Episode 31: Hey Sam, wake up. It’s 3am, and time to solve a murder mystery!

Have you ever been on-call duty as an IT person or otherwise? Woken up at 3 a.m. to solve a problem? Did you have to go through log files or look at a dashboard to figure out what was going on? Did you think there has got to be a better way to troubleshoot and solve problems? Today, we’re talking to Sam Bashton, who previously ran a premiere consulting partner with Amazon Web Services (AWS). Recently, he started runbook.cloud, which is a tool built on top of serverless technology that helps people find and troubleshoot problems within their AWS environment. Some of the highlights of the show include: Runbook.cloud looks at metrics to generate machine learning (ML) intelligence to pinpoint issues and present users with a pre-written set of solutions Runbook.cloud looks at all potential problems that can be detected in context with how the infrastructure is being used without being annoying and useless ML is used to do trend analysis and understand how a specific customer is using a service for a specific auto scaling group or Lambda functions Runbook.cloud takes all aggregate data to influence alerts; if there’s a problem in a specific region with a specific service, the tool is careful to caveat it Various monitoring solutions are on the market; runbook.cloud is designed for a mass market environment; it takes metrics that AWS provides for free and makes it so you don’t need to worry about them Will runbook.cloud compete with or sell out to AWS? Amazon wants to build underlying infrastructure, other people to use its APIs to build interfaces for users Runbook.cloud is sold through AWS Marketplace; it’s a subscription service where you pay by the hour and the charges are added to your AWS bill Amazon vs. Other Cloud Providers: Work is involved to detect problems that address multiple Clouds; it doesn’t make sense to branch out to other Clouds Runbook.cloud was built on top of serverless technology for business financial reasons; way to align outlay and costs because you pay for exactly what you use Analysis paralysis is real; it comes down to getting the emotional toil of making decisions down to as few decision points as possible Save money on Lambda; instead of using several Lambda functions concurrently, put everything into a single function using Go AWS responds to customers to discover how they use its services; it comes down to what customers need Links: Sam Bashton on Twitter runbook.cloud How We Massively Reduced Our AWS Lambda Bill with Go AWS AWS Lambda Microsoft Clippy Honeycomb AWS X-Ray Kubernetes Simon Wardley Go Secrets Manager DynamoDB EFS Digital Ocean