* Alerting: Optimize rule status gathering APIs when a limit is applied.
The frontend very commonly calls the `/rules` API with `limit_alerts=16`. When
there are a very large number of alert instances present, this API is quite
slow to respond, and profiling suggests that a big part of the problem is
sorting the alerts by importance, in order to select the first 16.
This changes the application of the limit to use a more efficient heap-based
top-k algorithm. This maintains a slice of only the highest ranked items whilst
iterating the full set of alert instances, which substantially reduces the
number of comparisons needed. This is particularly effective, as the
`AlertsByImportance` comparison is quite complex.
I've included a benchmark to compare the new TopK function to the existing
Sort/limit strategy. It shows that for small limits, the new approach is
much faster, especially at high numbers of alerts, e.g.
100K alerts / limit 16: 1.91s vs 0.02s (-99%)
For situations where there is no effective limit, sorting is marginally faster,
therefore in the API implementation, if there is either a) no limit or b) no
effective limit, then we just sort the alerts as before. There is also a space
overhead using a heap which would matter for large limits.
* Remove commented test cases
* Make linter happy
73873f5a8a