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.
## Describe your changes
Updates PITR and GC_PERIOD default value doc
## Issue ticket number and link
## 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.
* Add submodule postgres-15
* Support pg_15 in pgxn/neon
* Renamed zenith -> neon in Makefile
* fix name of codestyle check
* Refactor build system to prepare for building multiple Postgres versions.
Rename "vendor/postgres" to "vendor/postgres-v14"
Change Postgres build and install directory paths to be version-specific:
- tmp_install/build -> pg_install/build/14
- tmp_install/* -> pg_install/14/*
And Makefile targets:
- "make postgres" -> "make postgres-v14"
- "make postgres-headers" -> "make postgres-v14-headers"
- etc.
Add Makefile aliases:
- "make postgres" to build "postgres-v14" and in future, "postgres-v15"
- similarly for "make postgres-headers"
Fix POSTGRES_DISTRIB_DIR path in pytest scripts
* Make postgres version a variable in codestyle workflow
* Support vendor/postgres-v15 in codestyle check workflow
* Support postgres-v15 building in Makefile
* fix pg version in Dockerfile.compute-node
* fix kaniko path
* Build neon extensions in version-specific directories
* fix obsolete mentions of vendor/postgres
* use vendor/postgres-v14 in Dockerfile.compute-node.legacy
* Use PG_VERSION_NUM to gate dependencies in inmem_smgr.c
* Use versioned ECR repositories and image names for compute-node.
The image name format is compute-node-vXX, where XX is postgres major version number.
For now only v14 is supported.
Old format unversioned name (compute-node) is left, because cloud repo depends on it.
* update vendor/postgres submodule url (zenith->neondatabase rename)
* Fix postgres path in python tests after rebase
* fix path in regress test
* Use separate dockerfiles to build compute-node:
Dockerfile.compute-node-v15 should be identical to Dockerfile.compute-node-v14 except for the version number.
This is a hack, because Kaniko doesn't support build ARGs properly
* bump vendor/postgres-v14 and vendor/postgres-v15
* Don't use Kaniko cache for v14 and v15 compute-node images
* Build compute-node images for different versions in different jobs
Co-authored-by: Heikki Linnakangas <heikki@neon.tech>
To flush inmemory layer eventually when no new data arrives, which helps
safekeepers to suspend activity (stop pushing to the broker). Default 10m should
be ok.
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>
