We have a database with more than a billion daily statistical records. Each record has multiple metrics (m1 through m10), and several immutable tags.
Record can also be associated with zero or more groups. The idea was to use multiple tags (e.g. g1, g2) to indicate the belonging of specific record to specific group.
Our data is stored on daily level, and most time-series databases are really optimized for more granular data. This represents a problem when we want to produce monthly, or quarterly graphs (e.g. InfluxDB have maximum aggregation period of 7d). We need a database that is really optimized for day-level data points and can produce quick aggregations on month/quarter/year level.
Furthermore, the relationship between records and groups is mutable. We need the database to support the batch update of records (pseudo: ADD TAG group1 TO records WHERE record_id: 101), or at least fast deletion/reinserting of updated data. This operation should be relatively fast.
We need something that can produce near-real-time results when aggregating data across tens of millions (filtered) records.
Our original solution is based on elasticsearch and it works quite well, but wanted to explore alternatives in time-series databases niche. Can anyone recommend a time-series database that supports these features?