# Problem
The Pageserver read path exclusively uses direct IO if
`virtual_file_io_mode=direct`.
The write path is half-finished. Here is what the various writing
components use:
|what|buffering|flags on <br/>`v_f_io_mode`<br/>=`buffered`|flags on
<br/>`virtual_file_io_mode`<br/>=`direct`|
|-|-|-|-|
|`DeltaLayerWriter`| BlobWriter<BUFFERED=true> | () | () |
|`ImageLayerWriter`| BlobWriter<BUFFERED=false> | () | () |
|`download_layer_file`|BufferedWriter|()|()|
|`InMemoryLayer`|BufferedWriter|()|O_DIRECT|
The vehicle towards direct IO support is `BufferedWriter` which
- largely takes care of O_DIRECT alignment & size-multiple requirements
- double-buffering to mask latency
`DeltaLayerWriter`, `ImageLayerWriter` use `blob_io::BlobWriter` , which
has neither of these.
# Changes
## High-Level
At a high-level this PR makes the following primary changes:
- switch the two layer writer types to use `BufferedWriter` & make
sensitive to `virtual_file_io_mode` (via open_with_options_**v2**)
- make `download_layer_file` sensitive to `virtual_file_io_mode` (also
via open_with_options_**v2**)
- add `virtual_file_io_mode=direct-rw` as a feature gate
- we're hackish-ly piggybacking on OpenOptions's ask for write access
here
- this means with just `=direct` InMemoryLayer reads and writes no
longer uses O_DIRECT
- this is transitory and we'll remove the `direct-rw` variant once the
rollout is complete
(The `_v2` APIs for opening / creating VirtualFile are those that are
sensitive to `virtual_file_io_mode`)
The result is:
|what|uses <br/>`BufferedWriter`|flags on
<br/>`v_f_io_mode`<br/>=`buffered`|flags on
<br/>`v_f_io_mode`<br/>=`direct`|flags on
<br/>`v_f_io_mode`<br/>=`direct-rw`|
|-|-|-|-|-|
|`DeltaLayerWriter`| ~~Blob~~BufferedWriter | () | () | O_DIRECT |
|`ImageLayerWriter`| ~~Blob~~BufferedWriter | () | () | O_DIRECT |
|`download_layer_file`|BufferedWriter|()|()|O_DIRECT|
|`InMemoryLayer`|BufferedWriter|()|~~O_DIRECT~~()|O_DIRECT|
## Code-Level
The main change is:
- Switch `blob_io::BlobWriter` away from its own buffering method to use
`BufferedWriter`.
Additional prep for upholding `O_DIRECT` requirements:
- Layer writer `finish()` methods switched to use IoBufferMut for
guaranteed buffer address alignment. The size of the buffers is PAGE_SZ
and thereby implicitly assumed to fulfill O_DIRECT requirements.
For the hacky feature-gating via `=direct-rw`:
- Track `OpenOptions::write(true|false)` in a field; bunch of mechanical
churn.
- Consolidate the APIs in which we "open" or "create" VirtualFile for
better overview over which parts of the code use the `_v2` APIs.
Necessary refactorings & infra work:
- Add doc comments explaining how BufferedWriter ensures that writes are
compliant with O_DIRECT alignment & size constraints. This isn't new,
but should be spelled out.
- Add the concept of shutdown modes to `BufferedWriter::shutdown` to
make writer shutdown adhere to these constraints.
- The `PadThenTruncate` mode might not be necessary in practice because
I believe all layer files ever written are sized in multiples `PAGE_SZ`
and since `PAGE_SZ` is larger than the current alignment requirements
(512/4k depending on platform), it won't be necesary to pad.
- Some test (I believe `round_trip_test_compressed`?) required it though
- [ ] TODO: decide if we want to accept that complexity; if we do then
address TODO in the code to separate alignment requirement from buffer
capacity
- Add `set_len` (=`ftruncate`) VirtualFile operation to support the
above.
- Allow `BufferedWriter` to start at a non-zero offset (to make room for
the summary block).
Cleanups unlocked by this change:
- Remove non-positional APIs from VirtualFile (e.g. seek, write_full,
read_full)
Drive-by fixes:
- PR https://github.com/neondatabase/neon/pull/11585 aimed to run unit
tests for all `virtual_file_io_mode` combinations but didn't because of
a missing `_` in the env var.
# Performance
This section assesses this PR's impact on deployments with current
production setting (`=direct`) and anticipated impact of switching to
(`=direct-rw`).
For `DeltaLayerWriter`, `=direct` should remain unchanged to slightly
improved on throughput because the `BlobWriter`'s buffer had the same
size as the `BufferedWriter`'s buffer, but it didn't have the
double-buffering that `BufferedWriter` has.
The `=direct-rw` enables direct IO; throughput should not be suffering
because of double-buffering; benchmarks will show if this is true.
The `ImageLayerWriter` was previously not doing any buffering
(`BUFFERED=false`).
It went straight to issuing the IO operation to the underlying
VirtualFile and the buffering was done by the kernel.
The switch to `BufferedWriter` under `=direct` adds an additional memcpy
into the BufferedWriter's buffer.
We will win back that memcpy when enabling direct IO via `=direct-rw`.
A nice win from the switch to `BufferedWriter` is that ImageLayerWriter
performs >=16x fewer write operations to VirtualFile (the BlobWriter
performs one write per len field and one write per image value).
This should save low tens of microseconds of CPU overhead from doing all
these syscalls/io_uring operations, regardless of `=direct` or
`=direct-rw`.
Aside from problems with alignment, this write frequency without
double-buffering is prohibitive if we actually have to wait for the
disk, which is what will happen when we enable direct IO via
(`=direct-rw`).
Throughput should not be suffering because of BufferedWrite's
double-buffering; benchmarks will show if this is true.
`InMemoryLayer` at `=direct` will flip back to using buffered IO but
remain on BufferedWriter.
The buffered IO adds back one memcpy of CPU overhead.
Throughput should not suffer and will might improve on
not-memory-pressured Pageservers but let's remember that we're doing the
whole direct IO thing to eliminate global memory pressure as a source of
perf variability.
## bench_ingest
I reran `bench_ingest` on `im4gn.2xlarge` and `Hetzner AX102`.
Use `git diff` with `--word-diff` or similar to see the change.
General guidance on interpretation:
- immediate production impact of this PR without production config
change can be gauged by comparing the same `io_mode=Direct`
- end state of production switched over to `io_mode=DirectRw` can be
gauged by comparing old results' `io_mode=Direct` to new results'
`io_mode=DirectRw`
Given above guidance, on `im4gn.2xlarge`
- immediate impact is a significant improvement in all cases
- end state after switching has same significant improvements in all
cases
- ... except `ingest/io_mode=DirectRw volume_mib=128 key_size_bytes=8192
key_layout=Sequential write_delta=Yes` which only achieves `238 MiB/s`
instead of `253.43 MiB/s`
- this is a 6% degradation
- this workload is typical for image layer creation
# Refs
- epic https://github.com/neondatabase/neon/issues/9868
- stacked atop
- preliminary refactor https://github.com/neondatabase/neon/pull/11549
- bench_ingest overhaul https://github.com/neondatabase/neon/pull/11667
- derived from https://github.com/neondatabase/neon/pull/10063
Co-authored-by: Yuchen Liang <yuchen@neon.tech>
## Problem
We need to test the stability of Neon.
## Summary of changes
The test runs random operations on a Neon project. It performs via the
Public API calls the following operations: `create a branch`, `delete a
branch`, `add a read-only endpoint`, `delete a read-only endpoint`,
`restore a branch to a random position in the past`. All the branches
and endpoints are loaded with `pgbench`.
---------
Co-authored-by: Peter Bendel <peterbendel@neon.tech>
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
Metrics are saved in https://github.com/neondatabase/neon/pull/11559,
but the file is not matched by the attachment regex.
## Summary of changes
Make attachment regex match the metrics file.
## Problem
Benchmarks results are inconsistent on existing small-metal runners
## Summary of changes
Introduce new `unit-perf` runners, and lets run benchmark on them.
The new hardware has slower, but consistent, CPU frequency - if run with
default governor schedutil.
Thus we needed to adjust some testcases' timeouts and add some retry
steps where hard-coded timeouts couldn't be increased without changing
the system under test.
-
[wait_for_last_record_lsn](6592d69a67/test_runner/fixtures/pageserver/utils.py (L193))
1000s -> 2000s
-
[test_branch_creation_many](https://github.com/neondatabase/neon/pull/11409/files#diff-2ebfe76f89004d563c7e53e3ca82462e1d85e92e6d5588e8e8f598bbe119e927)
1000s
-
[test_ingest_insert_bulk](https://github.com/neondatabase/neon/pull/11409/files#diff-e90e685be4a87053bc264a68740969e6a8872c8897b8b748d0e8c5f683a68d9f)
- with back throttling disabled compute becomes unresponsive for more
than 60 seconds (PG hard-coded client authentication connection timeout)
-
[test_sharded_ingest](https://github.com/neondatabase/neon/pull/11409/files#diff-e8d870165bd44acb9a6d8350f8640b301c1385a4108430b8d6d659b697e4a3f1)
600s -> 1200s
Right now there are only 2 runners of that class, and if we decide to go
with them, we have to check how much that type of runners we need, so
jobs not stuck with waiting for that type of runners available.
However we now decided to run those runners with governor performance
instead of schedutil.
This achieves almost same performance as previous runners but still
achieves consistent results for same commit
Related issue to activate performance governor on these runners
https://github.com/neondatabase/runner/pull/138
## Verification that it helps
### analyze runtimes on new runner for same commit
Table of runtimes for the same commit on different runners in
[run](https://github.com/neondatabase/neon/actions/runs/14417589789)
| Run | Benchmarks (1) | Benchmarks (2) |Benchmarks (3) |Benchmarks (4)
| Benchmarks (5) |
|--------|--------|---------|---------|---------|---------|
| 1 | 1950.37s | 6374.55s | 3646.15s | 4149.48s | 2330.22s |
| 2 | - | 6369.27s | 3666.65s | 4162.42s | 2329.23s |
| Delta % | - | 0,07 % | 0,5 % | 0,3 % | 0,04 % |
| with governor performance | 1519.57s | 4131.62s | - | - | - |
| second run gov. perf. | 1513.62s | 4134.67s | - | - | - |
| Delta % | 0,3 % | 0,07 % | - | - | - |
| speedup gov. performance | 22 % | 35 % | - | - | - |
| current desktop class hetzner runners (main) | 1487.10s | 3699.67s | -
| - | - |
| slower than desktop class | 2 % | 12 % | - | - | - |
In summary, the runtimes for the same commit on this hardware varies
less than 1 %.
---------
Co-authored-by: BodoBolero <peterbendel@neon.tech>
## Problem
Sometimes it's useful to see the pageserver metrics after a test in
order to debug stuff.
For example, for https://github.com/neondatabase/neon/issues/11465 I'd
like to know
what the remote storage latencies are from the client.
## Summary of changes
When stopping the env, record the pageserver metrics into a file in the
pageserver's workdir.
## Problem
Pageservers now ignore unknown config fields, so this config tweaking is
no longer needed.
## Summary of changes
Get rid of the hack.
Closes https://github.com/neondatabase/neon/issues/11524
## Problem
https://github.com/neondatabase/neon/pull/11494 changes the batching
logic, but we don't have a way to evaluate it.
## Summary of changes
This PR introduces a global and per timeline metric which tracks the
reason for
which a batch was broken.
## Problem
Get page batching stops when we encounter requests at different LSNs.
We are leaving batching factor on the table.
## Summary of changes
The goal is to support keys with different LSNs in a single batch and
still serve them with a single vectored get.
Important restriction: the same key at different LSNs is not supported
in one batch. Returning different key
versions is a much more intrusive change.
Firstly, the read path is changed to support "scattered" queries. This
is a conceptually simple step from
https://github.com/neondatabase/neon/pull/11463. Instead of initializing
the fringe for one keyspace,
we do it for multiple at different LSNs and let the logic already
present into the fringe handle selection.
Secondly, page service code is updated to support batching at different
LSNs. Eeach request parsed from the wire determines its effective
request LSN and keeps it in mem for the batcher toinspect. The batcher
allows keys at
different LSNs in one batch as long one key is not requested at
different LSNs.
I'd suggest doing the first pass commit by commit to get a feel for the
changes.
## Results
I used the batching test from [Christian's
PR](https://github.com/neondatabase/neon/pull/11391) which increases the
change of batch breaks. Looking at the logs I think the new code is at
the max batching factor for the workload (we
only break batches due to them being oversized or because the executor
is idle).
```
Main:
Reasons for stopping batching: {'LSN changed': 22843, 'of batch size': 33417}
test_throughput[release-pg16-50-pipelining_config0-30-100-128-batchable {'max_batch_size': 32, 'execution': 'concurrent-futures', 'mode': 'pipelined'}].perfmetric.batching_factor: 14.6662
My branch:
Reasons for stopping batching: {'of batch size': 37024}
test_throughput[release-pg16-50-pipelining_config0-30-100-128-batchable {'max_batch_size': 32, 'execution': 'concurrent-futures', 'mode': 'pipelined'}].perfmetric.batching_factor: 19.8333
```
Related: https://github.com/neondatabase/neon/issues/10765
Previously, the structure of the spec file was just the compute spec.
However, the response from the control plane get spec request included
the compute spec and the compute_ctl config. This divergence was
hindering other work such as adding regression tests for compute_ctl
HTTP authorization.
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
The `pagebench` benchmarks set up an initial dataset by creating a
template tenant, copying the remote storage to a bunch of new tenants,
and attaching them to Pageservers.
In #11420, we found that
`test_pageserver_characterize_throughput_with_n_tenants` had degraded
performance because it set a custom tenant config in Pageservers that
was then replaced with the default tenant config by the storage
controller.
The initial fix was to register the tenants directly in the storage
controller, but this created the tenants with generation 1. This broke
`test_basebackup_with_high_slru_count`, where the template tenant was at
generation 2, leading to all layer files at generation 2 being ignored.
Resolves#11485.
Touches #11381.
## Summary of changes
This patch addresses both test issues by modifying `attach_hook` to also
take a custom tenant config. This allows attaching tenants to
Pageservers from pre-existing remote storage, specifying both the
generation and tenant config when registering them in the storage
controller.
The batching perf test workload is currently read-only sequential scans.
However, realistic workloads have concurrent writes (to other pages)
going on.
This PR simulates concurrent writes to other pages by emitting logical
replication messages.
These degrade the achieved batching factor, for the reason see
- https://github.com/neondatabase/neon/issues/10765
PR
- https://github.com/neondatabase/neon/pull/11494
will fix this problem and get batching factor back up.
---------
Co-authored-by: Vlad Lazar <vlad@neon.tech>
Because it wasn't recursive, there was a limit to the depth of updates.
This work is necessary because as we teach neon_local and compute_ctl
that the content in --spec-path should match a similar structure we get
from the control plane, the spec object itself will no longer be
toplevel. It will be under the "spec" key.
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
Walproposer should get elected and commit WAL on safekeepers specified
by the membership configuration.
## Summary of changes
- Add to wp `members_safekeepers` and `new_members_safekeepers` arrays
mapping configuration members to connection slots. Establish this
mapping (by node id) when safekeeper sends greeting, giving its id and
when mconf becomes known / changes.
- Add to TermsCollected, VotesCollected,
GetAcknowledgedByQuorumWALPosition membership aware logic. Currently it
partially duplicates existing one, but we'll drop the latter eventually.
- In python, rename Configuration to MembershipConfiguration for
clarity.
- Add test_quorum_sanity testing new logic.
ref https://github.com/neondatabase/neon/issues/10851
## Problem
`test_location_conf_churn` often fails with `neither image nor delta
layer`, but doesn't say what the file actually is. However, past local
failures have indicated that it might be `.___temp` files.
Touches https://github.com/neondatabase/neon/issues/11348.
## Summary of changes
Ignore `.___temp` files when evicting local layers, and include the file
name in the error message.
Service targeted for storing and retrieving LFC prewarm data.
Can be used for proxying S3 access for Postgres extensions like
pg_mooncake as well.
Requests must include a Bearer JWT token.
Token is validated using a pemfile (should be passed in infra/).
Note: app is not tolerant to extra trailing slashes, see app.rs
`delete_prefix` test for comments.
Resolves: https://github.com/neondatabase/cloud/issues/26342
Unrelated changes: gate a `rename_noreplace` feature and disable it in
`remote_storage` so as `object_storage` can be built with musl
## Problem
We don't have metrics to exactly quantify the end user impact of
on-demand downloads.
Perf tracing is underway (#11140) to supply us with high-resolution
*samples*.
But it will also be useful to have some aggregate per-timeline and
per-instance metrics that definitively contain all observations.
## Summary of changes
This PR consists of independent commits that should be reviewed
independently.
However, for convenience, we're going to merge them together.
- refactor(metrics): measure_remote_op can use async traits
- impr(pageserver metrics): task_kind dimension for
remote_timeline_client latency histo
- implements https://github.com/neondatabase/cloud/issues/26800
- refs
https://github.com/neondatabase/cloud/issues/26193#issuecomment-2769705793
- use the opportunity to rename the metric and add a _global suffix;
checked grafana export, it's only used in two personal dashboards, one
of them mine, the other by Heikki
- log on-demand download latency for expensive-to-query but precise
ground truth
- metric for wall clock time spent waiting for on-demand downloads
## Refs
- refs https://github.com/neondatabase/cloud/issues/26800
- a bunch of minor investigations / incidents into latency outliers
# Refs
- refs https://github.com/neondatabase/neon/issues/8915
- discussion thread:
https://neondb.slack.com/archives/C033RQ5SPDH/p1742406381132599
- stacked atop https://github.com/neondatabase/neon/pull/11298
- corresponding internal docs update that illustrates how this PR
removes friction: https://github.com/neondatabase/docs/pull/404
# Problem
Rejecting `pageserver.toml`s with unknown fields adds friction,
especially when using `pageserver.toml` fields as feature flags that
need to be decommissioned.
See the added paragraphs on `pageserver_api::models::ConfigToml` for
details on what kind of friction it causes.
Also read the corresponding internal docs update linked above to see a
more imperative guide for using `pageserver.toml` flags as feature
flags.
# Solution
## Ignoring unknown fields
Ignoring is the serde default behavior.
So, just remove `serde(deny_unknown_fields)` from all structs in
`pageserver_api::config::ConfigToml`
`pageserver_api::config::TenantConfigToml`.
I went through all the child fields and verified they don't use
`deny_unknown_fields` either, including those shared with
`pageserver_api::models`.
## Warning about unknown fields
We still want to warn about unknown fields to
- be informed about typos in the config template
- be reminded about feature-flag style configs that have been cleaned up
in code but not yet in config templates
We tried `serde_ignore` (cf draft #11319) but it doesn't work with
`serde(flatten)`.
The solution we arrived at is to compare the on-disk TOML with the TOML
that we produce if we serialize the `ConfigToml` again.
Any key specified in the on-disk TOML but not present in the serialized
TOML is flagged as an ignored key.
The mechanism to do it is a tiny recursive decent visitor on the
`toml_edit::DocumentMut`.
# Future Work
Invalid config _values_ in known fields will continue to fail pageserver
startup.
See
- https://github.com/neondatabase/cloud/issues/24349
for current worst case impact to deployments & ideas to improve.
## Problem
There are some places in the code where we create `reqwest::Client`
without providing SSL CA certs from `ssl_ca_file`. These will break
after we enable TLS everywhere.
- Part of https://github.com/neondatabase/cloud/issues/22686
## Summary of changes
- Support `ssl_ca_file` in storage scrubber.
- Add `use_https_safekeeper_api` option to safekeeper to use https for
peer requests.
- Propagate SSL CA certs to storage_controller/client, storcon's
ComputeHook, PeerClient and maybe_forward.
Based on https://github.com/neondatabase/neon/pull/11139
## Problem
We want to export performance traces from the pageserver in OTEL format.
End goal is to see them in Grafana.
## Summary of changes
https://github.com/neondatabase/neon/pull/11139 introduces the
infrastructure required to run the otel collector alongside the
pageserver.
### Design
Requirements:
1. We'd like to avoid implementing our own performance tracing stack if
possible and use the `tracing` crate if possible.
2. Ideally, we'd like zero overhead of a sampling rate of zero and be a
be able to change the tracing config for a tenant on the fly.
3. We should leave the current span hierarchy intact. This includes
adding perf traces without modifying existing tracing.
To satisfy (3) (and (2) in part) a separate span hierarchy is used.
`RequestContext` gains an optional `perf_span` member
that's only set when the request was chosen by sampling. All perf span
related methods added to `RequestContext` are no-ops for requests that
are not sampled.
This on its own is not enough for (3), so performance spans use a
separate tracing subscriber. The `tracing` crate doesn't have great
support for this, so there's a fair amount of boilerplate to override
the subscriber at all points of the perf span lifecycle.
### Perf Impact
[Periodic
pagebench](https://neonprod.grafana.net/d/ddqtbfykfqfi8d/e904990?orgId=1&from=2025-02-08T14:15:59.362Z&to=2025-03-10T14:15:59.362Z&timezone=utc)
shows no statistically significant regression with a sample ratio of 0.
There's an annotation on the dashboard on 2025-03-06.
### Overview of changes:
1. Clean up the `RequestContext` API a bit. Namely, get rid of the
`RequestContext::extend` API and use the builder instead.
2. Add pageserver level configs for tracing: sampling ratio, otel
endpoint, etc.
3. Introduce some perf span tracking utilities and expose them via
`RequestContext`. We add a `tracing::Span` wrapper to be used for perf
spans and a `tracing::Instrumented` equivalent for it. See doc comments
for reason.
4. Set up OTEL tracing infra according to configuration. A separate
runtime is used for the collector.
5. Add perf traces to the read path.
## Refs
- epic https://github.com/neondatabase/neon/issues/9873
---------
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
Since
0f367cb665
the timeout in `with_client_retries` is implemented via `tokio::timeout`
instead of `reqwest::ClientBuilder::timeout` (because we reuse the
client). It changed the error representation if the timeout is exceeded.
Such errors were suppressed in `allowed_errors.py`, but old regexps do
not match the new error.
Discussion:
https://neondb.slack.com/archives/C033RQ5SPDH/p1743533184736319
## Summary of changes
- Add new `Timeout` error to `allowed_errors.py`
## Problem
Pagebench creates a bunch of tenants by first creating a template tenant
and copying its remote storage, then attaching the copies to the
Pageserver.
These tenants had custom configurations to disable GC and compaction.
However, these configs were only picked up by the Pageserver on attach,
and not registered with the storage controller. This caused the storage
controller to replace the tenant configs with the default tenant config,
re-enabling GC and compaction which interferes with benchmark
performance.
Resolves#11381.
## Summary of changes
Register the copied tenants with the storage controller, instead of
directly attaching them to the Pageserver.
Right now we start safekeeper node ids at 0. However, other code treats
0 as invalid (see #11407). We decided on latter. Therefore, make the
register python tests register safekeepers starting at node id 1 instead
of 0, and forbid safekeepers with id 0 from registering.
Context:
https://github.com/neondatabase/neon/pull/11407#discussion_r2024852328
## Problem
close https://github.com/neondatabase/neon/issues/11279
## Summary of changes
* Allow passthrough of other methods in tenant timeline shard0
passthrough of storcon.
* Passthrough mark invisible API in storcon.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
In Neon DBaaS we adjust the shared_buffers to the size of the compute,
or better described we adjust the max number of connections to the
compute size and we adjust the shared_buffers size to the number of max
connections according to about the following sizes
`2 CU: 225mb; 4 CU: 450mb; 8 CU: 900mb`
[see](877e33b428/goapp/controlplane/internal/pkg/compute/computespec/pg_settings.go (L405))
## Summary of changes
We should run perf unit tests with settings that is realistic for a
paying customer and select 8 CU as the reference for those tests.
## Problem
For computes running inside NeonVM, the actual compute image tag is
buried inside the NeonVM spec, and we cannot get it as part of standard
k8s container metrics (it's always an image and a tag of the NeonVM
runner container). The workaround we currently use is to extract the
running computes info from the control plane database with SQL. It has
several drawbacks: i) it's complicated, separate DB per region; ii) it's
slow; iii) it's still an indirect source of info, i.e. k8s state could
be different from what the control plane expects.
## Summary of changes
Add a new `compute_ctl_up` gauge metric with `build_tag` and `status`
labels. It will help us to both overview what are the tags/versions of
all running computes; and to break them down by current status (`empty`,
`running`, `failed`, etc.)
Later, we could introduce low cardinality (no endpoint or compute ids)
streaming aggregates for such metrics, so they will be blazingly fast
and usable for monitoring the fleet-wide state.
## Problem
`TYPE_CHECKING` is used inconsistently across Python tests.
## Summary of changes
- Update `ruff`: 0.7.0 -> 0.11.2
- Enable TC (flake8-type-checking):
https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
- (auto)fix all new issues
## Problem
Previously, L0 flushes would wait for uploads, as a simple form of
backpressure. However, this prevented flush pipelining and upload
parallelism. It has since been disabled by default and replaced by L0
compaction backpressure.
Touches https://github.com/neondatabase/cloud/issues/24664.
## Summary of changes
This patch removes L0 flush upload waits, along with the
`l0_flush_wait_upload`. This can't be merged until the setting has been
removed across the fleet.
## Problem
While working on bulk import, I want to use the `control-plane-url` flag
for a different request.
Currently, the local compute hook is used whenever no control plane is
specified in the config.
My test requires local compute notifications and a configured
`control-plane-url` which isn't supported.
## Summary of changes
Add a `use-local-compute-notifications` flag. When this is set, we use
the local flow regardless of other config values.
It's enabled by default in neon_local and disabled by default in all
other envs. I had to turn the flag off in tests
that wish to bypass the local flow, but that's expected.
---------
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
## Problem
Issue https://github.com/neondatabase/neon/issues/11254 describes a case
where restart during a shard split can result in a bad end state in the
database.
## Summary of changes
- Add a reproducer for the issue
- Tighten an existing safety check around updated row counts in
complete_shard_split
## Problem
Currently, we only split tenants into 8 shards once, at the 64 GB split
threshold. For very large tenants, we need to keep splitting to avoid
huge shards. And we also want to eagerly split at a lower threshold to
improve throughput during initial ingestion.
See
https://github.com/neondatabase/cloud/issues/22532#issuecomment-2706215907
for details.
Touches https://github.com/neondatabase/cloud/issues/22532.
Requires #11157.
## Summary of changes
This adds parameters and logic to enable repeated splits when a tenant's
largest timeline divided by shard count exceeds `split_threshold`, as
well as eager initial splits at a lower threshold to speed up initial
ingestion. The default parameters are all set such that they retain the
current behavior in production (only split into 8 shards once, at 64
GB).
* `split_threshold` now specifies a maximum shard size. When a shard
exceeds it, all tenant shards are split by powers of 2 such that all
tenant shards fall below `split_threshold`. Disabled by default, like
today.
* Add `max_split_shards` to specify a max shard count for autosplits.
Defaults to 8 to retain current behavior.
* Add `initial_split_threshold` and `initial_split_shards` to specify a
threshold and target count for eager splits of unsharded tenants.
Defaults to 64 GB and 8 shards to retain current production behavior.
Because this PR sets `initial_split_threshold` to 64 GB by default, it
has the effect of enabling autosplits by default. This was not the case
previously, since `split_threshold` defaults to None, but it is already
enabled across production and staging. This is temporary until we
complete the production rollout.
For more details, see code comments.
This must wait until #11157 has been deployed to Pageservers.
Once this has been deployed to production, we plan to change the
parameters to:
* `split-threshold`: 256 GB
* `initial-split-threshold`: 16 GB
* `initial-split-shards`: 4
* `max-split-shards`: 16
The final split points will thus be:
* Start: 1 shard
* 16 GB: 4 shards
* 1 TB: 8 shards
* 2 TB: 16 shards
We will then change the default settings to be disabled by default.
---------
Co-authored-by: John Spray <john@neon.tech>
## Problem
`fast_import` binary is being run inside neonvms, and they do not
support proper `kubectl describe logs` now, there are a bunch of other
caveats as well: https://github.com/neondatabase/autoscaling/issues/1320
Anyway, we needed a signal if job finished successfully or not, and if
not — at least some error message for the cplane operation. And after [a
short
discussion](https://neondb.slack.com/archives/C07PG8J1L0P/p1741954251813609),
that s3 object is the most convenient at the moment.
## Summary of changes
If `s3_prefix` was provided to `fast_import` call, any job run puts a
status object file into `{s3_prefix}/status/fast_import` with contents
`{"done": true}` or `{"done": false, "error": "..."}`. Added a test as
well
## Problem
Pageservers use unencrypted HTTP requests for storage controller API.
- Closes: https://github.com/neondatabase/cloud/issues/25524
## Summary of changes
- Replace hyper0::server::Server with http_utils::server::Server in
storage controller.
- Add HTTPS handler for storage controller API.
- Support `ssl_ca_file` in pageserver.
Adds a basic test that makes the storcon issue explicit creation of a
timeline on safeekepers (main storcon PR in #11058). It was adapted from
`test_explicit_timeline_creation` from #11002.
Also, do a bunch of fixes needed to get the test work (the API
definitions weren't correct), and log more stuff when we can't create a
new timeline due to no safekeepers being active.
Part of #9011
---------
Co-authored-by: Arseny Sher <sher-ars@yandex.ru>
We want to switch away from and deprecate the `--compute-hook-url` param
for the storcon in favour of `--control-plane-url` because it allows us
to construct urls with `notify-safekeepers`.
This PR switches the pytests and neon_local from a
`control_plane_compute_hook_api` to a new param named
`control_plane_hooks_api` which is supposed to point to the parent of
the `notify-attach` URL.
We still support reading the old url from disk to not be too disruptive
with existing deployments, but we just ignore it.
Also add docs for the `notify-safekeepers` upcall API.
Follow-up of #11173
Part of https://github.com/neondatabase/neon/issues/11163
# Problem
We leave too few observability breadcrumbs in the case where wait_lsn is
exceptionally slow.
# Changes
- refactor: extract the monitoring logic out of `log_slow` into
`monitor_slow_future`
- add global + per-timeline counter for time spent waiting for wait_lsn
- It is updated while we're still waiting, similar to what we do for
page_service response flush.
- add per-timeline counterpair for started & finished wait_lsn count
- add slow-logging to leave breadcrumbs in logs, not just metrics
For the slow-logging, we need to consider not flooding the logs during a
broker or network outage/blip.
The solution is a "log-streak-level" concurrency limit per timeline.
At any given time, there is at most one slow wait_lsn that is logging
the "still running" and "completed" sequence of logs.
Other concurrent slow wait_lsn's don't log at all.
This leaves at least one breadcrumb in each timeline's logs if some
wait_lsn was exceptionally slow during a given period.
The full degree of slowness can then be determined by looking at the
per-timeline metric.
# Performance
Reran the `bench_log_slow` benchmark, no difference, so, existing call
sites are fine.
We do use a Semaphore, but only try_acquire it _after_ things have
already been determined to be slow. So, no baseline overhead
anticipated.
# Refs
-
https://github.com/neondatabase/cloud/issues/23486#issuecomment-2711587222
## Problem
close https://github.com/neondatabase/neon/issues/10310
## Summary of changes
This patch adds a new behavior for the detach_ancestor API: detach with
multi-level ancestor and no reparenting. Though we can potentially
support multi-level + do reparenting / single-level + no-reparenting in
the future, as it's not required for the recovery/snapshot epic, I'd
prefer keeping things simple now that we only handle the old one and the
new one instead of supporting the full feature matrix.
I only added a test case of successful detaching instead of testing
failures. I'd like to make this into staging and add more tests in the
future.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
As part of the disaster recovery tool. Partly for
https://github.com/neondatabase/neon/issues/9114.
## Summary of changes
* Add a new pageserver API to force patch the fields in index_part and
modify the timeline internal structures.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
The current migration API does a live migration, but if the destination
doesn't already have a secondary, that live migration is unlikely to be
able to warm up a tenant properly within its timeout (full warmup of a
big tenant can take tens of minutes).
Background optimisation code knows how to do this gracefully by creating
a secondary first, but we don't currently give a human a way to trigger
that.
Closes: https://github.com/neondatabase/neon/issues/10540
## Summary of changes
- Add `prefererred_node` parameter to TenantShard, which is respected by
optimize_attachment
- Modify migration API to have optional prewarm=true mode, in which we
set preferred_node and call optimize_attachment, rather than directly
modifying intentstate
- Require override_scheduler=true flag if migrating somewhere that is a
less-than-optimal scheduling location (e.g. wrong AZ)
- Add `origin_node_id` to migration API so that callers can ensure
they're moving from where they think they're moving from
- Add tests for the above
The storcon_cli wrapper for this has a 'watch' mode that waits for
eventual cutover. This doesn't show the warmth of the secondary evolve
because we don't currently have an API for that in the controller, as
the passthrough API only targets attached locations, not secondaries. It
would be straightforward to add later as a dedicated endpoint for
getting secondary status, then extend the storcon_cli to consume that
and print a nice progress indicator.
## Problem
We just had a regression reported at
https://neondb.slack.com/archives/C08EXUJF554/p1741102467515599, which
clearly came with one of the releases. It's not a huge problem yet, but
it's annoying that we cannot quickly attribute it to a specific commit.
## Summary of changes
Add a very simple `compute_ctl` HTTP API benchmark that does 10k
requests to `/status` and `metrics.json` and reports p50 and p99.
---------
Co-authored-by: Peter Bendel <peterbendel@neon.tech>
## Problem
For pg_regress test, we do both v1 and v2; for all the rest, we default
to v2.
part of https://github.com/neondatabase/neon/issues/9516
## Summary of changes
Use reldir v2 across test cases by default.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Our benchmarking workflows contain links to grafana dashboards to
troubleshoot problems.
This works fine for non-pooled endpoints.
For pooled endpoints we need to remove the `-pooler` suffix from the
endpoint's hostname to get a valid endpoint ID.
Example link that doesn't work in this run
https://github.com/neondatabase/neon/actions/runs/13678933253/job/38246028316#step:8:311
## Summary of changes
Check if connection string is a -pooler connection string and if so
remove this suffix from the endpoint ID.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
On unarchival, we update the previous heatmap with all visible layers.
When the primary generates a new heatmap it includes all those layers,
so the secondary will download them. Since they're not actually resident
on the primary (we didn't call the warm up API), they'll never be
evicted, so they remain in the heatmap.
This leads to oversized secondary locations like we saw in pre-prod.
## Summary of changes
Gate the loading of the previous heatmaps and the heatmap generation on
unarchival behind configuration
flags. They are disabled by default, but enabled in tests.
