Rethinking Logging: Why Traditional Practices Fail in the Era of Distributed Systems

Traditional logging, a long-standing cornerstone of software debugging, is increasingly being deemed obsolete and detrimental in the landscape of modern distributed systems. While individual developers may still find ad-hoc console logs invaluable for local troubleshooting, the prevailing sentiment, echoed by industry experts like Boris, highlights that current logging methodologies, often designed for monolithic applications of the early 2000s, are inadequate for today’s complex, multi-service environments. A single user request can now traverse numerous services, databases, caches, and message queues, rendering the sequential, uncontextualized nature of traditional logs almost useless for pinpointing issues when requests intermingle in production. This challenge is further compounded by an artificial and harmful distinction between ‘logs for debugging’ and ‘metrics for dashboards,’ which advocates argue should be unified.

The proposed solution to this paradigm shift lies in the adoption of ‘wide events.’ This approach advocates for capturing comprehensive, high-cardinality data points that serve as a single source of truth for both debugging and performance monitoring. By moving away from fragmented log entries to rich, contextualized event streams, development teams can transform debugging from a labor-intensive ‘archaeology’ exercise into a precise ‘analytics’ operation. This transformation is made possible by modern columnar databases like ClickHouse and BigQuery, as well as specialized solutions such as Axiom’s proprietary technology, which are purpose-built to efficiently store and query high-dimensionality data. These advanced tools enable developers to execute complex queries, such as identifying all checkout failures for premium users within a specific timeframe and grouping them by error code, with sub-second results, thereby rapidly isolating root causes. This unified approach, while challenging traditional practices like the simple application of OpenTelemetry, promises a more truthful and efficient observability stack tailored for the demands of contemporary distributed architectures, including the secure execution of AI-generated code in sandboxed environments facilitated by platforms like Daytona.