Closes#9387.
## Problem
`BufferedWriter` cannot proceed while the owned buffer is flushing to
disk. We want to implement double buffering so that the flush can happen
in the background. See #9387.
## Summary of changes
- Maintain two owned buffers in `BufferedWriter`.
- The writer is in charge of copying the data into owned, aligned
buffer, once full, submit it to the flush task.
- The flush background task is in charge of flushing the owned buffer to
disk, and returned the buffer to the writer for reuse.
- The writer and the flush background task communicate through a
bi-directional channel.
For in-memory layer, we also need to be able to read from the buffered
writer in `get_values_reconstruct_data`. To handle this case, we did the
following
- Use replace `VirtualFile::write_all` with `VirtualFile::write_all_at`,
and use `Arc` to share it between writer and background task.
- leverage `IoBufferMut::freeze` to get a cheaply clonable `IoBuffer`,
one clone will be submitted to the channel, the other clone will be
saved within the writer to serve reads. When we want to reuse the
buffer, we can invoke `IoBuffer::into_mut`, which gives us back the
mutable aligned buffer.
- InMemoryLayer reads is now aware of the maybe_flushed part of the
buffer.
**Caveat**
- We removed the owned version of write, because this interface does not
work well with buffer alignment. The result is that without direct IO
enabled,
[`download_object`](a439d57050/pageserver/src/tenant/remote_timeline_client/download.rs (L243))
does one more memcpy than before this PR due to the switch to use
`_borrowed` version of the write.
- "Bypass aligned part of write" could be implemented later to avoid
large amount of memcpy.
**Testing**
- use an oneshot channel based control mechanism to make flush behavior
deterministic in test.
- test reading from `EphemeralFile` when the last submitted buffer is
not flushed, in-progress, and done flushing to disk.
## Performance
We see performance improvement for small values, and regression on big
values, likely due to being CPU bound + disk write latency.
[Results](https://www.notion.so/neondatabase/Benchmarking-New-BufferedWriter-11-20-2024-143f189e0047805ba99acda89f984d51?pvs=4)
## Checklist before requesting a review
- [ ] I have performed a self-review of my code.
- [ ] If it is a core feature, I have added thorough tests.
- [ ] Do we need to implement analytics? if so did you add the relevant
metrics to the dashboard?
- [ ] If this PR requires public announcement, mark it with
/release-notes label and add several sentences in this section.
## Checklist before merging
- [ ] Do not forget to reformat commit message to not include the above
checklist
---------
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
# Problem
The timeout-based batching adds latency to unbatchable workloads.
We can choose a short batching timeout (e.g. 10us) but that requires
high-resolution timers, which tokio doesn't have.
I thoroughly explored options to use OS timers (see
[this](https://github.com/neondatabase/neon/pull/9822) abandoned PR).
In short, it's not an attractive option because any timer implementation
adds non-trivial overheads.
# Solution
The insight is that, in the steady state of a batchable workload, the
time we spend in `get_vectored` will be hundreds of microseconds anyway.
If we prepare the next batch concurrently to `get_vectored`, we will
have a sizeable batch ready once `get_vectored` of the current batch is
done and do not need an explicit timeout.
This can be reasonably described as **pipelining of the protocol
handler**.
# Implementation
We model the sub-protocol handler for pagestream requests
(`handle_pagrequests`) as two futures that form a pipeline:
2. Batching: read requests from the connection and fill the current
batch
3. Execution: `take` the current batch, execute it using `get_vectored`,
and send the response.
The Reading and Batching stage are connected through a new type of
channel called `spsc_fold`.
See the long comment in the `handle_pagerequests_pipelined` for details.
# Changes
- Refactor `handle_pagerequests`
- separate functions for
- reading one protocol message; produces a `BatchedFeMessage` with just
one page request in it
- batching; tried to merge an incoming `BatchedFeMessage` into an
existing `BatchedFeMessage`; returns `None` on success and returns back
the incoming message in case merging isn't possible
- execution of a batched message
- unify the timeline handle acquisition & request span construction; it
now happen in the function that reads the protocol message
- Implement serial and pipelined model
- serial: what we had before any of the batching changes
- read one protocol message
- execute protocol messages
- pipelined: the design described above
- optionality for execution of the pipeline: either via concurrent
futures vs tokio tasks
- Pageserver config
- remove batching timeout field
- add ability to configure pipelining mode
- add ability to limit max batch size for pipelined configurations
(required for the rollout, cf
https://github.com/neondatabase/cloud/issues/20620 )
- ability to configure execution mode
- Tests
- remove `batch_timeout` parametrization
- rename `test_getpage_merge_smoke` to `test_throughput`
- add parametrization to test different max batch sizes and execution
moes
- rename `test_timer_precision` to `test_latency`
- rename the test case file to `test_page_service_batching.py`
- better descriptions of what the tests actually do
## On the holding The `TimelineHandle` in the pending batch
While batching, we hold the `TimelineHandle` in the pending batch.
Therefore, the timeline will not finish shutting down while we're
batching.
This is not a problem in practice because the concurrently ongoing
`get_vectored` call will fail quickly with an error indicating that the
timeline is shutting down.
This results in the Execution stage returning a `QueryError::Shutdown`,
which causes the pipeline / entire page service connection to shut down.
This drops all references to the
`Arc<Mutex<Option<Box<BatchedFeMessage>>>>` object, thereby dropping the
contained `TimelineHandle`s.
- => fixes https://github.com/neondatabase/neon/issues/9850
# Performance
Local run of the benchmarks, results in [this empty
commit](1cf5b1463f)
in the PR branch.
Key take-aways:
* `concurrent-futures` and `tasks` deliver identical `batching_factor`
* tail latency impact unknown, cf
https://github.com/neondatabase/neon/issues/9837
* `concurrent-futures` has higher throughput than `tasks` in all
workloads (=lower `time` metric)
* In unbatchable workloads, `concurrent-futures` has 5% higher
`CPU-per-throughput` than that of `tasks`, and 15% higher than that of
`serial`.
* In batchable-32 workload, `concurrent-futures` has 8% lower
`CPU-per-throughput` than that of `tasks` (comparison to tput of
`serial` is irrelevant)
* in unbatchable workloads, mean and tail latencies of
`concurrent-futures` is practically identical to `serial`, whereas
`tasks` adds 20-30us of overhead
Overall, `concurrent-futures` seems like a slightly more attractive
choice.
# Rollout
This change is disabled-by-default.
Rollout plan:
- https://github.com/neondatabase/cloud/issues/20620
# Refs
- epic: https://github.com/neondatabase/neon/issues/9376
- this sub-task: https://github.com/neondatabase/neon/issues/9377
- the abandoned attempt to improve batching timeout resolution:
https://github.com/neondatabase/neon/pull/9820
- closes https://github.com/neondatabase/neon/issues/9850
- fixes https://github.com/neondatabase/neon/issues/9835
Addresses the 1.82 beta clippy lint `too_long_first_doc_paragraph` by
adding newlines to the first sentence if it is short enough, and making
a short first sentence if there is the need.
Ephemeral files cleanup on drop but did not delay shutdown, leading to
problems with restarting the tenant. The solution is as proposed:
- make ephemeral files carry the gate guard to delay `Timeline::gate`
closing
- flush in-memory layers and strong references to those on
`Timeline::shutdown`
The above are realized by making LayerManager an `enum` with `Open` and
`Closed` variants, and fail requests to modify `LayerMap`.
Additionally:
- fix too eager anyhow conversions in compaction
- unify how we freeze layers and handle errors
- optimize likely_resident_layers to read LayerFileManager hashmap
values instead of bouncing through LayerMap
Fixes: #7830
#7030 introduced an annoying papercut, deeming a failure to acquire a
strong reference to `LayerInner` from `DownloadedLayer::drop` as a
canceled eviction. Most of the time, it wasn't that, but just timeline
deletion or tenant detach with the layer not wanting to be deleted or
evicted.
When a Layer is dropped as part of a normal shutdown, the `Layer` is
dropped first, and the `DownloadedLayer` the second. Because of this, we
cannot detect eviction being canceled from the `DownloadedLayer::drop`.
We can detect it from `LayerInner::drop`, which this PR adds.
Test case is added which before had 1 started eviction, 2 canceled. Now
it accurately finds 1 started, 1 canceled.
Aiming for the design where `heavier_once_cell::OnceCell` is initialized
by a future factory lead to awkwardness with how
`LayerInner::get_or_maybe_download` looks right now with the `loop`. The
loop helps with two situations:
- an eviction has been scheduled but has not yet happened, and a read
access should cancel the eviction
- a previous `LayerInner::get_or_maybe_download` that canceled a pending
eviction was canceled leaving the `heavier_once_cell::OnceCell`
uninitialized but needing repair by the next
`LayerInner::get_or_maybe_download`
By instead supporting detached initialization in
`heavier_once_cell::OnceCell` via an `OnceCell::get_or_detached_init`,
we can fix what the monolithic #7030 does:
- spawned off download task initializes the
`heavier_once_cell::OnceCell` regardless of the download starter being
canceled
- a canceled `LayerInner::get_or_maybe_download` no longer stops
eviction but can win it if not canceled
Split off from #7030.
Cc: #5331
Nightly has added a bunch of compiler and linter warnings. There is also
two dependencies that fail compilation on latest nightly due to using
the old `stdsimd` feature name. This PR fixes them.
@problame noticed that the `tokio::sync::AcquireError` branch assertion
can be hit like in the added test. We haven't seen this yet in
production, but I'd prefer not to see it there. There `take_and_deinit`
is being used, but this race must be quite timing sensitive.
Rework of earlier: #6652.
This PR reverts
- https://github.com/neondatabase/neon/pull/6589
- https://github.com/neondatabase/neon/pull/6652
because there's a performance regression that's particularly visible at
high layer counts.
Most likely it's because the switch to RwLock inflates the
```
inner: heavier_once_cell::OnceCell<ResidentOrWantedEvicted>,
```
size from 48 to 88 bytes, which, by itself is almost a doubling of the
cache footprint, and probably the fact that it's now larger than a cache
line also doesn't help.
See this chat on the Neon discord for more context:
https://discord.com/channels/1176467419317940276/1204714372295958548/1205541184634617906
I'm reverting 6652 as well because it might also have perf implications,
and we're getting close to the next release. We should re-do its changes
after the next release, though.
cc @koivunej
cc @ivaxer
@problame noticed that the `tokio::sync::AcquireError` branch assertion
can be hit like in the first commit. We haven't seen this yet in
production, but I'd prefer not to see it there. There `take_and_deinit`
is being used, but this race must be quite timing sensitive.
changes:
- two messages instead of message every second when gate was closing
- replace the gate name string by using a pointer
- slow GateGuards are likely to log who they were (see example)
example found in regress tests: <https://github.com/neondatabase/neon/pull/6542#issuecomment-1919009256>
## Problem
- `shutdown_tasks` would log when a particular task was taking a long
time to shut down, but not when it eventually completed. That left one
uncertain as to whether the slow task was the source of a hang, or just
a precursor.
## Summary of changes
- Add a log line after a slow task shutdown
- Add an equivalent in Gate's `warn_if_stuck`, in case we ever need it.
This isn't related to the original issue but was noticed when checking
through these logging paths.
## Problem
#5711 and #5367 raced -- the `SlotGuard` type needs `Gate` to properly
enforce its invariant that we may not drop an `Arc<Tenant>` from a slot.
## Summary of changes
Replace the TODO with the intended check of Gate.
## Problem
When shutting down a Tenant, it isn't just important to cause any
background tasks to stop. It's also important to wait until they have
stopped before declaring shutdown complete, in cases where we may re-use
the tenant's local storage for something else, such as running in
secondary mode, or creating a new tenant with the same ID.
## Summary of changes
A `Gate` class is added, inspired by
[seastar::gate](https://docs.seastar.io/master/classseastar_1_1gate.html).
For types that have an important lifetime that corresponds to some
physical resource, use of a Gate as well as a CancellationToken provides
a robust pattern for async requests & shutdown:
- Requests must always acquire the gate as long as they are using the
object
- Shutdown must set the cancellation token, and then `close()` the gate
to wait for requests in progress before returning.
This is not for memory safety: it's for expressing the difference
between "Arc<Tenant> exists", and "This tenant's files on disk are
eligible to be read/written".
- Both Tenant and Timeline get a Gate & CancellationToken.
- The Timeline gate is held during eviction of layers, and during
page_service requests.
- Existing cancellation support in page_service is refined to use the
timeline-scope cancellation token instead of a process-scope
cancellation token. This replaces the use of `task_mgr::associate_with`:
tasks no longer change their tenant/timelineidentity after being
spawned.
The Tenant's Gate is not yet used, but will be important for
Tenant-scoped operations in secondary mode, where we must ensure that
our secondary-mode downloads for a tenant are gated wrt the activity of
an attached Tenant.
This is part of a broader move away from using the global-state driven
`task_mgr` shutdown tokens:
- less global state where we rely on implicit knowledge of what task a
given function is running in, and more explicit references to the
cancellation token that a particular function/type will respect, making
shutdown easier to reason about.
- eventually avoid the big global TASKS mutex.
---------
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Some of the log messages were lost with the #4938. This PR adds some of
them back, most notably:
- starting to on-demand download
- successful completion of on-demand download
- ability to see when there were many waiters for the layer download
- "unexpectedly on-demand downloading ..." is now `info!`
Additionally some rare events are logged as error, which should never
happen.
With the layer implementation as was done in #4938, it is possible via
cancellation to cause two concurrent downloads on the same path, due to
how `RemoteTimelineClient::download_remote_layer` does tempfiles. Thread
the init semaphore through the spawned task of downloading to make this
impossible to happen.
Implement a new `struct Layer` abstraction which manages downloadness
internally, requiring no LayerMap locking or rewriting to download or
evict providing a property "you have a layer, you can read it". The new
`struct Layer` provides ability to keep the file resident via a RAII
structure for new layers which still need to be uploaded. Previous
solution solved this `RemoteTimelineClient::wait_completion` which lead
to bugs like #5639. Evicting or the final local deletion after garbage
collection is done using Arc'd value `Drop`.
With a single `struct Layer` the closed open ended `trait Layer`, `trait
PersistentLayer` and `struct RemoteLayer` are removed following noting
that compaction could be simplified by simply not using any of the
traits in between: #4839.
The new `struct Layer` is a preliminary to remove
`Timeline::layer_removal_cs` documented in #4745.
Preliminaries: #4936, #4937, #5013, #5014, #5022, #5033, #5044, #5058,
#5059, #5061, #5074, #5103, epic #5172, #5645, #5649. Related split off:
#5057, #5134.
