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.
This PR simplifies the pageserver configuration parsing as follows:
* introduce the `pageserver_api::config::ConfigToml` type
* implement `Default` for `ConfigToml`
* use serde derive to do the brain-dead leg-work of processing the toml
document
* use `serde(default)` to fill in default values
* in `pageserver` crate:
* use `toml_edit` to deserialize the pageserver.toml string into a
`ConfigToml`
* `PageServerConfig::parse_and_validate` then
* consumes the `ConfigToml`
* destructures it exhaustively into its constituent fields
* constructs the `PageServerConfig`
The rules are:
* in `ConfigToml`, use `deny_unknown_fields` everywhere
* static default values go in `pageserver_api`
* if there cannot be a static default value (e.g. which default IO
engine to use, because it depends on the runtime), make the field in
`ConfigToml` an `Option`
* if runtime-augmentation of a value is needed, do that in
`parse_and_validate`
* a good example is `virtual_file_io_engine` or `l0_flush`, both of
which need to execute code to determine the effective value in
`PageServerConf`
The benefits:
* massive amount of brain-dead repetitive code can be deleted
* "unused variable" compile-time errors when removing a config value,
due to the exhaustive destructuring in `parse_and_validate`
* compile-time errors guide you when adding a new config field
Drawbacks:
* serde derive is sometimes a bit too magical
* `deny_unknown_fields` is easy to miss
Future Work / Benefits:
* make `neon_local` use `pageserver_api` to construct `ConfigToml` and
write it to `pageserver.toml`
* This provides more type safety / coompile-time errors than the current
approach.
### Refs
Fixes#3682
### Future Work
* `remote_storage` deser doesn't reject unknown fields
https://github.com/neondatabase/neon/issues/8915
* clean up `libs/pageserver_api/src/config.rs` further
* break up into multiple files, at least for tenant config
* move `models` as appropriate / refine distinction between config and
API models / be explicit about when it's the same
* use `pub(crate)` visibility on `mod defaults` to detect stale values
Removes additional async_trait usages from safekeeper and neon_local.
Also removes now redundant dependencies of the `async_trait` crate.
cc earlier work: #6305, #6464, #7303, #7342, #7212, #8296
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>
refs https://github.com/neondatabase/cloud/issues/13750
The logging in this commit will make it easier to detect lagging ingest.
We're trusting compute timestamps --- ideally we'd use SK timestmaps
instead.
But trusting the compute timestamp is ok for now.
This removes workspace hack from all libs, not from any binaries. This
does not change the behaviour of the hack.
Running
```
cargo clean
cargo build --release --bin proxy
```
Before this change took 5m16s. After this change took 3m3s. This is
because this allows the build to be parallelisable much more.
With the persistent gc blocking, we can now retry reparenting timelines
which had failed for whatever reason on the previous attempt(s).
Restructure the detach_ancestor into three phases:
- prepare (insert persistent gc blocking, copy lsn prefix, layers)
- detach and reparent
- reparenting can fail, so we might need to retry this portion
- complete (remove persistent gc blocking)
Cc: #6994
## Problem
We install and try to use `cachepot`. But it is not configured correctly
and doesn't work (after https://github.com/neondatabase/neon/pull/2290)
## Summary of changes
- Remove `cachepot`
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
Part of #8128, followed by #8502.
## Problem
Currently we lack mechanism to alert unhealthy `scan_metadata` status if
we start running this scrubber command as part of a cronjob. With the
storage controller client introduced to storage scrubber in #8196, it is
viable to set up alert by storing health status in the storage
controller database.
We intentionally do not store the full output to the database as the
json blobs potentially makes the table really huge. Instead, only a
health status and a timestamp recording the last time metadata health
status is posted on a tenant shard.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
