in concrete task I’m trying to tackle there is plenty of cardinality with floating point measurements
these measurements are having tags
they have one big series with plenty of float data and some tags that index smaller amount of values along that big series
I’m not wondering if I combine multiple measurements that have plenty of float values(I assume this is high cardinality)
then does it offer any improvement over having them in multiple measurements. The only benefit in the docs its like querying without regexp.
One way that I tried reasoning about this is like so
When combining multiple floating point measurements that have this assumed high cardinality then when they are indexed they will blow up on memory as some blog posts say
but there might be some saving in space due to dependent tags
but that might blow up memory quite a bit due to high cardinality
with having them separate in different measurements then there is bit extra space used due to not dependant tags
but there is no issue of big index being made
since only few floating points are indexed in current tags
if we’d combine two measurements influx would need to index pretty much entire long series to figure things out
does this reasoning sound legit?