octosql-plugin-random_data VS materialize

Hey!

> I think the main fundamental difference is that this wants all of the data upfront in a data file.

Absolutely not! Moreover, OctoSQL can push down predicates to databases so that it only has to download a small subset of the table, if the datasource and query allow it.

> Very easy to model HTTP APIs as a table.

"Very easy" is relative, but you can take a look at the random_data[0] datasource which is exactly this. I'm also planning to add a GitHub datasource fairly soon. That said, there is Steampipe[1] for which this is the main use case afaik (hitting API's and exposing them as tables through Postgres FWD's written in Go), so it might be a smoother and more polished experience. There's also tons of plugins already available for it.

> Easy to model basically anything as a table for example files on my filesystem.

Yep, definitely. That's the idea behind OctoSQL. Strive to create a tool for easily exposing anything through SQL (like your machine's processes list, an API, and join that with a file, or database). There's still lot's of documentation work left to do though, in order to make the plugin authoring experience easier.

> A decent query planner so that I can avoid expensive things (like API calls) if I can determine if I need the object based on something cheaper (like a local disk access).

Probably depends on the use-case, and it sometimes needs you to be fairly explicit, but OctoSQL does in fact do that. It will push down predicates to underlying databases, which means joining something small with something very big (while only taking very small amounts of the latter) can be very fast with LOOKUP JOIN's.

> I want something that is easy to extend to sources that are possibly non-listable or at the very least I don't want to have all of the data available.

Doable. An example of this is the `plugins.available_versions` table[2]. It requires you to provide the plugin name as a predicate, as the versions need to be downloaded from the plugin's own repository (and listing all plugin repositories on each query isn't really what you want to be doing). You can also LOOKUP JOIN with the `plugins.available_plugins` table if that is indeed what you want.

[0]: https://github.com/cube2222/octosql-plugin-random_data

[1]: https://steampipe.io

[2]: https://github.com/cube2222/octosql/blob/main/datasources/pl...

[2] https://materialize.com/

To fully leverage the data is the new oil concept, companies require a special database designed to manage vast amounts of data instantly. This need has led to different database forms, including NoSQL databases, vector databases, time-series databases, graph databases, in-memory databases, and in-memory data grids. Recent years have seen the rise of cloud-based streaming databases such as RisingWave, Materialize, DeltaStream, and TimePlus. While they each have distinct commercial and technical approaches, their overarching goal remains consistent: to offer users cloud-based streaming database services.

> Materialize no longer provide the latest code as an open-source software that you can download and try. It turned from a single binary design to cloud-only micro-service

Materialize CTO here. Just wanted to clarify that Materialize has always been source available, not OSS. Since our initial release in 2020, we've been licensed under the Business Source License (BSL), like MariaDB and CockroachDB. Under the BSL, each release does eventually transition to Apache 2.0, four years after its initial release.

Our core codebase is absolutely still publicly available on GitHub [0], and our developer guide for building and running Materialize on your own machine is still public [1].

It is true that we substantially rearchitected Materialize in 2022 to be more "cloud-native". Our new cloud offering offers horizontal scalability and fault tolerance—our two most requested features in the single-binary days. I wouldn't call the new architecture a microservices design though! There are only 2-3 services, each quite substantial, in the new architecture (loosely: a compute service, an orchestration service, and, soon, a load balancing service).

We do push folks to sign up for a free trial of our hosted cloud offering [2] these days, rather than trying to start off by running things locally, as we generally want folks' first impression of Materialize to be of the version that we support for production use cases. A all-in-one single machine Docker image does still exist, if you know where to look, but it's very much use-at-your-own-risk, and we don't recommend using it for anything serious, but it's there to support e.g. academic work that wants to evaluate Materialize's capabilities to incrementally maintain recursive SQL queries.

If folks have questions about Materialize, we've got a lively community Slack [3] where you can connect directly with our product and engineering teams.

[0]: https://github.com/MaterializeInc/materialize/tree/main

Some recent solutions to this problem include Differential Dataflow and Materialize. It would be neat if postgres adopted something similar for live-updating materialized views.

https://github.com/timelydataflow/differential-dataflow

https://materialize.com/

Materialize | Full-Time | NYC Office or Remote | https://materialize.com

Materialize is an Operational Data Warehouse: A cloud data warehouse with streaming internals, built for work that needs action on what’s happening right now. Keep the familiar SQL, keep the proven architecture of cloud warehouses but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

Materialize is the operational data warehouse built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.

Senior/Staff Product Manager - https://grnh.se/69754ebf4us

Senior Frontend Engineer - https://grnh.se/7010bdb64us

===

Investors include Redpoint, Lightspeed and Kleiner Perkins.

Materialize | EM (Compute), Senior PM | New York, New York | https://materialize.com/

You shouldn't have to throw away the database to build with fast-changing data. Keep the familiar SQL, keep the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date.

That is Materialize, the only true SQL streaming database built from the ground up to meet the needs of modern data products: Fresh, Correct, Scalable — all in a familiar SQL UI.

Engineering Manager, Compute - https://grnh.se/4e14099f4us

Senior Product Manager - https://grnh.se/587c36804us

VP of Marketing - https://grnh.se/9caac4b04us

How does it compare to https://materialize.com/ ?