# TimelyDataflow/differential-dataflow

An implementation of differential dataflow using timely dataflow on Rust.

Repository: https://github.com/TimelyDataflow/differential-dataflow
Canonical: https://ross.abutalabs.com/products/differential-dataflow
Language: Rust
License: MIT
License Family: permissive
Last push: 2026-08-26T20:53:31+00:00

## Health v2 (maintenance only)
Score: 93/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 81, longevity 100
- inputs: {"age_days": 4138, "days_push": 7, "days_rel": 49, "gap_med": 0, "n_releases_24m": 64}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2998, forks 210 (observed 2026-08-28T04:07:37.217080+00:00)

## What it is
A Rust library implementing differential dataflow on top of timely dataflow, enabling data-parallel programs that efficiently process large data collections and rapidly respond to arbitrary input changes. Programs are written as functional transformations (map, filter, join, reduce, iterate) that incrementally update outputs as inputs change.

## Use cases
- incrementally update query results as input data changes
- compute graph analytics like reachability on dynamically changing graphs
- process large streaming datasets with low-latency updates
- build data-parallel pipelines with joins and iterative computations
- implement real-time analytics that respond to arbitrary collection changes

## When to avoid
- you only need simple batch processing without incremental updates
- you prefer higher-level SQL or streaming engines over low-level dataflow programming
- your team is not comfortable with Rust and functional dataflow semantics

## Facets
- artifact type: library
- maturity: active
- function: streaming, data-science, etl
- domain: big-data, microservices
- platform: rust, cross-platform
- tags: dataflow, incremental-computation, data-parallel, timely-dataflow, data-engineering, algorithms

## Member repositories
- TimelyDataflow/differential-dataflow (main) score 93

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.217080+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 2026-08-30T07:30:36.349949+00:00, confidence not recorded.
  - readme: https://github.com/TimelyDataflow/differential-dataflow (fetched 2026-08-28T04:07:37.217080+00:00, sha 5c7eeffb62fb)
  - registry_crates: https://crates.io/api/v1/crates/differential-dataflow (fetched 2026-08-29T09:45:52.258205+00:00, sha c7c30a158d8f)
- Data as of 2026-08-30T08:39:29.467469+00:00.