Types of event data in observability
Traditionally, observability has been associated with three pillars, each effectively a specialized view of event data:- Logs: Logs record discrete events, such as error messages or access requests, typically associated with engineering or security.
- Traces: Traces track the path of requests through a system, capturing each step’s duration. By linking related spans within a trace, developers can identify bottlenecks and dependencies.
- Metrics: Metrics quantify state over time, recording data like CPU usage or user count at intervals. Product or engineering teams can then monitor and aggregate these values for performance insights.
Logs and traces support
Axiom excels at collecting, storing, and analyzing timestamped event data. For logs and traces, Axiom offers unparalleled efficiency and query performance. You can send logs and traces to Axiom from a wide range of popular sources. For information, see Send data to Axiom .Metrics support
For metrics data, Axiom is well-suited for event-level metrics that behave like logs, with each data point representing a discrete event. For example, you have the following timestamped data in Axiom:- Axiom doesn’t support pre-aggregated metrics such as scrape samples.
- Axiom isn’t optimized for high-dimensional metric time series with a very large number of metric/label combinations.