Logschema

Logschema is a term used to describe a standardized structure for log events. In practice, it refers to a schema that defines the fields, data types, and validation rules that govern log messages produced by software systems. Different organizations may implement their own variants, and there is no single universal Logschema across all platforms.

The main purpose of a logschema is to provide consistency for ingestion, storage, search, and correlation of logs. A formal logschema helps ensure that logs from different services can be parsed uniformly, enabling efficient querying, alerting, and analytics. It also supports schema evolution and backward compatibility when fields are added or deprecated.

Common elements in a logschema include timestamp, severity level, message, service or logger name, host, and process or thread identifiers. Context fields such as correlation_id, user_id, or session_id are frequently included. Additional fields may capture environment, version, event_id, and stack traces. The exact set of fields is often driven by the needs of the organization and the observability goals it pursues.

Logschemas are commonly expressed in machine-readable formats such as JSON Schema, Apache Avro, or Protocol Buffers, and can be enforced at ingestion by validation rules. They may exist as a formal document describing the schema or be implemented as part of a schema registry to enable versioning and distribution.

In practice, logs that conform to a logschema support interoperability with log management and observability platforms and align with conventions such as OpenTelemetry semantic conventions. Challenges include schema drift, performance considerations, optional fields, and balancing schema stability with evolving needs.