## Problem
There's no metrics for disk consistent LSN and remote LSN. This stuff is
useful when looking at ingest performance.
## Summary of changes
Two per timeline metrics are added: `pageserver_disk_consistent_lsn` and
`pageserver_projected_remote_consistent_lsn`. I went for the projected
remote lsn instead of the visible one
because that more closely matches remote storage write tput. Ideally we
would have both, but these metrics are expensive.
## Problem
I'm writing an ingest benchmark in #9812. To time S3 uploads, I need to
schedule a flush of the Pageserver's in-memory layer, but don't actually
want to wait around for it to complete (which will take a minute).
## Summary of changes
Add a parameter `wait_until_flush` (default `true`) for
`timeline/checkpoint` to control whether to wait for the flush to
complete.
## Problem
part of https://github.com/neondatabase/neon/issues/9114
gc-compaction can take a long time. This patch adds support for
scheduling a gc-compaction job. The compaction loop will first handle
L0->L1 compaction, and then gc compaction. The scheduled jobs are stored
in a non-persistent queue within the tenant structure.
This will be the building block for the partial compaction trigger -- if
the system determines that we need to do a gc compaction, it will
partition the keyspace and schedule several jobs. Each of these jobs
will run for a short amount of time (i.e, 1 min). L0 compaction will be
prioritized over gc compaction.
## Summary of changes
* Add compaction scheduler in tenant.
* Run scheduled compaction in integration tests.
* Change the manual compaction API to allow schedule a compaction
instead of immediately doing it.
* Add LSN upper bound as gc-compaction parameter. If we schedule partial
compactions, gc_cutoff might move across different runs. Therefore, we
need to pass a pre-determined gc_cutoff beforehand. (TODO: support LSN
lower bound so that we can compact arbitrary "rectangle" in the layer
map)
* Refactor the gc_compaction internal interface.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
We have a scale test for the storage controller which also acts as a
good stress test for scheduling stability. However, it created nodes
with no AZs set.
## Summary of changes
- Bump node count to 6 and set AZs on them.
This is a precursor to other AZ-related PRs, to make sure any new code
that's landed is getting scale tested in an AZ-aware environment.
(stacked on #9990 and #9995)
Partially fixes#1287 with a custom option field to enable the fixed
behaviour. This allows us to gradually roll out the fix without silently
changing the observed behaviour for our customers.
related to https://github.com/neondatabase/cloud/issues/15284
## 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
```
2024-12-03T15:42:46.5978335Z + poetry run python /__w/neon/neon/scripts/ingest_perf_test_result.py --ingest /__w/neon/neon/test_runner/perf-report-local
2024-12-03T15:42:49.5325077Z Traceback (most recent call last):
2024-12-03T15:42:49.5325603Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 165, in <module>
2024-12-03T15:42:49.5326029Z main()
2024-12-03T15:42:49.5326316Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 155, in main
2024-12-03T15:42:49.5326739Z ingested = ingest_perf_test_result(cur, item, recorded_at_timestamp)
2024-12-03T15:42:49.5327488Z ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
2024-12-03T15:42:49.5327914Z File "/__w/neon/neon/scripts/ingest_perf_test_result.py", line 99, in ingest_perf_test_result
2024-12-03T15:42:49.5328321Z psycopg2.extras.execute_values(
2024-12-03T15:42:49.5328940Z File "/github/home/.cache/pypoetry/virtualenvs/non-package-mode-_pxWMzVK-py3.11/lib/python3.11/site-packages/psycopg2/extras.py", line 1299, in execute_values
2024-12-03T15:42:49.5335618Z cur.execute(b''.join(parts))
2024-12-03T15:42:49.5335967Z psycopg2.errors.InvalidTextRepresentation: invalid input syntax for type numeric: "concurrent-futures"
2024-12-03T15:42:49.5336287Z LINE 57: 'concurrent-futures',
2024-12-03T15:42:49.5336462Z ^
```
## Summary of changes
- `test_page_service_batching`: save non-numeric params as `labels`
- Add a runtime check that `metric_value` is NUMERIC
Before this PR, some override callbacks used `.default()`, others
used `.setdefault()`.
As of this PR, all callbacks use `.setdefault()` which I think is least
prone to failure.
Aligning on a single way will set the right example for future tests
that need such customization.
The `test_pageserver_getpage_throttle.py` technically is a change in
behavior: before, it replaced the `tenant_config` field, now it just
configures the throttle. This is what I believe is intended anyway.
Support tenant manifests in the storage scrubber:
* list the manifests, order them by generation
* delete all manifests except for the two most recent generations
* for the latest manifest: try parsing it.
I've tested this patch by running the against a staging bucket and it
successfully deleted stuff (and avoided deleting the latest two
generations).
In follow-up work, we might want to also check some invariants of the
manifest, as mentioned in #8088.
Part of #9386
Part of #8088
---------
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
Sharded tenants should be run in a single AZ for best performance, so
that computes have AZ-local latency to all the shards.
Part of https://github.com/neondatabase/neon/issues/8264
## Summary of changes
- When we split a tenant, instead of updating each shard's preferred AZ
to wherever it is scheduled, propagate the preferred AZ from the parent.
- Drop the check in `test_shard_preferred_azs` that asserts shards end
up in their preferred AZ: this will not be true again until the
optimize_attachment logic is updated to make this so. The existing check
wasn't testing anything about scheduling, it was just asserting that we
set preferred AZ in a way that matches the way things happen to be
scheduled at time of split.
## Problem
In the batching PR
- https://github.com/neondatabase/neon/pull/9870
I stopped deducting the time-spent-in-throttle fro latency metrics,
i.e.,
- smgr latency metrics (`SmgrOpTimer`)
- basebackup latency (+scan latency, which I think is part of
basebackup).
The reason for stopping the deduction was that with the introduction of
batching, the trick with tracking time-spent-in-throttle inside
RequestContext and swap-replacing it from the `impl Drop for
SmgrOpTimer` no longer worked with >1 requests in a batch.
However, deducting time-spent-in-throttle is desirable because our
internal latency SLO definition does not account for throttling.
## Summary of changes
- Redefine throttling to be a page_service pagestream request throttle
instead of a throttle for repository `Key` reads through `Timeline::get`
/ `Timeline::get_vectored`.
- This means reads done by `basebackup` are no longer subject to any
throttle.
- The throttle applies after batching, before handling of the request.
- Drive-by fix: make throttle sensitive to cancellation.
- Rename metric label `kind` from `timeline_get` to `pagestream` to
reflect the new scope of throttling.
To avoid config format breakage, we leave the config field named
`timeline_get_throttle` and ignore the `task_kinds` field.
This will be cleaned up in a future PR.
## Trade-Offs
Ideally, we would apply the throttle before reading a request off the
connection, so that we queue the minimal amount of work inside the
process.
However, that's not possible because we need to do shard routing.
The redefinition of the throttle to limit pagestream request rate
instead of repository `Key` rate comes with several downsides:
- We're no longer able to use the throttle mechanism for other other
tasks, e.g. image layer creation.
However, in practice, we never used that capability anyways.
- We no longer throttle basebackup.
## Problem
`test_sharded_ingest` ingests a lot of data, which can cause shutdown to
be slow e.g. due to local "S3 uploads" or compactions. This can cause
test flakes during teardown.
Resolves#9740.
## Summary of changes
Perform an immediate shutdown of the cluster.
This PR
- fixes smgr metrics https://github.com/neondatabase/neon/issues/9925
- adds an additional startup log line logging the current batching
config
- adds a histogram of batch sizes global and per-tenant
- adds a metric exposing the current batching config
The issue described #9925 is that before this PR, request latency was
only observed *after* batching.
This means that smgr latency metrics (most importantly getpage latency)
don't account for
- `wait_lsn` time
- time spent waiting for batch to fill up / the executor stage to pick
up the batch.
The fix is to use a per-request batching timer, like we did before the
initial batching PR.
We funnel those timers through the entire request lifecycle.
I noticed that even before the initial batching changes, we weren't
accounting for the time spent writing & flushing the response to the
wire.
This PR drive-by fixes that deficiency by dropping the timers at the
very end of processing the batch, i.e., after the `pgb.flush()` call.
I was **unable to maintain the behavior that we deduct
time-spent-in-throttle from various latency metrics.
The reason is that we're using a *single* counter in `RequestContext` to
track micros spent in throttle.
But there are *N* metrics timers in the batch, one per request.
As a consequence, the practice of consuming the counter in the drop
handler of each timer no longer works because all but the first timer
will encounter error `close() called on closed state`.
A failed attempt to maintain the current behavior can be found in
https://github.com/neondatabase/neon/pull/9951.
So, this PR remvoes the deduction behavior from all metrics.
I started a discussion on Slack about it the implications this has for
our internal SLO calculation:
https://neondb.slack.com/archives/C033RQ5SPDH/p1732910861704029
# Refs
- fixes https://github.com/neondatabase/neon/issues/9925
- sub-issue https://github.com/neondatabase/neon/issues/9377
- epic: https://github.com/neondatabase/neon/issues/9376
Before this PR, the storcon_cli didn't have a way to show the
tenant-wide information of the TenantDescribeResponse.
Sadly, the `Serialize` impl for the tenant config doesn't skip on
`None`, so, the output becomes a bit bloated.
Maybe we can use `skip_serializing_if(Option::is_none)` in the future.
=> https://github.com/neondatabase/neon/issues/9983
## Problem
I was touching `test_storage_controller_node_deletion` because for AZ
scheduling work I was adding a change to the storage controller (kick
secondaries during optimisation) that made a FIXME in this test defunct.
While looking at it I also realized that we can easily fix the way node
deletion currently doesn't use a proper ScheduleContext, using the
iterator type recently added for that purpose.
## Summary of changes
- A testing-only behavior in storage controller where if a secondary
location isn't yet ready during optimisation, it will be actively
polled.
- Remove workaround in `test_storage_controller_node_deletion` that
previously was needed because optimisation would get stuck on cold
secondaries.
- Update node deletion code to use a `TenantShardContextIterator` and
thereby a proper ScheduleContext
## Problem
After enabling LFC in tests and lowering `shared_buffers` we started
having more problems with `test_pg_regress`.
## Summary of changes
Set `shared_buffers` to 1MB to both exercise getPage requests/LFC, and
still have enough room for Postgres to operate. Everything smaller might
be not enough for Postgres under load, and can cause errors like 'no
unpinned buffers available'.
See Konstantin's comment [1] as well.
Fixes#9956
[1]:
https://github.com/neondatabase/neon/issues/9956#issuecomment-2511608097
Improves `wait_until` by:
* Use `timeout` instead of `iterations`. This allows changing the
timeout/interval parameters independently.
* Make `timeout` and `interval` optional (default 20s and 0.5s). Most
callers don't care.
* Only output status every 1s by default, and add optional
`status_interval` parameter.
* Remove `show_intermediate_error`, this was always emitted anyway.
Most callers have been updated to use the defaults, except where they
had good reason otherwise.
## Problem
See https://neondb.slack.com/archives/C04DGM6SMTM/p1732110190129479
We observe the following error in the logs
```
[XX000] ERROR: [NEON_SMGR] [shard 3] Incorrect prefetch read: status=1 response=0x7fafef335138 my=128 receive=128
```
most likely caused by changing `neon.readahead_buffer_size`
## Summary of changes
1. Copy shard state
2. Do not use prefetch_set_unused in readahead_buffer_resize
3. Change prefetch buffer overflow criteria
---------
Co-authored-by: Konstantin Knizhnik <knizhnik@neon.tech>
# Problem
The timeout-based batching adds latency to unbatchable workloads.
We can choose a short batching timeout (e.g. 10us) but that requires
high-resolution timers, which tokio doesn't have.
I thoroughly explored options to use OS timers (see
[this](https://github.com/neondatabase/neon/pull/9822) abandoned PR).
In short, it's not an attractive option because any timer implementation
adds non-trivial overheads.
# Solution
The insight is that, in the steady state of a batchable workload, the
time we spend in `get_vectored` will be hundreds of microseconds anyway.
If we prepare the next batch concurrently to `get_vectored`, we will
have a sizeable batch ready once `get_vectored` of the current batch is
done and do not need an explicit timeout.
This can be reasonably described as **pipelining of the protocol
handler**.
# Implementation
We model the sub-protocol handler for pagestream requests
(`handle_pagrequests`) as two futures that form a pipeline:
2. Batching: read requests from the connection and fill the current
batch
3. Execution: `take` the current batch, execute it using `get_vectored`,
and send the response.
The Reading and Batching stage are connected through a new type of
channel called `spsc_fold`.
See the long comment in the `handle_pagerequests_pipelined` for details.
# Changes
- Refactor `handle_pagerequests`
- separate functions for
- reading one protocol message; produces a `BatchedFeMessage` with just
one page request in it
- batching; tried to merge an incoming `BatchedFeMessage` into an
existing `BatchedFeMessage`; returns `None` on success and returns back
the incoming message in case merging isn't possible
- execution of a batched message
- unify the timeline handle acquisition & request span construction; it
now happen in the function that reads the protocol message
- Implement serial and pipelined model
- serial: what we had before any of the batching changes
- read one protocol message
- execute protocol messages
- pipelined: the design described above
- optionality for execution of the pipeline: either via concurrent
futures vs tokio tasks
- Pageserver config
- remove batching timeout field
- add ability to configure pipelining mode
- add ability to limit max batch size for pipelined configurations
(required for the rollout, cf
https://github.com/neondatabase/cloud/issues/20620 )
- ability to configure execution mode
- Tests
- remove `batch_timeout` parametrization
- rename `test_getpage_merge_smoke` to `test_throughput`
- add parametrization to test different max batch sizes and execution
moes
- rename `test_timer_precision` to `test_latency`
- rename the test case file to `test_page_service_batching.py`
- better descriptions of what the tests actually do
## On the holding The `TimelineHandle` in the pending batch
While batching, we hold the `TimelineHandle` in the pending batch.
Therefore, the timeline will not finish shutting down while we're
batching.
This is not a problem in practice because the concurrently ongoing
`get_vectored` call will fail quickly with an error indicating that the
timeline is shutting down.
This results in the Execution stage returning a `QueryError::Shutdown`,
which causes the pipeline / entire page service connection to shut down.
This drops all references to the
`Arc<Mutex<Option<Box<BatchedFeMessage>>>>` object, thereby dropping the
contained `TimelineHandle`s.
- => fixes https://github.com/neondatabase/neon/issues/9850
# Performance
Local run of the benchmarks, results in [this empty
commit](1cf5b1463f)
in the PR branch.
Key take-aways:
* `concurrent-futures` and `tasks` deliver identical `batching_factor`
* tail latency impact unknown, cf
https://github.com/neondatabase/neon/issues/9837
* `concurrent-futures` has higher throughput than `tasks` in all
workloads (=lower `time` metric)
* In unbatchable workloads, `concurrent-futures` has 5% higher
`CPU-per-throughput` than that of `tasks`, and 15% higher than that of
`serial`.
* In batchable-32 workload, `concurrent-futures` has 8% lower
`CPU-per-throughput` than that of `tasks` (comparison to tput of
`serial` is irrelevant)
* in unbatchable workloads, mean and tail latencies of
`concurrent-futures` is practically identical to `serial`, whereas
`tasks` adds 20-30us of overhead
Overall, `concurrent-futures` seems like a slightly more attractive
choice.
# Rollout
This change is disabled-by-default.
Rollout plan:
- https://github.com/neondatabase/cloud/issues/20620
# Refs
- epic: https://github.com/neondatabase/neon/issues/9376
- this sub-task: https://github.com/neondatabase/neon/issues/9377
- the abandoned attempt to improve batching timeout resolution:
https://github.com/neondatabase/neon/pull/9820
- closes https://github.com/neondatabase/neon/issues/9850
- fixes https://github.com/neondatabase/neon/issues/9835
Adds a benchmark for logical message WAL ingestion throughput
end-to-end. Logical messages are essentially noops, and thus ignored by
the Pageserver.
Example results from my MacBook, with fsync enabled:
```
postgres_ingest: 14.445 s
safekeeper_ingest: 29.948 s
pageserver_ingest: 30.013 s
pageserver_recover_ingest: 8.633 s
wal_written: 10,340 MB
message_count: 1310720 messages
postgres_throughput: 715 MB/s
safekeeper_throughput: 345 MB/s
pageserver_throughput: 344 MB/s
pageserver_recover_throughput: 1197 MB/s
```
See
https://github.com/neondatabase/neon/issues/9642#issuecomment-2475995205
for running analysis.
Touches #9642.
## Problem
We used `set_path()` to replace the database name in the connection
string. It automatically does url-safe encoding if the path is not
already encoded, but it does it as per the URL standard, which assumes
that tabs can be safely removed from the path without changing the
meaning of the URL. See, e.g.,
https://url.spec.whatwg.org/#concept-basic-url-parser. It also breaks
for DBs with properly %-encoded names, like with `%20`, as they are kept
intact, but actually should be escaped.
Yet, this is not true for Postgres, where it's completely valid to have
trailing tabs in the database name.
I think this is the PR that caused this regression
https://github.com/neondatabase/neon/pull/9717, as it switched from
`postgres::config::Config` back to `set_path()`.
This was fixed a while ago already [1], btw, I just haven't added a test
to catch this regression back then :(
## Summary of changes
This commit changes the code back to use
`postgres/tokio_postgres::Config` everywhere.
While on it, also do some changes around, as I had to touch this code:
1. Bump some logging from `debug` to `info` in the spec apply path. We
do not use `debug` in prod, and it was tricky to understand what was
going on with this bug in prod.
2. Refactor configuration concurrency calculation code so it was
reusable. Yet, still keep `1` in the case of reconfiguration. The
database can be actively used at this moment, so we cannot guarantee
that there will be enough spare connection slots, and the underlying
code won't handle connection errors properly.
3. Simplify the installed extensions code. It was spawning a blocking
task inside async function, which doesn't make much sense. Instead, just
have a main sync function and call it with `spawn_blocking` in the API
code -- the only place we need it to be async.
4. Add regression python test to cover this and related problems in the
future. Also, add more extensive testing of schema dump and DBs and
roles listing API.
[1]:
4d1e48f3b9
[2]:
https://www.postgresql.org/message-id/flat/20151023003445.931.91267%40wrigleys.postgresql.orgResolvesneondatabase/cloud#20869
## 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
https://github.com/neondatabase/neon/pull/9746 lifted decoding and
interpretation of WAL to the safekeeper.
This reduced the ingested amount on the pageservers by around 10x for a
tenant with 8 shards, but doubled
the ingested amount for single sharded tenants.
Also, https://github.com/neondatabase/neon/pull/9746 uses bincode which
doesn't support schema evolution.
Technically the schema can be evolved, but it's very cumbersome.
## Summary of changes
This patch set addresses both problems by adding protobuf support for
the interpreted wal records and adding compression support. Compressed
protobuf reduced the ingested amount by 100x on the 32 shards
`test_sharded_ingest` case (compared to non-interpreted proto). For the
1 shard case the reduction is 5x.
Sister change to `rust-postgres` is
[here](https://github.com/neondatabase/rust-postgres/pull/33).
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
## Problem
ingest benchmark tests project migration to Neon involving steps
- COPY relation data
- create indexes
- create constraints
Previously we used only 4 copy jobs, 4 create index jobs and 7
maintenance workers. After increasing effective_io_concurrency on
compute we see that we can sustain more parallelism in the ingest bench
## Summary of changes
Increase copy jobs to 8, create index jobs to 8 and maintenance workers
to 16
## Problem
For any given tenant shard, pageservers receive all of the tenant's WAL
from the safekeeper.
This soft-blocks us from using larger shard counts due to bandwidth
concerns and CPU overhead of filtering
out the records.
## Summary of changes
This PR lifts the decoding and interpretation of WAL from the pageserver
into the safekeeper.
A customised PG replication protocol is used where instead of sending
raw WAL, the safekeeper sends
filtered, interpreted records. The receiver drives the protocol
selection, so, on the pageserver side, usage
of the new protocol is gated by a new pageserver config:
`wal_receiver_protocol`.
More granularly the changes are:
1. Optionally inject the protocol and shard identity into the arguments
used for starting replication
2. On the safekeeper side, implement a new wal sending primitive which
decodes and interprets records
before sending them over
3. On the pageserver side, implement the ingestion of this new
replication message type. It's very similar
to what we already have for raw wal (minus decoding and interpreting).
## Notes
* This PR currently uses my [branch of
rust-postgres](https://github.com/neondatabase/rust-postgres/tree/vlad/interpreted-wal-record-replication-support)
which includes the deserialization logic for the new replication message
type. PR for that is open
[here](https://github.com/neondatabase/rust-postgres/pull/32).
* This PR contains changes for both pageservers and safekeepers. It's
safe to merge because the new protocol is disabled by default on the
pageserver side. We can gradually start enabling it in subsequent
releases.
* CI tests are running on https://github.com/neondatabase/neon/pull/9747
## Links
Related: https://github.com/neondatabase/neon/issues/9336
Epic: https://github.com/neondatabase/neon/issues/9329
## Problem
close https://github.com/neondatabase/neon/issues/9761
The test assumed that no new L0 layers are flushed throughout the
process, which is not true.
## Summary of changes
Fix the test case `test_compaction_l0_memory` by flushing in-memory
layers before compaction.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## 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>
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
Co-authored-by: Stas Kelvic <stas@neon.tech>
# Context
This PR contains PoC-level changes for a product feature that allows
onboarding large databases into Neon without going through the regular
data path.
# Changes
This internal RFC provides all the context
* https://github.com/neondatabase/cloud/pull/19799
In the language of the RFC, this PR covers
* the Importer code (`fast_import`)
* all the Pageserver changes (mgmt API changes, flow implementation,
etc)
* a basic test for the Pageserver changes
# Reviewing
As acknowledged in the RFC, the code added in this PR is not ready for
general availability.
Also, the **architecture is not to be discussed in this PR**, but in the
RFC and associated Slack channel instead.
Reviewers of this PR should take that into consideration.
The quality bar to apply during review depends on what area of the code
is being reviewed:
* Importer code (`fast_import`): practically anything goes
* Core flow (`flow.rs`):
* Malicious input data must be expected and the existing threat models
apply.
* The code must not be safe to execute on *dedicated* Pageserver
instances:
* This means in particular that tenants *on other* Pageserver instances
must not be affected negatively wrt data confidentiality, integrity or
availability.
* Other code: the usual quality bar
* Pay special attention to correct use of gate guards, timeline
cancellation in all places during shutdown & migration, etc.
* Consider the broader system impact; if you find potentially
problematic interactions with Storage features that were not covered in
the RFC, bring that up during the review.
I recommend submitting three separate reviews, for the three high-level
areas with different quality bars.
# References
(Internal-only)
* refs https://github.com/neondatabase/cloud/issues/17507
* refs https://github.com/neondatabase/company_projects/issues/293
* refs https://github.com/neondatabase/company_projects/issues/309
* refs https://github.com/neondatabase/cloud/issues/20646
---------
Co-authored-by: Stas Kelvich <stas.kelvich@gmail.com>
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
Co-authored-by: John Spray <john@neon.tech>
to keep it consistent with existing compute metrics.
flux-fleet change is not needed, because it doesn't have any filter by
metric name for compute metrics.
## Problem
Follow up of https://github.com/neondatabase/neon/pull/9682, that patch
didn't fully address the problem: what if shutdown fails due to whatever
reason and then we reattach the tenant? Then we will still remove the
future layer. The underlying problem is that the fix for #5878 gets
voided because of the generation optimizations.
Of course, we also need to ensure that delete happens after uploads, but
note that we only schedule deletes when there are no ongoing upload
tasks, so that's fine.
## Summary of changes
* Add a test case to reproduce the behavior (by changing the original
test case to attach the same generation).
* If layer upload happens after the deletion, drain the deletion queue
before uploading.
* If blocked_deletion is enabled, directly remove it from the
blocked_deletion queue.
* Local fs backend fix to avoid race between deletion and preload.
* test_emergency_mode does not need to wait for uploads (and it's
generally not possible to wait for uploads).
* ~~Optimize deletion executor to skip validation if there are no files
to delete.~~ this doesn't work
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Follow up to https://github.com/neondatabase/neon/pull/9682, hopefully
we can detect some issues or assure ourselves that this is ready for
production.
## Summary of changes
* Add a compaction-detach-ancestor smoke test.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Along with the migration to Python 3.11, I switched `C(str, Enum)` with
`C(StrEnum)`; one such example is the `PgVersion` enum.
It required more changes in `PgVersion` itself (before, it accepted both
`str` and `int`, and after it, it supports only `str`), which caused the
`test_bulk_insert` test to fail.
## Summary of changes
- `test_bulk_insert`: explicitly cast pg_version from `timeline_detail`
to str
## 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
In https://github.com/neondatabase/neon/issues/9754 and the flakiness of
`test_readonly_node_gc`, we saw that although our logic for controlling
GC was sound, the validation of getpage requests was not, because it
could not consider LSN leases when requests arrived shortly after
restart.
Closes https://github.com/neondatabase/neon/issues/9754
## Summary of changes
This is the "Option 3" discussed verbally -- rather than holding back gc
cutoff, we waive the usual validation of request LSN if we are still
waiting for leases to be sent after startup
- When validating LSN in `wait_or_get_last_lsn`, skip the validation
relative to GC cutoff if the timeline is still in its LSN lease grace
period
- Re-enable test_readonly_node_gc
## 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
Long ago, in #5299 the tenant states for migration are added, but
respected only in a coarse-grained way: when hinted not to do deletions,
tenants will just avoid doing all GC or compaction.
Skipping compaction is not necessary for AttachedMulti, as we will soon
become the primary attached location, and it is not a waste of resources
to proceed with compaction. Instead, per the RFC
https://github.com/neondatabase/neon/pull/5029/files), deletions should
be queued up in this state, and executed later when we switch to
AttachedSingle.
Avoiding compaction in AttachedMulti can have an operational impact if a
tenant is under significant write load, as a long-running migration can
result in a large accumulation of delta layers with commensurate impact
on read latency.
Closes: https://github.com/neondatabase/neon/issues/5396
## Summary of changes
- Add a 'config' part to RemoteTimelineClient so that it can be aware of
the mode of the tenant it belongs to, and wire this through for
construction + updates
- Add a special buffer for delayed deletions, and when in AttachedMulti
route deletions here instead of into the main remote client queue. This
is drained when transitioning to AttachedSingle. If the tenant is
detached or our process dies before then, then these objects are leaked.
- As a quality of life improvement, also use the remote timeline
client's knowledge of the tenant state to avoid submitting remote
consistent LSN updates for validation when in AttachedStale (as we know
these will fail)
## Checklist before requesting a review
- [ ] I have performed a self-review of my code.
- [ ] If it is a core feature, I have added thorough tests.
- [ ] Do we need to implement analytics? if so did you add the relevant
metrics to the dashboard?
- [ ] If this PR requires public announcement, mark it with
/release-notes label and add several sentences in this section.
## Checklist before merging
- [ ] Do not forget to reformat commit message to not include the above
checklist
## Problem
SLRU blocks, which can add up to several gigabytes, are currently
ingested by all shards, multiplying their capacity cost by the shard
count and slowing down ingest. We do this because all shards need the
SLRU pages to do timestamp->LSN lookup for GC.
Related: https://github.com/neondatabase/neon/issues/7512
## Summary of changes
- On non-zero shards, learn the GC offset from shard 0's index instead
of calculating it.
- Add a test `test_sharding_gc` that exercises this
- Do GC in test_pg_regress as a general smoke test that GC functions run
(e.g. this would fail if we were using SLRUs we didn't have)
In this PR we are still ingesting SLRUs everywhere, but not using them
any more. Part 2 PR (https://github.com/neondatabase/neon/pull/9786)
makes the change to not store them at all.
## Checklist before requesting a review
- [ ] I have performed a self-review of my code.
- [ ] If it is a core feature, I have added thorough tests.
- [ ] Do we need to implement analytics? if so did you add the relevant
metrics to the dashboard?
- [ ] If this PR requires public announcement, mark it with
/release-notes label and add several sentences in this section.
## Checklist before merging
- [ ] Do not forget to reformat commit message to not include the above
checklist
## Problem
This test uses a gratuitous number of pageservers (16). This works fine
when there are plenty of system resources, but causes issues on test
runners that have limited resources and run many tests concurrently.
Related: https://github.com/neondatabase/neon/issues/9802
## Summary of changes
- Split from 2 shards to 4, instead of 4 to 8
- Don't give every shard a separate pageserver, let two locations share
each pageserver.
Net result is 4 pageservers instead of 16
## Problem
I've noticed that we have 2 flaky tests which failed with error:
```
re.error: missing ), unterminated subpattern at position 21
```
- `test_timeline_archival_chaos` — has been already fixed
- `test_sharded_tad_interleaved_after_partial_success` — I didn't manage
to find the incorrect regex
[Internal link](https://neonprod.grafana.net/goto/yfmVHV7NR?orgId=1)
## Summary of changes
- Wrap `re.match` in `try..except` block and print incorrect regex
## Problem
Two recently observed log errors indicate safekeeper tasks for a
timeline running after that timeline's deletion has started.
- https://github.com/neondatabase/neon/issues/8972
- https://github.com/neondatabase/neon/issues/8974
These code paths do not have a mechanism that coordinates task shutdown
with the overall shutdown of the timeline.
## Summary of changes
- Add a `Gate` to `Timeline`
- Take the gate as part of resident timeline guard: any code that holds
a guard over a timeline staying resident should also hold a guard over
the timeline's total lifetime.
- Take the gate from the wal removal task
- Respect Timeline::cancel in WAL send/recv code, so that we do not
block shutdown indefinitely.
- Add a test that deletes timelines with open pageserver+compute
connections, to check these get torn down as expected.
There is some risk to introducing gates: if there is code holding a gate
which does not properly respect a cancellation token, it can cause
shutdown hangs. The risk of this for safekeepers is lower in practice
than it is for other services, because in a healthy timeline deletion,
the compute is shutdown first, then the timeline is deleted on the
pageserver, and finally it is deleted on the safekeepers -- that makes
it much less likely that some protocol handler will still be running.
Closes: #8972Closes: #8974
part of https://github.com/neondatabase/neon/issues/9114, we want to be
able to run partial gc-compaction in tests. In the future, we can also
expand this functionality to legacy compaction, so that we can trigger
compaction for a specific key range.
## Summary of changes
* Support passing compaction key range through pageserver routes.
* Refactor input parameters of compact related function to take the new
`CompactOptions`.
* Add tests for partial compaction. Note that the test may or may not
trigger compaction based on GC horizon. We need to improve the test case
to ensure things always get below the gc_horizon and the gc-compaction
can be triggered.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
close https://github.com/neondatabase/neon/issues/9730
The test case tests if anything goes wrong during pageserver restart +
*during timeline creation not complete*. Therefore, queue is stopped
error is normal in this case, except that it should be categorized as a
shutdown error instead of a real error.
## Summary of changes
* More comments for the test case.
* Queue stopped error will now be forwarded as
CreateTimelineError::ShuttingDown.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## 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
