01Why Observability

Why Observability

FoundationsModule 014 topics~2h 40m

Topics, in order

01 Monitoring vs ObservabilityMonitoring answers questions you thought of in advance: is the error rate above 1%? Observability is the property of a system that lets you answer questions you did not think of in advance: … Beginner 30 min 02 The SignalsTelemetry comes in a small number of shapes, called signals. Metrics are numbers sampled over time: cheap, good for trends and alerts. Logs are timestamped records of individual things that … Beginner 40 min 03 SLIs, SLOs and Error BudgetsAn SLI (service level indicator) is a measurement of what users experience, written as a ratio: good events ÷ all events. An SLO (service level objective) is a target for that ratio over a … Beginner 45 min 04 Percentiles and TailsLatency is not one number; it is a distribution, and it is almost always lopsided: most requests are quick and a few are very slow. The average blends them into a figure that describes … Beginner 45 min

About this module

Before any tool: what are you trying to learn about a running system, and how do you state “working” precisely enough that a machine can check it?

#	Lesson	The question it answers
01	Monitoring vs Observability	What is the difference, and why does it matter?
02	The Signals	What kinds of telemetry exist, and what is each one good for?
03	SLIs, SLOs and Error Budgets	How do you write down “good enough” as a number?
04	Percentiles and Tails	Why is the average the wrong number, and what replaces it?

When you finish you can look at any service and say which three numbers describe whether its users are happy, and why none of them is an average.