## Problem
We saw a scale test failure when one shard went
secondary->attached->secondary in a short period of time -- the metrics
for the shard failed a validation assertion that is meant to ensure the
size metric matches the sum of layer sizes in the SecondaryDetail
struct.
This appears to be due to two SecondaryTenants being alive at the same
time -- the first one was shut down but still had its contributions to
the metrics.
Closes: https://github.com/neondatabase/neon/issues/9628
## Summary of changes
- Refactor code for validating metrics and call it in shutdown as well
as during downloads
- Move code for dropping per-tenant secondary metrics from drop() into
shutdown(), so that once shutdown() completes it is definitely safe to
instantiate another SecondaryTenant for the same tenant.
## Problem
We were hitting this assertion in debug mode tests sometimes.
This case was being hit when the parent shard has no resident layers.
For instance, this is the case on split retry where the previous attempt
shut-down the parent and deleted local state for it. If the logical size
calculation does not download some layers before we get to the
hardlinking, then the assertion is hit.
## Summary of Changes
Remove the assertion. It's fine for the ancestor to not have any
resident layers at the time of the split.
Closes https://github.com/neondatabase/neon/issues/9412
## Problem
Long ago, in #5299 the tenant states for migration are added, but
respected only in a coarse-grained way: when hinted not to do deletions,
tenants will just avoid doing all GC or compaction.
Skipping compaction is not necessary for AttachedMulti, as we will soon
become the primary attached location, and it is not a waste of resources
to proceed with compaction. Instead, per the RFC
https://github.com/neondatabase/neon/pull/5029/files), deletions should
be queued up in this state, and executed later when we switch to
AttachedSingle.
Avoiding compaction in AttachedMulti can have an operational impact if a
tenant is under significant write load, as a long-running migration can
result in a large accumulation of delta layers with commensurate impact
on read latency.
Closes: https://github.com/neondatabase/neon/issues/5396
## Summary of changes
- Add a 'config' part to RemoteTimelineClient so that it can be aware of
the mode of the tenant it belongs to, and wire this through for
construction + updates
- Add a special buffer for delayed deletions, and when in AttachedMulti
route deletions here instead of into the main remote client queue. This
is drained when transitioning to AttachedSingle. If the tenant is
detached or our process dies before then, then these objects are leaked.
- As a quality of life improvement, also use the remote timeline
client's knowledge of the tenant state to avoid submitting remote
consistent LSN updates for validation when in AttachedStale (as we know
these will fail)
## 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
## Problem
SLRU blocks, which can add up to several gigabytes, are currently
ingested by all shards, multiplying their capacity cost by the shard
count and slowing down ingest. We do this because all shards need the
SLRU pages to do timestamp->LSN lookup for GC.
Related: https://github.com/neondatabase/neon/issues/7512
## Summary of changes
- On non-zero shards, learn the GC offset from shard 0's index instead
of calculating it.
- Add a test `test_sharding_gc` that exercises this
- Do GC in test_pg_regress as a general smoke test that GC functions run
(e.g. this would fail if we were using SLRUs we didn't have)
In this PR we are still ingesting SLRUs everywhere, but not using them
any more. Part 2 PR (https://github.com/neondatabase/neon/pull/9786)
makes the change to not store them at all.
## 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
With this, 10us batching timeout works, but it has some other wrinkles:
- it uses the signal-based timer APIs instead of going through epoll (=> timerfd)
= it needs to make a syscall for each batch, which costs around 1-2us, so, probably significant CPU time wasted on this.
In timeline preloading, we also do a preload for offloaded timelines.
This includes the download of `index-part.json`. Ultimately, such a
download is wasteful, therefore avoid it. Same goes for the remote
client, we just discard it immediately thereafter.
Part of #8088
---------
Co-authored-by: Christian Schwarz <christian@neon.tech>
part of https://github.com/neondatabase/neon/issues/9114, we want to be
able to run partial gc-compaction in tests. In the future, we can also
expand this functionality to legacy compaction, so that we can trigger
compaction for a specific key range.
## Summary of changes
* Support passing compaction key range through pageserver routes.
* Refactor input parameters of compact related function to take the new
`CompactOptions`.
* Add tests for partial compaction. Note that the test may or may not
trigger compaction based on GC horizon. We need to improve the test case
to ensure things always get below the gc_horizon and the gc-compaction
can be triggered.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
close https://github.com/neondatabase/neon/issues/9730
The test case tests if anything goes wrong during pageserver restart +
*during timeline creation not complete*. Therefore, queue is stopped
error is normal in this case, except that it should be categorized as a
shutdown error instead of a real error.
## Summary of changes
* More comments for the test case.
* Queue stopped error will now be forwarded as
CreateTimelineError::ShuttingDown.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
There is a potential data corruption issue, not one I've encountered,
but it's still not hard to hit with some correct looking code given our
current architecture. It has to do with the timeline's memory object storage
via reference counted `Arc`s, and the removal of `retain_lsn` entries at
the drop of the last `Arc` reference.
The corruption steps are as follows:
1. timeline gets offloaded. timeline object A doesn't get dropped
though, because some long-running task accesses it
2. the same timeline gets unoffloaded again. timeline object B gets
created for it, timeline object A still referenced. both point to the
same timeline.
3. the task keeping the reference to timeline object A exits. destructor
for object A runs, removing `retain_lsn` in the timeline's parent.
4. the timeline's parent runs gc without the `retain_lsn` of the still
exant timleine's child, leading to data corruption.
In general we are susceptible each time when we recreate a `Timeline`
object in the same process, which happens both during a timeline
offload/unoffload cycle, as well as during an ancestor detach operation.
The solution this PR implements is to make the destructor for a timeline
as well as an offloaded timeline remove at most one `retain_lsn`.
PR #9760 has added a log line to print the refcounts at timeline
offload, but this only detects one of the places where we do such a
recycle operation. Plus it doesn't prevent the actual issue.
I doubt that this occurs in practice. It is more a defense in depth measure.
Usually I'd assume that the timeline gets dropped immediately in step 1,
as there is no background tasks referencing it after its shutdown.
But one never knows, and reducing the stakes of step 1 actually occurring
is a really good idea, from potential data corruption to waste of CPU time.
Part of #8088
## Problem
We don't take advantage of queue depth generated by the compute
on the pageserver. We can process getpage requests more efficiently
by batching them.
## Summary of changes
Batch up incoming getpage requests that arrive within a configurable
time window (`server_side_batch_timeout`).
Then process the entire batch via one `get_vectored` timeline operation.
By default, no merging takes place.
## Testing
* **Functional**: https://github.com/neondatabase/neon/pull/9792
* **Performance**: will be done in staging/pre-prod
# Refs
* https://github.com/neondatabase/neon/issues/9377
* https://github.com/neondatabase/neon/issues/9376
Co-authored-by: Christian Schwarz <christian@neon.tech>
