Materialized views are obviously useful

What a great post. Humble and honest and simple and focused on an issue most developers think is so simple (“why not just vibe code SQL?”, “whatever, just scale up the RDS instance”).

Compliments aside, where this article stops is where things get exciting. Postgres shines here, as does Vitess, Cassandra, ScyllaDB, even MongoDB has materialized views now. Vitess and Scylla are so good, it’s a shame they’re not more popular among smaller startups!

What I haven’t seen yet is a really good library for managing materialized views.

Convex's queries[0] are another example, with perhaps a somewhat simpler approach to the tracking and invalidation.

[0] https://stack.convex.dev/how-convex-works

Materialize.com and Snowflake have pretty reliable incremental materialised views now, with caveats that aren’t back breaking. If you can transform in SQL rather than having to build a whole new pipeline or microservice to do the work that’s a pure operational win. I consider this alongside hybrid transactional/analytical databases to be the holy grail of data infrastructure. Finally we can stop just shuffling data around, support almost all workloads in one place, and get some work done.

FYI, Zero uses incremental view maintenance internally as the core of its sync engine:

https://zero.rocicorp.dev/docs/introduction

IVM is what allows the core feature of our DX: that users can just do a fairly large query with sorts and joins, like:

  zero.query.posts.where('authorID', 42)
    .orderBy('created', 'desc')
    .limit(1000)
    .related('comments',
      c => c.orderBy('created', 'desc').limit(10))

TanStack DB also uses IVM, but only client-side currently.

If you are interested in such things, you can take a poke around the source here: https://github.com/rocicorp/mono/tree/main/packages/zql. Or come find us in Discord: https://discord.rocicorp.dev/

> And then by magic the results of this query will just always exist and be up-to-date.

With PostgreSQL the materialized view won't be automatically updated though, you need to do `REFRESH MATERIALIZED VIEW` manually.

> I don’t know yet if the implementations of this yet are good enough to use at scale. Maybe they’re slow or maybe the bugs aren’t ironed out yet.

This technique is very well supported in the big commercial engines. In MSSQL's Indexed View case, the views are synchronously updated when the underlying tables are modified. This has implications at insert/update/delete time, so if you are going to be doing a lot of these you might want to do it on a read replica to avoid impact to production writes.

https://learn.microsoft.com/en-us/sql/relational-databases/v...

https://learn.microsoft.com/en-us/sql/t-sql/statements/creat...

This triggered me in the article

'There are a few startups these days peddling a newfangled technology called “incremental view maintenance” or “differential dataflow”. '

Incremental view maintenance can change recomputation cost of view updates from O(N) to O(1). DBSP is based on z-sets, a generalization of relational algebra. The paper won best paper at SIGMOD. There is a startup, Feldera, commercializing it.

This is just ignorance to dismiss as 'new fangled'.

I curious why an index can't handle that first query well.

Here are some commercial and source-available options:

I'm not affiliated with any of these names, I'm just really interested in IVM:

Materialize Readyset Feldera RisingWave

> (Technically speaking, if 100 people load the same page at the same time and the cache isn’t populated yet, then we’ll end up sending 100 queries to the database which isn’t amazing, but let’s just pretend we didn’t hear that.)

Isn't their tech to address that, like golang's "singleflight"?

This is a weird article. The author doesn't even mention what database they are talking about then just drops in some SQL that looks like Postgres. If you think Postgres will magically have the right values in it for a materialized view you will be very disappointed...