blog
quokka
blog | quokka | |
---|---|---|
10 | 23 | |
1,926 | 1,082 | |
- | - | |
6.7 | 8.3 | |
9 days ago | 7 months ago | |
JavaScript | Python | |
- | 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.
blog
- Advent of Code 2023 in Recursive SQL
-
Big Data Is Dead
This reminds me of a great blog post by Frank McSherry (Materialize, timely dataflow, etc) talking about how using the right tools on a laptop could beat out a bunch of these JVM distributed querying tools because... data locality basically.
https://github.com/frankmcsherry/blog/blob/master/posts/2015...
- Quokka and Spark/Databricks
- Rust for Data-Intensive Computation (2020)
- Cost in the Land of Databases (2017)
-
Show HN: Cozo – new Graph DB with Datalog, embedded like SQLite, written in Rust
Oh, cool!
And yeah, licenses can be challenging and frustrating, especially the first time you release a major project.
I am really super excited by the idea of embedded Datalog in Rust. I run into a lot of situations where I need something that fits in that awkward gap between SQL and Prolog. I want more expressiveness, better composability, and better graph support than SQL. But I also want finite-sized results that I can materialize in bounded time.
There has been some very neat work with incrementally-updated Datalog in the Rust community. For example, I think Datafrog is really neat: https://github.com/frankmcsherry/blog/blob/master/posts/2018... But it's great to see more neat projects in this space, so thank you.
- [AskJS] JavaScript for data processing
-
Differential Dataflow for Mere Mortals
They used to but Frank McSherry (author of differential dataflow) wrote them a specialized version without all the dataflow infrastructure [1]. It's part of the rust-lang nursery [2] now but hasn't been updated in a while, so I'm not sure what happened to it.
[1] https://github.com/frankmcsherry/blog/blob/master/posts/2018...
[2] https://github.com/rust-lang/datafrog
-
Why isn't differential dataflow more popular?
Importantly, this doesn't just use memoization (it actually avoids having to spend memory on that), but rather uses operators (nodes in the dataflow graph) that directly work with `(time, data, delta)` tuples. The `time` is a general lattice, so fairly flexible (e.g. for expressing loop nesting/recursive computations, but also for handling multiple input sources with their own timestamps), and the `delta` type is between a (potentially commutative) semigroup (don't be confused, they use addition as the group operation) and an abelian group. E.g. collections that are iteratively refined in loops often need an abelian `delta` type, while monoids (semigroup + explicit zero element) allow for efficient append-only computations [0].
[0]: https://github.com/frankmcsherry/blog/blob/master/posts/2019...
quokka
-
How Query Engines Work
An awesome read!
Something related that I found out about from HN a few months back is another engine called quokka. It's particularly interesting and applicable how quokka schedules distributed queries to outperform Spark https://github.com/marsupialtail/quokka/blob/master/blog/why...
- Quokka – Distributed Polars on Ray
-
Algorithmic Trading with Go
Hi Justin, you might be interested in my blog: https://github.com/marsupialtail/quokka/blob/master/blog/bac... advocating a cloud based approach.
You don't have to use the system I am building, but it's worth thinking about that design.
-
Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
SQL support is very challenging.
I work on Quokka (https://github.com/marsupialtail/quokka). I support Iceberg reads. Recently we are adding SQL support from just parsing the DuckDB logical plan, though that is very challenging as well.
The Python world lacks a standard for a plug and play SQL query optimizer. Apache Calcite is good for the JVM world, but not great if you are trying to cut out the JVM.
- Why your dataframe library needs to understand vector embeddings
-
The Inner Workings of Distributed Databases
In case people are interested, I wrote a post about fault tolerance strategies of data systems like Spark and Flink: https://github.com/marsupialtail/quokka/blob/master/blog/fau...
The key difference here is that these systems don't store data, so fault tolerance means recovering within a query instead of not losing data.
-
Launch HN: DAGWorks – ML platform for data science teams
would love to collaborate on an integration with pyquokka (https://github.com/marsupialtail/quokka) once I put out a stable release end of this month :-)
-
is spark always your go to solution ?
Then you should keep an eye on quokka. This may become the "Spark" for Polars/DuckDB. It seems to be under active development though I'm not sure how stable it is.
- Distributed fault tolerance made simple
- Fault tolerance for distributed data systems is quite simple
What are some alternatives?
differential-dataflow - An implementation of differential dataflow using timely dataflow on Rust.
opteryx - 🦖 A SQL-on-everything Query Engine you can execute over multiple databases and file formats. Query your data, where it lives.
btree-typescript - A reasonably fast in-memory B+ tree with a powerful API based on the standard Map. Small minified. Well documented.
cempaka - "Write a trading bot which buys low and sells high." Sounds simple enough, right?
Hydra - Functional hybrid modelling (FHM) language for modelling and simulation of physical systems using implicitly formulated (undirected) Differential Algebraic Equations (DAEs)
awesome-pipeline - A curated list of awesome pipeline toolkits inspired by Awesome Sysadmin
rslint - A (WIP) Extremely fast JavaScript and TypeScript linter and Rust crate
spyql - Query data on the command line with SQL-like SELECTs powered by Python expressions
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.
pg8000 - A Pure-Python PostgreSQL Driver
pond - Immutable timeseries data structures built with Typescript
sqlglot - Python SQL Parser and Transpiler