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TrailDB is designed for discrete events (think JSON objects) rather than continuous metrics (like CPU utilization). Most time-series databases manage to store
by vtuulos 10y ago
TrailDB is designed for discrete events (think JSON objects) rather than continuous metrics (like CPU utilization).
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.
- ademarre 10y agoI've not yet used any of these, but I think InfluxDB stores discrete events. In fact, your description sounds very similar to this comparison of Prometheus and InfluxDB[0]: > 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/#prometheus-vs.-influxdb https://prometheus.io/docs/introduction/comparison/#promethe...
- oavdeev 10y agoYes, schema wise that's pretty close. 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.