go-logger-analyzer

To analyze Go JSON logs from mixed frameworks like zap, logrus, and zerolog, you can run a Python-based analyzer that loads your log files and computes statistical distributions. It automatically processes varied formats to surface patterns and hotspots.

Prefix-tree field analysis for Go log exploration is a method to discover and navigate common log field hierarchies and nested patterns. It maps structured JSON fields into a tree structure, allowing you to identify frequent field relationships and nested data.

Yes, you can compute logger distribution and caller insights for large Go services. The analyzer summarizes errors with context, calculates log level distributions, and identifies the most frequent call sites to help diagnose issues in large-scale applications.

The best way to find error patterns and hotspots in Go application logs is to use a structured analyzer that computes top messages, summarizes errors with context, and identifies frequent call sites. This approach accelerates issue diagnosis by highlighting statistical anomalies.

You need JSON log files to compute statistics and time-range distributions. The analyzer is applicable to varied Go log formats including zap, logrus, and zerolog, requiring structured JSON output to accurately parse fields and calculate logger distributions.