Avoids compiling the crate and its dependencies into binaries that don't
need them. Shrinks the compute_ctl binary from about 31MB to 28MB in the
release-line-debug-size-lto profile.
## Problem
The compaction loop currently runs periodically, which can cause it to
wait for up to 20 seconds before starting L0 compaction by default.
Also, when we later separate the semaphores for L0 compaction and image
compaction, we want to give up waiting for the image compaction
semaphore if L0 compaction is needed on any timeline.
Touches #10694.
## Summary of changes
Notify the compaction loop when an L0 flush (on any timeline) exceeds
`compaction_threshold`.
Also do some opportunistic cleanups in the area.
## Problem
Following #10641, let's add a few critical errors.
Resolves#10094.
## Summary of changes
Adds the following critical errors:
* WAL sender read/decode failure.
* WAL record ingestion failure.
* WAL redo failure.
* Missing key during compaction.
We don't add an error for missing keys during GetPage requests, since
we've seen a handful of these in production recently, and the cause is
still unclear (most likely a benign race).
## Problem
We don't currently have good alerts for critical errors, e.g. data
loss/corruption.
Touches #10094.
## Summary of changes
Add a `critical!` macro and corresponding
`libmetrics_tracing_event_count{level="critical"}` metric. This will:
* Emit an `ERROR` log message with prefix `"CRITICAL:"` and a backtrace.
* Increment `libmetrics_tracing_event_count{level="critical"}`, and
indirectly `level="error"`.
* Trigger a pageable alert (via the metric above).
* In debug builds, panic the process.
I'll add uses of the macro separately.
## Refs
- Epic: https://github.com/neondatabase/neon/issues/9378
Co-authored-by: Vlad Lazar <vlad@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
The read path does its IOs sequentially.
This means that if N values need to be read to reconstruct a page,
we will do N IOs and getpage latency is `O(N*IoLatency)`.
## Solution
With this PR we gain the ability to issue IO concurrently within one
layer visit **and** to move on to the next layer without waiting for IOs
from the previous visit to complete.
This is an evolved version of the work done at the Lisbon hackathon,
cf https://github.com/neondatabase/neon/pull/9002.
## Design
### `will_init` now sourced from disk btree index keys
On the algorithmic level, the only change is that the
`get_values_reconstruct_data`
now sources `will_init` from the disk btree index key (which is
PS-page_cache'd), instead
of from the `Value`, which is only available after the IO completes.
### Concurrent IOs, Submission & Completion
To separate IO submission from waiting for its completion, while
simultaneously
feature-gating the change, we introduce the notion of an `IoConcurrency`
struct
through which IO futures are "spawned".
An IO is an opaque future, and waiting for completions is handled
through
`tokio::sync::oneshot` channels.
The oneshot Receiver's take the place of the `img` and `records` fields
inside `VectoredValueReconstructState`.
When we're done visiting all the layers and submitting all the IOs along
the way
we concurrently `collect_pending_ios` for each value, which means
for each value there is a future that awaits all the oneshot receivers
and then calls into walredo to reconstruct the page image.
Walredo is now invoked concurrently for each value instead of
sequentially.
Walredo itself remains unchanged.
The spawned IO futures are driven to completion by a sidecar tokio task
that
is separate from the task that performs all the layer visiting and
spawning of IOs.
That tasks receives the IO futures via an unbounded mpsc channel and
drives them to completion inside a `FuturedUnordered`.
(The behavior from before this PR is available through
`IoConcurrency::Sequential`,
which awaits the IO futures in place, without "spawning" or "submitting"
them
anywhere.)
#### Alternatives Explored
A few words on the rationale behind having a sidecar *task* and what
alternatives were considered.
One option is to queue up all IO futures in a FuturesUnordered that is
polled
the first time when we `collect_pending_ios`.
Firstly, the IO futures are opaque, compiler-generated futures that need
to be polled at least once to submit their IO. "At least once" because
tokio-epoll-uring may not be able to submit the IO to the kernel on
first
poll right away.
Second, there are deadlocks if we don't drive the IO futures to
completion
independently of the spawning task.
The reason is that both the IO futures and the spawning task may hold
some
_and_ try to acquire _more_ shared limited resources.
For example, both spawning task and IO future may try to acquire
* a VirtualFile file descriptor cache slot async mutex (observed during
impl)
* a tokio-epoll-uring submission slot (observed during impl)
* a PageCache slot (currently this is not the case but we may move more
code into the IO futures in the future)
Another option is to spawn a short-lived `tokio::task` for each IO
future.
We implemented and benchmarked it during development, but found little
throughput improvement and moderate mean & tail latency degradation.
Concerns about pressure on the tokio scheduler made us discard this
variant.
The sidecar task could be obsoleted if the IOs were not arbitrary code
but a well-defined struct.
However,
1. the opaque futures approach taken in this PR allows leaving the
existing
code unchanged, which
2. allows us to implement the `IoConcurrency::Sequential` mode for
feature-gating
the change.
Once the new mode sidecar task implementation is rolled out everywhere,
and `::Sequential` removed, we can think about a descriptive submission
& completion interface.
The problems around deadlocks pointed out earlier will need to be solved
then.
For example, we could eliminate VirtualFile file descriptor cache and
tokio-epoll-uring slots.
The latter has been drafted in
https://github.com/neondatabase/tokio-epoll-uring/pull/63.
See the lengthy doc comment on `spawn_io()` for more details.
### Error handling
There are two error classes during reconstruct data retrieval:
* traversal errors: index lookup, move to next layer, and the like
* value read IO errors
A traversal error fails the entire get_vectored request, as before this
PR.
A value read error only fails that value.
In any case, we preserve the existing behavior that once
`get_vectored` returns, all IOs are done. Panics and failing
to poll `get_vectored` to completion will leave the IOs dangling,
which is safe but shouldn't happen, and so, a rate-limited
log statement will be emitted at warning level.
There is a doc comment on `collect_pending_ios` giving more code-level
details and rationale.
### Feature Gating
The new behavior is opt-in via pageserver config.
The `Sequential` mode is the default.
The only significant change in `Sequential` mode compared to before
this PR is the buffering of results in the `oneshot`s.
## Code-Level Changes
Prep work:
* Make `GateGuard` clonable.
Core Feature:
* Traversal code: track `will_init` in `BlobMeta` and source it from
the Delta/Image/InMemory layer index, instead of determining `will_init`
after we've read the value. This avoids having to read the value to
determine whether traversal can stop.
* Introduce `IoConcurrency` & its sidecar task.
* `IoConcurrency` is the clonable handle.
* It connects to the sidecar task via an `mpsc`.
* Plumb through `IoConcurrency` from high level code to the
individual layer implementations' `get_values_reconstruct_data`.
We piggy-back on the `ValuesReconstructState` for this.
* The sidecar task should be long-lived, so, `IoConcurrency` needs
to be rooted up "high" in the call stack.
* Roots as of this PR:
* `page_service`: outside of pagestream loop
* `create_image_layers`: when it is called
* `basebackup`(only auxfiles + replorigin + SLRU segments)
* Code with no roots that uses `IoConcurrency::sequential`
* any `Timeline::get` call
* `collect_keyspace` is a good example
* follow-up: https://github.com/neondatabase/neon/issues/10460
* `TimelineAdaptor` code used by the compaction simulator, unused in
practive
* `ingest_xlog_dbase_create`
* Transform Delta/Image/InMemoryLayer to
* do their values IO in a distinct `async {}` block
* extend the residence of the Delta/Image layer until the IO is done
* buffer their results in a `oneshot` channel instead of straight
in `ValuesReconstructState`
* the `oneshot` channel is wrapped in `OnDiskValueIo` /
`OnDiskValueIoWaiter`
types that aid in expressiveness and are used to keep track of
in-flight IOs so we can print warnings if we leave them dangling.
* Change `ValuesReconstructState` to hold the receiving end of the
`oneshot` channel aka `OnDiskValueIoWaiter`.
* Change `get_vectored_impl` to `collect_pending_ios` and issue walredo
concurrently, in a `FuturesUnordered`.
Testing / Benchmarking:
* Support queue-depth in pagebench for manual benchmarkinng.
* Add test suite support for setting concurrency mode ps config
field via a) an env var and b) via NeonEnvBuilder.
* Hacky helper to have sidecar-based IoConcurrency in tests.
This will be cleaned up later.
More benchmarking will happen post-merge in nightly benchmarks, plus in
staging/pre-prod.
Some intermediate helpers for manual benchmarking have been preserved in
https://github.com/neondatabase/neon/pull/10466 and will be landed in
later PRs.
(L0 layer stack generator!)
Drive-By:
* test suite actually didn't enable batching by default because
`config.compatibility_neon_binpath` is always Truthy in our CI
environment
=> https://neondb.slack.com/archives/C059ZC138NR/p1737490501941309
* initial logical size calculation wasn't always polled to completion,
which was
surfaced through the added WARN logs emitted when dropping a
`ValuesReconstructState` that still has inflight IOs.
* remove the timing histograms
`pageserver_getpage_get_reconstruct_data_seconds`
and `pageserver_getpage_reconstruct_seconds` because with planning,
value read
IO, and walredo happening concurrently, one can no longer attribute
latency
to any one of them; we'll revisit this when Vlad's work on
tracing/sampling
through RequestContext lands.
* remove code related to `get_cached_lsn()`.
The logic around this has been dead at runtime for a long time,
ever since the removal of the materialized page cache in #8105.
## Testing
Unit tests use the sidecar task by default and run both modes in CI.
Python regression tests and benchmarks also use the sidecar task by
default.
We'll test more in staging and possibly preprod.
# Future Work
Please refer to the parent epic for the full plan.
The next step will be to fold the plumbing of IoConcurrency
into RequestContext so that the function signatures get cleaned up.
Once `Sequential` isn't used anymore, we can take the next
big leap which is replacing the opaque IOs with structs
that have well-defined semantics.
---------
Co-authored-by: Christian Schwarz <christian@neon.tech>
# Refs
- extracted from https://github.com/neondatabase/neon/pull/9353
# Problem
Before this PR, when task_mgr shutdown is signalled, e.g. during
pageserver shutdown or Tenant shutdown, initial logical size calculation
stops polling and drops the future that represents the calculation.
This is against the current policy that we poll all futures to
completion.
This became apparent during development of concurrent IO which warns if
we drop a `Timeline::get_vectored` future that still has in-flight IOs.
We may revise the policy in the future, but, right now initial logical
size calculation is the only part of the codebase that doesn't adhere to
the policy, so let's fix it.
## Code Changes
- make sensitive exclusively to `Timeline::cancel`
- This should be sufficient for all cases of shutdowns; the sensitivity
to task_mgr shutdown is unnecessary.
- this broke the various cancel tests in `test_timeline_size.py`, e.g.,
`test_timeline_initial_logical_size_calculation_cancellation`
- the tests would time out because the await point was not sensitive to
cancellation
- to fix this, refactor `pausable_failpoint` so that it accepts a
cancellation token
- side note: we _really_ should write our own failpoint library; maybe
after we get heap-allocated RequestContext, we can plumb failpoints
through there.
## Problem
gc-compaction needs the partitioning data to decide the job split. This
refactor allows concurrent access/computing the partitioning.
## Summary of changes
Make `partitioning` an ArcSwap so that others can access the
partitioning while we compute it. Fully eliminate the `repartition is
called concurrently` warning when gc-compaction is going on.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Taking continuous profiles every 20 seconds is likely too expensive (in
dollar terms). Let's try 60-second profiles. We can now interrupt
running profiles via `?force=true`, so this should be fine.
With a new beta build of the rust compiler, it's good to check out the
new lints. Either to find false positives, or find flaws in our code.
Additionally, it helps reduce the effort required to update to 1.85 in 6
weeks.
## Problem
It's only possible to take one CPU profile at a time. With Grafana
continuous profiling, a (low-frequency) CPU profile will always be
running, making it hard to take an ad hoc CPU profile at the same time.
Resolves#10072.
## Summary of changes
Add a `force` parameter for `/profile/cpu` which will end and return an
already running CPU profile, starting a new one for the current caller.
## Problem
Unlike CPU profiles, the `/profile/heap` endpoint can't automatically
generate SVG flamegraphs. This requires the user to install and use
`pprof` tooling, which is unnecessary and annoying.
Resolves#10203.
## Summary of changes
Add `format=svg` for the `/profile/heap` route, and generate an SVG
flamegraph using the `inferno` crate, similarly to what `pprof-rs`
already does for CPU profiles.
# Problem
Before this PR, there were cases where send() in state
SenderWaitsForReceiverToConsume would never be woken up
by the receiver, because it never registered with `wake_sender`.
Example Scenario 1: we stop polling a send() future A that was waiting
for the receiver to consume. We drop A and create a new send() future B.
B would return Poll::Pending and never regsister a waker.
Example Scenario 2: a send() future A transitions from HasData
to SenderWaitsForReceiverToConsume. This registers the context X
with `wake_sender`. But before the Receiver consumes the data,
we poll A from a different context Y.
The state is still SenderWaitsForReceiverToConsume, but we wouldn't
register the new context with `wake_sender`.
When the Receiver comes around to consume and `wake_sender.notify()`s,
it wakes the old context X instead of Y.
# Fix
Register the waker in the case where we're polled in
state `SenderWaitsForReceiverToConsume`.
# Relation to #10309
I found this bug while investigating #10309.
There was never proof that this bug here is the root cause for #10309.
In the meantime we found a more probably hypothesis
for the root cause than what is being fixed here.
Regardless, let's walk through my thought process about
how it might have been relevant:
There (in page_service), Scenario 1 does not apply because
we poll the send() future to completion.
Scenario 2 (`tokio::join!`) also does not apply with the
current `tokio::join!()` impl, because it will just poll each
future every time, each with the same context.
Although if we ever used something like a FuturesUnordered anywhere,
that will be using a different context, so, in that case,
the bug might materialize.
Regarding tokio & spurious poll in general:
@conradludgate is not aware of any spurious wakeup cases in current
tokio,
but within a `tokio::join!()`, any wake meant for one future will poll
all
the futures, so that can appear as a spurious wake up to the N-1 futures
of the `tokio::join!()`.
## Problem
Jemalloc heap profiles aren't symbolized. This is inconvenient, and
doesn't work with Grafana Cloud Profiles.
Resolves#9964.
## Summary of changes
Symbolize the heap profiles in-process, and strip unnecessary cruft.
This uses about 100 MB additional memory to cache the DWARF information,
but I believe this is already the case with CPU profiles, which use the
same library for symbolization. With cached DWARF information, the
symbolization CPU overhead is negligible.
Example profiles:
*
[pageserver.pb.gz](https://github.com/user-attachments/files/18141395/pageserver.pb.gz)
*
[safekeeper.pb.gz](https://github.com/user-attachments/files/18141396/safekeeper.pb.gz)
## Problem
Cplane and storage controller tenant config changes are not additive.
Any change overrides all existing tenant configs. This would be fine if
both did client side patching, but that's not the case.
Once this merges, we must update cplane to use the PATCH endpoint.
## Summary of changes
### High Level
Allow for patching of tenant configuration with a `PATCH
/v1/tenant/config` endpoint.
It takes the same data as it's PUT counterpart. For example the payload
below will update `gc_period` and unset `compaction_period`. All other
fields are left in their original state.
```
{
"tenant_id": "1234",
"gc_period": "10s",
"compaction_period": null
}
```
### Low Level
* PS and storcon gain `PATCH /v1/tenant/config` endpoints. PS endpoint
is only used for cplane managed instances.
* `storcon_cli` is updated to have separate commands for
`set-tenant-config` and `patch-tenant-config`
Related https://github.com/neondatabase/cloud/issues/21043
We've seen cases where stray keys end up on the wrong shard. This
shouldn't happen. Add debug assertions to prevent this. In release
builds, we should be lenient in order to handle changing key ownership
policies.
Touches #9914.
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
We don't have good observability for memory usage. This would be useful
e.g. to debug OOM incidents or optimize performance or resource usage.
We would also like to use continuous profiling with e.g. [Grafana Cloud
Profiles](https://grafana.com/products/cloud/profiles-for-continuous-profiling/)
(see https://github.com/neondatabase/cloud/issues/14888).
This PR is intended as a proof of concept, to try it out in staging and
drive further discussions about profiling more broadly.
Touches https://github.com/neondatabase/neon/issues/9534.
Touches https://github.com/neondatabase/cloud/issues/14888.
Depends on #9779.
Depends on #9780.
## Summary of changes
Adds a HTTP route `/profile/heap` that takes a heap profile and returns
it. Query parameters:
* `format`: output format (`jemalloc` or `pprof`; default `pprof`).
Unlike CPU profiles (see #9764), heap profiles are not symbolized and
require the original binary to translate addresses to function names. To
make this work with Grafana, we'll probably have to symbolize the
process server-side -- this is left as future work, as is other output
formats like SVG.
Heap profiles don't work on macOS due to limitations in jemalloc.
# 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
## Problem
https://github.com/neondatabase/neon/pull/9746 lifted decoding and
interpretation of WAL to the safekeeper.
This reduced the ingested amount on the pageservers by around 10x for a
tenant with 8 shards, but doubled
the ingested amount for single sharded tenants.
Also, https://github.com/neondatabase/neon/pull/9746 uses bincode which
doesn't support schema evolution.
Technically the schema can be evolved, but it's very cumbersome.
## Summary of changes
This patch set addresses both problems by adding protobuf support for
the interpreted wal records and adding compression support. Compressed
protobuf reduced the ingested amount by 100x on the 32 shards
`test_sharded_ingest` case (compared to non-interpreted proto). For the
1 shard case the reduction is 5x.
Sister change to `rust-postgres` is
[here](https://github.com/neondatabase/rust-postgres/pull/33).
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
## Problem
For any given tenant shard, pageservers receive all of the tenant's WAL
from the safekeeper.
This soft-blocks us from using larger shard counts due to bandwidth
concerns and CPU overhead of filtering
out the records.
## Summary of changes
This PR lifts the decoding and interpretation of WAL from the pageserver
into the safekeeper.
A customised PG replication protocol is used where instead of sending
raw WAL, the safekeeper sends
filtered, interpreted records. The receiver drives the protocol
selection, so, on the pageserver side, usage
of the new protocol is gated by a new pageserver config:
`wal_receiver_protocol`.
More granularly the changes are:
1. Optionally inject the protocol and shard identity into the arguments
used for starting replication
2. On the safekeeper side, implement a new wal sending primitive which
decodes and interprets records
before sending them over
3. On the pageserver side, implement the ingestion of this new
replication message type. It's very similar
to what we already have for raw wal (minus decoding and interpreting).
## Notes
* This PR currently uses my [branch of
rust-postgres](https://github.com/neondatabase/rust-postgres/tree/vlad/interpreted-wal-record-replication-support)
which includes the deserialization logic for the new replication message
type. PR for that is open
[here](https://github.com/neondatabase/rust-postgres/pull/32).
* This PR contains changes for both pageservers and safekeepers. It's
safe to merge because the new protocol is disabled by default on the
pageserver side. We can gradually start enabling it in subsequent
releases.
* CI tests are running on https://github.com/neondatabase/neon/pull/9747
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
The notifications need to be sent whenever the waiters heap changes, per
the comment in `update_status`. But if 'advance' is called when there
are no waiters, or the new LSN is lower than the waiters so that no one
needs to be woken up, there's no need to send notifications. This saves
some CPU cycles in the common case that there are no waiters.
## Problem
We don't have a convenient way to gather CPU profiles from a running
binary, e.g. during production incidents or end-to-end benchmarks, nor
during microbenchmarks (particularly on macOS).
We would also like to have continuous profiling in production, likely
using [Grafana Cloud
Profiles](https://grafana.com/products/cloud/profiles-for-continuous-profiling/).
We may choose to use either eBPF profiles or pprof profiles for this
(pending testing and discussion with SREs), but pprof profiles appear
useful regardless for the reasons listed above. See
https://github.com/neondatabase/cloud/issues/14888.
This PR is intended as a proof of concept, to try it out in staging and
drive further discussions about profiling more broadly.
Touches #9534.
Touches https://github.com/neondatabase/cloud/issues/14888.
## Summary of changes
Adds a HTTP route `/profile/cpu` that takes a CPU profile and returns
it. Defaults to a 5-second pprof Protobuf profile for use with e.g.
`pprof` or Grafana Alloy, but can also emit an SVG flamegraph. Query
parameters:
* `format`: output format (`pprof` or `svg`)
* `frequency`: sampling frequency in microseconds (default 100)
* `seconds`: number of seconds to profile (default 5)
Also integrates pprof profiles into Criterion benchmarks, such that
flamegraph reports can be taken with `cargo bench ... --profile-duration
<seconds>`. Output under `target/criterion/*/profile/flamegraph.svg`.
Example profiles:
* pprof profile (use [`pprof`](https://github.com/google/pprof)):
[profile.pb.gz](https://github.com/user-attachments/files/17756788/profile.pb.gz)
* Web interface: `pprof -http :6060 profile.pb.gz`
* Interactive flamegraph:
[profile.svg.gz](https://github.com/user-attachments/files/17756782/profile.svg.gz)
part of https://github.com/neondatabase/neon/issues/9114, we want to be
able to run partial gc-compaction in tests. In the future, we can also
expand this functionality to legacy compaction, so that we can trigger
compaction for a specific key range.
## Summary of changes
* Support passing compaction key range through pageserver routes.
* Refactor input parameters of compact related function to take the new
`CompactOptions`.
* Add tests for partial compaction. Note that the test may or may not
trigger compaction based on GC horizon. We need to improve the test case
to ensure things always get below the gc_horizon and the gc-compaction
can be triggered.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
See https://github.com/neondatabase/neon/issues/7750
test_wal_restore.sh is copying file to current working directory which
can cause interfere of test_wa_restore.py tests spawned of different
configurations.
## Summary of changes
Copy file to $DATA_DIR
Co-authored-by: Konstantin Knizhnik <knizhnik@neon.tech>
## Problem
WAL segment fsyncs significantly affect WAL ingestion throughput.
`durable_rename()` is used when initializing every 16 MB segment, and
issues 3 fsyncs of which 1 was unnecessary.
## Summary of changes
Remove an fsync in `durable_rename` which is unnecessary with Linux and
ext4 (which we currently use). This improves WAL ingestion throughput by
up to 23% with large appends on my MacBook.
## Problem
The control file is flushed on the WAL ingest path when the commit LSN
advances by one segment, to bound the amount of recovery work in case of
a crash. This involves 3 additional fsyncs, which can have a significant
impact on WAL ingest throughput. This is to some extent mitigated by
`AppendResponse` not being emitted on segment bound flushes, since this
will prevent commit LSN advancement, which will be addressed separately.
## Summary of changes
Don't flush the control file on the WAL ingest path at all. Instead,
leave that responsibility to the timeline manager, but ask it to flush
eagerly if the control file lags the in-memory commit LSN by more than
one segment. This should not cause more than `REFRESH_INTERVAL` (300 ms)
additional latency before flushing the control file, which is
negligible.
## Problem
We wish to stop using admin tokens in the infra repo, but step down
requests use the admin token.
## Summary of Changes
Introduce a new "ControllerPeer" scope and use it for step-down requests.
Right now, our environments create databases with the C locale, which is
really unfortunate for users who have data stored in other languages
that they want to analyze. For instance, show_trgm on Hebrew text
currently doesn't work in staging or production.
I don't envision this being the final solution. I think this is just a
way to set a known value so the pageserver doesn't use its parent
environment. The final solution to me is exposing initdb parameters to
users in the console. Then they could use a different locale or encoding
if they so chose.
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
We don't have a convenient way to generate WAL records for benchmarks
and tests.
## Summary of changes
Adds a WAL generator, exposed as an iterator. It currently only
generates logical messages (noops), but will be extended to write actual
table rows later.
Some existing code for WAL generation has been replaced with this
generator, to reduce duplication.
## Problem
We were seeing timeouts on migrations in this test.
The test unfortunately tends to saturate local storage, which is shared
between the pageservers and the control plane database, which makes the
test kind of unrealistic. We will also want to increase the scale of
this test, so it's worth fixing that.
## Summary of changes
- Instead of randomly creating timelines at the same time as the other
background operations, explicitly identify a subset of tenant which will
have timelines, and create them at the start. This avoids pageservers
putting a lot of load on the test node during the main body of the test.
- Adjust the tenants created to create some number of 8 shard tenants
and the rest 1 shard tenants, instead of just creating a lot of 2 shard
tenants.
- Use archival_config to exercise tenant-mutating operations, instead of
using timeline creation for this.
- Adjust reconcile_until_idle calls to avoid waiting 5 seconds between
calls, which causes timelines with large shard count tenants.
- Fix a pageserver bug where calls to archival_config during activation
get 404
## Problem
Storage controller `/control` API mostly requires admin tokens, for
interactive use by engineers. But for endpoints used by scripts, we
should not require admin tokens.
Discussion at
https://neondb.slack.com/archives/C033RQ5SPDH/p1728550081788989?thread_ts=1728548232.265019&cid=C033RQ5SPDH
## Summary of changes
- Introduce the 'infra' JWT scope, which was not previously used in the
neon repo
- For pageserver & safekeeper node registrations, require infra scope
instead of admin
Note that admin will still work, as the controller auth checks permit
admin tokens for all endpoints irrespective of what scope they require.
Follow-up of #9234 to give hyper 1.0 the version-free name, and the
legacy version of hyper the one with the version number inside. As we
move away from hyper 0.14, we can remove the `hyper0` name piece by
piece.
Part of #9255
## Problem
Seems that PS might be too eager in reporting throttled tasks
## Summary of changes
Introduce a sleep counter. If the sleep counter increases, then the
acquire tasks was throttled.
close https://github.com/neondatabase/neon/issues/8903
In https://github.com/neondatabase/neon/issues/8903 we observed JSON
decoding error to have the following error message in the log:
```
Error processing HTTP request: Resource temporarily unavailable: 3956 (pageserver-6.ap-southeast-1.aws.neon.tech) error receiving body: error decoding response body
```
This is hard to understand. In this patch, we make the error message
more reasonable.
## Summary of changes
* receive body error is now an internal server error, passthrough the
`reqwest::Error` (only decoding error) as `anyhow::Error`.
* instead of formatting the error using `to_string`, we use the
alternative `anyhow::Error` formatting, so that it prints out the cause
of the error (i.e., what exactly cannot serde decode).
I would expect seeing something like `error receiving body: error
decoding response body: XXX field not found` after this patch, though I
didn't set up a testing environment to observe the exact behavior.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
I wanted to use some features from the newer version. The PR that needed
the new version is not ready yet (and might never be), but seems nice to
stay up in any case.
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.
