A schema and validator for YAML.
What's YAML? See the current spec here and an introduction to the syntax here.
- Python 2.7+
- Python 3.4+ (Only tested on 3.4, may work on older versions)
- PyYAML
- ruamel.yaml (optional)
$ pip install yamale
- Download Yamale from: https://github.com/23andMe/Yamale/archive/master.zip
- Unzip somewhere temporary
- Run
python setup.py install
(may have to prependsudo
)
Yamale can be run from the command line to validate one or many YAML files. Yamale will search the directory you supply (current directory is default) for YAML files. Each YAML file it finds it will look in the same directory as that file for its schema, if there is no schema Yamale will keep looking up the directory tree until it finds one. If Yamale can not find a schema it will tell you.
Usage:
usage: yamale [-h] [-s SCHEMA] [-n CPU_NUM] [-p PARSER] [PATH]
Validate yaml files.
positional arguments:
PATH folder to validate. Default is current directory.
optional arguments:
-h, --help show this help message and exit
-s SCHEMA, --schema SCHEMA
filename of schema. Default is schema.yaml.
-n CPU_NUM, --cpu-num CPU_NUM
number of CPUs to use. Default is 4.
-p PARSER, --parser PARSER
YAML library to load files. Choices are "ruamel" or
"pyyaml" (default).
There are several ways to feed Yamale schema and data files. The simplest way is to let Yamale take care of reading and parsing your YAML files.
All you need to do is supply the files' path:
# Import Yamale and make a schema object:
import yamale
schema = yamale.make_schema('./schema.yaml')
# Create a Data object
data = yamale.make_data('./data.yaml')
# Validate data against the schema. Throws a ValueError if data is invalid.
yamale.validate(schema, data)
If data
is valid, nothing will happen. However, if data
is invalid Yamale will throw a ValueError
with a message containing all the invalid nodes.
You can also specifiy an optional parser
if you'd like to use the ruamel.yaml
(YAML 1.2 support) instead:
# Import Yamale and make a schema object, make sure ruamel.yaml is installed already.
import yamale
schema = yamale.make_schema('./schema.yaml', parser='ruamel')
# Create a Data object
data = yamale.make_data('./data.yaml', parser='ruamel')
# Validate data against the schema same as before.
yamale.validate(schema, data)
To use Yamale you must make a schema. A schema is a valid YAML file with one or more documents inside. Each node terminates in a string which contains valid Yamale syntax. For example, str()
represents a String validator.
A basic schema:
name: str()
age: int(max=200)
height: num()
awesome: bool()
And some YAML that validates:
name: Bill
age: 26
height: 6.2
awesome: True
Take a look at the Examples section for more complex schema ideas.
Schema files may contain more than one YAML document (nodes separated by ---
). The first document found will be the base schema. Any additional documents will be treated as Includes. Includes allow you to define a valid structure once and use it several times. They also allow you to do recursion.
A schema with an Include validator:
person1: include('person')
person2: include('person')
---
person:
name: str()
age: int()
Some valid YAML:
person1:
name: Bill
age: 70
person2:
name: Jill
age: 20
Every root node not in the first YAML document will be treated like an include:
person: include('friend')
group: include('family')
---
friend:
name: str()
family:
name: str()
Is equivalent to:
person: include('friend')
group: include('family')
---
friend:
name: str()
---
family:
name: str()
You can get recursion using the Include validator.
This schema:
person: include('human')
---
human:
name: str()
age: int()
friend: include('human', required=False)
Will validate this data:
person:
name: Bill
age: 50
friend:
name: Jill
age: 20
friend:
name: Will
age: 10
After you construct a schema you can add extra, external include definitions by calling schema.add_include(dict)
. This method takes a dictionary and adds each key as another include.
Here are all the validators Yamale knows about. Every validator takes a required
keyword telling Yamale whether or not that node must exist. By default every node is required. Example: str(required=False)
You can also require that an optional value is not None
by using the none
keyword. By default Yamale will accept None
as a valid value for a key that's not required. Reject None
values with none=False
in any validator. Example: str(required=False, none=False)
.
Some validators take keywords and some take arguments, some take both. For instance the enum()
validator takes one or more constants as arguments and the required
keyword: enum('a string', 1, False, required=False)
Validates strings.
- keywords
min
: len(string) >= minmax
: len(string) <= maxexclude
: Rejects strings that contains any character in the excluded value.
Examples:
str(max=10, exclude='?!')
: Allows only strings less than 10 characters that don't contain?
or!
.
Validates integers.
- keywords
min
: int >= minmax
: int <= max
Validates integers and floats.
- keywords
min
: num >= minmax
: num <= max
Validates booleans.
Validates null values.
Validates from a list of constants.
- arguments: constants to test equality with
Examples:
enum('a string', 1, False)
: a value can be either'a string'
,1
orFalse
Validates a date in the form of YYYY-MM-DD.
- keywords
min
: date >= minmax
: date <= max
Examples:
day(min='2001-01-01', max='2100-01-01')
: Only allows dates between 2001-01-01 and 2100-01-01.
Validates a timestamp in the form of YYYY-MM-DD HH:MM:SS.
- keywords
min
: time >= minmax
: time <= max
Examples:
timestamp(min='2001-01-01 01:00:00', max='2100-01-01 23:00:00')
: Only allows times between 2001-01-01 01:00:00 and 2100-01-01 23:00:00.
Validates lists. If one or more validators are passed to list()
only nodes that pass at least one of those validators will be accepted.
-
arguments: one or more validators to test values with
-
keywords
min
: len(list) >= minmax
: len(list) <= max
Examples:
list()
: Validates any listlist(include('custom'), int(), min=4)
: Only validates lists that contain thecustom
include or integers and contains a minimum of 4 items.
Validates maps. Use when you want a node to contain freeform data. Similar to List
, Map
also takes a number of validators to
run against its children nodes. A child validates if at least one validator passes.
Examples:
map()
: Validates any mapmap(str(), int())
: Only validates maps whose children are strings or integers.
Validates against a union of types. Use when a node can contain one of several types. It is valid if at least one of the listed validators is valid.
- arguments: one or more validators to test values with
Examples:
any(int(), null())
: Validates an integer or a null value.any(num(), include('vector'))
: Validates a number or an included 'vector' type.
Validates included structures. Must supply the name of a valid include.
- arguments: single name of a defined include, surrounded by quotes.
Examples:
include('person')
It is also possible to add your own custom validators. This is an advanced topic, but here is an example of adding a Date
validator and using it in a schema as date()
import yamale
from yamale.validators import DefaultValidators, Validator
class Date(Validator):
""" Custom Date validator """
tag = 'date'
def _is_valid(self, value):
return isinstance(value, datetime.date)
validators = DefaultValidators.copy() # This is a dictionary
validators[Date.tag] = Date
schema = yamale.make_schema('./schema.yaml' validators=validators)
# Then use `schema` as normal
optional: str(required=False)
optional_min: int(min=1, required=False)
min: num(min=1.5)
max: int(max=100)
optional_min: 10
min: 1.6
max: 100
customerA: include('customer')
customerB: include('customer')
recursion: include('recurse')
---
customer:
name: str()
age: int()
custom: include('custom_type')
custom_type:
integer: int()
recurse:
level: int()
again: include('recurse', required=False)
customerA:
name: bob
age: 900
custom:
integer: 1
customerB:
name: jill
age: 1
custom:
integer: 3
recursion:
level: 1
again:
level: 2
again:
level: 3
again:
level: 4
list_with_two_types: list(str(), include('variant'))
questions: list(include('question'))
---
variant:
rsid: str()
name: str()
question:
choices: list(include('choices'))
questions: list(include('question'), required=False)
choices:
id: str()
list_with_two_types:
- 'some'
- rsid: 'rs123'
name: 'some SNP'
- 'thing'
- rsid: 'rs312'
name: 'another SNP'
questions:
- choices:
- id: 'id_str'
- id: 'id_str1'
questions:
- choices:
- id: 'id_str'
- id: 'id_str1'
To validate YAML files when you run your program's tests use Yamale's YamaleTestCase
Example:
class TestYaml(YamaleTestCase):
base_dir = os.path.dirname(os.path.realpath(__file__))
schema = 'schema.yaml'
yaml = 'data.yaml'
# or yaml = ['data-*.yaml', 'some_data.yaml']
def runTest(self):
self.assertTrue(self.validate())
base_dir
: String path to prepend to all other paths. This is optional.
schema
: String of path to the schema file to use. One schema file per test case.
yaml
: String or list of yaml files to validate. Accepts globs.
Yamale uses Tox to run its tests against multiple Python versions. To run tests, first checkout Yamale, install Tox, then run make test
in the Yamale's root directory. You may also have to install the correct Python versions to test with as well.
Yamale uses Travis to upload new tags to PyPi. To release a new version:
- Make a commit with the new version in
setup.py
. - Run
make release
.
Travis will take care of the rest.