dragonfly
Apache Arrow
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dragonfly | Apache Arrow | |
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49 | 75 | |
23,037 | 13,338 | |
3.7% | 2.0% | |
9.9 | 10.0 | |
3 days ago | 6 days 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.
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Redis License Changed
Check out DragonflyDB (BSL): https://www.dragonflydb.io/
BSL is not OSI-approved, but it’s a much more reasonable AWS-resistant license. It’s the same license CockroachDB uses, for example.
KeyDB (BSL, acquired by Snapchat) is also an option: https://keydb.dev/
BSL is a much better license, but it’s a gamble on how long KeyDB will be supported. I don’t want to mess around with such a core part of my architecture.
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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...
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Dragonfly – The most performant in-memory data store on Earth
2. You are right, I need to shut the fuck up and let self-righteous hn crowd with torches do what they do best - find a weakness, and push it until they get bored and switch to beat another builder. You asked me about my perspectives and priorities? These are my priorities: https://github.com/dragonflydb/dragonfly/graphs/contributors
> Developers do not want to manage a cluster of single cpu processes. Not on their laptops and not in the production. And it's not just about management complexity. See this https://github.com/dragonflydb/dragonfly/issues/1229 and it's just one example. Single cpu - is just not enough for today use-cases.
That may all very well be the case – let us assume it is for the sake of the argument although I have some comments about that as well – but that still means the argument is "Redis is too complex to run on multiple CPUs" and/or "Redis is poor for these workloads" (I didn't investigate that issue in-depth), and not "Redis is unable to do much work with this very powerful AWS instance". There two are very different things. There is no nuance anywhere in the benchmark. A reader might very well believe that this is all the performance they're going to get out of Redis on that machine, which that's clearly not the case.
License is important: https://github.com/dragonflydb/dragonfly/blob/main/LICENSE.m...
Dragonfly Business Source License 1.1
License: BSL 1.1
Licensor: DragonflyDB, Ltd.
Licensed Work: Dragonfly including the software components, or any portion of them, and any modification.
Change Date: March 15, 2028
Change License: Apache License, Version 2.0, as published by the Apache Foundation.
Additional Use Grant: You may make use of the Licensed Work (i) only as part of your own product or service, provided it is not an in-memory data store product or service; and (ii) provided that you do not use, provide, distribute, or make available the Licensed Work as a Service. A “Service” is a commercial offering, product, hosted, or managed service, that allows third parties (other than your own employees and contractors acting on your behalf) to access and/or use the Licensed Work or a substantial set of the features or functionality of the Licensed Work to third parties as a software-as-a-service, platform-as-a-service, infrastructure-as-a-service or other similar services that compete with Licensor products or services.
Nokia was designed as strongest and most affordable phone, yet you use Iphone that costs 1000$. it's not about how it was designed but whether it addresses your current needs. Developers do not want to manage a cluster of single cpu processes. Not on their laptops and not in the production. And it's not just about management complexity. See this https://github.com/dragonflydb/dragonfly/issues/1229 and it's just one example. Single cpu - is just not enough for today use-cases.
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The first version of Redis, written in Tcl
I think this is relevant... These are 3 OSS databases that can be an alternative to Redis:
- KeyDB: https://github.com/snapchat/keydb
- Dragonfly: https://github.com/dragonflydb/dragonfly
- Skytable: https://github.com/skytable/skytable
I have used keyDB before. The raft consensus makes building an HA Redis easy.
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.
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C++ Jobs - Q3 2023
Apache Arrow
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CSV or Parquet File Format
In fact I have asked Apache Github how to read select column of particular row group of a parquet file. https://github.com/apache/arrow/issues/35688
What are some alternatives?
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
KeyDB - A Multithreaded Fork of Redis
h5py - HDF5 for Python -- The h5py package is a Pythonic interface to the HDF5 binary data format.
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
ClickHouse - ClickHouse® is a free analytics DBMS for big data
beam - Apache Beam is a unified programming model for Batch and Streaming data processing.
ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).
duckdb_and_r - My thoughts and examples on DuckDB and R
Apache HBase - Apache HBase
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