Dagu

dagu

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No-code, self-contained, easy-to-use workflow engine

Dagu executes DAGs (Directed acyclic graph) defined in a simple YAML format. It comes with a web UI for visualizing and managing workflows.

Motivation: Why not Airflow or Prefect?

Popular workflow engines, Airflow and Prefect, are powerful and valuable tools, but they require writing Python code to manage the workflow. In many cases, there are already hundreds of thousands of existing lines of code written in other languages such as shell scripts or Perl. Adding another layer of Python code on top of these may not be ideal. Also, it is often not practical to rewrite everything from scratch in Python in such situations. For this reason, a more lightweight solution where you can manage DAGs without making them more complicated is needed. So we developed Dagu. It is easy to use and self-contained, making it ideal for smaller projects with fewer people.

️How does it work?

Self-contained

  • Dagu is a single binary and there’s no dependency on any.
  • It uses the file system to store execution history data. Therefore, no DBMS or cloud service is required.

Simple YAML format

  • Dagu executes DAGs defined in a simple YAML format.
  • Existing programs can be used without any modification.

️Quick start

1. Installation

Download the latest binary from the Releases page and place it in your $PATH. For example, you can download it in /usr/local/bin.

2. Launch the web UI

Start the server with dagu server and browse to http://127.0.0.1:8000 to explore the Web UI.

3. Create a new workflow

Create a workflow by clicking the New DAG button on the top page of the web UI. Input example.yaml in the dialog.

4. Edit the workflow

Go to the workflow detail page and click the Edit button in the Config Tab. Copy and paste from this example YAML and click the Save button.

5. Execute the workflow

You can execute the example by pressing the Start button.

example

Command Line User Interface

  • dagu start [--params=<params>] <file> – Runs the workflow
  • dagu status <file> – Displays the current status of the workflow
  • dagu retry --req=<request-id> <file> – Re-runs the specified workflow run
  • dagu stop <file> – Stops the workflow execution by sending TERM signals
  • dagu dry [--params=<params>] <file> – Dry-runs the workflow
  • dagu server – Starts the web server for web UI

Web User Interface

  • DAGs: Overview of all DAGs (workflows).

    DAGs page displays all workflows and real-time status. To create a new workflow, you can click the button in the top-right corner.

    DAGs

  • Detail: Realtime status of the workflow.

    The detail page displays the real-time status, logs, and all workflow configurations.

    Detail

  • History: History of the execution of the workflow.

    The history page allows you to check past execution results and logs.

    History

YAML format

You can define workflows in a simple YAML format.

Minimal

name: minimal configuration          # DAG's name
steps:                               # Steps inside the DAG
  - name: step 1                     # Step's name (should be unique within the file)
    command: python main_1.py        # Command and arguments to execute
  - name: step 2
    command: python main_2.py
    depends:
      - step 1                       # [optional] Name of the step to depend on

Environment Variables

You can define environment variables and refer using env field.

name: example
env:
  SOME_DIR: ${HOME}/batch
steps:
  - name: some task in some dir
    dir: ${SOME_DIR}
    command: python main.py

Parameters

You can define parameters using params field and refer to each parameter as $1, $2, etc. Parameters can also be command substitutions or environment variables. It can be overridden by --params= parameter of start command.

name: example
params: param1 param2
steps:
  - name: some task with parameters
    command: python main.py $1 $2

Command Substitution

You can use command substitution in field values. I.e., a string enclosed in backquotes (`) is evaluated as a command and replaced with the result of standard output.

name: example
env:
  TODAY: "`date '+%Y%m%d'`"
steps:
  - name: hello
    command: "echo hello, today is ${TODAY}"

Conditional Logic

Sometimes you have parts of a workflow that you only want to run under certain conditions. You can use the precondition field to add conditional branches to your workflow.

For example, the below task only runs on the first date of each month.

name: example
steps:
  - name: A monthly task
    command: monthly.sh
    preconditions:
      - condition: "`date '+%d'`"
        expected: "01"

If you want the workflow to continue to the next step regardless of the step’s conditional check result, you can use the continueOn field:

name: example
steps:
  - name: A monthly task
    command: monthly.sh
    preconditions:
      - condition: "`date '+%d'`"
        expected: "01"
    continueOn:
      skipped: true

State Handlers

It is often desirable to take action when a specific event happens, for example, when a workflow fails. To achieve this, you can use handlerOn fields.

name: example
handlerOn:
  failure:
    command: notify_error.sh
  exit:
    command: cleanup.sh
steps:
  - name: A task
    command: main.sh

Repeating Task

If you want a task to repeat execution at regular intervals, you can use the repeatPolicy field. If you want to stop the repeating task, you can use the stop command to gracefully stop the task.

name: example
steps:
  - name: A task
    command: main.sh
    repeatPolicy:
      repeat: true
      intervalSec: 60

All Available Fields

Combining these settings gives you granular control over how the workflow runs.

name: all configuration              # DAG's name
description: run a DAG               # DAG's description
env:                                 # Environment variables
  LOG_DIR: ${HOME}/logs
  PATH: /usr/local/bin:${PATH}
logDir: ${LOG_DIR}                   # Log directory to write standard output
histRetentionDays: 3                 # Execution history retention days (not for log files)
delaySec: 1                          # Interval seconds between steps
maxActiveRuns: 1                     # Max parallel number of running step
params: param1 param2                # Default parameters for the DAG that can be referred to by $1, $2, and so on
preconditions:                       # Precondisions for whether the DAG is allowed to run
  - condition: "`echo 1`"            # Command or variables to evaluate
    expected: "1"                    # Expected value for the condition
mailOn:
  failure: true                      # Send a mail when the DAG failed
  success: true                      # Send a mail when the DAG finished
MaxCleanUpTimeSec: 300               # The maximum amount of time to wait after sending a TERM signal to running steps before killing them
handlerOn:                           # Handlers on Success, Failure, Cancel, and Exit
  success:
    command: "echo succeed"          # Command to execute when the DAG execution succeed
  failure:
    command: "echo failed"           # Command to execute when the DAG execution failed
  cancel:
    command: "echo canceled"         # Command to execute when the DAG execution canceled
  exit:
    command: "echo finished"         # Command to execute when the DAG execution finished
steps:
  - name: some task                  # Step's name
    description: some task           # Step's description
    dir: ${HOME}/logs                # Working directory
    command: python main.py $1       # Command and parameters
    mailOn:
      failure: true                  # Send a mail when the step failed
      success: true                  # Send a mail when the step finished
    continueOn:
      failure: true                   # Continue to the next regardless of the step failed or not
      skipped: true                  # Continue to the next regardless the preconditions are met or not
    retryPolicy:                     # Retry policy for the step
      limit: 2                       # Retry up to 2 times when the step failed
    repeatPolicy:                    # Repeat policy for the step
      repeat: true                   # Boolean whether to repeat this step
      intervalSec: 60                # Interval time to repeat the step in seconds
    preconditions:                   # Precondisions for whether the step is allowed to run
      - condition: "`echo 1`"        # Command or variables to evaluate
        expected: "1"                # Expected Value for the condition

The global configuration file ~/.dagu/config.yaml is useful to gather common settings, such as logDir or env.

Admin Configuration

Environment Variables

You can customize the admin web UI by environment variables.

  • DAGU__DATA – path to directory for internal use by dagu (default : ~/.dagu/data)
  • DAGU__LOGS – path to directory for logging (default : ~/.dagu/logs)
  • DAGU__ADMIN_PORT – port number for web URL (default : 8000)
  • DAGU__ADMIN_NAVBAR_COLOR – navigation header color for web UI (optional)
  • DAGU__ADMIN_NAVBAR_TITLE – navigation header title for web UI (optional)

Web UI Configuration

Please create ~/.dagu/admin.yaml.

host: <hostname for web UI address>                          # default value is 127.0.0.1
port: <port number for web UI address>                       # default value is 8000
dags: <the location of DAG configuration files>              # default value is current working directory
command: <Absolute path to the dagu binary>                  # [optional] required if the dagu command not in $PATH
isBasicAuth: <true|false>                                    # [optional] basic auth config
basicAuthUsername: <username for basic auth of web UI>       # [optional] basic auth config
basicAuthPassword: <password for basic auth of web UI>       # [optional] basic auth config

Global Configuration

Creating a global configuration ~/.dagu/config.yaml is a convenient way to organize shared settings.

logDir: <path-to-write-log>         # log directory to write standard output
histRetentionDays: 3                # history retention days
smtp:                               # [optional] mail server configuration to send notifications
  host: <smtp server host>
  port: <stmp server port>
errorMail:                          # [optional] mail configuration for error-level
  from: <from address>
  to: <to address>
  prefix: <prefix of mail subject>
infoMail:
  from: <from address>              # [optional] mail configuration for info-level
  to: <to address>
  prefix: <prefix of mail subject>

Documentation

Dagu’s documentation, including a quick-start guide, and all reference, is available at https://dagu.pages.dev.

FAQ

How to contribute?

Feel free to contribute in any way you want. Share ideas, questions, submit issues, and create pull requests. Thank you!

Where is the history data stored?

It will store execution history data in the DAGU__DATA environment variable path. The default location is $HOME/.dagu/data.

Where are the log files stored?

It will store log files in the DAGU__LOGS environment variable path. The default location is $HOME/.dagu/logs. You can override the setting by the logDir field in a YAML file.

How long will the history data be stored?

The default retention period for execution history is seven days. However, you can override the setting by the histRetentionDays field in a YAML file.

How can a workflow be retried from a specific task?

You can change the status of any task to a failed state. Then, when you retry the workflow, it will execute the failed one and any subsequent.

Update Status

Does it have a scheduler function?

No, it doesn’t have scheduler functionality. It is meant to be used with cron or other schedulers.

How can it communicate with running processes?

Dagu uses Unix sockets to communicate with running processes.

dagu Architecture

License

This project is licensed under the GNU GPLv3 – see the LICENSE.md file for details

Contributors

Made with contrib.rocks.

GitHub

View Github