Best of Logging — July 2025

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   Article

   The Data Engineer·43w

   We stopped relying on bloom filters and now sort our ClickHouse primary key on a resource fingerprint. It cut our log query scans to 0.85% of blocks

   A development team optimized ClickHouse log query performance by replacing bloom filter skip indexes with a deterministic resource fingerprint approach. They sort their primary key on a hash of cluster, namespace, and pod information, which groups logs from the same source together. This change reduced block scanning from nearly 100% to just 0.85% (222 out of 26,135 blocks) for single namespace queries, significantly improving I/O and latency. The team is now exploring ClickHouse's native JSON column type to further optimize GROUP BY operations.

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2. 2
   Article

   Last9·47w

   How to Write Logs to a File in Go

   A comprehensive guide to implementing file logging in Go applications, covering everything from basic file output using the standard library to advanced structured logging with packages like Logrus and Zerolog. The guide addresses production considerations including log rotation, permission management, error handling, and performance optimization. It demonstrates practical patterns for environment-specific configuration, concurrent logging safety, and integration with observability platforms.

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3. 3
   Video

   Nick Chapsas·46w

   Logging correctly in .NET

   String interpolation in .NET logging creates unnecessary allocations even when logging is disabled. Structured logging using message templates with parameters is more efficient, avoiding allocations and formatting costs while maintaining queryable logs in tools like Kibana and Application Insights.

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