In proxy I switched to a leaky-bucket impl using the GCRA algorithm. I
figured I could share the code with pageserver and remove the
leaky_bucket crate dependency with some very basic tokio timers and
queues for fairness.
The underlying algorithm should be fairly clear how it works from the
comments I have left in the code.
---
In benchmarking pageserver, @problame found that the new implementation
fixes a getpage throughput discontinuity in pageserver under the
`pagebench get-page-latest-lsn` benchmark with the clickbench dataset
(`test_perf_olap.py`).
The discontinuity is that for any of `--num-clients={2,3,4}`, getpage
throughput remains 10k.
With `--num-clients=5` and greater, getpage throughput then jumps to the
configured 20k rate limit.
With the changes in this PR, the discontinuity is gone, and we scale
throughput linearly to `--num-clients` until the configured rate limit.
More context in
https://github.com/neondatabase/cloud/issues/16886#issuecomment-2315257641.
closes https://github.com/neondatabase/cloud/issues/16886
---------
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
The current bucket based rate limiter is not very intuitive and has some
bad failure cases.
## Summary of changes
Switches from fixed interval buckets to leaky bucket impl. A single
bucket per endpoint,
drains over time. Drains by checking the time since the last check, and
draining tokens en-masse. Garbage collection works similar to before, it
drains a shard (1/64th of the set) every 2048 checks, and it only
removes buckets that are empty.
To be compatible with the existing config, I've faffed to make it take
the min and the max rps of each as the sustained rps and the max bucket
size which should be roughly equivalent.
## Problem
Computes that are healthy can manage many connection attempts at a time.
Unhealthy computes cannot. We initially handled this with a fixed
concurrency limit, but it seems this inhibits pgbench.
## Summary of changes
Support AIMD for connect_to_compute lock to allow varying the
concurrency limit based on compute health
## Problem
There is an unused dead code.
## Summary of changes
Let's remove it. In case we would need it in the future, we can always
return it back.
Also removed cli arguments. They shouldn't be used by anyone but us.
## Problem
Many users have access to ipv6 subnets (eg a /64). That gives them 2^64
addresses to play with
## Summary of changes
Truncate the address to /64 to reduce the attack surface.
Todo:
~~Will NAT64 be an issue here? AFAIU they put the IPv4 address at the
end of the IPv6 address. By truncating we will lose all that detail.~~
It's the same problem as a host sharing IPv6 addresses between clients.
I don't think it's up to us to solve. If a customer is getting DDoSed,
then they likely need to arrange a dedicated IP with us.
## Problem
Actually read redis events.
## Summary of changes
This is revert of https://github.com/neondatabase/neon/pull/7350 +
fixes.
* Fixed events parsing
* Added timeout after connection failure
* Separated regional and global redis clients.
## Problem
Proxy doesn't know about existing endpoints.
## Summary of changes
* Added caching of all available endpoints.
* On the high load, use it before going to cplane.
* Report metrics for the outcome.
* For rate limiter and credentials caching don't distinguish between
`-pooled` and not
TODOs:
* Make metrics more meaningful
* Consider integrating it with the endpoint rate limiter
* Test it together with cplane in preview
## Problem
https://github.com/neondatabase/cloud/issues/9642
## Summary of changes
1. Make `EndpointRateLimiter` generic, renamed as `BucketRateLimiter`
2. Add support for claiming multiple tokens at once
3. Add `AuthRateLimiter` alias.
4. Check `(Endpoint, IP)` pair during authentication, weighted by how
many hashes proxy would be doing.
TODO: handle ipv6 subnets. will do this in a separate PR.
## Problem
1. Using chrono for durations only is wasteful
2. The arc/mutex was not being utilised
3. Locking every shard in the dashmap every GC could cause latency
spikes
4. More buckets
## Summary of changes
1. Use `Instant` instead of `NaiveTime`.
2. Remove the `Arc<Mutex<_>>` wrapper, utilising that dashmap entry
returns mut access
3. Clear only a random shard, update gc interval accordingly
4. Multiple buckets can be checked before allowing access
When I benchmarked the check function, it took on average 811ns when
multithreaded over the course of 10 million checks.
