## Problem
This code was to mitigate risk in
https://github.com/neondatabase/neon/pull/8427
As expected, we did not hit this code path - the new continuous updates
of gc_info are working fine, we can remove this code now.
## Summary of changes
- Remove block that double-checks retain_lsns
avoid "leaking" the completions of BackgroundPurges by:
1. switching it to TaskTracker for provided close+wait
2. stop using tokio::fs::remove_dir_all which will consume two units of
memory instead of one blocking task
Additionally, use more graceful shutdown in tests which do actually some
background cleanup.
## Problem
Some developers build on MacOS, which doesn't have io_uring.
## Summary of changes
- Add `io_engine_for_bench`, which on linux will give io_uring or panic
if it's unavailable, and on MacOS will always panic.
We do not want to run such benchmarks with StdFs: the results aren't
interesting, and will actively waste the time of any developers who
start investigating performance before they realize they're using a
known-slow I/O backend.
Why not just conditionally compile this benchmark on linux only? Because
even on linux, I still want it to refuse to run if it can't get
io_uring.
Part of #8130, [RFC: Direct IO For Pageserver](https://github.com/neondatabase/neon/blob/problame/direct-io-rfc/docs/rfcs/034-direct-io-for-pageserver.md)
## Description
Add pageserver config for evaluating/enabling direct I/O.
- Disabled: current default, uses buffered io as is.
- Evaluate: still uses buffered io, but could do alignment checking and
perf simulation (pad latency by direct io RW to a fake file).
- Enabled: uses direct io, behavior on alignment error is configurable.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
We've noticed increased memory usage with the latest release. Drain the
joinset of `page_service` connection handlers to avoid leaking them
until shutdown. An alternative would be to use a TaskTracker.
TaskTracker was not discussed in original PR #8339 review, so not hot
fixing it in here either.
Earlier I was thinking we'd need a (ancestor_lsn, timeline_id) ordered
list of reparented. Turns out we did not need it at all. Replace it with
an unordered hashset. Additionally refactor the reparented direct
children query out, it will later be used from more places.
Split off from #8430.
Cc: #6994
Ephemeral files cleanup on drop but did not delay shutdown, leading to
problems with restarting the tenant. The solution is as proposed:
- make ephemeral files carry the gate guard to delay `Timeline::gate`
closing
- flush in-memory layers and strong references to those on
`Timeline::shutdown`
The above are realized by making LayerManager an `enum` with `Open` and
`Closed` variants, and fail requests to modify `LayerMap`.
Additionally:
- fix too eager anyhow conversions in compaction
- unify how we freeze layers and handle errors
- optimize likely_resident_layers to read LayerFileManager hashmap
values instead of bouncing through LayerMap
Fixes: #7830
Timeline cancellation running in parallel with gc yields error log lines
like:
```
Gc failed 1 times, retrying in 2s: TimelineCancelled
```
They are completely harmless though and normal to occur. Therefore, only
print those messages at an info level. Still print them at all so that
we know what is going on if we focus on a single timeline.
## Problem
We lack a rust bench for the inmemory layer and delta layer write paths:
it is useful to benchmark these components independent of postgres & WAL
decoding.
Related: https://github.com/neondatabase/neon/issues/8452
## Summary of changes
- Refactor DeltaLayerWriter to avoid carrying a Timeline, so that it can
be cleanly tested + benched without a Tenant/Timeline test harness. It
only needed the Timeline for building `Layer`, so this can be done in a
separate step.
- Add `bench_ingest`, which exercises a variety of workload "shapes"
(big values, small values, sequential keys, random keys)
- Include a small uncontroversial optimization: in `freeze`, only
exhaustively walk values to assert ordering relative to end_lsn in debug
mode.
These benches are limited by drive performance on a lot of machines, but
still useful as a local tool for iterating on CPU/memory improvements
around this code path.
Anecdotal measurements on Hetzner AX102 (Ryzen 7950xd):
```
ingest-small-values/ingest 128MB/100b seq
time: [1.1160 s 1.1230 s 1.1289 s]
thrpt: [113.38 MiB/s 113.98 MiB/s 114.70 MiB/s]
Found 1 outliers among 10 measurements (10.00%)
1 (10.00%) low mild
Benchmarking ingest-small-values/ingest 128MB/100b rand: Warming up for 3.0000 s
Warning: Unable to complete 10 samples in 10.0s. You may wish to increase target time to 18.9s.
ingest-small-values/ingest 128MB/100b rand
time: [1.9001 s 1.9056 s 1.9110 s]
thrpt: [66.982 MiB/s 67.171 MiB/s 67.365 MiB/s]
Benchmarking ingest-small-values/ingest 128MB/100b rand-1024keys: Warming up for 3.0000 s
Warning: Unable to complete 10 samples in 10.0s. You may wish to increase target time to 11.0s.
ingest-small-values/ingest 128MB/100b rand-1024keys
time: [1.0715 s 1.0828 s 1.0937 s]
thrpt: [117.04 MiB/s 118.21 MiB/s 119.46 MiB/s]
ingest-small-values/ingest 128MB/100b seq, no delta
time: [425.49 ms 429.07 ms 432.04 ms]
thrpt: [296.27 MiB/s 298.32 MiB/s 300.83 MiB/s]
Found 1 outliers among 10 measurements (10.00%)
1 (10.00%) low mild
ingest-big-values/ingest 128MB/8k seq
time: [373.03 ms 375.84 ms 379.17 ms]
thrpt: [337.58 MiB/s 340.57 MiB/s 343.13 MiB/s]
Found 1 outliers among 10 measurements (10.00%)
1 (10.00%) high mild
ingest-big-values/ingest 128MB/8k seq, no delta
time: [81.534 ms 82.811 ms 83.364 ms]
thrpt: [1.4994 GiB/s 1.5095 GiB/s 1.5331 GiB/s]
Found 1 outliers among 10 measurements (10.00%)
```
## Problem
Sometimes, a layer is Covered by hasn't yet been evicted from local disk
(e.g. shortly after image layer generation). It is not good use of
resources to download these to a secondary location, as there's a good
chance they will never be read.
This follows the previous change that added layer visibility:
- #8511
Part of epic:
- https://github.com/neondatabase/neon/issues/8398
## Summary of changes
- When generating heatmaps, only include Visible layers
- Update test_secondary_downloads to filter to visible layers when
listing layers from an attached location
## Problem
In staging, we could see that occasionally tenants were wrapping their
pageserver_visible_physical_size metric past zero to 2^64.
This is harmless right now, but will matter more later when we start
using visible size in things like the /utilization endpoint.
## Summary of changes
- Add debug asserts that detect this case. `test_gc_of_remote_layers`
works as a reproducer for this issue once the asserts are added.
- Tighten up the interface around access_stats so that only Layer can
mutate it.
- In Layer, wrap calls to `record_access` in code that will update the
visible size statistic if the access implicitly marks the layer visible
(this was what caused the bug)
- In LayerManager::rewrite_layers, use the proper set_visibility layer
function instead of directly using access_stats (this is an additional
path where metrics could go bad.)
- Removed unused instances of LayerAccessStats in DeltaLayer and
ImageLayer which I noticed while reviewing the code paths that call
record_access.
#8600 missed the hunk changing index_part.json informative version.
Include it in this PR, in addition add more non-warning index_part.json
versions to scrubber.
## Problem
We have been maintaining two read paths (legacy and vectored) for a
while now. The legacy read-path was only used for cross validation in some tests.
## Summary of changes
* Tweak all tests that were using the legacy read path to use the
vectored read path instead
* Remove the read path dispatching based on the pageserver configs
* Remove the legacy read path code
We will be able to remove the single blob io code in
`pageserver/src/tenant/blob_io.rs` when https://github.com/neondatabase/neon/issues/7386 is complete.
Closes https://github.com/neondatabase/neon/issues/8005
Currently, we do not have facilities to persistently block GC on a
tenant for whatever reason. We could do a tenant configuration update,
but that is risky for generation numbers and would also be transient.
Introduce a `gc_block` facility in the tenant, which manages per
timeline blocking reasons.
Additionally, add HTTP endpoints for enabling/disabling manual gc
blocking for a specific timeline. For debugging, individual tenant
status now includes a similar string representation logged when GC is
skipped.
Cc: #6994
Add dry-run mode that does not produce any image layer + delta layer. I
will use this code to do some experiments and see how much space we can
reclaim for tenants on staging. Part of
https://github.com/neondatabase/neon/issues/8002
* Add dry-run mode that runs the full compaction process without
updating the layer map. (We never call finish on the writers and the
files will be removed before exiting the function).
* Add compaction statistics and print them at the end of compaction.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Currently if `GET
/v1/tenant/x/timeline/y?force-await-initial-logical-size=true` is
requested for a root timeline created within the current pageserver
session, the request handler panics hitting the debug assertion. These
timelines will always have an accurate (at initdb import) calculated
logical size. Fix is to never attempt prioritizing timeline size
calculation if we already have an exact value.
Split off from #8528.
part of https://github.com/neondatabase/neon/issues/8002
## Summary of changes
Add a `SplitImageWriter` that automatically splits image layer based on
estimated target image layer size. This does not consider compression
and we might need a better metrics.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
We need both compaction and gc lock for gc-compaction. The lock order
should be the same everywhere, otherwise there could be a deadlock where
A waits for B and B waits for A.
We also had a double-lock issue. The compaction lock gets acquired in
the outer `compact` function. Note that the unit tests directly call
`compact_with_gc`, and therefore not triggering the issue.
## Summary of changes
Ensure all places acquire compact lock and then gc lock. Remove an extra
compact lock acqusition.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Makes `flush_frozen_layer` add a barrier to the upload queue and makes
it wait for that barrier to be reached until it lets the flushing be
completed.
This gives us backpressure and ensures that writes can't build up in an
unbounded fashion.
Fixes#7317
## Problem
Previously, when we do a timeline deletion, shards will delete layers
that belong to an ancestor. That is not a correctness issue, because
when we delete a timeline, we're always deleting it from all shards, and
destroying data for that timeline is clearly fine.
However, there exists a race where one shard might start doing this
deletion while another shard has not yet received the deletion request,
and might try to access an ancestral layer. This creates ambiguity over
the "all layers referenced by my index should always exist" invariant,
which is important to detecting and reporting corruption.
Now that we have a GC mode for clearing up ancestral layers, we can rely
on that to clean up such layers, and avoid deleting them right away.
This makes things easier to reason about: there are now no cases where a
shard will delete a layer that belongs to a ShardIndex other than
itself.
## Summary of changes
- Modify behavior of RemoteTimelineClient::delete_all
- Add `test_scrubber_physical_gc_timeline_deletion` to exercise this
case
- Tweak AWS SDK config in the scrubber to enable retries. Motivated by
seeing the test for this feature encounter some transient "service
error" S3 errors (which are probably nothing to do with the changes in
this PR)
part of https://github.com/neondatabase/neon/issues/8002
Due to the limitation of the current layer map implementation, we cannot
directly replace a layer. It's interpreted as an insert and a deletion,
and there will be file exist error when renaming the newly-created layer
to replace the old layer. We work around that by changing the end key of
the image layer. A long-term fix would involve a refactor around the
layer file naming. For delta layers, we simply skip layers with the same
key range produced, though it is possible to add an extra key as an
alternative solution.
* The image layer range for the layers generated from gc-compaction will
be Key::MIN..(Key..MAX-1), to avoid being recognized as an L0 delta
layer.
* Skip existing layers if it turns out that we need to generate a layer
with the same persistent key in the same generation.
Note that it is possible that the newly-generated layer has different
content from the existing layer. For example, when the user drops a
retain_lsn, the compaction could have combined or dropped some records,
therefore creating a smaller layer than the existing one. We discard the
"optimized" layer for now because we cannot deal with such rewrites
within the same generation.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## Problem
We recently added a "visibility" state to layers, but nothing
initializes it.
Part of:
- #8398
## Summary of changes
- Add a dependency on `range-set-blaze`, which is used as a fast
incrementally updated alternative to KeySpace. We could also use this to
replace the internals of KeySpaceRandomAccum if we wanted to. Writing a
type that does this kind of "BtreeMap & merge overlapping entries" thing
isn't super complicated, but no reason to write this ourselves when
there's a third party impl available.
- Add a function to layermap to calculate visibilities for each layer
- Add a function to Timeline to call into layermap and then apply these
visibilities to the Layer objects.
- Invoke the calculation during startup, after image layer creations,
and when removing branches. Branch removal and image layer creation are
the two ways that a layer can go from Visible to Covered.
- Add unit test & benchmark for the visibility calculation
- Expose `pageserver_visible_physical_size` metric, which should always
be <= `pageserver_remote_physical_size`.
- This metric will feed into the /v1/utilization endpoint later: the
visible size indicates how much space we would like to use on this
pageserver for this tenant.
- When `pageserver_visible_physical_size` is greater than
`pageserver_resident_physical_size`, this is a sign that the tenant has
long-idle branches, which result in layers that are visible in
principle, but not used in practice.
This does not keep visibility hints up to date in all cases:
particularly, when creating a child timeline, any previously covered
layers will not get marked Visible until they are accessed.
Updates after image layer creation could be implemented as more of a
special case, but this would require more new code: the existing depth
calculation code doesn't maintain+yield the list of deltas that would be
covered by an image layer.
## Performance
This operation is done rarely (at startup and at timeline deletion), so
needs to be efficient but not ultra-fast.
There is a new `visibility` bench that measures runtime for a synthetic
100k layers case (`sequential`) and a real layer map (`real_map`) with
~26k layers.
The benchmark shows runtimes of single digit milliseconds (on a ryzen
7950). This confirms that the runtime shouldn't be a problem at startup
(as we already incur S3-level latencies there), but that it's slow
enough that we definitely shouldn't call it more often than necessary,
and it may be worthwhile to optimize further later (things like: when
removing a branch, only bother scanning layers below the branchpoint)
```
visibility/sequential time: [4.5087 ms 4.5894 ms 4.6775 ms]
change: [+2.0826% +3.9097% +5.8995%] (p = 0.00 < 0.05)
Performance has regressed.
Found 24 outliers among 100 measurements (24.00%)
2 (2.00%) high mild
22 (22.00%) high severe
min: 0/1696070, max: 93/1C0887F0
visibility/real_map time: [7.0796 ms 7.0832 ms 7.0871 ms]
change: [+0.3900% +0.4505% +0.5164%] (p = 0.00 < 0.05)
Change within noise threshold.
Found 4 outliers among 100 measurements (4.00%)
3 (3.00%) high mild
1 (1.00%) high severe
min: 0/1696070, max: 93/1C0887F0
visibility/real_map_many_branches
time: [4.5285 ms 4.5355 ms 4.5434 ms]
change: [-1.0012% -0.8004% -0.5969%] (p = 0.00 < 0.05)
Change within noise threshold.
```
# Motivation
The working theory for hung systemd during PS deploy
(https://github.com/neondatabase/cloud/issues/11387) is that leftover
walredo processes trigger a race condition.
In https://github.com/neondatabase/neon/pull/8150 I arranged that a
clean Tenant shutdown does actually kill its walredo processes.
But many prod machines don't manage to shut down all their tenants until
the 10s systemd timeout hits and, presumably, triggers the race
condition in systemd / the Linux kernel that causes the frozen systemd
# Solution
This PR bolts on a rather ugly mechanism to shut down tenant managers
out of order 8s after we've received the SIGTERM from systemd.
# Changes
- add a global registry of `Weak<WalRedoManager>`
- add a special thread spawned during `shutdown_pageserver` that sleeps
for 8s, then shuts down all redo managers in the registry and prevents
new redo managers from being created
- propagate the new failure mode of tenant spawning throughout the code
base
- make sure shut down tenant manager results in
PageReconstructError::Cancelled so that if Timeline::get calls come in
after the shutdown, they do the right thing
Since the introduction of sharding, the protocol handling loop in
`handle_pagerequests` cannot know anymore which concrete
`Tenant`/`Timeline` object any of the incoming `PagestreamFeMessage`
resolves to.
In fact, one message might resolve to one `Tenant`/`Timeline` while
the next one may resolve to another one.
To avoid going to tenant manager, we added the `shard_timelines` which
acted as an ever-growing cache that held timeline gate guards open for
the lifetime of the connection.
The consequence of holding the gate guards open was that we had to be
sensitive to every cached `Timeline::cancel` on each interaction with
the network connection, so that Timeline shutdown would not have to wait
for network connection interaction.
We can do better than that, meaning more efficiency & better
abstraction.
I proposed a sketch for it in
* https://github.com/neondatabase/neon/pull/8286
and this PR implements an evolution of that sketch.
The main idea is is that `mod page_service` shall be solely concerned
with the following:
1. receiving requests by speaking the protocol / pagestream subprotocol
2. dispatching the request to a corresponding method on the correct
shard/`Timeline` object
3. sending response by speaking the protocol / pagestream subprotocol.
The cancellation sensitivity responsibilities are clear cut:
* while in `page_service` code, sensitivity to page_service cancellation
is sufficient
* while in `Timeline` code, sensitivity to `Timeline::cancel` is
sufficient
To enforce these responsibilities, we introduce the notion of a
`timeline::handle::Handle` to a `Timeline` object that is checked out
from a `timeline::handle::Cache` for **each request**.
The `Handle` derefs to `Timeline` and is supposed to be used for a
single async method invocation on `Timeline`.
See the lengthy doc comment in `mod handle` for details of the design.
part of https://github.com/neondatabase/neon/issues/8002
For child branches, we will pull the image of the modified keys from the
parant into the child branch, which creates a full history for
generating key retention. If there are not enough delta keys, the image
won't be wrote eventually, and we will only keep the deltas inside the
child branch. We could avoid the wasteful work to pull the image from
the parent if we can know the number of deltas in advance, in the future
(currently we always pull image for all modified keys in the child
branch)
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
part of https://github.com/neondatabase/neon/issues/8184
# Problem
We want to bypass PS PageCache for all data block reads, but
`compact_level0_phase1` currently uses `ValueRef::load` to load the WAL
records from delta layers.
Internally, that maps to `FileBlockReader:read_blk` which hits the
PageCache
[here](e78341e1c2/pageserver/src/tenant/block_io.rs (L229-L236)).
# Solution
This PR adds a mode for `compact_level0_phase1` that uses the
`MergeIterator` for reading the `Value`s from the delta layer files.
`MergeIterator` is a streaming k-merge that uses vectored blob_io under
the hood, which bypasses the PS PageCache for data blocks.
Other notable changes:
* change the `DiskBtreeReader::into_stream` to buffer the node, instead
of holding a `PageCache` `PageReadGuard`.
* Without this, we run out of page cache slots in
`test_pageserver_compaction_smoke`.
* Generally, `PageReadGuard`s aren't supposed to be held across await
points, so, this is a general bugfix.
# Testing / Validation / Performance
`MergeIterator` has not yet been used in production; it's being
developed as part of
* https://github.com/neondatabase/neon/issues/8002
Therefore, this PR adds a validation mode that compares the existing
approach's value iterator with the new approach's stream output, item by
item.
If they're not identical, we log a warning / fail the unit/regression
test.
To avoid flooding the logs, we apply a global rate limit of once per 10
seconds.
In any case, we use the existing approach's value.
Expected performance impact that will be monitored in staging / nightly
benchmarks / eventually pre-prod:
* with validation:
* increased CPU usage
* ~doubled VirtualFile read bytes/second metric
* no change in disk IO usage because the kernel page cache will likely
have the pages buffered on the second read
* without validation:
* slightly higher DRAM usage because each iterator participating in the
k-merge has a dedicated buffer (as opposed to before, where compactions
would rely on the PS PageCaceh as a shared evicting buffer)
* less disk IO if previously there were repeat PageCache misses (likely
case on a busy production Pageserver)
* lower CPU usage: PageCache out of the picture, fewer syscalls are made
(vectored blob io batches reads)
# Rollout
The new code is used with validation mode enabled-by-default.
This gets us validation everywhere by default, specifically in
- Rust unit tests
- Python tests
- Nightly pagebench (shouldn't really matter)
- Staging
Before the next release, I'll merge the following aws.git PR that
configures prod to continue using the existing behavior:
* https://github.com/neondatabase/aws/pull/1663
# Interactions With Other Features
This work & rollout should complete before Direct IO is enabled because
Direct IO would double the IOPS & latency for each compaction read
(#8240).
# Future Work
The streaming k-merge's memory usage is proportional to the amount of
memory per participating layer.
But `compact_level0_phase1` still loads all keys into memory for
`all_keys_iter`.
Thus, it continues to have active memory usage proportional to the
number of keys involved in the compaction.
Future work should replace `all_keys_iter` with a streaming keys
iterator.
This PR has a draft in its first commit, which I later reverted because
it's not necessary to achieve the goal of this PR / issue #8184.
If compression is enabled, we currently try compressing each image
larger than a specific size and if the compressed version is smaller, we
write that one, otherwise we use the uncompressed image. However, this
might sometimes be a wasteful process, if there is a substantial amount
of images that don't compress well.
The compression metrics added in #8420
`pageserver_compression_image_in_bytes_total` and
`pageserver_compression_image_out_bytes_total` are well designed for
answering the question how space efficient the total compression process
is end-to-end, which helps one to decide whether to enable it or not.
To answer the question of how much waste there is in terms of trial
compression, so CPU time, we add two metrics:
* one about the images that have been trial-compressed (considered), and
* one about the images where the compressed image has actually been
written (chosen).
There is different ways of weighting them, like for example one could
look at the count, or the compressed data. But the main contributor to
compression CPU usage is amount of data processed, so we weight the
images by their *uncompressed* size. In other words, the two metrics
are:
* `pageserver_compression_image_in_bytes_considered`
* `pageserver_compression_image_in_bytes_chosen`
Part of #5431
## Problem
The secondary download HTTP API is meant to return 200 if the download
is complete, and 202 if it is still in progress. In #8198 the download
implementation was changed to drop out with success early if it
over-runs a time budget, which resulted in 200 responses for incomplete
downloads.
This breaks storcon_cli's "tenant-warmup" command, which uses the OK
status to indicate download complete.
## Summary of changes
- Only return 200 if we get an Ok() _and_ the progress stats indicate
the download is complete.
Uses the Stream based `list_streaming` function added by #8457 in tenant
deletion, as suggested in https://github.com/neondatabase/neon/pull/7932#issuecomment-2150480180 .
We don't have to worry about retries, as the function is wrapped inside
an outer retry block. If there is a retryable error either during the
listing or during deletion, we just do a fresh start.
Also adds `+ Send` bounds as they are required by the
`delete_tenant_remote` function.
Persists whether a timeline is archived or not in `index_part.json`. We
only return success if the upload has actually worked successfully.
Also introduces a new `index_part.json` version number.
Fixes#8459
Part of #8088
close https://github.com/neondatabase/neon/issues/8435
## Summary of changes
If L0 compaction did not include all L0 layers, skip image generation.
There are multiple possible solutions to the original issue, i.e., an
alternative is to wrap the partial L0 compaction in a loop until it
compacts all L0 layers. However, considering that we should weight all
tenants equally, the current solution can ensure everyone gets a chance
to run compaction, and those who write too much won't get a chance to
create image layers. This creates a natural backpressure feedback that
they get a slower read due to no image layers are created, slowing down
their writes, and eventually compaction could keep up with their writes
+ generate image layers.
Consider deployment, we should add an alert on "skipping image layer
generation", so that we won't run into the case that image layers are
not generated => incidents again.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Problem
-------
wait_lsn timeouts result in a user-facing errors like
```
$ /tmp/neon/pg_install/v16/bin/pgbench -s3424 -i -I dtGvp user=neondb_owner dbname=neondb host=ep-tiny-wave-w23owa37.eastus2.azure.neon.build sslmode=require options='-cstatement_timeout=0 '
dropping old tables...
NOTICE: table "pgbench_accounts" does not exist, skipping
NOTICE: table "pgbench_branches" does not exist, skipping
NOTICE: table "pgbench_history" does not exist, skipping
NOTICE: table "pgbench_tellers" does not exist, skipping
creating tables...
generating data (server-side)...
vacuuming...
pgbench: error: query failed: ERROR: [NEON_SMGR] [shard 0] could not read block 214338 in rel 1663/16389/16839.0 from page server at lsn C/E1C12828
DETAIL: page server returned error: LSN timeout: Timed out while waiting for WAL record at LSN C/E1418528 to arrive, last_record_lsn 6/999D9CA8 disk consistent LSN=6/999D9CA8, WalReceiver status: (update 2024-07-25 08:30:07): connecting to node 25, safekeeper candidates (id|update_time|commit_lsn): [(21|08:30:16|C/E1C129E0), (23|08:30:16|C/E1C129E0), (25|08:30:17|C/E1C129E0)]
CONTEXT: while scanning block 214338 of relation "public.pgbench_accounts"
pgbench: detail: Query was: vacuum analyze pgbench_accounts
```
Solution
--------
Its better to be slow than to fail the queries.
If the app has a deadline, it can use `statement_timeout`.
In the long term, we want to eliminate wait_lsn timeout.
In the short term (this PR), we bump the wait_lsn timeout to
a larger value to reduce the frequency at which these wait_lsn timeouts
occur.
We will observe SLOs and specifically
`pageserver_wait_lsn_seconds_bucket`
before we eliminate the timeout completely.
## Problem
Vectored get is already enabled in all prod regions without validation.
The pageserver defaults
are out of sync however.
## Summary of changes
Update the pageserver defaults to match the prod config. Also means that
when running tests locally,
people don't have to use the env vars to get the prod config.
## Problem
The scrubber would like to check the highest mtime in a tenant's objects
as a safety check during purges. It recently switched to use
GenericRemoteStorage, so we need to expose that in the listing methods.
## Summary of changes
- In Listing.keys, return a ListingObject{} including a last_modified
field, instead of a RemotePath
---------
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
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
This pull request (should) fix the failure of test_gc_feedback. See the
explanation in the newly-added test case.
Part of https://github.com/neondatabase/neon/issues/8002
Allow incomplete history for the compaction algorithm.
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Re-attach blocks the pageserver http server from starting up. Hence, it
can't reply to heartbeats
until that's done. This makes the storage controller mark the node
off-line (not good). We worked
around this by setting the interval after which nodes are marked offline
to 5 minutes. This isn't a
long term solution.
## Summary of changes
* Introduce a new `NodeAvailability` state: `WarmingUp`. This state
models the following time interval:
* From receiving the re-attach request until the pageserver replies to
the first heartbeat post re-attach
* The heartbeat delta generator becomes aware of this state and uses a
separate longer interval
* Flag `max-warming-up-interval` now models the longer timeout and
`max-offline-interval` the shorter one to
match the names of the states
Closes https://github.com/neondatabase/neon/issues/7552
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
PR that modified compaction raced with PR that modified the GcInfo
structure
## Summary of changes
Fix it
Co-authored-by: Vlad Lazar <vlalazar.vlad@gmail.com>
## Problem
The in-memory layer vectored read was very slow in some conditions
(walingest::test_large_rel) test. Upon profiling, I realised that 80% of
the time was spent building up the binary heap of reads. This stage
isn't actually needed.
## Summary of changes
Remove the planning stage as we never took advantage of it in order to
merge reads. There should be no functional change from this patch.
