Salt Formulas

Formulas are pre-written Salt States. They are as open-ended as Salt States themselves and can be used for tasks such as installing a package, configuring, and starting a service, setting up users or permissions, and many other common tasks.

All official Salt Formulas are found as separate Git repositories in the "saltstack-formulas" organization on GitHub:

As a simple example, to install the popular Apache web server (using the normal defaults for the underlying distro) simply include the apache-formula from a top file:

    - apache


Each Salt Formula is an individual Git repository designed as a drop-in addition to an existing Salt State tree. Formulas can be installed in the following ways.

Adding a Formula as a GitFS remote

One design goal of Salt's GitFS fileserver backend was to facilitate reusable States. GitFS is a quick and natural way to use Formulas.

  1. Install and configure GitFS.

  2. Add one or more Formula repository URLs as remotes in the gitfs_remotes list in the Salt Master configuration file:


    We strongly recommend forking a formula repository into your own GitHub account to avoid unexpected changes to your infrastructure.

    Many Salt Formulas are highly active repositories so pull new changes with care. Plus any additions you make to your fork can be easily sent back upstream with a quick pull request!

  3. Restart the Salt master.

Adding a Formula directory manually

Formulas are simply directories that can be copied onto the local file system by using Git to clone the repository or by downloading and expanding a tarball or zip file of the repository. The directory structure is designed to work with file_roots in the Salt master configuration.

  1. Clone or download the repository into a directory:

    mkdir -p /srv/formulas
    cd /srv/formulas
    git clone
    # or
    mkdir -p /srv/formulas
    cd /srv/formulas
    tar xf apache-formula-master.tar.gz
  2. Add the new directory to file_roots:

        - /srv/salt
        - /srv/formulas/apache-formula
  3. Restart the Salt Master.


Each Formula is intended to be immediately usable with sane defaults without any additional configuration. Many formulas are also configurable by including data in Pillar; see the pillar.example file in each Formula repository for available options.

Including a Formula in an existing State tree

Formula may be included in an existing sls file. This is often useful when a state you are writing needs to require or extend a state defined in the formula.

Here is an example of a state that uses the epel-formula in a require declaration which directs Salt to not install the python26 package until after the EPEL repository has also been installed:

  - epel

    - require:
      - pkg: epel

Including a Formula from a Top File

Some Formula perform completely standalone installations that are not referenced from other state files. It is usually cleanest to include these Formula directly from a Top File.

For example the easiest way to set up an OpenStack deployment on a single machine is to include the openstack-standalone-formula directly from a top.sls file:

    - openstack

Quickly deploying OpenStack across several dedicated machines could also be done directly from a Top File and may look something like this:

    - openstack.horizon
    - openstack.keystone
    - openstack.nova
    - openstack.glance
    - openstack.swift

Configuring Formula using Pillar

Salt Formulas are designed to work out of the box with no additional configuration. However, many Formula support additional configuration and customization through Pillar. Examples of available options can be found in a file named pillar.example in the root directory of each Formula repository.

Using Formula with your own states

Remember that Formula are regular Salt States and can be used with all Salt's normal state mechanisms. Formula can be required from other States with require declarations, they can be modified using extend, they can made to watch other states with The _in versions of requisites.

The following example uses the stock apache-formula alongside a custom state to create a vhost on a Debian/Ubuntu system and to reload the Apache service whenever the vhost is changed.

# Include the stock, upstream apache formula.
  - apache

# Use the watch_in requisite to cause the apache service state to reload
# apache whenever the my-example-com-vhost state changes.
    - managed
    - name: /etc/apache2/sites-available/my-example-com
    - watch_in:
      - service: apache

Don't be shy to read through the source for each Formula!

Reporting problems & making additions

Each Formula is a separate repository on GitHub. If you encounter a bug with a Formula please file an issue in the respective repository! Send fixes and additions as a pull request. Add tips and tricks to the repository wiki.

Writing Formulas

Each Formula is a separate repository in the saltstack-formulas organization on GitHub.


Get involved creating new Formulas

The best way to create new Formula repositories for now is to create a repository in your own account on GitHub and notify a SaltStack employee when it is ready. We will add you to the contributors team on the saltstack-formulas organization and help you transfer the repository over. Ping a SaltStack employee on IRC (#salt on Freenode) or send an email to the salt-users mailing list.

There are a lot of repositories in that organization! Team members can manage which repositories they are subscribed to on GitHub's watching page:


Maintainability, readability, and reusability are all marks of a good Salt sls file. This section contains several suggestions and examples.

# Deploy the stable master branch unless version overridden by passing
# Pillar at the CLI or via the Reactor.

    - name:
    - version: {{ salt.pillar.get('myapp:version', 'master') }}

Use a descriptive State ID

The ID of a state is used as a unique identifier that may be referenced via other states in requisites. It must be unique across the whole state tree (it is a key in a dictionary, after all).

In addition a state ID should be descriptive and serve as a high-level hint of what it will do, or manage, or change. For example, deploy_webapp, or apache, or reload_firewall.

Use module.function notation

So-called "short-declaration" notation is preferred for referencing state modules and state functions. It provides a consistent pattern of module.function shared between Salt States, the Reactor, Salt Mine, the Scheduler, as well as with the CLI.

# Do
    - name: httpd

# Don't
    - installed
    - name: httpd

Salt's state compiler will transform "short-decs" into the longer format when compiling the human-friendly highstate structure into the machine-friendly lowstate structure.

Specify the name parameter

Use a unique and permanent identifier for the state ID and reserve name for data with variability.

The name declaration is a required parameter for all state functions. The state ID will implicitly be used as name if it is not explicitly set in the state.

In many state functions the name parameter is used for data that varies such as OS-specific package names, OS-specific file system paths, repository addresses, etc. Any time the ID of a state changes all references to that ID must also be changed. Use a permanent ID when writing a state the first time to future-proof that state and allow for easier refactors down the road.

Comment state files

YAML allows comments at varying indentation levels. It is a good practice to comment state files. Use vertical whitespace to visually separate different concepts or actions.

# Start with a high-level description of the current sls file.
# Explain the scope of what it will do or manage.

# Comment individual states as necessary.
  # Provide details on why an unusual choice was made. For example:
  # This template is fetched from a third-party and does not fit our
  # company norm of using Jinja. This must be processed using Mako.
    - name: /path/to/file.cfg
    - source: salt://path/to/file.cfg.template
    - template: mako

  # Provide a description or explanation that did not fit within the state
  # ID. For example:
  # Update the application's last-deployed timestamp.
  # This is a workaround until Bob configures Jenkins to automate RPM
  # builds of the app.
    # FIXME: Joe needs this to run on Windows by next quarter. Switch these
    # from shell commands to Salt's file.managed and file.replace state
    # modules.
    - name: |
        touch /path/to/file_last_updated
        sed -e 's/foo/bar/g' /path/to/file_environment
    - onchanges:
      - file: a_config_file

Be careful to use Jinja comments for commenting Jinja code and YAML comments for commenting YAML code.

# The Jinja in this YAML comment is still executed!
# {% set apache_is_installed = 'apache' in salt.pkg.list_pkgs() %}

# The Jinja in this Jinja comment will not be executed.
{# {% set apache_is_installed = 'apache' in salt.pkg.list_pkgs() %} #}

Easy on the Jinja!

Jinja templating provides vast flexibility and power when building Salt sls files. It can also create an unmaintainable tangle of logic and data. Speaking broadly, Jinja is best used when kept apart from the states (as much as is possible).

Below are guidelines and examples of how Jinja can be used effectively.

Know the evaluation and execution order

High-level knowledge of how Salt states are compiled and run is useful when writing states.

The default renderer setting in Salt is Jinja piped to YAML. Each is a separate step. Each step is not aware of the previous or following step. Jinja is not YAML aware, YAML is not Jinja aware; they cannot share variables or interact.

  • Whatever the Jinja step produces must be valid YAML.
  • Whatever the YAML step produces must be a valid highstate data structure. (This is also true of the final step for any of the alternate renderers in Salt.)
  • Highstate can be thought of as a human-friendly data structure; easy to write and easy to read.
  • Salt's state compiler validates the highstate and compiles it to low state.
  • Low state can be thought of as a machine-friendly data structure. It is a list of dictionaries that each map directly to a function call.
  • Salt's state system finally starts and executes on each "chunk" in the low state. Remember that requisites are evaluated at runtime.
  • The return for each function call is added to the "running" dictionary which is the final output at the end of the state run.

The full evaluation and execution order:

Jinja -> YAML -> Highstate -> low state -> execution

Avoid changing the underlying system with Jinja

Avoid calling commands from Jinja that change the underlying system. Commands run via Jinja do not respect Salt's dry-run mode (test=True)! This is usually in conflict with the idempotent nature of Salt states unless the command being run is also idempotent.

Inspect the local system

A common use for Jinja in Salt states is to gather information about the underlying system. The grains dictionary available in the Jinja context is a great example of common data points that Salt itself has already gathered. Less common values are often found by running commands. For example:

{% set is_selinux_enabled ='sestatus') == '1' %}

This is usually best done with a variable assignment in order to separate the data from the state that will make use of the data.

Gather external data

One of the most common uses for Jinja is to pull external data into the state file. External data can come from anywhere like API calls or database queries, but it most commonly comes from flat files on the file system or Pillar data from the Salt Master. For example:

{% set some_data = salt.pillar.get('some_data', {'sane default': True}) %}

{# or #}

{% import_yaml 'path/to/file.yaml' as some_data %}

{# or #}

{% import_json 'path/to/file.json' as some_data %}

{# or #}

{% import_text 'path/to/' as ssh_pub_key %}

{# or #}

{% from 'path/to/other_file.jinja' import some_data with context %}

This is usually best done with a variable assignment in order to separate the data from the state that will make use of the data.

Light conditionals and looping

Jinja is extremely powerful for programmatically generating Salt states. It is also easy to overuse. As a rule of thumb, if it is hard to read it will be hard to maintain!

Separate Jinja control-flow statements from the states as much as is possible to create readable states. Limit Jinja within states to simple variable lookups.

Below is a simple example of a readable loop:

{% for user in salt.pillar.get('list_of_users', []) %}

{# Ensure unique state IDs when looping. #}
{{ }}-{{ loop.index }}:
    - name: {{ }}
    - shell: {{ }}

{% endfor %}

Avoid putting a Jinja conditionals within Salt states where possible. Readability suffers and the correct YAML indentation is difficult to see in the surrounding visual noise. Parametrization (discussed below) and variables are both useful techniques to avoid this. For example:

{# ---- Bad example ---- #}

    {% if grains.os_family == 'RedHat' %}
    - name: httpd
    {% elif grains.os_family == 'Debian' %}
    - name: apache2
    {% endif %}

{# ---- Better example ---- #}

{% if grains.os_family == 'RedHat' %}
{% set name = 'httpd' %}
{% elif grains.os_family == 'Debian' %}
{% set name = 'apache2' %}
{% endif %}

    - name: {{ name }}

{# ---- Good example ---- #}

{% set name = {
    'RedHat': 'httpd',
    'Debian': 'apache2',
}.get(grains.os_family) %}

    - name: {{ name }}

Dictionaries are useful to effectively "namespace" a collection of variables. This is useful with parametrization (discussed below). Dictionaries are also easily combined and merged. And they can be directly serialized into YAML which is often easier than trying to create valid YAML through templating. For example:

{# ---- Bad example ---- #}

    - name: /etc/haproxy/haproxy.cfg
    - template: jinja
    {% if 'external_loadbalancer' in grains.roles %}
    - source: salt://haproxy/external_haproxy.cfg
    {% elif 'internal_loadbalancer' in grains.roles %}
    - source: salt://haproxy/internal_haproxy.cfg
    {% endif %}
    - context:
        {% if 'external_loadbalancer' in grains.roles %}
        ssl_termination: True
        {% elif 'internal_loadbalancer' in grains.roles %}
        ssl_termination: False
        {% endif %}

{# ---- Better example ---- #}

{% load_yaml as haproxy_defaults %}
  bind_port: 80

  source: salt://haproxy/internal_haproxy.cfg
    bind_port: 8080
    ssl_termination: False

  source: salt://haproxy/external_haproxy.cfg
    ssl_termination: True
{% endload %}

{% if 'external_loadbalancer' in grains.roles %}
{% set haproxy = haproxy_defaults['external_loadbalancer'] %}
{% elif 'internal_loadbalancer' in grains.roles %}
{% set haproxy = haproxy_defaults['internal_loadbalancer'] %}
{% endif %}

{% do haproxy.settings.update(haproxy_defaults.common_settings) %}

    - name: /etc/haproxy/haproxy.cfg
    - template: jinja
    - source: {{ haproxy.source }}
    - context: {{ haproxy.settings | yaml() }}

There is still room for improvement in the above example. For example, extracting into an external file or replacing the if-elif conditional with a function call to filter the correct data more succinctly. However, the state itself is simple and legible, the data is separate and also simple and legible. And those suggested improvements can be made at some future date without altering the state at all!

Avoid heavy logic and programming

Jinja is not Python. It was made by Python programmers and shares many semantics and some syntax but it does not allow for abitrary Python function calls or Python imports. Jinja is a fast and efficient templating language but the syntax can be verbose and visually noisy.

Once Jinja use within an sls file becomes slightly complicated -- long chains of if-elif-elif-else statements, nested conditionals, complicated dictionary merges, wanting to use sets -- instead consider using a different Salt renderer, such as the Python renderer. As a rule of thumb, if it is hard to read it will be hard to maintain -- switch to a format that is easier to read.

Using alternate renderers is very simple to do using Salt's "she-bang" syntax at the top of the file. The Python renderer must simply return the correct highstate data structure. The following example is a state tree of two sls files, one simple and one complicated.


    - common_configuration
    - roles_configuration


    - names: [larry, curly, moe]


def run():
    list_of_roles = set()

    # This example has the minion id in the form 'web-03-dev'.
    # Easily access the grains dictionary:
        app, instance_number, environment = __grains__['id'].split('-')
        instance_number = int(instance_number)
    except ValueError:
        app, instance_number, environment = ['Unknown', 0, 'dev']


    if app == 'web' and environment == 'dev':
    elif app == 'web' and environment == 'staging':
        if instance_number == 0:

    # Easily cross-call Salt execution modules:
    if __salt__['myutils.query_valid_ec2_instance']():

    return {
        'set_roles_grains': {
            'grains.present': [
                {'name': 'roles'},
                {'value': list(list_of_roles)},

Jinja Macros

In Salt sls files Jinja macros are useful for one thing and one thing only: creating mini templates that can be reused and rendered on demand. Do not fall into the trap of thinking of macros as functions; Jinja is not Python (see above).

Macros are useful for creating reusable, parameterized states. For example:

{% macro user_state(state_id, user_name, shell='/bin/bash', groups=[]) %}
{{ state_id }}:
    - name: {{ user_name }}
    - shell: {{ shell }}
    - groups: {{ groups | json() }}
{% endmacro %}

{% for user_info in salt.pillar.get('my_users', []) %}
{{ user_state('user_number_' ~ loop.index, **user_info) }}
{% endfor %}

Macros are also useful for creating one-off "serializers" that can accept a data structure and write that out as a domain-specific configuration file. For example, the following macro could be used to write a php.ini config file:


    - name: /etc/php.ini
    - source: salt://php.ini.tmpl
    - template: jinja
    - context:
        php_ini_settings: {{ salt.pillar.get('php_ini', {}) | json() }}


    engine: 'On'
    short_open_tag: 'Off'
    error_reporting: 'E_ALL & ~E_DEPRECATED & ~E_STRICT'


{% macro php_ini_serializer(data) %}
{% for section_name, name_val_pairs in data.items() %}
[{{ section_name }}]
{% for name, val in name_val_pairs.items() -%}
{{ name }} = "{{ val }}"
{% endfor %}
{% endfor %}
{% endmacro %}

; File managed by Salt at <{{ source }}>.
; Your changes will be overwritten.

{{ php_ini_serializer(php_ini_settings) }}

Abstracting static defaults into a lookup table

Separate data that a state uses from the state itself to increases the flexibility and reusability of a state.

An obvious and common example of this is platform-specific package names and file system paths. Another example is sane defaults for an application, or common settings within a company or organization. Organizing such data as a dictionary (aka hash map, lookup table, associative array) often provides a lightweight namespacing and allows for quick and easy lookups. In addition, using a dictionary allows for easily merging and overriding static values within a lookup table with dynamic values fetched from Pillar.

A strong convention in Salt Formulas is to place platform-specific data, such as package names and file system paths, into a file named map.jinja that is placed alongside the state files.

The following is an example from the MySQL Formula. The grains.filter_by function performs a lookup on that table using the os_family grain (by default).

The result is that the mysql variable is assigned to a subset of the lookup table for the current platform. This allows states to reference, for example, the name of a package without worrying about the underlying OS. The syntax for referencing a value is a normal dictionary lookup in Jinja, such as {{ mysql['service'] }} or the shorthand {{ mysql.service }}.


{% set mysql = salt['grains.filter_by']({
    'Debian': {
        'server': 'mysql-server',
        'client': 'mysql-client',
        'service': 'mysql',
        'config': '/etc/mysql/my.cnf',
        'python': 'python-mysqldb',
    'RedHat': {
        'server': 'mysql-server',
        'client': 'mysql',
        'service': 'mysqld',
        'config': '/etc/my.cnf',
        'python': 'MySQL-python',
    'Gentoo': {
        'server': 'dev-db/mysql',
        'client': 'dev-db/mysql',
        'service': 'mysql',
        'config': '/etc/mysql/my.cnf',
        'python': 'dev-python/mysql-python',
}, merge=salt['pillar.get']('mysql:lookup')) %}

Values defined in the map file can be fetched for the current platform in any state file using the following syntax:

{% from "mysql/map.jinja" import mysql with context %}

    - name: {{ mysql.server }}
    - name: {{ mysql.service }}

Organizing Pillar data

It is considered a best practice to make formulas expect all formula-related parameters to be placed under second-level lookup key, within a main namespace designated for holding data for particular service/software/etc, managed by the formula:

    version: 5.7.11

Collecting common values

Common values can be collected into a base dictionary. This minimizes repetition of identical values in each of the lookup_dict sub-dictionaries. Now only the values that are different from the base must be specified by the alternates:


{% set mysql = salt['grains.filter_by']({
    'default': {
        'server': 'mysql-server',
        'client': 'mysql-client',
        'service': 'mysql',
        'config': '/etc/mysql/my.cnf',
        'python': 'python-mysqldb',
    'Debian': {
    'RedHat': {
        'client': 'mysql',
        'service': 'mysqld',
        'config': '/etc/my.cnf',
        'python': 'MySQL-python',
    'Gentoo': {
        'server': 'dev-db/mysql',
        'client': 'dev-db/mysql',
        'python': 'dev-python/mysql-python',
merge=salt['pillar.get']('mysql:lookup'), base='default') %}

Overriding values in the lookup table

Allow static values within lookup tables to be overridden. This is a simple pattern which once again increases flexibility and reusability for state files.

The merge argument in filter_by specifies the location of a dictionary in Pillar that can be used to override values returned from the lookup table. If the value exists in Pillar it will take precedence.

This is useful when software or configuration files is installed to non-standard locations or on unsupported platforms. For example, the following Pillar would replace the config value from the call above.

    config: /usr/local/etc/mysql/my.cnf


Protecting Expansion of Content with Special Characters

When templating keep in mind that YAML does have special characters for quoting, flows, and other special structure and content. When a Jinja substitution may have special characters that will be incorrectly parsed by YAML care must be taken. It is a good policy to use the yaml_encode or the yaml_dquote Jinja filters:

{%- set foo = 7.7 %}
{%- set bar = none %}
{%- set baz = true %}
{%- set zap = 'The word of the day is "salty".' %}
{%- set zip = '"The quick brown fox . . ."' %}

foo: {{ foo|yaml_encode }}
bar: {{ bar|yaml_encode }}
baz: {{ baz|yaml_encode }}
zap: {{ zap|yaml_encode }}
zip: {{ zip|yaml_dquote }}

The above will be rendered as below:

foo: 7.7
bar: null
baz: true
zap: "The word of the day is \"salty\"."
zip: "\"The quick brown fox . . .\""

The filter_by function performs a simple dictionary lookup but also allows for fetching data from Pillar and overriding data stored in the lookup table. That same workflow can be easily performed without using filter_by; other dictionaries besides data from Pillar can also be used.

{% set lookup_table = {...} %}
{% do lookup_table.update(salt.pillar.get('my:custom:data')) %}

When to use lookup tables

The map.jinja file is only a convention within Salt Formulas. This greater pattern is useful for a wide variety of data in a wide variety of workflows. This pattern is not limited to pulling data from a single file or data source. This pattern is useful in States, Pillar and the Reactor, for example.

Working with a data structure instead of, say, a config file allows the data to be cobbled together from multiple sources (local files, remote Pillar, database queries, etc), combined, overridden, and searched.

Below are a few examples of what lookup tables may be useful for and how they may be used and represented.

Platform-specific information

An obvious pattern and one used heavily in Salt Formulas is extracting platform-specific information such as package names and file system paths in a file named map.jinja. The pattern is explained in detail above.

Sane defaults

Application settings can be a good fit for this pattern. Store default settings along with the states themselves and keep overrides and sensitive settings in Pillar. Combine both into a single dictionary and then write the application config or settings file.

The example below stores most of the Apache Tomcat server.xml file alongside the Tomcat states and then allows values to be updated or augmented via Pillar. (This example uses the BadgerFish format for transforming JSON to XML.)


  '@port': '8005'
  '@shutdown': SHUTDOWN
      '@auth': Container
      '@description': User database that can be updated and saved
      '@factory': org.apache.catalina.users.MemoryUserDatabaseFactory
      '@name': UserDatabase
      '@pathname': conf/tomcat-users.xml
      '@type': org.apache.catalina.UserDatabase
  # <...snip...>


        '@name': Catalina
          '@port': '8009'
          '@protocol': AJP/1.3
          '@redirectPort': '8443'
          # <...snip...>


{% import_yaml 'tomcat/defaults.yaml' as server_xml_defaults %}
{% set server_xml_final_values = salt.pillar.get(

    - name: /etc/tomcat/server.xml
    - dataset: {{ server_xml_final_values | json() }}
    - formatter: xml_badgerfish

The file.serialize state can provide a shorthand for creating some files from data structures. There are also many examples within Salt Formulas of creating one-off "serializers" (often as Jinja macros) that reformat a data structure to a specific config file format. For example, `Nginx vhosts`__ or the `php.ini`__

__: __:

Environment specific information

A single state can be reused when it is parameterized as described in the section below, by separating the data the state will use from the state that performs the work. This can be the difference between deploying Application X and Application Y, or the difference between production and development. For example:


{# Load the map file. #}
{% import_yaml 'app/defaults.yaml' as app_defaults %}

{# Extract the relevant subset for the app configured on the current
   machine (configured via a grain in this example). #}
{% app = app_defaults.get(salt.grains.get('role') %}

{# Allow values from Pillar to (optionally) update values from the lookup
   table. #}
{% do app_defaults.update(salt.pillar.get('myapp', {}) %}

    - name: {{ app.repo_url }}
    - version: {{ app.version }}
    - target: {{ app.deploy_dir }}

    - data:
        version: {{ app.version }}
    - onchanges:
      - git: deploy_application


  target: /var/www/appX
  version: master
  target: /var/www/appY
  version: v1.2.3.4

Single-purpose SLS files

Each sls file in a Formula should strive to do a single thing. This increases the reusability of this file by keeping unrelated tasks from getting coupled together.

As an example, the base Apache formula should only install the Apache httpd server and start the httpd service. This is the basic, expected behavior when installing Apache. It should not perform additional changes such as set the Apache configuration file or create vhosts.

If a formula is single-purpose as in the example above, other formulas, and also other states can include and use that formula with Requisites and Other Global State Arguments without also including undesirable or unintended side-effects.

The following is a best-practice example for a reusable Apache formula. (This skips platform-specific options for brevity. See the full apache-formula for more.)

# apache/init.sls

# apache/mod_wsgi.sls
  - apache

    - require:
      - pkg: apache

# apache/conf.sls
  - apache

    - watch_in:
      - service: apache

To illustrate a bad example, say the above Apache formula installed Apache and also created a default vhost. The mod_wsgi state would not be able to include the Apache formula to create that dependency tree without also installing the unneeded default vhost.

Formulas should be reusable. Avoid coupling unrelated actions together.


Parameterization is a key feature of Salt Formulas and also for Salt States. Parameterization allows a single Formula to be reused across many operating systems; to be reused across production, development, or staging environments; and to be reused by many people all with varying goals.

Writing states, specifying ordering and dependencies is the part that takes the longest to write and to test. Filling those states out with data such as users or package names or file locations is the easy part. How many users, what those users are named, or where the files live are all implementation details that should be parameterized. This separation between a state and the data that populates a state creates a reusable formula.

In the example below the data that populates the state can come from anywhere -- it can be hard-coded at the top of the state, it can come from an external file, it can come from Pillar, it can come from an execution function call, or it can come from a database query. The state itself doesn't change regardless of where the data comes from. Production data will vary from development data will vary from data from one company to another, however the state itself stays the same.

{% set user_list = [
    {'name': 'larry', 'shell': 'bash'},
    {'name': 'curly', 'shell': 'bash'},
    {'name': 'moe', 'shell': 'zsh'},
] %}

{# or #}

{% set user_list = salt['pillar.get']('user_list') %}

{# or #}

{% load_json "default_users.json" as user_list %}

{# or #}

{% set user_list = salt['acme_utils.get_user_list']() %}

{% for user in list_list %}
{{ }}:
    - name: {{ }}
    - shell: {{ }}
{% endfor %}


Formulas should strive to use the defaults of the underlying platform, followed by defaults from the upstream project, followed by sane defaults for the formula itself.

As an example, a formula to install Apache should not change the default Apache configuration file installed by the OS package. However, the Apache formula should include a state to change or override the default configuration file.

Pillar overrides

Pillar lookups must use the safe get() and must provide a default value. Create local variables using the Jinja set construct to increase redability and to avoid potentially hundreds or thousands of function calls across a large state tree.

{% from "apache/map.jinja" import apache with context %}
{% set settings = salt['pillar.get']('apache', {}) %}

    - name: {{ apache.conf_dir }}
    - source: {{ settings.get('mod_status_conf', 'salt://apache/mod_status.conf') }}
    - template: {{ settings.get('template_engine', 'jinja') }}

Any default values used in the Formula must also be documented in the pillar.example file in the root of the repository. Comments should be used liberally to explain the intent of each configuration value. In addition, users should be able copy-and-paste the contents of this file into their own Pillar to make any desired changes.


Remember that both State files and Pillar files can easily call out to Salt execution modules and have access to all the system grains as well.

{% if '/storage' in salt['']() %}
    - symlink
    - target: /storage/myfile.conf
{% endif %}

Jinja macros to encapsulate logic or conditionals are discouraged in favor of writing custom execution modules in Python.

Repository structure

A basic Formula repository should have the following layout:

|-- foo/
|   |-- map.jinja
|   |-- init.sls
|   `-- bar.sls
|-- pillar.example
|-- README.rst

See also


The template-formula repository has a pre-built layout that serves as the basic structure for a new formula repository. Just copy the files from there and edit them.


The README should detail each available .sls file by explaining what it does, whether it has any dependencies on other formulas, whether it has a target platform, and any other installation or usage instructions or tips.

A sample skeleton for the README.rst file:


Install and configure the FOO service.

.. note::

    See the full `Salt Formulas installation and usage instructions

Available states

.. contents::


Install the ``foo`` package and enable the service.


Install the ``bar`` package.


The CHANGELOG.rst file should detail the individual versions, their release date and a set of bullet points for each version highlighting the overall changes in a given version of the formula.

A sample skeleton for the CHANGELOG.rst file:


foo formula

0.0.2 (2013-01-01)

- Re-organized formula file layout
- Fixed filename used for upstart logger template
- Allow for pillar message to have default if none specified


Formula are versioned according to Semantic Versioning,


Given a version number MAJOR.MINOR.PATCH, increment the:

  1. MAJOR version when you make incompatible API changes,
  2. MINOR version when you add functionality in a backwards-compatible manner, and
  3. PATCH version when you make backwards-compatible bug fixes.

Additional labels for pre-release and build metadata are available as extensions to the MAJOR.MINOR.PATCH format.

Formula versions are tracked using Git tags as well as the VERSION file in the formula repository. The VERSION file should contain the currently released version of the particular formula.

Testing Formulas

A smoke-test for invalid Jinja, invalid YAML, or an invalid Salt state structure can be performed by with the state.show_sls function:

salt '*' state.show_sls apache

Salt Formulas can then be tested by running each .sls file via state.apply and checking the output for the success or failure of each state in the Formula. This should be done for each supported platform.