Re:Timescale raises $110M Series C
by CyberDildonics on Tuesday February 22, 2022 @03:00PM
(#30432882)
There are two different things here - the first is people not needing an elaborate solution because computers are fast and the second is that if someone does need a solution with less overhead, why is that difficult?
Values over time like audio is a one dimensional signal. Seeking is basic data structure stuff, filtering is basic signal stuff. There aren't going to be other dimensions like time, which makes the other values just other channels. If you need to combine dense values they can be not only filtered, but filtered into individual distributions.
People give abstract descriptions like you have here, but I'm just not seeing a difficult problem in all of this.
The problem something like timescale tries to solve is dealing with "high cardinality." When you have many unique values the indexing approaches needed to ensure performance start becoming different. You'll run into write performance issues if need indexes on every single column, and every single combination of columns, and each column has a large number of unique values. While the common factor many of these datasets tend to share is that they are being constantly generated by some kind of sensor/probe/live-system, they tend to have a variety of other dimensions that are also high-cardinality.