Before this PR, some override callbacks used `.default()`, others
used `.setdefault()`.
As of this PR, all callbacks use `.setdefault()` which I think is least
prone to failure.
Aligning on a single way will set the right example for future tests
that need such customization.
The `test_pageserver_getpage_throttle.py` technically is a change in
behavior: before, it replaced the `tenant_config` field, now it just
configures the throttle. This is what I believe is intended anyway.
## Problem
In the batching PR
- https://github.com/neondatabase/neon/pull/9870
I stopped deducting the time-spent-in-throttle fro latency metrics,
i.e.,
- smgr latency metrics (`SmgrOpTimer`)
- basebackup latency (+scan latency, which I think is part of
basebackup).
The reason for stopping the deduction was that with the introduction of
batching, the trick with tracking time-spent-in-throttle inside
RequestContext and swap-replacing it from the `impl Drop for
SmgrOpTimer` no longer worked with >1 requests in a batch.
However, deducting time-spent-in-throttle is desirable because our
internal latency SLO definition does not account for throttling.
## Summary of changes
- Redefine throttling to be a page_service pagestream request throttle
instead of a throttle for repository `Key` reads through `Timeline::get`
/ `Timeline::get_vectored`.
- This means reads done by `basebackup` are no longer subject to any
throttle.
- The throttle applies after batching, before handling of the request.
- Drive-by fix: make throttle sensitive to cancellation.
- Rename metric label `kind` from `timeline_get` to `pagestream` to
reflect the new scope of throttling.
To avoid config format breakage, we leave the config field named
`timeline_get_throttle` and ignore the `task_kinds` field.
This will be cleaned up in a future PR.
## Trade-Offs
Ideally, we would apply the throttle before reading a request off the
connection, so that we queue the minimal amount of work inside the
process.
However, that's not possible because we need to do shard routing.
The redefinition of the throttle to limit pagestream request rate
instead of repository `Key` rate comes with several downsides:
- We're no longer able to use the throttle mechanism for other other
tasks, e.g. image layer creation.
However, in practice, we never used that capability anyways.
- We no longer throttle basebackup.
This PR
- fixes smgr metrics https://github.com/neondatabase/neon/issues/9925
- adds an additional startup log line logging the current batching
config
- adds a histogram of batch sizes global and per-tenant
- adds a metric exposing the current batching config
The issue described #9925 is that before this PR, request latency was
only observed *after* batching.
This means that smgr latency metrics (most importantly getpage latency)
don't account for
- `wait_lsn` time
- time spent waiting for batch to fill up / the executor stage to pick
up the batch.
The fix is to use a per-request batching timer, like we did before the
initial batching PR.
We funnel those timers through the entire request lifecycle.
I noticed that even before the initial batching changes, we weren't
accounting for the time spent writing & flushing the response to the
wire.
This PR drive-by fixes that deficiency by dropping the timers at the
very end of processing the batch, i.e., after the `pgb.flush()` call.
I was **unable to maintain the behavior that we deduct
time-spent-in-throttle from various latency metrics.
The reason is that we're using a *single* counter in `RequestContext` to
track micros spent in throttle.
But there are *N* metrics timers in the batch, one per request.
As a consequence, the practice of consuming the counter in the drop
handler of each timer no longer works because all but the first timer
will encounter error `close() called on closed state`.
A failed attempt to maintain the current behavior can be found in
https://github.com/neondatabase/neon/pull/9951.
So, this PR remvoes the deduction behavior from all metrics.
I started a discussion on Slack about it the implications this has for
our internal SLO calculation:
https://neondb.slack.com/archives/C033RQ5SPDH/p1732910861704029
# Refs
- fixes https://github.com/neondatabase/neon/issues/9925
- sub-issue https://github.com/neondatabase/neon/issues/9377
- epic: https://github.com/neondatabase/neon/issues/9376
Improves `wait_until` by:
* Use `timeout` instead of `iterations`. This allows changing the
timeout/interval parameters independently.
* Make `timeout` and `interval` optional (default 20s and 0.5s). Most
callers don't care.
* Only output status every 1s by default, and add optional
`status_interval` parameter.
* Remove `show_intermediate_error`, this was always emitted anyway.
Most callers have been updated to use the defaults, except where they
had good reason otherwise.
Problem
-------
Tests that directly call the Pageserver Management API to set tenant
config are flaky if the Pageserver is managed by Storcon because Storcon
is the source of truth and may (theoretically) reconcile a tenant at any
time.
Solution
--------
Switch all users of
`set_tenant_config`/`patch_tenant_config_client_side`
to use the `env.storage_controller.pageserver_api()`
Future Work
-----------
Prevent regressions from creeping in.
And generally clean up up tenant configuration.
Maybe we can avoid the Pageserver having a default tenant config at all
and put the default into Storcon instead?
* => https://github.com/neondatabase/neon/issues/9621
Refs
----
fixes https://github.com/neondatabase/neon/issues/9522
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>
part of https://github.com/neondatabase/neon/issues/5899
Problem
-------
Before this PR, the time spent waiting on the throttle was charged
towards the higher-level page_service metrics, i.e.,
`pageserver_smgr_query_seconds`.
The metrics are the foundation of internal SLIs / SLOs.
A throttled tenant would cause the SLI to degrade / SLO alerts to fire.
Changes
-------
- don't charge time spent in throttle towards the page_service metrics
- record time spent in throttle in RequestContext and subtract it from
the elapsed time
- this works because the page_service path doesn't create child context,
so, all the throttle time is recorded in the parent
- it's quite brittle and will break if we ever decide to spawn child
tasks that need child RequestContexts, which would have separate
instances of the `micros_spent_throttled` counter.
- however, let's punt that to a more general refactoring of
RequestContext
- add a test case that ensures that
- throttling happens for getpage requests; this aspect of the test
passed before this PR
- throttling delays aren't charged towards the page_service metrics;
this aspect of the test only passes with this PR
- drive-by: make the throttle log message `info!`, it's an expected
condition
Performance
-----------
I took the same measurements as in #6706 , no meaningful change in CPU
overhead.
Future Work
-----------
This PR enables us to experiment with the throttle for select tenants
without affecting the SLI metrics / triggering SLO alerts.
Before declaring this feature done, we need more work to happen,
specifically:
- decide on whether we want to retain the flexibility of throttling any
`Timeline::get` call, filtered by TaskKind
- versus: separate throttles for each page_service endpoint, potentially
with separate config options
- the trouble here is that this decision implies changes to the
TenantConfig, so, if we start using the current config style now, then
decide to switch to a different config, it'll be a breaking change
Nice-to-haves but probably not worth the time right now:
- Equivalent tests to ensure the throttle applies to all other
page_service handlers.
