quix VS logica

Aerospike Data Browser is basically a stack that consists of Quix, Presto, and the Aerospike Connector for Presto, and is dockerized. Figure 1 depicts an under the hood view. The Quix UI provides a DB Explorer and a SQL editor, in addition to a notebook manager for managing your notebooks. Presto exposes a JDBC interface to Quix and uses the Aerospike Connector to translate SQL queries into API calls to the DB. Building a stack with the aforementioned components for a desktop installation is not trivial by any means. Presto can scale to 100’s of nodes for a large scale deployment, but we wanted to limit the data browser to a single Presto instance that would run both the coordinator and worker in the developer's desktop environment. Our initial size of the Presto docker image was over 2GB, which was not acceptable. Hence, we stripped out all but the Aerospike connector from the plugin directory. Similarly, we had to downsize the Quix connector. Finally, we got the compressed docker image size under 1GB. We also made a design decision to default to schema inference so that a user that does not know the schema apriori is not left out.

If you're interested in this I would also recommend you check out Logica[0], which is a datalog-like language that is explicitly made to compile to SQL queries.

0: https://logica.dev/

> I guess the intention is to be better than SQL but then I was left with "under which circumstances?"

Excellent question.

Two of the most common use cases for databases are "transactional processing" (manipulating small numbers of rows in real time) and "analytical processing" (querying enormous numbers of rows, typically in a read-only fashion).

SQL is generally fine for transactional workloads.

But analytical queries sometimes involve multi-page queries, with lots of JOINs and CTEs. And these queries are often automatically generated.

And once you start writing actual multi-page "programs" in SQL, you may decide that it's a fairly clunky and miserable programming language. What Datalog typically buys you is a way to cleanly decompose large queries into "subroutines." And it offers a simpler syntax for many kinds of complex JOINs.

Unfortunately, there isn't really a standard dialect of Datalog, or even a particular dialect with mainstream traction. So choosing Datalog is a bit of a tradeoff: does it buy you enough, for your use case, that it's worth being a bit outside the mainstream? Maybe! But I'd love to see something like Logica gain more traction: https://logica.dev/

Interesting; a Google engineer previously published a Datalog variant for BigQuery: https://logica.dev/

This new language seems similar to differential-Datalog (which is sadly in maintenance mode): https://news.ycombinator.com/item?id=33521561

Looks pretty cool. I'd be interested if the README had a comparison with Google's Logica (https://github.com/EvgSkv/logica)

Oh wow that is neat!

And yes, this kind of thing is why datalog is a lot more amenable to fast query plans & runtimes than prolog. This part is especially cool: https://github.com/EvgSkv/logica/blob/main/compiler/dialects...

Google new programming Language that compiles to SQL (Support BigQuery and Postgres) feels very exciting. Blog: https://opensource.googleblog.com/2021/04/logica-organizing-your-data-queries.html Github: https://github.com/EvgSkv/logica

Going to be? It already is. In fact, one thing the article misses is right there at the bottom of the project page: