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FeatureBaseDB
featurebase
Go2017–2025
FeatureBaseDB / featurebase · Archived

featurebase

A crazy fast analytical database, built on bitmaps. Perfect for ML applications. Learn more at: http://docs.featurebase.com/. Start a Docker instance: https://hub.docker.com/r/featurebasedb/featurebase

9Distinct people
17Mentions
0Explicit recommenders
2.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A crazy fast analytical database, built on bitmaps. Perfect for ML applications. Learn more at: http://docs.featurebase.com/. Start a Docker instance: https://hub.docker.com/r/featurebasedb/featurebase

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 12:54:37 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 year17 dated mentions

Observed mentions: 2017-05-03 19:48:07 UTC to 2025-07-30 17:48:06 UTC.

0123452017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 5 mentions; 4 submissions, 1 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 5 mentions; 4 submissions, 1 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025

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
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC413
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC101
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC101
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC000
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC541
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC541
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC000
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101

What Hacker News says

17 sources
User behavior analytics have created some interesting specialized data systems. It's interesting that the authors chose to use Redis but how does it scale with a lot of events? A few other interesting projects from the past that either have to do with user behavior analytics or using bitmaps. TrailDB: https://github.com/traildb/traildb old but still a fascinating project in my opinion. Not related to bitmaps but they've done some very clever things on the storage level to compress and query the events in a way that fits well this particular workload. FeatureBase: https://github.com/FeatureBaseDB/featurebase this one was built on top of bitmaps but they didn't market it as a solution specifically for behavioral analytics, although I'm sure it was used for that. Of course there are the Mixpanels and Amplitudes of the work too that they had
Read the rest
to build specialized storage and query engines for this particular workload. Regardless of how fascinating I find these systems though as specialized compute engines for a specific workload, it seems that the use case itself is not lucrative enough to sustain companies built around them. I'm not sure why is that but it's interesting to see companies building infrastructure for this particular case on top of Spark for example which ends up being so painful in the mid-long run. (edit: apparently they answered my question already while I was writing this!)
In Bitmapist: We built an open-source cohorts analytics tool that saved millions
1. It is written in Go, and open source! https://github.com/pilosa/pilosa 2. It's going to depend on your use case, but generally you will be streaming writes into your current data store as well as Pilosa. Then read queries will be serviced by Pilosa, or by a combination of Pilosa and your system of record depending on the data you need. We're more than happy to help explore how you might use Pilosa - get in touch with us on github, or through https://www.pilosa.com/about/#contact
In Pilosa Performance – Billion Taxi Ride Dataset

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