## 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.
Note: this has to merge after the release is cut on `2025-01-17` for
compat tests to start passing.
## Problem
SK wal reader fan-out is not enabled in tests by default.
## Summary of changes
Enable it.
## Problem
There is no direct backpressure for compaction and L0 read
amplification. This allows a large buildup of compaction debt and read
amplification.
Resolves#5415.
Requires #10402.
## Summary of changes
Delay layer flushes based on the number of level 0 delta layers:
* `l0_flush_delay_threshold`: delay flushes such that they take 2x as
long (default `2 * compaction_threshold`).
* `l0_flush_stall_threshold`: stall flushes until level 0 delta layers
drop below threshold (default `4 * compaction_threshold`).
If either threshold is reached, ephemeral layer rolls also synchronously
wait for layer flushes to propagate this backpressure up into WAL
ingestion. This will bound the number of frozen layers to 1 once
backpressure kicks in, since all other frozen layers must flush before
the rolled layer.
## Analysis
This will significantly change the compute backpressure characteristics.
Recall the three compute backpressure knobs:
* `max_replication_write_lag`: 500 MB (based on Pageserver
`last_received_lsn`).
* `max_replication_flush_lag`: 10 GB (based on Pageserver
`disk_consistent_lsn`).
* `max_replication_apply_lag`: disabled (based on Pageserver
`remote_consistent_lsn`).
Previously, the Pageserver would keep ingesting WAL and build up
ephemeral layers and L0 layers until the compute hit
`max_replication_flush_lag` at 10 GB and began backpressuring. Now, once
we delay/stall WAL ingestion, the compute will begin backpressuring
after `max_replication_write_lag`, i.e. 500 MB. This is probably a good
thing (we're not building up a ton of compaction debt), but we should
consider tuning these settings.
`max_replication_flush_lag` probably doesn't serve a purpose anymore,
and we should consider removing it.
Furthermore, the removal of the upload barrier in #10402 will mean that
we no longer backpressure flushes based on S3 uploads, since
`max_replication_apply_lag` is disabled. We should consider enabling
this as well.
### When and what do we compact?
Default compaction settings:
* `compaction_threshold`: 10 L0 delta layers.
* `compaction_period`: 20 seconds (between each compaction loop check).
* `checkpoint_distance`: 256 MB (size of L0 delta layers).
* `l0_flush_delay_threshold`: 20 L0 delta layers.
* `l0_flush_stall_threshold`: 40 L0 delta layers.
Compaction characteristics:
* Minimum compaction volume: 10 layers * 256 MB = 2.5 GB.
* Additional compaction volume (assuming 128 MB/s WAL): 128 MB/s * 20
seconds = 2.5 GB (10 L0 layers).
* Required compaction bandwidth: 5.0 GB / 20 seconds = 256 MB/s.
### When do we hit `max_replication_write_lag`?
Depending on how fast compaction and flushes happens, the compute will
backpressure somewhere between `l0_flush_delay_threshold` or
`l0_flush_stall_threshold` + `max_replication_write_lag`.
* Minimum compute backpressure lag: 20 layers * 256 MB + 500 MB = 5.6 GB
* Maximum compute backpressure lag: 40 layers * 256 MB + 500 MB = 10.0
GB
This seems like a reasonable range to me.
This reapplies #10135. Just removing this flush backpressure without
further mitigations caused read amp increases during bulk ingestion
(predictably), so it was reverted. We will replace it by
compaction-based backpressure.
## Problem
In #8550, we made the flush loop wait for uploads after every layer.
This was to avoid unbounded buildup of uploads, and to reduce compaction
debt. However, the approach has several problems:
* It prevents upload parallelism.
* It prevents flush and upload pipelining.
* It slows down ingestion even when there is no need to backpressure.
* It does not directly backpressure based on compaction debt and read
amplification.
We will instead implement compaction-based backpressure in a PR
immediately following this removal (#5415).
Touches #5415.
Touches #10095.
## Summary of changes
Remove waiting on the upload queue in the flush loop.
## Problem
If gc-compaction decides to rewrite an image layer, it will now cause
index_part to lose reference to that layer. In details,
* Assume there's only one image layer of key 0000...AAAA at LSN 0x100
and generation 0xA in the system.
* gc-compaction kicks in at gc-horizon 0x100, and then produce
0000...AAAA at LSN 0x100 and generation 0xB.
* It submits a compaction result update into the index part that unlinks
0000-AAAA-100-A and adds 0000-AAAA-100-B
On the remote storage / local disk side, this is fine -- it unlinks
things correctly and uploads the new file. However, the
`index_part.json` itself doesn't record generations. The buggy procedure
is as follows:
1. upload the new file
2. update the index part to remove the old file and add the new file
3. remove the new file
Therefore, the correct update result process for gc-compaction should be
as follows:
* When modifying the layer map, delete the old one and upload the new
one.
* When updating the index, uploading the new one in the index without
deleting the old one.
## Summary of changes
* Modify `finish_gc_compaction` to correctly order insertions and
deletions.
* Update the way gc-compaction uploads the layer files.
* Add new tests.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
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
Sometimes, especially when the host running the tests is overloaded, we
can run into reconcile timeouts in
`test_timeline_ancestor_detach_idempotent_success`, making the test
flaky. By increasing the timeouts from 30 seconds to 120 seconds, we can
address the flakiness.
Fixes#10464
## Problem
Currently, the report does not contain the LFC state of the failed
tests.
## Summary of changes
Added the LFC state to the link to the allure report.
---------
Co-authored-by: Alexander Bayandin <alexander@neon.tech>
Drop logical replication subscribers
before compute starts on a non-main branch.
Add new compute_ctl spec flag: drop_subscriptions_before_start
If it is set, drop all the subscriptions from the compute node
before it starts.
To avoid race on compute start, use new GUC
neon.disable_logical_replication_subscribers
to temporarily disable logical replication workers until we drop the
subscriptions.
Ensure that we drop subscriptions exactly once when endpoint starts on a
new branch.
It is essential, because otherwise, we may drop not only inherited, but
newly created subscriptions.
We cannot rely only on spec.drop_subscriptions_before_start flag,
because if for some reason compute restarts inside VM,
it will start again with the same spec and flag value.
To handle this, we save the fact of the operation in the database
in the neon.drop_subscriptions_done table.
If the table does not exist, we assume that the operation was never
performed, so we must do it.
If table exists, we check if the operation was performed on the current
timeline.
fixes: https://github.com/neondatabase/neon/issues/8790
## Problem
Not really a bug fix, but hopefully can reproduce
https://github.com/neondatabase/neon/issues/10482 more.
If the layer map does not contain layers that end at exactly the end
range of the compaction job, the current split algorithm will produce
the last job that ends at the maximum layer key. This patch extends it
all the way to the compaction job end key.
For example, the user requests a compaction of 0000...FFFF. However, we
only have a layer 0000..3000 in the layer map, and the split job will
have a range of 0000..3000 instead of 0000..FFFF.
This is not a correctness issue but it would be better to fix it so that
we can get consistent job splits.
## Summary of changes
Compaction job split will always cover the full specified key range.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
PR #10457 was supposed to fix the flakiness of
`test_scrubber_physical_gc_ancestors`, but instead it made it even more
flaky. However, the original error causes disappeared, now to be
replaced by key not found errors.
See this for a longer explanation:
https://github.com/neondatabase/neon/issues/10391#issuecomment-2608018967
## Solution
This does one churn rows after all compactions, and before we do any
timeline gc's. That way, we remain more accessible at older lsn's.
## Problem
The trust connection to the compute required for `pg_anon` was removed.
However, the PGPASSWORD environment variable was not added to
`docker-compose.yml`.
This caused connection errors, which were interpreted as success due to
errors in the bash script.
## Summary of changes
The environment variable was added, and the logic in the bash script was
fixed.
## 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
Both these versions are binary compatible, but the way pgvector
structures the SQL files forbids installing 0.7.4 if you have a 0.8.0
distribution. Yet, some users may need a previous version for backward
compatibility, e.g., restoring the dump.
See this thread for discussion
https://neondb.slack.com/archives/C04DGM6SMTM/p1735911490242919?thread_ts=1731343604.259169&cid=C04DGM6SMTM
## Summary of changes
Put `vector--0.7.4.sql` file into compute image to allow installing this
version as well.
Tested on staging and it seems to be working as expected:
```sql
select * from pg_available_extensions where name = 'vector';
name | default_version | installed_version | comment
--------+-----------------+-------------------+------------------------------------------------------
vector | 0.8.0 | (null) | vector data type and ivfflat and hnsw access methods
create extension vector version '0.7.4';
select * from pg_available_extensions where name = 'vector';
name | default_version | installed_version | comment
--------+-----------------+-------------------+------------------------------------------------------
vector | 0.8.0 | 0.7.4 | vector data type and ivfflat and hnsw access methods
alter extension vector update;
select * from pg_available_extensions where name = 'vector';
name | default_version | installed_version | comment
--------+-----------------+-------------------+------------------------------------------------------
vector | 0.8.0 | 0.8.0 | vector data type and ivfflat and hnsw access methods
drop extension vector;
create extension vector;
select * from pg_available_extensions where name = 'vector';
name | default_version | installed_version | comment
--------+-----------------+-------------------+------------------------------------------------------
vector | 0.8.0 | 0.8.0 | vector data type and ivfflat and hnsw access methods
```
If we find out it's a good approach, we can adopt the same for other
extensions with a stable ABI -- support both `current` and `current - 1`
releases.
# Refs
- extracted from https://github.com/neondatabase/neon/pull/9353
# Problem
Before this PR, when task_mgr shutdown is signalled, e.g. during
pageserver shutdown or Tenant shutdown, initial logical size calculation
stops polling and drops the future that represents the calculation.
This is against the current policy that we poll all futures to
completion.
This became apparent during development of concurrent IO which warns if
we drop a `Timeline::get_vectored` future that still has in-flight IOs.
We may revise the policy in the future, but, right now initial logical
size calculation is the only part of the codebase that doesn't adhere to
the policy, so let's fix it.
## Code Changes
- make sensitive exclusively to `Timeline::cancel`
- This should be sufficient for all cases of shutdowns; the sensitivity
to task_mgr shutdown is unnecessary.
- this broke the various cancel tests in `test_timeline_size.py`, e.g.,
`test_timeline_initial_logical_size_calculation_cancellation`
- the tests would time out because the await point was not sensitive to
cancellation
- to fix this, refactor `pausable_failpoint` so that it accepts a
cancellation token
- side note: we _really_ should write our own failpoint library; maybe
after we get heap-allocated RequestContext, we can plumb failpoints
through there.
## Problem
PR #9993 was supposed to enable `page_service_pipelining` by default for
all `NeonEnv`s, but this was ineffective in our CI environment.
Thus, CI Python-based tests and benchmarks, unless explicitly
configuring pipelining, were still using serial protocol handling.
## Analysis
The root cause was that in our CI environment,
`config.compatibility_neon_binpath` is always Truthy.
It's not in local environments, which is why this slipped through in
local testing.
Lesson: always add a log line ot pageserver startup and spot-check tests
to ensure the intended default is picked up.
## Summary of changes
Fix it. Since enough time has passed, the compatiblity snapshot contains
a recent enough software version so we don't need to worry about
`compatibility_neon_binpath` anymore.
## Future Work
The question how to add a new default except for compatibliity tests,
which is what the broken code was supposed to do, is still unsolved.
Slack discussion:
https://neondb.slack.com/archives/C059ZC138NR/p1737490501941309
## Problem
Currently, the layer flush loop will continue flushing layers as long as
any are pending, and only notify waiters once there are no further
layers to flush. This can cause waiters to wait longer than necessary,
and potentially starve them if pending layers keep arriving faster than
they can be flushed. The impact of this will increase when we add
compaction backpressure and propagate it up into the WAL receiver.
Extracted from #10405.
## Summary of changes
Break out of the layer flush loop once we've flushed up to the requested
LSN. If further flush requests have arrived in the meanwhile, flushing
will resume immediately after.
## Problem
For compaction backpressure, we need a mechanism to signal when
compaction has reduced the L0 delta layer count below the backpressure
threshold.
Extracted from #10405.
## Summary of changes
Add `LayerMap::watch_level0_deltas()` which returns a
`tokio::sync:⌚:Receiver` signalling the current L0 delta layer
count.
## Problem
It's sometimes useful to obtain the elapsed duration from a
`StorageTimeMetricsTimer` for purposes beyond just recording it in
metrics (e.g. to log it).
Extracted from #10405.
## Summary of changes
Add `StorageTimeMetricsTimer.elapsed()` and return the duration from
`stop_and_record()`.
## Problem
part of https://github.com/neondatabase/neon/issues/9114
The automatic trigger is already implemented at
https://github.com/neondatabase/neon/pull/10221 but I need to write some
tests and finish my experiments in staging before I can merge it with
confidence. Given that I have some other patches that will modify the
config items, I'd like to get the config items merged first to reduce
conflicts.
## Summary of changes
* add `l2_lsn` to index_part.json -- below that LSN, data have been
processed by gc-compaction
* add a set of gc-compaction auto trigger control items into the config
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We are removing the `pg_anon` v1 extension from Neon. So we don't need
to test it anymore and can remove the code for simplicity.
## Summary of changes
The code required for testing `pg_anon` is removed.
We did not have any tests on fast_import binary yet.
In this PR I have introduced:
- `FastImport` class and tools for testing in python
- basic test that runs fast import against vanilla postgres and checks
that data is there
Should be merged after https://github.com/neondatabase/neon/pull/10251
We currently have some flakiness in
`test_scrubber_physical_gc_ancestors`, see #10391.
The first flakiness kind is about the reconciler not actually becoming
idle within the timeout of 30 seconds. We see continuous forward
progress so this is likely not a hang. We also see this happen in
parallel to a test failure, so is likely due to runners being
overloaded. Therefore, we increase the timeout.
The second flakiness kind is an assertion failure. This one is a little
bit more tricky, but we saw in the successful run that there was some
advance of the lsn between the compaction ran (which created layer
files) and the gc run. Apparently gc rejects reductions to the single
image layer setting if the cutoff lsn is the same as the lsn of the
image layer: it will claim that that layer is newer than the space
cutoff and therefore skip it, while thinking the old layer (that we want
to delete) is the latest one (so it's not deleted).
We address the second flakiness kind by inserting a tiny amount of WAL
between the compaction and gc. This should hopefully fix things.
Related issue: #10391
(not closing it with the merger of the PR as we'll need to validate that
these changes had the intended effect).
Thanks to Chi for going over this together with me in a call.
## 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
This simplifies the code in `pageserver_physical_gc` a little bit after
the feedback in #10007 that the code is too complicated.
Most importantly, we don't pass around `GcSummary` any more in a
complicated fashion, and we save on async stream-combinator-inception in
one place in favour of `try_stream!{}`.
Follow-up of #10007
Otherwise we might hit ERRORs in otherwise safe situations (such as user
queries), which isn't a great user experience.
## Problem
https://github.com/neondatabase/neon/pull/10376
## Summary of changes
Instead of accepting internal errors as acceptable, we ensure we don't
exceed our allocated usage.
## Refs
- fixes https://github.com/neondatabase/neon/issues/10444
## Problem
We're seeing a panic `handles are only shut down once in their lifetime`
in our performance testbed.
## Hypothesis
Annotated code in
https://github.com/neondatabase/neon/issues/10444#issuecomment-2602286415.
```
T1: drop Cache, executes up to (1)
=> HandleInner is now in state ShutDown
T2: Timeline::shutdown => PerTimelineState::shutdown executes shutdown() again => panics
```
Likely this snuck in the final touches of #10386 where I narrowed down
the locking rules.
## Summary of changes
Make duplicate shutdowns a no-op.
## Summary
Whereas currently we send all WAL to all pageserver shards, and each
shard filters out the data that it needs,
in this RFC we add a mechanism to filter the WAL on the safekeeper, so
that each shard receives
only the data it needs.
This will place some extra CPU load on the safekeepers, in exchange for
reducing the network bandwidth
for ingesting WAL back to scaling as O(1) with shard count, rather than
O(N_shards).
Touches #9329.
## 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
---------
Co-authored-by: Vlad Lazar <vlalazar.vlad@gmail.com>
Co-authored-by: Vlad Lazar <vlad@neon.tech>
The other test focus on the external interface usage while the tests
added in this PR add some testing around HandleInner's lifecycle,
ensuring we don't leak it once either connection gets dropped or
per-timeline-state is shut down explicitly.
It was requested by review in #10305 to use an enum or something like it
for distinguishing the different modes instead of two parameters,
because two flags allow four combinations, and two of them don't really
make sense/ aren't used.
follow-up of #10305
## Problem
Since #9916 , the chaos code is actively fighting the optimizer: tenants
tend to be attached in their preferred AZ, so most chaos migrations were
moving them to a non-preferred AZ.
## Summary of changes
- When picking migrations, prefer to migrate things _toward_ their
preferred AZ when possible. Then pick shards to move the other way when
necessary.
The resulting behavior should be an alternating "back and forth" where
the chaos code migrates thiings away from home, and then migrates them
back on the next iteration.
The side effect will be that the chaos code actively helps to push
things into their home AZ. That's not contrary to its purpose though: we
mainly just want it to continuously migrate things to exercise
migration+notification code.
## Problem
Occasionally, we encounter bugs in test environments that can be
detected at the point of uploading an index, but we proceed to upload it
anyway and leave a tenant in a broken state that's awkward to handle.
## Summary of changes
- Validate index when submitting it for upload, so that we can see the
issue quickly e.g. in an API invoking compaction
- Validate index before executing the upload, so that we have a hard
enforcement that any code path that tries to upload an index will not
overwrite a valid index with an invalid one.
Add an endpoint to obtain the utilization of a safekeeper. Future
changes to the storage controller can use this endpoint to find the most
suitable safekeepers for newly created timelines, analogously to how
it's done for pageservers already.
Initially we just want to assign by timeline count, then we can iterate
from there.
Part of https://github.com/neondatabase/neon/issues/9011
## 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`.
Instead of generating our own request ID, we can just use the one
provided by the control plane. In the event, we get a request from a
client which doesn't set X-Request-ID, then we just generate one which
is useful for tracing purposes.
Signed-off-by: Tristan Partin <tristan@neon.tech>
# Refs
- fixes https://github.com/neondatabase/neon/issues/10309
- fixup of batching design, first introduced in
https://github.com/neondatabase/neon/pull/9851
- refinement of https://github.com/neondatabase/neon/pull/8339
# Problem
`Tenant::shutdown` was occasionally taking many minutes (sometimes up to
20) in staging and prod if the
`page_service_pipelining.mode="concurrent-futures"` is enabled.
# Symptoms
The issue happens during shard migration between pageservers.
There is page_service unavailability and hence effectively downtime for
customers in the following case:
1. The source (state `AttachedStale`) gets stuck in `Tenant::shutdown`,
waiting for the gate to close.
2. Cplane/Storcon decides to transition the target `AttachedMulti` to
`AttachedSingle`.
3. That transition comes with a bump of the generation number, causing
the `PUT .../location_config` endpoint to do a full `Tenant::shutdown` /
`Tenant::attach` cycle for the target location.
4. That `Tenant::shutdown` on the target gets stuck, waiting for the
gate to close.
5. Eventually the gate closes (`close completed`), correlating with a
`page_service` connection handler logging that it's exiting because of a
network error (`Connection reset by peer` or `Broken pipe`).
While in (4):
- `Tenant::shutdown` is stuck waiting for all `Timeline::shutdown` calls
to complete.
So, really, this is a `Timeline::shutdown` bug.
- retries from Cplane/Storcon to complete above state transitions, fail
with errors related to the tenant mgr slot being in state
`TenantSlot::InProgress`, the tenant state being
`TenantState::Stopping`, and the timelines being in
`TimelineState::Stopping`, and the `Timeline::cancel` being cancelled.
- Existing (and/or new?) page_service connections log errors `error
reading relation or page version: Not found: Timed out waiting 30s for
tenant active state. Latest state: None`
# Root-Cause
After a lengthy investigation ([internal
write-up](https://www.notion.so/neondatabase/2025-01-09-batching-deadlock-Slow-Log-Analysis-in-Staging-176f189e00478050bc21c1a072157ca4?pvs=4))
I arrived at the following root cause.
The `spsc_fold` channel (`batch_tx`/`batch_rx`) that connects the
Batcher and Executor stages of the pipelined mode was storing a `Handle`
and thus `GateGuard` of the Timeline that was not shutting down.
The design assumption with pipelining was that this would always be a
short transient state.
However, that was incorrect: the Executor was stuck on writing/flushing
an earlier response into the connection to the client, i.e., socket
write being slow because of TCP backpressure.
The probable scenario of how we end up in that case:
1. Compute backend process sends a continuous stream of getpage prefetch
requests into the connection, but never reads the responses (why this
happens: see Appendix section).
2. Batch N is processed by Batcher and Executor, up to the point where
Executor starts flushing the response.
3. Batch N+1 is procssed by Batcher and queued in the `spsc_fold`.
4. Executor is still waiting for batch N flush to finish.
5. Batcher eventually hits the `TimeoutReader` error (10min).
From here on it waits on the
`spsc_fold.send(Err(QueryError(TimeoutReader_error)))`
which will never finish because the batch already inside the `spsc_fold`
is not
being read by the Executor, because the Executor is still stuck in the
flush.
(This state is not observable at our default `info` log level)
6. Eventually, Compute backend process is killed (`close()` on the
socket) or Compute as a whole gets killed (probably no clean TCP
shutdown happening in that case).
7. Eventually, Pageserver TCP stack learns about (6) through RST packets
and the Executor's flush() call fails with an error.
8. The Executor exits, dropping `cancel_batcher` and its end of the
spsc_fold.
This wakes Batcher, causing the `spsc_fold.send` to fail.
Batcher exits.
The pipeline shuts down as intended.
We return from `process_query` and log the `Connection reset by peer` or
`Broken pipe` error.
The following diagram visualizes the wait-for graph at (5)
```mermaid
flowchart TD
Batcher --spsc_fold.send(TimeoutReader_error)--> Executor
Executor --flush batch N responses--> socket.write_end
socket.write_end --wait for TCP window to move forward--> Compute
```
# Analysis
By holding the GateGuard inside the `spsc_fold` open, the pipelining
implementation
violated the principle established in
(https://github.com/neondatabase/neon/pull/8339).
That is, that `Handle`s must only be held across an await point if that
await point
is sensitive to the `<Handle as Deref<Target=Timeline>>::cancel` token.
In this case, we were holding the Handle inside the `spsc_fold` while
awaiting the
`pgb_writer.flush()` future.
One may jump to the conclusion that we should simply peek into the
spsc_fold to get
that Timeline cancel token and be sensitive to it during flush, then.
But that violates another principle of the design from
https://github.com/neondatabase/neon/pull/8339.
That is, that the page_service connection lifecycle and the Timeline
lifecycles must be completely decoupled.
Tt must be possible to shut down one shard without shutting down the
page_service connection, because on that single connection we might be
serving other shards attached to this pageserver.
(The current compute client opens separate connections per shard, but,
there are plans to change that.)
# Solution
This PR adds a `handle::WeakHandle` struct that does _not_ hold the
timeline gate open.
It must be `upgrade()`d to get a `handle::Handle`.
That `handle::Handle` _does_ hold the timeline gate open.
The batch queued inside the `spsc_fold` only holds a `WeakHandle`.
We only upgrade it while calling into the various `handle_` methods,
i.e., while interacting with the `Timeline` via `<Handle as
Deref<Target=Timeline>>`.
All that code has always been required to be (and is!) sensitive to
`Timeline::cancel`, and therefore we're guaranteed to bail from it
quickly when `Timeline::shutdown` starts.
We will drop the `Handle` immediately, before we start
`pgb_writer.flush()`ing the responses.
Thereby letting go of our hold on the `GateGuard`, allowing the timeline
shutdown to complete while the page_service handler remains intact.
# Code Changes
* Reproducer & Regression Test
* Developed and proven to reproduce the issue in
https://github.com/neondatabase/neon/pull/10399
* Add a `Test` message to the pagestream protocol (`cfg(feature =
"testing")`).
* Drive-by minimal improvement to the parsing code, we now have a
`PagestreamFeMessageTag`.
* Refactor `pageserver/client` to allow sending and receiving
`page_service` requests independently.
* Add a Rust helper binary to produce situation (4) from above
* Rationale: (4) and (5) are the same bug class, we're holding a gate
open while `flush()`ing.
* Add a Python regression test that uses the helper binary to
demonstrate the problem.
* Fix
* Introduce and use `WeakHandle` as explained earlier.
* Replace the `shut_down` atomic with two enum states for `HandleInner`,
wrapped in a `Mutex`.
* To make `WeakHandle::upgrade()` and `Handle::downgrade()`
cache-efficient:
* Wrap the `Types::Timeline` in an `Arc`
* Wrap the `GateGuard` in an `Arc`
* The separate `Arc`s enable uncontended cloning of the timeline
reference in `upgrade()` and `downgrade()`.
If instead we were `Arc<Timeline>::clone`, different connection handlers
would be hitting the same cache line on every upgrade()/downgrade(),
causing contention.
* Please read the udpated module-level comment in `mod handle`
module-level comment for details.
# Testing & Performance
The reproducer test that failed before the changes now passes, and
obviously other tests are passing as well.
We'll do more testing in staging, where the issue happens every ~4h if
chaos migrations are enabled in storcon.
Existing perf testing will be sufficient, no perf degradation is
expected.
It's a few more alloctations due to the added Arc's, but, they're low
frequency.
# Appendix: Why Compute Sometimes Doesn't Read Responses
Remember, the whole problem surfaced because flush() was slow because
Compute was not reading responses. Why is that?
In short, the way the compute works, it only advances the page_service
protocol processing when it has an interest in data, i.e., when the
pagestore smgr is called to return pages.
Thus, if compute issues a bunch of requests as part of prefetch but then
it turns out it can service the query without reading those pages, it
may very well happen that these messages stay in the TCP until the next
smgr read happens, either in that session, or possibly in another
session.
If there’s too many unread responses in the TCP, the pageserver kernel
is going to backpressure into userspace, resulting in our stuck flush().
All of this stems from the way vanilla Postgres does prefetching and
"async IO":
it issues `fadvise()` to make the kernel do the IO in the background,
buffering results in the kernel page cache.
It then consumes the results through synchronous `read()` system calls,
which hopefully will be fast because of the `fadvise()`.
If it turns out that some / all of the prefetch results are not needed,
Postgres will not be issuing those `read()` system calls.
The kernel will eventually react to that by reusing page cache pages
that hold completed prefetched data.
Uncompleted prefetch requests may or may not be processed -- it's up to
the kernel.
In Neon, the smgr + Pageserver together take on the role of the kernel
in above paragraphs.
In the current implementation, all prefetches are sent as GetPage
requests to Pageserver.
The responses are only processed in the places where vanilla Postgres
would do the synchronous `read()` system call.
If we never get to that, the responses are queued inside the TCP
connection, which, once buffers run full, will backpressure into
Pageserver's sending code, i.e., the `pgb_writer.flush()` that was the
root cause of the problems we're fixing in this PR.
The extension now supports Postgres 17. The release also seems to be
binary compatible with the previous version.
Signed-off-by: Tristan Partin <tristan@neon.tech>
## Problem
`test_storage_controller_node_deletion` sometimes failed because shards
were moving around during timeline creation, and neon_local isn't
tolerant of that. The movements were unexpected because the shards had
only just been created.
This was a regression from #9916Closes: #10383
## Summary of changes
- Make this test use multiple AZs -- this makes the storage controller's
scheduling reliably stable
Why this works: in #9916 , I made a simplifying assumption that we would
have multiple AZs to get nice stable scheduling -- it's much easier,
because each tenant has a well defined primary+secondary location when
they have an AZ preference and nodes have different AZs. Everything
still works if you don't have multiple AZs, but you just have this quirk
that sometimes the optimizer can disagree with initial scheduling, so
once in a while a shard moves after being created -- annoying for tests,
harmless IRL.
## Problem
All pageserver have the same application name which makes it hard to
distinguish them.
## Summary of changes
Include the node id in the application name sent to the safekeeper. This
should gives us
more visibility in logs. There's a few metrics that will increase in
cardinality by `pageserver_count`,
but that's fine.
## Problem
Node fills were limited to moving (total shards / node_count) shards. In
systems that aren't perfectly balanced already, that leads us to skip
migrating some of the shards that belong on this node, generating work
for the optimizer later to gradually move them back.
## Summary of changes
- Where a shard has a preferred AZ and is currently attached outside
this AZ, then always promote it during fill, irrespective of target fill
count
## Problem
We were comparing serialized configs from the database with serialized
configs from memory. If fields have been added/removed to TenantConfig,
this generates spurious consistency errors. This is fine in test
environments, but limits the usefulness of this debug API in the field.
Closes: https://github.com/neondatabase/neon/issues/10369
## Summary of changes
- Do a decode/encode cycle on the config before comparing it, so that it
will have exactly the expected fields.
## Problem
gc-compaction needs the partitioning data to decide the job split. This
refactor allows concurrent access/computing the partitioning.
## Summary of changes
Make `partitioning` an ArcSwap so that others can access the
partitioning while we compute it. Fully eliminate the `repartition is
called concurrently` warning when gc-compaction is going on.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Rename the safekeeper scheduling policy "disabled" to "pause".
A rename was requested in
https://github.com/neondatabase/neon/pull/10400#discussion_r1916259124,
as the "disabled" policy is meant to be analogous to the "pause" policy
for pageservers.
Also simplify the `SkSchedulingPolicyArg::from_str` function, relying on
the `from_str` implementation of `SkSchedulingPolicy`. Latter is used
for the database format as well, so it is quite stable. If we ever want
to change the UI, we'll need to duplicate the function again but this is
cheap.
## Problem
Threads spawned in `test_tenant_delete_races_timeline_creation` are not
joined before the test ends, and can generate
`PytestUnhandledThreadExceptionWarning` in other tests.
https://neon-github-public-dev.s3.amazonaws.com/reports/pr-10419/12805365523/index.html#/testresult/53a72568acd04dbd
## Summary of changes
- Wrap threads in ThreadPoolExecutor which will join them before the
test ends
- Remove a spurious deletion call -- the background thread doing
deletion ought to succeed.
