Commit
e69c3d632b
added metrics (used for alerting) to indicate whether Safekeepers are
operating with a degraded quorum due to some of them being down.
However, even if all SKs are active/reachable, we probably still want to
raise an alert if some of them are really slow or otherwise lagging
behind, as it is technically still a "degraded quorum" situation.
Added a new field `max_active_safekeeper_commit_lag` to the
`neon_perf_counters` view that reports the lag between the most advanced
and most lagging commit LSNs among active Safekeepers.
Commit LSNs are received from `AppendResponse` messages from SKs and
recorded in the `WalProposer`'s shared memory state.
Note that this lag is calculated among active SKs only to keep this
alert clean. If there are inactive SKs the previous metric on active SKs
will capture that instead.
Note: @chen-luo_data pointed out during the PR review that we can
probably get the benefits of this metric with PromQL query like `max
(safekeeper_commit_lsn) by (tenant_id, timeline_id) -
min(safekeeper_commit_lsn) by (tenant_id, timeline_id)` on existing
metrics exported by SKs.
Given that this code is already ready, @haoyu-huang_data suggested that
I just check in this change anyway, as the reliability of prometheus
metrics (and especially the aggregation operators when the result set
cardinality is high) is somewhat questionable based on our prior
experience.
Added integration test
`test_wal_acceptor.py::test_max_active_safekeeper_commit_lag`.
Today we don't have any indications (other than spammy logs in PG that
nobody monitors) if the Walproposer in PG cannot connect to/get votes
from all Safekeepers. This means we don't have signals indicating that
the Safekeepers are operating at degraded redundancy. We need these
signals.
Added plumbing in PG extension so that the `neon_perf_counters` view
exports the following gauge metrics on safekeeper health:
- `num_configured_safekeepers`: The total number of safekeepers
configured in PG.
- `num_active_safekeepers`: The number of safekeepers that PG is
actively streaming WAL to.
An alert should be raised whenever `num_active_safekeepers` <
`num_configured_safekeepers`.
The metrics are implemented by adding additional state to the
Walproposer shared memory keeping track of the active statuses of
safekeepers using a simple array. The status of the safekeeper is set to
active (1) after the Walproposer acquires a quorum and starts streaming
data to the safekeeper, and is set to inactive (0) when the connection
with a safekeeper is shut down. We scan the safekeeper status array in
Walproposer shared memory when collecting the metrics to produce results
for the gauges.
Added coverage for the metrics to integration test
`test_wal_acceptor.py::test_timeline_disk_usage_limit`.
Data corruptions are typically detected on the pageserver side when it
replays WAL records. However, since PS doesn't synchronously replay WAL
records as they are being ingested through safekeepers, we need some
extra plumbing to feed information about pageserver-detected corruptions
during compaction (and/or WAL redo in general) back to SK and PG for
proper action.
We don't yet know what actions PG/SK should take upon receiving the
signal, but we should have the detection and feedback in place.
Add an extra `corruption_detected` field to the `PageserverFeedback`
message that is sent from PS -> SK -> PG. It's a boolean value that is
set to true when PS detects a "critical error" that signals data
corruption, and it's sent in all `PageserverFeedback` messages. Upon
receiving this signal, the safekeeper raises a
`safekeeper_ps_corruption_detected` gauge metric (value set to 1). The
safekeeper then forwards this signal to PG where a
`ps_corruption_detected` gauge metric (value also set to 1) is raised in
the `neon_perf_counters` view.
Added an integration test in
`test_compaction.py::test_ps_corruption_detection_feedback` that
confirms that the safekeeper and PG can receive the data corruption
signal in the `PageserverFeedback` message in a simulated data
corruption.
## Problem
- Benchmark periodic pagebench had inconsistent benchmarking results
even when run with the same commit hash.
Hypothesis is this was due to running on dedicated but virtualized EC
instance with varying CPU frequency.
- the dedicated instance type used for the benchmark is quite "old" and
we increasingly get `An error occurred (InsufficientInstanceCapacity)
when calling the StartInstances operation (reached max retries: 2):
Insufficient capacity.`
- periodic pagebench uses a snapshot of pageserver timelines to have the
same layer structure in each run and get consistent performance.
Re-creating the snapshot was a painful manual process (see
https://github.com/neondatabase/cloud/issues/27051 and
https://github.com/neondatabase/cloud/issues/27653)
## Summary of changes
- Run the periodic pagebench on a custom hetzner GitHub runner with
large nvme disk and governor set to defined perf profile
- provide a manual dispatch option for the workflow that allows to
create a new snapshot
- keep the manual dispatch option to specify a commit hash useful for
bi-secting regressions
- always use the newest created snapshot (S3 bucket uses date suffix in
S3 key, example
`s3://neon-github-public-dev/performance/pagebench/shared-snapshots-2025-05-17/`
- `--ignore`
`test_runner/performance/pageserver/pagebench/test_pageserver_max_throughput_getpage_at_latest_lsn.py`
in regular benchmarks run for each commit
- improve perf copying snapshot by using `cp` subprocess instead of
traversing tree in python
## Example runs with code in this PR:
- run which creates new snapshot
https://github.com/neondatabase/neon/actions/runs/15083408849/job/42402986376#step:19:55
- run which uses latest snapshot
-
https://github.com/neondatabase/neon/actions/runs/15084907676/job/42406240745#step:11:65
## 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
Release CI is slow, because we're doing unnecessary work, for example
building compute images on storage releases and vice versa.
## Summary of changes
- Extract tag generation into reusable workflow and extend it with
fetching of previous component releases
- Don't build neon images on compute releases and don't build compute
images on proxy and storage releases
- Reuse images from previous releases for tests on branches where we
don't build those images
## Open questions
- We differentiate between `TAG` and `COMPUTE_TAG` in a few places, but
we don't differentiate between storage and proxy releases. Since they
use the same image, this will continue to work, but I'm not sure this is
what we want.
## Problem
There's new rate-limits coming on docker hub. To reduce our reliance on
docker hub and the problems the limits are going to cause for us, we
want to prepare for this by also pushing our container images to ghcr.io
## Summary of changes
Push our images to ghcr.io as well and not just docker hub.
## Problem
Our AWS account IDs are copy-pasted all over the place. A wrong paste
might only be caught late if we hardcode them, but will get flagged
instantly by actionlint if we access them from github actions variables.
Resolves https://github.com/neondatabase/neon/issues/10787, follow-up
for https://github.com/neondatabase/neon/pull/10613.
## Summary of changes
Access AWS account IDs using Github Actions variables.
## Problem
Retagging container images and pushing container images taken from one
registry to another is very tangled up with artifact building and not
separated by component. This makes not building compute for storage
releases and vice versa pretty tricky. To enable that, I want to clean
up retagging and pushing of container images and then continue on making
the pipelines for releases leaner by not building unnecessary things.
## Summary of changes
- Add a reusable workflow that can push to ACR, ECR and Docker Hub,
while being very flexible in terms of source and target images. This
allows for retagging and pushing images between container registries.
- Stop pushing images to registries aside of docker hub in the jobs that
build the images
- Split image pushing into 4 different jobs (not mentioning special
cases):
- neon-dev
- neon-prod
- compute-dev
- compute-prod
## TODO
- Consider also using this for `pin-build-tools-image`, as it's
basically another instance of the same thing.
## Known limitations
- The ECR part of this workflow supports authenticating to multiple AWS
accounts and therefore multiple ECR endpoints, but the ACR part only
supports one Azure Account. If someone with more knowledge on Azure can
tell me whether an equivalent to
https://github.com/aws-actions/amazon-ecr-login?tab=readme-ov-file#login-to-ecr-on-multiple-aws-accounts
is easily possible, that'd be great.
- The `image_map` input is a bit complex. It expects something along the
lines of
```
{
"docker.io/neondatabase/compute-node-v14:13196061314": [
"docker.io/neondatabase/compute-node-v14:13196061314",
"369495373322.dkr.ecr.eu-central-1.amazonaws.com/compute-node-v14:13196061314",
"neoneastus2.azurecr.io/neondatabase/compute-node-v14:13196061314"
],
"docker.io/neondatabase/compute-node-v15:13196061314": [
"docker.io/neondatabase/compute-node-v15:13196061314",
"369495373322.dkr.ecr.eu-central-1.amazonaws.com/compute-node-v15:13196061314",
"neoneastus2.azurecr.io/neondatabase/compute-node-v15:13196061314"
]
}
```
to map from source to target image. We have a small python step to
generate this map for the 4 main image pushing jobs. The concrete
example is taken from
https://github.com/neondatabase/neon/actions/runs/13196061314/job/36838584098?pr=10613#step:3:6
and shortened to two images.
## Problem
Currently, the report does not contain the LFC state of the failed
tests.
## Summary of changes
Added the LFC state to the link to the allure report.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
Currently, we rerun only known flaky tests. This approach was chosen to
reduce the number of tests that go unnoticed (by forcing people to take
a look at failed tests and rerun the job manually), but it has some
drawbacks:
- In PRs, people tend to push new changes without checking failed tests
(that's ok)
- In the main, tests are just restarted without checking
(understandable)
- Parametrised tests become flaky one by one, i.e. if `test[1]` is flaky
`, test[2]` is not marked as flaky automatically (which may or may not
be the case).
I suggest rerunning all failed tests to increase the stability of GitHub
jobs and using the Grafana Dashboard with flaky tests for deeper
analysis.
## Summary of changes
- Rerun all failed tests twice at max
## Problem
LFC is not enabled by default in tests, but it is enabled in production.
This increases the risk of errors in the production environment, which
were not found during the routine workflow.
However, enabling LFC for all the tests may overload the disk on our
servers and increase the number of failures.
So, we try enabling LFC in one case to evaluate the possible risk.
## Summary of changes
A new environment variable, USE_LFC is introduced. If it is set to true,
LFC is enabled by default in all the tests.
In our workflow, we enable LFC for PG17, release, x86-64, and disabled
for all other combinations.
---------
Co-authored-by: Alexey Masterov <alexeymasterov@neon.tech>
Co-authored-by: a-masterov <72613290+a-masterov@users.noreply.github.com>
## Problem
We use a pretty old version of `mypy` 1.3 (released 1.5 years ago), it
produces false positives for `typing.Self`.
## Summary of changes
- Bump `mypy` from 1.3 to 1.13
- Fix new warnings and errors
- Use `typing.Self` whenever we `return self`
## Problem
On Debian 12 (Bookworm), Python 3.11 is the latest available version.
## Summary of changes
- Update Python to 3.11 in build-tools
- Fix ruff check / format
- Fix mypy
- Use `StrEnum` instead of pair `str`, `Enum`
- Update docs
## Problem
The first version of the ingest benchmark had some parsing and reporting
logic in shell script inside GitHub workflow.
it is better to move that logic into a python testcase so that we can
also run it locally.
## Summary of changes
- Create new python testcase
- invoke pgcopydb inside python test case
- move the following logic into python testcase
- determine backpressure
- invoke pgcopydb and report its progress
- parse pgcopydb log and extract metrics
- insert metrics into perf test database
- add additional column to perf test database that can receive endpoint
ID used for pgcopydb run to have it available in grafana dashboard when
retrieving other metrics for an endpoint
## Example run
https://github.com/neondatabase/neon/actions/runs/11860622170/job/33056264386
## Problem
Failed / flaky tests for different arches don't have any difference in
GitHub Autocomment
## Summary of changes
- Add arch to build type for GitHub autocomment
## Problem
We want to run our regression test suite on ARM.
## Summary of changes
- run regression tests on release ARM builds
- run `build-neon` (including rust tests) on debug ARM builds
- add `arch` parameter to test to distinguish them in the allure report
and in a database
## Problem
The default Postgres version is set to 15 in code, while we use 16 in
most of the other places (and Postgres 17 is coming)
## Summary of changes
- Run `benchmarks` job with Postgres 16 (instead of Postgres 14)
- Set `DEFAULT_PG_VERSION` to 16 in all places
- Remove deprecated `--pg-version` pytest argument
- Update `test_metadata_bincode_serde_ensure_roundtrip` for Postgres 16
This PR allows setting the
`PAGESERVER_DEFAULT_TENANT_CONFIG_COMPACTION_ALGORITHM` env var to
override the `tenant_config.compaction_algorithm` field in the initial
`pageserver.toml` for all tests.
I tested manually that this works by halting a test using pdb and
inspecting the `effective_config` in the tenant status managment API.
If the env var is set, the tests are parametrized by the `kind` tag
field, allowing to do a matrix build in CI and let Allure summarize
everything in a nice report.
If the env var is not set, the tests are not parametrized. So, merging
this PR doesn't cause problems for flaky test detection. In fact, it
doesn't cause any runtime change if the env var is not set.
There are some tests in the test suite that set used to override
the entire tenant_config using
`NeonEnvBuilder.pageserver_config_override`.
Since config overrides are merged non-recursively, such overrides
that don't specify `kind = ` cause a fallback to pageserver's built-in
`DEFAULT_COMPACTION_ALGORITHM`.
Such cases can be found using
```
["']tenant_config\s*[='"]
```
We'll deal with these tests in a future PR.
closes https://github.com/neondatabase/neon/issues/7555
## Problem
The `export_import_between_pageservers` script us to do major storage format changes
in the past. If we have to do such breaking changes in the future this approach
wouldn't be suitable because:
1. It doesn't scale to the current size of the fleet
2. It loses history
## Summary of changes
Remove the script and its associated test.
Keep `fullbasebackup` and friends because it's useful for debugging.
Closes https://github.com/neondatabase/cloud/issues/11648
## Problem
```
Could not resolve host: console.stage.neon.tech
```
## Summary of changes
- replace `console.stage.neon.tech` with `console-stage.neon.build`
## Problem
After switching the default pageserver io-engine to `tokio-epoll-uring`
on CI, we tuned a query that finds flaky tests (in
https://github.com/neondatabase/neon/pull/7077).
It has been almost a month since then, additional query tuning is not
required anymore.
## Summary of changes
- Remove extra condition from flaky tests query
- Also return back parameterisation to the query
# Problem
On-demand downloads are still using `tokio::fs`, which we know is
inefficient.
# Changes
- Add `pagebench ondemand-download-churn` to quantify on-demand download
throughput
- Requires dumping layer map, which required making `history_buffer`
impl `Deserialize`
- Implement an equivalent of `tokio::io::copy_buf` for owned buffers =>
`owned_buffers_io` module and children.
- Make layer file download sensitive to `io_engine::get()`, using
VirtualFile + above copy loop
- For this, I had to move some code into the `retry_download`, e.g.,
`sync_all()` call.
Drive-by:
- fix missing escaping in `scripts/ps_ec2_setup_instance_store`
- if we failed in retry_download to create a file, we'd try to remove
it, encounter `NotFound`, and `abort()` the process using
`on_fatal_io_error`. This PR adds treats `NotFound` as a success.
# Testing
Functional
- The copy loop is generic & unit tested.
Performance
- Used the `ondemand-download-churn` benchmark to manually test against
real S3.
- Results (public Notion page):
https://neondatabase.notion.site/Benchmarking-tokio-epoll-uring-on-demand-downloads-2024-04-15-newer-code-03c0fdc475c54492b44d9627b6e4e710?pvs=4
- Performance is equivalent at low concurrency. Jumpier situation at
high concurrency, but, still less CPU / throughput with
tokio-epoll-uring.
- It’s a win.
# Future Work
Turn the manual performance testing described in the above results
document into a performance regression test:
https://github.com/neondatabase/neon/issues/7146
All of production is using it now as of
https://github.com/neondatabase/aws/pull/1121
The change in `flaky_tests.py` resets the flakiness detection logic.
The alternative would have been to repeat the choice of io engine in
each test name, which would junk up the various test reports too much.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
We've got several issues with the current `benchmarks` job setup:
- `benchmark_durations.json` file (that we generate in runtime to
split tests into several jobs[0]) is not consistent between these
jobs (and very not consistent with the file if we rerun the job). I.e.
test selection for each job can be different, which could end up in
missed tests in a test run.
- `scripts/benchmark_durations` doesn't fetch all tests from the
database (it doesn't expect any extra directories inside
`test_runner/performance`)
- For some reason, currently split into 4 groups ends up with the 4th
group has no tests to run, which fails the job[1]
- [0] https://github.com/neondatabase/neon/pull/4683
- [1] https://github.com/neondatabase/neon/issues/6629
## Summary of changes
- Generate `benchmark_durations.json` file once before we start
`benchmarks` jobs (this makes it consistent across the jobs) and pass
the file content through the GitHub Actions input (this makes it
consistent for reruns)
- `scripts/benchmark_durations` fix SQL query for getting all required
tests
- Split benchmarks into 5 jobs instead of 4 jobs.
## Problem
We were archiving the pref benchmarks to
- neon DB
- git repo `zenith-perf-data`
As the pref batch ran in parallel when the uploading of results to
zenith-perf-data` git repo resulted in merge conflicts.
Which made the run flaky and as a side effect the build started failing
.
The problem is been expressed in
https://github.com/neondatabase/neon/issues/5160
## Summary of changes
As the results were not used from the git repo it was redundant hence in
this PR cleaning up the results uploading of of perf results to git repo
The shell script `generate_and_push_perf_report.sh` was using a py
script
[git-upload](https://github.com/neondatabase/neon/compare/remove-perf-benchmark-git-upload?expand=1#diff-c6d938e7f060e487367d9dc8055245c82b51a73c1f97956111a495a8a86e9a33)
and
[scripts/generate_perf_report_page.py](https://github.com/neondatabase/neon/pull/6590/files#diff-81af2147e72d07e4cf8ee4395632596d805d6168ba75c71cab58db2659956ef8)
which are not used anywhere else in repo hence also cleaning that up
## 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 the commit message to not include the
above checklist
## Problem
We have switched to new test results and new coverage results, so no
need to collect these data in old formats.
## Summary of changes
- Remove "Upload coverage report" for old coverage report
- Remove "Store Allure test stat in the DB" for old test results format
## Problem
`black` is slow sometimes, we can replace it with `ruff format` (a new
feature in 0.1.2 [0]), which produces pretty similar to black style [1].
On my local machine (MacBook M1 Pro 16GB):
```
# `black` on main
$ hyperfine "BLACK_CACHE_DIR=/dev/null poetry run black ."
Benchmark 1: BLACK_CACHE_DIR=/dev/null poetry run black .
Time (mean ± σ): 3.131 s ± 0.090 s [User: 5.194 s, System: 0.859 s]
Range (min … max): 3.047 s … 3.354 s 10 runs
```
```
# `ruff format` on the current PR
$ hyperfine "RUFF_NO_CACHE=true poetry run ruff format"
Benchmark 1: RUFF_NO_CACHE=true poetry run ruff format
Time (mean ± σ): 300.7 ms ± 50.2 ms [User: 259.5 ms, System: 76.1 ms]
Range (min … max): 267.5 ms … 420.2 ms 10 runs
```
## Summary of changes
- Replace `black` with `ruff format` everywhere
- [0] https://docs.astral.sh/ruff/formatter/
- [1] https://docs.astral.sh/ruff/formatter/#black-compatibility
## Problem
Some existing tests are written in a way that's incompatible with tenant
generations.
## Summary of changes
Update all the tests that need updating: this is things like calling
through the NeonPageserver.tenant_attach helper to get a generation
number, instead of calling directly into the pageserver API. There are
various more subtle cases.
## Problem
We started to store test results in a new format in
https://github.com/neondatabase/neon/pull/4549.
This PR switches scripts to query this db.
(we can completely remove old DB/ingestions scripts in a couple of
weeks after the PR merged)
## Summary of changes
- `scripts/benchmark_durations.py` query new database
- `scripts/flaky_tests.py` query new database
## Problem
When PR `ci-run/pr-*` is created the GitHub Autocomment with test
results are supposed to be posted to the original PR, currently, this
doesn't work.
I created this PR from a personal fork to debug and fix the issue.
## Summary of changes
- `scripts/comment-test-report.js`: use `pull_request.head` instead of
`pull_request.base` 🤦
## Problem
A bunch of fixes for different test-related things
## Summary of changes
- Fix test_runner/pg_clients (`subprocess_capture` return value has
changed)
- Do not run create-test-report if check-permissions failed for not
cancelled jobs
- Fix Code Coverage comment layout after flaky tests. Add another
healing "\n"
- test_compatibility: add an instruction for local run
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
