dragonfly
Apache Arrow
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dragonfly | Apache Arrow | |
---|---|---|
49 | 75 | |
23,791 | 13,523 | |
6.1% | 2.2% | |
9.9 | 10.0 | |
6 days ago | 1 day ago | |
C++ | C++ | |
BSL 1.1 | 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.
dragonfly
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Redict is an independent, copyleft fork of Redis
https://github.com/dragonflydb/dragonfly is another option. Not a fork but API-compatible reimplementation.
- Redis License Changed
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Scaling Real-Time Leaderboards with Dragonfly
Our journey will involve leveraging the capabilities of Dragonfly, a highly efficient drop-in replacement for Redis, known for its ultra-high throughput and multi-threaded share-nothing architecture. Specifically, we'll be utilizing two of Dragonfly's data types: Sorted-Set and Hash. These data structures are perfect for handling real-time data and ranking systems, making them ideal for our leaderboards.
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Announcing Dragonfly Search
2023 has been a year with remarkable advancements in AI capabilities, and at Dragonfly, we are thrilled to power new use cases with our latest release: Dragonfly Search. This new feature set, debuting in Dragonfly v1.13, is a subset of RediSearch-compatible commands implemented natively in Dragonfly, allowing for both vector search and faceted search use cases in the highly scalable and performant Dragonfly in-memory data store.
- Dragonfly v1.10.0
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Dragonfly Cache Design
If you have not heard about Dragonfly - please check it out. It uses - what I hope - novel and interesting ideas backed up by the research from recent years [1] and [2]. It's meant to fix many problems that exist with Redis today. I have been working on Dragonfly for the last 7 months and it has been one of the more interesting and challenging projects I've ever done!
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Generating Income from Open Source
I recently ran across the the license for Dragonfly [1] which has some restrictions (rights reserved), but 5 years after the license date the license switches to Apache 2.0. Basically a timed-limited rights reservation. I don't hate it. I might even contribute to such a project for free.
I would consider something like this: When I release code, it's rights reserved for 5 years, then open-source (and this baked into an irrevocable license). Anyone may use the software for non-commercial purposes. Anyone may contribute, those who contribute will be granted permission for commercial use if I deem their contributions significant enough. Anyone may distribute the software under these terms.
If such a model became popular, I have a hard time imagining it could make things any worse. It might even accelerate open-source development. You might say, "but it's not open-source", fair enough, but we can view it as open-source contribution with a delay. For example, if this model became wildely popular this year, and we saw great progress with this model, then come 2028 we would be flooded with new open-source software and ultimately might be better off than it would have been without this model.
(And this whole thing makes me rethink copyright and patents and how much they really contribute to society. Perhaps they should be shortened?)
[1]: https://github.com/dragonflydb/dragonfly/blob/main/LICENSE.m...
- dragonflydb/dragonfly: A modern replacement for Redis and Memcached
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Redis HA on k8s without Sentinel?
Maybe check out https://www.dragonflydb.io/ It claims to have a full redis implementation.
- Dragonfly is about 10x slower than Redis
Apache Arrow
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How moving from Pandas to Polars made me write better code without writing better code
In comes Polars: a brand new dataframe library, or how the author Ritchie Vink describes it... a query engine with a dataframe frontend. Polars is built on top of the Arrow memory format and is written in Rust, which is a modern performant and memory-safe systems programming language similar to C/C++.
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From slow to SIMD: A Go optimization story
I learned yesterday about GoLang's assembler https://go.dev/doc/asm - after browsing how arrow is implemented for different languages (my experience is mainly C/C++) - https://github.com/apache/arrow/tree/main/go/arrow/math - there are bunch of .S ("asm" files) and I'm still not able to comprehend how these work exactly (I guess it'll take more reading) - it seems very peculiar.
The last time I've used inlined assembly was back in Turbo/Borland Pascal, then bit in Visual Studio (32-bit), until they got disabled. Then did very little gcc with their more strict specification (while the former you had to know how the ABI worked, the latter too - but it was specced out).
Anyway - I wasn't expecting to find this in "Go" :) But I guess you can always start with .go code then produce assembly (-S) then optimize it, or find/hire someone to do it.
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Time Series Analysis with Polars
One is related to the heritage of being built around the NumPy library, which is great for processing numerical data, but becomes an issue as soon as the data is anything else. Pandas 2.0 has started to bring in Arrow, but it's not yet the standard (you have to opt-in and according to the developers it's going to stay that way for the foreseeable future). Also, pandas's Arrow-based features are not yet entirely on par with its NumPy-based features. Polars was built around Arrow from the get go. This makes it very powerful when it comes to exchanging data with other languages and reducing the number of in-memory copying operations, thus leading to better performance.
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TXR Lisp
IMO a good first step would be to use the txr FFI to write a library for Apache arrow: https://arrow.apache.org/
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3D desktop Game Engine scriptable in Python
https://www.reddit.com/r/O3DE/comments/rdvxhx/why_python/ :
> Python is used for scripting the editor only, not in-game behaviors.
> For implementing entity behaviors the only out of box ways are C++, ScriptCanvas (visual scripting) or Lua. Python is currently not available for implementing game logic.
C++, Lua, and Python all implement CFFI (C Foreign Function Interface) for remote function and method calls.
"Using CFFI for embedding" https://cffi.readthedocs.io/en/latest/embedding.html :
> You can use CFFI to generate C code which exports the API of your choice to any C application that wants to link with this C code. This API, which you define yourself, ends up as the API of a .so/.dll/.dylib library—or you can statically link it within a larger application.
Apache Arrow already supports C, C++, Python, Rust, Go and has C GLib support Lua:
https://github.com/apache/arrow/tree/main/c_glib/example/lua :
> Arrow Lua example: All example codes use LGI to use Arrow GLib based bindings
pyarrow.from_numpy_dtype:
- Show HN: Udsv.js – A faster CSV parser in 5KB (min)
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Interacting with Amazon S3 using AWS Data Wrangler (awswrangler) SDK for Pandas: A Comprehensive Guide
AWS Data Wrangler is a Python library that simplifies the process of interacting with various AWS services, built on top of some useful data tools and open-source projects such as Pandas, Apache Arrow and Boto3. It offers streamlined functions to connect to, retrieve, transform, and load data from AWS services, with a strong focus on Amazon S3.
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Cap'n Proto 1.0
Worker should really adopt Apache Arrow, which has a much bigger ecosystem.
https://github.com/apache/arrow
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C++ Jobs - Q3 2023
Apache Arrow
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Wheel fails for pyarrow installation
I am aware of the fact that there are other posts about this issue but none of the ideas to solve it worked for me or sometimes none were found. The issue was discussed in the wheel git hub last December and seems to be solved but then it seems like I'm installing the wrong version? I simply used pip3 install pyarrow, is that wrong?
What are some alternatives?
KeyDB - A Multithreaded Fork of Redis
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
skytable - Skytable is a modern scalable NoSQL database with BlueQL, designed for performance, scalability and flexibility. Skytable gives you spaces, models, data types, complex collections and more to build powerful experiences
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
Redis - Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
Memcached - memcached development tree
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
Aerospike - Aerospike Database Server – flash-optimized, in-memory, nosql database
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
glommio - Glommio is a thread-per-core crate that makes writing highly parallel asynchronous applications in a thread-per-core architecture easier for rustaceans.
ClickHouse - ClickHouse® is a free analytics DBMS for big data