How does this compare to RisingWave and Materialize?
https://github.com/risingwavelabs/risingwave https://github.com/MaterializeInc/materialize
https://github.com/risingwavelabs/risingwave https://github.com/MaterializeInc/materialize
In short, data streaming is getting popular. Apache Flink, Apache Spark, ksqlDB are traditional players. They work well in some cases, but have challenges on easy-to-use, easy-to-deploy, on even performance sometimes
Proton, RisingWave, Materialize, etc are the alternatives. In the end, it's always 'case by case' to pick up the best tools working for your team in your projects.
If you think this is too general, well - 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 - RisingWave has been open-sourced for 1.5 years and released their cloud mid of last year. It builds its own row-based historical storage, while Proton leverages ClickHouse for faster OLAP-like workload. Also there are more SQL functions supported in Proton, because it's powered by ClickHouse. Proton uses local SSD as the main storage and has an option to send older data to object storage. While RisingWave primarily uses object storage, and use local disk as cache. So in general Proton can achieve lower latency for both streaming or historical queries.
I am a big fan of coffee and drink different kinds of coffee over time, even on the same day. It's all about the use cases and preferences. This could be applied to choose open-source tools to query live data. In the last Current conference, me and Gang had a talk about different tools and different coffee. You may check https://www.timeplus.com/post/query-kafka-with-sql-current23
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 impressions 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
[1]: https://github.com/MaterializeInc/materialize/blob/main/doc/...
Proton is written in C++, adding streaming processing to ClickHouse and make stream the main concept. It is designed to support streaming analytics where aggregating large number of data over time is the focus.
In geneneral, these three are very similar.