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anthonynsimon
timeflake
Python2020–2021
anthonynsimon / timeflake

timeflake

Timeflake is a 128-bit, roughly-ordered, URL-safe UUID.

Explore on GitHubRead Hacker News discussions
4Distinct people
5Mentions
0Explicit recommenders
809GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Timeflake is a 128-bit, roughly-ordered, URL-safe UUID.

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

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 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 month5 dated mentions

Observed mentions: 2020-01-23 16:50:30 UTC to 2021-06-01 06:57:35 UTC.

0122020-01-01 00:00:00 UTC to 2020-02-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 commentsJan 20202020-02-01 00:00:00 UTC to 2020-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-03-01 00:00:00 UTC to 2020-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-04-01 00:00:00 UTC to 2020-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-05-01 00:00:00 UTC to 2020-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-06-01 00:00:00 UTC to 2020-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-07-01 00:00:00 UTC to 2020-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-08-01 00:00:00 UTC to 2020-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-09-01 00:00:00 UTC to 2020-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsSep 20202020-10-01 00:00:00 UTC to 2020-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-11-01 00:00:00 UTC to 2020-12-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2020-12-01 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 2021-02-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2021-02-01 00:00:00 UTC to 2021-03-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-03-01 00:00:00 UTC to 2021-04-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-04-01 00:00:00 UTC to 2021-05-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2021-05-01 00:00:00 UTC to 2021-06-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2021-06-01 00:00:00 UTC to 2021-07-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJun 2021

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
Jan 20202020-01-01 00:00:00 UTC2020-02-01 00:00:00 UTC110
Feb 20202020-02-01 00:00:00 UTC2020-03-01 00:00:00 UTC000
Mar 20202020-03-01 00:00:00 UTC2020-04-01 00:00:00 UTC000
Apr 20202020-04-01 00:00:00 UTC2020-05-01 00:00:00 UTC000
May 20202020-05-01 00:00:00 UTC2020-06-01 00:00:00 UTC000
Jun 20202020-06-01 00:00:00 UTC2020-07-01 00:00:00 UTC000
Jul 20202020-07-01 00:00:00 UTC2020-08-01 00:00:00 UTC000
Aug 20202020-08-01 00:00:00 UTC2020-09-01 00:00:00 UTC000
Sep 20202020-09-01 00:00:00 UTC2020-10-01 00:00:00 UTC000
Oct 20202020-10-01 00:00:00 UTC2020-11-01 00:00:00 UTC000
Nov 20202020-11-01 00:00:00 UTC2020-12-01 00:00:00 UTC000
Dec 20202020-12-01 00:00:00 UTC2021-01-01 00:00:00 UTC000
Jan 20212021-01-01 00:00:00 UTC2021-02-01 00:00:00 UTC211
Feb 20212021-02-01 00:00:00 UTC2021-03-01 00:00:00 UTC000
Mar 20212021-03-01 00:00:00 UTC2021-04-01 00:00:00 UTC000
Apr 20212021-04-01 00:00:00 UTC2021-05-01 00:00:00 UTC000
May 20212021-05-01 00:00:00 UTC2021-06-01 00:00:00 UTC101
Jun 20212021-06-01 00:00:00 UTC2021-07-01 00:00:00 UTC101

What Hacker News says

5 sources
hardwaresofton·Jun 1, 2021
Yeah, just use a UUID unless the bits to store the UUID really are your driving limitation (they're not), having a UUID that is non-linear is almost always the most straight-forward option for identifying things, for the tradeoff of human readability (though you can get some of that back with prefixes and some other schemes). I'm not going to rehash the benefits that people have brought up for UUIDs, but they're in this thread. At this point what I'm concerned about is just... what is the best kind of UUID to use -- I've recently started using mostly v1 because time relationship is important to me (despite the unfortunate order issues) and v6[0] isn't quite so spread yet. Here's a list of other approaches out there worth looking at - isntauuid[1] (mentioned in this thread, I've given it a name here) - timeflake[2] - HiLo[3][4] - ulid[5] -
Read the rest
ksuid[6] (made popular by segment.io) - v1-v6 UUIDs (the ones we all know and some love) - sequential interval based UUIDs in Postgres[7] Just add a UUID -- this almost surely isn't going to be what bricks your architecture unless you have some crazy high write use case like time series or IoT or something maybe. [0]: http://gh.peabody.io/uuidv6/ [1]: https://instagram-engineering.com/sharding-ids-at-instagram-... [2]: https://github.com/anthonynsimon/timeflake [3]: https://en.wikipedia.org/wiki/Hi/Lo_algorithm [4]: https://www.npgsql.org/efcore/modeling/generated-properties.... [5]: https://github.com/edoceo/pg-ulid [6]: https://github.com/segmentio/ksuid [7]: https://www.2ndquadrant.com/en/blog/sequential-uuid-generato...
In UUID, serial or identity columns for PostgreSQL auto-generated primary keys?
js2·May 3, 2021
I'd advise UUID v6 over this which is at least an RFC 4122 extension. As coded, this isn't UUID compatible other than being 128 bits. http://gh.peabody.io/uuidv6/ Also some recent similar submissions: Timeflake is a 128-bit, roughly-ordered, URL-safe UUID. https://news.ycombinator.com/item?id=25870482 https://github.com/anthonynsimon/timeflake ULIDs: https://news.ycombinator.com/item?id=18768909 Sonyflake: https://news.ycombinator.com/item?id=25592325 KSUIDs (can't find any discussion here): https://github.com/segmentio/ksuid This comment lists other prior art: https://github.com/bradleypeabody/gouuidv6/issues/3
In Sortable Collision-Free UUIDs
mleonhard·Jan 22, 2021
I filed an issue on the project, "Point out the privacy risks of timestamps in object IDs" [0]. ---- Non-random object IDs have privacy issues. Developers need to learn about these before they choose to use non-random IDs over simple random IDs. How about adding a PRIVACY section to the readme? Timeflake encodes the precise time that a user created an object. Timestamps in IDs can reveal: User timezone. Geographic location: If the client software creates multiple associated IDs at the same time (like an article and embedded media), then the differences in timestamps of the IDs can reveal the latency of the client's network connection to the server. This reveals user geographic location. This can also happen if the client creates a single ID and the server adds an additional timestamp to the object. User identity (de-anonymizing) - Mo
Read the rest
st Android apps include Google's libraries for working with push notifications. And some iOS apps that use Google Cloud services also load the libraries. These Google libraries automatically load Google Analytics [1] which records the names of every screen the users view in the app, and sends them to Google. So Google knows that userN switched from screen "New Post" to screen "Published Post" at time K. - Some ISPs record and sell user behavior data. For example, SAP knows [2] that userN made a request to appM's API at time K. - Even if the posting app does not share its user behavior data with third-parties, the user could post and then immediately switch to an app that does share user behavior data. This provides data points like "userN stopped using an app that does not record analytics at time K". - Operating Systems (Android [3], Windows [4], macOS [5]) send user behavior data to their respective companies. - Browsers and Browser Extensions send user behavior data to many companies. Data points like "userN visited a URL at example.com at time K" can end up in many databases and sold. - Posting times combined with traffic analysis can perfectly de-anonymize users. How long the user took to write the post. This can happen if the app creates the ID when the user starts editing the post and also shares a timestamp of the publication or save time. Whether or not the user edited the post after posting it. This can happen if the posts's displayed time doesn't match the timestamp in the ID. Whether or not the user prepared the post in advance and set it to post automatically. If the timestamp is very close to a round numbered time like 21:00:00, it was likely posted automatically. If the posting platform does not provide such functionality, then the user must be using some third-party software or custom software to do it. This information can help de-anonymize the user. ---- [0] https://github.com/anthonynsimon/timeflake/issues/3 [1] https://firebase.google.com/docs/cloud-messaging/android/cli... [2] https://www.eff.org/deeplinks/2017/03/five-creepy-things-you... [3] https://digitalcontentnext.org/wp-content/uploads/2018/08/DC... [4] https://www.eff.org/deeplinks/2016/08/windows-10-microsoft-b... [5] https://www.eff.org/deeplinks/2020/11/macos-leaks-applicatio...
In Show HN: 128-bit, roughly-ordered, URL-safe UUIDs

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