# vmware-archive/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.

Repository: https://github.com/vmware-archive/differential-datalog
Canonical: https://ross.abutalabs.com/products/differential-datalog
Language: Java
License: MIT
License Family: permissive
Topics: programming-language, datalog, ddlog, rust, incremental
Archived: true
Last push: 2023-07-07T21:56:01+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3088, "days_push": 1153, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1501, forks 135 (observed 2026-08-28T04:04:54.315595+00:00)

## What it is
DDlog is a programming language and compiler for incremental computation, based on a dialect of Datalog and differential dataflow. Programmers declare input-output mappings declaratively, and the compiler synthesizes efficient Rust implementations that continuously update outputs in response to streaming input changes.

## Use cases
- maintain derived results incrementally as input relations change
- build real-time analytics over streaming relational updates
- implement static program analysis tools
- write declarative rules for cloud management systems
- compute all derived facts bottom-up from input relations
- process insert/delete/modify updates with minimal recomputation

## When to choose
- your application continuously updates outputs in response to input changes
- you want declarative relational rules without writing incremental algorithms
- you need bottom-up computation of all derived facts
- you work with relational data streams and want amortized performance

## When to avoid
- you need ad-hoc top-down queries rather than maintaining all derived facts
- your data does not fit in memory
- you need a general-purpose programming language rather than a declarative DSL
- you require active community maintenance - the repo is archived

## Facets
- artifact type: library
- maturity: maintenance
- function: programming-language, compiler, interpreter
- domain: programming-languages, developer-tools
- platform: rust, windows, cli
- tags: datalog, incremental-computation, differential-dataflow, declarative, dataflow, relational, algorithms, data-engineering, linux, macos

## Member repositories
- vmware-archive/differential-datalog (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.315595+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:32:58.023512+00:00, confidence not recorded.
  - readme: https://github.com/vmware-archive/differential-datalog (fetched 2026-08-28T04:04:54.315595+00:00, sha de8e69ed5a16)
- Data as of 2026-08-30T08:39:29.467469+00:00.