On 8/26/19 6:48 PM, Peter Geoghegan wrote:
> Such data often consists of timestamps from a large number
> of low cost devices -- event data that arrives *approximately* in
> order. This is more or less the problem that the TimescaleDB extension
> targets, so it seems likely that a fair number of users care about
> getting it right, even if they don't know it.
This problem is not limited to IoT but to RT financial transaction
ingestion as well.
I found BRIN indices to work exceptionally well for that, while B-tree
taking enormous amounts of space with no performance difference or win
going to BRIN.
The situation gets even worse when B-tree index is subjected to
identical tuples which often happens when you have an avalanche of
timestamps that are within less than 1ms of each other (frequent TS
rounding resolution).
--
Arcadiy Ivanov
arcadiy@gmail.com | @arcivanov | https://ivanov.biz
https://github.com/arcivanov