This reverts commit 1ca0ffeaf2.
kube-proxy is not recreating the rules associated to the
KUBE-MARK-DROP chain, that is created by the kubelet.
Is preferrable avoid the dependency between the kubelet and
kube-proxy and that each of them handle their own rules.
This includes IPv4 and IPv6 address types and IPVS dual stack support.
Importantly this ensures that EndpointSlices with a FQDN address type
are not processed by kube-proxy.
Computing EndpointChanges is a relatively expensive operation for
kube-proxy when Endpoint Slices are used. This had been computed on
every EndpointSlice update which became quite inefficient at high levels
of scale when multiple EndpointSlice update events would be triggered
before a syncProxyRules call.
Profiling results showed that computing this on each update could
consume ~80% of total kube-proxy CPU utilization at high levels of
scale. This change reduced that to as little as 3% of total kube-proxy
utilization at high levels of scale.
It's worth noting that the difference is minimal when there is a 1:1
relationship between EndpointSlice updates and proxier syncs. This is
primarily beneficial when there are many EndpointSlice updates between
proxier sync loops.
Until now, iptables probabilities had 5 decimal places of granularity.
That meant that probabilities would start to repeat once a Service
had 319 or more endpoints.
This doubles the granularity to 10 decimal places, ensuring that
probabilities will not repeat until a Service reaches 100,223 endpoints.
The proxy healthz server assumed that kube-proxy would regularly call
UpdateTimestamp() even when nothing changed, but that's no longer
true. Fix it to only report unhealthiness when updates have been
received from the apiserver but not promptly pushed out to
iptables/ipvs.
Kube-proxy runs two different health servers; one for monitoring the
health of kube-proxy itself, and one for monitoring the health of
specific services. Rename them to "ProxierHealthServer" and
"ServiceHealthServer" to make this clearer, and do a bit of API
cleanup too.
The detectStaleConnections function in kube-proxy is very expensive in
terms of CPU utilization. The results of this function are only actually
used for UDP ports. This adds a protocol attribute to ServicePortName to
make it simple to only run this function for UDP connections. For
clusters with primarily TCP connections this can improve kube-proxy
performance by 2x.
The .IP() call that was previously used for sorting resulted in a call
to netutil to parse an IP out of an IP:Port string. This was very slow
and resulted in this sort taking up ~50% of total CPU util for
kube-proxy.
Kubelet and kube-proxy both had loops to ensure that their iptables
rules didn't get deleted, by repeatedly recreating them. But on
systems with lots of iptables rules (ie, thousands of services), this
can be very slow (and thus might end up holding the iptables lock for
several seconds, blocking other operations, etc).
The specific threat that they need to worry about is
firewall-management commands that flush *all* dynamic iptables rules.
So add a new iptables.Monitor() function that handles this by creating
iptables-flush canaries and only triggering a full rule reload after
noticing that someone has deleted those chains.
