Updates storage components to edition 2024. We like to stay on the
latest edition if possible. There is no functional changes, however some
code changes had to be done to accommodate the edition's breaking
changes.
The PR has two commits:
* the first commit updates storage crates to edition 2024 and appeases
`cargo clippy` by changing code. i have accidentially ran the formatter
on some files that had other edits.
* the second commit performs a `cargo fmt`
I would recommend a closer review of the first commit and a less close
review of the second one (as it just runs `cargo fmt`).
part of https://github.com/neondatabase/neon/issues/10918
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
Co-authored-by: Stas Kelvic <stas@neon.tech>
# Context
This PR contains PoC-level changes for a product feature that allows
onboarding large databases into Neon without going through the regular
data path.
# Changes
This internal RFC provides all the context
* https://github.com/neondatabase/cloud/pull/19799
In the language of the RFC, this PR covers
* the Importer code (`fast_import`)
* all the Pageserver changes (mgmt API changes, flow implementation,
etc)
* a basic test for the Pageserver changes
# Reviewing
As acknowledged in the RFC, the code added in this PR is not ready for
general availability.
Also, the **architecture is not to be discussed in this PR**, but in the
RFC and associated Slack channel instead.
Reviewers of this PR should take that into consideration.
The quality bar to apply during review depends on what area of the code
is being reviewed:
* Importer code (`fast_import`): practically anything goes
* Core flow (`flow.rs`):
* Malicious input data must be expected and the existing threat models
apply.
* The code must not be safe to execute on *dedicated* Pageserver
instances:
* This means in particular that tenants *on other* Pageserver instances
must not be affected negatively wrt data confidentiality, integrity or
availability.
* Other code: the usual quality bar
* Pay special attention to correct use of gate guards, timeline
cancellation in all places during shutdown & migration, etc.
* Consider the broader system impact; if you find potentially
problematic interactions with Storage features that were not covered in
the RFC, bring that up during the review.
I recommend submitting three separate reviews, for the three high-level
areas with different quality bars.
# References
(Internal-only)
* refs https://github.com/neondatabase/cloud/issues/17507
* refs https://github.com/neondatabase/company_projects/issues/293
* refs https://github.com/neondatabase/company_projects/issues/309
* refs https://github.com/neondatabase/cloud/issues/20646
---------
Co-authored-by: Stas Kelvich <stas.kelvich@gmail.com>
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
Co-authored-by: John Spray <john@neon.tech>
## Problem
At high percentiles we see more than 800 layers being visited by the
read path. We need the tenant/timeline to investigate.
## Summary of changes
Add a rate limited log line when the average number of layers visited
per key is in the last specified histogram bucket.
I plan to use this to identify tenants in us-east-2 staging that exhibit
this behaviour. Will revert before next week's release.
## Problem
Part of https://github.com/neondatabase/neon/issues/7462
On metadata keyspace, vectored get will not stop if a key is not found,
and will read past the image layer. However, the semantics is different
from single get, because if a key does not exist in the image layer, it
means that the key does not exist in the past, or have been deleted.
This pull request fixed it by recording image layer coverage during the
vectored get process and stop when the full keyspace is covered by an
image layer. A corresponding test case is added to ensure generating
image layer reduces the number of delta layers.
This optimization (or bug fix) also applies to rel block keyspaces. If a
key is missing, we can know it's missing once the first image layer is
reached. Page server will not attempt to read lower layers, which
potentially incurs layer downloads + evictions.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
After a shard split of a large existing tenant, child tenants can end up
with oversized historic layers indefinitely, if those layers are
prevented from being GC'd by branchpoints.
This PR is followed by https://github.com/neondatabase/neon/pull/7531
Related issue: https://github.com/neondatabase/neon/issues/7504
## Summary of changes
- Add a new compaction phase `compact_shard_ancestors`, which identifies
layers that are no longer needed after a shard split.
- Add a Timeline->LayerMap code path called `rewrite_layers` , which is
currently only used to drop layers, but will later be used to rewrite
them as well in https://github.com/neondatabase/neon/pull/7531
- Add a new test that compacts after a split, and checks that something
is deleted.
Note that this doesn't have much impact on a tenant's resident size
(since unused layers would end up evicted anyway), but it:
- Makes index_part.json much smaller
- Makes the system easier to reason about: avoid having tenants which
are like "my physical size is 4TiB but don't worry I'll never actually
download it", instead have tenants report the real physical size of what
they might download.
Why do we remove these layers in compaction rather than during the
split? Because we have existing split tenants that need cleaning up. We
can add it to the split operation in future as an optimization.
extracted (and tested) from
https://github.com/neondatabase/neon/pull/7468, part of
https://github.com/neondatabase/neon/issues/7462.
The current codebase assumes the keyspace is dense -- which means that
if we have a keyspace of 0x00-0x100, we assume every key (e.g., 0x00,
0x01, 0x02, ...) exists in the storage engine. However, the assumption
does not hold any more in metadata keyspace. The metadata keyspace is
sparse. It is impossible to do per-key check.
Ideally, we should not have the assumption of dense keyspace at all, but
this would incur a lot of refactors. Therefore, we split the keyspaces
we have to dense/sparse and handle them differently in the code for now.
At some point in the future, we should assume all keyspaces are sparse.
## Summary of changes
* Split collect_keyspace to return dense+sparse keyspace.
* Do not allow generating image layers for sparse keyspace (for now --
will fix this next week, we need image layers anyways).
* Generate delta layers for sparse keyspace.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
Followup to https://github.com/neondatabase/neon/pull/6776
While #6776 makes compaction safe on sharded tenants, the logic for
keyspace partitioning remains inefficient: it assumes that the size of
data on a pageserver can be calculated simply as the range between start
and end of a Range -- this is not the case in sharded tenants, where
data within a range belongs to a variety of shards.
Closes: https://github.com/neondatabase/neon/issues/6774
## Summary of changes
I experimented with using a sharding-aware range type in KeySpace to
replace all the Range<Key> uses, but the impact on other code was quite
large (many places use the ranges), and not all of them need this
property of being able to approximate the physical size of data within a
key range.
So I compromised on expressing this as a ShardedRange type, but only
using that type selctively: during keyspace repartition, and in tiered
compaction when accumulating key ranges.
- keyspace partitioning methods take sharding parameters as an input
- new `ShardedRange` type wraps a Range<Key> and a shard identity
- ShardedRange::page_count is the shard-aware replacement for
key_range_size
- Callers that don't need to be shard-aware (e.g. vectored get code that
just wants to count the number of keys in a keyspace) can use
ShardedRange::raw_size to get the faster, shard-naive code (same as old
`key_range_size`)
- Compaction code is updated to carry a shard identity so that it can
use shard aware calculations
- Unit tests for the new fragmentation logic.
- Add a test for compaction on sharded tenants, that validates that we
generate appropriately sized image layers (this fails before fixing
keyspace partitioning)
extracted from https://github.com/neondatabase/neon/pull/7468, part of
https://github.com/neondatabase/neon/issues/7462.
In the page server, we use i128 (instead of u128) to do the integer
representation of the key, which indicates that the highest bit of the
key should not be 1. This constraints our keyspace to <= 0x7F.
Also fix the bug of `to_i128` that dropped the highest 4b. Now we keep
3b of them, dropping the sign bit.
And on that, we shrink the metadata keyspace to 0x60-0x7F for now, and
once we add support for u128, we can have a larger metadata keyspace.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
We are currently supporting two read paths. No bueno.
## Summary of changes
High level: use vectored read path to serve get page requests - gated by
`get_impl` config
Low level:
1. Add ps config, `get_impl` to specify which read path to use when
serving get page requests
2. Fix base cached image handling for the vectored read path. This was
subtly broken: previously we
would not mark keys that went past their cached lsn as complete. This is
a self standing change which
could be its own PR, but I've included it here because writing separate
tests for it is tricky.
3. Fork get page to use either the legacy or vectored implementation
4. Validate the use of vectored read path when serving get page requests
against the legacy implementation.
Controlled by `validate_vectored_get` ps config.
5. Use the vectored read path to serve get page requests in tests (with
validation).
## Note
Since the vectored read path does not go through the page cache to read
buffers, this change also amounts to a removal of the buffer page cache. Materialized page cache
is still used.
## Problem
If the previous step of the vectored left no further keyspace to
investigate (i.e. keyspace remains empty after removing keys completed in the previous step),
then we'd still grab the layers lock, potentially add an in-mem layer to the fringe
and at some further point read its index without reading any values from it.
## Summary of changes
If there's nothing left in the current keyspace, then skip the search
and just select the next item from the fringe as usual.
When running `test_pg_regress[release-pg16]` with the vectored read path
for singular gets this improved perf drastically (see PR cover letter).
## Correctness
Since no keys remained from the previous range (i.e. we are on a leaf
node) there's nothing that search can find in deeper nodes.
## Problem
Vectored get would descend into ancestor timelines for aux files.
This is not the behaviour of the legacy read path and blocks cutting
over to the vectored read path.
Fixes https://github.com/neondatabase/neon/issues/7379
## Summary of Changes
Treat non inherited keys specially in vectored get. At the point when
we want to descend into the ancestor mark all pending non inherited keys
as errored out at the key level. Note that this diverges from the
standard vectored get behaviour for missing keys which is a top level
error. This divergence is required to avoid blocking compaction in case
such an error is encountered when compaction aux files keys. I'm pretty
sure the bug I just described predates the vectored get implementation,
but it's still worth fixing.
Rebased version of #5234, part of #6768
This consists of three parts:
1. A refactoring and new contract for implementing and testing
compaction.
The logic is now in a separate crate, with no dependency on the
'pageserver' crate. It defines an interface that the real pageserver
must implement, in order to call the compaction algorithm. The interface
models things like delta and image layers, but just the parts that the
compaction algorithm needs to make decisions. That makes it easier unit
test the algorithm and experiment with different implementations.
I did not convert the current code to the new abstraction, however. When
compaction algorithm is set to "Legacy", we just use the old code. It
might be worthwhile to convert the old code to the new abstraction, so
that we can compare the behavior of the new algorithm against the old
one, using the same simulated cases. If we do that, have to be careful
that the converted code really is equivalent to the old.
This inclues only trivial changes to the main pageserver code. All the
new code is behind a tenant config option. So this should be pretty safe
to merge, even if the new implementation is buggy, as long as we don't
enable it.
2. A new compaction algorithm, implemented using the new abstraction.
The new algorithm is tiered compaction. It is inspired by the PoC at PR
#4539, although I did not use that code directly, as I needed the new
implementation to fit the new abstraction. The algorithm here is less
advanced, I did not implement partial image layers, for example. I
wanted to keep it simple on purpose, so that as we add bells and
whistles, we can see the effects using the included simulator.
One difference to #4539 and your typical LSM tree implementations is how
we keep track of the LSM tree levels. This PR doesn't have a permanent
concept of a level, tier or sorted run at all. There are just delta and
image layers. However, when compaction starts, we look at the layers
that exist, and arrange them into levels, depending on their shapes.
That is ephemeral: when the compaction finishes, we forget that
information. This allows the new algorithm to work without any extra
bookkeeping. That makes it easier to transition from the old algorithm
to new, and back again.
There is just a new tenant config option to choose the compaction
algorithm. The default is "Legacy", meaning the current algorithm in
'main'. If you set it to "Tiered", the new algorithm is used.
3. A simulator, which implements the new abstraction.
The simulator can be used to analyze write and storage amplification,
without running a test with the full pageserver. It can also draw an SVG
animation of the simulation, to visualize how layers are created and
deleted.
To run the simulator:
cargo run --bin compaction-simulator run-suite
---------
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
This PR introduces a new vectored implementation of the read path.
The search is basically a DFS if you squint at it long enough.
LayerFringe tracks the next layers to visit and acts as our stack.
Vertices are tuples of (layer, keyspace, lsn range). Continuously
pop the top of the stack (most recent layer) and do all the reads
for one layer at once.
The search maintains a fringe (`LayerFringe`) which tracks all the
layers that intersect the current keyspace being searched. Continuously
pop the top of the fringe (layer with highest LSN) and get all the data
required from the layer in one go.
Said search is done on one timeline at a time. If data is still required for
some keys, then search the ancestor timeline.
Apart from the high level layer traversal, vectored variants have been
introduced for grabbing data from each layer type. They still suffer from
read amplification issues and that will be addressed in a different PR.
You might notice that in some places we duplicate the code for the
existing read path. All of that code will be removed when we switch
the non-vectored read path to proxy into the vectored read path.
In the meantime, we'll have to contend with the extra cruft for the sake
of testing and gentle releasing.
This commit adds a function to `KeySpace` which updates a key key space
by removing all overlaps with a second key space. This can involve
splitting or removing of existing ranges.
The implementation is not particularly efficient: O(M * N * log(N))
where N is the number of ranges in the current key space and M is the
number of ranges in the key space we are checking against. In practice,
this shouldn't matter much since, in the short term, the only caller of
this function will be the vectored read path and the number of key
spaces invovled will be small. This follows from the upper bound placed
on the number of keys accepted by the vectored read path.
A couple other small utility functions are added. They'll be used by the
vectored search path as well.
1. Introduce a naive `Timeline::get_vectored` implementation
The return type is intended to be flexible enough for various types of
callers. We return the pages in a map keyed by `Key` such that the
caller doesn't have to map back to the key if it needs to know it. Some
callers can ignore errors
for specific pages, so we return a separate `Result<Bytes,
PageReconstructError>` for each page and an overarching
`GetVectoredError` for API misuse. The overhead of the mapping will be
small and bounded since we enforce a maximum key count for the
operation.
2. Use the `get_vectored` API for SLRU segment reconstruction and image
layer creation.
PR #6266 broke the getpage_latest_lsn benchmark.
Before this patch, we'd fail with
```
not implemented: split up range
```
because `r.start = rel size key` and `r.end = rel size key + 1`.
The filtering of the key ranges in that loop is a bit ugly, but,
I measured:
* setup with 180k layer files (20k tenants * 9 layers).
* total physical size is 463GiB
* 5k tenants, the range filtering takes `0.6 seconds` on an
i3en.3xlarge.
That's a tiny fraction of the overall time it takes for pagebench to get
ready to send requests. So, this is good enough for now / there are
other bottlenecks that are bigger.
