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On the recordRead 9 Oct 202619 facts
Timely Dataflow
On the record
- Built-in operators
- The project includes operators such as map, filter, concat, and operators for entering and exiting loops.github.com · read 9 Oct 2026
- Cluster execution
- The README describes running multiple processes using a host file with hostname-and-port entries, plus process-count and process-index arguments.github.com · read 4 Oct 2026
- Custom operators
- Users can supply closures to generic unary and binary operators, and can create source operators for external inputs.timelydataflow.github.io · read 9 Oct 2026
- Distributed execution
- It scales the same program from one thread on a laptop to execution across a cluster of computers.github.com · read 9 Oct 2026
- Documentation status
- The README describes the documentation as a work in progress and cautions that some blog examples may need tweaks to build against current code.github.com · read 9 Oct 2026
- Ecosystem
- Differential Dataflow is described as a higher-level language built on Timely Dataflow, with operators including group, join, and iterate.github.com · read 9 Oct 2026
- Installation
- The README shows adding the timely crate to a Rust project's Cargo.toml dependencies.github.com · read 9 Oct 2026
- Intended users
- The project invites people building dataflow programs and contributors to report their experiences and help improve examples and documentation.github.com · read 9 Oct 2026
- Iteration
- The programming model supports higher-level control constructs such as iteration in stream computations.timelydataflow.github.io · read 9 Oct 2026
- License
- The GitHub repository identifies the project as MIT licensed.github.com · read 9 Oct 2026
- Operators
- Built-in operators include map, filter, concat, enter, and leave, with generic unary and binary operators also available.github.com · read 4 Oct 2026
- Origin
- The project site says Timely Dataflow arose from work at Microsoft Research on scalable distributed data processing platforms.timelydataflow.github.io · read 4 Oct 2026
- Parallelism
- Its operators can process independent parts of data concurrently across parallel workers.timelydataflow.github.io · read 9 Oct 2026
- Programming model
- It is described as a low-latency cyclic dataflow computational model implemented in Rust.github.com · read 4 Oct 2026
- Progress tracking
- Operators use timestamp capabilities and input frontiers to track whether more records may arrive at particular times.timelydataflow.github.io · read 9 Oct 2026
- Purpose
- Timely Dataflow is a system for implementing distributed streaming computation and a way to structure computation generally.timelydataflow.github.io · read 4 Oct 2026
- Scaling
- The same program can scale from one thread on a laptop to distributed execution across a cluster of computers.github.com · read 4 Oct 2026
- Streaming
- Its core data type is a stream that can represent unbounded data arriving as a computation proceeds.timelydataflow.github.io · read 9 Oct 2026
