Amazon Elastic Container Service (ECS) is an orchestration service for Docker containers running within the Amazon Web Services (AWS) cloud. You can declare the components of a container-based infrastructure, and ECS will deploy, maintain, and remove those components automatically. The resulting ECS cluster lends itself to a microservice architecture where containers are scaled and scheduled based on need.
In Part 1, we introduced a number of key metrics that you can use for ECS monitoring. Monitoring ECS involves paying attention to two levels of abstraction: the status of your services, tasks, and containers, as well as the resource use from the underlying compute and storage infrastructure, monitored per EC2 host or Docker container. In this post, we’ll survey some techniques you can use to monitor both levels of your ECS deployment.
As we explained in Part 1, it’s important to monitor task status and resource use at the level of ECS constructs like clusters and services, while also paying attention to what’s taking place within each host or container. In this post, we’ll show you how Datadog can help you: Automatically collect metrics from every layer of your ECS deployment, Track data from your ECS cluster, plus its hosts and running services in dashboards, and more.
Lambda@Edge is a compute service that allows you to write JavaScript code that executes in any of the 150+ AWS edge locations making up the Amazon CloudFront content delivery network (CDN) service. In this post, I’ll provide some background on CDN technologies. I will also build out an application stack that serves country-specific content depending on where the user request originates from.
There is a lot to love about Kubernetes. It offers one of the best ways to deploy and run applications on a large pool of resources. With its easy-to-use UI and out-of-the-box capabilities like RBAC, monitoring, auditing, logging, and more, Rancher makes it easy to stand up and manage enterprise grade Kubernetes. Using Rancher, IT Operators can point to their cloud provider (AWS, GCP, Azure, etc.) or datacenter and create a cluster with just a few clicks.
Since joining Rancher Labs to head up the Australia, New Zealand, and Singapore region, my day revolves around discussing containers/Kubernetes use cases and adoption with many of the top enterprises, DevOps groups, and executives in the area. Not only is this a great learning experience and a fantastic way to meet people, it is also a huge eye opener into the many reasons why Kubernetes adoption is growing so rapidly and what the current challenges are.
Many of my fellow engineers ask me what it means to be an SRE (Site Reliability Engineer). When I tell them it’s a type of DevOps engineer, they get a glazed look in their eyes and then ask what a DevOps engineer is. I then find myself googling both job titles and reading twelve very different definitions until I reach the conclusion that these definitions vary wildly from company to company and from team to team.
When creating EBS snapshots, it’s important that the snapshots be “consistent”. This means that the data on the snapshot is whole and complete. An EBS snapshot can be considered “inconsistent” if not all data was flushed to the filesystem, and/or if an application running on the EC2 instance was mid-write when the EBS snapshot was initiated.
As Stackery’s Ecosystems Manager, a huge part of my work revolves around meeting new people and developing relationships with them for the good of our company. I love this work not only because I’m passionate about people and serverless, but also because it keeps my curiosity muscle strong. To be good at my job, I need to do right by my personal connection to curiosity and learning— but sometimes I get off-track.
