The main use case is to see how many requests are being made, and how
many are second/third/etc attempts. If there are large number of retries
then that likely indicates a delivery problem.
#8567 started a span for every background process. This is good as it means all Synapse code that gets run should be in a span (unless in the sentinel logging context), but it means we generate about 15x the number of spans as we did previously.
This PR attempts to reduce that number by a) not starting one for send commands to Redis, and b) deferring starting background processes until after we're sure they're necessary.
I don't really know how much this will help.
This was a bit unweildy for what I wanted: in particular, I wanted to assign
each measurement straight into a bucket, rather than storing an intermediate
Counter which didn't do any bucketing at all.
I've replaced it with something that is hopefully a bit easier to use.
(I'm not entirely sure what the difference between a HistogramMetricFamily and
a GaugeHistogramMetricFamily is, but given our counters can go down as well as
up the latter *sounds* more accurate?)
Our hacked-up `_exposition.py` was stripping out some samples it shouldn't
have been. Put them back in, to more closely match the upstream
`exposition.py`.
slots use less memory (and attribute access is faster) while slightly
limiting the flexibility of the class attributes. This focuses on objects
which are instantiated "often" and for short periods of time.
HTTP requires the response to contain a Content-Length header unless chunked encoding is being used.
Prometheus metrics endpoint did not set this, causing software such as prometheus-proxy to not be able to scrape synapse for metrics.
Signed-off-by: Christian Svensson <blue@cmd.nu>
PyPy's gc.get_stats() returns an object containing detailed allocator statistics
which could be beneficial to collect as metrics.
Signed-off-by: Ivan Shapovalov <intelfx@intelfx.name>
It turns out that looping_call does check the deferred returned by its
callback, and (at least in the case of client_ips), we were relying on this,
and I broke it in #3604.
Update run_as_background_process to return the deferred, and make sure we
return it to clock.looping_call.
This introduces a mechanism for tracking resource usage by background
processes, along with an example of how it will be used.
This will help address #3518, but more importantly will give us better insights
into things which are happening but not being shown up by the request metrics.
We *could* do this with Measure blocks, but:
- I think having them pulled out as a completely separate metric class will
make it easier to distinguish top-level processes from those which are
nested.
- I want to be able to report on in-flight background processes, and I don't
think we want to do this for *all* Measure blocks.
