distributed
Dask
| distributed | Dask | |
|---|---|---|
| 3 | 32 | |
| 1,543 | 12,022 | |
| 0.6% | 0.8% | |
| 9.6 | 9.6 | |
| 1 day ago | 1 day ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | BSD 3-clause "New" or "Revised" License |
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distributed
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Shuffling large data at constant memory in Dask
Thanks, if you give it a try, you can share your experience in this GitHub issue, where developers are collecting info for further improvements. https://github.com/dask/distributed/discussions/7509
- Great forward progress on squashing cluster deadlocks
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Dask – a flexible library for parallel computing in Python
I would not recommend Dask. We use it just for simple job scheduling (that is, none of its fancy data structures) and run into issues just getting the work done efficiently. This issue, for instance, keeps the cluster from actually being utilized fully: https://github.com/dask/distributed/issues/4501. I feel like I'm on crazy pills, because it seems pretty serious yet it's gotten no attention.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
mpire - A Python package for easy multiprocessing, but faster than multiprocessing
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
Numba - NumPy aware dynamic Python compiler using LLVM
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
tdigest - t-Digest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark
NetworkX - Network Analysis in Python
go-micro - A Go microservices framework
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
FindTheMag - A tool to determine optimal projects for Gridcoin crunchers. Maximize your magnitude!
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python