## Problem
Given a container image it is difficult to figure out dependencies and
doesn't work automatically.
## Summary of changes
- Build all rust binaries with `cargo auditable`, to allow sbom scanners
to find it's dependencies.
- Adjust `attests` for `docker/build-push-action`, so that buildkit
creates sbom and provenance attestations.
- Dropping `--locked` for `rustfilt`, because `rustfilt` can't build
with locked dependencies[^5]
## Further details
Building with `cargo auditable`[^1] embeds a dependency list into Linux,
Windows, MacOS and WebAssembly artifacts. A bunch of tools support
discovering dependencies from this, among them `syft`[^2], which is used
by the BuildKit Syft scanner[^3] plugin. This BuildKit plugin is the
default[^4] used in docker for generating sbom attestations, but we're
making that default explicit by referencing the container image.
[^1]: https://github.com/rust-secure-code/cargo-auditable
[^2]: https://github.com/anchore/syft
[^3]: https://github.com/docker/buildkit-syft-scanner
[^4]:
https://docs.docker.com/build/metadata/attestations/sbom/#sbom-generator
[^5]: https://github.com/luser/rustfilt/issues/23
## Problem
We want to have the data-api served by the proxy directly instead of
relying on a 3rd party to run a deployment for each project/endpoint.
## Summary of changes
With the changes below, the proxy (auth-broker) becomes also a
"rest-broker", that can be thought of as a "Multi-tenant" data-api which
provides an automated REST api for all the databases in the region.
The core of the implementation (that leverages the subzero library) is
in proxy/src/serverless/rest.rs and this is the only place that has "new
logic".
---------
Co-authored-by: Ruslan Talpa <ruslan.talpa@databricks.com>
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
Co-authored-by: Conrad Ludgate <conrad@neon.tech>
## Problem
We have several linters that use Node.js, but they are currently set up
differently, both locally and on CI.
## Summary of changes
- Add Node.js to `build-tools` image
- Move `compute/package.json` -> `build-tools/package.json` and add
`redocly` to it `@redocly/cli`
- Unify and merge into one job `lint-openapi-spec` and
`validate-compute-manifest`
## Problem
Extension tests were previously run sequentially, resulting in
unnecessary wait time and underutilization of available CPU cores.
## Summary of changes
Tests are now executed in a customizable number of parallel threads
using separate database branches. This reduces overall test time by
approximately 50% (e.g., on my laptop, parallel test lasts 173s, while
sequential one lasts 340s) and increases the load on the pageserver,
providing better test coverage.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Co-authored-by: Alexey Masterov <alexey.masterov@databricks.com>
Reverts neondatabase/neon#11663 and
https://github.com/neondatabase/neon/pull/11265/
Step Security is not yet approved by Databricks team, in order to
prevent issues during Github org migration, I'll revert this PR to use
the previous action instead of Step Security maintained action.
The build-tools image contains various build tools and dependencies,
mostly Rust-related. The compute image build used it to build
compute_ctl and a few other little rust binaries that are included in
the compute image. However, for extensions built in Rust (pgrx), the
build used a different layer which installed the rust toolchain using
rustup.
Switch to using the same rust toolchain for both pgrx-based extensions
and compute_ctl et al. Since we don't need anything else from the
build-tools image, I switched to using the toolchain installed with
rustup, and eliminated the dependency to build-tools altogether. The
compute image build no longer depends on build-tools.
Note: We no longer use 'mold' for linking compute_ctl et al, since mold
is not included in the build-deps-with-cargo layer. We could add it
there, but it doesn't seem worth it. I proposed stopping using mold
altogether in https://github.com/neondatabase/neon/pull/10735, but that
was rejected because 'mold' is faster for incremental builds. That
doesn't matter much for docker builds however, since they're not
incremental, and the compute binaries are not as large as the storage
server binaries anyway.
## 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
Use the current production config for batching & concurrent IO.
Remove the permutation testing for unit tests from CI.
(The pageserver unit test matrix takes ~10min for debug builds).
Drive-by-fix use of `if cfg!(test)` inside crate `pageserver_api`.
It is ineffective for early-enabling new defaults for pageserver unit
tests only.
The reason is that the `test` cfg is only set for the crate under test
but not its dependencies.
So, `cargo test -p pageserver` will build `pageserver_api` with
`cfg!(test) == false`.
Resort to checking for feature flag `testing` instead, since all our
unit tests are run with `--feature testing`.
refs
- `scattered-lsn` batching has been implemented and rolled out in all
envs, cf https://github.com/neondatabase/neon/issues/10765
- preliminary for https://github.com/neondatabase/neon/pull/10466
- epic https://github.com/neondatabase/neon/issues/9377
- epic https://github.com/neondatabase/neon/issues/9378
- drive-by fix
https://neondb.slack.com/archives/C0277TKAJCA/p1746821515504219
## Problem
We notify only Storage team about failed deploys, but Compute and Proxy
teams can also benefit from that
## Summary of changes
- Adjust `notify-storage-release-deploy-failure` to notify the relevant
team about failed deploy
## Problem
Our CI/CD security tool StepSecurity maintains safer forks of popular
GitHub Actions with low security scores. We're replacing
dorny/paths-filter with the maintained step-security/paths-filter
version to reduce risk of supply chain breaches and potential CVEs.
## Summary of changes
replace
```uses: dorny/paths-filter@de90cc6fb3 ``` with ```uses: step-security/paths-filter@v3```
This PR will fix: neondatabase/cloud#26141
# 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
There is too much delay between merging a PR into `main` and deploying
the changes to staging
## Summary of changes
- Trigger `deploy` job without waiting for `build-and-test-locally` job
## 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
Changes in compute can cause errors in tests if another version of
`neon-test-extensions` image is used.
## Summary of changes
Use the same version of `neon-test-extensions` image as `compute` one
for docker-compose based extension tests.
## Problem
Hotfix releases mean that sometimes changes in release PRs haven't been
tested and linted yet. Disabling tests and lints is therefore not
necessarily safe. In the future we will check whether tests have run on
the same git tree already to speed things up, but for now we need to
turn tests back on fully. This partially reverts:
https://github.com/neondatabase/neon/pull/11272
## Summary of changes
Run checks on `.*-rc-pr` runs.
## Problem
`github.sha` contains a merge commit of `head` and `base` if we're in a
PR. In release PRs, this makes no sense, because we fast-forward the
`base` branch to contain the changes from `head`.
Even though we correctly use `${{ github.event.pull_request.head.sha ||
github.sha }}` to reference the git commit when building artifacts, we
don't use that when checking out code, because we want to test the merge
of head and base usually. In the case of release PRs, we definitely
always want to test on the head sha though, because we're going to
forward that, and it already has the base sha as a parent, so the merge
would end up with the same tree anyway.
As a side effect, not checking out `${{
github.event.pull_request.head.sha || github.sha }}` also caused
https://github.com/neondatabase/neon/actions/runs/13986389780/job/39173256184#step:6:49
to say `release-tag=release-compute-8187`, while
https://github.com/neondatabase/neon/actions/runs/14084613121/job/39445314780#step:6:48
is talking about `build-tag=release-compute-8186`
## Summary of changes
Run a few things on `github.event.pull_request.head.sha`, if we're in a
release PR.
## Problem
Occasionally getting data from GH cache could be slow, with less than
10MB/s and taking 5+ minutes to download cache:
```
Received 20971520 of 2987085791 (0.7%), 9.9 MBs/sec
Received 50331648 of 2987085791 (1.7%), 15.9 MBs/sec
...
Received 1065353216 of 2987085791 (35.7%), 4.8 MBs/sec
Received 1065353216 of 2987085791 (35.7%), 4.7 MBs/sec
...
```
https://github.com/neondatabase/neon/actions/runs/13956437454/job/39068664599#step:7:17
Resulting in getting cache even longer that build time.
## Summary of changes
Switch to the caches, that are closer to the runners, and they provided
stable throughput about 70-80MB/s
## Problem
#11061 changed how artifacts for releases are built, by
reusing/retagging the artifacts from release PRs. This resulted in the
BUILD_TAG that's baked into the images to not be as expected.
Context: https://neondb.slack.com/archives/C08JBTT3R1Q/p1742333300129069
## Summary of changes
Set BUILD_TAG to the release tag of the upcoming release when running
inside release PRs.
## Problem
https://github.com/neondatabase/neon/pull/11210 migrated pushing images
to ghcr. Unfortunately, it was incomplete in using images from ghcr,
which resulted in a few places referencing the ghcr build-tools image,
while trying to use docker hub credentials.
## Summary of changes
Use build-tools image from ghcr consistently.
## Problem
The pipelines after release merges are slower than they need to be at
the moment. This is because some kinds of tests/checks run on all kinds
of pipelines, even though they only matter in some of those.
## Summary of changes
Run `check-codestyle-{rust,python,jsonnet}`, `build-and-test-locally`
and `trigger-e2e-tests` only on regular PRs, not release PR or pushes to
main or release branches.
## Problem
Docker Hub has new rate limits coming up, and to avoid problems coming
with those we're switching to GHCR.
## Summary of changes
- Push images to GHCR initially and distribute them from there
- Use images from GHCR in docker-compose
## Problem
ef0d4a48a adjusted how we build container images and how we push them,
and the neon-test-extensions image was overlooked. Additionally, is was
also missed in 1f0dea9a1, which pushed our container images to GHCR.
## Summary of changes
Push neon-test-extensions to GHCR and also push release tags for it.
## Problem
When we release our components, we perform builds in the release PR,
then test the components, then merge the PR, and then build everything
*again*, run tests *again*, and only then start deployments.
To speed things up, we want to perform builds and run tests in the PR,
and start deployments using the existing artifacts from the release PR.
To make that possible, we need to have both CI pipelines running on the
same commit hash, which requires fast forwarding release. That only
works, if we have a commit in the PR that has the current release branch
state as an ancestor.
## Summary of changes
- Changes to release PR creation:
- Remove templates and automatic bodies for release PRs. The previous
template wasn't used anymore, and the automatic body we created in the
pipeline didn't contain any useful content anymore after the changees
here.
- Make it possible to select the source branch. For releases that aren't
cut from `main`, like https://github.com/neondatabase/neon/pull/11051,
we need a way to trigger the new flow from a different branch.
- Determine `release-branch` automatically from the component name
instead of passing that as well.
- Changes to the merge queue job:
- Rename `get-changed-files` to `meta` in preparation of additional data
being fetched as part of that job
- Fail the merge queue if we're trying to merge into a branch other than
main - this is to prevent non-fast-forward merges.
- Label PRs to branches other than main as `fast-forward`, to trigger
the fast-forward job
- Add a fast-forward job that can be triggered with the `fast-forward`
label that performs a fast-forward merge. This only happens if the PR
has `mergeable_state == clean`, so CI having passed.
- Build and Test on releases now skips building images, skips testing
images and skips triggering e2e tests. We add new tags to the images
from the release PR to tag them as release images, and we push them to
the prod registries.
## Problem
Pinging groups on slack didn't work, because I didn't use the correct
syntax.
## Summary of changes
Use the correct syntax for pinging groups.
## Problem
If an image fails to push to dev registries, we shouldn't trigger the
deploy job, because that depends on images existing in dev registries.
To ensure this is the case, the deploy job needs to depend on pushing to
dev registries.
## Summary of changes
Make `deploy` depend on `push-neon-image-dev` and
`push-compute-image-dev`.
## Problem
We build compute-nodes as multi-arch images, but not the
vm-compute-nodes. The PR adds multiarch vm images the same way as in
autoscaling repo.
## Summary of changes
Add architecture to the matrix for vm compute build steps
Add merge job
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
## Problem
CI does not pass for the compute release due to the absence of some
images
## Summary of changes
Now we use the images from the old non-compute releases for non-compute
images
## Problem
https://github.com/neondatabase/neon/pull/10841 made building compute
and neon images optional on releases that don't need them. The
`push-<component>-image-prod` jobs had transitive dependencies that were
skipped due to that, causing the images not to be pushed to production
registries.
## Summary of changes
Add `!failure() && !cancelled() &&` to the beginning of the conditions
for these jobs to ensure they run even if some of their transitive
dependencies are skipped.
## Problem
https://github.com/neondatabase/neon/pull/10841 broke CI on PRs that
aren't based on main or a release branch but want to merge into another
PR.
## Summary of changes
Replace `run-kind=pr-main` with `run-kind=pr`, so that all PRs that
aren't release PRs are treated equally.
## 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
Pinning build tools still replicated the ACR/ECR/Docker Hub login and
pushing, even though we have a reusable workflow for this. Was mentioned
as a TODO in https://github.com/neondatabase/neon/pull/10613.
## Summary of changes
Reuse `_push-to-container-registry.yml` for pinning the build-tools
images.
## 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
If the deploy job on the release branch doesn't succeed, the preprod
deployment will not have happened. It was requested that this triggers a
notification in https://github.com/neondatabase/neon/issues/10662.
## Summary of changes
If we're on the release branch and the deploy job doesn't end up in
"success", notify storage oncall on slack.
## Problem
`benchmarks` is a long-running and non-blocking job. If, on Staging, a
deploy-blocking job fails, restarting it requires cancelling any running
`benchmarks` jobs, which is a waste of CI resources and requires a
couple of extra clicks for a human to do.
Ref: https://neondb.slack.com/archives/C059ZC138NR/p1739292995400899
## Summary of changes
- Run `benchmarks` after `deploy` job
- Handle `benchmarks` run in PRs with `run-benchmarks` label but without
`deploy` job.
## Problem
https://github.com/neondatabase/neon/pull/10613 changed how images are
pushed, and there was a small mismatch between the github workflow and
the script generating what to push where. This resulted in the workflow
trying to push images to prod registries from the main branch, even
though we don't do that and therefore didn't generate a mapping for
those registries in the script that decides what to push where.
This misconception happened because promote-images-dev pushed to dev
registries, and promote-images-prod pushed to prod registries, but
promote-images-prod also updated the latest tag in the dev registries if
and only if we are on the main branch. This last bit is why the
push-<component>-image-prod jobs were trying to run on the main branch.
## Summary of changes
Don't try pushing to prod registries from the main branch.
## 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.
Refactor how extensions are built in compute Dockerfile
1. Rename some of the extension layers, so that names correspond more
precisely to the upstream repository name and the source directory
name. For example, instead of "pg-jsonschema-pg-build", spell it
"pg_jsonschema-build". Some of the layer names had the extra "pg-"
part, and some didn't; harmonize on not having it. And use an
underscore if the upstream project name uses an underscore.
2. Each extension now consists of two dockerfile targets:
[extension]-src and [extension]-build. By convention, the -src
target downloads the sources and applies any neon-specific patches
if necessary. The source tarball is downloaded and extracted under
/ext-src. For example, the 'pgvector' extension creates the
following files and directory:
/ext-src/pgvector.tar.gz # original tarball
/ext-src/pgvector.patch # neon-specific patch, copied from patches/ dir
/ext-src/pgvector-src/ # extracted tarball, with patch applied
This separation avoids re-downloading the sources every time the
extension is recompiled. The 'extension-tests' target also uses the
[extension]-src layers, by copying the /ext-src/ dirs from all
the extensions together into one image.
This refactoring came about when I was experimenting with different
ways of splitting up the Dockerfile so that each extension would be in
a separate file. That's not part of this PR yet, but this is a good
step in modularizing the extensions.
