Most time-series databases manage to store a large number of data points by aggregating data over time. Discrete events can't be aggregated. Instead, TrailDB leverages predictability of events to compress data over time.
> InfluxDB is better geared towards the following use cases: Storing all individual events, not just time series of values. E.g. storing every HTTP request with full metadata vs. storing the cumulative count of HTTP requests for certain dimensions.
[0] https://prometheus.io/docs/introduction/comparison/#promethe...
However, last time I looked that didn't seem like a use case InfluxDB invested a ton of effort optimizing for, especially if you tried to store a lot of separate "event histories" either as separate measurements or by using tags. By "a lot" I mean 100K-100M histories. It may have improved since.
Besides, there is somewhat fundamental tradeoff between allowing efficient realtime granular writes, which I believe a priority for InfluxDB, and building efficient indexed store for millions of histories/event trails, which is more of a TrailDB use case. Kind of OLAP vs OLTP.