The snapshot id doesn't make sense when two snapshots are squashed.
Better to use the timeline id anyway.
It's still a little strange to squash two timelines together, but it
must be possible to flatten history even if there have been many branch
events, so this is should be a control plane / snapshot management
problem.
Add logic to squash snapshot files.
Add snaptool (a binary for inspecting and manipulating snapshot files).
Use bookfile 0.3, which allows concurrent reads.
The snapfile crate implements a snapshot file format. The format relies
heavily on the bookfile crate for the structured file format, and the
aversion crate for versioned data structures.
The overall structure of the file looks like this:
- first 4KB: bookfile header
- next 8KB * N: raw page data for N pages
- page index map (from page identifier to data offset)
- bookfile chapter index
When a SnapFile is opened for reading, the page index map is read into
memory; any page can be read directly from the file from that point.
There can be only one "open" layer for each segment. That's the last one,
implemented by InMemoryLayer. That's the only one where new records can
be appended to. Much of the code needed to distinguish between the last
open layer and other layers anyway, so make the distinction explicit
in LayerMap.
There was a a lot of duplicated code between the get_page_at_lsn()
implementations in InMemoryLayer and SnapshotLayer. Move the code for
requesting WAL redo from the Layer trait into LayeredTimeline. The
get-function in Layer now just returns the WAL records and base image
to the caller, and the caller is responsible for performing the WAL
redo on them.
Split each relish into fixed-sized 10 MB segments. Separate layers are
created for each segment. This reduces the write amplification if you
have a large relation and update only parts of it; the downside is
that you have a lot more files. The 10 MB is just a guess, we should
do some modeling and testing in the future to figure out the optimal
size.
Each segment tracks the size of the segment separately. To figure out
the total size of a relish, you need to loop through the segment to
find the highest segment that's in use. That's a bit inefficient, but
will do for now. We might want to add a cache or something later.
Change CLI so that we always create node from scratch at 'pg start'.
This operation preserve previously existing config
Add new flag '--config-only' to 'pg create'.
If this flag is passed, don't perform basebackup, just fill initial postgresql.conf for the node.
Track the time spent on replaying WAL records by the special Postgres
process, the time spent waiting for acces to the Postgres process (since
there is only one per tenant), and the number of records replayed.
This replaces the RocksDB based implementation with an approach using
"snapshot files" on disk, and in-memory btreemaps to hold the recent
changes.
This make the repository implementation a configuration option. You can
choose 'layered' or 'rocksdb' with "zenith init --repository-format=<format>"
The unit tests have been refactored to exercise both implementations.
'layered' is now the default.
Push/pull is not implemented. The 'test_history_inmemory' test has been
commented out accordingly. It's not clear how we will implement that
functionality; probably by copying the snapshot files directly.
Most of the work here was done on the postgres side. There's more
information in the commit message there.
(see: 04cfa326a5)
On the WAL acceptor side, we're now expecting 'START_WAL_PUSH' to
initialize the WAL keeper protocol. Everything else is mostly the same,
with the only real difference being that protocol messages are now
discrete CopyData messages sent over the postgres protocol.
For the sake of documentation, the full set of these messages is:
<- recv: START_WAL_PUSH query
<- recv: server info from postgres (type `ServerInfo`)
-> send: walkeeper info (type `SafeKeeperInfo`)
<- recv: vote info (type `RequestVote`)
if node id mismatch:
-> send: self node id (type `NodeId`); exit
-> send: confirm vote (with node id) (type `NodeId`)
loop:
<- recv: info and maybe WAL block (type `SafeKeeperRequest` + bytes)
(break loop if done)
-> send: confirm receipt (type `SafeKeeperResponse`)
My main motivation is to make it easier to attribute time spent in WAL
redo to the request that needed the WAL redo. With this patch, the WAL
redo is performed by the requester thread, so it shows up in stack traces
and in 'perf' report as part of the requester's call stack. This is also
slightly simpler (less lines of code) and should be a bit faster too.
The upcoming layered storage implementation handles GC as a
repository-wide operation because it needs to pay attention to the branch
points of all timelines.
On my laptop, the server was receiving the token as a string with extra
b'...' escaping, e.g as "b'eyJ0....0ifQA'" instead of just "eyJ0....0ifQA".
That was causing the test to fail.
I'm using Python 3.9, while the CI is using Python 3.8. I suspect that's
why. My version of pyjwt might be different too.
See also https://github.com/jpadilla/pyjwt/issues/391.
They represent files and use RelationSizeEntry to track existing and dropped files.
They can be both blocky and non-blocky.
get_relish_size() and get_rel_exists() functions work with physical relishes, not only with blocky ones.
Follow PostgreSQL logic: remove Twophase files when prepared transaction is committed/aborted.
Always store Twophase segments as materialized page images (no wal records).
Current state with authentication.
Page server validates JWT token passed as a password during connection
phase and later when performing an action such as create branch tenant
parameter of an operation is validated to match one submitted in token.
To allow access from console there is dedicated scope: PageServerApi,
this scope allows access to all tenants. See code for access validation in:
PageServerHandler::check_permission.
Because we are in progress of refactoring of communication layer
involving wal proposer protocol, and safekeeper<->pageserver. Safekeeper
now doesn’t check token passed from compute, and uses “hardcoded” token
passed via environment variable to communicate with pageserver.
Compute postgres now takes token from environment variable and passes it
as a password field in pageserver connection. It is not passed through
settings because then user will be able to retrieve it using pg_settings
or SHOW ..
I’ve added basic test in test_auth.py. Probably after we add
authentication to remaining network paths we should enable it by default
and switch all existing tests to use it.
