[2605.29099] One Ring to Shuffle Them All: Scalable Intra-Process Data Redistribution with Ring-Buffer Shuffle in Redpanda Oxla
-->
Computer Science > Databases
arXiv:2605.29099 (cs)
[Submitted on 27 May 2026]
Title:One Ring to Shuffle Them All: Scalable Intra-Process Data Redistribution with Ring-Buffer Shuffle in Redpanda Oxla
Authors:Adam Szymański, Tyler Akidau<br>View a PDF of the paper titled One Ring to Shuffle Them All: Scalable Intra-Process Data Redistribution with Ring-Buffer Shuffle in Redpanda Oxla, by Adam Szyma\'nski and 1 other authors
View PDF<br>HTML (experimental)
Abstract:As server CPUs scale to dozens and now hundreds of cores per socket, parallel query engines must rethink how they redistribute data between threads. Partitioned operators such as hash joins and aggregations require frequent data redistribution across threads, yet existing intra-process shuffle designs fundamentally fail to scale with core count: batch partitioning avoids cross-thread synchronization in the hot path but materializes all intermediate data, introduces a global producer/consumer barrier, and requires a consumption approach with low cache locality, while channel-based streaming avoids materialization but incurs per-channel synchronization that scales poorly with core count. As core counts rise, these architectural tradeoffs increasingly prevent engines from fully utilizing modern hardware.
We present a ring-buffer streaming shuffle design that addresses these shortcomings through lock-free atomic slot acquisition into fixed-size batch groups, achieving amortized O(1) synchronization cost per batch and O(M) memory independent of input size. Ring-buffer shuffle has been implemented in Redpanda's Oxla query engine for two years, where it currently powers production queries for Redpanda SQL users.
We evaluate all three approaches on a 72-core NVIDIA GraceHopper, a 192-core dual-socket AWS Graviton4, and a 96-core (192-thread) AMD EPYC. On a 72-core single-socket system the ring buffer outperforms channel streaming by up to 44% and batch partitioning by up to 79%; at 192 cores the advantage over channel grows to over 100% and over 300% versus batch partitioning. Even so, on chiplet architectures with many partitioned L3 caches, the shared atomic counter becomes a cross-die bottleneck and channel-based streaming remains competitive. End-to-end Graviton4 evaluation on TPC-H (21 queries) and ClickBench (43 queries) shows the advantage is workload-shape-dependent.
Comments:<br>13 pages, 8 figures, accepted at VLDB 2026, Industrial Track
Subjects:
Databases (cs.DB)
ACM classes:<br>H.2.4
Cite as:<br>arXiv:2605.29099 [cs.DB]
(or<br>arXiv:2605.29099v1 [cs.DB] for this version)
https://doi.org/10.48550/arXiv.2605.29099
Focus to learn more
arXiv-issued DOI via DataCite (pending registration)
Submission history<br>From: Tyler Akidau [view email]<br>[v1]<br>Wed, 27 May 2026 21:00:22 UTC (84 KB)
Full-text links:<br>Access Paper:
View a PDF of the paper titled One Ring to Shuffle Them All: Scalable Intra-Process Data Redistribution with Ring-Buffer Shuffle in Redpanda Oxla, by Adam Szyma\'nski and 1 other authors<br>View PDF<br>HTML (experimental)<br>TeX Source
view license
Current browse context:
cs.DB
next >
new<br>recent<br>| 2026-05
Change to browse by:
cs
References & Citations
NASA ADS<br>Google Scholar
Semantic Scholar
export BibTeX citation<br>Loading...
BibTeX formatted citation
×
loading...
Data provided by:
Bookmark
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to...