## Problem
`test_metadata_image_creation ` became flaky with #11212, since image
compaction may yield to L0 compaction.
## Summary of changes
Set `NoYield` when compacting in tenant tests.
## Problem
`l0_flush_delay_threshold` has already been set to 30 in production for
a couple of weeks. Let's harmonize the default.
## Summary of changes
Update `DEFAULT_L0_FLUSH_DELAY_FACTOR` to 3 such that the default
`l0_flush_delay_threshold` is `3 * compaction_threshold`.
This differs from the production setting, which is hardcoded to 30 (with
`compaction_threshold` at 10), and is more appropriate for any tenants
that have custom `compaction_threshold` overrides.
# Problem
We leave too few observability breadcrumbs in the case where wait_lsn is
exceptionally slow.
# Changes
- refactor: extract the monitoring logic out of `log_slow` into
`monitor_slow_future`
- add global + per-timeline counter for time spent waiting for wait_lsn
- It is updated while we're still waiting, similar to what we do for
page_service response flush.
- add per-timeline counterpair for started & finished wait_lsn count
- add slow-logging to leave breadcrumbs in logs, not just metrics
For the slow-logging, we need to consider not flooding the logs during a
broker or network outage/blip.
The solution is a "log-streak-level" concurrency limit per timeline.
At any given time, there is at most one slow wait_lsn that is logging
the "still running" and "completed" sequence of logs.
Other concurrent slow wait_lsn's don't log at all.
This leaves at least one breadcrumb in each timeline's logs if some
wait_lsn was exceptionally slow during a given period.
The full degree of slowness can then be determined by looking at the
per-timeline metric.
# Performance
Reran the `bench_log_slow` benchmark, no difference, so, existing call
sites are fine.
We do use a Semaphore, but only try_acquire it _after_ things have
already been determined to be slow. So, no baseline overhead
anticipated.
# Refs
-
https://github.com/neondatabase/cloud/issues/23486#issuecomment-2711587222
## Problem
There was a panic on staging that compaction didn't find any keys. This
is possible if all layers selected for compaction does not contain any
keys within the current shard.
## Summary of changes
Make panic an error. In the future, we can try creating an empty image
layer so that GC can clean up those layers. Otherwise, for now, we can
only rely on shard ancestor compaction to remove these data.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
`compaction_l0_first` has already been enabled in production for a
couple of weeks.
## Summary of changes
Enable `compaction_l0_first` by default.
Also set `CompactFlags::NoYield` in `timeline_checkpoint_handler`, to
ensure explicitly requested compaction runs to completion. This endpoint
is mainly used in tests, and caused some flakiness where tests expected
compaction to complete.
## Problem
close https://github.com/neondatabase/neon/issues/10310
## Summary of changes
This patch adds a new behavior for the detach_ancestor API: detach with
multi-level ancestor and no reparenting. Though we can potentially
support multi-level + do reparenting / single-level + no-reparenting in
the future, as it's not required for the recovery/snapshot epic, I'd
prefer keeping things simple now that we only handle the old one and the
new one instead of supporting the full feature matrix.
I only added a test case of successful detaching instead of testing
failures. I'd like to make this into staging and add more tests in the
future.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
When the storage controller and Pageserver loads tenants from persisted
storage, it uses `ShardIdentity::unsharded()` for unsharded tenants.
However, this replaces the persisted stripe size of unsharded tenants
with the default stripe size.
This doesn't really matter for practical purposes, since the stripe size
is meaningless for unsharded tenants anyway, but can cause consistency
check failures if the persisted stripe size differs from the default.
This was seen in #11168, where we change the default stripe size.
Touches #11168.
## Summary of changes
Carry over the persisted stripe size from `TenantShardPersistence` for
unsharded tenants, and from `LocationConf` on Pageservers.
Also add bounds checks for type casts when loading persisted shard
metadata.
## Problem
`info_span!` is only used in a `linux` branch, causing the unused lint
to fire on macOS.
## Summary of changes
Fully qualify the `info_span!` use.
We want to export performance traces from the pageserver in OTEL format.
End goal is to see them in Grafana.
To this end, there are two changes here:
1. Update the `tracing-utils` crate to allow for explicitly specifying
the export configuration. Pageserver configuration is loaded from a file
on start-up. This allows us to use the same flow for export configs
there.
2. Update the `utils::logging::init` common entry point to set up OTEL
tracing infrastructure if requested. Note that an entirely different
tracing subscriber is used. This is to avoid interference with the
existing tracing set-up. For now, no service uses this functionality.
PR to plug this into the pageserver is
[here](https://github.com/neondatabase/neon/pull/11140).
Related https://github.com/neondatabase/neon/issues/9873
## Problem
This field was retained for backward compat only in
https://github.com/neondatabase/neon/pull/10707.
Once https://github.com/neondatabase/cloud/pull/25233 is released,
nothing external will be reading this field.
Internally, this was a mandatory field so storage controller is still
trying to decode it, so we must do this removal in two steps: this PR
makes the field optional, and after one release we can fully remove it.
Related: https://github.com/neondatabase/cloud/issues/24250
## Summary of changes
- Rename field to `_unused`
- Remove field from swagger
- Make field optional
# Problem
If the Pageserver ingest path
(InMemoryLayer=>EphemeralFile=>BufferedWriter)
encounters ENOSPC or any other write IO error when flushing the mutable
buffer
of the BufferedWriter, the buffered writer is left in a state where
subsequent _reads_
from the InMemoryLayer it will cause a `must not use after we returned
an error` panic.
The reason is that
1. the flush background task bails on flush failure,
2. causing the `FlushHandle::flush` function to fail at channel.recv()
and
3. causing the `FlushHandle::flush` function to bail with the flush
error,
4. leaving its caller `BufferedWriter::flush` with
`BufferedWriter::mutable = None`,
5. once the InMemoryLayer's RwLock::write guard is dropped, subsequent
reads can enter,
6. those reads find `mutable = None` and cause the panic.
# Context
It has always been the contract that writes against the BufferedWriter
API
must not be retried because the writer/stream-style/append-only
interface makes no atomicity guarantees ("On error, did nothing or a
piece of the buffer get appended?").
The idea was that the error would bubble up to upper layers that can
throw away the buffered writer and create a new one. (See our [internal
error handling policy document on how to handle e.g.
`ENOSPC`](c870a50bc0/src/storage/handling_io_and_logical_errors.md (L36-L43))).
That _might_ be true for delta/image layer writers, I haven't checked.
But it's certainly not true for the ingest path: there are no provisions
to throw away an InMemoryLayer that encountered a write error an
reingest the WAL already written to it.
Adding such higher-level retries would involve either resetting
last_record_lsn to a lower value and restarting walreceiver. The code
isn't flexible enough to do that, and such complexity likely isn't worth
it given that write errors are rare.
# Solution
The solution in this PR is to retry _any_ failing write operation
_indefinitely_ inside the buffered writer flush task, except of course
those that are fatal as per `maybe_fatal_err`.
Retrying indefinitely ensures that `BufferedWriter::mutable` is never
left `None` in the case of IO errors, thereby solving the problem
described above.
It's a clear improvement over the status quo.
However, while we're retrying, we build up backpressure because the
`flush` is only double-buffered, not infinitely buffered.
Backpressure here is generally good to avoid resource exhaustion, **but
blocks reads** and hence stalls GetPage requests because InMemoryLayer
reads and writes are mutually exclusive.
That's orthogonal to the problem that is solved here, though.
## Caveats
Note that there are some remaining conditions in the flush background
task where it can bail with an error. I have annotated one of them with
a TODO comment.
Hence the `FlushHandle::flush` is still fallible and hence the overall
scenario of leaving `mutable = None` on the bail path is still possible.
We can clean that up in a later commit.
Note also that retrying indefinitely is great for temporary errors like
ENOSPC but likely undesirable in case the `std::io::Error` we get is
really due to higher-level logic bugs.
For example, we could fail to flush because the timeline or tenant
directory got deleted and VirtualFile's reopen fails with ENOENT.
Note finally that cancellation is not respected while we're retrying.
This means we will block timeline/tenant/pageserver shutdown.
The reason is that the existing cancellation story for the buffered
writer background task was to recv from flush op channel until the
sending side (FlushHandle) is explicitly shut down or dropped.
Failing to handle cancellation carries the operational risk that even if
a single timeline gets stuck because of a logic bug such as the one laid
out above, we must still restart the whole pageserver process.
# Alternatives Considered
As pointed out in the `Context` section, throwing away a InMemoryLayer
that encountered an error and reingesting the WAL is a lot of complexity
that IMO isn't justified for such an edge case.
Also, it's wasteful.
I think it's a local optimum.
A more general and simpler solution for ENOSPC is to `abort()` the
process and run eviction on startup before bringing up the rest of
pageserver.
I argued for it in the past, the pro arguments are still valid and
complete:
https://neondb.slack.com/archives/C033RQ5SPDH/p1716896265296329
The trouble at the time was implementing eviction on startup.
However, maybe things are simpler now that we are fully storcon-managed
and all tenants have secondaries.
For example, if pageserver `abort()`s on ENOSPC and then simply don't
respond to storcon heartbeats while we're running eviction on startup,
storcon will fail tenants over to the secondary anyway, giving us all
the time we need to clean up.
The downside is that if there's a systemic space management bug, above
proposal will just propagate the problem to other nodes. But I imagine
that because of the delays involved with filling up disks, the system
might reach a half-stable state, providing operators more time to react.
# Demo
Intermediary commit `a03f335121480afc0171b0f34606bdf929e962c5` is demoed
in this (internal) screen recording:
https://drive.google.com/file/d/1nBC6lFV2himQ8vRXDXrY30yfWmI2JL5J/view?usp=drive_link
# Perf Testing
Ran `bench_ingest` on tmpfs, no measurable difference.
Spans are uniquely owned by the flush task, and the span stack isn't too
deep, so, enter and exit should be cheap.
Plus, each flush takes ~150us with direct IO enabled, so, not _that_
high frequency event anyways.
# Refs
- fixes https://github.com/neondatabase/neon/issues/10856
## Problem
This should also resolve the test flakiness of `test_gc_feedback`.
close https://github.com/neondatabase/neon/issues/11144
## Summary of changes
If `NoYield` is set, do not yield in gc-compaction.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Investigate https://github.com/neondatabase/neon/issues/11159
## Summary of changes
This doesn't fix the issue, but at least we can narrow down the cause
next time it happens by logging ancestor referenced layer cnt even if
it's 0.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
In #11122, we want to split shards once the logical size of the largest
timeline exceeds a split threshold. However, `get_top_tenants` currently
only returns `max_logical_size`, which tracks the max _total_ logical
size of a timeline across all shards.
This is problematic, because the storage controller needs to fetch a
list of N tenants that are eligible for splits, but the API doesn't
currently have a way to express this. For example, with a split
threshold of 1 GB, a tenant with `max_logical_size` of 4 GB is eligible
to split if it has 1 or 2 shards, but not if it already has 4 shards. We
need to express this in per-shard terms, otherwise the `get_top_tenants`
endpoint may end up only returning tenants that can't be split, blocking
splits entirely.
Touches https://github.com/neondatabase/neon/pull/11122.
Touches https://github.com/neondatabase/cloud/issues/22532.
## Summary of changes
Add `TenantShardItem::max_logical_size_per_shard` containing
`max_logical_size / shard_count`, and
`TenantSorting::MaxLogicalSizePerShard` to order and filter by it.
# Refs
- fixes https://github.com/neondatabase/neon/issues/6107
# Problem
`VirtualFile` currently parses the path it is opened with to identify
the `tenant,shard,timeline` labels to be used for the `STORAGE_IO_SIZE`
metric.
Further, for each read or write call to VirtualFile, it uses
`with_label_values` to retrieve the correct metrics object, which under
the hood is a global hashmap guarded by a parking_lot mutex.
We perform tens of thousands of reads and writes per second on every
pageserver instance; thus, doing the mutex lock + hashmap lookup is
wasteful.
# Changes
Apply the technique we use for all other timeline-scoped metrics to
avoid the repeat `with_label_values`: add it to `TimelineMetrics`.
Wrap `TimelineMetrics` into an `Arc`.
Propagate the `Arc<TimelineMetrics>` down do `VirtualFile`, and use
`Timeline::metrics::storage_io_size`.
To avoid contention on the `Arc<TimelineMetrics>`'s refcount atomics
between different connection handlers for the same timeline, we wrap it
into another Arc.
To avoid frequent allocations, we store that Arc<Arc<TimelineMetrics>>
inside the per-connection timeline cache.
Preliminary refactorings to enable this change:
- https://github.com/neondatabase/neon/pull/11001
- https://github.com/neondatabase/neon/pull/11030
# Performance
I ran the benchmarks in
`test_runner/performance/pageserver/pagebench/test_pageserver_max_throughput_getpage_at_latest_lsn.py`
on an `i3en.3xlarge` because that's what we currently run them on.
None of the benchmarks shows a meaningful difference in latency or
throughput or CPU utilization.
I would have expected some improvement in the
many-tenants-one-client-each workload because they all hit that hashmap
constantly, and clone the same `UintCounter` / `Arc` inside of it.
But apparently the overhead is miniscule compared to the remaining work
we do per getpage.
Yet, since the changes are already made, the added complexity is
manageable, and the perf overhead of `with_label_values` demonstrable in
micro-benchmarks, let's have this change anyway.
Also, propagating TimelineMetrics through RequestContext might come in
handy down the line.
The micro-benchmark that demonstrates perf impact of
`with_label_values`, along with other pitfalls and mitigation techniques
around the `metrics`/`prometheus` crate:
- https://github.com/neondatabase/neon/pull/11019
# Alternative Designs
An earlier iteration of this PR stored an `Arc<Arc<Timeline>>` inside
`RequestContext`.
The problem is that this risks reference cycles if the RequestContext
gets stored in an object that is owned directly or indirectly by
`Timeline`.
Ideally, we wouldn't be using this mess of Arc's at all and propagate
Rust references instead.
But tokio requires tasks to be `'static`, and so, we wouldn't be able to
propagate references across task boundaries, which is incompatible with
any sort of fan-out code we already have (e.g. concurrent IO) or future
code (parallel compaction).
So, opt for Arc for now.
We use the `metrics` / `prometheus` crate in the Pageserver code base.
This PR demonstrates
- typical performance pitfalls with that crate
- our current set of techniques to avoid most of these pitfalls.
refs
- https://github.com/neondatabase/neon/issues/10948
- https://github.com/neondatabase/neon/pull/7202
- I applied the `label_values__cache_label_values_lookup` technique
there.
- It didn't yield measurable results in high-level benchmarks though.
## Problem
As part of the disaster recovery tool. Partly for
https://github.com/neondatabase/neon/issues/9114.
## Summary of changes
* Add a new pageserver API to force patch the fields in index_part and
modify the timeline internal structures.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Part of https://github.com/neondatabase/neon/issues/9114
## Summary of changes
gc-compaction could take a long time in some cases, for example, if the
job split heuristics is wrong and we selected a too large region for
compaction that can't be finished within a reasonable amount of time. We
will give up such tasks and yield to L0 compaction. Each gc-compaction
sub-compaction job is atomic and cannot be split further so we have to
give up (instead of storing a state and continue later as in image
creation).
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
part of https://github.com/neondatabase/neon/issues/9114
## Summary of changes
* Add timers for each phase of the gc-compaction.
* Add a final ratio computation to directly show the garbage collection
ratio in the logs.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
On unarchival, we update the previous heatmap with all visible layers.
When the primary generates a new heatmap it includes all those layers,
so the secondary will download them. Since they're not actually resident
on the primary (we didn't call the warm up API), they'll never be
evicted,
so they remain in the heatmap.
We want these layers in the heatmap, since we might wish to warm-up an
unarchived timeline after a shard migration. However, we don't want them
to be downloaded on the secondary until we've warmed up the primary.
## Summary of Changes
Include these layers in the heatmap and mark them as cold. All heatmap
operations act on non-cold layers apart from the attached location
warming up API,
which will download the cold layers. Once the cold layers are downloaded
on the primary,
they'll be included in the next heatmap as hot and the secondary starts
fetching them too.
## Problem
I like using `cargo stress` to hammer on a test, but it doesn't work out
of the box because it does parallel runs by default and tests always use
the same repo dir.
## Summary of changes
Add an uuid to the test repo dir when generating it.
## Problem
In a batch, `pageserver_layers_per_read_global` counts all layer visits
towards every read in the batch, since this directly affects the
observed latency of the read. However, this doesn't give a good picture
of the amortized read amplification due to batching.
## Summary of changes
Add two more global read amp metrics:
* `pageserver_layers_per_read_batch_global`: number of layers visited
per batch.
* `pageserver_layers_per_read_amortized_global`: number of layers
divided by reads in a batch.
## Problem
For pg_regress test, we do both v1 and v2; for all the rest, we default
to v2.
part of https://github.com/neondatabase/neon/issues/9516
## Summary of changes
Use reldir v2 across test cases by default.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
part of https://github.com/neondatabase/neon/issues/9114
We used anyhow::Error everywhere and it's time to fix.
## Summary of changes
* Make sure that cancel errors are correctly propagated as
CompactionError::ShuttingDown.
* Skip all the trigger computation work if gc_cutoff is not generated
yet.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
On unarchival, we update the previous heatmap with all visible layers.
When the primary generates a new heatmap it includes all those layers,
so the secondary will download them. Since they're not actually resident
on the primary (we didn't call the warm up API), they'll never be
evicted, so they remain in the heatmap.
This leads to oversized secondary locations like we saw in pre-prod.
## Summary of changes
Gate the loading of the previous heatmaps and the heatmap generation on
unarchival behind configuration
flags. They are disabled by default, but enabled in tests.
## Problem
Each page of the slru segment is fetched individually when it's loaded
on demand.
## Summary of Changes
Use `Timeline::get_vectored` to fetch 16 at a time.
## Problem
part of https://github.com/neondatabase/neon/issues/9516
## Summary of changes
Similar to the aux v2 migration, we persist the relv2 migration status
into index_part, so that even the config item is set to false, we will
still read from the v2 storage to avoid loss of data.
Note that only the two variants `None` and
`Some(RelSizeMigration::Migrating)` are used for now. We don't have full
migration implemented so it will never be set to
`RelSizeMigration::Migrated`.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
The "did not trigger" gets logged at 10k/minute in staging.
## Summary of changes
Change it to debug level.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Image layers may be nested inside in-memory layers as diagnosed
[here](https://github.com/neondatabase/neon/issues/10720#issuecomment-2649419252).
The read path doesn't support this and may skip over the image layer,
resulting in a failure to reconstruct the page.
## Summary of changes
We already support nesting of image layers inside delta layers. The
logic lives in `LayerMap::select_layer`.
The main goal of this PR is to propagate the candidate in-memory layer
down to that point and update
the selection logic.
Important changes are:
1. Support partial reads for the in-memory layer. Previously, we could
only specify the start LSN of the read.
We need to control the end LSN too.
2. `LayerMap::ranged_search` considers in-memory layers too. Previously,
the search for in-memory layers
was done explicitly in `Timeline::get_reconstruct_data_timeline`. Note
that `LayerMap::ranged_search` now returns
a weak readable layer which the `LayerManager` can upgrade. This dance
is such that we can unit test the layer selection logic.
3. Update `LayerMap::select_layer` to consider the candidate in-memory
layer too
Loosely related drive bys:
1. Remove the "keys not found" tracking in the ranged search. This
wasn't used anywhere and it just complicates things.
2. Remove the difficulty map stuff from the layer map. Again, not used
anywhere.
Closes https://github.com/neondatabase/neon/issues/9185
Closes https://github.com/neondatabase/neon/issues/10720
## Problem
Timeline shutdown during basebackup logs at error level because the the
canecellation error is smushed into BasebackupError::Server.
## Summary of changes
Introduce BasebackupError::Shutdown and use it. `log_query_error` will
now see `QueryError::Shutdown` and log at info level.
## Problem
`wait_for_active_tenant()`, used when starting background tasks, has a
race condition that can cause it to wait forever (until cancelled). It
first checks the current tenant state, and then subscribes for state
updates, but if the state changes between these then it won't be
notified about it.
We've seen this wedge compaction tasks, which can cause unbounded layer
file buildup and read amplification.
## Summary of changes
Use `watch::Receiver::wait_for()` to check both the current and new
tenant states.
# Changes
While working on
- https://github.com/neondatabase/neon/pull/7202
I found myself needing to cache another expensive Arc::clone inside
inside the timeline::handle::Cache by wrapping it in another Arc.
Before this PR, it seemed like the only expensive thing we were caching
was the
connection handler tasks' clone of `Arc<Timeline>`.
But in fact the GateGuard was another such thing, but it was
special-cased
in the implementation.
So, this refactoring PR de-special-cases the GateGuard.
# Performance
With this PR we are doing strictly _less_ operations per `Cache::get`.
The reason is that we wrap the entire `Types::Timeline` into one Arc.
Before this PR, it was a separate Arc around the Arc<Timeline> and
one around the Arc<GateGuard>.
With this PR, we avoid an allocation per cached item, namely,
the separate Arc around the GateGuard.
This PR does not change the amount of shared mutable state.
So, all in all, it should be a net positive, albeit probably not
noticable
with our small non-NUMA instances and generally high CPU usage per
request.
# Reviewing
To understand the refactoring logistics, look at the changes to the unit
test types first.
Then read the improved module doc comment.
Then the remaining changes.
In the future, we could rename things to be even more generic.
For example, `Types::TenantMgr` could really be a `Types::Resolver`.
And `Types::Timeline` should, to avoid constant confusion in the doc
comment,
be called `Types::Cached` or `Types::Resolved`.
Because the `handle` module, after this PR, really doesn't care that
we're
using it for storing Arc's and GateGuards.
Then again, specicifity is sometimes more useful than being generic.
And writing the module doc comment in a totally generic way would
probably also be more confusing than helpful.
## Problem
We intend for cplane to use the heatmap layer download API to warm up
timelines after unarchival. It's tricky for them to recurse in the
ancestors,
and the current implementation doesn't work well when unarchiving a
chain
of branches and warming them up.
## Summary of changes
* Add a `recurse` flag to the API. When the flag is set, the operation
recurses into the parent
timeline after the current one is done.
* Be resilient to warming up a chain of unarchived branches. Let's say
we unarchived `B` and `C` from
the `A -> B -> C` branch hierarchy. `B` got unarchived first. We
generated the unarchival heatmaps
and stash them in `A` and `B`. When `C` unarchived, it dropped it's
unarchival heatmap since `A` and `B`
already had one. If `C` needed layers from `A` and `B`, it was out of
luck. Now, when choosing whether
to keep an unarchival heatmap we look at its end LSN. If it's more
inclusive than what we currently have,
keep it.
## Problem
layer_map access was unwrapped. It might return an error during
shutdown.
## Summary of changes
Propagate the layer_map access error back to the compaction loop.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We see `Inmem storage overflow` in page server logs:
https://neondb.slack.com/archives/C033RQ5SPDH/p1740157873114339
walked process is using inseam SMGR with storage size limited by 64
pages with warning watermark 32 (based ion the assumption that
XLR_MAX_BLOCK_ID is 32, so WAL record can not access more than 32
pages).
Actually it is not true. We can update up to 3 forks for each block
(including update of FSM and VM forks).
## Summary of changes
This PR increases inseam SMGR size for walled process to 100 pages and
print stack trace in case of overflow.
---------
Co-authored-by: Konstantin Knizhnik <knizhnik@neon.tech>
Before this PR, re-attach and validate would log the same warning
```
calling control plane generation validation API failed
```
on retry errors.
This can be confusing.
This PR makes the message generically valid for any upcall and adds
additional tracing spans to capture context.
Along the way, clean up some copy-pasta variable naming.
refs
-
https://github.com/neondatabase/neon/issues/10381#issuecomment-2684755827
---------
Co-authored-by: Alexander Lakhin <alexander.lakhin@neon.tech>
Migrates the remaining crates to edition 2024. We like to stay on the
latest edition if possible. There is no functional changes, however some
code changes had to be done to accommodate the edition's breaking
changes.
Like the previous migration PRs, this is comprised of three commits:
* the first does the edition update and makes `cargo check`/`cargo
clippy` pass. we had to update bindgen to make its output [satisfy the
requirements of edition
2024](https://doc.rust-lang.org/edition-guide/rust-2024/unsafe-extern.html)
* the second commit does a `cargo fmt` for the new style edition.
* the third commit reorders imports as a one-off change. As before, it
is entirely optional.
Part of #10918
## Problem
ref https://github.com/neondatabase/neon/issues/10927
## Summary of changes
* Implement `is_critical` and `is_cancel` over `CompactionError`.
* Revisit all places that uses `CollectKeyspaceError` to ensure they are
handled correctly.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Delete layers that we have hardlinked so far when there is an error in
`remote_copy`. This prevents a retry of the ancestor detach from
stumbling over already present layer files: the hardlink would fail with
an error.
If there is a crash, we already clean up during the timeline attach: we
loop over all layer files and purge all layers that are not referenced
by the `index_part.json`.
Make sure to hold the timeline gate to prevent races with
detach&attach&read from the layer file.
These cleanups aren't completely enough however, as there is code after
`prepare` as well. To handle errors there, we add a special case for
`AlreadyExists` errors during the hardlink, where we check if the layer
is an orphan, and if yes, we delete it from local disk. That is ideally
not the case we hit, as it is less clear in that scenario where the
layer came from, but it provides good defense in depth.
Related #10729Fixes#10970
## Problem
If the client connection goes dead without an explicit close (e.g. due
to network infrastructure dropping the connection) then we currently
won't detect it for a long time, which may e.g. block GetPage flushes
and keep the task running.
Touches https://github.com/neondatabase/cloud/issues/23515.
## Summary of changes
Enable `SO_KEEPALIVE` on the page service socket, to enable periodic TCP
keepalive probes. These are configured via Linux sysctls, which will be
deployed separately. By default, the first probe is sent after 2 hours,
so this doesn't have a practical effect until we change the sysctls.
Updates storage components to edition 2024. We like to stay on the
latest edition if possible. There is no functional changes, however some
code changes had to be done to accommodate the edition's breaking
changes.
The PR has two commits:
* the first commit updates storage crates to edition 2024 and appeases
`cargo clippy` by changing code. i have accidentially ran the formatter
on some files that had other edits.
* the second commit performs a `cargo fmt`
I would recommend a closer review of the first commit and a less close
review of the second one (as it just runs `cargo fmt`).
part of https://github.com/neondatabase/neon/issues/10918
## Problem
part of https://github.com/neondatabase/neon/issues/9114
## Summary of changes
Add the auto trigger for gc-compaction. It computes two values: L1 size
and L2 size. When L1 size >= initial trigger threshold, we will trigger
an initial gc-compaction. When l1_size / l2_size >=
gc_compaction_ratio_percent, we will trigger the "tiered" gc-compaction.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We recently added slow GetPage request logging. However, this
unintentionally included the flush time when logging (which we already
have separate logging for). It also logs at WARN level, which is a bit
aggressive since we see this fire quite frequently.
Follows https://github.com/neondatabase/neon/pull/10906.
## Summary of changes
* Only log the request execution time, not the flush time.
* Extract a `pagestream_dispatch_batched_message()` helper.
* Rename `warn_slow()` to `log_slow()` and downgrade to INFO.
