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differential-datalog
Java2019–2026
vmware-archive / differential-datalog · Archived

differential-datalog

DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.

24Distinct people
32Mentions
1Explicit recommenders
1.5KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

Full disclosure, I do work for VMWare on the DDlog project If you want a sort of FRP-backed datalog, I highly recommend Differential Datalog…

kixiron used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

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 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 4:00:59 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 year32 dated mentions

Observed mentions: 2019-11-29 21:12:51 UTC to 2026-05-18 01:30:58 UTC.

0369122019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20192020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 12 mentions; 1 submissions, 11 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 6 mentions; 1 submissions, 5 comments20222023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

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
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 UTC422
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC12111
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC615
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC505
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC211
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC101
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC101

What Hacker News says

5 sources
Hi, > I wonder if it lives up to the hype. We do think so! (disclaimer: I'm a co-founder at Feldera) To give some more background: We are trialing feldera with several industry/enterprise partners from different domains. Our core team also built differential datalog (https://github.com/vmware/differential-datalog) in the past. And while ddlog is used quite successfully in products today, we believe the many lessons we learned with ddlog will help us to build an even better continuous analytics platform. FYI our code is open-source at https://github.com/feldera/feldera if you'd like to try it out. Also feel free to join our community slack channel (https://www.feldera.com/slack/) if you have more questions.
In Feldera – a more performant streaming database based on Z-sets
The relational model (and generally working at the level of sets/collections, instead of the level of individual values/objects) actually makes it easier to have this kind of incremental computation in a consistent way, I think. There's a bunch of work being done on making relational systems work this way. Some interesting reading: - https://www.scattered-thoughts.net/writing/an-opinionated-ma... - https://materialize.com/ which is built on https://timelydataflow.github.io/differential-dataflow/, which has a lot of research behind it - Which also can be a compilation target for Datalog: https://github.com/vmware/differential-datalog - Some prototype work on building UI systems in exactly the way you describe using a relational approach: https://riffle.systems/essays/prelude/ (and HN discussion: https://news.ycombinator.com/item?id=3053012
Read the rest
0) (There's a lot more too -- I have a hobby interest in this space, so I have a small collection of links)
In Why Are There No Relational DBMSs? [pdf] (2015)

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