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