logslog

Logslog is a term used in software engineering to describe a style and ecosystem for structured logging that blends human-friendly text with machine-readable metadata. It defines a practical format for log entries and a reference set of libraries to produce, consume, and transport those entries across systems. The goal is to improve searchability, correlation, and auditing while preserving readability for developers debugging locally.

Entries are line-oriented. Each line includes a timestamp in ISO 8601, a severity level, a concise message, and an optional context object. The context is a nested map of key-value pairs that can encode identifiers, user information, request IDs, and other telemetry. Context may be serialized as JSON or a compact key-value syntax, depending on the implementation. When an entry spans multiple lines, a continuation indicator or quoting convention is used.

Designed for streaming and bulk processing, logslog supports lazy parsing, schema evolution, and efficient indexing. The schema is flexible but recommends a schema definition that remains backward compatible. The format aims to be human-readable when inspected directly, while remaining easily machine-parsable for log aggregators and search engines.

Various language bindings exist, including libraries for Python, Java, Go, and JavaScript, and are compatible with common log shippers and aggregators such as Filebeat, Fluentd, and Kafka-based pipelines. Logslog is used in microservice architectures to link events through identifiers like trace or correlation IDs, enhance incident response, and support compliance by retaining structured metadata along with messages.

Critics note that logslog cannot eliminate the need for standardization across tools, as different ecosystems may adopt divergent metadata schemas. While it improves structure over plain text logs, successful adoption relies on discipline in metadata naming and key usage.