refs https://github.com/neondatabase/neon/issues/6989
Problem
-------
After unclean shutdown, we get restarted, start reading the local
filesystem,
and make decisions based on those reads. However, some of the data might
have
not yet been fsynced when the unclean shutdown completed.
Durability matters even though Pageservers are conceptually just a cache
of state in S3. For example:
- the cloud control plane is no control loop => pageserver responses
to tenant attachmentm, etc, needs to be durable.
- the storage controller does not rely on this (as much?)
- we don't have layer file checksumming, so, downloaded+renamed but not
fsynced layer files are technically not to be trusted
- https://github.com/neondatabase/neon/issues/2683
Solution
--------
`syncfs` the tenants directory during startup, before we start reading
from it.
This is a bit overkill because we do remove some temp files
(InMemoryLayer!)
later during startup. Further, these temp files are particularly likely
to
be dirty in the kernel page cache. However, we don't want to refactor
that
cleanup code right now, and the dirty data on pageservers is generally
not that high. Last, with [direct
IO](https://github.com/neondatabase/neon/issues/8130) we're going to
have near-zero kernel page cache anyway quite soon.
This PR:
* Implements the rule that archived timelines require all of their
children to be archived as well, as specified in the RFC. There is no
fancy locking mechanism though, so the precondition can still be broken.
As a TODO for later, we still allow unarchiving timelines with archived
parents.
* Adds an `is_archived` flag to `TimelineInfo`
* Adds timeline_archival_config to `PageserverHttpClient`
* Adds a new `test_timeline_archive` test, loosely based on
`test_timeline_delete`
Part of #8088
## Problem
We need some metric to sneak peek into how many people use inbound
logical replication (Neon is a subscriber).
## Summary of changes
This commit adds a new metric `compute_subscriptions_count`, which is
number of subscriptions grouped by enabled/disabled state.
Resolves: neondatabase/cloud#16146
Add binary for local-proxy that uses the local auth backend. Runs only
the http serverless driver support and offers config reload based on a
config file and SIGHUP
## Problem
- If a reconciler was waiting to be able to notify computes about a
change, but the control plane was waiting for the controller to finish a
timeline creation/deletion, the overall system can deadlock.
- If a tenant shard was migrated concurrently with a timeline
creation/deletion, there was a risk that the timeline operation could be
applied to a non-latest-generation location, and thereby not really be
persistent. This has never happened in practice, but would eventually
happen at scale.
Closes: #8743
## Summary of changes
- Introduce `Service::tenant_remote_mutation` helper, which looks up
shards & generations and passes them into an inner function that may do
remote I/O to pageservers. Before returning success, this helper checks
that generations haven't incremented, to guarantee that changes are
persistent.
- Convert tenant_timeline_create, tenant_timeline_delete, and
tenant_timeline_detach_ancestor to use this helper.
- These functions no longer block on ensure_attached unless the tenant
was never attached at all, so they should make progress even if we can't
complete compute notifications.
This increases the database load from timeline/create operations, but
only with cheap read transactions.
## Problem
Previously, the controller only used the shard counts for scheduling.
This works well when hosting only many-sharded tenants, but works much
less well when hosting single-sharded tenants that have a greater
deviation in size-per-shard.
Closes: https://github.com/neondatabase/neon/issues/7798
## Summary of changes
- Instead of UtilizationScore, carry the full PageserverUtilization
through into the Scheduler.
- Use the PageserverUtilization::score() instead of shard count when
ordering nodes in scheduling.
Q: Why did test_sharding_split_smoke need updating in this PR?
A: There's an interesting side effect during shard splits: because we do
not decrement the shard count in the utilization when we de-schedule the
shards from before the split, the controller will now prefer to pick
_different_ nodes for shards compared with which ones held secondaries
before the split. We could use our knowledge of splitting to fix up the
utilizations more actively in this situation, but I'm leaning toward
leaving the code simpler, as in practical systems the impact of one
shard on the utilization of a node should be fairly low (single digit
%).
In case of corrupted delta layers, we can detect the corruption and bail
out the compaction.
## Summary of changes
* Detect wrong delta desc of key range
* Detect unordered deltas
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We have been naughty and curl-ed storcon to fix-up drains and fills.
## Summary of changes
Add support for starting/cancelling drain/fill operations via
`storcon_cli`.
close https://github.com/neondatabase/neon/issues/8579
## Summary of changes
The `is_l0` check now takes both layer key range and the layer type.
This allows us to have image layers covering the full key range in
btm-most compaction (upcoming PR). However, we still don't allow delta
layers to cover the full key range, and I will make btm-most compaction
to generate delta layers with the key range of the keys existing in the
layer instead of `Key::MIN..Key::HACK_MAX` (upcoming PR).
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Failed / flaky tests for different arches don't have any difference in
GitHub Autocomment
## Summary of changes
- Add arch to build type for GitHub autocomment
Our new policy is to use the "rebase" method and slice all the Neon
commits into a nice patch set when doing a new major version, and use
"merge" method on minor version upgrades on the release branches.
"git merge" preserves the git history of Neon commits on the Postgres
branches. While it's nice to rebase all the Neon changes to a logical
patch set against upstream, having to do it between every minor release
is a fair amount work, and it loses the history, and is more
error-prone.
part of https://github.com/neondatabase/neon/issues/8623
We want to discover potential aux v1 customers that we might have missed
from the migrations.
## Summary of changes
Log warnings on basebackup, load timeline, and the first put_file.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
See https://neondb.slack.com/archives/C07J14D8GTX/p1724347552023709
Manipulations with LRU list in relation size cache are performed under
shared lock
## Summary of changes
Take exclusive lock
## Checklist before requesting a review
- [ ] I have performed a self-review of my code.
- [ ] If it is a core feature, I have added thorough tests.
- [ ] Do we need to implement analytics? if so did you add the relevant
metrics to the dashboard?
- [ ] If this PR requires public announcement, mark it with
/release-notes label and add several sentences in this section.
## Checklist before merging
- [ ] Do not forget to reformat commit message to not include the above
checklist
Co-authored-by: Konstantin Knizhnik <knizhnik@neon.tech>
This reverts commit cbe8c77997.
This change was originally made to test a hypothesis, but after that,
the proper fix#8669 was merged, so now it's not needed. Moreover, the
test is still flaky, so probably this bug was not a reason of the
flakiness.
Related to #8097
This copies a piece of code from `test_scrubber_physical_gc_ancestors`
to fix a source of flakiness: later on we rely on stuff being older than
a second, but the test can run faster under optimal conditions (as
happened to me locally, but also obvservable in
[this](https://neon-github-public-dev.s3.amazonaws.com/reports/main/10470762360/index.html#testresult/f713b02657db4b4c/retries)
allure report):
```
test_runner/regress/test_storage_scrubber.py:169: in test_scrubber_physical_gc
assert gc_summary["remote_storage_errors"] == 0
E assert 1 == 0
```
## Problem/Solution
TimelineWriter::put_batch is simply a loop over individual puts. Each
put acquires and releases locks, and checks for potentially starting a
new layer. Batching these is more efficient, but more importantly
unlocks future changes where we can pre-build serialized buffers much
earlier in the ingest process, potentially even on the safekeeper
(imagine a future model where some variant of DatadirModification lives
on the safekeeper).
Ensuring that the values in put_batch are written to one layer also
enables a simplification upstream, where we no longer need to write
values in LSN-order. This saves us a sort, but also simplifies follow-on
refactors to DatadirModification: we can store metadata keys and data
keys separately at that level without needing to zip them together in
LSN order later.
## Why?
In this PR, these changes are simplify optimizations, but they are
motivated by evolving the ingest path in the direction of disentangling
extracting DatadirModification from Timeline. It may not obvious how
right now, but the general idea is that we'll end up with three phases
of ingest:
- A) Decode walrecords and build a datadirmodification with all the
simple data contents already in a big serialized buffer ready to write
to an ephemeral layer **<-- this part can be pipelined and parallelized,
and done on a safekeeper!**
- B) Let that datadirmodification see a Timeline, so that it can also
generate all the metadata updates that require a read-modify-write of
existing pages
- C) Dump the results of B into an ephemeral layer.
Related: https://github.com/neondatabase/neon/issues/8452
## Caveats
Doing a big monolithic buffer of values to write to disk is ordinarily
an anti-pattern: we prefer nice streaming I/O. However:
- In future, when we do this first decode stage on the safekeeper, it
would be inefficient to serialize a Vec of Value, and then later
deserialize it just to add blob size headers while writing into the
ephemeral layer format. The idea is that for bulk write data, we will
serialize exactly once.
- The monolithic buffer is a stepping stone to pipelining more of this:
by seriailizing earlier (rather than at the final put_value), we will be
able to parallelize the wal decoding and bulk serialization of data page
writes.
- The ephemeral layer's buffered writer already stalls writes while it
waits to flush: so while yes we'll stall for a couple milliseconds to
write a couple megabytes, we already have stalls like this, just
distributed across smaller writes.
## Benchmarks
This PR is primarily a stepping stone to safekeeper ingest filtering,
but also provides a modest efficiency improvement to the `wal_recovery`
part of `test_bulk_ingest`.
test_bulk_ingest:
```
test_bulk_insert[neon-release-pg16].insert: 23.659 s
test_bulk_insert[neon-release-pg16].pageserver_writes: 5,428 MB
test_bulk_insert[neon-release-pg16].peak_mem: 626 MB
test_bulk_insert[neon-release-pg16].size: 0 MB
test_bulk_insert[neon-release-pg16].data_uploaded: 1,922 MB
test_bulk_insert[neon-release-pg16].num_files_uploaded: 8
test_bulk_insert[neon-release-pg16].wal_written: 1,382 MB
test_bulk_insert[neon-release-pg16].wal_recovery: 18.981 s
test_bulk_insert[neon-release-pg16].compaction: 0.055 s
vs. tip of main:
test_bulk_insert[neon-release-pg16].insert: 24.001 s
test_bulk_insert[neon-release-pg16].pageserver_writes: 5,428 MB
test_bulk_insert[neon-release-pg16].peak_mem: 604 MB
test_bulk_insert[neon-release-pg16].size: 0 MB
test_bulk_insert[neon-release-pg16].data_uploaded: 1,922 MB
test_bulk_insert[neon-release-pg16].num_files_uploaded: 8
test_bulk_insert[neon-release-pg16].wal_written: 1,382 MB
test_bulk_insert[neon-release-pg16].wal_recovery: 23.586 s
test_bulk_insert[neon-release-pg16].compaction: 0.054 s
```
close https://github.com/neondatabase/neon/issues/8558
* Directly generate image layers for sparse keyspaces during initdb
optimization.
* Support force image layer generation for sparse keyspaces.
* Fix a bug of incorrect image layer key range in case of duplicated
keys. (The added line: `start = img_range.end;`) This can cause
overlapping image layers and keys to disappear.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
After #8655, we needed to mark some tests to shut down immediately. To
aid these tests, try the new pattern of `flush_ep_to_pageserver`
followed by a non-compacting checkpoint. This moves the general graceful
shutdown problem of having too much to flush at shutdown into the test.
Also, add logging for how long the graceful shutdown took, if we got to
complete it for faster log eyeballing.
Fixes: #8712
Cc: #8715, #8708
Going through the list of recent flaky tests, trying to fix those
related to graceful shutdown.
- test_forward_compatibility: flush and wait for uploads to avoid
graceful shutdown
- test_layer_bloating: in the end the endpoint and vanilla are still up
=> immediate shutdown
- test_lagging_sk: pageserver shutdown is not related to the test =>
immediate shutdown
- test_lsn_lease_size: pageserver flushing is not needed => immediate
shutdown
Additionally:
- remove `wait_for_upload` usage from workload fixture
Cc: #8708Fixes: #8710
This removes workspace hack from all libs, not from any binaries. This
does not change the behaviour of the hack.
Running
```
cargo clean
cargo build --release --bin proxy
```
Before this change took 5m16s. After this change took 3m3s. This is
because this allows the build to be parallelisable much more.
## Problem
We want to run our regression test suite on ARM.
## Summary of changes
- run regression tests on release ARM builds
- run `build-neon` (including rust tests) on debug ARM builds
- add `arch` parameter to test to distinguish them in the allure report
and in a database
## Problem
Compaction jobs and other background loops are concurrency-limited
through a global semaphore.
The current counters allow quantifying how _many_ tasks are waiting.
But there is no way to tell how _much_ delay is added by the semaphore.
So, add a counter that aggregates the wall clock time seconds spent
acquiring the semaphore.
The metrics can be used as follows:
* retroactively calculate average acquisition time in a given time range
* compare the degree of background loop backlog among pageservers
The metric is insufficient to calculate
* run-up of ongoing acquisitions that haven't finished acquiring yet
* Not easily feasible because ["Cancelling a call to acquire makes you
lose your place in the
queue"](https://docs.rs/tokio/latest/tokio/sync/struct.Semaphore.html#method.acquire)
## Summary of changes
* Refactor the metrics to follow the current best practice for typed
metrics in `metrics.rs`.
* Add the new counter.
## Problem
We don't have a convenient way for a human to ask "how far are secondary
downloads along for this tenant".
This is useful when driving migrations of tenants to the storage
controller, as we first create a secondary location and want to see it
warm up before we cut over. That can already be done via storcon_cli,
but we would like a way that doesn't require direct API access.
## Summary of changes
Add a metric that reports to total size of layers in the heatmap: this
may be used in conjunction with the existing
`pageserver_secondary_resident_physical_size` to estimate "warmth" of
the secondary location.
## Problem
Storage controllers did not have the right token to speak to their peers
for leadership transitions.
## Summary of changes
Accept a peer jwt token for the storage controller.
Epic: https://github.com/neondatabase/cloud/issues/14701
## Problem
#8736 is getting too big. splitting off some simple changes here
## Summary of changes
Local proxy wont always be using tls, so make it optional. Local proxy
wont be using ws for now, so make it optional. Remove a dead config var.
## Problem
Previously, we would run db migrations before doing the step-down
sequence. This meant that the current leader would have to deal with
the schema changes and that's generally not safe.
## Summary of changes
Push the step-down procedure earlier in start-up and
do db migrations right after it (but before we load-up the in-memory
state from the db).
Epic: https://github.com/neondatabase/cloud/issues/14701
