Tag Archives: Access management

Identify unused IAM roles and remove them confidently with the last used timestamp

Post Syndicated from Mathangi Ramesh original https://aws.amazon.com/blogs/security/identify-unused-iam-roles-remove-confidently-last-used-timestamp/

As you build on AWS, you create AWS Identity and Access Management (IAM) roles to enable teams and applications to use AWS services. As those teams and applications evolve, you might only rely on a sub-set of your original roles to meet your needs. This can leave unused roles in your AWS account. To help you identify these unused roles, IAM now reports the last-used timestamp that represents when a role was last used to make an AWS request. You or your security team can use this information to identify, analyze, and then confidently remove unused roles. This helps you improve the security posture of your AWS environments. Additionally, by removing unused roles, you can simplify your monitoring and auditing efforts by focusing only on roles that are in use. You can review when a role was last used to access your AWS environment in the IAM console, using the AWS Command Line Interface (AWS CLI), or AWS SDK.

In this post, I demonstrate how to identify and remove roles that your team or applications don’t use by viewing the last-used timestamp in the IAM console.  Before I share an example, I’ll describe the existing IAM APIs where we now also report the last-used timestamp:

  • Get-role: Returns role details, including the path, ARN (Amazon resource number), and trust policy. You can now use this API to retrieve the last-used timestamp.
  • Get-account-authorization-details: Retrieves information about all the IAM users, groups, roles, and policies in your AWS account. You can now view the last-used timestamp along with the other role details.

How to use the AWS Management Console to view last-used information for roles

Imagine you’re a system administrator for Example Inc. and your development team is working on a new application. To enable them to get started with AWS quickly, you create roles for the team and their application. As the application goes through final review, you learn the team and application now rely on a smaller set of roles to access AWS services. This leaves unused roles in your AWS accounts that you might want to remove. You’re going to check the last time each role made a request to AWS and use this information to determine whether the team is using the role. If they aren’t, you plan to remove it knowing the team doesn’t need it for the application.

To view role-last-used information in the IAM Console, select Roles in the IAM navigation pane, then look for the Last activity column (see Figure 1 below). This displays the number of days that have passed since each role made an AWS service request. AWS records last-used information for the trailing 400 days. This is referred to as the tracking period. You can sort the column to identify the roles your team has not used recently.

In the case of Example Inc., let’s say you want to get rid of any roles that have been inactive for 90 days or more. From the information in Figure 1, you see that your team is using ApplicationEC2Access, TestRole, and CodeDeployRole. You also see they haven’t used AdminAccess, EC2FullAccess, and InfraSetupRole in the last 90 days. You can now delete these roles confidently. (Last activity “None”, as seen for the AdminAccess role, means that the role was not used within the trailing 400-day tracking period to make any service request.)
 

Figure 1: "Last activity" column in IAM console

Figure 1: “Last activity” column in IAM console

While analyzing the last-used timestamp for each role, you notice that the MigrationRole role was last active two months ago. You want to gather more information about the role’s access patterns to determine whether you ought to delete it. To do this, select the name of the role. From the role detail page, navigate to the Access Advisor tab and investigate the list of accessed services and verify what the role was used for. Access advisor provides a report that displays a list of services and timestamps that indicate when the selected IAM principal last accessed each of the services that it has permissions to. Based on this report, you can decide to follow up with the development team to see if they still need this role. Thus, you have reduced the number of roles in your account from 9 to 6, making it easier to monitor active roles and restrict access to your AWS environments.
 

Figure 2: Access Advisor report

Summary

In this post, I showed you how to use role-last-used information to identify and remove unused roles. By removing unused roles, you can simplify monitoring and improve your security posture. To learn more about deleting roles, visit the deleting roles or instance profiles documentation.

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

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Mathangi Ramesh

Mathangi Ramesh

Mathangi is the product manager for AWS Identity and Access Management. She enjoys talking to customers and working with data to solve problems. Outside of work, Mathangi is a fitness enthusiast and a Bharatanatyam dancer. She holds an MBA degree from Carnegie Mellon University.

New .BOT gTLD from Amazon

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/new-bot-gtld-from-amazon/

Today, I’m excited to announce the launch of .BOT, a new generic top-level domain (gTLD) from Amazon. Customers can use .BOT domains to provide an identity and portal for their bots. Fitness bots, slack bots, e-commerce bots, and more can all benefit from an easy-to-access .BOT domain. The phrase “bot” was the 4th most registered domain keyword within the .COM TLD in 2016 with more than 6000 domains per month. A .BOT domain allows customers to provide a definitive internet identity for their bots as well as enhancing SEO performance.

At the time of this writing .BOT domains start at $75 each and must be verified and published with a supported tool like: Amazon Lex, Botkit Studio, Dialogflow, Gupshup, Microsoft Bot Framework, or Pandorabots. You can expect support for more tools over time and if your favorite bot framework isn’t supported feel free to contact us here: [email protected].

Below, I’ll walk through the experience of registering and provisioning a domain for my bot, whereml.bot. Then we’ll look at setting up the domain as a hosted zone in Amazon Route 53. Let’s get started.

Registering a .BOT domain

First, I’ll head over to https://amazonregistry.com/bot, type in a new domain, and click magnifying class to make sure my domain is available and get taken to the registration wizard.

Next, I have the opportunity to choose how I want to verify my bot. I build all of my bots with Amazon Lex so I’ll select that in the drop down and get prompted for instructions specific to AWS. If I had my bot hosted somewhere else I would need to follow the unique verification instructions for that particular framework.

To verify my Lex bot I need to give the Amazon Registry permissions to invoke the bot and verify it’s existence. I’ll do this by creating an AWS Identity and Access Management (IAM) cross account role and providing the AmazonLexReadOnly permissions to that role. This is easily accomplished in the AWS Console. Be sure to provide the account number and external ID shown on the registration page.

Now I’ll add read only permissions to our Amazon Lex bots.

I’ll give my role a fancy name like DotBotCrossAccountVerifyRole and a description so it’s easy to remember why I made this then I’ll click create to create the role and be transported to the role summary page.

Finally, I’ll copy the ARN from the created role and save it for my next step.

Here I’ll add all the details of my Amazon Lex bot. If you haven’t made a bot yet you can follow the tutorial to build a basic bot. I can refer to any alias I’ve deployed but if I just want to grab the latest published bot I can pass in $LATEST as the alias. Finally I’ll click Validate and proceed to registering my domain.

Amazon Registry works with a partner EnCirca to register our domains so we’ll select them and optionally grab Site Builder. I know how to sling some HTML and Javascript together so I’ll pass on the Site Builder side of things.

 

After I click continue we’re taken to EnCirca’s website to finalize the registration and with any luck within a few minutes of purchasing and completing the registration we should receive an email with some good news:

Alright, now that we have a domain name let’s find out how to host things on it.

Using Amazon Route53 with a .BOT domain

Amazon Route 53 is a highly available and scalable DNS with robust APIs, healthchecks, service discovery, and many other features. I definitely want to use this to host my new domain. The first thing I’ll do is navigate to the Route53 console and create a hosted zone with the same name as my domain.


Great! Now, I need to take the Name Server (NS) records that Route53 created for me and use EnCirca’s portal to add these as the authoritative nameservers on the domain.

Now I just add my records to my hosted zone and I should be able to serve traffic! Way cool, I’ve got my very own .bot domain for @WhereML.

Next Steps

  • I could and should add to the security of my site by creating TLS certificates for people who intend to access my domain over TLS. Luckily with AWS Certificate Manager (ACM) this is extremely straightforward and I’ve got my subdomains and root domain verified in just a few clicks.
  • I could create a cloudfront distrobution to front an S3 static single page application to host my entire chatbot and invoke Amazon Lex with a cognito identity right from the browser.

Randall

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Janna Pellegrino original https://aws.amazon.com/blogs/architecture/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: following our most popular beta exam ever, the AWS Certified Security – Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam
The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates:

  • An understanding of specialized data classifications and AWS data protection mechanisms.
  • An understanding of data encryption methods and AWS mechanisms to implement them.
  • An understanding of secure Internet protocols and AWS mechanisms to implement them.
  • A working knowledge of AWS security services and features of services to provide a secure production environment.
  • Competency gained from two or more years of production deployment experience using AWS security services and features.
  • Ability to make trade-off decisions with regard to cost, security, and deployment complexity given a set of application requirements.
  • An understanding of security operations and risk.

Learn more and register >>

How to prepare
We have training and other resources to help you prepare for the exam:

AWS Training (aws.amazon.com/training)

Additional Resources

Learn more and register >>

Please contact us if you have questions about exam registration.

Good luck!

Announcing the new AWS Certified Security – Specialty exam

Post Syndicated from Ozlem Yilmaz original https://aws.amazon.com/blogs/security/announcing-the-new-aws-certified-security-specialty-exam/

Good news for cloud security experts: the AWS Certified Security — Specialty exam is here. This new exam allows experienced cloud security professionals to demonstrate and validate their knowledge of how to secure the AWS platform.

About the exam

The security exam covers incident response, logging and monitoring, infrastructure security, identity and access management, and data protection. The exam is open to anyone who currently holds a Cloud Practitioner or Associate-level certification. We recommend candidates have five years of IT security experience designing and implementing security solutions, and at least two years of hands-on experience securing AWS workloads.

The exam validates your understanding of:

  • Specialized data classifications and AWS data protection mechanisms
  • Data encryption methods and AWS mechanisms to implement them
  • Secure Internet protocols and AWS mechanisms to implement them
  • AWS security services and features of services to provide a secure production environment
  • Making tradeoff decisions with regard to cost, security, and deployment complexity given a set of application requirements
  • Security operations and risk

How to prepare

We have training and other resources to help you prepare for the exam.

AWS Training that includes:

Additional Resources

Learn more and register here, and please contact us if you have questions about exam registration.

Want more AWS Security news? Follow us on Twitter.

Rotate Amazon RDS database credentials automatically with AWS Secrets Manager

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

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

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

Key features of Secrets Manager

These features include the ability to:

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

Get started with Secrets Manager

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

Phase 1: Store a secret in Secrets Manager

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

    Note: For this example, you need permissions to store secrets in Secrets Manager. To grant these permissions, you can use the AWSSecretsManagerReadWriteAccess managed policy. Read the AWS Secrets Manager Documentation for more information about the minimum IAM permissions required to store a secret.

  3. Next, I review the encryption setting and choose to use the default encryption settings. Secrets Manager will encrypt this secret using the Secrets Manager DefaultEncryptionKeyDefaultEncryptionKey in this account. Alternatively, I can choose to encrypt using a customer master key (CMK) that I have stored in AWS KMS.
     
    Select the encryption key interface
     
  4. Next, I view the list of Amazon RDS instances in my account and select the database this credential accesses. For this example, I select the DB instance mysql-rds-database, and then I select Next.
     
    Select the RDS database interface
     
  5. In this step, I specify values for Secret Name and Description. For this example, I use Applications/MyApp/MySQL-RDS-Database as the name and enter a description of this secret, and then select Next.
     
    Secret Name and description interface
     
  6. For the next step, I keep the default setting Disable automatic rotation because my secret is used by my application running on Amazon EC2. I’ll enable rotation after I’ve updated my application (see Phase 2 below) to use Secrets Manager APIs to retrieve secrets. I then select Next.

    Note: If you’re storing a secret that you’re not using in your application, select Enable automatic rotation. See our AWS Secrets Manager getting started guide on rotation for details.

     
    Configure automatic rotation interface
     

  7. Review the information on the next screen and, if everything looks correct, select Store. We’ve now successfully stored a secret in Secrets Manager.
  8. Next, I select See sample code.
     
    The See sample code button
     
  9. Take note of the code samples provided. I will use this code to update my application to retrieve the secret using Secrets Manager APIs.
     
    Python sample code
     

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

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

  1. I connect to my Amazon EC2 instance via Secure Shell (SSH).
  2. Previously, I configured my application to retrieve the database user name and password from the configuration file. Below is the source code for my application.
    import MySQLdb
    import config

    def no_secrets_manager_sample()

    # Get the user name, password, and database connection information from a config file.
    database = config.database
    user_name = config.user_name
    password = config.password

    # Use the user name, password, and database connection information to connect to the database
    db = MySQLdb.connect(database.endpoint, user_name, password, database.db_name, database.port)

  3. I use the sample code from Phase 1 above and update my application to retrieve the user name and password from Secrets Manager. This code sets up the client and retrieves and decrypts the secret Applications/MyApp/MySQL-RDS-Database. I’ve added comments to the code to make the code easier to understand.
    # Use the code snippet provided by Secrets Manager.
    import boto3
    from botocore.exceptions import ClientError

    def get_secret():
    #Define the secret you want to retrieve
    secret_name = "Applications/MyApp/MySQL-RDS-Database"
    #Define the Secrets mManager end-point your code should use.
    endpoint_url = "https://secretsmanager.us-east-1.amazonaws.com"
    region_name = "us-east-1"

    #Setup the client
    session = boto3.session.Session()
    client = session.client(
    service_name='secretsmanager',
    region_name=region_name,
    endpoint_url=endpoint_url
    )

    #Use the client to retrieve the secret
    try:
    get_secret_value_response = client.get_secret_value(
    SecretId=secret_name
    )
    #Error handling to make it easier for your code to tolerate faults
    except ClientError as e:
    if e.response['Error']['Code'] == 'ResourceNotFoundException':
    print("The requested secret " + secret_name + " was not found")
    elif e.response['Error']['Code'] == 'InvalidRequestException':
    print("The request was invalid due to:", e)
    elif e.response['Error']['Code'] == 'InvalidParameterException':
    print("The request had invalid params:", e)
    else:
    # Decrypted secret using the associated KMS CMK
    # Depending on whether the secret was a string or binary, one of these fields will be populated
    if 'SecretString' in get_secret_value_response:
    secret = get_secret_value_response['SecretString']
    else:
    binary_secret_data = get_secret_value_response['SecretBinary']

    # Your code goes here.

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

Phase 3: Enable Rotation for Your Secret

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

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

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

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

Summary

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

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

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Best Practices for Running Apache Cassandra on Amazon EC2

Post Syndicated from Prasad Alle original https://aws.amazon.com/blogs/big-data/best-practices-for-running-apache-cassandra-on-amazon-ec2/

Apache Cassandra is a commonly used, high performance NoSQL database. AWS customers that currently maintain Cassandra on-premises may want to take advantage of the scalability, reliability, security, and economic benefits of running Cassandra on Amazon EC2.

Amazon EC2 and Amazon Elastic Block Store (Amazon EBS) provide secure, resizable compute capacity and storage in the AWS Cloud. When combined, you can deploy Cassandra, allowing you to scale capacity according to your requirements. Given the number of possible deployment topologies, it’s not always trivial to select the most appropriate strategy suitable for your use case.

In this post, we outline three Cassandra deployment options, as well as provide guidance about determining the best practices for your use case in the following areas:

  • Cassandra resource overview
  • Deployment considerations
  • Storage options
  • Networking
  • High availability and resiliency
  • Maintenance
  • Security

Before we jump into best practices for running Cassandra on AWS, we should mention that we have many customers who decided to use DynamoDB instead of managing their own Cassandra cluster. DynamoDB is fully managed, serverless, and provides multi-master cross-region replication, encryption at rest, and managed backup and restore. Integration with AWS Identity and Access Management (IAM) enables DynamoDB customers to implement fine-grained access control for their data security needs.

Several customers who have been using large Cassandra clusters for many years have moved to DynamoDB to eliminate the complications of administering Cassandra clusters and maintaining high availability and durability themselves. Gumgum.com is one customer who migrated to DynamoDB and observed significant savings. For more information, see Moving to Amazon DynamoDB from Hosted Cassandra: A Leap Towards 60% Cost Saving per Year.

AWS provides options, so you’re covered whether you want to run your own NoSQL Cassandra database, or move to a fully managed, serverless DynamoDB database.

Cassandra resource overview

Here’s a short introduction to standard Cassandra resources and how they are implemented with AWS infrastructure. If you’re already familiar with Cassandra or AWS deployments, this can serve as a refresher.

ResourceCassandraAWS
Cluster

A single Cassandra deployment.

 

This typically consists of multiple physical locations, keyspaces, and physical servers.

A logical deployment construct in AWS that maps to an AWS CloudFormation StackSet, which consists of one or many CloudFormation stacks to deploy Cassandra.
DatacenterA group of nodes configured as a single replication group.

A logical deployment construct in AWS.

 

A datacenter is deployed with a single CloudFormation stack consisting of Amazon EC2 instances, networking, storage, and security resources.

Rack

A collection of servers.

 

A datacenter consists of at least one rack. Cassandra tries to place the replicas on different racks.

A single Availability Zone.
Server/nodeA physical virtual machine running Cassandra software.An EC2 instance.
TokenConceptually, the data managed by a cluster is represented as a ring. The ring is then divided into ranges equal to the number of nodes. Each node being responsible for one or more ranges of the data. Each node gets assigned with a token, which is essentially a random number from the range. The token value determines the node’s position in the ring and its range of data.Managed within Cassandra.
Virtual node (vnode)Responsible for storing a range of data. Each vnode receives one token in the ring. A cluster (by default) consists of 256 tokens, which are uniformly distributed across all servers in the Cassandra datacenter.Managed within Cassandra.
Replication factorThe total number of replicas across the cluster.Managed within Cassandra.

Deployment considerations

One of the many benefits of deploying Cassandra on Amazon EC2 is that you can automate many deployment tasks. In addition, AWS includes services, such as CloudFormation, that allow you to describe and provision all your infrastructure resources in your cloud environment.

We recommend orchestrating each Cassandra ring with one CloudFormation template. If you are deploying in multiple AWS Regions, you can use a CloudFormation StackSet to manage those stacks. All the maintenance actions (scaling, upgrading, and backing up) should be scripted with an AWS SDK. These may live as standalone AWS Lambda functions that can be invoked on demand during maintenance.

You can get started by following the Cassandra Quick Start deployment guide. Keep in mind that this guide does not address the requirements to operate a production deployment and should be used only for learning more about Cassandra.

Deployment patterns

In this section, we discuss various deployment options available for Cassandra in Amazon EC2. A successful deployment starts with thoughtful consideration of these options. Consider the amount of data, network environment, throughput, and availability.

  • Single AWS Region, 3 Availability Zones
  • Active-active, multi-Region
  • Active-standby, multi-Region

Single region, 3 Availability Zones

In this pattern, you deploy the Cassandra cluster in one AWS Region and three Availability Zones. There is only one ring in the cluster. By using EC2 instances in three zones, you ensure that the replicas are distributed uniformly in all zones.

To ensure the even distribution of data across all Availability Zones, we recommend that you distribute the EC2 instances evenly in all three Availability Zones. The number of EC2 instances in the cluster is a multiple of three (the replication factor).

This pattern is suitable in situations where the application is deployed in one Region or where deployments in different Regions should be constrained to the same Region because of data privacy or other legal requirements.

ProsCons

●     Highly available, can sustain failure of one Availability Zone.

●     Simple deployment

●     Does not protect in a situation when many of the resources in a Region are experiencing intermittent failure.

 

Active-active, multi-Region

In this pattern, you deploy two rings in two different Regions and link them. The VPCs in the two Regions are peered so that data can be replicated between two rings.

We recommend that the two rings in the two Regions be identical in nature, having the same number of nodes, instance types, and storage configuration.

This pattern is most suitable when the applications using the Cassandra cluster are deployed in more than one Region.

ProsCons

●     No data loss during failover.

●     Highly available, can sustain when many of the resources in a Region are experiencing intermittent failures.

●     Read/write traffic can be localized to the closest Region for the user for lower latency and higher performance.

●     High operational overhead

●     The second Region effectively doubles the cost

 

Active-standby, multi-region

In this pattern, you deploy two rings in two different Regions and link them. The VPCs in the two Regions are peered so that data can be replicated between two rings.

However, the second Region does not receive traffic from the applications. It only functions as a secondary location for disaster recovery reasons. If the primary Region is not available, the second Region receives traffic.

We recommend that the two rings in the two Regions be identical in nature, having the same number of nodes, instance types, and storage configuration.

This pattern is most suitable when the applications using the Cassandra cluster require low recovery point objective (RPO) and recovery time objective (RTO).

ProsCons

●     No data loss during failover.

●     Highly available, can sustain failure or partitioning of one whole Region.

●     High operational overhead.

●     High latency for writes for eventual consistency.

●     The second Region effectively doubles the cost.

Storage options

In on-premises deployments, Cassandra deployments use local disks to store data. There are two storage options for EC2 instances:

Your choice of storage is closely related to the type of workload supported by the Cassandra cluster. Instance store works best for most general purpose Cassandra deployments. However, in certain read-heavy clusters, Amazon EBS is a better choice.

The choice of instance type is generally driven by the type of storage:

  • If ephemeral storage is required for your application, a storage-optimized (I3) instance is the best option.
  • If your workload requires Amazon EBS, it is best to go with compute-optimized (C5) instances.
  • Burstable instance types (T2) don’t offer good performance for Cassandra deployments.

Instance store

Ephemeral storage is local to the EC2 instance. It may provide high input/output operations per second (IOPs) based on the instance type. An SSD-based instance store can support up to 3.3M IOPS in I3 instances. This high performance makes it an ideal choice for transactional or write-intensive applications such as Cassandra.

In general, instance storage is recommended for transactional, large, and medium-size Cassandra clusters. For a large cluster, read/write traffic is distributed across a higher number of nodes, so the loss of one node has less of an impact. However, for smaller clusters, a quick recovery for the failed node is important.

As an example, for a cluster with 100 nodes, the loss of 1 node is 3.33% loss (with a replication factor of 3). Similarly, for a cluster with 10 nodes, the loss of 1 node is 33% less capacity (with a replication factor of 3).

 Ephemeral storageAmazon EBSComments

IOPS

(translates to higher query performance)

Up to 3.3M on I3

80K/instance

10K/gp2/volume

32K/io1/volume

This results in a higher query performance on each host. However, Cassandra implicitly scales well in terms of horizontal scale. In general, we recommend scaling horizontally first. Then, scale vertically to mitigate specific issues.

 

Note: 3.3M IOPS is observed with 100% random read with a 4-KB block size on Amazon Linux.

AWS instance typesI3Compute optimized, C5Being able to choose between different instance types is an advantage in terms of CPU, memory, etc., for horizontal and vertical scaling.
Backup/ recoveryCustomBasic building blocks are available from AWS.

Amazon EBS offers distinct advantage here. It is small engineering effort to establish a backup/restore strategy.

a) In case of an instance failure, the EBS volumes from the failing instance are attached to a new instance.

b) In case of an EBS volume failure, the data is restored by creating a new EBS volume from last snapshot.

Amazon EBS

EBS volumes offer higher resiliency, and IOPs can be configured based on your storage needs. EBS volumes also offer some distinct advantages in terms of recovery time. EBS volumes can support up to 32K IOPS per volume and up to 80K IOPS per instance in RAID configuration. They have an annualized failure rate (AFR) of 0.1–0.2%, which makes EBS volumes 20 times more reliable than typical commodity disk drives.

The primary advantage of using Amazon EBS in a Cassandra deployment is that it reduces data-transfer traffic significantly when a node fails or must be replaced. The replacement node joins the cluster much faster. However, Amazon EBS could be more expensive, depending on your data storage needs.

Cassandra has built-in fault tolerance by replicating data to partitions across a configurable number of nodes. It can not only withstand node failures but if a node fails, it can also recover by copying data from other replicas into a new node. Depending on your application, this could mean copying tens of gigabytes of data. This adds additional delay to the recovery process, increases network traffic, and could possibly impact the performance of the Cassandra cluster during recovery.

Data stored on Amazon EBS is persisted in case of an instance failure or termination. The node’s data stored on an EBS volume remains intact and the EBS volume can be mounted to a new EC2 instance. Most of the replicated data for the replacement node is already available in the EBS volume and won’t need to be copied over the network from another node. Only the changes made after the original node failed need to be transferred across the network. That makes this process much faster.

EBS volumes are snapshotted periodically. So, if a volume fails, a new volume can be created from the last known good snapshot and be attached to a new instance. This is faster than creating a new volume and coping all the data to it.

Most Cassandra deployments use a replication factor of three. However, Amazon EBS does its own replication under the covers for fault tolerance. In practice, EBS volumes are about 20 times more reliable than typical disk drives. So, it is possible to go with a replication factor of two. This not only saves cost, but also enables deployments in a region that has two Availability Zones.

EBS volumes are recommended in case of read-heavy, small clusters (fewer nodes) that require storage of a large amount of data. Keep in mind that the Amazon EBS provisioned IOPS could get expensive. General purpose EBS volumes work best when sized for required performance.

Networking

If your cluster is expected to receive high read/write traffic, select an instance type that offers 10–Gb/s performance. As an example, i3.8xlarge and c5.9xlarge both offer 10–Gb/s networking performance. A smaller instance type in the same family leads to a relatively lower networking throughput.

Cassandra generates a universal unique identifier (UUID) for each node based on IP address for the instance. This UUID is used for distributing vnodes on the ring.

In the case of an AWS deployment, IP addresses are assigned automatically to the instance when an EC2 instance is created. With the new IP address, the data distribution changes and the whole ring has to be rebalanced. This is not desirable.

To preserve the assigned IP address, use a secondary elastic network interface with a fixed IP address. Before swapping an EC2 instance with a new one, detach the secondary network interface from the old instance and attach it to the new one. This way, the UUID remains same and there is no change in the way that data is distributed in the cluster.

If you are deploying in more than one region, you can connect the two VPCs in two regions using cross-region VPC peering.

High availability and resiliency

Cassandra is designed to be fault-tolerant and highly available during multiple node failures. In the patterns described earlier in this post, you deploy Cassandra to three Availability Zones with a replication factor of three. Even though it limits the AWS Region choices to the Regions with three or more Availability Zones, it offers protection for the cases of one-zone failure and network partitioning within a single Region. The multi-Region deployments described earlier in this post protect when many of the resources in a Region are experiencing intermittent failure.

Resiliency is ensured through infrastructure automation. The deployment patterns all require a quick replacement of the failing nodes. In the case of a regionwide failure, when you deploy with the multi-Region option, traffic can be directed to the other active Region while the infrastructure is recovering in the failing Region. In the case of unforeseen data corruption, the standby cluster can be restored with point-in-time backups stored in Amazon S3.

Maintenance

In this section, we look at ways to ensure that your Cassandra cluster is healthy:

  • Scaling
  • Upgrades
  • Backup and restore

Scaling

Cassandra is horizontally scaled by adding more instances to the ring. We recommend doubling the number of nodes in a cluster to scale up in one scale operation. This leaves the data homogeneously distributed across Availability Zones. Similarly, when scaling down, it’s best to halve the number of instances to keep the data homogeneously distributed.

Cassandra is vertically scaled by increasing the compute power of each node. Larger instance types have proportionally bigger memory. Use deployment automation to swap instances for bigger instances without downtime or data loss.

Upgrades

All three types of upgrades (Cassandra, operating system patching, and instance type changes) follow the same rolling upgrade pattern.

In this process, you start with a new EC2 instance and install software and patches on it. Thereafter, remove one node from the ring. For more information, see Cassandra cluster Rolling upgrade. Then, you detach the secondary network interface from one of the EC2 instances in the ring and attach it to the new EC2 instance. Restart the Cassandra service and wait for it to sync. Repeat this process for all nodes in the cluster.

Backup and restore

Your backup and restore strategy is dependent on the type of storage used in the deployment. Cassandra supports snapshots and incremental backups. When using instance store, a file-based backup tool works best. Customers use rsync or other third-party products to copy data backups from the instance to long-term storage. For more information, see Backing up and restoring data in the DataStax documentation. This process has to be repeated for all instances in the cluster for a complete backup. These backup files are copied back to new instances to restore. We recommend using S3 to durably store backup files for long-term storage.

For Amazon EBS based deployments, you can enable automated snapshots of EBS volumes to back up volumes. New EBS volumes can be easily created from these snapshots for restoration.

Security

We recommend that you think about security in all aspects of deployment. The first step is to ensure that the data is encrypted at rest and in transit. The second step is to restrict access to unauthorized users. For more information about security, see the Cassandra documentation.

Encryption at rest

Encryption at rest can be achieved by using EBS volumes with encryption enabled. Amazon EBS uses AWS KMS for encryption. For more information, see Amazon EBS Encryption.

Instance store–based deployments require using an encrypted file system or an AWS partner solution. If you are using DataStax Enterprise, it supports transparent data encryption.

Encryption in transit

Cassandra uses Transport Layer Security (TLS) for client and internode communications.

Authentication

The security mechanism is pluggable, which means that you can easily swap out one authentication method for another. You can also provide your own method of authenticating to Cassandra, such as a Kerberos ticket, or if you want to store passwords in a different location, such as an LDAP directory.

Authorization

The authorizer that’s plugged in by default is org.apache.cassandra.auth.Allow AllAuthorizer. Cassandra also provides a role-based access control (RBAC) capability, which allows you to create roles and assign permissions to these roles.

Conclusion

In this post, we discussed several patterns for running Cassandra in the AWS Cloud. This post describes how you can manage Cassandra databases running on Amazon EC2. AWS also provides managed offerings for a number of databases. To learn more, see Purpose-built databases for all your application needs.

If you have questions or suggestions, please comment below.


Additional Reading

If you found this post useful, be sure to check out Analyze Your Data on Amazon DynamoDB with Apache Spark and Analysis of Top-N DynamoDB Objects using Amazon Athena and Amazon QuickSight.


About the Authors

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

 

 

 

Provanshu Dey is a Senior IoT Consultant with AWS Professional Services. He works on highly scalable and reliable IoT, data and machine learning solutions with our customers. In his spare time, he enjoys spending time with his family and tinkering with electronics & gadgets.

 

 

 

Amazon Relational Database Service – Looking Back at 2017

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/amazon-relational-database-service-looking-back-at-2017/

The Amazon RDS team launched nearly 80 features in 2017. Some of them were covered in this blog, others on the AWS Database Blog, and the rest in What’s New or Forum posts. To wrap up my week, I thought it would be worthwhile to give you an organized recap. So here we go!

Certification & Security

Features

Engine Versions & Features

Regional Support

Instance Support

Price Reductions

And That’s a Wrap
I’m pretty sure that’s everything. As you can see, 2017 was quite the year! I can’t wait to see what the team delivers in 2018.

Jeff;