Tag Archives: CloudFormation stacks

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.

Resource Cassandra AWS
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.
Datacenter A 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/node A physical virtual machine running Cassandra software. An EC2 instance.
Token Conceptually, 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 factor The 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.

Pros Cons

●     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.

Pros Cons

●     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).

Pros Cons

●     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 storage Amazon EBS Comments

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 types I3 Compute optimized, C5 Being able to choose between different instance types is an advantage in terms of CPU, memory, etc., for horizontal and vertical scaling.
Backup/ recovery Custom Basic 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.

 

 

 

Prime Day 2017 – Powered by AWS

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/prime-day-2017-powered-by-aws/

The third annual Prime Day set another round of records for global orders, topping Black Friday and Cyber Monday, making it the biggest day in Amazon retail history. Over the course of the 30 hour event, tens of millions of Prime members purchased things like Echo Dots, Fire tablets, programmable pressure cookers, espresso machines, rechargeable batteries, and much more! July 11th also set a record for the number of new Prime memberships, as people signed up in order to take advantage of hundreds of thousands of deals. Amazon customers shopped online and made heavy use of the Amazon App, with mobile orders more than doubling from last Prime Day.

Powered by AWS
Last year I told you about How AWS Powered Amazon’s Biggest Day Ever, and shared what the team had learned with regard to preparation, automation, monitoring, and thinking big. All of those lessons still apply and you can read that post to learn more. Preparation for this year’s Prime Day (which started just days after Prime Day 2016 wrapped up) started by collecting and sharing best practices and identifying areas for improvement, proceeding to implementation and stress testing as the big day approached. Two of the best practices involve auditing and GameDay:

Auditing – This is a formal way for us to track preparations, identify risks, and to track progress against our objectives. Each team must respond to a series of detailed technical and operational questions that are designed to help them determine their readiness. On the technical side, questions could revolve around time to recovery after a database failure, including the all-important check of the TTL (time to live) for the CNAME. Operational questions address schedules for on-call personnel, points of contact, and ownership of services & instances.

GameDay – This practice (which I believe originated with former Amazonian Jesse Robbins), is intended to validate all of the capacity planning & preparation and to verify that all of the necessary operational practices are in place and work as expected. It introduces simulated failures and helps to train the team to identify and quickly resolve issues, building muscle memory in the process. It also tests failover and recovery capabilities, and can expose latent defects that are lurking under the covers. GameDays help teams to understand scaling drivers (page views, orders, and so forth) and gives them an opportunity to test their scaling practices. To learn more, read Resilience Engineering: Learning to Embrace Failure or watch the video: GameDay: Creating Resiliency Through Destruction.

Prime Day 2017 Metrics
So, how did we do this year?

The AWS teams checked their dashboards and log files, and were happy to share their metrics with me. Here are a few of the most interesting ones:

Block Storage – Use of Amazon Elastic Block Store (EBS) grew by 40% year-over-year, with aggregate data transfer jumping to 52 petabytes (a 50% increase) for the day and total I/O requests rising to 835 million (a 30% increase). The team told me that they loved the elasticity of EBS, and that they were able to ramp down on capacity after Prime Day concluded instead of being stuck with it.

NoSQL Database – Amazon DynamoDB requests from Alexa, the Amazon.com sites, and the Amazon fulfillment centers totaled 3.34 trillion, peaking at 12.9 million per second. According to the team, the extreme scale, consistent performance, and high availability of DynamoDB let them meet needs of Prime Day without breaking a sweat.

Stack Creation – Nearly 31,000 AWS CloudFormation stacks were created for Prime Day in order to bring additional AWS resources on line.

API Usage – AWS CloudTrail processed over 50 billion events and tracked more than 419 billion calls to various AWS APIs, all in support of Prime Day.

Configuration TrackingAWS Config generated over 14 million Configuration items for AWS resources.

You Can Do It
Running an event that is as large, complex, and mission-critical as Prime Day takes a lot of planning. If you have an event of this type in mind, please take a look at our new Infrastructure Event Readiness white paper. Inside, you will learn how to design and provision your applications to smoothly handle planned scaling events such as product launches or seasonal traffic spikes, with sections on automation, resiliency, cost optimization, event management, and more.

Jeff;

 

Use CloudFormation StackSets to Provision Resources Across Multiple AWS Accounts and Regions

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/use-cloudformation-stacksets-to-provision-resources-across-multiple-aws-accounts-and-regions/

AWS CloudFormation helps AWS customers implement an Infrastructure as Code model. Instead of setting up their environments and applications by hand, they build a template and use it to create all of the necessary resources, collectively known as a CloudFormation stack. This model removes opportunities for manual error, increases efficiency, and ensures consistent configurations over time.

Today I would like to tell you about a new feature that makes CloudFormation even more useful. This feature is designed to help you to address the challenges that you face when you use Infrastructure as Code in situations that include multiple AWS accounts and/or AWS Regions. As a quick review:

Accounts – As I have told you in the past, many organizations use a multitude of AWS accounts, often using AWS Organizations to arrange the accounts into a hierarchy and to group them into Organizational Units, or OUs (read AWS Organizations – Policy-Based Management for Multiple AWS Accounts to learn more). Our customers use multiple accounts for business units, applications, and developers. They often create separate accounts for development, testing, staging, and production on a per-application basis.

Regions – Customers also make great use of the large (and ever-growing) set of AWS Regions. They build global applications that span two or more regions, implement sophisticated multi-region disaster recovery models, replicate S3, Aurora, PostgreSQL, and MySQL data in real time, and choose locations for storage and processing of sensitive data in accord with national and regional regulations.

This expansion into multiple accounts and regions comes with some new challenges with respect to governance and consistency. Our customers tell us that they want to make sure that each new account is set up in accord with their internal standards. Among other things, they want to set up IAM users and roles, VPCs and VPC subnets, security groups, Config Rules, logging, and AWS Lambda functions in a consistent and reliable way.

Introducing StackSet
In order to address these important customer needs, we are launching CloudFormation StackSet today. You can now define an AWS resource configuration in a CloudFormation template and then roll it out across multiple AWS accounts and/or Regions with a couple of clicks. You can use this to set up a baseline level of AWS functionality that addresses the cross-account and cross-region scenarios that I listed above. Once you have set this up, you can easily expand coverage to additional accounts and regions.

This feature always works on a cross-account basis. The master account owns one or more StackSets and controls deployment to one or more target accounts. The master account must include an assumable IAM role and the target accounts must delegate trust to this role. To learn how to do this, read Prerequisites in the StackSet Documentation.

Each StackSet references a CloudFormation template and contains lists of accounts and regions. All operations apply to the cross-product of the accounts and regions in the StackSet. If the StackSet references three accounts (A1, A2, and A3) and four regions (R1, R2, R3, and R4), there are twelve targets:

  • Region R1: Accounts A1, A2, and A3.
  • Region R2: Accounts A1, A2, and A3.
  • Region R3: Accounts A1, A2, and A3.
  • Region R4: Accounts A1, A2, and A3.

Deploying a template initiates creation of a CloudFormation stack in an account/region pair. Templates are deployed sequentially to regions (you control the order) to multiple accounts within the region (you control the amount of parallelism). You can also set an error threshold that will terminate deployments if stack creation fails.

You can use your existing CloudFormation templates (taking care to make sure that they are ready to work across accounts and regions), create new ones, or use one of our sample templates. We are launching with support for the AWS partition (all public regions except those in China), and expect to expand it to to the others before too long.

Using StackSets
You can create and deploy StackSets from the CloudFormation Console, via the CloudFormation APIs, or from the command line.

Using the Console, I start by clicking on Create StackSet. I can use my own template or one of the samples. I’ll use the last sample (Add config rule encrypted volumes):

I click on View template to learn more about the template and the rule:

I give my StackSet a name. The template that I selected accepts an optional parameter, and I can enter it at this time:

Next, I choose the accounts and regions. I can enter account numbers directly, reference an AWS organizational unit, or upload a list of account numbers:

I can set up the regions and control the deployment order:

I can also set the deployment options. Once I am done I click on Next to proceed:

I can add tags to my StackSet. They will be applied to the AWS resources created during the deployment:

The deployment begins, and I can track the status from the Console:

I can open up the Stacks section to see each stack. Initially, the status of each stack is OUTDATED, indicating that the template has yet to be deployed to the stack; this will change to CURRENT after a successful deployment. If a stack cannot be deleted, the status will change to INOPERABLE.

After my initial deployment, I can click on Manage StackSet to add additional accounts, regions, or both, to create additional stacks:

Now Available
This new feature is available now and you can start using it today at no extra charge (you pay only for the AWS resources created on your behalf).

Jeff;

PS – If you create some useful templates and would like to share them with other AWS users, please send a pull request to our AWS Labs GitHub repo.

New Security Whitepaper Now Available: Use AWS WAF to Mitigate OWASP’s Top 10 Web Application Vulnerabilities

Post Syndicated from Vlad Vlasceanu original https://aws.amazon.com/blogs/security/new-security-whitepaper-now-available-use-aws-waf-to-mitigate-owasps-top-10-web-application-vulnerabilities/

Whitepaper image

Today, we released a new security whitepaper: Use AWS WAF to Mitigate OWASP’s Top 10 Web Application Vulnerabilities. This whitepaper describes how you can use AWS WAF, a web application firewall, to address the top application security flaws as named by the Open Web Application Security Project (OWASP). Using AWS WAF, you can write rules to match patterns of exploitation attempts in HTTP requests and block requests from reaching your web servers. This whitepaper discusses manifestations of these security vulnerabilities, AWS WAF–based mitigation strategies, and other AWS services or solutions that can help address these threats.

– Vlad