Dimensionality can be seen as the number of possible columns in a database. Imagine a dataset containing information about customers, such as age, gender, location, and purchase history. The dimensionality of this dataset would be the number of these attributes, which determines the complexity of analyzing and understanding the dataset as a whole.
For observability, dimensionality represents the number of unique fields present in logs, traces or wide events. High dimensionality datasets are important in observability as they enable you to add as much data to a single event / log / span, each field giving you more context about the behaviour of your application.
Explore related concepts
Wide event
A wide event is a comprehensive data structure that contains all relevant details of a particular request or transaction within a single event, rather than distributing them across multiple events or logs.
Cardinality
Cardinality is the number of unique values in a dataset. For example, the number of unique values a specific field in a set of logs can take.
Observability
Observability is the ability to understand how your application is working and behaving in production through telemetry data (logs, metrics, traces, wide events, etc.). It enables you detect, diagnose and resolve issues in your app before they impact your users and become problems.