There is a race condition between timeline shutdown and the split task.
Timeline shutdown first shuts down the upload queue, and only then fires
the cancellation token. A parallel running timeline split operation
might thus encounter a cancelled upload queue before the cancellation
token is fired, and print a noisy error.
Fix this by mapping `anyhow::Error{ NotInitialized::ShuttingDown }) to
`FlushLayerError::Cancelled` instead of `FlushLayerError::Other(_)`.
Fixes#8496
Before this PR
1.The circuit breaker would trip on CompactionError::Shutdown. That's
wrong, we want to ignore those cases.
2. remote timeline client shutdown would not be mapped to
CompactionError::Shutdown in all circumstances.
We observed this in staging, see
https://neondb.slack.com/archives/C033RQ5SPDH/p1721829745384449
This PR fixes (1) with a simple `match` statement, and (2) by switching
a bunch of `anyhow` usage over to distinguished errors that ultimately
get mapped to `CompactionError::Shutdown`.
I removed the implicit `#[from]` conversion from `anyhow::Error` to
`CompactionError::Other` to discover all the places that were mapping
remote timeline client shutdown to `anyhow::Error`.
In my opinion `#[from]` is an antipattern and we should avoid it,
especially for `anyhow::Error`. If some callee is going to return
anyhow, the very least the caller should to is to acknowledge, through a
`map_err(MyError::Other)` that they're conflating different failure
reasons.
## Problem
Previously, Timeline::gc_info was only updated in a batch operation at
the start of GC. That means that timelines didn't generally have
accurate information about who their children were before the first GC,
or between GC cycles.
Knowledge of child branches is important for calculating layer
visibility in #8398
## Summary of changes
- Split out part of refresh_gc_info into initialize_gc_info, which is
now called early in startup
- Include TimelineId in retain_lsns so that we can later add/remove the
LSNs for particular children
- When timelines are added/removed, update their parent's retain_lsns
## Problem
LayerAccessStats contains a lot of detail that we don't use: short
histories of most recent accesses, specifics on what kind of task
accessed a layer, etc. This is all stored inside a Mutex, which is
locked every time something accesses a layer.
## Summary of changes
- Store timestamps at a very low resolution (to the nearest second),
sufficient for use on the timescales of eviction.
- Pack access time and last residence change time into a single u64
- Use the high bits of the u64 for other flags, including the new layer
visibility concept.
- Simplify the external-facing model for access stats to just include
what we now track.
Note that the `HistoryBufferWithDropCounter` is removed here because it
is no longer used. I do not dislike this type, we just happen not to use
it for anything else at present.
Co-authored-by: Christian Schwarz <christian@neon.tech>
part of https://github.com/neondatabase/neon/issues/8002
The main thing in this pull request is the new `generate_key_retention`
function. It decides which deltas to retain and generate images for a
given key based on its history + retain_lsn + horizon.
On that, we generate a flat single level of delta layers over all deltas
included in the compaction. In the future, we can decide whether to
split them over the LSN axis as described in the RFC.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Motivation & Context
We want to move away from `task_mgr` towards explicit tracking of child
tasks.
This PR is extracted from https://github.com/neondatabase/neon/pull/8339
where I refactor `PageRequestHandler` to not depend on task_mgr anymore.
## Changes
This PR refactors all global tasks but `PageRequestHandler` to use some
combination of `JoinHandle`/`JoinSet` + `CancellationToken`.
The `task_mgr::spawn(.., shutdown_process_on_error)` functionality is
preserved through the new `exit_on_panic_or_error` wrapper.
Some global tasks were not using it before, but as of this PR, they are.
The rationale is that all global tasks are relevant for correct
operation of the overall Neon system in one way or another.
## Future Work
After #8339, we can make `task_mgr::spawn` require a `TenantId` instead
of an `Option<TenantId>` which concludes this step of cleanup work and
will help discourage future usage of task_mgr for global tasks.
Part of #8128.
## Problem
Scrubber uses `scan_metadata` command to flag metadata inconsistencies.
To trust it at scale, we need to make sure the errors we emit is a
reflection of real scenario. One check performed in the scrubber is to
see whether layers listed in the latest `index_part.json` is present in
object listing. Currently, the scrubber does not robustly handle the
case where objects are uploaded/deleted during the scan.
## Summary of changes
**Condition for success:** An object in the index is (1) in the object
listing we acquire from S3 or (2) found in a HeadObject request (new
object).
- Add in the `HeadObject` requests for the layers missing from the
object listing.
- Keep the order of first getting the object listing and then
downloading the layers.
- Update check to only consider shards with highest shard count.
- Skip analyzing a timeline if `deleted_at` tombstone is marked in
`index_part.json`.
- Add new test to see if scrubber actually detect the metadata
inconsistency.
_Misc_
- A timeline with no ancestor should always have some layers.
- Removed experimental histograms
_Caveat_
- Ancestor layer is not cleaned until #8308 is implemented. If ancestor
layers reference non-existing layers in the index, the scrubber will
emit false positives.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
PR #8299 has switched the storage scrubber to use
`DefaultCredentialsChain`. Now we do this for `remote_storage`, as it
allows us to use `remote_storage` from inside kubernetes. Most of the
diff is due to `GenericRemoteStorage::from_config` becoming `async fn`.
- `horizon` is a confusing term, it's not at all obvious that this means
space-based retention limit, rather than the total GC history limit.
Rename to `GcCutoffs::space`.
- `pitr` is less confusing, but still an unecessary level of indirection
from what we really mean: a time-based condition. The fact that we use
that that time-history for Point In Time Recovery doesn't mean we have
to refer to time as "pitr" everywhere. Rename to `GcCutoffs::time`.
## Problem
This structure was in an Arc<> unnecessarily, making it harder to reason
about its lifetime (i.e. it was superficially possible for LayerManager
to outlive timeline, even though no code used it that way)
## Summary of changes
- Remove the Arc<>
Right now timeline detach ancestor reports an error (409, "no ancestor")
on a new attempt after successful completion. This makes it troublesome
for storage controller retries. Fix it to respond with `200 OK` as if
the operation had just completed quickly.
Additionally, the returned timeline identifiers in the 200 OK response
are now ordered so that responses between different nodes for error
comparison are done by the storage controller added in #8353.
Design-wise, this PR introduces a new strategy for accessing the latest
uploaded IndexPart:
`RemoteTimelineClient::initialized_upload_queue(&self) ->
Result<UploadQueueAccessor<'_>, NotInitialized>`. It should be a more
scalable way to query the latest uploaded `IndexPart` than to add a
query method for each question directly on `RemoteTimelineClient`.
GC blocking will need to be introduced to make the operation fully
idempotent. However, it is idempotent for the cases demonstrated by
tests.
Cc: #6994
## Problem
Pageserver GC uses a size-based condition (GC "horizon" in addition to
time-based "PITR").
Eventually we plan to retire the size-based condition:
https://github.com/neondatabase/neon/issues/6374
Currently, we always apply the more conservative of the two, meaning
that tenants always retain at least 64MB of history (default horizon),
even after a very long time has passed. This is particularly acute in
cases where someone has dropped tables/databases, and then leaves a
database idle: the horizon can prevent GCing very large quantities of
historical data (we already account for this in synthetic size by
ignoring gc horizon).
We're not entirely removing GC horizon right now because we don't want
to 100% rely on standby_horizon for robustness of physical replication,
but we can tweak our logic to avoid retaining that 64MB LSN length
indefinitely.
## Summary of changes
- Rework `Timeline::find_gc_cutoffs`, with new logic:
- If there is no PITR set, then use `DEFAULT_PITR_INTERVAL` (1 week) to
calculate a time threshold. Retain either the horizon or up to that
thresholds, whichever requires less data.
- When there is a PITR set, and we have unambiguously resolved the
timestamp to an LSN, then ignore the GC horizon entirely. For typical
PITRs (1 day, 1 week), this will still easily retain enough data to
avoid stressing read only replicas.
The key property we end up with, whether a PITR is set or not, is that
after enough time has passed, our GC cutoff on an idle timeline will
catch up with the last_record_lsn.
Using `DEFAULT_PITR_INTERVAL` is a bit of an arbitrary hack, but this
feels like it isn't really worth the noise of exposing in TenantConfig.
We could just make it a different named constant though. The end-end
state will be that there is no gc_horizon at all, and that tenants with
pitr_interval=0 would truly retain no history, so this constant would go
away.
## Problem
new clippy warnings on nightly.
## Summary of changes
broken up each commit by warning type.
1. Remove some unnecessary refs.
2. In edition 2024, inference will default to `!` and not `()`.
3. Clippy complains about doc comment indentation
4. Fix `Trait + ?Sized` where `Trait: Sized`.
5. diesel_derives triggering `non_local_defintions`
## Problem
This test incorrectly assumed that a post-split compaction would only
drop content. This was easily destabilized by any changes to image
generation rules.
## Summary of changes
- Before split, do a full image layer generation pass, to guarantee that
post-split compaction should only drop data, never create it.
- Fix the force_image_layer_creation mode of compaction that we use from
tests like this: previously it would try and generate image layers even
if one already existed with the same layer key, which caused compaction
to fail.
## Problem
Debug-mode runs of test_pg_regress are rather slow since
https://github.com/neondatabase/neon/pull/8105, and occasionally exceed
their 600s timeout.
## Summary of changes
- Use 8MiB layer files, avoiding large ephemeral layers
On a hetzner AX102, this takes the runtime from 230s to 190s. Which
hopefully will be enough to get the runtime on github runners more
reliably below its 600s timeout.
This has the side benefit of exercising more of the pageserver stack
(including compaction) under a workload that exercises a more diverse
set of postgres functionality than most of our tests.
## Problem
Assume a timeline with the following workload: very slow ingest of
updates to a small number of keys that fit within the same partition (as decided by
`KeySpace::partition`). These tenants will create small L0 layers since due to time
based rolling, and, consequently, the L1 layers will also be small.
Currently, by default, we need to ingest 512 MiB of WAL before checking
if an image layer is required. This scheme works fine under the assumption that L1s are roughly of
checkpoint distance size, but as the first paragraph explained, that's not the case for all workloads.
## Summary of changes
Check if new image layers are required at least once every checkpoint timeout interval.
Part of #7497, closes#8071. (accidentally closed#8208, reopened here)
## Problem
After the changes in #8084, we need synthetic size to also account for
leased LSNs so that users do not get free retention by running a small
ephemeral endpoint for a long time.
## Summary of changes
This PR integrates LSN leases into the synthetic size calculation. We
model leases as read-only branches started at the leased LSN (except it
does not have a timeline id).
Other changes:
- Add new unit tests testing whether a lease behaves like a read-only
branch.
- Change `/size_debug` response to include lease point in the SVG
visualization.
- Fix `/lsn_lease` HTTP API to do proper parsing for POST.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
I'd like to keep this in the tree since it might be useful in prod as
well. It's a bit too noisy as is and missing the lsn.
## Summary of changes
Add an lsn field and and increase the rate limit duration.
## Problem
The metrics we have today aren't convenient for planning around the
impact of timeline archival on costs.
Closes: https://github.com/neondatabase/neon/issues/8108
## Summary of changes
- Add metric `pageserver_archive_size`, which indicates the logical
bytes of data which we would expect to write into an archived branch.
- Add metric `pageserver_pitr_history_size`, which indicates the
distance between last_record_lsn and the PITR cutoff.
These metrics are somewhat temporary: when we implement #8088 and
associated consumption metric changes, these will reach a final form.
For now, an "archived" branch is just any branch outside of its parent's
PITR window: later, archival will become an explicit state (which will
_usually_ correspond to falling outside the parent's PITR window).
The overall volume of timeline metrics is something to watch, but we are
removing many more in https://github.com/neondatabase/neon/pull/8245
than this PR is adding.
I'd like to add some constraints to the layer map we generate in tests.
(1) is the layer map that the current compaction algorithm will produce.
There is a property that for all delta layer, all delta layer overlaps
with it on the LSN axis will have the same LSN range.
(2) is the layer map that cannot be produced with the legacy compaction
algorithm.
(3) is the layer map that will be produced by the future
tiered-compaction algorithm. The current validator does not allow that
but we can modify the algorithm to allow it in the future.
## Summary of changes
Add a validator to check if the layer map is valid and refactor the test
cases to include delta layer start/end LSN.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
part of https://github.com/neondatabase/neon/issues/7418
# Motivation
(reproducing #7418)
When we do an `InMemoryLayer::write_to_disk`, there is a tremendous
amount of random read I/O, as deltas from the ephemeral file (written in
LSN order) are written out to the delta layer in key order.
In benchmarks (https://github.com/neondatabase/neon/pull/7409) we can
see that this delta layer writing phase is substantially more expensive
than the initial ingest of data, and that within the delta layer write a
significant amount of the CPU time is spent traversing the page cache.
# High-Level Changes
Add a new mode for L0 flush that works as follows:
* Read the full ephemeral file into memory -- layers are much smaller
than total memory, so this is afforable
* Do all the random reads directly from this in memory buffer instead of
using blob IO/page cache/disk reads.
* Add a semaphore to limit how many timelines may concurrently do this
(limit peak memory).
* Make the semaphore configurable via PS config.
# Implementation Details
The new `BlobReaderRef::Slice` is a temporary hack until we can ditch
`blob_io` for `InMemoryLayer` => Plan for this is laid out in
https://github.com/neondatabase/neon/issues/8183
# Correctness
The correctness of this change is quite obvious to me: we do what we did
before (`blob_io`) but read from memory instead of going to disk.
The highest bug potential is in doing owned-buffers IO. I refactored the
API a bit in preliminary PR
https://github.com/neondatabase/neon/pull/8186 to make it less
error-prone, but still, careful review is requested.
# Performance
I manually measured single-client ingest performance from `pgbench -i
...`.
Full report:
https://neondatabase.notion.site/2024-06-28-benchmarking-l0-flush-performance-e98cff3807f94cb38f2054d8c818fe84?pvs=4
tl;dr:
* no speed improvements during ingest, but
* significantly lower pressure on PS PageCache (eviction rate drops to
1/3)
* (that's why I'm working on this)
* noticable but modestly lower CPU time
This is good enough for merging this PR because the changes require
opt-in.
We'll do more testing in staging & pre-prod.
# Stability / Monitoring
**memory consumption**: there's no _hard_ limit on max `InMemoryLayer`
size (aka "checkpoint distance") , hence there's no hard limit on the
memory allocation we do for flushing. In practice, we a) [log a
warning](23827c6b0d/pageserver/src/tenant/timeline.rs (L5741-L5743))
when we flush oversized layers, so we'd know which tenant is to blame
and b) if we were to put a hard limit in place, we would have to decide
what to do if there is an InMemoryLayer that exceeds the limit.
It seems like a better option to guarantee a max size for frozen layer,
dependent on `checkpoint_distance`. Then limit concurrency based on
that.
**metrics**: we do have the
[flush_time_histo](23827c6b0d/pageserver/src/tenant/timeline.rs (L3725-L3726)),
but that includes the wait time for the semaphore. We could add a
separate metric for the time spent after acquiring the semaphore, so one
can infer the wait time. Seems unnecessary at this point, though.
## Problem
At high percentiles we see more than 800 layers being visited by the
read path. We need the tenant/timeline to investigate.
## Summary of changes
Add a rate limited log line when the average number of layers visited
per key is in the last specified histogram bucket.
I plan to use this to identify tenants in us-east-2 staging that exhibit
this behaviour. Will revert before next week's release.
Adds manual compaction trigger; add gc compaction to test_gc_feedback
Part of https://github.com/neondatabase/neon/issues/8002
```
test_gc_feedback[debug-pg15].logical_size: 50 Mb
test_gc_feedback[debug-pg15].physical_size: 2269 Mb
test_gc_feedback[debug-pg15].physical/logical ratio: 44.5302
test_gc_feedback[debug-pg15].max_total_num_of_deltas: 7
test_gc_feedback[debug-pg15].max_num_of_deltas_above_image: 2
test_gc_feedback[debug-pg15].logical_size_after_bottom_most_compaction: 50 Mb
test_gc_feedback[debug-pg15].physical_size_after_bottom_most_compaction: 287 Mb
test_gc_feedback[debug-pg15].physical/logical ratio after bottom_most_compaction: 5.6312
test_gc_feedback[debug-pg15].max_total_num_of_deltas_after_bottom_most_compaction: 4
test_gc_feedback[debug-pg15].max_num_of_deltas_above_image_after_bottom_most_compaction: 1
```
## Summary of changes
* Add the manual compaction trigger
* Use in test_gc_feedback
* Add a guard to avoid running it with retain_lsns
* Fix: Do `schedule_compaction_update` after compaction
* Fix: Supply deltas in the correct order to reconstruct value
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
part of Epic https://github.com/neondatabase/neon/issues/7386
# Motivation
The materialized page cache adds complexity to the code base, which
increases the maintenance burden and risk for subtle and hard to
reproduce bugs such as #8050.
Further, the best hit rate that we currently achieve in production is ca
1% of materialized page cache lookups for
`task_kind=PageRequestHandler`. Other task kinds have hit rates <0.2%.
Last, caching page images in Pageserver rewards under-sized caches in
Computes because reading from Pageserver's materialized page cache over
the network is often sufficiently fast (low hundreds of microseconds).
Such Computes should upscale their local caches to fit their working
set, rather than repeatedly requesting the same page from Pageserver.
Some more discussion and context in internal thread
https://neondb.slack.com/archives/C033RQ5SPDH/p1718714037708459
# Changes
This PR removes the materialized page cache code & metrics.
The infrastructure for different key kinds in `PageCache` is left in
place, even though the "Immutable" key kind is the only remaining one.
This can be further simplified in a future commit.
Some tests started failing because their total runtime was dependent on
high materialized page cache hit rates. This test makes them
fixed-runtime or raises pytest timeouts:
* test_local_file_cache_unlink
* test_physical_replication
* test_pg_regress
# Performance
I focussed on ensuring that this PR will not result in a performance
regression in prod.
* **getpage** requests: our production metrics have shown the
materialized page cache to be irrelevant (low hit rate). Also,
Pageserver is the wrong place to cache page images, it should happen in
compute.
* **ingest** (`task_kind=WalReceiverConnectionHandler`): prod metrics
show 0 percent hit rate, so, removing will not be a regression.
* **get_lsn_by_timestamp**: important API for branch creation, used by
control pane. The clog pages that this code uses are not
materialize-page-cached because they're not 8k. No risk of introducing a
regression here.
We will watch the various nightly benchmarks closely for more results
before shipping to prod.
Part of #7497, extracts from #7996, closes#8063.
## Problem
With the LSN lease API introduced in
https://github.com/neondatabase/neon/issues/7808, we want to implement
the real lease logic so that GC will
keep all the layers needed to reconstruct all pages at all the leased
LSNs with valid leases at a given time.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
## Problem
rust 1.79 new enabled by default lints
## Summary of changes
* update to rust 1.79
* `s/default_features/default-features/`
* fix proxy dead code.
* fix pageserver dead code.
## Problem
This PR refactors some error handling to avoid log spam on
tenant/timeline shutdown.
- "ignoring failure to find gc cutoffs: timeline shutting down." logs
(https://github.com/neondatabase/neon/issues/8012)
- "synthetic_size_worker: failed to calculate synthetic size for tenant
...: Failed to refresh gc_info before gathering inputs: tenant shutting
down", for example here:
https://neon-github-public-dev.s3.amazonaws.com/reports/pr-8049/9502988669/index.html#suites/3fc871d9ee8127d8501d607e03205abb/1a074a66548bbcea
Closes: https://github.com/neondatabase/neon/issues/8012
## Summary of changes
- Refactor: Add a PageReconstructError variant to GcError: this is the
only kind of error that find_gc_cutoffs can emit.
- Functional change: only ignore shutdown PageReconstructError variant:
for other variants, treat it as a real error
- Refactor: add a structured CalculateSyntheticSizeError type and use it
instead of anyhow::Error in synthetic size calculations
- Functional change: while iterating through timelines gathering logical
sizes, only drop out if the whole tenant is cancelled: individual
timeline cancellations indicate deletion in progress and we can just
ignore those.
Some test cases add random keys into the timeline, but it is not part of
the `collect_keyspace`, this will cause compaction remove the keys.
The pull request adds a field to supply extra keyspaces during unit
tests.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
A demo for a building block for compaction. The GC-compaction operation
iterates all layers below/intersect with the GC horizon, and do a full
layer rewrite of all of them. The end result will be image layer
covering the full keyspace at GC-horizon, and a bunch of delta layers
above the GC-horizon. This helps us collect the garbages of the
test_gc_feedback test case to reduce space amplification.
This operation can be manually triggered using an HTTP API or be
triggered based on some metrics. Actual method TBD.
The test is very basic and it's very likely that most part of the
algorithm will be rewritten. I would like to get this merged so that I
can have a basic skeleton for the algorithm and then make incremental
changes.
<img width="924" alt="image"
src="https://github.com/neondatabase/neon/assets/4198311/f3d49f4e-634f-4f56-986d-bfefc6ae6ee2">
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
The new features have deteriorated layer flushing, most recently with
#7927. Changes:
- inline `Timeline::freeze_inmem_layer` to the only caller
- carry the TimelineWriterState guard to the actual point of freezing
the layer
- this allows us to `#[cfg(feature = "testing")]` the assertion added in
#7927
- remove duplicate `flush_frozen_layer` in favor of splitting the
`flush_frozen_layers_and_wait`
- this requires starting the flush loop earlier for `checkpoint_distance
< initdb size` tests
As seen with the pgvector 0.7.0 index builds, we can receive large
batches of images, leading to very large L0 layers in the range of 1GB.
These large layers are produced because we are only able to roll the
layer after we have witnessed two different Lsns in a single
`DataDirModification::commit`. As the single Lsn batches of images can
span over multiple `DataDirModification` lifespans, we will rarely get
to write two different Lsns in a single `put_batch` currently.
The solution is to remember the TimelineWriterState instead of eagerly
forgetting it until we really open the next layer or someone else
flushes (while holding the write_guard).
Additional changes are test fixes to avoid "initdb image layer
optimization" or ignoring initdb layers for assertion.
Cc: #7197 because small `checkpoint_distance` will now trigger the
"initdb image layer optimization"
A simple API to collect some statistics after compaction to easily
understand the result.
The tool reads the layer map, and analyze range by range instead of
doing single-key operations, which is more efficient than doing a
benchmark to collect the result. It currently computes two key metrics:
* Latest data access efficiency, which finds how many delta layers /
image layers the system needs to iterate before returning any key in a
key range.
* (Approximate) PiTR efficiency, as in
https://github.com/neondatabase/neon/issues/7770, which is simply the
number of delta files in the range. The reason behind that is, assume no
image layer is created, PiTR efficiency is simply the cost of collect
records from the delta layers, and the replay time. Number of delta
files (or in the future, estimated size of reads) is a simple yet
efficient way of estimating how much effort the page server needs to
reconstruct a page.
Signed-off-by: Alex Chi Z <chi@neon.tech>
currently we warn even by going over a single byte. even that will be
hit much more rarely once #7927 lands, but get this in earlier.
rationale for 2*checkpoint_distance: anything smaller is not really
worth a warn.
we have an global allowed_error for this warning, which still cannot be
removed nor can it be removed with #7927 because of many tests with very
small `checkpoint_distance`.
Store logical replication origin in KV storage
## Problem
See #6977
## Summary of changes
* Extract origin_lsn from commit WAl record
* Add ReplOrigin key to KV storage and store origin_lsn
* In basebackup replace snapshot origin_lsn with last committed
origin_lsn at basebackup LSN
## 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
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Konstantin Knizhnik <knizhnik@neon.tech>
Co-authored-by: Alex Chi Z <chi@neon.tech>
The keyspace utils like `is_rel_size_key` or `is_rel_fsm_block_key` and
many others are free functions and have to be either imported separately
or specified with the full path starting in `pageserver_api:🔑:`.
This is less convenient than if these functions were just inherent
impls.
Follow-up of #7890Fixes#6438
## Problem
- Because GC exposes all errors as an anyhow::Error, we have
intermittent issues with spurious log errors during shutdown, e.g. in
this failure of a performance test
https://neon-github-public-dev.s3.amazonaws.com/reports/main/9300804302/index.html#suites/07874de07c4a1c9effe0d92da7755ebf/214a2154f6f0217a/
```
Gc failed 1 times, retrying in 2s: shutting down
```
GC really doesn't do a lot of complicated IO: it doesn't benefit from
the backtrace capabilities of anyhow::Error, and can be expressed more
robustly as an enum.
## Summary of changes
- Add GcError type and use it instead of anyhow::Error in GC functions
- In `gc_iteration_internal`, return GcError::Cancelled on shutdown
rather than Ok(()) (we only used Ok before because we didn't have a
clear cancellation error variant to use).
- In `gc_iteration_internal`, skip past timelines that are shutting
down, to avoid having to go through another GC iteration if we happen to
see a deleting timeline during a GC run.
- In `refresh_gc_info_internal`, avoid an error case where a timeline
might not be found after being looked up, by carrying an Arc<Timeline>
instead of a TimelineId between the first loop and second loop in the
function.
- In HTTP request handler, handle Cancelled variants as 503 instead of
turning all GC errors into 500s.
## Problem
In all cases, AncestorStopping is equivalent to Cancelled.
This became more obvious in
https://github.com/neondatabase/neon/pull/7912#discussion_r1620582309
when updating these error types.
## Summary of changes
- Remove AncestorStopping, always use Cancelled instead
This pull request adds necessary interfaces to deterministically create
scenarios we want to test. Simplify some test cases to use this
interface to make it stable + reproducible.
Compaction test will be able to use this interface. Also the upcoming
delete tombstone tests will use this interface to make test
reproducible.
## Summary of changes
* `force_create_image_layer`
* `force_create_delta_layer`
* `force_advance_lsn`
* `create_test_timeline_with_states`
* `branch_timeline_test_with_states`
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
- Initial size calculations tend to fail with `Bad state (not active)`
Closes: https://github.com/neondatabase/neon/issues/7911
## Summary of changes
- In `wait_lsn`, return WaitLsnError::Cancelled rather than BadState
when the state is Stopping
- Replace PageReconstructError's `Other` variant with a specific
`BadState` variant
- Avoid returning anyhow::Error from get_ready_ancestor_timeline -- this
was only used for the case where there was no ancestor. All callers of
this function had implicitly checked that the ancestor timeline exists
before calling it, so they can pass in the ancestor instead of handling
an error.
## Problem
Looking at several noisy shutdown logs:
- In https://github.com/neondatabase/neon/issues/7861 we're hitting a
log error with `InternalServerError(timeline shutting down\n'` on the
checkpoint API handler.
- In the field, we see initial_logical_size_calculation errors on
shutdown, via DownloadError
- In the field, we see errors logged from layer download code
(independent of the error propagated) during shutdown
Closes: https://github.com/neondatabase/neon/issues/7861
## Summary of changes
The theme of these changes is to avoid propagating anyhow::Errors for
cases that aren't really unexpected error cases that we might want a
stacktrace for, and avoid "Other" error variants unless we really do
have unexpected error cases to propagate.
- On the flush_frozen_layers path, use the `FlushLayerError` type
throughout, rather than munging it into an anyhow::Error. Give
FlushLayerError an explicit from_anyhow helper that checks for timeline
cancellation, and uses it to give a Cancelled error instead of an Other
error when the timeline is shutting down.
- In logical size calculation, remove BackgroundCalculationError (this
type was just a Cancelled variant and an Other variant), and instead use
CalculateLogicalSizeError throughout. This can express a
PageReconstructError, and has a From impl that translates cancel-like
page reconstruct errors to Cancelled.
- Replace CalculateLogicalSizeError's Other(anyhow::Error) variant case
with a Decode(DeserializeError) variant, as this was the only kind of
error we actually used in the Other case.
- During layer download, drop out early if the timeline is shutting
down, so that we don't do an `error!()` log of the shutdown error in
this case.
Do pull_timeline while WAL is being removed. To this end
- extract pausable_failpoint to utils, sprinkle pull_timeline with it
- add 'checkpoint' sk http endpoint to force WAL removal.
After fixing checking for pull file status code test fails so far which is
expected.
