Change the signature so that it takes an Arc<Timeline> reference to the
source timeline, instead of just the ID. All the callers have an Arc
reference at hand, so this is more convenient for everyone.
Reorder the code a bit and improve the comments, to make it more clear
what it does and why.
Motivation
==========
Layer Eviction Needs Context
----------------------------
Before we start implementing layer eviction, we need to collect some
access statistics per layer file or maybe even page.
Part of these statistics should be the initiator of a page read request
to answer the question of whether it was page_service vs. one of the
background loops, and if the latter, which of them?
Further, it would be nice to learn more about what activity in the pageserver
initiated an on-demand download of a layer file.
We will use this information to test out layer eviction policies.
Read more about the current plan for layer eviction here:
https://github.com/neondatabase/neon/issues/2476#issuecomment-1370822104
task_mgr problems + cancellation + tenant/timeline lifecycle
------------------------------------------------------------
Apart from layer eviction, we have long-standing problems with task_mgr,
task cancellation, and various races around tenant / timeline lifecycle
transitions.
One approach to solve these is to abandon task_mgr in favor of a
mechanism similar to Golang's context.Context, albeit extended to
support waiting for completion, and specialized to the needs in the
pageserver.
Heikki solves all of the above at once in PR
https://github.com/neondatabase/neon/pull/3228 , which is not yet
merged at the time of writing.
What Is This Patch About
========================
This patch addresses the immediate needs of layer eviction by
introducing a `RequestContext` structure that is plumbed through the
pageserver - all the way from the various entrypoints (page_service,
management API, tenant background loops) down to
Timeline::{get,get_reconstruct_data}.
The struct carries a description of the kind of activity that initiated
the call. We re-use task_mgr::TaskKind for this.
Also, it carries the desired on-demand download behavior of the entrypoint.
Timeline::get_reconstruct_data can then log the TaskKind that initiated
the on-demand download.
I developed this patch by git-checking-out Heikki's big RequestContext
PR https://github.com/neondatabase/neon/pull/3228 , then deleting all
the functionality that we do not need to address the needs for layer
eviction.
After that, I added a few things on top:
1. The concept of attached_child and detached_child in preparation for
cancellation signalling through RequestContext, which will be added in
a future patch.
2. A kill switch to turn DownloadBehavior::Error into a warning.
3. Renamed WalReceiverConnection to WalReceiverConnectionPoller and
added an additional TaskKind WalReceiverConnectionHandler.These were
necessary to create proper detached_child-type RequestContexts for the
various tasks that walreceiver starts.
How To Review This Patch
========================
Start your review with the module-level comment in context.rs.
It explains the idea of RequestContext, what parts of it are implemented
in this patch, and the future plans for RequestContext.
Then review the various `task_mgr::spawn` call sites. At each of them,
we should be creating a new detached_child RequestContext.
Then review the (few) RequestContext::attached_child call sites and
ensure that the spawned tasks do not outlive the task that spawns them.
If they do, these call sites should use detached_child() instead.
Then review the todo_child() call sites and judge whether it's worth the
trouble of plumbing through a parent context from the caller(s).
Lastly, go through the bulk of mechanical changes that simply forwards
the &ctx.
This makes Timeline::get() async, and all functions that call it
directly or indirectly with it. The with_ondemand_download() mechanism
is gone, Timeline::get() now always downloads files, whether you want
it or not. That is what all the current callers want, so even though
this loses the capability to get a page only if it's already in the
pageserver, without downloading, we were not using that capability.
There were some places that used 'no_ondemand_download' in the WAL
ingestion code that would error out if a layer file was not found
locally, but those were dubious. We do actually want to on-demand
download in all of those places.
Per discussion at
https://github.com/neondatabase/neon/pull/3233#issuecomment-1368032358
The PR aims to fix two missing redownloads in a flacky
test_remote_storage_upload_queue_retries[local_fs]
([example](https://neon-github-public-dev.s3.amazonaws.com/reports/pr-3190/release/3759194738/index.html#categories/80f1dcdd7c08252126be7e9f44fe84e6/8a70800f7ab13620/))
1. missing redownload during walreceiver work
```
2022-12-22T16:09:51.509891Z ERROR wal_connection_manager{tenant=fb62b97553e40f949de8bdeab7f93563 timeline=4f153bf6a58fd63832f6ee175638d049}: wal receiver task finished with an error: walreceiver connection handling failure
Caused by:
Layer needs downloading
Stack backtrace:
0: pageserver::tenant::timeline::PageReconstructResult<T>::no_ondemand_download
at /__w/neon/neon/pageserver/src/tenant/timeline.rs:467:59
1: pageserver::walingest::WalIngest::new
at /__w/neon/neon/pageserver/src/walingest.rs:61:32
2: pageserver::walreceiver::walreceiver_connection::handle_walreceiver_connection::{{closure}}
at /__w/neon/neon/pageserver/src/walreceiver/walreceiver_connection.rs:178:25
....
```
That looks sad, but inevitable during the current approach: seems that
we need to wait for old layers to arrive in order to accept new data.
For that, `WalIngest::new` now started to return the
`PageReconstructResult`.
Sync methods from `import_datadir.rs` use `WalIngest::new` too, but both
of them import WAL during timeline creation, so no layers to download
are needed there, ergo the `PageReconstructResult` is converted to
`anyhow::Result` with `no_ondemand_download`.
2. missing redownload during compaction work
```
2022-12-22T16:09:51.090296Z ERROR compaction_loop{tenant_id=fb62b97553e40f949de8bdeab7f93563}:compact_timeline{timeline=4f153bf6a58fd63832f6ee175638d049}: could not compact, repartitioning keyspace failed: Layer needs downloading
Stack backtrace:
0: pageserver::tenant::timeline::PageReconstructResult<T>::no_ondemand_download
at /__w/neon/neon/pageserver/src/tenant/timeline.rs:467:59
1: pageserver::pgdatadir_mapping::<impl pageserver::tenant::timeline::Timeline>::collect_keyspace::{{closure}}
at /__w/neon/neon/pageserver/src/pgdatadir_mapping.rs:506:41
<core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
at /rustc/e092d0b6b43f2de967af0887873151bb1c0b18d3/library/core/src/future/mod.rs:91:19
pageserver::tenant::timeline::Timeline::repartition::{{closure}}
at /__w/neon/neon/pageserver/src/tenant/timeline.rs:2161:50
<core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
at /rustc/e092d0b6b43f2de967af0887873151bb1c0b18d3/library/core/src/future/mod.rs:91:19
2: pageserver::tenant::timeline::Timeline::compact::{{closure}}
at /__w/neon/neon/pageserver/src/tenant/timeline.rs:700:14
<core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
at /rustc/e092d0b6b43f2de967af0887873151bb1c0b18d3/library/core/src/future/mod.rs:91:19
3: <tracing::instrument::Instrumented<T> as core::future::future::Future>::poll
at /github/home/.cargo/registry/src/github.com-1ecc6299db9ec823/tracing-0.1.37/src/instrument.rs:272:9
4: pageserver::tenant::Tenant::compaction_iteration::{{closure}}
at /__w/neon/neon/pageserver/src/tenant.rs:1232:85
<core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
at /rustc/e092d0b6b43f2de967af0887873151bb1c0b18d3/library/core/src/future/mod.rs:91:19
pageserver::tenant_tasks::compaction_loop::{{closure}}::{{closure}}
at /__w/neon/neon/pageserver/src/tenant_tasks.rs:76:62
<core::future::from_generator::GenFuture<T> as core::future::future::Future>::poll
at /rustc/e092d0b6b43f2de967af0887873151bb1c0b18d3/library/core/src/future/mod.rs:91:19
pageserver::tenant_tasks::compaction_loop::{{closure}}
at /__w/neon/neon/pageserver/src/tenant_tasks.rs:91:6
```
1.66 release speeds up compile times for over 10% according to tests.
Also its Clippy finds plenty of old nits in our code:
* useless conversion, `foo as u8` where `foo: u8` and similar, removed
`as u8` and similar
* useless references and dereferenced (that were automatically adjusted
by the compiler), removed various `&` and `*`
* bool -> u8 conversion via `if/else`, changed to `u8::from`
* Map `.iter()` calls where only values were used, changed to
`.values()` instead
Standing out lints:
* `Eq` is missing in our protoc generated structs. Silenced, does not
seem crucial for us.
* `fn default` looks like the one from `Default` trait, so I've
implemented that instead and replaced the `dummy_*` method in tests with
`::default()` invocation
* Clippy detected that
```
if retry_attempt < u32::MAX {
retry_attempt += 1;
}
```
is a saturating add and proposed to replace it.
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.
- Refactor logical_size_calculation_task, moving the pieces that are
specific to try_spawn_size_init_task into that function.
This allows us to spawn additional size calculation tasks that are not
init size calculation tasks.
- As part of this refactoring, stop logging cancellations as errors.
They are part of regular operations.
Logging them as errors was inadvertently introduced in earlier commit
427c1b2e9661161439e65aabc173d695cfc03ab4
initial logical size calculation: if it fails, retry on next call
- Change tenant size model request code to spawn task_mgr tasks using
the refactored logical_size_calculation_task function.
Using a task_mgr task ensures that the calculation cannot outlive
the timeline.
- There are presumably still some subtle race conditions if a size
requests comes in at exactly the same time as a detach / delete
request.
- But that's the concern of diferent area of the code (e.g., tenant_mgr)
and requires holistic solutions, such as the proposed TenantGuard.
- Make size calculation cancellable using CancellationToken.
This is more of a cherry on top.
NB: the test code doesn't use this because we _must_ return from
the failpoint, because the failpoint lib doesn't allow to just
continue execution in combination with executing the closure.
This commit fixes the tests introduced earlier in this patch series.
If we cannot reconstruct an FSM or VM page, while creating image
layers, fill it with zeros instead. That should always be safe, for
the FSM and VM, in the sense that you won't lose actual user data. It
will get cleaned up by VACUUM later.
We had a bug with FSM/VM truncation, where we truncated the FSM and VM
at WAL replay to a smaller size than PostgreSQL originally did. We
thought was harmless, as the FSM and VM are not critical for
correctness and can be zeroed out or truncated without affecting user
data. However, it lead to a situation where PostgreSQL created
incremental WAL records for pages that we had already truncated away
in the pageserver, and when we tried to replay those WAL records, that
failed. That lead to a permanent error in image layer creation, and
prevented it from ever finishing. See
https://github.com/neondatabase/neon/issues/2601. With this patch,
those pages will be filled with zeros in the image layer, which allows
the image layer creation to finish.
Part of https://github.com/neondatabase/neon/pull/2239
Regular, from scratch, timeline creation involves initdb to be run in a separate directory, data from this directory to be imported into pageserver and, finally, timeline-related background tasks to start.
This PR ensures we don't leave behind any directories that are not marked as temporary and that pageserver removes such directories on restart, allowing timeline creation to be retried with the same IDs, if needed.
It would be good to later rewrite the logic to use a temporary directory, similar what tenant creation does.
Yet currently it's harder than this change, so not done.
- Split postgres_ffi into two version specific files.
- Preserve pg_version in timeline metadata.
- Use pg_version in safekeeper code. Check for postgres major version mismatch.
- Clean up the code to use DEFAULT_PG_VERSION constant everywhere, instead of hardcoding.
- Parameterize python tests: use DEFAULT_PG_VERSION env and pg_version fixture.
To run tests using a specific PostgreSQL version, pass the DEFAULT_PG_VERSION environment variable:
'DEFAULT_PG_VERSION='15' ./scripts/pytest test_runner/regress'
Currently don't all tests pass, because rust code relies on the default version of PostgreSQL in a few places.
Another preparatory commit for pg15 support:
* generate bindings for both pg14 and pg15;
* update Makefile and CI scripts: now neon build depends on both PostgreSQL versions;
* some code refactoring to decrease version-specific dependencies.
* Update relation size cache only when latest LSN is requested
* Fix tests
* Add a test case for timetravel query after pageserver restart.
This test is currently failing, the queries return incorrect results.
I don't know why, needs to be investigated.
FAILED test_runner/batch_others/test_readonly_node.py::test_timetravel - assert 85 == 100000
If you remove the pageserver restart from the test, it passes.
* yapf3 test_readonly_node.py
* Add comment about cache correction in case of setting incorrect latest flag
* Fix formatting for test_readonly_node.py
* Remove unused imports
* Fix mypy warning for test_readonly_node.py
* Fix formatting of test_readonly_node.py
* Bump postgres version
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
Start the calculation on the first size request, return
partially calculated size during calculation, retry if failed.
Remove "fast" size init through the ancestor: the current approach is
fast enough for now and there are better ways to optimize the
calculation via incremental ancestor size computation
usize/isize type corresponds to the CPU architecture's pointer width,
i.e. 64 bits on a 64-bit platform and 32 bits on a 32-bit platform.
The logical size of a database has nothing to do with the that, so
u64/i64 is more appropriate.
It doesn't make any difference in practice as long as you're on a
64-bit platform, and it's hard to imagine anyone wanting to run the
pageserver on a 32-bit platform, but let's be tidy.
Also add a comment on why we use signed i64 for the logical size
variable, even though size should never be negative. I'm not sure the
reasons are very good, but at least this documents them, and hints at
some possible better solutions.
Re-export only things that are used by other modules.
In the future, I'm imagining that we run bindgen twice, for Postgres
v14 and v15. The two sets of bindings would go into separate
'bindings_v14' and 'bindings_v15' modules.
Rearrange postgres_ffi modules.
Move function, to avoid Postgres version dependency in timelines.rs
Move function to generate a logical-message WAL record to postgres_ffi.
Resolves#2097
- use timeline modification's `lsn` and timeline's `last_record_lsn` to determine the corresponding LSN to query data in `DatadirModification::get`
- update `test_import_from_pageserver`. Split the test into 2 variants: `small` and `multisegment`.
+ `small` is the old test
+ `multisegment` is to simulate #2097 by using a larger number of inserted rows to create multiple segment files of a relation. `multisegment` is configured to only run with a `release` build
Previously DatadirTimeline was a separate struct, and there was a 1:1
relationship between each DatadirTimeline and LayeredTimeline. That was
a bit awkward; whenever you created a timeline, you also needed to create
the DatadirTimeline wrapper around it, and if you only had a reference
to the LayeredTimeline, you would need to look up the corresponding
DatadirTimeline struct through tenant_mgr::get_local_timeline_with_load().
There were a couple of calls like that from LayeredTimeline itself.
Refactor DatadirTimeline, so that it's a trait, and mark LayeredTimeline
as implementing that trait. That way, there's only one object,
LayeredTimeline, and you can call both Timeline and DatadirTimeline
functions on that. You can now also call DatadirTimeline functions from
LayeredTimeline itself.
I considered just moving all the functions from DatadirTimeline directly
to Timeline/LayeredTimeline, but I still like to have some separation.
Timeline provides a simple key-value API, and handles durably storing
key/value pairs, and branching. Whereas DatadirTimeline is stateless, and
provides an abstraction over the key-value store, to present an interface
with relations, databases, etc. Postgres concepts.
This simplified the logical size calculation fast-path for branch
creation, introduced in commit 28243d68e6. LayerTimeline can now
access the ancestor's logical size directly, so it doesn't need the
caller to pass it to it. I moved the fast-path to init_logical_size()
function itself. It now checks if the ancestor's last LSN is the same
as the branch point, i.e. if there haven't been any changes on the
ancestor after the branch, and copies the size from there. An
additional bonus is that the optimization will now work any time you
have a branch of another branch, with no changes from the ancestor,
not only at a create-branch command.
## Overview
This patch reduces the number of memory allocations when running the page server under a heavy write workload. This mostly helps improve the speed of WAL record ingestion.
## Changes
- modified `DatadirModification` to allow reuse the struct's allocated memory after each modification
- modified `decode_wal_record` to allow passing a `DecodedWALRecord` reference. This helps reuse the struct in each `decode_wal_record` call
- added a reusable buffer for serializing object inside the `InMemoryLayer::put_value` function
- added a performance test simulating a heavy write workload for testing the changes in this patch
### Semi-related changes
- remove redundant serializations when calling `DeltaLayer::put_value` during `InMemoryLayer::write_to_disk` function call [1]
- removed the info span `info_span!("processing record", lsn = %lsn)` during each WAL ingestion [2]
## Notes
- [1]: in `InMemoryLayer::write_to_disk`, a deserialization is called
```
let val = Value::des(&buf)?;
delta_layer_writer.put_value(key, *lsn, val)?;
```
`DeltaLayer::put_value` then creates a serialization based on the previous deserialization
```
let off = self.blob_writer.write_blob(&Value::ser(&val)?)?;
```
- [2]: related: https://github.com/neondatabase/neon/issues/733
Before this patch, importing a physical backup followed the same path
as ingesting any WAL records:
1. All the data pages from the backup are first collected in the
DatadirModification object.
2. Then, they are "committed" to the Repository. They are written to
the in-memory layer
3. Finally, the in-memory layer is frozen, and flushed to disk as a
L0 delta layer file.
This was pretty inefficient. In step 1, the whole physical backup was
held in memory. If the backup is large, you simply run out of
memory. And in step 3, the resulting L0 delta layer file is large,
holding all the data again. That's a problem if the backup is larger
than 5 GB: Amazon S3 doesn't allow uploading files larger than 5 GB
(without using multi-part upload, see github issue #1910). So we want
to avoid that.
To alleviate those problems, optimize the codepath for importing a
physical backup. The basic flow is the same as before, but step 1
is optimized so that it doesn't accumulate all the data in memory,
and step 3 writes the data in image layers instead of one large delta
layer.
A new `get_lsn_by_timestamp` command is added to the libpq page service
API.
An extra timestamp field is now stored in an extra field after each
Clog page. It is the timestamp of the latest commit, among all the
transactions on the Clog page. To find the overall latest commit, we
need to scan all Clog pages, but this isn't a very frequent operation
so that's not too bad.
To find the LSN that corresponds to a timestamp, we perform a binary
search. The binary search starts with min = last LSN when GC ran, and
max = latest LSN on the timeline. On each iteration of the search we
check if there are any commits with a higher-than-requested timestamp
at that LSN.
Implements github issue 1361.
This is a backwards-incompatible change. The new pageserver cannot
read repositories created with an old pageserver binary, or vice
versa.
Simplify Repository to a value-store
------------------------------------
Move the responsibility of tracking relation metadata, like which
relations exist and what are their sizes, from Repository to a new
module, pgdatadir_mapping.rs. The interface to Repository is now a
simple key-value PUT/GET operations.
It's still not any old key-value store though. A Repository is still
responsible from handling branching, and every GET operation comes
with an LSN.
Mapping from Postgres data directory to keys/values
---------------------------------------------------
All the data is now stored in the key-value store. The
'pgdatadir_mapping.rs' module handles mapping from PostgreSQL objects
like relation pages and SLRUs, to key-value pairs.
The key to the Repository key-value store is a Key struct, which
consists of a few integer fields. It's wide enough to store a full
RelFileNode, fork and block number, and to distinguish those from
metadata keys.
'pgdatadir_mapping.rs' is also responsible for maintaining a
"partitioning" of the keyspace. Partitioning means splitting the
keyspace so that each partition holds a roughly equal number of keys.
The partitioning is used when new image layer files are created, so
that each image layer file is roughly the same size.
The partitioning is also responsible for reclaiming space used by
deleted keys. The Repository implementation doesn't have any explicit
support for deleting keys. Instead, the deleted keys are simply
omitted from the partitioning, and when a new image layer is created,
the omitted keys are not copied over to the new image layer. We might
want to implement tombstone keys in the future, to reclaim space
faster, but this will work for now.
Changes to low-level layer file code
------------------------------------
The concept of a "segment" is gone. Each layer file can now store an
arbitrary range of Keys.
Checkpointing, compaction
-------------------------
The background tasks are somewhat different now. Whenever
checkpoint_distance is reached, the WAL receiver thread "freezes" the
current in-memory layer, and creates a new one. This is a quick
operation and doesn't perform any I/O yet. It then launches a
background "layer flushing thread" to write the frozen layer to disk,
as a new L0 delta layer. This mechanism takes care of durability. It
replaces the checkpointing thread.
Compaction is a new background operation that takes a bunch of L0
delta layers, and reshuffles the data in them. It runs in a separate
compaction thread.
Deployment
----------
This also contains changes to the ansible scripts that enable having
multiple different pageservers running at the same time in the staging
environment. We will use that to keep an old version of the pageserver
running, for clusters created with the old version, at the same time
with a new pageserver with the new binary.
Author: Heikki Linnakangas
Author: Konstantin Knizhnik <knizhnik@zenith.tech>
Author: Andrey Taranik <andrey@zenith.tech>
Reviewed-by: Matthias Van De Meent <matthias@zenith.tech>
Reviewed-by: Bojan Serafimov <bojan@zenith.tech>
Reviewed-by: Konstantin Knizhnik <knizhnik@zenith.tech>
Reviewed-by: Anton Shyrabokau <antons@zenith.tech>
Reviewed-by: Dhammika Pathirana <dham@zenith.tech>
Reviewed-by: Kirill Bulatov <kirill@zenith.tech>
Reviewed-by: Anastasia Lubennikova <anastasia@zenith.tech>
Reviewed-by: Alexey Kondratov <alexey@zenith.tech>
