This chapter covers
- What we mean by back-end destinations and what types of destinations exist
- Back-end destination options for handling logs, metrics, and traces
- What time series datastores are and what you can use them for
- Introducing columnar datastores and formats along with their use cases
- Considerations for selecting back-end destinations
By now you should know what major signal types exist (2), where they are coming from (3), and what options exist to collect and pre-process said signals (4). In this chapter we will dive deep on back-end destinations, the targets or sinks for agents. Back-end destinations, or back-ends for short, are the source of truth for your observability questions. They allow you to store and query signals and make them available to front-end destinations (6).
Why do I make the distinction between back-end and front-end? Well, there are a few examples that fall exactly in the one or other category. For example, Grafana is a pure front-end destination (sometimes also called the presentation stage) that queries back-end destinations to visualize or alert on signals. The Cloud Native Computing Foundation (CNCF) project Cortex on the other hand, is a pure back-end destination (beside rudimentary admin Web UI you only have the API to ingest and query metrics).