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Yamp - Yet Another Macro Processor - for YAML

YAML is human friendly data serialization standard. [1] Yamp is a general-purpose macroprocessor for YAML files. Both its input and output are YAML. It scans the input for symbols and makes substitutions and expansions on the output. yamp is 100% YAML so the syntax for defining and calling macros is YAML also.

TL;DR

Input
- defmacro:
    name: foo
    args: [who]
    value:
        Hello: who
- foo:
    who: World
Output
- Hello: World

Quick Start

You just want to run it already? OK, use the docker image. Put your first Yamp file in hello.yamp:

hello.yamp
"Hello {{argv.2}}"

And run it

$ docker run --rm -u $(id -u):$(id -g) -v "$HOME":/work docker.io/birchb1024/yamp /work/hello.yamp $USER

Get Started

Yamp is a Python 2.7 program contained in a single file. Hence one has to install Python and the pyyaml module.

  • First obtain a copy of the Yamp program. If you’re reading this you probably did that already!

  • Install Python 2.7 [2] : $ sudo yum install -y python2

  • Then install the Python YAML module: $ pip install pyyaml

The program is run from the command-line giving the input file to parse as the first argument followed by optional arguments to the expansion. The expansion is written to the standard output, which you normally redirect to another file.:

Usage
$ python yamp.py <myinputfile>.yaml [arg1..argn]

An example - Pipelines as Code.

Supposing we are building some GoCD pipeline definitions in YAML each of which uses the same Git repository. The YAML we have to write looks like this:

output.yaml
pipelines:
  mypipe1:
    group: mygroup
    label_template: ${COUNT}
    materials: # (1)
      mygit:
        branch: master
        git: http://my.example.org/mygit.git
    stages: null
  mypipe2:
    group: mygroup
    label_template: ${COUNT}
    materials: # (1)
      mygit:
        branch: ci
        git: http://my.example.org/mygit.git
    stages: null
  1. Duplicated

We don’t want re-key duplicated code so we define a macro which yamp expands whenever it is invoked. Our yamp source code now looks like this:

YAML source
define: # (1)
    name: mygit_repo_url
    value: http://my.example.org/mygit.git

defmacro: # (2)
    name: mygit_materials
    args: [branch_name]
    value:
      mygit:
        git: mygit_repo_url # (3)
        branch: branch_name
---
pipelines:
  mypipe1:
    group: mygroup
    label_template: "${COUNT}"
    materials: {mygit_materials: {branch_name: master}} # (4)
    stages:

  mypipe2:
    group: mygroup
    label_template: "${COUNT}"
    materials:
        mygit_materials:
            branch_name: ci # (5)
    stages:
  1. simple variable definition

  2. a macro Definition

  3. variable used

  4. a macro call - flow style

  5. a macro call - block style

When run through yamp, the output is as above. Now we have a single place where the git repository is defined, if we need to change it we can change it once.

Applications

This program is general-purpose, it can be used wherever YAML is required. Its first uses were for GoCd pipelines and Ansible playbooks. These are human-readable source code which is a subset of YAML. Hence yamp may not be applied to all aspects of YAML especially those which result from data transmission. We will not be attempting to exercise yamp with such inputs.

Since YAML is a superset of JSON it can also be used to generate JSON for, say, Azure ARM files.

Similar Tools

There are many great general-purpose macro-processors available, starting with the venerable GPM, through m4, cpp, and lately, Jinja2. However these are predominantly character-based and the programmer has to compute the indentation required by YAML by counting spaces. Like previous authors we started on this course of writing yet another macro-processor primarily for reasons of laziness. Since yamp transforms maps and sequences not character strings, indentation is automatic.

Reference

This section describes the operation of the processor and the macros available.

The Command Line

The command to run yamp is a python invocation taking a single filename followed by optional arguments.

Usage
$ python yamp.py [Filename | - ] [arg1..argn]

If the filename is the minus sign - Yamp reads YAML from the standard input, so it serves as a filter. As in

$ echo "[define: {data: {load: test/fixtures/blade-runner.json}}, data.director]" | python src/yamp.py -
- ' Ridley Scott'

File Suffixes

If the Filename’s suffix is `yaml, yml, or yamp the file is assumed to contain a YAML file . If the suffix is json the file is parsed as JSON.

Any other file suffix can be used - it is assumed to be YAML. A warning is printed to alert you of a possible input mistake.

In practice yaml will be recognised by most text editors' YAML editting mode. You will need to configure your text editor if you use a non-standard suffix.

Docker

A docker image is provided in docker.io (Docker Hub) here. This image includes Python and its libraries on a slim Debian base. To use it you need to map your workspace into the container and use your current user id. In general:

$ docker run --rm -u $(id -u):$(id -g) -v "$HOME":/work docker.io/birchb1024/yamp:0.2.0 /work/{path to yout code}.yaml [arg1, arg2...] > outputfile.yaml

Processing

When Yamp starts, it collects the command-line arguments and assigns the list to the variable argv. It collects the process environment and assigns it to the map variable env. Yamp then reads the input file, attempts to parse the YAML and holds the resulting data as objects in memory. (If the YAML does not parse Yamp exits). It recursively scans the objects looking for strings which are the same as defined variables or which contain variables inside the string in curly braces. If it finds a match, it substitutes the object with the variable’s value.

Yamp is a substitution engine. It looks for things in it’s input an when it sees them replaces them with the substitution. The things to look for and the substitutions we call variables and bindings. For example:

Table 1. Variables Bindings
Variable Name Value to substitute

mygit_repo_url

http://my.example.org/mygit.git

mygit_materials

args: [branch_name]
mygit:
        git: mygit_repo_url
        branch: branch_name

When scanning maps, Yamp does not expand map keys unless either the map key is explicitly identified as a variable with the ^ caret character, or the map key is a string with embedded curly braces. In these two special cases Yamp looks up variables or interpolates the string.

Some special variables contain 'macros' - these must be within a map of their own, with a value containing a map of arguments which can contain anything. Normally a macro will contain more than the original, so we call this 'macro expansion' [3] ;-).

Yamp is looking for macro calls with this structure:

<Macro>:
   <Argument>: <value>
   <Argument>: <value>
    . . .

Some macros have special functions and are built-in to Yamp. Those are described in the reference section.

Here’s examples of three kinds of things Yamp is scanning for replacement:

Simple Variables
- Username
- 'directory'
Embeded Variables
- 'The username is {{Username}}'
Macro Calls
- add_user:
    name: Kevin
    phone: (555) 098 880

When all the objects in the data have been scanned and in some cases, substituted, Yamp outputs the new object tree on the standard output in YAML format. Becuase YAML maps are unordered, the order of the keys and their corresponding values on output maybe be different from the input [4].

Variables

During processing yamp maintains a hierarchy of bindings of variable names to variable values. The top level of bindings is the gobal environment. As each macro is applied the application creates a unique environment for the macro variables which is popped when the macro finishes.

define - Definition of Variables

You can define new variable bindings or update existing variables with the define macro. The value can be any YAML expansion. Variable names are expected to be strings.

- define: {name: age, value: 32}
- age
- define: {name: age2, value: [age, age]}
- age2
- define: {name: age2, value: [{define: {name: age, value: 99}}, age]}
- age2
# Produces:
#- 32
#- - 32
#  - 32
#- - 99

Scalars

Variables can contain any YAML scalar, int float, string, True, False and null.

Collections

Variables can contain any YAML collection ie, maps and lists.

Variable Expansion

When yamp scans YAML it looks for variables in the lists and map values. When one is found it is replaced with the current value of variable binding. It searches the stack of macro bindings until the global environment is reached. If no bindng is found the string is output unchanged.

Variables Embedded in Strings

Inside strings, yamp will insert expansions delimited by the double-curlies {{ and }}. It’s looking for variable names.

- define: {name: X, value: Christopher}
- define: {name: AXA, value: 'A{{ X }}A'}
---
- AXA
# Produces AChristopherA

This processing is also done in map keys so that map keys can be computed during the expansion. For example:

repeat:
  for: loop_variable
  in : {range: [1,3] }
  body:
    'KEY_{{loop_variable}}': some step
Interpolation with dot syntax

If a string contains periods, such as data.height Yamp looks for a exactly matching variable name, which is expanded with the value. Otherwise the first item (ie data) is assumed to be a variable name.

If a binding for the first part is found the value of the variable is assumed to be a collection. The other items which we call sub-variables are used to index the collection (ie height). If the collection is a map, the sub-variable name is used as the key. If it is a list the subvariable must evaluate to an integer which is zero-indexed into the list. These subvariable names are also expanded before use so other variables can be used to index the collection.

- define: { zero: 0 }
- define:
    name: data
    value:
        - type: webserver
          hostname: web01
          ip: 1.1.2.3
        - type: database
          hostname: db01
          ip: 1.1.2.2
- define: {data.1 : Wednesday}
---
- data.1
- data.1.hostname
- data.zero.hostname

Produces

- Wednesday
- db01
- web01
Variable Map Keys with the Caret

Normally map keys are not expanded, but with a preceding caret character Yamp looks up the variable name in the current binding and uses its value. For example:

- defmacro:
    name: my-macro
    args: [ param ]
    value:
      ^param:
        LtUaE : RU
---
- my-macro: { param: 42 }

Evaluates to:

- 42:
    LtUaE: 42

This facility even allows macros to be called indirectly since the macro being called is provided by the variable rather than in the code itself. Here’s an example, although the practical value of this is yet to surface. This code applies four different macros to the same arguments in turn:

repeat:
  for: macro
  in: [+, range, flatten, quote]
  body:
    ^macro: [1, 5]
Defining Multiple Variables

Declarations don’t need the 'name' and 'value' keys, and multiple variables are simultaneously bound.

- define: { quick: 'shorthand' }
- define:
    name: Sara
    age: 34
    height: 123

Refactoring Yamp with undefine

Sometimes a variable needs to be renamed or removed. For example if a Yamp macro name conflicts with a name used in the output format required. The undefine macro removes a variable binding from the current environment. Usage:

undefine: variablename

Used at the top level (outside of a macro) undefine can be used to change the definitions of Yamp built-in macros themselves. This is done by first assigning a new name with the currently used macro, then undefining the original name. If this is done before any files are included, it can be used to redefine Yamp syntax. For example we can use plus instead of the + symbol as follows

- define:
    plus: +
- undefine: +
- {plus: [1,2,3]}

Macros

Macros are re-usable templates of YAML objects that can be called up almost anywhere in the expansion. They differ from variables becuase they have parameters which are used to fill holes in the template. The are similar to functions, but unlike functions their entire text is always the result. By defining oft-repeated YAML fragments in macros repetitive work is avoided. Also a singular macro definition makes maintainance easy since there is a single defintion for a concept which can be easily changed.

Defining with defmacro

Macros are defined with the define macro which gives the macro a name and specifies the arguments it has and the expansion to return, the body. A macro definition looks like this:

- defmacro:
    name: <the name of the macro>
    args: [<list of argument names>, ...]
    value:
      <Some YAML to be expanded>

Example - Database upgrade steps:

defmacro:
  name: app-upgrade
  args: [appname, dbname]
  value:
      Database upgrade for {{ appname }}:
        - stop application {{ appname }}
        - backup app database {{ dbname }}
        - upgrade the database {{ dbname }}
        - restart the application {{ appname }}
        - smoke test {{ appname }}
---
- {app-upgrade: { appname: Netflix, dbname: db8812}}
- app-upgrade:
    appname: Stan
    dbname: postgres123123

Produces:

- Database upgrade for Netflix:
  - stop application Netflix
  - backup app database db8812
  - upgrade the database db8812
  - restart the application Netflix
  - smoke test Netflix
- Database upgrade for Stan:
  - stop application Stan
  - backup app database postgres123123
  - upgrade the database postgres123123
  - restart the application Stan
  - smoke test Stan

Invoking/calling Macros

As above, macro calls are just maps with a particular structure:

<macro name>:
   <arg1> : <arg 1 value>
   ...
   <argN> : <arg N value>

Macros with variable arguments

If the arguments in the definition are specified as a string, not a list, the string is the single argument. All the actual arguments at call-time are collected and bound to the variable in a map.

- defmacro:
    name: <the name of the macro>
    args: <argument_variable_name>
    value:
      <Some YAML to be expanded>

Example:

# Definition
- defmacro:
    name: package
    args: all
    value:
      name: all.doc
      yum:
        name: apache
        state: all.state

---
# Call
package:
  doc: Install apache
  name: httpd
  state: latest

Produces

name: Install apache
yum:
  name: apache
  state: latest

The disadvantage of vararg macros is that Yamp cannot ensure that all the required arguments have been supplied in the call.

Nesting Macros

Macro calls can be nested i.e. a macro can can contain a call to another in its arguments. Likewise macro definitions can be nested. The macro arguments are lexically scoped, a closure is collected at the time of definition. The macro call executes in the environment in the define-time closure. Macros can call themselves directly or indirectly.

Conditional Expansion with if then else

The if macro renders one value from a choice of two based on whether the condition argument is true. Where true means it’s true or not false or null. The then argument is expanded if so, otherwise the else argument. It’s not required to have both then and else arguments - when the condition requires the missing one, it expands to null.

if: <Booleanish (true, false or null)>
then: <value if true>
else: <value if false or null>

Example:

# Some variable
define:
  application:
    name: CSIRAC
    has_database: true
    arch: valves
---
if: application.has_database
then:
  - shutdown database
else:
  - shutdown not required

Produces:

- shutdown database

Example - short form

if: true
else: 'This value if false or Null'

Produces null

Testing equality with ==

Macros can have almost any name, this one is the symbol '=='. It expands to true or false if the items in the list are equal. Most often used inside an enclosing if macro.

{ ==: [arg1, arg2, ...] }

Example:

{ ==: [1, 1, 10] }

Produces the value false.

Preventing Expansion with quote

The quote macro does not expand its input arguments returning them unexpanded.

Example:

- define: { data1: { sub: 2}}
- data1.sub
- quote: data1.sub

Produces

- 2
- data1.sub

Looping with repeat

This macro repeatedly expands the same object, either returning a list or a map. If the key argument is present it returns a map, using the key argument as the item’s key. This must have embedded variables derived from the looping execution otherwise there will be a key collision error. With no key argument, it returns a list.

repeat:
  for: <loop variable name>
  in: [list of items]
  key: <string key with embedded varaibles in {{}}> # Optional
  body: <any value>

Example - returning a dictionary:

repeat:
  for: environment_name
  in:
    - DEV1
    - SVT
    - PROD
  key: 'Deploy_App_{{environment_name}}'
  body:
    stage: step

Produces:

Deploy_App_DEV1:
  stage: step
Deploy_App_PROD:
  stage: step
Deploy_App_SVT:
  stage: step

Example - returning a list:

repeat:
  for: loop_variable
  in: {range: [1,3]}
  body:
    loop_variable: 'KEY_{{loop_variable}}'
    some: step
    another:

Produces:

- another: null
  loop_variable: KEY_1
  some: step
- another: null
  loop_variable: KEY_2
  some: step
- another: null
  loop_variable: KEY_3
  some: step

Example - looped list with changing keys. Here the keys and values of a child map are changed. :

repeat:
  for: loop_variable
  in: {range: [12,13]}
  body:
    'index_{{loop_variable}}': { +:  [100, loop_variable] }
    some: step

Produces:

- index_12: 112
  some: step
- index_13: 113
  some: step

Looping with range

The range macro substitutes a list of numbers that can be used in repeat macros. (Or anywhere else a list of numbers is needed). The start and end values are passed as a list argument. The range can count up or down, always by one.

range: [3,5]

Produces [3,4,5]

range also accepts a map object, in which case it expands the sequence of map keys. For example

- define: {map: {ra: 879, rb: 662}}
- range: map

Produces [ra, rb]. This can then be used in repeat to loop over the items in a map. Dot notation is used to expand individual members of the map. For example here the loop variable is set to ra then rb which map.keyz resolves to 879 and 662:

repeat:
  for: keyz
  in: {range: map}
  body:
    map.keyz

Be aware that map keys in data (such as ra) might conflict with already defined variables.

Combining Lists with flatten

Sometimes you need to combine lists, perhaps from different macro expansions. The flatten macro combines multiple lists into a single, flat, list. The flattening is recursive. Syntax:

flatten: < list of objects >

For example:

define: {home-directories: [/home/elvis, /home/madonna]}
---
flatten: [[home-directories], /var, /log]
---
flatten: [1, 2, [3], [[4, 5]], [[[ 6,7]]] ]

Produces:

- /home/elvis
- /home/madonna
- /var
- /log
---
- 1
- 2
- 3
- 4
- 5
- 6
- 7

Combining One Level of Lists with flatone

The flatone macro combines multiple lists into a single, flat, list. The flattening is not recursive, only the first level is flattened. Syntax:

flatone: < list of objects >

For example:

flatone: [1, 2, [3], [[4, 5]], [[[ 6,7]]] ]

Produces:

- 1
- 2
- 3
- - 4
  - 5
- - - 6
    - 7

Combining Maps with merge

The merge macro takes a list of maps and merges them togther to make a single map. When there are keys shared between the supplied maps, the program uses the last one seen, it over-writes the earlier value. Hence the order in the list dictates the priority. Syntax:

merge: < list of maps >

For example:

merge:
  - { a : 1 }
  - { b : 2 }
  - { c : 3 , a : -1}

Produces:

a: -1
b: 2
c: 3

A more complex example shows combining data from multiple sources:

- define:
    network-data:
      hostname: tetris.games.org
- defmacro:
    name: mymacro
    args: [arg1]
    value:
      hostname: arg1
      ip: 1.1.1.1
      app: tetris
- merge:
  - { hostname: tetris.home.org }
  - { site: Kansas }
  - mymacro:
      arg1: tetris
  - network-data

Which boils down to:

- app: tetris
  hostname: tetris.games.org
  ip: 1.1.1.1
  site: Kansas

Arithmetic with +

The + macro adds a list of numbers, int or float.

+: [1,2,4,8]

Produces 15

Reading files with include

include reads and expands the list of Yamp YAML files in order. The filenames can be the result of prior macro expansion. So derived filenames like "{{ROOT_DIR}}/{{arch}}/config.yaml" are possible.

include:
- <filename>
- <filename>

Reading Data Files

Sometimes you want to use raw data for parameters and variable values. For example you may have an inventory or database of facts. Yamp can load YAML or JSON data.

Reading Data with load

The load macro reads a single file of YAML or JSON data and returns the result. No variable substitutions or macro expansions are performed on the data. YAML data is returned as a list, one object for each 'doc'. [5]

{load: <filename>}

Examples:

- define: {name: file, value: 'load_data.yaml'}
- define:
    name: somedata
    value: {load: file}
- define:
    movie1: {load: '../test/fixtures/blade-runner.json'}

Loading Shell Script Data

When you have shell variables in files which you want to use as input to expansion, you can load them into the environment of the yamp execution. For example here’s a script with some dynamic data:

data.sh
export VARIABLE1=value1
export VARIABLE2="${VARIABLE1}_value2"
export VARIABLE3="${VARIABLE2}_value3"

The shell script must executed to determine the values. To load this into the Yamp environment, use shell wrappers like this:

$ env -i bash --noprofile --norc -c '. data.sh ; echo env | python src/yamp.py - '

How does this work?

  • env -i bash creates a bash process with an empty environment.

  • --noprofile --norc prevent bash from reading profile files on startup

  • -c '. data.sh sources the shell script in the current (empty) environment

  • echo env | python src/yamp.py - runs Yamp with an input of just env - this will output all the environment variables

The YAML output contains the variables we want plus a couple of variables bash always needs:

PWD: /home/birchb/workspace/yamp
SHLVL: '1'
VARIABLE1: value1
VARIABLE2: value1_value2
VARIABLE3: value1_value2_value3
_: /usr/bin/python

Evaluating Python expressions

The python_eval macro allows you to execute expressions inside the Python interpreter running Yamp. Yamp uses the Python eval() function, passing the current variable bindings to eval as the locals. This allows Python to access the Yamp-defined variables, including bindings in the environment hierarchy defined by macros.

Warning - Yamp internals can change at any time.

- defmacro:
    name: factorial
    args: [N]
    value:
      python_eval: "math.factorial(N)"
---
factorial: {N: 10}

Produces 3628800

This macro is recommended for simple tasks about objects such as:

  • getting the length of a list,

  • converting the case of a string

  • getting the time

Examples:

- python_eval: 'len(argv)'

- python_eval: 'env["USERNAME"].upper()'

- python_eval: 'datetime.datetime.now()'

- python_eval: 'lookup(__parent__, "argv")[0][0]' # ==> yamp.py

- python_eval: 'iter(_ for _ in ()).throw(Exception("{} This is how to raise an exception!".format(env["USERNAME"])))'

Builtin Variables

Yamp automatically populates some variables as it executes. These are:

  • FILE - the current source filename

  • parent - the current macro application’s parent environment

  • env - the process environment

  • argv - the command line arguments

Using Yamp as a Python Module

Maybe later…​

Maintenance of Yamp

Known Issues

There is a bug in the Python YAML parser in which duplicated map keys are not flagged as erroneous. See yaml/pyyaml#165 Quote:

YAML spec in version 1.2 says that in a valid YAML file, mapping keys are unique. This is not the case in pyyaml, as can be seen by loading this sample file. The correct result from loading this file is an error. pyyaml instead naively overwrites results with the last key, resulting in this dict: {'a': {'q': 'b'}}.

Sample File

a:
  - b
  - c
a:
  q: a
  q: b

Suggest validation of the incomimg YAML with an alternate parser such as yamlok or yamllint.

Code

Run the unit tests with python test/test_expand_01.py.

Updating This Document

This document is in AsciiDoc format. Use the Linux asciidoc packages. To Highlight the YAML syntax also install source-highlight and the YAML syntax module. Save the HTML version in doc/README.html.


1. YAML stands for YAML Ain’t Markup Language. See https://yaml.org/
2. This is for RedHat Linux.
3. But it could actually be a reduction!
4. Order-preservation may happen in a future version, but it’s complicated
5. YAML files are subdivided into 'docs' separated by '---'
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