Robust, flexible and resource-efficient pipelines using Go and the commandline

Why SciPipe?

• Intuitive: SciPipe works by flowing data through a network of channels and processes
• Flexible: Wrapped command-line programs can be combined with processes in Go
• Convenient: Full control over how your files are named
• Efficient: Workflows are compiled to binary code that run fast
• Parallel: Pipeline paralellism between processes as well as task parallelism for multiple inputs, making efficient use of multiple CPU cores
• Supports streaming: Stream data between programs to avoid wasting disk space
• Easy to debug: Use available Go debugging tools or just println()
• Portable: Distribute workflows as Go code or as self-contained executable files

Project links: GitHub repo | Issue Tracker | Chat

Project updates

Jan 2020: New screencast: "Hello World" scientific workflow in SciPipe May 2019: The SciPipe paper published open access in GigaScience: SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines Nov 2018: Scientific study using SciPipe: Predicting off-target binding profiles with confidence using Conformal Prediction Slides: Presentation on SciPipe and more at Go Stockholm Conference Blog post: Provenance reports in Scientific Workflows - going into details about how SciPipe is addressing provenance. Blog post: First production workflow run with SciPipe

Introduction

SciPipe is a library for writing Scientific Workflows, sometimes also called "pipelines", in the Go programming language.

When you need to run many commandline programs that depend on each other in complex ways, SciPipe helps by making the process of running these programs flexible, robust and reproducible. SciPipe also lets you restart an interrupted run without over-writing already produced output and produces an audit report of what was run, among many other things.

SciPipe is built on the proven principles of Flow-Based Programming (FBP) to achieve maximum flexibility, productivity and agility when designing workflows. Compared to plain dataflow, FBP provides the benefits that processes are fully self-contained, so that a library of re-usable components can be created, and plugged into new workflows ad-hoc.

Similar to other FBP systems, SciPipe workflows can be likened to a network of assembly lines in a factory, where items (files) are flowing through a network of conveyor belts, stopping at different independently running stations (processes) for processing, as depicted in the picture above.

SciPipe was initially created for problems in bioinformatics and cheminformatics, but works equally well for any problem involving pipelines of commandline applications.

Project status: SciPipe pretty stable now, and only very minor API changes might still occur. We have successfully used SciPipe in a handful of both real and experimental projects, and it has had occasional use outside the research group as well.

Known limitations

• There are still a number of missing good-to-have features for workflow design. See the issue tracker for details.
• There is not (yet) support for the Common Workflow Language.

Installing

For full installation instructions, see the intallation page. For quick getting started steps, you can do:

1. Download and install Go
2. Run the following command, to install the scipipe Go library (don't miss the trailing dots!), and create a Go module for your script:

go install github.com/scipipe/scipipe/...@latest
go mod init myfirstworkflow-module

Hello World example

Let's look at an example workflow to get a feel for what writing workflows in SciPipe looks like:

package main

import (
    // Import SciPipe, aliased to sp
    sp "github.com/scipipe/scipipe"
)

func main() {
    // Init workflow and max concurrent tasks
    wf := sp.NewWorkflow("hello_world", 4)

    // Initialize processes, and file extensions
    hello := wf.NewProc("hello", "echo 'Hello ' > {o:out|.txt}")
    world := wf.NewProc("world", "echo $(cat {i:in}) World > {o:out|.txt}")

    // Define data flow
    world.In("in").From(hello.Out("out"))

    // Run workflow
    wf.Run()
}

To create a file with a similar simple example, you can run:

scipipe new hello_world.go

Running the example

Let's put the code in a file named hello_world.go and run it.

First you need to make sure that the dependencies (SciPipe in this case) is installed in your local Go module. This you can do with:

go mod tidy

Then you can go ahead and run the workflow:

$ go run hello_world.go
AUDIT   2018/07/17 21:42:26 | workflow:hello_world             | Starting workflow (Writing log to log/scipipe-20180717-214226-hello_world.log)
AUDIT   2018/07/17 21:42:26 | hello                            | Executing: echo 'Hello ' > hello.out.txt
AUDIT   2018/07/17 21:42:26 | hello                            | Finished: echo 'Hello ' > hello.out.txt
AUDIT   2018/07/17 21:42:26 | world                            | Executing: echo $(cat ../hello.out.txt) World > hello.out.txt.world.out.txt
AUDIT   2018/07/17 21:42:26 | world                            | Finished: echo $(cat ../hello.out.txt) World > hello.out.txt.world.out.txt
AUDIT   2018/07/17 21:42:26 | workflow:hello_world             | Finished workflow (Log written to log/scipipe-20180717-214226-hello_world.log)

Let's check what file SciPipe has generated:

$ ls -1 hello*
hello.out.txt
hello.out.txt.audit.json
hello.out.txt.world.out.txt
hello.out.txt.world.out.txt.audit.json

As you can see, it has created a file hello.out.txt, and hello.out.world.out.txt, and an accompanying .audit.json for each of these files.

Now, let's check the output of the final resulting file:

$ cat hello.out.txt.world.out.txt
Hello World

Now we can rejoice that it contains the text "Hello World", exactly as a proper Hello World example should :)

Now, these were a little long and cumbersome filenames, weren't they? SciPipe gives you very good control over how to name your files, if you don't want to rely on the automatic file naming. For example, we could set the first filename to a static one, and then use the first name as a basis for the file name for the second process, like so:

package main

import (
    // Import the SciPipe package, aliased to 'sp'
    sp "github.com/scipipe/scipipe"
)

func main() {
    // Init workflow with a name, and max concurrent tasks
    wf := sp.NewWorkflow("hello_world", 4)

    // Initialize processes and set output file paths
    hello := wf.NewProc("hello", "echo 'Hello ' > {o:out}")
    hello.SetOut("out", "hello.txt")

    world := wf.NewProc("world", "echo $(cat {i:in}) World >> {o:out}")
    world.SetOut("out", "{i:in|%.txt}_world.txt")

    // Connect network
    world.In("in").From(hello.Out("out"))

    // Run workflow
    wf.Run()
}

In the {i:in... part, we are re-using the file path from the file received on the in-port named 'in', and then running a Bash-style trim-from-end command on it to remove the .txt extension.

Now, if we run this, the file names get a little cleaner:

$ ls -1 hello*
hello.txt
hello.txt.audit.json
hello_world.go
hello_world.txt
hello_world.txt.audit.json

The audit logs

Finally, we could have a look at one of those audit file created:

$ cat hello_world.txt.audit.json
{
    "ID": "99i5vxhtd41pmaewc8pr",
    "ProcessName": "world",
    "Command": "echo $(cat hello.txt) World \u003e\u003e hello_world.txt.tmp/hello_world.txt",
    "Params": {},
    "Tags": {},
    "StartTime": "2018-06-15T19:10:37.955602979+02:00",
    "FinishTime": "2018-06-15T19:10:37.959410102+02:00",
    "ExecTimeNS": 3000000,
    "Upstream": {
        "hello.txt": {
            "ID": "w4oeiii9h5j7sckq7aqq",
            "ProcessName": "hello",
            "Command": "echo 'Hello ' \u003e hello.txt.tmp/hello.txt",
            "Params": {},
            "Tags": {},
            "StartTime": "2018-06-15T19:10:37.950032676+02:00",
            "FinishTime": "2018-06-15T19:10:37.95468214+02:00",
            "ExecTimeNS": 4000000,
            "Upstream": {}
        }
    }

Each such audit-file contains a hierarchic JSON-representation of the full workflow path that was executed in order to produce this file. On the first level is the command that directly produced the corresponding file, and then, indexed by their filenames, under "Upstream", there is a similar chunk describing how all of its input files were generated. This process will be repeated in a recursive way for large workflows, so that, for each file generated by the workflow, there is always a full, hierarchic, history of all the commands run - with their associated metadata - to produce that file.

You can find many more examples in the examples folder in the GitHub repo.

For more information about how to write workflows using SciPipe, use the menu to the left, to browse the various topics!

Citing SciPipe

If you use SciPipe in academic or scholarly work, please cite the following paper as source:

Lampa S, Dahlö M, Alvarsson J, Spjuth O. SciPipe: A workflow library for agile development of complex and dynamic bioinformatics pipelines Gigascience. 8, 5 (2019). DOI: 10.1093/gigascience/giz044