Page server architecture
The Page Server has a few different duties:
- Respond to GetPage@LSN requests from the Compute Nodes
- Receive WAL from WAL safekeeper
- Replay WAL that's applicable to the chunks that the Page Server maintains
- Backup to S3
S3 is the main fault-tolerant storage of all data, as there are no Page Server replicas. We use a separate fault-tolerant WAL service to reduce latency. It keeps track of WAL records which are not synced to S3 yet.
The Page Server consists of multiple threads that operate on a shared repository of page versions:
| WAL
V
+--------------+
| |
| WAL receiver |
| |
+--------------+
+----+
+---------+ .......... | |
| | . . | |
GetPage@LSN | | . backup . -------> | S3 | -------------> | Page | repository . . | | | Service | .......... | | page | | +----+ <------------- | | +---------+ +--------------------+ | Checkpointing / | | Garbage collection | +--------------------+
Legend:
+--+ | | A thread or multi-threaded service +--+
.... . . Component at its early development phase. ....
---> Data flow <---
Page Service
The Page Service listens for GetPage@LSN requests from the Compute Nodes, and responds with pages from the repository.
WAL Receiver
The WAL receiver connects to the external WAL safekeeping service (or directly to the primary) using PostgreSQL physical streaming replication, and continuously receives WAL. It decodes the WAL records, and stores them to the repository.
Repository
The repository stores all the page versions, or WAL records needed to reconstruct them. Each tenant has a separate Repository, which is stored in the .zenith/tenants/ directory.
Repository is an abstract trait, defined in repository.rs. It is
implemented by the LayeredRepository object in
layered_repository.rs. There is only that one implementation of the
Repository trait, but it's still a useful abstraction that keeps the
interface for the low-level storage functionality clean. The layered
storage format is described in layered_repository/README.md.
Each repository consists of multiple Timelines. Timeline is a workhorse that accepts page changes from the WAL, and serves get_page_at_lsn() and get_rel_size() requests. Note: this has nothing to do with PostgreSQL WAL timeline. The term "timeline" is mostly interchangeable with "branch", there is a one-to-one mapping from branch to timeline. A timeline has a unique ID within the tenant, represented as 16-byte hex string that never changes, whereas a branch is a user-given name for a timeline.
Each repository also has a WAL redo manager associated with it, see
walredo.rs. The WAL redo manager is used to replay PostgreSQL WAL
records, whenever we need to reconstruct a page version from WAL to
satisfy a GetPage@LSN request, or to avoid accumulating too much WAL
for a page. The WAL redo manager uses a Postgres process running in
special zenith wal-redo mode to do the actual WAL redo, and
communicates with the process using a pipe.
Checkpointing / Garbage Collection
Periodically, the checkpointer thread wakes up and performs housekeeping duties on the repository. It has two duties:
Checkpointing
Flush WAL that has accumulated in memory to disk, so that the old WAL can be truncated away in the WAL safekeepers. Also, to free up memory for receiving new WAL. This process is called "checkpointing". It's similar to checkpointing in PostgreSQL or other DBMSs, but in the page server, checkpointing happens on a per-segment basis.
Garbage collection
Remove old on-disk layer files that are no longer needed according to the PITR retention policy
Backup service
The backup service, responsible for storing pageserver recovery data externally.
Currently, pageserver stores its files in a filesystem directory it's pointed to. That working directory could be rather ephemeral for such cases as "a pageserver pod running in k8s with no persistent volumes attached". Therefore, the server interacts with external, more reliable storage to back up and restore its state.
The code for storage support is extensible and can support arbitrary ones as long as they implement a certain Rust trait. There are the following implementations present:
- local filesystem — to use in tests mainly
- AWS S3 - to use in production
Implementation details are covered in the backup readme and corresponding Rust file docs, parameters documentation can be found at settings docs.
The backup service is disabled by default and can be enabled to interact with a single remote storage.
CLI examples:
- Local FS:
${PAGESERVER_BIN} -c "remote_storage={local_path='/some/local/path/'}" - AWS S3 :
${PAGESERVER_BIN} -c "remote_storage={bucket_name='some-sample-bucket',bucket_region='eu-north-1', prefix_in_bucket='/test_prefix/',access_key_id='SOMEKEYAAAAASADSAH*#',secret_access_key='SOMEsEcReTsd292v'}"
For Amazon AWS S3, a key id and secret access key could be located in ~/.aws/credentials if awscli was ever configured to work with the desired bucket, on the AWS Settings page for a certain user. Also note, that the bucket names does not contain any protocols when used on AWS.
For local S3 installations, refer to the their documentation for name format and credentials.
Similar to other pageserver settings, toml config file can be used to configure either of the storages as backup targets. Required sections are:
[remote_storage]
local_path = '/Users/someonetoignore/Downloads/tmp_dir/'
or
[remote_storage]
bucket_name = 'some-sample-bucket'
bucket_region = 'eu-north-1'
prefix_in_bucket = '/test_prefix/'
access_key_id = 'SOMEKEYAAAAASADSAH*#'
secret_access_key = 'SOMEsEcReTsd292v'
Also, AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID variables can be used to specify the credentials instead of any of the ways above.
TODO: Sharding
We should be able to run multiple Page Servers that handle sharded data.