This line shows up as about 5% of cpu time on a synchrotron:
not_known_entities = set(entities) - set(self._entity_to_key)
Presumably the problem here is that _entity_to_key can be largeish, and
building a set for its keys every time this function is called is slow.
Here we rewrite the logic to avoid building so many sets.
When _get_state_for_groups is given a wildcard filter, just do a complete
lookup. Hopefully this will give us the best of both worlds by not filling up
the ram if we only need one or two keys, but also making the cache still work
for the federation reader usecase.
When we finish processing a request, log the number of events we fetched from
the database to handle it.
[I'm trying to figure out which requests are responsible for large amounts of
event cache churn. It may turn out to be more helpful to add counts to the
prometheus per-request/block metrics, but that is an extension to this code
anyway.]
There's a frequent idiom I noticed where an iterable is split up into a
number of chunks/batches. Unfortunately that method does not work with
iterators like dict.keys() in python3. This implementation works with
iterators.
Signed-off-by: Adrian Tschira <nota@notafile.com>
So, it turns out that if you have a first `Deferred` `D1`, you can add a
callback which returns another `Deferred` `D2`, and `D2` must then complete
before any further callbacks on `D1` will execute (and later callbacks on `D1`
get the *result* of `D2` rather than `D2` itself).
So, `D1` might have `called=True` (as in, it has started running its
callbacks), but any new callbacks added to `D1` won't get run until `D2`
completes - so if you `yield D1` in an `inlineCallbacks` function, your `yield`
will 'block'.
In conclusion: some of our assumptions in `logcontext` were invalid. We need to
make sure that we don't optimise out the logcontext juggling when this
situation happens. Fortunately, it is easy to detect by checking `D1.paused`.
