laboro 0.1.5

The Laboro workflow framework

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Laboro

Laboro is a NO-Code / Low-Code workflow manager that helps you to build and run automated tasks without any coding or system administration skills.

You don't need to know how to install a specific development environment to use Laboro.

You don't need to have advanced system administration knowledge to run your Laboro automated tasks.

Development status

Laboro is in beta stage and is not ready for production yet.

Status of this documentation

This documentation is an incomplete work in progress and changes may occur frequently until the release of the Laboro-1.0.0 version.

Install

Laboro leverages the modern container environments such as Docker or Podman.

The easiest way to install Laboro is to install the pre-built and pre-configured container by issuing the following command:

docker pull mcosystem/laboro:latest

That's it !

However, Laboro is available through the Python Package Index for adventurous and expert users willing to simply install Laboro on their computer using pip3 install laboro and configure it manually.

To get the latest Laboro version, run the following command line:

Install a specific Laboro version

The Laboro version matches the Python Laboro package version.

Thus, to install Laboro in a specific version, use the corresponding tag:

docker pull mcosystem/laboro:1.0.0

Running workflows

Once a workflow is configured and saved in the /path/to/local/workflowdir, you can run it by issuing the following command:

Example:

docker run --name laboro \
            -e TZ=Pacific/Noumea \
            -v /path/to/local/workflowdir:/opt/laboro/workflows:ro \
            -v /path/to/local/logs:/opt/laboro/log:rw \
            mcosystem/laboro \
            -w workflow_demo_1 workflow_demo_2 workflow_demo_3

In this example the 3 workflows workflow_demo_1, workflow_demo_2, workflow_demo_3 will be run sequentially in the specified order within a container which time zone is set to Pacific/Noumea and with the /path/to/local/workflowdir and /path/to/local/logs host directories mounted respectively mounted as /opt/laboro/workflows and /opt/laboro/log container directories.

Example of a Laboro container running a workflow named workflow_demo :

Modules

Laboro capabilities are extended through modules delivered as Python packages.

Available Modules

Not documented yet.

Code your own module

Not documented yet.

Write the module specification

Not documented yet.

Concepts

Laboro lays on 5 fundamental concepts: workflow, packages, steps, actions, method.

Each of these concepts are described in technical details in the configuration section.

Workflow

A workflow is a set of packages and steps. Basically it is the container that embed all the tasks needed that you want to achieve by running it.

Package

A package is a package in the way Python, the programming language underlying Laboro, call the deliverable set of code that can be installed.

Laboro itself is a Python package.

Step

A step is a set of actions. It describes all actions in that set and the condition in which they will be executed.

Action

Laboro relies on the Python programming language which is an object oriented programming language.

This type of languages propose some abstract representation called objects that have some properties and methods.

An object configured with specific parameters is called an instance and by calling the methods of an instance, you can execute the code that resides inside these methods.

An action is the description of an instance, its parameters and the condition in which its method will be be executed.

More on that in the workflow configuration section.

Method

As seen in the above Action section, a method is a description of an object method, its parameters and the condition in which it will be be executed.

Configuration

All aspects of Laboro, its main configuration as the workflow configuration are managed through YAML configuration files.

Main configuration

The default global configuration file is /etc/laboro/laboro.yml. This file is provided by the Laboro container image and should not be modified.

However, this file is auto-generated at build time from the parameters given to container build command. Passing parameters at build time ascertains that all needed directories are created with the expected permissions (See the container/README.md for further details on the available configuration parameters and how to build a custom Laboro container image).

Even if Laboro is a container based application, modification of the default global configuration file outside build time (i.e. by mounting a container volume at run time) is not expected nor supported.

If you choose to do so, please, don't forget to create the directories according to your configuration file and apply the right permissions to them.

Example of global configuration file:

---
laboro:
  histdir: /opt/laboro/log/hist
  workspacedir: /opt/laboro/workspaces
  workflowdir:  /opt/laboro/workflows
  log:
    dir:  /opt/laboro/log
    level: INFO

Workflow configuration

A workflow is described by a YAML configuration file. Each workflow configuration file must be in the laboro.workflowdir directory specified in the main configuration file.

TL;DR: A complete working workflow configuration is available in the test suite.

A workflow has the following properties:

• name: The name of the workflow. A simple string identifying your workflow
• packages: A list of Python packages that should be installed before running the workflow. All python packages listed here will be installed prior to workflow execution. To prevent abusive and hazardous package installation, each package name must match the following regular expression ^laboro_.*.
• workspace: A section which has a unique key delete_on_exit that accepts a boolean value. If set to True, the workspace will be deleted at the end of the workflow.
• steps: A list of step. Steps will be executed sequentially in the specified order.

Example:

---
name: Demo workflow
packages:
  - laboro_demo
workspace:
  delete_on_exit: True
steps:
  - ...

In this example the Python package laboro_demo will be installed prior to the execution of the workflow named Demo workflow. When the workflow end, the workspace associated with it and all its content will be deleted.

Step

A step has the following properties:

• name: The name of the step. A string that should be unique in the steps list.
• loop: Optional. The description of an iterable object (a list or a dictionary) on which the step will be repeated. If no loop property is defined, the step will be executed only once. See the (Loops section)(#loops) for further details on loops.
• when: Optional. The string representation of an expression that must evaluates as a boolean. The result of the evaluation of this expression will be used as a condition to the step execution. See the (Conditional execution section)(#conditional-execution) for further details on conditional executions.
• actions: A list of action. Actions will be executed sequentially in the specified order.

Example:

name: Step 1
loop:
  - Nobody
  - expects
  - 'the Spanish Inquisition !'
  - spam
  - spam
  - spam
when: "$step_item$ != 'spam'"
actions:
  - ...

In this example the step named Step 1 will loop over all values declared in its loop property and will be executed only when the step loop variable $step_item$ will have any value different than spam.

See the variables section for further details on how to use variables.

Action

An action has the following properties:

• name: A string specifying the name of the action. This name should be unique in the workflow.
• loop: Optional. The description of an iterable object (a list or a dictionary) on which the acton will be repeated. If no loop property is defined, the action will be executed only once. See the (Loops section)(#loops) for further details on loops.
• when: Optional. The string representation of an expression that must evaluates as a boolean. The result of the evaluation of this expression will be used as a condition to the action execution. See the (Conditional execution section)(#conditional-execution) for further details on conditional executions.
• instance: the description of the instance

Example:

name: Action 1
when: '$step_item$ != "spam"'
instance:
  ...

In this example the action named Action 1 will be executed when the step loop variable $step_item$ will have any other value than spam.

See the variables section for further details on how to use variables.

Instance

An instance describes how an object will be instantiated. It has the following properties:

• name: A string specifying the name of the instance. This name should be unique in the workflow and will be used as the variable name in which the instance will be stored. Multiple instance declaration with the same name property will override each other and the last declared within the execution scope will be kept.
• module: The Python module in which the object class can be found. This module must be available (see packages) will be imported.
• class: A string specifying the class of the object to be instantiated. The specified class must be part of the specified module.
• args: Optional. A dictionary representation of the arguments that will be used for the object instantiation. This dictionary must use the expected argument names as keys, and the argument values as value. The args property can be omitted if no argument is necessary to the method call.
• instantiate: A boolean. If set to True, the object will be instantiated and the instance stored in the variable named after the name property. If set to False, the object will not be instantiated and all methods will be called upon the the object previously stored in the variable named after the name property. This allow instance reutilization across steps or actions.
• methods: A list of all methods that should be executed. Methods will be executed sequentially in the specified order.

Example:

instance:
  name: my_demo
  module: laboro_demo
  class: Demo
  args:
    is_demo: True
    name: Demo Class
    password: p455w0rD_01
    list_only: True
    demo_list:
      - Laboro
      - Rocks
  instantiate: True
  methods:
    - ...

In this example, the module name laboro_demo will be imported, an object of the class laboro_demo.Demo will be instantiated withe the arguments is_demo, name, password, list_only and demo_list. The resulting instance will be stored in a variable called my_demo.

See the variables section for further details on how to use variables.

Method

A method has the following properties:

• name: A string specifying the method name. This name must be the method name as declared in the instance object class source code.
• loop: Optional. The description of an iterable object (a list or a dictionary) on which the method will be repeated. If no loop property is defined, the method will be executed only once. See the (Loops section)(#loops) for further details on loops.
• when: Optional. The string representation of an expression that must evaluates as a boolean. The result of the evaluation of this expression will be used as a condition to the method execution. See the (Conditional execution section)(#conditional-execution) for further details on conditional executions.
• args: Optional. A dictionary representation of the arguments that will be passed to the method. This dictionary must use the expected argument names as keys, and the argument values as value. The args property can be omitted if no argument is necessary to the method call.
• output: A string specifying a variable name in which the return value of the method will be stored. If no output property is specified the return value of the method will be lost.

Example:

name: get_title
loop: $store$books
when: $method_item$['author'] == "Plato"
args:
  title: $method_item$
output: book_title

In this example the method get_title will be executed for each item stored in the books variable and when method loop variable author item value is "Plato". The argument name passed to the method is title and its value is the value of the method loop variable.

The value returned by the get_title method will be stored in a variable named book_title.

See the variables section for further details on how to use variables.

Variables

By using their output property, Laboro can store values returned by methods.

Once stored, a variable is available on the whole workflow scope and can be used in any subsequent property including loop and when properties).

Calling a variable in a loop is done by prefixing its name by $store$.

Example:

methods:
  - name: get_books
    output: books
  - name: get_title
    loop: $store$books
    when: $method_item$['author'] == "Plato"
    args:
      title: $method_item$
    output: book_title

In this example the return value of the get_books method is stored in the books variable and then used:

• As a get_title method loop iterable.
• As part of the execution condition of the method get_title.

Loops

Steps, actions and method have a loop property that accepts any iterable object as a value.

When the loop property is set, a specific variable will store each item in the iterable object for each execution.

The specific variable name depends on the object the loop property is set:

Object	Loop variable	Call syntax
Step	step_item	$step_item$
Action	action_item	$action_item$
Method	method_item	$method_item$

Once stored, a loop variable is available for all objects within and beneath its declaration level and can be used in any subsequent property including loop and when properties).

Example:

steps:
  - name: Step 1
    loop:
      - item1: Nobody
        item2: expects
        item3: 'the Spanish Inquisition !'
      - item1: spam
        item2: spam
        item3: spam
    actions:
      - name: Action 1
        when: '$step_item$["item3"] != "spam"'
        instance:
          name: my_instance
          module: laboro_fake.submodule
          class: FakeObject
          instantiate: True
          methods:
            - name: get_arg_value
              args:
                argument: $step_item$

In this example, a list containing two dictionaries is declared as a step loop iterable. When the step is executed, the step variable step_item will get the value of each of the dictionary making it available for all actions is the step.

Workflow sessions

Each execution of a specific workflow is identified by a unique identifier called a session. The session identifier is a string representation of an uuid.uuid4 object. This session identifier is stored in the workflow execution history file.

The workflow log file and the session workspace are named after this property.

Execution history

Each workflow has its own history database situated in the laboro.histdir directory in which each workflow execution session parameters are stored

The default laboro.histdir value is /opt/laboro/log/hist.

This history database is a sqlite3 database named after the workflow name property.

For each workflow execution the following data are stored:

• The workflow name
• The workflow session id
• The workflow start date as a timestamp
• The workflow end date as a timestamp
• The workflow execution time in a human readable format
• The workflow parameters as a dictionary representation
• The exit code: 0 if exited without error, a detailed error message otherwise.

Workspaces

Each workflow has a file storage space named after the workflow name property and situated under the laboro.workspacedir directory.

For each workflow execution a sub-directory named after the workflow session id is created under the main workflow directory. Any Laboro modules can use this workspace to store file for later use within the workflow.

Note: When the workspace.delete_on_exit workflow property is set to True, the workspace and its content is deleted at the end of the workflow execution even when the workflow exit on error.

-+- workspacedir/
      |
      +- my_workflow_1/
      |    |
      |    +- workflow_1_session_1/
      |    |    |
      |    |    + file_1
      |    |
      |    +- workflow_1_session_2/
      |         |
      |         + file_1
      |
      +- my_workflow_2/
           |
           +- workflow_2_session_1/
           |    |
           |    + file_1
           |
           +- workflow_ 2_session_2/

Logging

Laboro logging configuration is set from the main laboro configuration file.

The default container configuration set the "${LABORO_LOGDIR}" variable to /var/log/laboro so the log directory can be easily mounted at runtime from the host with the following command:

docker run --name laboro \
           -e TZ=Pacific/Noumea \
           -v /path/to/local/workflowdir:/opt/laboro/workflows:ro \
           -v /path/to/local/logdir:/var/log/laboro:rw \
           mcosystem/laboro \
           -w my_workflow_1 my_workflow_2

All workflows logs are stored in the laboro.log.dir directory and each workflow has a specific directory where its session logs will be stored.

-+- logdir/
     |
     +- my_workflow_1/
     |    |
     |    +- workflow_1_session_1.log
     |    |
     |    +- workflow_1_session_2.log
     |
       +- my_workflow_2/
            |
            +- workflow_2_session_1.log

Log levels

Log level is a workflow configuration and must be set to one of the following values:

Log levels
DEBUG
INFO
WARNING
ERROR
CRITICAL

Secrets management

Not documented yet.

Log date and time

The default time zone of the Laboro container image is UTC.

This can be changed by setting the desired time zone setting by using the TZ environment variable to the container.

Example:

docker run --name laboro \
            -e TZ=Pacific/Noumea \
            -v /path/to/local/workflowdir:/opt/laboro/workflows:ro \
            -v /path/to/local/logs:/opt/laboro/log:rw \
            mcosystem/laboro \
            -w workflow_demo