GROQ
logica
GROQ | logica | |
---|---|---|
3 | 19 | |
365 | 1,683 | |
2.5% | - | |
3.4 | 9.1 | |
18 days ago | 27 days ago | |
JavaScript | Jupyter Notebook | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
GROQ
-
[AskJS] Frontend tool to interactively filter a JSON
Check out the GROQ Arcade: https://groq.dev/
- JSONiq: The JSON Query Language
-
PathQuery, Google's Graph Query Language
PathQuery is of course a lot more complex, but the basic structure seems very similar. One thing GROQ does not have yet is recursive querying of the type needed to traverse graphs, but this is on our roadmap to implement.
(Disclosure: I work on GROQ at Sanity.)
[1] https://github.com/sanity-io/GROQ
[2] https://www.sanity.io/
logica
-
Prolog language for PostgreSQL proof of concept
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/
- Logica
- New welcome page for Logica language
-
Introduction to Datalog
> 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/
-
Mangle, a programming language for deductive database programming
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
- Show HN: PRQL 0.2 – Releasing a better SQL
-
Show HN: PRQL – A Proposal for a Better SQL
Looks pretty cool. I'd be interested if the README had a comparison with Google's Logica (https://github.com/EvgSkv/logica)
-
PathQuery, Google's Graph Query Language
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...
-
Thought about Logica: Google new programming language that compiles to SQL ?
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
-
Google Logica Aims To Make SQL Queries More Reusable and Readable
Going to be? It already is. In fact, one thing the article misses is right there at the bottom of the project page:
What are some alternatives?
jfq - JSONata on the command line
scryer-prolog - A modern Prolog implementation written mostly in Rust.
jmespath.py - JMESPath is a query language for JSON.
ungoogled-chromium-archlinux - Arch Linux packaging for ungoogled-chromium
sanity - Sanity Studio – Rapidly configure content workspaces powered by structured content
malloy - Malloy is an experimental language for describing data relationships and transformations.
brackit - Query processor with proven optimizations, ready to use for your JSON store to query semi-structured data with JSONiq. Can also be used as an ad-hoc in-memory query processor.
prql - PRQL is a modern language for transforming data — a simple, powerful, pipelined SQL replacement
gron - Make JSON greppable!
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
sirix - SirixDB is an an embeddable, bitemporal, append-only database system and event store, storing immutable lightweight snapshots. It keeps the full history of each resource. Every commit stores a space-efficient snapshot through structural sharing. It is log-structured and never overwrites data. SirixDB uses a novel page-level versioning approach.
differential-datalog - DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.