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maxi-k
btrblocks
C++2023–2024
maxi-k / btrblocks

btrblocks

BtrBlocks: Efficient Columnar Compression for Data Lakes (SIGMOD 2023 Paper)

3Distinct people
3Mentions
0Explicit recommenders
290GitHub stars
REPOSITORY AT A GLANCE

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What it is

BtrBlocks: Efficient Columnar Compression for Data Lakes (SIGMOD 2023 Paper)

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Where it is used

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What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month3 dated mentions

Observed mentions: 2023-09-16 11:28:45 UTC to 2024-05-15 22:25:22 UTC.

0122023-09-01 00:00:00 UTC to 2023-10-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsSep 20232023-10-01 00:00:00 UTC to 2023-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-11-01 00:00:00 UTC to 2023-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-01-01 00:00:00 UTC to 2024-02-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJan 20242024-02-01 00:00:00 UTC to 2024-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-03-01 00:00:00 UTC to 2024-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-04-01 00:00:00 UTC to 2024-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-05-01 00:00:00 UTC to 2024-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMay 2024

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 20232023-09-01 00:00:00 UTC2023-10-01 00:00:00 UTC202
Oct 20232023-10-01 00:00:00 UTC2023-11-01 00:00:00 UTC000
Nov 20232023-11-01 00:00:00 UTC2023-12-01 00:00:00 UTC000
Dec 20232023-12-01 00:00:00 UTC2024-01-01 00:00:00 UTC000
Jan 20242024-01-01 00:00:00 UTC2024-02-01 00:00:00 UTC000
Feb 20242024-02-01 00:00:00 UTC2024-03-01 00:00:00 UTC000
Mar 20242024-03-01 00:00:00 UTC2024-04-01 00:00:00 UTC000
Apr 20242024-04-01 00:00:00 UTC2024-05-01 00:00:00 UTC000
May 20242024-05-01 00:00:00 UTC2024-06-01 00:00:00 UTC101

What Hacker News says

3 sources
Lance v2 looks interesting. I like their meta-data + container story. Lacking SOTA encoding schemes though. There is also Vortex (https://github.com/fulcrum-so/vortex). That has modern encoding schemes that we want to use. BtrBlocks (https://github.com/maxi-k/btrblocks) from the Germans is another Parquet alternative. Nimble (formerly Alpha) is a complicated story. We worked with the Velox team for over a year to open-source and extend it. But plans got stymied by legal. This was in collaboration with Meta + CWI + Nvidia + Voltron. We decided to go a separate path because Nimble code has no spec/docs. Too tightly coupled with Velox/Folly. Given that, we are working on a new file format. We hope to share our ideas/code later this year.
In An Empirical Evaluation of Columnar Storage Formats [pdf]
> CONCLUSION > We introduced BtrBlocks, an open columnar compression format for data lakes. By analyzing a collection of real-world datasets, we selected a pool of fast encoding schemes for this use case. Additionally, we introduced Pseudodecimal Encoding, a novel compression scheme for floating-point numbers. Using our sample-based compression scheme selection algorithm and our generic framework for cascading compression, we showed that, compared to existing data lake formats, BtrBlocks achieves a high compression factor, competitive compression speed and superior decompression performance. BtrBlocks is open source and available at https://github.com/maxi-k/btrblocks.
In BtrBlocks: Efficient Columnar Compression for Data Lakes [pdf]

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