AWS Announcements at a Glance: April 2021

Ross Lawrie

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With spring here, AWS has been taking advantage of the longer days and warming weather to release a host of new features, services and updates. As with any month, April has been full of announcements from AWS — with Bot Control in AWS WAF, updates to Amazon CodeGuru, the release of Amazon Lookout for Equipment, and Warm Pools in EC2 Auto Scaling, there’s a lot to dive into.


New AWS WAF Bot Control

AWS now gives organizations the means to more easily control traffic from bots, before it reaches your sites and applications, allowing the resources serving your platform to operate more efficiently. New functionality and features within AWS WAF allow for identification of bots, as well as metrics capture and visualization, and the ability to define customized actions based on the bot type.

WAF Bot Control is a new managed rule group within AWS WAF, and it includes pre-built dashboards that allow for a great at-a-glance understanding of how much of your traffic comes from bots, as well as what types of bots those are, such as content scrapers, monitoring tools or SEO crawlers. The default action is to block the unwanted bot traffic, but the custom actions can be defined to return a customized response or to use a new header to identify the request for later handling. By using Bot Control to remove unwanted traffic, the business metrics you collect from your end systems will be cleaner and more useful in understanding your client base, as well as allowing you to scale your systems more efficiently and reduce costs.

AWS WAF Bot Control is available now, and the visualization dashboard is included in the AWS WAF free tier.


Amazon CodeGuru Reviewer Updates

Amazon CodeGuru has been generally available since last June, allowing organizations to automate their code review process while also improving the quality of their code bases. This month, a couple of big announcements have been made to make this service even better.

First up, AWS has improved CodeGuru’s pricing model. It was previously based on the number of lines of code analyzed each month, which had the impact of dis-incentivizing its use for extremely active code repositories. The new model instead is a fixed monthly rate calculated on the total size of the connected repositories — the initial tier is $10 per month for 100k lines of code, and then an additional $30 per month for every 100k lines of code above that. With this change, organizations can expect to see up to a 90% decrease in CodeGuru costs.

The other big announcement is that Python support is now generally available, where previously only Java was supported. As a massively popular language, this will be a welcomed addition for many development teams, allowing them to take advantage of the recommendations afforded by CodeGuru. From suggestions on Python best practices to optimal data structures and concurrency, teams will be able to use CodeGuru to keep their code optimized and safe.

Learn more on the Amazon CodeGuru product page, and then start adding in your Python repositories to help maintain your high code standards.


Amazon Lookout for Equipment (GA)

At AWS re:Invent 2020, Amazon Lookout for Equipment was announced — a service to analyze equipment sensor data and provide predictive insight into the health of that equipment. Lookout for Equipment automatically builds and trains a machine learning model based on your sensor data, and then uses that to identify abnormalities and issues in real-time. By using Lookout for Equipment, organizations can quickly gain the advantages of machine learning on their sensor data without the need for expert data scientists. Specific sensors exhibiting abnormal behaviour can be quickly identified, as well as the impact of the event, letting teams move quickly to diagnose and remediate the emerging issue and avoid large scale problems.

With the general availability of Amazon Lookout for Equipment, organizations with large bases of equipment — such as those in the manufacturing vertical — can utilize their sensor data and machine learning to keep that equipment running at top performance.


Amazon EC2 Auto Scaling introduces warm pools

Auto Scaling has always been an amazing tool for increasing and decreasing EC2 instances in response to changes in demand, such as a spike in website visitors or fluctuations of work in a queue. One challenge that has surfaced are applications on EC2 instances that require extensive initialization or compilation that can’t be pre-built into the AMI, like imports of large amounts of data on boot, or custom provisioning.

AWS has introduced warm pools to EC2 Auto Scaling to help address this problem. Warm pools maintains a set of stopped, but pre-initialized instances that allow the scaling group to quickly scale out to its maximum when needed. As the instances are pre-initialized and then kept in a stopped state, costs are minimized while ensuring that they can be ready to respond quickly. Lifecycle hooks allow the instances to be properly prepared before they join the warm pool, as well as controlling actions as they leave the warm pool.

Refer to the Amazon EC2 Auto Scaling documentation on warm pools for more information on configuring and utilizing them.


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