What I emphasized in this article (author here) is scaling postgres backups. This works because there's already rock-solid systems built into postgres + surrounding tooling to build from.
The broader takeaway is the principle of, "how do I take something that doesn't scale on its own and make it so?" This applies to backups, compute, storage layers, proxies. It's why Neki and Vitess are so powerful for everything from small 1GB databases to petabytes.
Hello! Very nice article. I have a couple of questions if you don't mind.
When doing the last streaming of the wal from the primary the article mentions that the nodes will catch up to replication time `T`.
How do the nodes coordinate this time `T`? Is it simply just choosing a time in the future (after the backup has started) and waiting till they all catch up or is there more realtime coordination happening?
Also, in another part it's mentioned that "Time T is saved to ensure we know the precise time, down to the second, included in this backup." My question is that if 1 second is granular enough? I'm assuming that this is a simplification for the sake of explanation and Time T is a timestamp with at least millisecond granularity. I regularly play with otel data that can have nano-second granularity so I'm assuming is millisecond or more
(Also PlanetScale employee here)
Each shard finishes a backup at it's own time T, so two different shards could finish minutes apart (or even more, depending on the difference in size).
for pretty much any use of a backup, you'll effectively be doing a PITR, not a raw restore from the cluster. The PITR timestamp is what unifies all the clusters together, regardless on when each backup finishes. think of backups as jumpstarts for actual restores (like for cluster resizes), where WAL replay and replication get the node to real time (or some specific point)
with that said you can restore without PITR if you don't care about synchronization, but generally you'll just use PITR
Broadly for Pg, minor version upgrades are straightforward as the data on disk is guaranteed to be compatible. All it takes is a restart or switchover to upgrade.
Major versions are more challenging for "vanilla" Postgres because that's not the case. Storage/catalog formats may change. There needs to be an explicit upgrade process for the data (eg, a database created with v18 won't work out of the box with v19).
The cool thing is, software like Neki (and Vitess for MySQL, which we maintain) have architectures that lend themselves to making this much more feasible. Because the actual database nodes sit behind a router + parser layer with Pg/MySQL compatibility, the upgrades can be done transparently to the user. We plan to write more about how this works in the future.
I believe most major versions of postgres only change metadata, so the downtime only scales with the size of the schema (usually small) and not the size of the data.
You can do live parallel routing and blue-green migrations transparently. In your docs you already describe how to do it but the user has to do it manually at the application level when you can do it just fine at the DB load balancer/router level.
Exactly. When you have a router + query parser in front of the db nodes, the burden of managing multiple versions simultaneously can be taken out of the app and into the database layer.
What I emphasized in this article (author here) is scaling postgres backups. This works because there's already rock-solid systems built into postgres + surrounding tooling to build from.
The broader takeaway is the principle of, "how do I take something that doesn't scale on its own and make it so?" This applies to backups, compute, storage layers, proxies. It's why Neki and Vitess are so powerful for everything from small 1GB databases to petabytes.
Hanging around to answer questions, too :)
Hello! Very nice article. I have a couple of questions if you don't mind.
When doing the last streaming of the wal from the primary the article mentions that the nodes will catch up to replication time `T`.
How do the nodes coordinate this time `T`? Is it simply just choosing a time in the future (after the backup has started) and waiting till they all catch up or is there more realtime coordination happening?
Also, in another part it's mentioned that "Time T is saved to ensure we know the precise time, down to the second, included in this backup." My question is that if 1 second is granular enough? I'm assuming that this is a simplification for the sake of explanation and Time T is a timestamp with at least millisecond granularity. I regularly play with otel data that can have nano-second granularity so I'm assuming is millisecond or more
(Also PlanetScale employee here) Each shard finishes a backup at it's own time T, so two different shards could finish minutes apart (or even more, depending on the difference in size). for pretty much any use of a backup, you'll effectively be doing a PITR, not a raw restore from the cluster. The PITR timestamp is what unifies all the clusters together, regardless on when each backup finishes. think of backups as jumpstarts for actual restores (like for cluster resizes), where WAL replay and replication get the node to real time (or some specific point)
with that said you can restore without PITR if you don't care about synchronization, but generally you'll just use PITR
Interesting, last I checked PlanetScale still doesn't have in place Postgres version updates.
Broadly for Pg, minor version upgrades are straightforward as the data on disk is guaranteed to be compatible. All it takes is a restart or switchover to upgrade.
Major versions are more challenging for "vanilla" Postgres because that's not the case. Storage/catalog formats may change. There needs to be an explicit upgrade process for the data (eg, a database created with v18 won't work out of the box with v19).
The cool thing is, software like Neki (and Vitess for MySQL, which we maintain) have architectures that lend themselves to making this much more feasible. Because the actual database nodes sit behind a router + parser layer with Pg/MySQL compatibility, the upgrades can be done transparently to the user. We plan to write more about how this works in the future.
I believe most major versions of postgres only change metadata, so the downtime only scales with the size of the schema (usually small) and not the size of the data.
You can do live parallel routing and blue-green migrations transparently. In your docs you already describe how to do it but the user has to do it manually at the application level when you can do it just fine at the DB load balancer/router level.
Exactly. When you have a router + query parser in front of the db nodes, the burden of managing multiple versions simultaneously can be taken out of the app and into the database layer.