Back in Part I of Deploying a Serverless Data Processing Workflow with AWS Step Functions, Nuatu mentioned one key benefit of using step functions is their visibility into business critical workflows. Outside stakeholders, support staff, and other engineers can look at a state machine execution in AWS or Stackery, and can easily understand the process.
Last week I was at Monitorama and it was an amazing time! Not only where the talks fantastic, but I also had the opportunity to join Liz Fong-Jones from Honeycomb.io for a podcast with The New Stack to dig a little deeper into some of the topics in monitoring and site reliability engineering.
Welcome to our series of blog posts about all the nitty-gritty details that go into building a great debug experience at scale. Today, we’re looking at Symbolicator, the service that processes all native crash reports and minidumps at Sentry.
What are the essential things to monitor in your infrastructure? Sure, CPU utilization, memory usage, and IO throughput. However, once you notice a significant load somewhere in your infrastructure you want to know what is causing it, and that typically boils down to needing to find the process that’s using too much CPU or memory or that’s doing disk or network IO like there’s no tomorrow.
