authentication and authorization procedures centrally. Amazon Redshift now supports database authentication with IAM, enabling user authentication though enterprise federation. No need to manage separate database users and passwords to further ease the database administration. You can now manage users outside of AWS and authenticate them for access to an Amazon Redshift data warehouse. Do this by integrating IAM authentication and a third-party SAML-2.0 identity provider (IdP), such as AD FS, PingFederate, or Okta. In addition, database users can also be automatically created at their first login based on corporate permissions.

In this post, I demonstrate how you can extend the federation to enable single sign-on (SSO) to the Amazon Redshift data warehouse.

SAML and Amazon Redshift

AWS supports Security Assertion Markup Language (SAML) 2.0, which is an open standard for identity federation used by many IdPs. SAML enables federated SSO, which enables your users to sign in to the AWS Management Console. Users can also make programmatic calls to AWS API actions by using assertions from a SAML-compliant IdP. For example, if you use Microsoft Active Directory for corporate directories, you may be familiar with how Active Directory and AD FS work together to enable federation. For more information, see the Enabling Federation to AWS Using Windows Active Directory, AD FS, and SAML 2.0 AWS Security Blog post.

Amazon Redshift now provides the GetClusterCredentials API operation that allows you to generate temporary database user credentials for authentication. You can set up an IAM permissions policy that generates these credentials for connecting to Amazon Redshift. Extending the IAM authentication, you can configure the federation of AWS access though a SAML 2.0–compliant IdP. An IAM role can be configured to permit the federated users call the GetClusterCredentials action and generate temporary credentials to log in to Amazon Redshift databases. You can also set up policies to restrict access to Amazon Redshift clusters, databases, database user names, and user group.

Amazon Redshift federation workflow

In this post, I demonstrate how you can use a JDBC– or ODBC-based SQL client to log in to the Amazon Redshift cluster using this feature. The SQL clients used with Amazon Redshift JDBC or ODBC drivers automatically manage the process of calling the GetClusterCredentials action, retrieving the database user credentials, and establishing a connection to your Amazon Redshift database. You can also use your database application to programmatically call the GetClusterCredentials action, retrieve database user credentials, and connect to the database. I demonstrate these features using an example company to show how different database users accounts can be managed easily using federation.

The following diagram shows how the SSO process works:

  1. JDBC/ODBC
  2. Authenticate using Corp Username/Password
  3. IdP sends SAML assertion
  4. Call STS to assume role with SAML
  5. STS Returns Temp Credentials
  6. Use Temp Credentials to get Temp cluster credentials
  7. Connect to Amazon Redshift using temp credentials

Walkthrough

Example Corp. is using Active Directory (idp host:demo.examplecorp.com) to manage federated access for users in its organization. It has an AWS account: 123456789012 and currently manages an Amazon Redshift cluster with the cluster ID “examplecorp-dw”, database “analytics” in us-west-2 region for its Sales and Data Science teams. It wants the following access:

  • Sales users can access the examplecorp-dw cluster using the sales_grp database group
  • Sales users access examplecorp-dw through a JDBC-based SQL client
  • Sales users access examplecorp-dw through an ODBC connection, for their reporting tools
  • Data Science users access the examplecorp-dw cluster using the data_science_grp database group.
  • Partners access the examplecorp-dw cluster and query using the partner_grp database group.
  • Partners are not federated through Active Directory and are provided with separate IAM user credentials (with IAM user name examplecorpsalespartner).
  • Partners can connect to the examplecorp-dw cluster programmatically, using language such as Python.
  • All users are automatically created in Amazon Redshift when they log in for the first time.
  • (Optional) Internal users do not specify database user or group information in their connection string. It is automatically assigned.
  • Data warehouse users can use SSO for the Amazon Redshift data warehouse using the preceding permissions.

Step 1:  Set up IdPs and federation

The Enabling Federation to AWS Using Windows Active Directory post demonstrated how to prepare Active Directory and enable federation to AWS. Using those instructions, you can establish trust between your AWS account and the IdP and enable user access to AWS using SSO.  For more information, see Identity Providers and Federation.

For this walkthrough, assume that this company has already configured SSO to their AWS account: 123456789012 for their Active Directory domain demo.examplecorp.com. The Sales and Data Science teams are not required to specify database user and group information in the connection string. The connection string can be configured by adding SAML Attribute elements to your IdP. Configuring these optional attributes enables internal users to conveniently avoid providing the DbUser and DbGroup parameters when they log in to Amazon Redshift.

The user-name attribute can be set up as follows, with a user ID (for example, nancy) or an email address (for example. [email protected]):

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbUser">  
  <AttributeValue>user-name</AttributeValue>
</Attribute>

The AutoCreate attribute can be defined as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/AutoCreate">
    <AttributeValue>true</AttributeValue>
</Attribute>

The sales_grp database group can be included as follows:

<Attribute Name="https://redshift.amazon.com/SAML/Attributes/DbGroups">
    <AttributeValue>sales_grp</AttributeValue>
</Attribute>

For more information about attribute element configuration, see Configure SAML Assertions for Your IdP.

Step 2: Create IAM roles for access to the Amazon Redshift cluster

The next step is to create IAM policies with permissions to call GetClusterCredentials and provide authorization for Amazon Redshift resources. To grant a SQL client the ability to retrieve the cluster endpoint, region, and port automatically, include the redshift:DescribeClusters action with the Amazon Redshift cluster resource in the IAM role.  For example, users can connect to the Amazon Redshift cluster using a JDBC URL without the need to hardcode the Amazon Redshift endpoint:

Previous:  jdbc:redshift://endpoint:port/database

Current:  jdbc:redshift:iam://clustername:region/dbname

Use IAM to create the following policies. You can also use an existing user or role and assign these policies. For example, if you already created an IAM role for IdP access, you can attach the necessary policies to that role. Here is the policy created for sales users for this example:

Sales_DW_IAM_Policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "redshift:DescribeClusters"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:GetClusterCredentials"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ],
            "Condition": {
                "StringEquals": {
                    "aws:userid": "AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com"
                }
            }
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:CreateClusterUser"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:JoinGroup"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp"
            ]
        }
    ]
}

The policy uses the following parameter values:

  • Region: us-west-2
  • AWS Account: 123456789012
  • Cluster name: examplecorp-dw
  • Database group: sales_grp
  • IAM role: AIDIODR4TAW7CSEXAMPLE
Policy Statement Description
{
"Effect":"Allow",
"Action":[
"redshift:DescribeClusters"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
]
}

Allow users to retrieve the cluster endpoint, region, and port automatically for the Amazon Redshift cluster examplecorp-dw. This specification uses the resource format arn:aws:redshift:region:account-id:cluster:clustername. For example, the SQL client JDBC can be specified in the format jdbc:redshift:iam://clustername:region/dbname.

For more information, see Amazon Resource Names.

{
"Effect":"Allow",
"Action":[
"redshift:GetClusterCredentials"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
"arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
],
"Condition":{
"StringEquals":{
"aws:userid":"AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com"
}
}
}

Generates a temporary token to authenticate into the examplecorp-dw cluster. “arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}” restricts the corporate user name to the database user name for that user. This resource is specified using the format: arn:aws:redshift:region:account-id:dbuser:clustername/dbusername.

The Condition block enforces that the AWS user ID should match “AIDIODR4TAW7CSEXAMPLE:${redshift:DbUser}@examplecorp.com”, so that individual users can authenticate only as themselves. The AIDIODR4TAW7CSEXAMPLE role has the Sales_DW_IAM_Policy policy attached.

{
"Effect":"Allow",
"Action":[
"redshift:CreateClusterUser"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
]
}
Automatically creates database users in examplecorp-dw, when they log in for the first time. Subsequent logins reuse the existing database user.
{
"Effect":"Allow",
"Action":[
"redshift:JoinGroup"
],
"Resource":[
"arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp"
]
}
Allows sales users to join the sales_grp database group through the resource “arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/sales_grp” that is specified in the format arn:aws:redshift:region:account-id:dbgroup:clustername/dbgroupname.

Similar policies can be created for Data Science users with access to join the data_science_grp group in examplecorp-dw. You can now attach the Sales_DW_IAM_Policy policy to the role that is mapped to IdP application for SSO.
 For more information about how to define the claim rules, see Configuring SAML Assertions for the Authentication Response.

Because partners are not authorized using Active Directory, they are provided with IAM credentials and added to the partner_grp database group. The Partner_DW_IAM_Policy is attached to the IAM users for partners. The following policy allows partners to log in using the IAM user name as the database user name.

Partner_DW_IAM_Policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "redshift:DescribeClusters"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:GetClusterCredentials"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:cluster:examplecorp-dw",
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ],
            "Condition": {
                "StringEquals": {
                    "redshift:DbUser": "${aws:username}"
                }
            }
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:CreateClusterUser"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbuser:examplecorp-dw/${redshift:DbUser}"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "redshift:JoinGroup"
            ],
            "Resource": [
                "arn:aws:redshift:us-west-2:123456789012:dbgroup:examplecorp-dw/partner_grp"
            ]
        }
    ]
}

redshift:DbUser“: “${aws:username}” forces an IAM user to use the IAM user name as the database user name.

With the previous steps configured, you can now establish the connection to Amazon Redshift through JDBC– or ODBC-supported clients.

Step 3: Set up database user access

Before you start connecting to Amazon Redshift using the SQL client, set up the database groups for appropriate data access. Log in to your Amazon Redshift database as superuser to create a database group, using CREATE GROUP.

Log in to examplecorp-dw/analytics as superuser and create the following groups and users:

CREATE GROUP sales_grp;
CREATE GROUP datascience_grp;
CREATE GROUP partner_grp;

Use the GRANT command to define access permissions to database objects (tables/views) for the preceding groups.

Step 4: Connect to Amazon Redshift using the JDBC SQL client

Assume that sales user “nancy” is using the SQL Workbench client and JDBC driver to log in to the Amazon Redshift data warehouse. The following steps help set up the client and establish the connection:

  1. Download the latest Amazon Redshift JDBC driver from the Configure a JDBC Connection page
  2. Build the JDBC URL with the IAM option in the following format:
    jdbc:redshift:iam://examplecorp-dw:us-west-2/sales_db

Because the redshift:DescribeClusters action is assigned to the preceding IAM roles, it automatically resolves the cluster endpoints and the port. Otherwise, you can specify the endpoint and port information in the JDBC URL, as described in Configure a JDBC Connection.

Identify the following JDBC options for providing the IAM credentials (see the “Prepare your environment” section) and configure in the SQL Workbench Connection Profile:

plugin_name=com.amazon.redshift.plugin.AdfsCredentialsProvider 
idp_host=demo.examplecorp.com (The name of the corporate identity provider host)
idp_port=443  (The port of the corporate identity provider host)
user=examplecorp\nancy(corporate user name)
password=***(corporate user password)

The SQL workbench configuration looks similar to the following screenshot:

Now, “nancy” can connect to examplecorp-dw by authenticating using the corporate Active Directory. Because the SAML attributes elements are already configured for nancy, she logs in as database user nancy and is assigned the sales_grp. Similarly, other Sales and Data Science users can connect to the examplecorp-dw cluster. A custom Amazon Redshift ODBC driver can also be used to connect using a SQL client. For more information, see Configure an ODBC Connection.

Step 5: Connecting to Amazon Redshift using JDBC SQL Client and IAM Credentials

This optional step is necessary only when you want to enable users that are not authenticated with Active Directory. Partners are provided with IAM credentials that they can use to connect to the examplecorp-dw Amazon Redshift clusters. These IAM users are attached to Partner_DW_IAM_Policy that assigns them to be assigned to the public database group in Amazon Redshift. The following JDBC URLs enable them to connect to the Amazon Redshift cluster:

jdbc:redshift:iam//examplecorp-dw/analytics?AccessKeyID=XXX&SecretAccessKey=YYY&DbUser=examplecorpsalespartner&DbGroup= partner_grp&AutoCreate=true

The AutoCreate option automatically creates a new database user the first time the partner logs in. There are several other options available to conveniently specify the IAM user credentials. For more information, see Options for providing IAM credentials.

Step 6: Connecting to Amazon Redshift using an ODBC client for Microsoft Windows

Assume that another sales user “uma” is using an ODBC-based client to log in to the Amazon Redshift data warehouse using Example Corp Active Directory. The following steps help set up the ODBC client and establish the Amazon Redshift connection in a Microsoft Windows operating system connected to your corporate network:

  1. Download and install the latest Amazon Redshift ODBC driver.
  2. Create a system DSN entry.
    1. In the Start menu, locate the driver folder or folders:
      • Amazon Redshift ODBC Driver (32-bit)
      • Amazon Redshift ODBC Driver (64-bit)
      • If you installed both drivers, you have a folder for each driver.
    2. Choose ODBC Administrator, and then type your administrator credentials.
    3. To configure the driver for all users on the computer, choose System DSN. To configure the driver for your user account only, choose User DSN.
    4. Choose Add.
  3. Select the Amazon Redshift ODBC driver, and choose Finish. Configure the following attributes:
    Data Source Name =any friendly name to identify the ODBC connection 
    Database=analytics
    user=uma(corporate user name)
    Auth Type-Identity Provider: AD FS
    password=leave blank (Windows automatically authenticates)
    Cluster ID: examplecorp-dw
    idp_host=demo.examplecorp.com (The name of the corporate IdP host)

This configuration looks like the following:

  1. Choose OK to save the ODBC connection.
  2. Verify that uma is set up with the SAML attributes, as described in the “Set up IdPs and federation” section.

The user uma can now use this ODBC connection to establish the connection to the Amazon Redshift cluster using any ODBC-based tools or reporting tools such as Tableau. Internally, uma authenticates using the Sales_DW_IAM_Policy  IAM role and is assigned the sales_grp database group.

Step 7: Connecting to Amazon Redshift using Python and IAM credentials

To enable partners, connect to the examplecorp-dw cluster programmatically, using Python on a computer such as Amazon EC2 instance. Reuse the IAM users that are attached to the Partner_DW_IAM_Policy policy defined in Step 2.

The following steps show this set up on an EC2 instance:

  1. Launch a new EC2 instance with the Partner_DW_IAM_Policy role, as described in Using an IAM Role to Grant Permissions to Applications Running on Amazon EC2 Instances. Alternatively, you can attach an existing IAM role to an EC2 instance.
  2. This example uses Python PostgreSQL Driver (PyGreSQL) to connect to your Amazon Redshift clusters. To install PyGreSQL on Amazon Linux, use the following command as the ec2-user:
    sudo easy_install pip
    sudo yum install postgresql postgresql-devel gcc python-devel
    sudo pip install PyGreSQL

  1. The following code snippet demonstrates programmatic access to Amazon Redshift for partner users:
    #!/usr/bin/env python
    """
    Usage:
    python redshift-unload-copy.py <config file> <region>
    
    * Copyright 2014, Amazon.com, Inc. or its affiliates. All Rights Reserved.
    *
    * Licensed under the Amazon Software License (the "License").
    * You may not use this file except in compliance with the License.
    * A copy of the License is located at
    *
    * http://aws.amazon.com/asl/
    *
    * or in the "license" file accompanying this file. This file is distributed
    * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
    * express or implied. See the License for the specific language governing
    * permissions and limitations under the License.
    """
    
    import sys
    import pg
    import boto3
    
    REGION = 'us-west-2'
    CLUSTER_IDENTIFIER = 'examplecorp-dw'
    DB_NAME = 'sales_db'
    DB_USER = 'examplecorpsalespartner'
    
    options = """keepalives=1 keepalives_idle=200 keepalives_interval=200
                 keepalives_count=6"""
    
    set_timeout_stmt = "set statement_timeout = 1200000"
    
    def conn_to_rs(host, port, db, usr, pwd, opt=options, timeout=set_timeout_stmt):
        rs_conn_string = """host=%s port=%s dbname=%s user=%s password=%s
                             %s""" % (host, port, db, usr, pwd, opt)
        print "Connecting to %s:%s:%s as %s" % (host, port, db, usr)
        rs_conn = pg.connect(dbname=rs_conn_string)
        rs_conn.query(timeout)
        return rs_conn
    
    def main():
        # describe the cluster and fetch the IAM temporary credentials
        global redshift_client
        redshift_client = boto3.client('redshift', region_name=REGION)
        response_cluster_details = redshift_client.describe_clusters(ClusterIdentifier=CLUSTER_IDENTIFIER)
        response_credentials = redshift_client.get_cluster_credentials(DbUser=DB_USER,DbName=DB_NAME,ClusterIdentifier=CLUSTER_IDENTIFIER,DurationSeconds=3600)
        rs_host = response_cluster_details['Clusters'][0]['Endpoint']['Address']
        rs_port = response_cluster_details['Clusters'][0]['Endpoint']['Port']
        rs_db = DB_NAME
        rs_iam_user = response_credentials['DbUser']
        rs_iam_pwd = response_credentials['DbPassword']
        # connect to the Amazon Redshift cluster
        conn = conn_to_rs(rs_host, rs_port, rs_db, rs_iam_user,rs_iam_pwd)
        # execute a query
        result = conn.query("SELECT sysdate as dt")
        # fetch results from the query
        for dt_val in result.getresult() :
            print dt_val
        # close the Amazon Redshift connection
        conn.close()
    
    if __name__ == "__main__":
        main()

You can save this Python program in a file (redshiftscript.py) and execute it at the command line as ec2-user:

python redshiftscript.py

Now partners can connect to the Amazon Redshift cluster using the Python script, and authentication is federated through the IAM user.

Summary

In this post, I demonstrated how to use federated access using Active Directory and IAM roles to enable single sign-on to an Amazon Redshift cluster. I also showed how partners outside an organization can be managed easily using IAM credentials.  Using the GetClusterCredentials API action, now supported by Amazon Redshift, lets you manage a large number of database users and have them use corporate credentials to log in. You don’t have to maintain separate database user accounts.

Although this post demonstrated the integration of IAM with AD FS and Active Directory, you can replicate this solution across with your choice of SAML 2.0 third-party identity providers (IdP), such as PingFederate or Okta. For the different supported federation options, see Configure SAML Assertions for Your IdP.

If you have questions or suggestions, please comment below.


Additional Reading

Learn how to establish federated access to your AWS resources by using Active Directory user attributes.


About the Author

Thiyagarajan Arumugam is a Big Data Solutions Architect at Amazon Web Services and designs customer architectures to process data at scale. Prior to AWS, he built data warehouse solutions at Amazon.com. In his free time, he enjoys all outdoor sports and practices the Indian classical drum mridangam.

 

AWS Developer Tools Expands Integration to Include GitHub

Post Syndicated from Balaji Iyer original https://aws.amazon.com/blogs/devops/aws-developer-tools-expands-integration-to-include-github/

AWS Developer Tools is a set of services that include AWS CodeCommit, AWS CodePipeline, AWS CodeBuild, and AWS CodeDeploy. Together, these services help you securely store and maintain version control of your application’s source code and automatically build, test, and deploy your application to AWS or your on-premises environment. These services are designed to enable developers and IT professionals to rapidly and safely deliver software.

As part of our continued commitment to extend the AWS Developer Tools ecosystem to third-party tools and services, we’re pleased to announce AWS CodeStar and AWS CodeBuild now integrate with GitHub. This will make it easier for GitHub users to set up a continuous integration and continuous delivery toolchain as part of their release process using AWS Developer Tools.

In this post, I will walk through the following:

Prerequisites:

You’ll need an AWS account, a GitHub account, an Amazon EC2 key pair, and administrator-level permissions for AWS Identity and Access Management (IAM), AWS CodeStar, AWS CodeBuild, AWS CodePipeline, Amazon EC2, Amazon S3.

 

Integrating GitHub with AWS CodeStar

AWS CodeStar enables you to quickly develop, build, and deploy applications on AWS. Its unified user interface helps you easily manage your software development activities in one place. With AWS CodeStar, you can set up your entire continuous delivery toolchain in minutes, so you can start releasing code faster.

When AWS CodeStar launched in April of this year, it used AWS CodeCommit as the hosted source repository. You can now choose between AWS CodeCommit or GitHub as the source control service for your CodeStar projects. In addition, your CodeStar project dashboard lets you centrally track GitHub activities, including commits, issues, and pull requests. This makes it easy to manage project activity across the components of your CI/CD toolchain. Adding the GitHub dashboard view will simplify development of your AWS applications.

In this section, I will show you how to use GitHub as the source provider for your CodeStar projects. I’ll also show you how to work with recent commits, issues, and pull requests in the CodeStar dashboard.

Sign in to the AWS Management Console and from the Services menu, choose CodeStar. In the CodeStar console, choose Create a new project. You should see the Choose a project template page.

CodeStar Project

Choose an option by programming language, application category, or AWS service. I am going to choose the Ruby on Rails web application that will be running on Amazon EC2.

On the Project details page, you’ll now see the GitHub option. Type a name for your project, and then choose Connect to GitHub.

Project details

You’ll see a message requesting authorization to connect to your GitHub repository. When prompted, choose Authorize, and then type your GitHub account password.

Authorize

This connects your GitHub identity to AWS CodeStar through OAuth. You can always review your settings by navigating to your GitHub application settings.

Installed GitHub Apps

You’ll see AWS CodeStar is now connected to GitHub:

Create project

You can choose a public or private repository. GitHub offers free accounts for users and organizations working on public and open source projects and paid accounts that offer unlimited private repositories and optional user management and security features.

In this example, I am going to choose the public repository option. Edit the repository description, if you like, and then choose Next.

Review your CodeStar project details, and then choose Create Project. On Choose an Amazon EC2 Key Pair, choose Create Project.

Key Pair

On the Review project details page, you’ll see Edit Amazon EC2 configuration. Choose this link to configure instance type, VPC, and subnet options. AWS CodeStar requires a service role to create and manage AWS resources and IAM permissions. This role will be created for you when you select the AWS CodeStar would like permission to administer AWS resources on your behalf check box.

Choose Create Project. It might take a few minutes to create your project and resources.

Review project details

When you create a CodeStar project, you’re added to the project team as an owner. If this is the first time you’ve used AWS CodeStar, you’ll be asked to provide the following information, which will be shown to others:

  • Your display name.
  • Your email address.

This information is used in your AWS CodeStar user profile. User profiles are not project-specific, but they are limited to a single AWS region. If you are a team member in projects in more than one region, you’ll have to create a user profile in each region.

User settings

User settings

Choose Next. AWS CodeStar will create a GitHub repository with your configuration settings (for example, https://github.com/biyer/ruby-on-rails-service).

When you integrate your integrated development environment (IDE) with AWS CodeStar, you can continue to write and develop code in your preferred environment. The changes you make will be included in the AWS CodeStar project each time you commit and push your code.

IDE

After setting up your IDE, choose Next to go to the CodeStar dashboard. Take a few minutes to familiarize yourself with the dashboard. You can easily track progress across your entire software development process, from your backlog of work items to recent code deployments.

Dashboard

After the application deployment is complete, choose the endpoint that will display the application.

Pipeline

This is what you’ll see when you open the application endpoint:

The Commit history section of the dashboard lists the commits made to the Git repository. If you choose the commit ID or the Open in GitHub option, you can use a hotlink to your GitHub repository.

Commit history

Your AWS CodeStar project dashboard is where you and your team view the status of your project resources, including the latest commits to your project, the state of your continuous delivery pipeline, and the performance of your instances. This information is displayed on tiles that are dedicated to a particular resource. To see more information about any of these resources, choose the details link on the tile. The console for that AWS service will open on the details page for that resource.

Issues

You can also filter issues based on their status and the assigned user.

Filter

AWS CodeBuild Now Supports Building GitHub Pull Requests

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

We recently announced support for GitHub pull requests in AWS CodeBuild. This functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild. You can use the AWS CodeBuild or AWS CodePipeline consoles to run AWS CodeBuild. You can also automate the running of AWS CodeBuild by using the AWS Command Line Interface (AWS CLI), the AWS SDKs, or the AWS CodeBuild Plugin for Jenkins.

AWS CodeBuild

In this section, I will show you how to trigger a build in AWS CodeBuild with a pull request from GitHub through webhooks.

Open the AWS CodeBuild console at https://console.aws.amazon.com/codebuild/. Choose Create project. If you already have a CodeBuild project, you can choose Edit project, and then follow along. CodeBuild can connect to AWS CodeCommit, S3, BitBucket, and GitHub to pull source code for builds. For Source provider, choose GitHub, and then choose Connect to GitHub.

Configure

After you’ve successfully linked GitHub and your CodeBuild project, you can choose a repository in your GitHub account. CodeBuild also supports connections to any public repository. You can review your settings by navigating to your GitHub application settings.

GitHub Apps

On Source: What to Build, for Webhook, select the Rebuild every time a code change is pushed to this repository check box.

Note: You can select this option only if, under Repository, you chose Use a repository in my account.

Source

In Environment: How to build, for Environment image, select Use an image managed by AWS CodeBuild. For Operating system, choose Ubuntu. For Runtime, choose Base. For Version, choose the latest available version. For Build specification, you can provide a collection of build commands and related settings, in YAML format (buildspec.yml) or you can override the build spec by inserting build commands directly in the console. AWS CodeBuild uses these commands to run a build. In this example, the output is the string “hello.”

Environment

On Artifacts: Where to put the artifacts from this build project, for Type, choose No artifacts. (This is also the type to choose if you are just running tests or pushing a Docker image to Amazon ECR.) You also need an AWS CodeBuild service role so that AWS CodeBuild can interact with dependent AWS services on your behalf. Unless you already have a role, choose Create a role, and for Role name, type a name for your role.

Artifacts

In this example, leave the advanced settings at their defaults.

If you expand Show advanced settings, you’ll see options for customizing your build, including:

  • A build timeout.
  • A KMS key to encrypt all the artifacts that the builds for this project will use.
  • Options for building a Docker image.
  • Elevated permissions during your build action (for example, accessing Docker inside your build container to build a Dockerfile).
  • Resource options for the build compute type.
  • Environment variables (built-in or custom). For more information, see Create a Build Project in the AWS CodeBuild User Guide.

Advanced settings

You can use the AWS CodeBuild console to create a parameter in Amazon EC2 Systems Manager. Choose Create a parameter, and then follow the instructions in the dialog box. (In that dialog box, for KMS key, you can optionally specify the ARN of an AWS KMS key in your account. Amazon EC2 Systems Manager uses this key to encrypt the parameter’s value during storage and decrypt during retrieval.)

Create parameter

Choose Continue. On the Review page, either choose Save and build or choose Save to run the build later.

Choose Start build. When the build is complete, the Build logs section should display detailed information about the build.

Logs

To demonstrate a pull request, I will fork the repository as a different GitHub user, make commits to the forked repo, check in the changes to a newly created branch, and then open a pull request.

Pull request

As soon as the pull request is submitted, you’ll see CodeBuild start executing the build.

Build

GitHub sends an HTTP POST payload to the webhook’s configured URL (highlighted here), which CodeBuild uses to download the latest source code and execute the build phases.

Build project

If you expand the Show all checks option for the GitHub pull request, you’ll see that CodeBuild has completed the build, all checks have passed, and a deep link is provided in Details, which opens the build history in the CodeBuild console.

Pull request

Summary:

In this post, I showed you how to use GitHub as the source provider for your CodeStar projects and how to work with recent commits, issues, and pull requests in the CodeStar dashboard. I also showed you how you can use GitHub pull requests to automatically trigger a build in AWS CodeBuild — specifically, how this functionality makes it easier to collaborate across your team while editing and building your application code with CodeBuild.


About the author:

Balaji Iyer is an Enterprise Consultant for the Professional Services Team at Amazon Web Services. In this role, he has helped several customers successfully navigate their journey to AWS. His specialties include architecting and implementing highly scalable distributed systems, serverless architectures, large scale migrations, operational security, and leading strategic AWS initiatives. Before he joined Amazon, Balaji spent more than a decade building operating systems, big data analytics solutions, mobile services, and web applications. In his spare time, he enjoys experiencing the great outdoors and spending time with his family.

 

How to Automatically Revert and Receive Notifications About Changes to Your Amazon VPC Security Groups

Post Syndicated from Rob Barnes original https://aws.amazon.com/blogs/security/how-to-automatically-revert-and-receive-notifications-about-changes-to-your-amazon-vpc-security-groups/

In a previous AWS Security Blog post, Jeff Levine showed how you can monitor changes to your Amazon EC2 security groups. The methods he describes in that post are examples of detective controls, which can help you determine when changes are made to security controls on your AWS resources.

In this post, I take that approach a step further by introducing an example of a responsive control, which you can use to automatically respond to a detected security event by applying a chosen security mitigation. I demonstrate a solution that continuously monitors changes made to an Amazon VPC security group, and if a new ingress rule (the same as an inbound rule) is added to that security group, the solution removes the rule and then sends you a notification after the changes have been automatically reverted.

The scenario

Let’s say you want to reduce your infrastructure complexity by replacing your Secure Shell (SSH) bastion hosts with Amazon EC2 Systems Manager (SSM). SSM allows you to run commands on your hosts remotely, removing the need to manage bastion hosts or rely on SSH to execute commands. To support this objective, you must prevent your staff members from opening SSH ports to your web server’s Amazon VPC security group. If one of your staff members does modify the VPC security group to allow SSH access, you want the change to be automatically reverted and then receive a notification that the change to the security group was automatically reverted. If you are not yet familiar with security groups, see Security Groups for Your VPC before reading the rest of this post.

Solution overview

This solution begins with a directive control to mandate that no web server should be accessible using SSH. The directive control is enforced using a preventive control, which is implemented using a security group rule that prevents ingress from port 22 (typically used for SSH). The detective control is a “listener” that identifies any changes made to your security group. Finally, the responsive control reverts changes made to the security group and then sends a notification of this security mitigation.

The detective control, in this case, is an Amazon CloudWatch event that detects changes to your security group and triggers the responsive control, which in this case is an AWS Lambda function. I use AWS CloudFormation to simplify the deployment.

The following diagram shows the architecture of this solution.

Solution architecture diagram

Here is how the process works:

  1. Someone on your staff adds a new ingress rule to your security group.
  2. A CloudWatch event that continually monitors changes to your security groups detects the new ingress rule and invokes a designated Lambda function (with Lambda, you can run code without provisioning or managing servers).
  3. The Lambda function evaluates the event to determine whether you are monitoring this security group and reverts the new security group ingress rule.
  4. Finally, the Lambda function sends you an email to let you know what the change was, who made it, and that the change was reverted.

Deploy the solution by using CloudFormation

In this section, you will click the Launch Stack button shown below to launch the CloudFormation stack and deploy the solution.

Prerequisites

  • You must have AWS CloudTrail already enabled in the AWS Region where you will be deploying the solution. CloudTrail lets you log, continuously monitor, and retain events related to API calls across your AWS infrastructure. See Getting Started with CloudTrail for more information.
  • You must have a default VPC in the region in which you will be deploying the solution. AWS accounts have one default VPC per AWS Region. If you’ve deleted your VPC, see Creating a Default VPC to recreate it.

Resources that this solution creates

When you launch the CloudFormation stack, it creates the following resources:

  • A sample VPC security group in your default VPC, which is used as the target for reverting ingress rule changes.
  • A CloudWatch event rule that monitors changes to your AWS infrastructure.
  • A Lambda function that reverts changes to the security group and sends you email notifications.
  • A permission that allows CloudWatch to invoke your Lambda function.
  • An AWS Identity and Access Management (IAM) role with limited privileges that the Lambda function assumes when it is executed.
  • An Amazon SNS topic to which the Lambda function publishes notifications.

Launch the CloudFormation stack

The link in this section uses the us-east-1 Region (the US East [N. Virginia] Region). Change the region if you want to use this solution in a different region. See Selecting a Region for more information about changing the region.

To deploy the solution, click the following Launch Stack button to launch the stack. After you click the button, you must sign in to the AWS Management Console if you have not already done so.

Click this "Launch Stack" button

Then:

  1. Choose Next to proceed to the Specify Details page.
  2. On the Specify Details page, type your email address in the Send notifications to box. This is the email address to which change notifications will be sent. (After the stack is launched, you will receive a confirmation email that you must accept before you can receive notifications.)
  3. Choose Next until you get to the Review page, and then choose the I acknowledge that AWS CloudFormation might create IAM resources check box. This confirms that you are aware that the CloudFormation template includes an IAM resource.
  4. Choose Create. CloudFormation displays the stack status, CREATE_COMPLETE, when the stack has launched completely, which should take less than two minutes.Screenshot showing that the stack has launched completely

Testing the solution

  1. Check your email for the SNS confirmation email. You must confirm this subscription to receive future notification emails. If you don’t confirm the subscription, your security group ingress rules still will be automatically reverted, but you will not receive notification emails.
  2. Navigate to the EC2 console and choose Security Groups in the navigation pane.
  3. Choose the security group created by CloudFormation. Its name is Web Server Security Group.
  4. Choose the Inbound tab in the bottom pane of the page. Note that only one rule allows HTTPS ingress on port 443 from 0.0.0.0/0 (from anywhere).Screenshot showing the "Inbound" tab in the bottom pane of the page
  1. Choose Edit to display the Edit inbound rules dialog box (again, an inbound rule and an ingress rule are the same thing).
  2. Choose Add Rule.
  3. Choose SSH from the Type drop-down list.
  4. Choose My IP from the Source drop-down list. Your IP address is populated for you. By adding this rule, you are simulating one of your staff members violating your organization’s policy (in this blog post’s hypothetical example) against allowing SSH access to your EC2 servers. You are testing the solution created when you launched the CloudFormation stack in the previous section. The solution should remove this newly created SSH rule automatically.
    Screenshot of editing inbound rules
  5. Choose Save.

Adding this rule creates an EC2 AuthorizeSecurityGroupIngress service event, which triggers the Lambda function created in the CloudFormation stack. After a few moments, choose the refresh button ( The "refresh" icon ) to see that the new SSH ingress rule that you just created has been removed by the solution you deployed earlier with the CloudFormation stack. If the rule is still there, wait a few more moments and choose the refresh button again.

Screenshot of refreshing the page to see that the SSH ingress rule has been removed

You should also receive an email to notify you that the ingress rule was added and subsequently reverted.

Screenshot of the notification email

Cleaning up

If you want to remove the resources created by this CloudFormation stack, you can delete the CloudFormation stack:

  1. Navigate to the CloudFormation console.
  2. Choose the stack that you created earlier.
  3. Choose the Actions drop-down list.
  4. Choose Delete Stack, and then choose Yes, Delete.
  5. CloudFormation will display a status of DELETE_IN_PROGRESS while it deletes the resources created with the stack. After a few moments, the stack should no longer appear in the list of completed stacks.
    Screenshot of stack "DELETE_IN_PROGRESS"

Other applications of this solution

I have shown one way to use multiple AWS services to help continuously ensure that your security controls haven’t deviated from your security baseline. However, you also could use the CIS Amazon Web Services Foundations Benchmarks, for example, to establish a governance baseline across your AWS accounts and then use the principles in this blog post to automatically mitigate changes to that baseline.

To scale this solution, you can create a framework that uses resource tags to identify particular resources for monitoring. You also can use a consolidated monitoring approach by using cross-account event delivery. See Sending and Receiving Events Between AWS Accounts for more information. You also can extend the principle of automatic mitigation to detect and revert changes to other resources such as IAM policies and Amazon S3 bucket policies.

Summary

In this blog post, I demonstrated how you can automatically revert changes to a VPC security group and have a notification sent about the changes. You can use this solution in your own AWS accounts to enforce your security requirements continuously.

If you have comments about this blog post or other ideas for ways to use this solution, submit a comment in the “Comments” section below. If you have implementation questions, start a new thread in the EC2 forum or contact AWS Support.

– Rob

Now Available – Microsoft SQL Server 2017 for Amazon EC2

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/now-available-microsoft-sql-server-2017-for-amazon-ec2/

Microsoft SQL Server 2017 (launched just a few days ago) includes lots of powerful new features including support for graph databases, automatic database tuning, and the ability to create clusterless Always On Availability Groups. It can also be run on Linux and in Docker containers.

Run on EC2
I’m happy to announce that you can now launch EC2 instances that run Windows Server 2016 and four editions (Web, Express, Standard, and Enterprise) of SQL Server 2017. The AMIs (Amazon Machine Images) are available today in all AWS Regions and run on a wide variety of EC2 instance types, including the new x1e.32xlarge with 128 vCPUs and almost 4 TB of memory.

You can launch these instances from the AWS Management Console or through AWS Marketplace. Here’s what they look like in the console:

And in AWS Marketplace:

Licensing Options Galore
You have lots of licensing options for SQL Server:

Pay As You Go – This option works well if you would prefer to avoid buying licenses, are already running an older version of SQL Server, and want to upgrade. You don’t have to deal with true-ups, software compliance audits, or Software Assurance and you don’t need to make a long-term purchase. If you are running the Standard Edition of SQL Server, you also benefit from our recent price reduction, with savings of up to 52%.

License Mobility – This option lets your use your active Software Assurance agreement to bring your existing licenses to EC2, and allows you to run SQL Server on Windows or Linux instances.

Bring Your Own Licenses – This option lets you take advantage of your existing license investment while minimizing upgrade costs. You can run SQL Server on EC2 Dedicated Instances or EC2 Dedicated Hosts, with the potential to reduce operating costs by licensing SQL Server on a per-core basis. This option allows you to run SQL Server 2017 on EC2 Linux instances (SUSE, RHEL, and Ubuntu are supported) and also supports Docker-based environments running on EC2 Windows and Linux instances. To learn more about these options, read the Installation Guidance for SQL Server on Linux and Run SQL Server 2017 Container Image with Docker.

Learn More
To learn more about SQL Server 2017 and to explore your licensing options in depth, take a look at the SQL Server on AWS page.

If you need advice and guidance as you plan your migration effort, check out the AWS Partners who have qualified for the Microsoft Workloads competency and focus on database solutions.

Amazon RDS support for SQL Server 2017 is planned for November. This will give you a fully managed option.

Plan to join the AWS team at the PASS Summit (November 1-3 in Seattle) and at AWS re:Invent (November 27th to December 1st in Las Vegas).

Jeff;

PS – Special thanks to my colleague Tom Staab (Partner Solutions Architect) for his help with this post!