We separated client error from basebackup error log lines in
https://github.com/neondatabase/neon/pull/7523, but we didn't do
anything for the metrics. In this patch, we fixed it.
ref https://github.com/neondatabase/neon/issues/8970
## Summary of changes
We use the same criteria as in `log_query_error` producing an info line
(instead of error) for the metrics. We added a `client_error` category
for the basebackup query time metrics.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
I wanted to use some features from the newer version. The PR that needed
the new version is not ready yet (and might never be), but seems nice to
stay up in any case.
Protocol version 2 has been the default for a while now, and we no
longer have any computes running in production that used protocol
version 1. This completes the migration by removing support for v1 in
both the pageserver and the compute.
See issue #6211.
## 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
%).
## 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
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.
```
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
Successor of #8288 , just enable zstd in tests. Also adds a test that
creates easily compressable data.
Part of #5431
---------
Co-authored-by: John Spray <john@neon.tech>
Co-authored-by: Joonas Koivunen <joonas@neon.tech>
## Problem
We lack insight into:
- How much of a tenant's physical size is image vs. delta layers
- Average sizes of image vs. delta layers
- Total layer counts per timeline, indicating size of index_part object
As well as general observability love, this is motivated by
https://github.com/neondatabase/neon/issues/6738, where we need to
define some sensible thresholds for storage amplification, and using
total physical size may not work well (if someone does a lot of DROPs
then it's legitimate for the physical-synthetic ratio to be huge), but
the ratio between image layer size and delta layer size may be a better
indicator of whether we're generating unreasonable quantities of image
layers.
## Summary of changes
- Add pageserver_layer_bytes and pageserver_layer_count metrics,
labelled by timeline and `kind` (delta or image)
- Add & subtract these with LayerInner's lifetime.
I'm intentionally avoiding using a generic metric RAII guard object, to
avoid bloating LayerInner: it already has all the information it needs
to update metric on new+drop.
## Problem
We already back off on compaction retries, but the impact of a failing
compaction can be so great that backing off up to 300s isn't enough. The
impact is consuming a lot of I/O+CPU in the case of image layer
generation for large tenants, and potentially also leaking disk space.
Compaction failures are extremely rare and almost always indicate a bug,
frequently a bug that will not let compaction to proceed until it is
fixed.
Related: https://github.com/neondatabase/neon/issues/6738
## Summary of changes
- Introduce a CircuitBreaker type
- Add a circuit breaker for compaction, with a policy that after 5
failures, compaction will not be attempted again for 24 hours.
- Add metrics that we can alert on: any >0 value for
`pageserver_circuit_breaker_broken_total` should generate an alert.
- Add a test that checks this works as intended.
Couple notes to reviewers:
- Circuit breakers are intrinsically a defense-in-depth measure: this is
not the solution to any underlying issues, it is just a general
mitigation for "unknown unknowns" that might be encountered in future.
- This PR isn't primarily about writing a perfect CircuitBreaker type:
the one in this PR is meant to be just enough to mitigate issues in
compaction, and make it easy to monitor/alert on these failures. We can
refine this type in future as/when we want to use it elsewhere.
I want to fix bugs in `page_service`
([issue](https://github.com/neondatabase/neon/issues/7427)) and the
`import basebackup` / `import wal` stand in the way / make the
refactoring more complicated.
We don't use these methods anyway in practice, but, there have been some
objections to removing the functionality completely.
So, this PR preserves the existing functionality but moves it into the
HTTP management API.
Note that I don't try to fix existing bugs in the code, specifically not
fixing
* it only ever worked correctly for unsharded tenants
* it doesn't clean up on error
All errors are mapped to `ApiError::InternalServerError`.
## Problem
The metrics we have today aren't convenient for planning around the
impact of timeline archival on costs.
Closes: https://github.com/neondatabase/neon/issues/8108
## Summary of changes
- Add metric `pageserver_archive_size`, which indicates the logical
bytes of data which we would expect to write into an archived branch.
- Add metric `pageserver_pitr_history_size`, which indicates the
distance between last_record_lsn and the PITR cutoff.
These metrics are somewhat temporary: when we implement #8088 and
associated consumption metric changes, these will reach a final form.
For now, an "archived" branch is just any branch outside of its parent's
PITR window: later, archival will become an explicit state (which will
_usually_ correspond to falling outside the parent's PITR window).
The overall volume of timeline metrics is something to watch, but we are
removing many more in https://github.com/neondatabase/neon/pull/8245
than this PR is adding.
## Problem
We record detailed histograms for all page_service op types, which
mostly aren't very interesting, but make our prometheus scrapes huge.
Closes: #8223
## Summary of changes
- Only track GetPageAtLsn histograms on a per-timeline granularity. For
all other operation types, rely on existing node-wide histograms.
## Problem
We lack visibility of how much local disk space is used by secondary
tenant locations
Close: https://github.com/neondatabase/neon/issues/8181
## Summary of changes
- Add `pageserver_secondary_resident_physical_size`, tagged by tenant
- Register & de-register label sets from SecondaryTenant
- Add+use wrappers in SecondaryDetail that update metrics when
adding+removing layers/timelines
## Problem
For some time, we have created tenants with calls to location_conf. The
legacy "POST /v1/tenant" path was only used in some tests.
## Summary of changes
- Remove the API
- Relocate TenantCreateRequest to the controller API file (this used to
be used in both pageserver and controller APIs)
- Rewrite tenant_create test helper to use location_config API, as
control plane and storage controller do
- Update docker-compose test script to create tenants with
location_config API (this small commit is also present in
https://github.com/neondatabase/neon/pull/7947)
I was looking for metrics on how many computes are still using protocol
version 1 and 2. This provides counters for that as "pagestream" and
"pagestream_v2" commands, but also all the other commands. The new
metrics are global for the whole pageserver instance rather than
per-tenant, so the additional metrics bloat should be fairly small.
Part of #7497, closes#8120.
## Summary of changes
This PR adds a metric to track the number of valid leases after `GCInfo`
gets refreshed each time.
Besides this metric, we should also track disk space and synthetic size
(after #8071 is closed) to make sure leases are used properly.
Signed-off-by: Yuchen Liang <yuchen@neon.tech>
part of Epic https://github.com/neondatabase/neon/issues/7386
# Motivation
The materialized page cache adds complexity to the code base, which
increases the maintenance burden and risk for subtle and hard to
reproduce bugs such as #8050.
Further, the best hit rate that we currently achieve in production is ca
1% of materialized page cache lookups for
`task_kind=PageRequestHandler`. Other task kinds have hit rates <0.2%.
Last, caching page images in Pageserver rewards under-sized caches in
Computes because reading from Pageserver's materialized page cache over
the network is often sufficiently fast (low hundreds of microseconds).
Such Computes should upscale their local caches to fit their working
set, rather than repeatedly requesting the same page from Pageserver.
Some more discussion and context in internal thread
https://neondb.slack.com/archives/C033RQ5SPDH/p1718714037708459
# Changes
This PR removes the materialized page cache code & metrics.
The infrastructure for different key kinds in `PageCache` is left in
place, even though the "Immutable" key kind is the only remaining one.
This can be further simplified in a future commit.
Some tests started failing because their total runtime was dependent on
high materialized page cache hit rates. This test makes them
fixed-runtime or raises pytest timeouts:
* test_local_file_cache_unlink
* test_physical_replication
* test_pg_regress
# Performance
I focussed on ensuring that this PR will not result in a performance
regression in prod.
* **getpage** requests: our production metrics have shown the
materialized page cache to be irrelevant (low hit rate). Also,
Pageserver is the wrong place to cache page images, it should happen in
compute.
* **ingest** (`task_kind=WalReceiverConnectionHandler`): prod metrics
show 0 percent hit rate, so, removing will not be a regression.
* **get_lsn_by_timestamp**: important API for branch creation, used by
control pane. The clog pages that this code uses are not
materialize-page-cached because they're not 8k. No risk of introducing a
regression here.
We will watch the various nightly benchmarks closely for more results
before shipping to prod.
Related to #7341 tenant deletion will end up shutting down timelines
twice, once before actually starting and the second time when per
timeline deletion is requested. Shutting down TimelineMetrics causes
underflows. Add an atomic boolean and only do the shutdown once.
## Problem
Currently tenants are only split into multiple shards if a human being
calls the API to do it.
Issue: #7388
## Summary of changes
- Add a pageserver API for returning the top tenants by size
- Add a step to the controller's background loop where if there is no
reconciliation or optimization to be done, it looks for things to split.
- Add a test that runs pgbench on many tenants concurrently, and checks
that splitting happens as expected as tenants grow, without interrupting
the client I/O.
This PR is quite basic: there is a tasklist in
https://github.com/neondatabase/neon/issues/7388 for further work. This
PR is meant to be safe (off by default), and sufficient to enable our
staging environment to run lots of sharded tenants without a human
having to set them up.
refs https://github.com/neondatabase/neon/issues/7753
This PR is step (1) of removing sync walredo from Pageserver.
Changes:
* Remove the sync impl
* If sync is configured, warn! and use async instead
* Remove the metric that exposes `kind`
* Remove the tenant status API that exposes `kind`
Future Work
-----------
After we've released this change to prod and are sure we won't
roll back, we will
1. update the prod Ansible to remove the config flag from the prod
pageserver.toml.
2. remove the remaining `kind` code in pageserver
These two changes need no release inbetween.
See https://github.com/neondatabase/neon/issues/7753 for details.
ref https://github.com/neondatabase/neon/issues/7443
## Summary of changes
This pull request adds a size estimator for aux files. Each timeline
stores a cached `isize` for the estimated total size of aux files. It
gets reset on basebackup, and gets updated for each aux file
modification. TODO: print a warning when it exceeds the size.
The size metrics is not accurate. Race between `on_basebackup` and other
functions could create a negative basebackup size, but the chance is
rare. Anyways, this does not impose any extra I/Os to the storage as
everything is computed in-memory.
The aux files are only stored on shard 0. As basebackups are only
generated on shard 0, only shard 0 will report this metrics.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
## Problem
In https://github.com/neondatabase/neon/pull/7531, we would like to be
able to rewrite layers safely. One option is to make `Layer` able to
rewrite files in place safely (e.g. by blocking evictions/deletions for
an old Layer while a new one is created), but that's relatively fragile.
It's more robust in general if we simply never overwrite the same local
file: we can do that by putting the generation number in the filename.
## Summary of changes
- Add `local_layer_path` (counterpart to `remote_layer_path`) and
convert all locations that manually constructed a local layer path by
joining LayerFileName to timeline path
- In the layer upload path, construct remote paths with
`remote_layer_path` rather than trying to build them out of a local
path.
- During startup, carry the full path to layer files through
`init::reconcile`, and pass it into `Layer::for_resident`
- Add a test to make sure we handle upgrades properly.
- Comment out the generation part of `local_layer_path`, since we need
to maintain forward compatibility for one release. A tiny followup PR
will enable it afterwards.
We could make this a bit simpler if we bulk renamed existing layers on
startup instead of carrying literal paths through init, but that is
operationally risky on existing servers with millions of layer files. We
can always do a renaming change in future if it becomes annoying, but
for the moment it's kind of nice to have a structure that enables us to
change local path names again in future quite easily.
We should rename `LayerFileName` to `LayerName` or somesuch, to make it
more obvious that it's not a literal filename: this was already a bit
confusing where that type is used in remote paths. That will be a
followup, to avoid polluting this PR's diff.
## Problem
The current `tenant_slots` metric becomes less useful once we have lots
of secondaries, because we can't tell how many tenants are really
attached (without doing a sum() on some other metric).
## Summary of changes
- Add a `mode` label to this metric
- Update the metric with `slot_added` and `slot_removed` helpers that
are called at all the places we mutate the tenants map.
- Add a debug assertion at shutdown that checks the metrics add up to
the right number, as a cheap way of validating that we're calling the
metric hooks in all the right places.
This pull request adds the scan interface. Scan operates on a sparse
keyspace and retrieves all the key-value pairs from the keyspaces.
Currently, scan only supports the metadata keyspace, and by default do
not retrieve anything from the ancestor branch. This should be fixed in
the future if we need to have some keyspaces that inherits from the
parent.
The scan interface reuses the vectored get code path by disabling the
missing key errors.
This pull request also changes the behavior of vectored get on aux file
v1/v2 key/keyspace: if the key is not found, it is simply not included in the
result, instead of throwing a missing key error.
TODOs in future pull requests: limit memory consumption, ensure the
search stops when all keys are covered by the image layer, remove
`#[allow(dead_code)]` once the code path is used in basebackups / aux
files, remove unnecessary fine-grained keyspace tracking in vectored get
(or have another code path for scan) to improve performance.
---------
Signed-off-by: Alex Chi Z <chi@neon.tech>
Split `GcInfo` and replace `Timeline::update_gc_info` with a method that
simply finds gc cutoffs `Timeline::find_gc_cutoffs` to be combined as
`Timeline::gc_info` at the caller.
This change will be followed up with a change that finds the GC cutoff
values before taking the `Tenant::gc_cs` lock.
Cc: #7560
## Problem
Vectored and non-vectored read paths don't publish the same set of
metrics. Metrics parity is needed for coalescing the read paths.
## Summary of changes
* Publish reconstruct time and fetching data for reconstruct time from
the vectored read path
* Remove pageserver_getpage_reconstruct_seconds{res="err"} - wasn't used
anyway
## Problem
We recently went through an incident where compaction was inhibited by a
bug. We didn't observe this until quite late because we did not have alerting
on deep reads.
## Summary of changes
+ Tweak an existing metric that tracks the depth of a read on the
non-vectored read path:
* Give it a better name
* Track all layers
* Larger buckets
+ Add a similar metric for the vectored read path
+ Add a compaction smoke test which uses these metrics. This test would
have caught
the compaction issue mentioned earlier.
Related https://github.com/neondatabase/neon/issues/7428
## Problem
Some tenants were observed to stop doing downloads after some time
## Summary of changes
- Fix a rogue `<` that was incorrectly scheduling work when `now` was
_before_ the scheduling target, rather than after. This usually resulted
in too-frequent execution, but could also result in never executing, if
the current time has advanced ahead of `next_download` at the time we
call `schedule()`.
- Fix in-memory list of timelines not being amended after timeline
deletion: the resulted in repeated harmless logs about the timeline
being removed, and redundant calls to remove_dir_all for the timeline
path.
- Add a log at startup to make it easier to see a particular tenant
starting in secondary mode (this is for parity with the logging that
exists when spawning an attached tenant). Previously searching on tenant
ID didn't provide a clear signal as to how the tenant was started during
pageserver start.
- Add a test that exercises secondary downloads using the background
scheduling, whereas existing tests were using the API hook to invoke
download directly.
Before this PR, the `nix::poll::poll` call would stall the executor.
This PR refactors the `walredo::process` module to allow for different
implementations, and adds a new `async` implementation which uses
`tokio::process::ChildStd{in,out}` for IPC.
The `sync` variant remains the default for now; we'll do more testing in
staging and gradual rollout to prod using the config variable.
Performance
-----------
I updated `bench_walredo.rs`, demonstrating that a single `async`-based
walredo manager used by N=1...128 tokio tasks has lower latency and
higher throughput.
I further did manual less-micro-benchmarking in the real pageserver
binary.
Methodology & results are published here:
https://neondatabase.notion.site/2024-04-08-async-walredo-benchmarking-8c0ed3cc8d364a44937c4cb50b6d7019?pvs=4
tl;dr:
- use pagebench against a pageserver patched to answer getpage request &
small-enough working set to fit into PS PageCache / kernel page cache.
- compare knee in the latency/throughput curve
- N tenants, each 1 pagebench clients
- sync better throughput at N < 30, async better at higher N
- async generally noticable but not much worse p99.X tail latencies
- eyeballing CPU efficiency in htop, `async` seems significantly more
CPU efficient at ca N=[0.5*ncpus, 1.5*ncpus], worse than `sync` outside
of that band
Mental Model For Walredo & Scheduler Interactions
-------------------------------------------------
Walredo is CPU-/DRAM-only work.
This means that as soon as the Pageserver writes to the pipe, the
walredo process becomes runnable.
To the Linux kernel scheduler, the `$ncpus` executor threads and the
walredo process thread are just `struct task_struct`, and it will divide
CPU time fairly among them.
In `sync` mode, there are always `$ncpus` runnable `struct task_struct`
because the executor thread blocks while `walredo` runs, and the
executor thread becomes runnable when the `walredo` process is done
handling the request.
In `async` mode, the executor threads remain runnable unless there are
no more runnable tokio tasks, which is unlikely in a production
pageserver.
The above means that in `sync` mode, there is an implicit concurrency
limit on concurrent walredo requests (`$num_runtimes *
$num_executor_threads_per_runtime`).
And executor threads do not compete in the Linux kernel scheduler for
CPU time, due to the blocked-runnable-ping-pong.
In `async` mode, there is no concurrency limit, and the walredo tasks
compete with the executor threads for CPU time in the kernel scheduler.
If we're not CPU-bound, `async` has a pipelining and hence throughput
advantage over `sync` because one executor thread can continue
processing requests while a walredo request is in flight.
If we're CPU-bound, under a fair CPU scheduler, the *fixed* number of
executor threads has to share CPU time with the aggregate of walredo
processes.
It's trivial to reason about this in `sync` mode due to the
blocked-runnable-ping-pong.
In `async` mode, at 100% CPU, the system arrives at some (potentially
sub-optiomal) equilibrium where the executor threads get just enough CPU
time to fill up the remaining CPU time with runnable walredo process.
Why `async` mode Doesn't Limit Walredo Concurrency
--------------------------------------------------
To control that equilibrium in `async` mode, one may add a tokio
semaphore to limit the number of in-flight walredo requests.
However, the placement of such a semaphore is non-trivial because it
means that tasks queuing up behind it hold on to their request-scoped
allocations.
In the case of walredo, that might be the entire reconstruct data.
We don't limit the number of total inflight Timeline::get (we only
throttle admission).
So, that queue might lead to an OOM.
The alternative is to acquire the semaphore permit *before* collecting
reconstruct data.
However, what if we need to on-demand download?
A combination of semaphores might help: one for reconstruct data, one
for walredo.
The reconstruct data semaphore permit is dropped after acquiring the
walredo semaphore permit.
This scheme effectively enables both a limit on in-flight reconstruct
data and walredo concurrency.
However, sizing the amount of permits for the semaphores is tricky:
- Reconstruct data retrieval is a mix of disk IO and CPU work.
- If we need to do on-demand downloads, it's network IO + disk IO + CPU
work.
- At this time, we have no good data on how the wall clock time is
distributed.
It turns out that, in my benchmarking, the system worked fine without a
semaphore. So, we're shipping async walredo without one for now.
Future Work
-----------
We will do more testing of `async` mode and gradual rollout to prod
using the config flag.
Once that is done, we'll remove `sync` mode to avoid the temporary code
duplication introduced by this PR.
The flag will be removed.
The `wait()` for the child process to exit is still synchronous; the
comment [here](
655d3b6468/pageserver/src/walredo.rs (L294-L306))
is still a valid argument in favor of that.
The `sync` mode had another implicit advantage: from tokio's
perspective, the calling task was using up coop budget.
But with `async` mode, that's no longer the case -- to tokio, the writes
to the child process pipe look like IO.
We could/should inform tokio about the CPU time budget consumed by the
task to achieve fairness similar to `sync`.
However, the [runtime function for this is
`tokio_unstable`](`https://docs.rs/tokio/latest/tokio/task/fn.consume_budget.html).
Refs
----
refs #6628
refs https://github.com/neondatabase/neon/issues/2975
No functional changes, this is a comments/naming PR.
While merging sharding changes, some cleanup of the shard.rs types was
deferred.
In this PR:
- Rename `is_zero` to `is_shard_zero` to make clear that this method
doesn't literally mean that the entire object is zeros, just that it
refers to the 0th shard in a tenant.
- Pull definitions of types to the top of shard.rs and add a big comment
giving an overview of which type is for what.
Closes: https://github.com/neondatabase/neon/issues/6072
This PR is an off-by-default revision v2 of the (since-reverted) PR
#6555 / commit `3220f830b7fbb785d6db8a93775f46314f10a99b`.
See that PR for details on why running with a single runtime is
desirable and why we should be ready.
We reverted #6555 because it showed regressions in prodlike cloudbench,
see the revert commit message `ad072de4209193fd21314cf7f03f14df4fa55eb1`
for more context.
This PR makes it an opt-in choice via an env var.
The default is to use the 4 separate runtimes that we have today, there
shouldn't be any performance change.
I tested manually that the env var & added metric works.
```
# undefined env var => no change to before this PR, uses 4 runtimes
./target/debug/neon_local start
# defining the env var enables one-runtime mode, value defines that one runtime's configuration
NEON_PAGESERVER_USE_ONE_RUNTIME=current_thread ./target/debug/neon_local start
NEON_PAGESERVER_USE_ONE_RUNTIME=multi_thread:1 ./target/debug/neon_local start
NEON_PAGESERVER_USE_ONE_RUNTIME=multi_thread:2 ./target/debug/neon_local start
NEON_PAGESERVER_USE_ONE_RUNTIME=multi_thread:default ./target/debug/neon_local start
```
I want to use this change to do more manualy testing and potentially
testing in staging.
Future Work
-----------
Testing / deployment ergonomics would be better if this were a variable
in `pageserver.toml`.
It can be done, but, I don't need it right now, so let's stick with the
env var.
