Operations | Monitoring | ITSM | DevOps | Cloud

Ten years ago, Amazon found that every 100ms of latency would cost them roughly 1% in sales. This is a pretty clear statement on the importance of user experience! It’s especially true in today’s ultra-competitive market where the cost of switching (to another provider) for consumers is lower than ever. And one of the most common performance issues in serverless architectures is related to elevated latencies from services we depend on.

We’re pleased to introduce you to the latest Elastic Cloud features and functionality. Grab a cup of your favorite beverage and five minutes, and let’s dive in.

AWS API Gateway is a great service but can be quite expensive, and even cost-prohibitive in some cases. An Application Load Balancer is a viable alternative since it integrates seamlessly with Lambda functions and is also highly scalable and reliable. For very small traffic, API Gateway will probably be the winner, but in high-throughput cases, ALB is capable of providing up to 90% savings.

In this article we are going to consider the two most common methods for Autoscaling in EKS cluster: The Horizontal Pod Autoscaler or HPA is a Kubernetes component that automatically scales your service based on metrics such as CPU utilization or others, as defined through the Kubernetes metric server. The HPA scales the pods in either a deployment or replica set, and is implemented as a Kubernetes API resource and a controller.

Monitoring your Azure cloud computing environment requires an understanding of each component of your Azure infrastructure and how they all interact. Cloud computing with Azure offers each of the three main advantages of cloud service providers: infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). Combined, these three services provide a comprehensive cloud-based computing setup for any business.

At Elastic we are constantly innovating and releasing new features. As we release new features we are also working to make sure that they are tested, solid, and reliable — and sometimes we do find bugs or other issues. While testing a new feature we discovered a Linux kernel bug affecting SSD disks on certain Linux kernels. In this blog article we cover the story around the investigation and how it involved a great collaboration with two close partners, Google Cloud and Canonical.

AWS Lambda enables you to run serverless functions in the AWS cloud, by manually triggering functions or by creating trigger events. To ensure your Lambda functions are running smoothly, you can monitor metrics that measure performance, invocations, and concurrencies. However, even if you continuously monitor, once in a while you are going to run into what’s termed a Lamba cold start. There are various ways to prevent AWS Lambda cold starts.

Since almost the beginning of programming, the idea of write-once and deploy everywhere, on all platforms, has been an unreachable ideal to minimize development costs for cross-platform applications, drive UI consistency and reduce security service area. In programming, the cross-platform languages Java and Python have topped developer utilization charts for decades.

Some, maybe. But not most. Look, I have this theory. I also have data. And as with most stories in my life, it all starts with a trip to Las Vegas. In 2017, I attended my first AWS re:Invent in Las Vegas. For a while I heard how re:Invent was either a “cloud” or a “developer” event. I now believe neither to be true. Most people I met at re:Invent were operations folks. They were there to learn about monitoring and managing applications running in AWS.