dyngle 0.7.0

Run lightweight local workflows

Dyngle

Use cases

• A task runner
• A lightweight workflow engine
• A replacement for Make in Python projects
• A replacement for short functions in RC files
• Freedom from quirky Bash syntax

Technical foundations

• Configuration, task definition, and flow control in YAML
• Operations as system commands using a familiar shell-like syntax
• Expressions and logic in pure Python

Quick installation (MacOS)

brew install python@3.11
python3.11 -m pip install pipx
pipx install dyngle

Getting started

Create a file .dyngle.yml:

dyngle:
  operations:
    hello:
      - echo "Hello world"

Run an operation:

dyngle run hello

Configuration

Dyngle reads configuration from YAML files. You can specify the config file location using:

• --config command line option
• DYNGLE_CONFIG environment variable
• .dyngle.yml in current directory
• ~/.dyngle.yml in home directory

Operations

Operations are defined under dyngle: in the configuration. In its simplest form, an Operation takes the form of a YAML array defining the steps, as a system command with space-separated arguments. In that sense, a Dyngle operation looks something akin to a "PHONY" Make target, a short Bash script, or a CI/CD job. As a serious example, consider the init operation from the Dyngle configuration delivered with the project's source code.

dyngle:
  operations:
    init:
      - rm -rf .venv
      - python3.11 -m venv .venv
      - .venv/bin/pip install --upgrade pip poetry

The elements of the YAML array look like lines of Bash, but Dyngle replaces the shell with YAML-based flow control and Python-based expressions (described below). So shell-specific operators such as |, >, and $VARIABLES won't work.

Data and Templates

Dyngle maintains a block of Data throughout an operation, which is a set of named values (Python dict, YAML "mapping"). The values are usually strings but can also be other data types that are valid in both YAML and Python.

The dyngle run command feeds the contents of stdin to the Operation as Data, by converting a YAML mapping to named Python values. The values may be substituted into commands or arguments in Steps using double-curly-bracket syntax ({{ and }}) similar to Jinja2.

For example, consider the following configuration:

dyngle:
  operations:
    hello:
      - echo "Hello {{name}}!"

Cram some YAML into stdin to try it in your shell:

echo "name: Francis" | dyngle run hello

The output will say:

Hello Francis!

Expressions

Operations may contain Expressions, written in Python, that can be referenced in operation steps using the same syntax as for Data. In the case of a naming conflict, an Expression takes precedence over Data with the same name. Expressions can reference names in the Data directly.

Expressions may be defined in either of two ways in the configuration:

1. Global Expressions, under the dyngle: mapping, using the expressions: key.
2. Local Expressions, within a single Operation, in which case the Steps of the operation require a steps: key.

Here's an example of a global Expression

dyngle:
  expressions:
    count: len(name)    
  operations:
    say-hello:
      - echo "Hello {{name}}! Your name has {{count}} characters."

For completeness, consider the following example using a local Expression for the same purpose.

dyngle:
  operations:
    say-hello:
      expressions:
        count: len(name)
      steps:
        - echo "Hello {{name}}! Your name has {{count}} characters."

Expressions can use a controlled subset of the Python standard library, including:

• Built-in data types such as str()
• Essential built-in functions such as len()
• The core modules from the datetime package (but some methods such as strftime() will fail)
• A specialized function called formatted() to perform string formatting operations on a datetime object
• A restricted version of Path() that only operates within the current working directory
• Various other useful utilities, mostly read-only, such as the math module
• A special function called resolve which resolves data expressions using the same logic as in templates
• An array args containing arguments passed to the dyngle run command after the Operation name

NOTE Some capabilities of the Expression namespace might be limited in the future. The goal is support purely read-only operations within Expressions.

Expressions behave like functions that take no arguments, using the Data as a namespace. So Expressions reference Data directly as local names in Python.

YAML keys can contain hyphens, which are fully supported in Dyngle. To reference a hyphenated key in an Expression, choose:

• Reference the name using underscores instead of hyphens (they are automatically replaced), OR
• Use the built-in special-purpose resolve() function (which can also be used to reference other expressions)

dyngle:
  expressions:
    say-hello: >-
        'Hello ' + full_name + '!'

... or using the resolve() function, which also allows expressions to essentially call other expressions, using the same underlying data set.

dyngle:
  expressions:
    hello: >-
        'Hello ' + resolve('formal-name') + '!'
    formal-name: >-
        'Ms. ' + full_name

Note it's also possible to call other expressions by name as functions, if they only return hard-coded values (i.e. constants).

dyngle:
  expressions:
    author-name: Francis Potter
    author-hello: >-
        'Hello ' + author_name()

Here are some slightly more sophisticated exercises using Expression reference syntax:

dyngle:
  operations:
    reference-hyphenated-data-key:
      expressions:
        spaced-name: "' '.join([x for x in first_name])"
        count-name: len(resolve('first-name'))
        x-name: "'X' * int(resolve('count-name'))"
      steps:
        - echo "Your name is {{first-name}} with {{count-name}} characters, but I will call you '{{spaced-name}}' or maybe '{{x-name}}'"
    reference-expression-using-function-syntax:
      expressions:
        name: "'George'"
        works: "name()"
        double: "name * 2"
        fails: double()
      steps:
        - echo "It works to call you {{works}}"
        # - echo "I have trouble calling you {{fails}}"

Finally, here's an example using args:

dyngle:
  operations:
    name-from-arg:
      expressions:
        name: "args[0]"
      steps:
        - echo "Hello {{name}}"

Data assignment operator

The Steps parser supports one special operator which assigns the output (stdout) of its command to a Data field for use in subsequent steps.

The operator is => and must go after the command and its arguments. Follow the operator with the name of the Data key to assign.

Example:

dyngle:
  operations:
    today:
      expressions:
        just-the-date: resolve('full-date')[0:10]
      steps:
        - date => full-date
        - echo "Today is {{just-the-date}}"

(Note Dyngle does provide Python date operations in the Expression namespace, which might provide a better way to perform the same operation as this example, but it suffices to demonstrate assignment)

Lifecycle

The lifecycle of an operation is:

1. Load Data if it exists from YAML on stdin (if no tty)
2. Find the named Operation in the configuration
3. Perform template rendering on the first Step, using Data and Expressions
4. Execute the Step in a subprocess
5. Continue with the next Step

Note that operations in the config are not full shell lines. They are passed directly to the system.

Imports

Configuration files can import other configuration files, by providing an entry imports: with an array of filepaths. The most obvious example is a Dyngle config in a local directory which imports the user-level configuration.

dyngle:
  imports:
    - ~/.dyngle.yml
  expressions:
  operations:

In the event of item name conflicts, expressions and operations are loaded from imports in the order specified, so imports lower in the array will override those higher up. The expressions and operations defined in the main file override the imports. Imports are not recursive.

Security

Commands are executed using Python's subprocess.run() with arguments split in a shell-like fashion. The shell is not used, which reduces the likelihood of shell injection attacks. However, note that Dyngle is not robust to malicious configuration. Use with caution.