Observability
Monitoring is one of the most important areas of maintaining a product. Knowing when something breaks or degrades the performance for your users is incredibly important, and to be efficient when investigating, you'll need to do some work upfront that'll help you through hard times. Observability,
Observability
In my recent posts on managing a commit-oriented infrastructure, I explained the benefits of deploying services of one specific version. This version would be the hashed Git commit reference, or commit SHA. When running this kind of setup, you might encounter the question of which version is running at the
NewRelic
When systems grow bigger, we quickly start lacking a degree of oversight that would be comfortable. Our services interact with third-party tools, query databases, send webhook requests, and perform countless other operations. Even for simple projects, analyzing how long certain operations take, or being able to trace individual requests can
GraphQL
After releasing the post about tracing basics with an example of setting up tracing with a GraphQL service, I thought of a solution to make the development of tracing features for this kind of services even easier - the result of that effort is called graphql-middleware-jaeger. The simple functionality of
GraphQL
Tracing has emerged as one of the most widely-used techniques to monitor the performance and health of your services and applications, from development to production. Due to this heavy usage, a large amount of tooling is published and improved constantly, focusing on features related to distributed systems and recent industry