[bddicken]

B+trees combined with sequential IDs are great for writes. This is because we are essentially just appending new rows to the "linked list" at the bottom level of the tree. We can also keep a high fill % if we know there isn't a lot of data churn.

If you're sharding based purely on sequential ID ranges, then yes this is a problem. Its better practice to shard based on a hash of your ID, so sequential id assignments turn into non-sequential shard keys, keeping things evenly distributed.

[amarant]

Oh wow, that's a super simple solution, and I can immediately see how this gets you the best of both worlds!

And since it's only used for speedy lookup we can even use a fast, cheap and non-secure hashing algorithm, so it's really a low-cost operation!

Thanks! This was really one of those aha-moments where I feel kinda stupid to not have thought of it myself!

[bddicken]

I've also written about sharding.

https://planetscale.com/blog/database-sharding

[amarant]

Thanks! Another great article! It strikes me that modulo sharding on a sequential id would probably work rather well, but it was not mentioned in this article. Is there a reason I'm not seeing that this is bad? I guess resharding might be problematic, as you can't easily split a shard in two without rewriting every shard if you do that...

[evil-olive]

> I guess resharding might be problematic

yes, that's the crux of the problem. when you have a sharded database, typically you want to be able to add (and/or remove) shards easily and non-disruptively.

for example - your database is currently sharded across N nodes, and it's overloaded due to increased traffic, so you want to increase it to N+1 nodes (or N+M nodes, which can add complexity in some cases)

if adding a shard causes a significant increase in load on the database, that's usually a non-starter for a production workload, because at the time you want to do it, the database is already overloaded

you can read about this in the original Dynamo paper [0] from almost 20 years ago - consistent hashing is used to select 3 of the N nodes to host a given key. when node N+1 is added, it joins the cluster in such a way that it will "take over" hosting 1/Nth of the data, from each of the N nodes - meaning that a) the joining process places a relatively small load on each of those N nodes and b) once the node is fully joined, it reduces overall load evenly across all N nodes.

0: https://www.allthingsdistributed.com/2007/10/amazons_dynamo....

[cogman10]

For our DBs (which are often unsharded), we've found the best performance using the user account ID as the first part of the cluster key and then a sequential id for whatever the record is as the second.

It's not as good as just a sequential ID at keeping the fragmentation and data movement down. However, it does ultimately lead to the best write performance for us because the user data ends up likely still appending to an empty page. It allows for more concurrent writes to the same table because they aren't all fighting over that end page.

UUIDv4 is madness.