Cloud Observability Basics
Cloud
observability is the
practice of understanding the internal state of cloud-based systems by
analyzing their external outputs. It goes beyond traditional monitoring by
helping teams answer not just what is wrong, but why it
is happening in complex, distributed environments like microservices and
Kubernetes.
Core
Pillars of Observability
Effective
cloud observability relies on three primary data types, often called the
"three pillars"
- Metrics: Numerical values (like CPU
usage, memory consumption, or error rates) measured over time. They are
essential for spotting trends and triggering real-time alerts.
- Logs: Detailed, timestamped
records of specific events within an application or server. They provide
the "narrative" context needed for deep troubleshooting.
- Traces: Data that tracks a single
request as it moves through various services in a distributed system. This
is critical for identifying latency and bottlenecks in microservices
architectures.
Monitoring
vs. Observability
While often
used interchangeably, they represent different approaches:
1.
Monitoring (Reactive): Focuses on "known unknowns." It uses
predefined dashboards and alerts to tell you when a specific threshold (e.g.,
90% CPU) is breached.
2.
Observability (Proactive/Diagnostic): Focuses on "unknown unknowns." it
allows teams to explore data on the fly to diagnose unexpected failures and
complex interdependencies that weren't predicted in advance.