This reverts commit 826e89b9ce.
The problem with that commit was that it deletes the TempDir while
there are still EphemeralFile instances open.
At first I thought this could be fixed by simply adding
Handle::current().block_on(task_mgr::shutdown(None, Some(tenant_id), None))
to TenantHarness::drop, but it turned out to be insufficient.
So, reverting the commit until we find a proper solution.
refs https://github.com/neondatabase/neon/issues/3385
The code in this change was extracted from #2595 (Heikki’s on-demand
download draft PR).
High-Level Changes
- New RemoteLayer Type
- On-Demand Download As An Effect Of Page Reconstruction
- Breaking Semantics For Physical Size Metrics
There are several follow-up work items planned.
Refer to the Epic issue on GitHub: https://github.com/neondatabase/neon/issues/2029
closes https://github.com/neondatabase/neon/pull/3013
Co-authored-by: Kirill Bulatov <kirill@neon.tech>
Co-authored-by: Christian Schwarz <christian@neon.tech>
New RemoteLayer Type
====================
Instead of downloading all layers during tenant attach, we create
RemoteLayer instances for each of them and add them to the layer map.
On-Demand Download As An Effect Of Page Reconstruction
======================================================
At the heart of pageserver is Timeline::get_reconstruct_data(). It
traverses the layer map until it has collected all the data it needs to
produce the page image. Most code in the code base uses it, though many
layers of indirection.
Before this patch, the function would use synchronous filesystem IO to
load data from disk-resident layer files if the data was not cached.
That is not possible with RemoteLayer, because the layer file has not
been downloaded yet. So, we do the download when get_reconstruct_data
gets there, i.e., “on demand”.
The mechanics of how the download is done are rather involved, because
of the infamous async-sync-async sandwich problem that plagues the async
Rust world. We use the new PageReconstructResult type to work around
this. Its introduction is the cause for a good amount of code churn in
this patch. Refer to the block comment on `with_ondemand_download()`
for details.
Breaking Semantics For Physical Size Metrics
============================================
We rename prometheus metric pageserver_{current,resident}_physical_size to
reflect what this metric actually represents with on-demand download.
This intentionally BREAKS existing grafana dashboard and the cost model data
pipeline. Breaking is desirable because the meaning of this metrics has changed
with on-demand download. See
https://docs.google.com/document/d/12AFpvKY-7FZdR5a4CaD6Ir_rI3QokdCLSPJ6upHxJBo/edit#
for how we will handle this breakage.
Likewise, we rename the new billing_metrics’s PhysicalSize => ResidentSize.
This is not yet used anywhere, so, this is not a breaking change.
There is still a field called TimelineInfo::current_physical_size. It
is now the sum of the layer sizes in layer map, regardless of whether
local or remote. To compute that sum, we added a new trait method
PersistentLayer::file_size().
When updating the Python tests, we got rid of
current_physical_size_non_incremental. An earlier commit removed it from
the OpenAPI spec already, so this is not a breaking change.
test_timeline_size.py has grown additional assertions on the
resident_physical_size metric.
* Add test for pageserver metric cleanup once a tenant is detached.
* Remove tenant specific timeline metrics on detach.
* Use definitions from timeline_metrics in page service.
* Move metrics to own file from layered_repository/timeline.rs
* TIMELINE_METRICS: define smgr metrics
* REMOVE SMGR cleanup from timeline_metrics. Doesn't seem to work as
expected.
* Vritual file centralized metrics, except for evicted file as there's no
tenat id or timeline id.
* Use STORAGE_TIME from timeline_metrics in layered_repository.
* Remove timelineless gc metrics for tenant on detach.
* Rename timeline metrics -> metrics as it's more generic.
* Don't create a TimelineMetrics instance for VirtualFile
* Move the rest of the metric definitions to metrics.rs too.
* UUID -> ZTenantId
* Use consistent style for dict.
* Use Repository's Drop trait for dropping STORAGE_TIME metrics.
* No need for Arc, TimelineMetrics is used in just one place. Due to that,
we can fall back using ZTenantId and ZTimelineId too to avoid additional
string allocation.
Try to follow Prometheus style-guide https://prometheus.io/docs/practices/naming/ for metrics names. More specifically:
- Use `pageserver_` prefix for all pagserver metrics
- Specify `_seconds` unit in time metrics
- Use unit as a suffix in other cases, such as `_hits`, `_bytes`, `_records`
- Use `_total` suffix for accumulating counters (note that Histograms append that suffix internally)
This introduces two new abstraction layers for I/O:
- Block I/O, and
- Blob I/O.
The BlockReader trait abstracts a file or something else that can be read
in 8kB pages. It is implemented by EphemeralFiles, and by a new
FileBlockReader struct that allows reading arbitrary VirtualFiles in that
manner, utilizing the page cache.
There is also a new BlockCursor struct that works as a cursor over a
BlockReader. When you create a BlockCursor and read the first page using
it, it keeps the reference to the page. If you access the same page again,
it avoids going to page cache and quickly returns the same page again.
That can save a lot of lookups in the page cache if you perform multiple
reads.
The Blob-oriented API allows reading and writing "blobs" of arbitrary
length. It is a layer on top of the block-oriented API. When you write
a blob with the write_blob() function, it writes a length field
followed by the actual data to the underlying block storage, and
returns the offset where the blob was stored. The blob can be
retrieved later using the offset.
Finally, this replaces the I/O code in image-, delta-, and in-memory
layers to use the new abstractions. These replace the 'bookfile'
crate.
This is a backwards-incompatible change to the storage format.
The "in-memory layer" is misnomer now, each in-memory layer is now actually
backed by a file. The files are ephemeral, in that they don't survive page
server crash or shutdown.
To avoid reading the file for every operation,
"ephemeral files" are cached in a page cache.
This includes changes from 'inmemory-layer-chunks' branch to serialize /
the page versions when they are added to the open layer. The difference is
that they are not serialized to the expandable in-memory "chunk buffer", but
written out to the file.
There were two separate locking issues that could lead to a deadlock,
both related to holding a lock for longer than necessary:
1. In the loop in `VirtualFile::with_file`, the "handle_guard" was
held across iterations of the loop. Because of that, if the handle was
changed by a concurrent thread, the loop would try to acquire the
handle lock, when it was still holding the lock from previous
iteration. To fix, release the lock earlier. There was no need to hold
it across iterations, it was just accidental.
2. In the same function, we also held the "slot_guard" longer than
necessary. It's only needed in the first part of the loop, where we
check if the current handle is valid. If it's not, the slot lock can
be immediately released. But it was not, it was kept over the
acquisition of the handle lock. I'm not sure if that alone could cause
problems, but let's release the lock as soon as possible anyway.
Add a test case, based on Konstantin's test program to demonstrate the
deadlock.
Currently, whenever a page version is needed from an image or delta
layer, we open the file and read and parse the bookfile headers. That's
pretty expensive. To reduce the overhead, introduce a cache of open file
descriptors, and use that to cache the Book objects so that we don't need
to read the metadata on every access.
