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v0.7.0 has unified local and distributed execution layer, local thread-based scheduling has been removed, instead, the unified runtime is based on multiprocess-based scheduling which could get rid of infamous GIL problem .
Thus, for local usage, please new a local default session via:
importmarsmars.new_session() # create a default local session
If not doing so, it will be initialized once in the background, however, keep in mind that the multiprocess scheduling consumes more time compared to multithread one.
We tried our best to keep other compatibilities, if you find any incompatible place, please open an issue to reach out to us.
Highlights
v0.7.0 implements a unified execution layer, all deployment including bare metal, Kubernetes, Ray as well as Yarn shares the same fundamental components. This unified execution layer optimized many aspects compare to the old one including:
Better serialization based on pickle5 protocol, which is 5-7x faster than old version.
Completely rewritten execution layer which has better performance, even 20%-50% faster than the old version on a laptop.
Based on multiprocess scheduling which avoids infamous GIL issue.
Mars on Ray is way more better due to the reason that Ray actor is leveraged to build the Ray backend of Oscar which is a lightweight actor framework that is the fundamental part of the entire execution layer.
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This is the release notes of v0.7.0. See here for the complete list of solved issues and merged PRs.
This release note only covers the difference from v0.7.0rc2; for all highlights and changes, please refer to the release notes of the pre-releases:
alpha1
alpha2
alpha3
alpha4
alpha5
alpha6
alpha7
alpha8
beta1
beta2
rc1
rc2
Changes that break compatibility
v0.7.0 has unified local and distributed execution layer, local thread-based scheduling has been removed, instead, the unified runtime is based on multiprocess-based scheduling which could get rid of infamous GIL problem .
Thus, for local usage, please new a local default session via:
If not doing so, it will be initialized once in the background, however, keep in mind that the multiprocess scheduling consumes more time compared to multithread one.
We tried our best to keep other compatibilities, if you find any incompatible place, please open an issue to reach out to us.
Highlights
v0.7.0 implements a unified execution layer, all deployment including bare metal, Kubernetes, Ray as well as Yarn shares the same fundamental components. This unified execution layer optimized many aspects compare to the old one including:
New Features
seek
method in memory cases (#2250) #2264)Enhancements
md.read_csv
([BACKPORT] Ensure range index incremental for data source op like md.read_csv (#2232) #2244)run_script
(#2251) #2256)fetch_log
for web session (#2257) #2262)Bug fixes
NoDataToSpill
when multiple storage quota requests happen simultaneously (#2203) #2223)Documentation
Installation
Tests
This discussion was created from the release v0.7.0.
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