How to use LogQL range aggregations in Loki

In this blog post, the author explores how to leverage LogQL range aggregations in Loki to effectively aggregate log data over time, introducing two main types of aggregation operations. The first type operates on log entries as a whole with functions like rate, count_over_time, and bytes_rate, while the second type uses unwrapped ranges to treat extracted labels as sample values, supporting functions such as avg_over_time and quantile_over_time. The post emphasizes the utility of these operations for aggregating data on specific dimensions without vector operations, allowing for enhanced metric queries by parsing log data to include new dimensions. The author notes the importance of grouping for reducing series in metric queries that use logfmt and JSON parsers. Highlighting the potential of combining range vector operations with LogQL parsers, the post also encourages readers to explore the documentation further for a deeper understanding and suggests trying Loki through self-installation or Grafana Cloud, which offers new plans catering to various use cases.