postgresql-contrib VS risingwave

You can use https://github.com/twosigma/postgresql-contrib/blob/master/m... and write triggers to update your views or mark them as needing asynchronous refreshes. It's not as automatic as what you'd like, but it's a lot better than having to keep waiting.

I use PG with an alternative materialized views implementation[0] that is pure PlPgSQL and that exposes real tables that can be used to write to in triggers, and where the views can be marked stale too.

This means hand-coding triggers to keep the materializations up to date, or else to mark them as out of date (because maybe some operations would be slow or hard to hand-code triggers for), but this works remarkably well.

As a bonus, I get an update history table that can be used to generate updates to external systems.

In principle one can get the AST for a VIEW's query from the PG catalog and use that generate triggers on the tables it queries to keep it up to date. In practice that's only trivial for some kinds of queries, and I've not written such a tool yet.

[0] https://github.com/twosigma/postgresql-contrib/blob/master/m...

How does this compare to RisingWave and Materialize?

https://github.com/risingwavelabs/risingwave

Hey everyone - One and a half year ago, we open sourced RisingWave, a Rust-built streaming database, under Apache 2.0 license. Two weeks ago, we released RisingWave 1.3. Just last week, we unveiled RisingWave's roadmap.

Look out for next month’s edition to see what new, exciting features will be added. Check out the RisingWave GitHub repository to stay up to date on the newest features and planned releases.

If interested, please feel free to join our Slack community! Thanks eveyone for your generous support!

We will spin up on our local environment the existing RisingWave fully-featured demo cluster on GitHub which is composed of multiple RisingWave components. To simplify this task, it leverages docker-compose.yaml file which includes additional containers for Kafka message broker, and data generation service.

To complete the steps in this guide, you must download/clone and work on an existing sample project on GitHub. The project uses Docker for convenience and consistency. It provides a containerized development environment that includes the services you need to build the sample data pipeline.

Hey have you looked at RisingWave (https://github.com/risingwavelabs/risingwave) before? It's a stream processing system with PostgreSQL interface. It also have integrations similar to Flink CDC.