We used to employ the worst strategy for parallelism possibly: The rate
limiter capped us at one concurrent request per second, while 100+ items
were handled in parallel. This lead to every item taking the full
duration of the job to proceed, making the data fetched at the beginning
of the job stale at the end. This leads to smaller hiccups when
labeling, or to the merge-bot posting comments after the PR has already
been closed.
GitHub allows 100 concurrent requests, but considers it a best practice
to serialize them. Since serializing all of them causes problems for us,
we should try to go higher.
Since other jobs are running in parallel, we use a conservative value of
20 concurrent requests here. We also introduce the same number of
workers going through the list of items, to make sure that each item is
handled in the shortest time possible from start to finish, before
proceeding to the next. This gives us roughly 2.5 seconds per individual
item - but speeds up the overall execution of the scheduled job to 20-30
seconds from 3-4 minutes before.
This turns the check-cherry-pick script into a github-script based
JavaScript program. This makes it much easier to extend to check reverts
or merge commits later on.
