Dagu

Dagu is a powerful Cron alternative that comes with a Web UI. It allows you to define dependencies between commands as a Directed Acyclic Graph (DAG) in a declarative YAML format. Additionally, Dagu natively supports running Docker containers, making HTTP requests, and executing commands over SSH.

• Documentation
• Discord Community

Highlights

• Single binary file installation
• Declarative YAML format for defining DAGs
• Web UI for visually managing, rerunning, and monitoring pipelines
• Use existing programs without any modification
• Self-contained, with no need for a DBMS

Contents

• Highlights
• Contents
• Features
• Usecase
• Web User Interface
• Installation
  • Via Homebrew
  • Via Bash script
  • Via Docker
  • Via GitHub Release Page
• ️Quick Start Guide
  • 1. Launch the Web UI
  • 2. Create a New DAG
  • 3. Edit the DAG
  • 4. Execute the DAG
• Documentation
• Example Workflow
• Motivation
• Why Not Use an Existing Workflow Scheduler Like Airflow?
• How It Works
• Roadmap
• Contributors
• License
• Support and Community

Features

• Web User Interface
• Command Line Interface (CLI) with several commands for running and managing DAGs
• YAML format for defining DAGs, with support for various features including:
  • Execution of custom code snippetts
  • Parameters
  • Command substitution
  • Conditional logic
  • Redirection of stdout and stderr
  • Lifecycle hooks
  • Repeating task
  • Automatic retry
• Executors for running different types of tasks:
  • Running arbitrary Docker containers
  • Making HTTP requests
  • Sending emails
  • Running jq command
  • Executing remote commands via SSH
• Email notification
• Scheduling with Cron expressions
• REST API Interface
• Basic Authentication over HTTPS

Usecase

• Data Pipeline Automation: Schedule ETL tasks for data processing and centralization.
• Infrastructure Monitoring: Periodically check infrastructure components with HTTP requests or SSH commands.
• Automated Reporting: Generate and send periodic reports via email.
• Batch Processing: Schedule batch jobs for tasks like data cleansing or model training.
• Task Dependency Management: Manage complex workflows with interdependent tasks.
• Microservices Orchestration: Define and manage dependencies between microservices.
• CI/CD Integration: Automate code deployment, testing, and environment updates.
• Alerting System: Create notifications based on specific triggers or conditions.
• Custom Task Automation: Define and schedule custom tasks using code snippets.

Web User Interface

• DAG Details: It shows the real-time status, logs, and DAG configurations. You can edit DAG configurations on a browser.

  

  You can switch to the vertical graph with the button on the top right corner.

  

• DAGs: It shows all DAGs and the real-time status.

  

• Search DAGs: It greps given text across all DAGs.

• Execution History: It shows past execution results and logs.

  

• DAG Execution Log: It shows the detail log and standard output of each execution and step.

  

Installation

You can install Dagu quickly using Homebrew or by downloading the latest binary from the Releases page on GitHub.

Via Homebrew

brew install yohamta/tap/dagu

Upgrade to the latest version:

brew upgrade yohamta/tap/dagu

Via Bash script

curl -L https://raw.githubusercontent.com/yohamta/dagu/main/scripts/downloader.sh | bash

Via Docker

docker run \
--rm \
-p 8080:8080 \
-v $HOME/.dagu/dags:/home/dagu/.dagu/dags \
-v $HOME/.dagu/data:/home/dagu/.dagu/data \
-v $HOME/.dagu/logs:/home/dagu/.dagu/logs \
yohamta/dagu:latest

Via GitHub Release Page

Download the latest binary from the Releases page and place it in your $PATH (e.g. /usr/local/bin).

️Quick Start Guide

1. Launch the Web UI

Start the server with the command dagu server and browse to http://127.0.0.1:8080 to explore the Web UI.

2. Create a New DAG

Navigate to the DAG List page by clicking the menu in the left panel of the Web UI. Then create a DAG by clicking the New DAG button at the top of the page. Enter example in the dialog.

Note: DAG (YAML) files will be placed in ~/.dagu/dags by default. See Configuration Options for more details.

3. Edit the DAG

Go to the SPEC Tab and hit the Edit button. Copy & Paste the following example and click the Save button.

Example:

steps:
  - name: s1
    command: echo Hello Dagu
  - name: s2
    command: echo done!
    depends:
      - s1

4. Execute the DAG

You can execute the example by pressing the Start button. You can see "Hello Dagu" in the log page in the Web UI.

Documentation

• Installation Instructions
• ️Quick Start Guide
• Command Line Interface
• Web User Interface
• YAML Format
  • Minimal DAG Definition
  • Running Arbitrary Code Snippets
  • Defining Environment Variables
  • Defining and Using Parameters
  • Using Command Substitution
  • Adding Conditional Logic
  • Setting Environment Variables with Standard Output
  • Redirecting Stdout and Stderr
  • Adding Lifecycle Hooks
  • Repeating a Task at Regular Intervals
  • All Available Fields for DAGs
  • All Available Fields for Steps
• Example DAGs
  • Hello World
  • Conditional Steps
  • File Output
  • Passing Output to Next Step
  • Running a Docker Container
  • Sending HTTP Requests
  • Querying JSON Data with jq
  • Sending Email
• Configurations
• Scheduler
• Docker Compose

Example Workflow

This example workflow showcases a data pipeline typically implemented in DevOps and Data Engineering scenarios. It demonstrates an end-to-end data processing cycle starting from data acquisition and cleansing to transformation, loading, analysis, reporting, and ultimately, cleanup.

The YAML code below represents this workflow:

# Environment variables used throughout the pipeline
env:
  - DATA_DIR: /data
  - SCRIPT_DIR: /scripts
  - LOG_DIR: /log

# Handlers to manage errors and cleanup after execution
handlerOn:
  failure:
    command: "echo error"
  exit:
    command: "echo clean up"

# The schedule for the workflow execution in cron format
# This schedule runs the workflow daily at 12:00 AM
schedule: "0 0 * * *"

steps:
  # Step 1: Pull the latest data from a data source
  - name: pull_data
    command: "bash"
    script: |
      echo `date '+%Y-%m-%d'`
    output: DATE

 # Step 2: Cleanse and prepare the data
  - name: cleanse_data
    command: echo cleansing ${DATA_DIR}/${DATE}.csv
    depends:
      - pull_data

  # Step 3: Transform the data
  - name: transform_data
    command: echo transforming ${DATA_DIR}/${DATE}_clean.csv
    depends:
      - cleanse_data

  # Parallel Step 1: Load the data into a database
  - name: load_data
    command: echo loading ${DATA_DIR}/${DATE}_transformed.csv
    depends:
      - transform_data

  # Parallel Step 2: Generate a statistical report
  - name: generate_report
    command: echo generating report ${DATA_DIR}/${DATE}_transformed.csv
    depends:
      - transform_data

  # Step 4: Run some analytics
  - name: run_analytics
    command: echo running analytics ${DATA_DIR}/${DATE}_transformed.csv
    depends:
      - load_data

  # Step 5: Send an email report
  - name: send_report
    command: echo sending email ${DATA_DIR}/${DATE}_analytics.csv
    depends:
      - run_analytics
      - generate_report

  # Step 6: Cleanup temporary files
  - name: cleanup
    command: echo removing ${DATE}*.csv
    depends:
      - send_report

Motivation

Legacy systems often have complex and implicit dependencies between jobs. When there are hundreds of cron jobs on a server, it can be difficult to keep track of these dependencies and to determine which job to rerun if one fails. It can also be a hassle to SSH into a server to view logs and manually rerun shell scripts one by one. Dagu aims to solve these problems by allowing you to explicitly visualize and manage pipeline dependencies as a DAG, and by providing a web UI for checking dependencies, execution status, and logs and for rerunning or stopping jobs with a simple mouse click.

Why Not Use an Existing Workflow Scheduler Like Airflow?

There are many existing tools such as Airflow, but many of these require you to write code in a programming language like Python to define your DAG. For systems that have been in operation for a long time, there may already be complex jobs with hundreds of thousands of lines of code written in languages like Perl or Shell Script. Adding another layer of complexity on top of these codes can reduce maintainability. Dagu was designed to be easy to use, self-contained, and require no coding, making it ideal for small projects.

How It Works

Dagu is a single command line tool that uses the local file system to store data, so no database management system or cloud service is required. DAGs are defined in a declarative YAML format, and existing programs can be used without modification.

Roadmap

• Writing dags in the Starlark Language
• Writing dags in Cue
• AWS Lambda Execution
• Slack Integration
• User Defined Function
• Observability
• Project concept for grouping dags
• Simple User management and RBAC
• Keycloak Integration Option
• HA Cluster Mode
• Database Option
• DAG Versioning
• Built-in TLS

Contributors

Feel free to contribute in any way you want! Share ideas, questions, submit issues, and create pull requests. Check out our Contribution Guide for help getting started.

We welcome any and all contributions!

License

This project is licensed under the GNU GPLv3.

Support and Community

Join our Discord community to ask questions, request features, and share your ideas.