loggst

Loggst is a term used in discussions of data logging and time-series analysis to describe a logarithmic aggregation technique for high-volume log streams. The core idea is to convert a continuous stream of events into a compact, multi-resolution representation by grouping events into buckets whose widths grow logarithmically with time and by computing summary statistics within each bucket.

In practice, loggst maintains several layers of buckets. Each layer uses a different time scale, such as short, medium, and long windows, with bucket boundaries expanding geometrically. Updates are performed online as events arrive, updating the relevant buckets without storing every individual event. Queries return approximate counts, means, and percentile-like statistics for a requested interval, with accuracy improving at coarser resolutions and diminishing for very fine-grained requests.

Applications of loggst include real-time monitoring dashboards, anomaly detection, and capacity planning in large-scale systems. The approach is particularly suitable where extreme data volumes would make exact storage or queries impractical, and where approximate results suffice for trend analysis or alerting.

Limitations include the inherent approximation error, the need to tune base factors and decay parameters, and added complexity compared with simple counters or raw-storage schemes. It is typically used alongside other time-series database features and can be integrated into streaming analytics pipelines.

See also: time-series analysis, histograms, data compression, streaming analytics.