2 unstable releases

0.2.0 Oct 24, 2021
0.1.0 Oct 20, 2021

#5 in #work-stealing

35 downloads per month

GPL-3.0 license

928 lines

Build Status


Command-line work-stealing scheduler.


If you use the Nix package manager, there's a package in my overlay.

Otherwise, you can build jerbs with cargo:

cargo install jerbs


Create a job database:

$ jerbs work.db init

Define a job and enqueue some repetitions:

$ jerbs work.db create --count 17 --data "info for thing to do 17 times"

The output is the job id, which you can use to edit the job later.

See what's scheduled:

$ jerbs work.db list-available -v
1       17      "info for thing to do 17 times"

(Note: do not use verbose output (-v) for scripting. It is intended to be human-readable and the format is unstable.)

Run a worker:

$ while jerbs work.db take $$ | read JOB; do echo $JOB; done

Now start some more!

Typical Usage

I made this so I could have a tmux with a worker process in each pane, all taking jobs from the same queue. The worker processes run a shell script that uses this utility to pick the next job.

A job's payload is a blob of data. What's in the blob is up to you. If a job needs multiple parameters, the blobs could be filenames indicating where to find the job data; or, you might pack the data directly into the blob with a delimiter-based format or jq or something.

Worker IDs can be any utf-8 string. If your worker is a bash script, you can pass $$ to use your worker's PID.

Because the data blob for your task may contain characters that are subject to string interpolation hazards, any command that requires a blob will read it from standard input by default. If your blobs are shell-safe, you can instead use --data to include your blob in the arguments.

Comparison to alternatives

Other work-stealing schedulers (like GNU Parallel) are frameworks; they own the worker processes, so you can only configure workers through the framework. jerbs inverts this paradigm: jerbs is a utility to be used from your worker script. With jerbs you can easily assign unique resources to the workers, pin workers to CPUs/NUMA nodes, or dynamically vary the number of simultaneous jobs. At last, the workers control the means of production.


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