## 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
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>
## Problem
It turns out that `WalStreamDecoder::poll_decode` returns the start LSN
of the next record and not the end LSN of the current record. They are
not always equal. For example, they're not equal when the record in
question is an XLOG SWITCH record.
## Summary of changes
Rename things to reflect that.
PR #9308 has modified tenant activation code to take offloaded child
timelines into account for populating the list of `retain_lsn` values.
However, there is more places than just tenant activation where one
needs to update the `retain_lsn`s.
This PR fixes some bugs of the current code that could lead to
corruption in the worst case:
1. Deleting of an offloaded timeline would not get its `retain_lsn`
purged from its parent. With the patch we now do it, but as the parent
can be offloaded as well, the situatoin is a bit trickier than for
non-offloaded timelines which can just keep a pointer to their parent.
Here we can't keep a pointer because the parent might get offloaded,
then unoffloaded again, creating a dangling pointer situation. Keeping a
pointer to the *tenant* is not good either, because we might drop the
offloaded timeline in a context where a `offloaded_timelines` lock is
already held: so we don't want to acquire a lock in the drop code of
OffloadedTimeline.
2. Unoffloading a timeline would not get its `retain_lsn` values
populated, leading to it maybe garbage collecting values that its
children might need. We now call `initialize_gc_info` on the parent.
3. Offloading of a timeline would not get its `retain_lsn` values
registered as offloaded at the parent. So if we drop the `Timeline`
object, and its registration is removed, the parent would not have any
of the child's `retain_lsn`s around. Also, before, the `Timeline` object
would delete anything related to its timeline ID, now it only deletes
`retain_lsn`s that have `MaybeOffloaded::No` set.
Incorporates Chi's reproducer from #9753. cc
https://github.com/neondatabase/cloud/issues/20199
The `test_timeline_retain_lsn` test is extended:
1. it gains a new dimension, duplicating each mode, to either have the
"main" branch be the direct parent of the timeline we archive, or the
"test_archived_parent" branch intermediary, creating a three timeline
structure. This doesn't test anything fixed by this PR in particular,
just explores the vast space of possible configurations a little bit
more.
2. it gains two new modes, `offload-parent`, which tests the second
point, and `offload-no-restart` which tests the third point.
It's easy to verify the test actually is "sharp" by removing one of the
respective `self.initialize_gc_info()`, `gc_info.insert_child()` or
`ancestor_children.push()`.
Part of #8088
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Alex Chi Z <chi@neon.tech>
The final patch for partial compaction, part of
https://github.com/neondatabase/neon/issues/9114, close
https://github.com/neondatabase/neon/issues/8921 (note that we didn't
implement parallel compaction or compaction scheduler for partial
compaction -- currently this needs to be scheduled by using a Python
script to split the keyspace, and in the future, automatically split
based on the key partitioning when the pageserver wants to trigger a
gc-compaction)
## Summary of changes
* Update the layer selection algorithm to use the same selection as full
compaction (everything intersect/below gc horizon)
* Update the layer selection algorithm to also generate a list of delta
layers that need to be rewritten
* Add the logic to rewrite delta layers and add them back to the layer
map
* Update test case to do partial compaction on deltas
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
It is possible at the point we shutdown the timeline, there are
still layer files we did not upload.
## Summary of changes
* If the queue is not empty, avoid offloading.
* Shutdown the timeline gracefully using the flush mode to
ensure all local files are uploaded before deleting the timeline
directory.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
We need to use the shard associated with the layer file, not the shard
associated with our current tenant shard ID.
Due to shard splits, the shard IDs can refer to older files.
close https://github.com/neondatabase/neon/issues/9667
## Problem
https://github.com/neondatabase/neon/pull/9524 split the decoding and
interpretation step from ingestion.
The output of the first phase is a `wal_decoder::models::InterpretedWalRecord`.
Before this patch set that struct contained a list of `Value` instances.
We wish to lift the decoding and interpretation step to the safekeeper,
but it would be nice if the safekeeper gave us a batch containing the raw data instead of actual values.
## Summary of changes
Main goal here is to make `InterpretedWalRecord` hold a raw buffer which
contains pre-serialized Values.
For this we do:
1. Add a `SerializedValueBatch` type. This is `inmemory_layer::SerializedBatch` with some
extra functionality for extension, observing values for shard 0 and tests.
2. Replace `inmemory_layer::SerializedBatch` with `SerializedValueBatch`
3. Make `DatadirModification` maintain a `SerializedValueBatch`.
### `DatadirModification` changes
`DatadirModification` now maintains a `SerializedValueBatch` and extends
it as new WAL records come in (to avoid flushing to disk on every
record).
In turn, this cascaded into a number of modifications to
`DatadirModification`:
1. Replace `pending_data_pages` and `pending_zero_data_pages` with `pending_data_batch`.
2. Removal of `pending_zero_data_pages` and its cousin `on_wal_record_end`
3. Rename `pending_bytes` to `pending_metadata_bytes` since this is what it tracks now.
4. Adapting of various utility methods like `len`, `approx_pending_bytes` and `has_dirty_data_pages`.
Removal of `pending_zero_data_pages` and the optimisation associated
with it ((1) and (2)) deserves more detail.
Previously all zero data pages went through `pending_zero_data_pages`.
We wrote zero data pages when filling gaps caused by relation extension
(case A) and when handling special wal records (case B). If it happened
that the same WAL record contained a non zero write for an entry in
`pending_zero_data_pages` we skipped the zero write.
Case A: We handle this differently now. When ingesting the
`SerialiezdValueBatch` associated with one PG WAL record, we identify the gaps and fill the
them in one go. Essentially, we move from a per key process (gaps were filled after each
new key), and replace it with a per record process. Hence, the optimisation is not
required anymore.
Case B: When the handling of a special record needs to zero out a key,
it just adds that to the current batch. I inspected the code, and I
don't think the optimisation kicked in here.
## Problem
In https://github.com/neondatabase/neon/pull/9589, timeline offload code
is modified to return an explicit error type rather than propagating
anyhow::Error. One of the 'Other' cases there is I/O errors from local
timeline deletion, which shouldn't need to exist, because our policy is
not to try and continue running if the local disk gives us errors.
## Summary of changes
- Make `delete_local_timeline_directory` and use `.fatal_err(` on I/O
errors
---------
Co-authored-by: Erik Grinaker <erik@neon.tech>
## Problem
The final part of https://github.com/neondatabase/neon/issues/9543 will
be a chaos test that creates/deletes/archives/offloads timelines while
restarting pageservers and migrating tenants. Developing that test
showed up a few places where we log errors during normal shutdown.
## Summary of changes
- UninitializedTimeline's drop should log at info severity: this is a
normal code path when some part of timeline creation encounters a
cancellation `?` path.
- When offloading and finding a `RemoteTimelineClient` in a
non-initialized state, this is not an error and should not be logged as
such.
- The `offload_timeline` function returned an anyhow error, so callers
couldn't gracefully pick out cancellation errors from real errors:
update this to have a structured error type and use it throughout.
## Problem
Decoding and ingestion are still coupled in `pageserver::WalIngest`.
## Summary of changes
A new type is added to `wal_decoder::models`, InterpretedWalRecord. This
type contains everything that the pageserver requires in order to ingest
a WAL record. The highlights are the `metadata_record` which is an
optional special record type to be handled and `blocks` which stores
key, value pairs to be persisted to storage.
This type is produced by
`wal_decoder::models::InterpretedWalRecord::from_bytes` from a raw PG
wal record.
The rest of this commit separates decoding and interpretation of the PG
WAL record from its application in `WalIngest::ingest_record`.
Related: https://github.com/neondatabase/neon/issues/9335
Epic: https://github.com/neondatabase/neon/issues/9329
If we delete a timeline that has childen, those children will have their
data corrupted. Therefore, extend the already existing safety check to
offloaded timelines as well.
Part of #8088
Disallow a request for timeline ancestor detach if either the to be
detached timeline, or any of the to be reparented timelines are
offloaded or archived.
In theory we could support timelines that are archived but not
offloaded, but archived timelines are at the risk of being offloaded, so
we treat them like offloaded timelines. As for offloaded timelines, any
code to "support" them would amount to unoffloading them, at which point
we can just demand to have the timelines be unarchived.
Part of #8088
## Problem
Uploads of the tenant manifest could race between different tasks,
resulting in unexpected results in remote storage.
Closes: https://github.com/neondatabase/neon/issues/9556
## Summary of changes
- Create a central function for uploads that takes a tokio::sync::Mutex
- Store the latest upload in that Mutex, so that when there is lots of
concurrency (e.g. archive 20 timelines at once) we can coalesce their
manifest writes somewhat.
As pointed out in
https://github.com/neondatabase/neon/pull/9489#discussion_r1814699683 ,
we currently didn't support deletion for offloaded timelines after the
timeline has been loaded from the manifest instead of having been
offloaded.
This was because the upload queue hasn't been initialized yet. This PR
thus initializes the timeline and shuts it down immediately.
Part of #8088
## Problem
We wish to have high level WAL decoding logic in `wal_decoder::decoder`
module.
## Summary of Changes
For this we need the `Value` and `NeonWalRecord` types accessible there, so:
1. Move `Value` and `NeonWalRecord` to `pageserver::value` and
`pageserver::record` respectively.
2. Get rid of `pageserver::repository` (follow up from (1))
3. Move PG specific WAL record types to `postgres_ffi::walrecord`. In
theory they could live in `wal_decoder`, but it would create a circular
dependency between `wal_decoder` and `postgres_ffi`. Long term it makes
sense for those types to be PG version specific, so that will work out nicely.
4. Move higher level WAL record types (to be ingested by pageserver)
into `wal_decoder::models`
Related: https://github.com/neondatabase/neon/issues/9335
Epic: https://github.com/neondatabase/neon/issues/9329
This PR does two things:
1. Obtain a `TimelineCreateGuard` object in `unoffload_timeline`. This
prevents two unoffload tasks from racing with each other. While they
already obtain locks for `timelines` and `offloaded_timelines`, they
aren't sufficient, as we have already constructed an entire timeline at
that point. We shouldn't ever have two `Timeline` objects in the same
process at the same time.
2. don't allow timeline creations for timelines that have been
offloaded. Obviously they already exist, so we should not allow
creation. the previous logic only looked at the timelines list.
Part of #8088
# Context
In the PGDATA import code
(https://github.com/neondatabase/neon/pull/9218) I add a third way to
create timelines, namely, by importing from a copy of a vanilla PGDATA
directory in object storage.
For idempotency, I'm using the PGDATA object storage location
specification, which is stored in the IndexPart for the entire lifespan
of the timeline. When loading the timeline from remote storage, that
value gets stored inside `struct Timeline` and timeline creation
compares the creation argument with that value to determine idempotency
of the request.
# Changes
This PR refactors the existing idempotency handling of Timeline
bootstrap and branching such that we simply compare the
`CreateTimelineIdempotency` struct, using the derive-generated
`PartialEq` implementation.
Also, by spelling idempotency out in the type names, I find it adds a
lot of clarity.
The pathway to idempotency via requester-provided idempotency key also
becomes very straight-forward, if we ever want to do this in the future.
# Refs
* platform context: https://github.com/neondatabase/neon/pull/9218
* product context: https://github.com/neondatabase/cloud/issues/17507
* stacks on top of https://github.com/neondatabase/neon/pull/9366
This PR adds a pageserver mgmt API to scan a layer file for disposable
keys.
It hooks it up to the sharding compaction test, demonstrating that we're
not filtering out all disposable keys.
This is extracted from PGDATA import
(https://github.com/neondatabase/neon/pull/9218)
where I do the filtering of layer files based on `is_key_disposable`.
part of https://github.com/neondatabase/neon/issues/9114,
https://github.com/neondatabase/neon/issues/8836,
https://github.com/neondatabase/neon/issues/8362
The split layer writer code can be used in a more general way: the
caller puts unfinished writers into the batch layer writer and let batch
layer writer to ensure the atomicity of the layer produces.
## Summary of changes
* Add batch layer writer, which atomically finishes the layers.
`BatchLayerWriter::finish` is simply a copy-paste from previous split
layer writers.
* Refactor split writers to use the batch layer writer.
* The current split writer tests cover all code path of batch layer
writer.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
similar to https://github.com/neondatabase/neon/pull/8841, we make the
delta layer writer atomic when finishing the layers.
## Summary of changes
* `put_value` not taking discard fn anymore
* `finish` decides what layers to keep
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Persist timeline offloaded state to S3.
Right now, as of #8907, at each restart of the pageserver, all offloaded
state is lost, so we load the full timeline again. As it starts with an
empty local directory, we might potentially download some files again,
leading to downloads that are ultimately wasteful.
This patch adds support for persisting the offloaded state, allowing us
to never load offloaded timelines in the first place. The persistence
feature is facilitated via a new file in S3 that is tenant-global, which
contains a list of all offloaded timelines. It is updated each time we
offload or unoffload a timeline, and otherwise never touched.
This choice means that tenants where no offloading is happening will not
immediately get a manifest, keeping the change very minimal at the
start.
We leave generation support for future work. It is important to support
generations, as in the worst case, the manifest might be overwritten by
an older generation after a timeline has been unoffloaded (and
unarchived), so the next pageserver process instantiation might wrongly
believe that some timeline is still offloaded even though it should be
active.
Part of #9386, #8088
Part of https://github.com/neondatabase/neon/issues/8836
## Summary of changes
This pull request makes the image layer split writer atomic when
finishing the layers. All the produced layers either finish at the same
time, or discard at the same time. Note that this does not prevent
atomicity when crash, but anyways, it will be cleaned up on pageserver
restart.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
Part of the aux v1 retirement
https://github.com/neondatabase/neon/issues/8623
## Summary of changes
Remove write/read path for aux v1, but keeping the config item and the
index part field for now.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
part of https://github.com/neondatabase/neon/issues/9114
## Summary of changes
gc-compaction may take a lot of disk space, and if it does, the caller
should do a partial gc-compaction. This patch adds space check for the
compaction job.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Add a test for timeline offloading, and subsequent unoffloading.
Also adds a manual endpoint, and issues a proper timeline shutdown
during offloading which prevents a pageserver hang at shutdown.
Part of #8088.
Also consider offloaded timelines for obtaining `retain_lsn`. This is
required for correctness for all timelines that have not been flattened
yet: otherwise we GC data that might still be required for reading.
This somewhat counteracts the original purpose of timeline offloading of
not having to iterate over offloaded timelines, but sadly it's required.
In the future, we can improve the way the offloaded timelines are
stored.
We also make the `retain_lsn` optional so that in the future, when we
implement flattening, we can make it None. This also applies to full
timeline objects by the way, where it would probably make most sense to
add a bool flag whether the timeline is successfully flattened, and if
it is, one can exclude it from `retain_lsn` as well.
Also, track whether a timeline was offloaded or not in `retain_lsn` so
that the `retain_lsn` can be excluded from visibility and size
calculation.
Part of #8088
Implements an initial mechanism for offloading of archived timelines.
Offloading is implemented as specified in the RFC.
For now, there is no persistence, so a restart of the pageserver will
retrigger downloads until the timeline is offloaded again.
We trigger offloading in the compaction loop because we need the signal
for whether compaction is done and everything has been uploaded or not.
Part of #8088
close https://github.com/neondatabase/neon/issues/9160
For whatever reason, pg17's WAL pattern seems different from others,
which triggers some flaky behavior within the compaction smoke test.
## Summary of changes
* Run L0 compaction before proceeding with the read benchmark.
* So that we can ensure the num of L0 layers is 0 and test the
compaction behavior only with L1 layers.
We have a threshold for triggering L0 compaction. In some cases, the
test case did not produce enough L0 layers to do a L0 compaction,
therefore leaving the layer map with 3+ L0 layers above the L1 layers.
This increases the average read depth for the timeline.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
In the `imitate_synthetic_size_calculation_worker` function, we might
obtain the `Cancelled` error variant instead of hitting the cancellation
token based path. Therefore, catch `Cancelled` and handle it analogously
to the cancellation case.
Fixes#8886.
Part of https://github.com/neondatabase/neon/issues/8002
Close https://github.com/neondatabase/neon/issues/8920
Legacy compaction (as well as gc-compaction) rely on the GC process to
remove unused layer files, but this relies on many factors (i.e., key
partition) to ensure data in a dropped table can be eventually removed.
In gc-compaction, we consider the keyspace information when doing the
compaction process. If a key is not in the keyspace, we will skip that
key and not include it in the final output.
However, this is not easy to implement because gc-compaction considers
branch points (i.e., retain_lsns) and the retained keyspaces could
change across different LSNs. Therefore, for now, we only remove aux v1
keys in the compaction process.
## Summary of changes
* Add `FilterIterator` to filter out keys.
* Integrate `FilterIterator` with gc-compaction.
* Add `collect_gc_compaction_keyspace` for a spec of keyspaces that can
be retained during the gc-compaction process.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
We have 3 places where we implement layer map checks.
## Summary of changes
Now we have a single check function being called in all places.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
There's some more code that still checks for uninit and delete
markers, see callers of is_delete_mark and is_uninit_mark, and github
issue #5718. But these functions were outright dead.
Commit ca5390a89d made a similar change to DeltaLayerWriter.
We bumped into this with Stas with our hackathon project, to create a
standalong program to create image layers directly from a Postgres data
directory. It needs to create image layers without having a Timeline and
other pageserver machinery.
This downgrades the "created image layer {}" message from INFO to TRACE
level. TRACE is used for the corresponding message on delta layer
creation too. The path logged in the message is now the temporary path,
before the file is renamed to its final name. Again commit ca5390a89d
made the same change for the message on delta layer creation.
close https://github.com/neondatabase/neon/issues/8838
## Summary of changes
This patch modifies the split delta layer writer to avoid taking
start_key and end_key when creating/finishing the layer writer. The
start_key for the delta layers will be the first key provided to the
layer writer, and the end_key would be the `last_key.next()`. This
simplifies the delta layer writer API.
On that, the layer key hack is removed. Image layers now use the full
key range, and delta layers use the first/last key provided by the user.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
This PR simplifies the pageserver configuration parsing as follows:
* introduce the `pageserver_api::config::ConfigToml` type
* implement `Default` for `ConfigToml`
* use serde derive to do the brain-dead leg-work of processing the toml
document
* use `serde(default)` to fill in default values
* in `pageserver` crate:
* use `toml_edit` to deserialize the pageserver.toml string into a
`ConfigToml`
* `PageServerConfig::parse_and_validate` then
* consumes the `ConfigToml`
* destructures it exhaustively into its constituent fields
* constructs the `PageServerConfig`
The rules are:
* in `ConfigToml`, use `deny_unknown_fields` everywhere
* static default values go in `pageserver_api`
* if there cannot be a static default value (e.g. which default IO
engine to use, because it depends on the runtime), make the field in
`ConfigToml` an `Option`
* if runtime-augmentation of a value is needed, do that in
`parse_and_validate`
* a good example is `virtual_file_io_engine` or `l0_flush`, both of
which need to execute code to determine the effective value in
`PageServerConf`
The benefits:
* massive amount of brain-dead repetitive code can be deleted
* "unused variable" compile-time errors when removing a config value,
due to the exhaustive destructuring in `parse_and_validate`
* compile-time errors guide you when adding a new config field
Drawbacks:
* serde derive is sometimes a bit too magical
* `deny_unknown_fields` is easy to miss
Future Work / Benefits:
* make `neon_local` use `pageserver_api` to construct `ConfigToml` and
write it to `pageserver.toml`
* This provides more type safety / coompile-time errors than the current
approach.
### Refs
Fixes#3682
### Future Work
* `remote_storage` deser doesn't reject unknown fields
https://github.com/neondatabase/neon/issues/8915
* clean up `libs/pageserver_api/src/config.rs` further
* break up into multiple files, at least for tenant config
* move `models` as appropriate / refine distinction between config and
API models / be explicit about when it's the same
* use `pub(crate)` visibility on `mod defaults` to detect stale values
## Problem
Currently, DatadirModification keeps a key-indexed map of all pending
writes, even though we (almost) never need to read back dirty pages for
anything other than metadata pages (e.g. relation sizes).
Related: https://github.com/neondatabase/neon/issues/6345
## Summary of changes
- commit() modifications before ingesting database creation wal records,
so that they are guaranteed to be able to get() everything they need
directly from the underlying Timeline.
- Split dirty pages in DatadirModification into pending_metadata_pages
and pending_data_pages. The data ones don't need to be in a
key-addressable format, so they just go in a Vec instead.
- Special case handling of zero-page writes in DatadirModification,
putting them in a map which is flushed on the end of a WAL record. This
handles the case where during ingest, we might first write a zero page,
and then ingest a postgres write to that page. We used to do this via
the key-indexed map of writes, but in this PR we change the data page
write path to not bother indexing these by key.
My least favorite thing about this PR is that I needed to change the
DatadirModification interface to add the on_record_end call. This is not
very invasive because there's really only one place we use it, but it
changes the object's behaviour from being clearly an aggregation of many
records to having some per-record state. I could avoid this by
implicitly doing the work when someone calls set_lsn or commit -- I'm
open to opinions on whether that's cleaner or dirtier.
## Performance
There may be some efficiency improvement here, but the primary
motivation is to enable an earlier stage of ingest to operate without
access to a Timeline. The `pending_data_pages` part is the "fast path"
bulk write data that can in principle be generated without a Timeline,
in parallel with other ingest batches, and ultimately on the safekeeper.
`test_bulk_insert` on AX102 shows approximately the same results as in
the previous PR #8591:
```
------------------------------ Benchmark results -------------------------------
test_bulk_insert[neon-release-pg16].insert: 23.577 s
test_bulk_insert[neon-release-pg16].pageserver_writes: 5,428 MB
test_bulk_insert[neon-release-pg16].peak_mem: 637 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: 18.264 s
test_bulk_insert[neon-release-pg16].compaction: 0.052 s
```
Part of #8002, the final big PR in the batch.
## Summary of changes
This pull request uses the new split layer writer in the gc-compaction.
* It changes how layers are split. Previously, we split layers based on
the original split point, but this creates too many layers
(test_gc_feedback has one key per layer).
* Therefore, we first verify if the layer map can be processed by the
current algorithm (See https://github.com/neondatabase/neon/pull/8191,
it's basically the same check)
* On that, we proceed with the compaction. This way, it creates a large
enough layer close to the target layer size.
* Added a new set of functions `with_discard` in the split layer writer.
This helps us skip layers if we are going to produce the same persistent
key.
* The delta writer will keep the updates of the same key in a single
file. This might create a super large layer, but we can optimize it
later.
* The split layer writer is used in the gc-compaction algorithm, and it
will split layers based on size.
* Fix the image layer summary block encoded the wrong key range.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Co-authored-by: Arpad Müller <arpad-m@users.noreply.github.com>
Co-authored-by: Christian Schwarz <christian@neon.tech>
## 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
```
Some benchmarks and tests might still fail because of #8655 (tracked in
#8708) because we are not fast enough to shut down ([one evidence]).
Partially this is explained by the current validation mode of streaming
k-merge, but otherwise because that is where we use a lot of time in
compaction. Outside of L0 => L1 compaction, the image layer generation
is already guarded by vectored reads doing cancellation checks.
32768 is a wild guess based on looking how many keys we put in each
layer in a bench (1-2 million), but I assume it will be good enough
divisor. Doing checks more often will start showing up as contention
which we cannot currently measure. Doing checks less often might be
reasonable.
[one evidence]:
https://neon-github-public-dev.s3.amazonaws.com/reports/main/10384136483/index.html#suites/9681106e61a1222669b9d22ab136d07b/96e6d53af234924/
Earlier PR: #8706.
We can get CompactionError::Other(Cancelled) via the error handling with
a few ways.
[evidence](https://neon-github-public-dev.s3.amazonaws.com/reports/pr-8655/10301613380/index.html#suites/cae012a1e6acdd9fdd8b81541972b6ce/653a33de17802bb1/).
Hopefully fix it by:
1. replace the `map_err` which hid the
`GetReadyAncestorError::Cancelled` with `From<GetReadyAncestorError> for
GetVectoredError` conversion
2. simplifying the code in pgdatadir_mapping to eliminate the token
anyhow wrapping for deserialization errors
3. stop wrapping GetVectoredError as anyhow errors
4. stop wrapping PageReconstructError as anyhow errors
Additionally, produce warnings if we treat any other error (as was legal
before this PR) as missing key.
Cc: #8708.
## Problem
When pageservers do compaction, they frequently create image layers that
make earlier layers un-needed for reads, but then keep those earlier
layers around for 24 hours waiting for time-based eviction to expire
them.
Now that we track layer visibility, we can use it as an input to
eviction, and avoid the 24 hour "disk bump" that happens around
pageserver restarts.
## Summary of changes
- During time-based eviction, if a layer is marked Covered, use the
eviction period as the threshold: i.e. these layers get to remain
resident for at least one iteration of the eviction loop, but then get
evicted. With current settings this means they get evicted after 1h
instead of 24h.
- During disk usage eviction, prioritized evicting covered layers above
all other layers.
Caveats:
- Using the period as the threshold for time based eviction in this case
is a bit of a hack, but it avoids adding yet another configuration
property, and in any case the value of a new property would be somewhat
arbitrary: there's no "right" length of time to keep covered layers
around just in case.
- We had previously planned on removing time-based eviction: this change
would motivate us to keep it around, but we can still simplify the code
later to just do the eviction of covered layers, rather than applying a
TTL policy to all layers.
