## Problem
The containers' log output is mixed with the tests' output, so you must
scroll up to find the error.
## Summary of changes
Printing of containers' logs moved to a separate step.
We've finally transitioned to using a separate `release-compute` branch.
Now, we can finally automatically create release PRs on Fri and release
them during the following week.
Part of neondatabase/cloud#11698
## Problem
https://github.com/neondatabase/neon/actions/runs/12896686483/job/35961290336#step:5:107
showed that `promote-images-prod` was missing another dependency.
## Summary of changes
Modify `promote-images-prod` to tag based on docker-hub images, so that
`promote-images-prod` does not rely on `promote-images-dev`. The result
should be the exact same, but allows the two jobs to run in parallel.
## Refs
- Epic: https://github.com/neondatabase/neon/issues/9378
Co-authored-by: Vlad Lazar <vlad@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
The read path does its IOs sequentially.
This means that if N values need to be read to reconstruct a page,
we will do N IOs and getpage latency is `O(N*IoLatency)`.
## Solution
With this PR we gain the ability to issue IO concurrently within one
layer visit **and** to move on to the next layer without waiting for IOs
from the previous visit to complete.
This is an evolved version of the work done at the Lisbon hackathon,
cf https://github.com/neondatabase/neon/pull/9002.
## Design
### `will_init` now sourced from disk btree index keys
On the algorithmic level, the only change is that the
`get_values_reconstruct_data`
now sources `will_init` from the disk btree index key (which is
PS-page_cache'd), instead
of from the `Value`, which is only available after the IO completes.
### Concurrent IOs, Submission & Completion
To separate IO submission from waiting for its completion, while
simultaneously
feature-gating the change, we introduce the notion of an `IoConcurrency`
struct
through which IO futures are "spawned".
An IO is an opaque future, and waiting for completions is handled
through
`tokio::sync::oneshot` channels.
The oneshot Receiver's take the place of the `img` and `records` fields
inside `VectoredValueReconstructState`.
When we're done visiting all the layers and submitting all the IOs along
the way
we concurrently `collect_pending_ios` for each value, which means
for each value there is a future that awaits all the oneshot receivers
and then calls into walredo to reconstruct the page image.
Walredo is now invoked concurrently for each value instead of
sequentially.
Walredo itself remains unchanged.
The spawned IO futures are driven to completion by a sidecar tokio task
that
is separate from the task that performs all the layer visiting and
spawning of IOs.
That tasks receives the IO futures via an unbounded mpsc channel and
drives them to completion inside a `FuturedUnordered`.
(The behavior from before this PR is available through
`IoConcurrency::Sequential`,
which awaits the IO futures in place, without "spawning" or "submitting"
them
anywhere.)
#### Alternatives Explored
A few words on the rationale behind having a sidecar *task* and what
alternatives were considered.
One option is to queue up all IO futures in a FuturesUnordered that is
polled
the first time when we `collect_pending_ios`.
Firstly, the IO futures are opaque, compiler-generated futures that need
to be polled at least once to submit their IO. "At least once" because
tokio-epoll-uring may not be able to submit the IO to the kernel on
first
poll right away.
Second, there are deadlocks if we don't drive the IO futures to
completion
independently of the spawning task.
The reason is that both the IO futures and the spawning task may hold
some
_and_ try to acquire _more_ shared limited resources.
For example, both spawning task and IO future may try to acquire
* a VirtualFile file descriptor cache slot async mutex (observed during
impl)
* a tokio-epoll-uring submission slot (observed during impl)
* a PageCache slot (currently this is not the case but we may move more
code into the IO futures in the future)
Another option is to spawn a short-lived `tokio::task` for each IO
future.
We implemented and benchmarked it during development, but found little
throughput improvement and moderate mean & tail latency degradation.
Concerns about pressure on the tokio scheduler made us discard this
variant.
The sidecar task could be obsoleted if the IOs were not arbitrary code
but a well-defined struct.
However,
1. the opaque futures approach taken in this PR allows leaving the
existing
code unchanged, which
2. allows us to implement the `IoConcurrency::Sequential` mode for
feature-gating
the change.
Once the new mode sidecar task implementation is rolled out everywhere,
and `::Sequential` removed, we can think about a descriptive submission
& completion interface.
The problems around deadlocks pointed out earlier will need to be solved
then.
For example, we could eliminate VirtualFile file descriptor cache and
tokio-epoll-uring slots.
The latter has been drafted in
https://github.com/neondatabase/tokio-epoll-uring/pull/63.
See the lengthy doc comment on `spawn_io()` for more details.
### Error handling
There are two error classes during reconstruct data retrieval:
* traversal errors: index lookup, move to next layer, and the like
* value read IO errors
A traversal error fails the entire get_vectored request, as before this
PR.
A value read error only fails that value.
In any case, we preserve the existing behavior that once
`get_vectored` returns, all IOs are done. Panics and failing
to poll `get_vectored` to completion will leave the IOs dangling,
which is safe but shouldn't happen, and so, a rate-limited
log statement will be emitted at warning level.
There is a doc comment on `collect_pending_ios` giving more code-level
details and rationale.
### Feature Gating
The new behavior is opt-in via pageserver config.
The `Sequential` mode is the default.
The only significant change in `Sequential` mode compared to before
this PR is the buffering of results in the `oneshot`s.
## Code-Level Changes
Prep work:
* Make `GateGuard` clonable.
Core Feature:
* Traversal code: track `will_init` in `BlobMeta` and source it from
the Delta/Image/InMemory layer index, instead of determining `will_init`
after we've read the value. This avoids having to read the value to
determine whether traversal can stop.
* Introduce `IoConcurrency` & its sidecar task.
* `IoConcurrency` is the clonable handle.
* It connects to the sidecar task via an `mpsc`.
* Plumb through `IoConcurrency` from high level code to the
individual layer implementations' `get_values_reconstruct_data`.
We piggy-back on the `ValuesReconstructState` for this.
* The sidecar task should be long-lived, so, `IoConcurrency` needs
to be rooted up "high" in the call stack.
* Roots as of this PR:
* `page_service`: outside of pagestream loop
* `create_image_layers`: when it is called
* `basebackup`(only auxfiles + replorigin + SLRU segments)
* Code with no roots that uses `IoConcurrency::sequential`
* any `Timeline::get` call
* `collect_keyspace` is a good example
* follow-up: https://github.com/neondatabase/neon/issues/10460
* `TimelineAdaptor` code used by the compaction simulator, unused in
practive
* `ingest_xlog_dbase_create`
* Transform Delta/Image/InMemoryLayer to
* do their values IO in a distinct `async {}` block
* extend the residence of the Delta/Image layer until the IO is done
* buffer their results in a `oneshot` channel instead of straight
in `ValuesReconstructState`
* the `oneshot` channel is wrapped in `OnDiskValueIo` /
`OnDiskValueIoWaiter`
types that aid in expressiveness and are used to keep track of
in-flight IOs so we can print warnings if we leave them dangling.
* Change `ValuesReconstructState` to hold the receiving end of the
`oneshot` channel aka `OnDiskValueIoWaiter`.
* Change `get_vectored_impl` to `collect_pending_ios` and issue walredo
concurrently, in a `FuturesUnordered`.
Testing / Benchmarking:
* Support queue-depth in pagebench for manual benchmarkinng.
* Add test suite support for setting concurrency mode ps config
field via a) an env var and b) via NeonEnvBuilder.
* Hacky helper to have sidecar-based IoConcurrency in tests.
This will be cleaned up later.
More benchmarking will happen post-merge in nightly benchmarks, plus in
staging/pre-prod.
Some intermediate helpers for manual benchmarking have been preserved in
https://github.com/neondatabase/neon/pull/10466 and will be landed in
later PRs.
(L0 layer stack generator!)
Drive-By:
* test suite actually didn't enable batching by default because
`config.compatibility_neon_binpath` is always Truthy in our CI
environment
=> https://neondb.slack.com/archives/C059ZC138NR/p1737490501941309
* initial logical size calculation wasn't always polled to completion,
which was
surfaced through the added WARN logs emitted when dropping a
`ValuesReconstructState` that still has inflight IOs.
* remove the timing histograms
`pageserver_getpage_get_reconstruct_data_seconds`
and `pageserver_getpage_reconstruct_seconds` because with planning,
value read
IO, and walredo happening concurrently, one can no longer attribute
latency
to any one of them; we'll revisit this when Vlad's work on
tracing/sampling
through RequestContext lands.
* remove code related to `get_cached_lsn()`.
The logic around this has been dead at runtime for a long time,
ever since the removal of the materialized page cache in #8105.
## Testing
Unit tests use the sidecar task by default and run both modes in CI.
Python regression tests and benchmarks also use the sidecar task by
default.
We'll test more in staging and possibly preprod.
# Future Work
Please refer to the parent epic for the full plan.
The next step will be to fold the plumbing of IoConcurrency
into RequestContext so that the function signatures get cleaned up.
Once `Sequential` isn't used anymore, we can take the next
big leap which is replacing the opaque IOs with structs
that have well-defined semantics.
---------
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
When releasing `release-7574`, the Github Release creation failed with
"body is too long" (see
https://github.com/neondatabase/neon/actions/runs/12834025431/job/35792346745#step:5:77).
There's lots of room for improvement of the release notes, but for now
we'll disable them instead.
## Summary of changes
- Disable automatic generation of release notes for Github releases
- Enable creation of Github releases for proxy/compute
## Problem
871e8b325f failed CI on main because a job
ran to soon. This was caused by
ea84ec357f. While `promote-images-dev`
does not inherently need `neon-image`, a few jobs depending on
`promote-images-dev` do need it, and previously had it when it was
`promote-images`, which depended on `test-images`, which in turn
depended on `neon-image`.
## Summary of changes
To ensure jobs depending `docker.io/neondatabase/neon` images get them,
`promote-images-dev` gets the dependency to `neon-image` back which it
previously had transitively through `test-images`.
## Problem
When multiple changes are grouped in a merge group to be merged as part
of the merge queue, the changes might individually pass
`check-codestyle-rust` but not in their combined form.
## Summary of changes
- Move `check-codestyle-rust` into a reusable workflow that is called
from it's previous location in `build_and_test.yml`, and additionally
call it from `pre_merge_checks.yml`. The additional call does not run on
ARM, only x86, to ensure the merge queue continues being responsive.
- Trigger `pre_merge_checks.yml` on PRs that change any of the workflows
running in `pre_merge_checks.yml`, so that we get feedback on those
early an not only after trying to merge those changes.
## Problem
Successful `benchmarks` runs doesn't have enough visibility
Ref https://neondb.slack.com/archives/C069Z2199DL/p1736868055094539
## Summary of changes
- Report both successful and failed `benchmarks` to Slack
- Update `slackapi/slack-github-action` action
Closes: https://github.com/neondatabase/cloud/issues/17784
## Problem
Currently, we run the whole CI pipeline for any changes. It's slow and
expensive.
## Suggestion
Starting with MacOs builds:
- check what files were changed
- rebuild only needed parts
- reuse results from previous builds when available
- run builds in parallel when possible
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
`promote-images` was split into `promote-images-dev` and
`promote-images-prod` in
https://github.com/neondatabase/neon/pull/10267.
`dev` credentials were loaded in `promote-images-dev` and `prod`
credentials were loaded in `promote-images-prod`, but
`promote-images-prod` needs `dev` credentials as well to access the
`dev` images to replicate them from `dev` to `prod`.
## Summary of changes
Load `dev` credentials in `promote-images-prod` as well.
## Problem
`trigger-e2e-tests` waits half an hour before starting to run. Nearly
half of that time can be saved by promoting images before tests on them
are complete, so the e2e tests can run in parallel.
On `main` and `release{,-proxy,-compute}`, `promote-images` updates
`latest` and pushes things to prod ecr, so we want to run
`promote-images` only after `test-images` is done, but on other
branches, there is no harm in promoting images that aren't tested yet.
## Summary of changes
To promote images into dev container registries sooner, `promote-images`
is split into `promote-images-dev` and `promote-images-prod`. The former
pushes to dev container registries, the latter to prod ones. The latter
also waits for `test-images`, while the former doesn't. This allows to
run `trigger-e2e-tests` sooner.
## Problem
Running clippy with `cargo hack --feature-powerset` in CI isn't
particularly fast. This PR follows-up on
https://github.com/neondatabase/neon/pull/8912 to improve the speed of
our clippy runs.
Parallelism as suggested in
https://github.com/neondatabase/neon/issues/9901 was tested, but didn't
show consistent enough improvements to be worth it. It actually
increased the amount of work done, as there's less cache hits when
clippy runs are spread out over multiple target directories.
Additionally, parallelism makes it so caching needs to be thought about
more actively and copying around target directories to enable
parallelism eats the rest of the performance gains from parallel
execution.
After some discussion, the decision was to instead cut down on the
number of jobs that are running further. The easiest way to do this is
to not run clippy *without* default features. The list of default
features is empty for all crates, and I haven't found anything using
`cfg(feature = "default")` either, so this is likely not going to change
anything except speeding the runs up.
## Summary of changes
Reduce the amount of feature combinations tried by `cargo hack` (as
suggested in
https://github.com/neondatabase/neon/pull/8912#pullrequestreview-2286482368)
by never disabling default features.
## Alternatives
- We can split things out into different jobs which reduces the time
until everything is finished by running more things in parallel. This
does however decreases the amount of cache hits and increases the amount
of time spent on overhead tasks like repo cloning and restoring caches
by doing those multiple times instead of once.
- We could replace `cargo hack [...] clippy` with `cargo clippy [...];
cargo clippy --features testing`. I'm not 100% sure how this compares to
the change here in the PR, but it does seem to run a bit faster. That
likely means it's doing less work, but without understanding what
exactly we loose by that I'd rather not do that for now. I'd appreciate
input on this though.
## Problem
We want to verify how much / if pgbench throughput and latency on Neon
suffers if the database contains many other relations, too.
## Summary of changes
Modify the benchmarking.yml pgbench-compare job to
- create an addiitional project at scale factor 10 GiB
- before running pgbench add n tables (initially 10k) to the database
- then compare the pgbench throughput and latency to the existing
pgbench-compare at 10 Gib scale factor
We use a realistic template for the n relations that is a partitioned
table with some realistic data types, indexes and constraints - similar
to a table that we use internally.
Example run:
https://github.com/neondatabase/neon/actions/runs/12377565956/job/34547386959
## Problem
The allure report finishes with the error `HttpError: Resource not
accessible by integration` while running the `pg_regress` test against a
cloud staging project due to a lack of permissions.
## Summary of changes
The permissions are added.
## Problem
`benchmarking` job fails because `aws-oicd-role-arn` input is not set
## Summary of changes:
- Set `aws-oicd-role-arn` for `benchmarking job
- Always require `aws-oicd-role-arn` to be set
- Rename `aws_oicd_role_arn` to `aws-oicd-role-arn` for consistency
## Problem
When moving the comment on proxy-releases from the yaml doc into a
javascript code block, I missed converting the comment marker from `#`
to `//`.
## Summary of changes
Correctly convert comment marker.
## Problem
I've noticed that debug builds with LFC fail more frequently and for
some reason ,their failure do block merging (but it should not)
## Summary of changes
- Do not run Debug builds with LFC
## Problem
Now notifications about failures in `pg_regress` tests run on the
staging cloud instance, reach the channel `on-call-staging-stream`,
while they should reach `on-call-qa-staging-stream`
## Summary of changes
The channel changed.
## Problem
CI currently uses static credentials in some places. These are less
secure and hard to maintain, so we are going to deprecate them and use
OIDC auth.
## Summary of changes
- ci(fix): Use OIDC auth to upload artifact on s3
- ci(fix): Use OIDC auth to login on ECR
## Problem
To give Storage more time on preprod — create a release branch on Friday
## Summary of changes
- Automatically create Storage release PR on Friday instead of Monday
## Problem
We added support for LFC for tests but are still using it only for the
PG17 release.
## Summary of changes
LFC is enabled for all PG versions. Errors in tests with LFC enabled now
block merging as usual. We keep tests with disabled LFC for PG17
release. Tests on debug builds with LFC enabled still don't affect
permission to merge.
## Problem
When dev deployments are disabled (or fail), the tags for releases
aren't created. It makes more sense to have tag and release creation
before the deployment to prevent situations like
[this](https://github.com/neondatabase/neon/pull/9959).
It is not enough to move the tag creation before the deployment. If the
deployment fails, re-running the job isn't possible because the API call
to create the tag will fail.
## Summary of changes
- Tag/Release creation now happens before the deployment
- The two steps for tag and release have been merged into a bigger one
- There's new checks to ensure the that if the tags/releases already
exist as expected, things will continue just fine.
## Problem
Currently, we run the `pg_regress` tests only for PG16
However, PG17 is a part of Neon and should be tested as well
## Summary of changes
Modified the workflow and added a patch for PG17 enabling the
`pg_regress` tests.
The problem with leftovers was solved by using branches.
## Problem
We practice a manual release flow for the compute module. This will
allow automation of the compute release process.
## Summary of changes
The workflow was modified to make a compute release automatically on the
branch release-compute.
## Checklist before requesting a review
- [x] 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
## Problem
we tried different parallelism settings for ingest bench
## Summary of changes
the following settings seem optimal after merging
- SK side Wal filtering
- batched getpages
Settings:
- effective_io_concurrency 100
- concurrency limit 200 (different from Prod!)
- jobs 4, maintenance workers 7
- 10 GB chunk size
## Problem
The extensions for Postgres v17 are ready but we do not test the
extensions shipped with v17
## Summary of changes
Build the test image based on Postgres v17. Run the tests for v17.
---------
Co-authored-by: Anastasia Lubennikova <anastasia@neon.tech>
## Problem
`if: ${{ github.event.schedule }}` gets skipped if a previous step has
failed, but we want to run the step for both `success` and `failure`
## Summary of changes
- Add `!cancelled()` to notification step if-condition, to skip only
cancelled jobs
## 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
The `pre-merge-checks` workflow relies on the build-tools image.
If changes to the `build-tools` image have been merged into the main
branch since the last CI run for a PR (with other changes to the
`build-tools`), the image will be rebuilt during the merge queue run.
Otherwise, cached images are used.
Rebuilding the image adds approximately 10 minutes on x86-64 and 20
minutes on arm64 to the process.
## Summary of changes
- parametrise `build-build-tools-image` job with arch and Debian version
- Run `pre-merge-checks` only on Debian 12 x86-64 image
## Problem
We have a couple of CI workflows that still run on Debian Bullseye, and
the default Debian version in images is Bullseye as well (we explicitly
set building on Bookworm)
## Summary of changes
- Run `pgbench-pgvector` on Bookworm (fix a couple of packages)
- Run `trigger_bench_on_ec2_machine_in_eu_central_1` on Bookworm
- Change default `DEBIAN_VERSION` in Dockerfiles to Bookworm
- Make `pinned` docker tag an alias to `pinned-bookworm`
## 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 want to keep `#on-call-staging-stream` channel close to the prod one
and redirect notifications from failing benchmarks to another channel
for investigation.
## Summary of changes
- Send notifications regarding failures in `benchmarking` job to
`#on-call-staging-stream`
- Send notifications regarding failures in `periodic_pagebench` job to
`#on-call-staging-stream`
## Problem
We have a bunch of duplicated code for automated releases. There will be
even more, once we have `release-compute` branch
(https://github.com/neondatabase/neon/pull/9637).
Another issue with the current `release` workflow is that it creates a
PR from the main as is. If we create 2 different releases from the
same commit, GitHub could mix up results from different PRs.
## Summary of changes
- Create a reusable workflow for releases
- Create an empty commit to differentiate releases
## 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
We call `check-build-tools-image` twice for each workflow whenever we
use it, along with `build-build-tools-image`, once as a workflow itself,
and the second time from `build-build-tools-image`. This is not
necessary.
## Summary of changes
- Inline `check-build-tools-image` into `build-build-tools-image`
- Remove separate `check-build-tools-image` workflow
We found that exporting GH Workflow Runs in batch is more efficient due
to
- better utilisation of Github API
- and gh runners usage is rounded to minutes, so even when ad-hoc export
is done in 5-10 seconds, we billed for one minute usage
So now we introduce batch exporting, with version v0.2.x of github
workflow stats exporter.
How it's expected to work now:
- every 15 minutes we query for the workflow runs, created in last 2
hours
- to avoid missing workflows that ran for more than 2 hours, every night
(00:25) we will query workflows created in past 24 hours and export them
as well
- should we have query for even longer periods?
- lets see how it works with current schedule
- for longer periods like for days or weeks, it may require to adjust
logic and concurrency of querying data, so lets for now use simpler
version
## Problem
We have no specific benchmark testing project migration of postgresql
project with existing data into Neon.
Typical steps of such a project migration are
- schema creation in the neon project
- initial COPY of relations
- creation of indexes and constraints
- vacuum analyze
## Summary of changes
Add a periodic benchmark running 9 AM UTC every day.
In each run:
- copy a 200 GiB project that has realistic schema, data, tables,
indexes and constraints from another project into
- a new Neon project (7 CU fixed)
- an existing tenant, (but new branch and new database) that already has
4 TiB of data
- use pgcopydb tool to automate all steps and parallelize COPY and index
creation
- parse pgcopydb output and report performance metrics in Neon
performance test database
## Logs
This benchmark has been tested first manually and then as part of
benchmarking.yml workflow, example run see
https://github.com/neondatabase/neon/actions/runs/11757679870
## Problem
Once we enable the merge queue for the `main` branch, it won't be
possible to adjust the commit message right after pressing the "Squash
and merge" button and the PR title + description will be used as is.
To avoid extra noise in the commits in the `main` with the checklist
leftovers, I propose removing the checklist from the PR template and
keeping only the Problem / Summary of changes.
## Summary of changes
- Remove the checklist from the PR template
