Simple config parser with evaluator library.
This project inspired of the necessity complex config in a project. By means complex:
- Reusability
- Import values from file
- Reference values from other object
- Secure
- Encrypt/decrypt sensitive values
- Flexible
- Make logical expression to derive values
- Powerful
- Add custom functions in Python
- Link name data from Python
In Python 3.5, the dict data type has inconsistent ordering, it is STRONGLY recommended to use OrderedDict if you manually parse object. If you load from YAML file, the library already handled it.
[sudo] pip install conff
To get very basic parsing:
import conff
r = conff.parse({'math': '1 + 3'})
r = {'math': '4'}
import conff
r = conff.load('path_of_file.yaml')
import conff
## y1.yml
# shared_conf: 1
## y2.yml
# conf: F.inc('y1.yml')
r = conff.load('y2.yml')
r = {'conf': {'shared_conf': 1}}
More advances examples:
import conff
r = conff.parse('1 + 2')
r = 3
import conff
r = conff.parse({"math": "1 + 2"})
r = {'math': 3}
import conff
r = conff.parse('"1 + 2"')
r = '1 + 2'
import conff
errors = []
r = conff.parse({"math": "1 / 0"}, errors=errors)
r = {'math': '1 / 0'}
errors = [['1 / 0', ZeroDivisionError('division by zero',)]]
import conff
def fn_add(a, b):
return a + b
r = conff.parse('F.add(1, 2)', fns={'add': fn_add})
r = 3
import conff
r = conff.parse('a + b', names={'a': 1, 'b': 2})
r = 3
import conff
data = {
't1': {'a': 'a'},
't2': {
'F.extend': 'R.t1',
'b': 'b'
}
}
r = conff.parse(data)
r = {'t1': {'a': 'a'}, 't2': {'a': 'a', 'b': 'b'}}
import conff
data = {
't1': {'a': 'a'},
't2': {
'b': 'b',
'F.update': {
'c': 'c'
},
}
}
r = conff.parse(data)
r = {'t1': {'a': 'a'}, 't2': {'b': 'b', 'c': 'c'}}
import conff
data = {
't1': {'a': 'a'},
't2': {
'F.extend': 'R.t1',
'b': 'b',
'F.update': {
'a': 'A',
'c': 'c'
},
}
}
r = conff.parse(data)
r = {'t1': {'a': 'a'}, 't2': {'a': 'A', 'b': 'b', 'c': 'c'}}
This creates a list of floats, similar to numpy.linspace
import conff
data = {'t2': 'F.linspace(0, 10, 5)'}
r = conff.parse(data)
r = {'t2': [0.0, 2.5, 5.0, 7.5, 10.0]}
This also creates a list of floats, but behaves like numpy.arange (although slightly different in that it is inclusive of the endpoint).
import conff
data = {'t2': 'F.arange(0, 10, 2)'}
r = conff.parse(data)
r = {'t2': [0, 2, 4, 6, 8, 10]}
One can mimic the logic of a for loop with the following example
import conff
data = {'t1': 2,
'F.foreach': {
'values': 'F.linspace(0, 10, 2)',
# You have access to loop.index, loop.value, and loop.length
# within the template, as well as all the usual names
'template': {
'"test%i"%loop.index': 'R.t1*loop.value',
'length': 'loop.length'
}
}
}
r = conff.parse(data)
r = {'length': 3, 't1': 2, 'test0': 0.0, 'test1': 10.0, 'test2': 20.0}
This section to help you to quickly generate encryption key, initial encrypt values and test to decrypt the value.
import conff
# generate key, save it somewhere safe
names = {'R': {'_': {'etype': 'fernet'}}}
etype = conff.generate_key(names)()
# or just
ekey = conff.generate_key()('fernet')
# encrypt data
# BIG WARNING: this should be retrieved somewhere secured for example in ~/.secret
# below just for example purposes
ekey = 'FOb7DBRftamqsyRFIaP01q57ZLZZV6MVB2xg1Cg_E7g='
names = {'R': {'_': {'etype': 'fernet', 'ekey': ekey}}}
# gAAAAABbBBhOJDMoQSbF9jfNgt97FwyflQEZRxv2L2buv6YD_Jiq8XNrxv8VqFis__J7YlpZQA07nDvzYwMU562Mlm978uP9BQf6M9Priy3btidL6Pm406w=
encrypted_value = conff.encrypt(names)('ACCESSSECRETPLAIN1234')
# decrypt data
ekey = 'FOb7DBRftamqsyRFIaP01q57ZLZZV6MVB2xg1Cg_E7g='
names = {'R': {'_': {'etype': 'fernet', 'ekey': ekey}}}
encrypted_value = 'gAAAAABbBBhOJDMoQSbF9jfNgt97FwyflQEZRxv2L2buv6YD_Jiq8XNrxv8VqFis__J7YlpZQA07nDvzYwMU562Mlm978uP9BQf6M9Priy3btidL6Pm406w='
conff.decrypt(names)(encrypted_value)
All the example below located in data directory. Imagine you start an important project, your code need to analyse image/videos which involves workflow with set of tasks with AWS Rekognition. The steps will be more/less like this:
- Read images/videos from a specific folder, if images goes to (2), if videos goes to (3).
- Analyse the images with AWS API, then goes (4)
- Analyse the videos with AWS API, then goes (4)
- Write the result back to JSON file, finished
The configuration required:
- Read images/videos (where is the folder)
- Analyse images (AWS API credential and max resolution for image)
- Analyse videos (AWS API credential and max resolution for video)
- Write results (where is the result should be written)
File: data/sample_config_01.yml
Where it is all started, if we require to store the configuration as per normally, it should be like this.
job:
read_image:
# R01
root_path: /data/project/images_and_videos/
analyse_image:
# R02
api_cred:
region_name: ap-southeast-2
aws_access_key_id: ACCESSKEY1234
# R03
aws_secret_access_key: ACCESSSECRETPLAIN1234
max_res: [1024, 768]
analyse_video:
# R04
api_cred:
region_name: ap-southeast-2
aws_access_key_id: ACCESSKEY1234
aws_secret_access_key: ACCESSSECRETPLAIN1234
max_res: [800, 600]
write_result:
# R05
output_path: /data/project/result.json
import yaml
with open('data/sample_config_01.yml') as stream:
r1 = yaml.safe_load(stream)
Notes:
- R01: The subpath of "/data/project" is repeated between R01 and R05
- R02: api_cred is repeatedly defined with R04
- R03: the secret is plain visible, if this stored in GIT, it is pure disaster
File: data/sample_config_02.yml
Repeating values/configuration is bad, this could potentially cause human mistake if changes made is not consistently applied in all occurences.
# this can be any name, as long as not reserved in Python
shared:
project_path: /data/project
aws_cred:
region_name: ap-southeast-2
aws_access_key_id: ACCESSKEY1234
# F03
aws_secret_access_key: F.decrypt('gAAAAABbBBhOJDMoQSbF9jfNgt97FwyflQEZRxv2L2buv6YD_Jiq8XNrxv8VqFis__J7YlpZQA07nDvzYwMU562Mlm978uP9BQf6M9Priy3btidL6Pm406w=')
job:
read_image:
# F01
root_path: R.shared.project_path + '/images_and_videos/'
analyse_image:
# F02
api_cred: R.shared.aws_cred
max_res: [1024, 768]
analyse_video:
# F04
api_cred: R.shared.aws_cred
max_res: [800, 600]
write_result:
# F05
output_path: R.shared.project_path + '/result.json'
import conff
# ekey is the secured encryption key
# WARNING: this is just demonstration purposes
ekey = 'FOb7DBRftamqsyRFIaP01q57ZLZZV6MVB2xg1Cg_E7g='
r2 = conff.load(fs_path='data/sample_config_02.yml', params={'ekey': ekey})
Notes:
- F01: it is safe if the prefix '/data/project' need to be changed, it will automatically changed for F05
- F02: no more duplicated config with F04
- F03: it is secured to save this to GIT, as long as the encryption key is stored securely somewhere in server such as ~/.secret
File: data/sample_config_03.yml
This is just demonstration purposes to see the full capabilities of this library.
# this can be any name, as long as not reserved in Python
shared:
project_path: /data/project
analyse_image_video:
api_cred:
region_name: ap-southeast-2
aws_access_key_id: ACCESSKEY1234
aws_secret_access_key: F.decrypt('gAAAAABbBBhOJDMoQSbF9jfNgt97FwyflQEZRxv2L2buv6YD_Jiq8XNrxv8VqFis__J7YlpZQA07nDvzYwMU562Mlm978uP9BQf6M9Priy3btidL6Pm406w=')
max_res: [1024, 768]
job:
read_image:
root_path: R.shared.project_path + '/images_and_videos/'
analyse_image: R.shared.analyse_image_video
analyse_video:
F.extend: R.shared.analyse_image_video
F.update:
max_res: [800, 600]
write_result:
output_path: R.shared.project_path + '/result.json'
For completeness, ensuring data is consistent and correct between sample_config_01.yml, sample_config_02.yml and sample_config_03.yml.
# nose2 conff.test.ConffTestCase.test_sample
fs_path = 'data/sample_config_01.yml'
with open(fs_path) as stream:
r1 = yaml.safe_load(stream)
fs_path = 'data/sample_config_02.yml'
ekey = 'FOb7DBRftamqsyRFIaP01q57ZLZZV6MVB2xg1Cg_E7g='
r2 = conff.load(fs_path=fs_path, params={'ekey': ekey})
fs_path = 'data/sample_config_03.yml'
r3 = conff.load(fs_path=fs_path, params={'ekey': ekey})
self.assertDictEqual(r1['job'], r2['job'], 'Mismatch value')
self.assertDictEqual(r2['job'], r3['job'], 'Mismatch value')
To test this project:
# default test
nose2
# test with coverage
nose2 --with-coverage
# test specific
nose2 conff.test.ConffTestCase.test_sample
- [X] Setup basic necessity
- [X] Stop procrastinating
- [X] Project registration in pypi
- [X] Create unit tests
- [X] Setup travis
- [X] Setup coveralls
- [ ] Add more support on Python versions
- [ ] 2.7
- [ ] 3.4
- [X] 3.5
- [X] 3.6
- [ ] Features
- [X] Add more functions for encryption
- [ ] Test on multilanguage
- [ ] Add circular dependencies error
- [ ] Ensure this is good on production environment
- [X] Add options to give more flexibility
- [ ] Check safety on the evaluator, expose more of its options such as (MAX_STRING)
- [ ] Improve F.extend to allow list to be extended
- [ ] Allow conff to update existing config object
- [ ] Have more converter from/to excel, xml
- [ ] Feature to display warning and error messages (for example, missing parameters, logging purposes)
- [ ] Add options to extend structure in template (for example, https://github.com/defunkt/pystache), the idea is render the values in string, convert it to object and attach it back. This will also inherit lots of feature such as loop, decision blocks
- [ ] Separate current functions into built-in and restructure it (first break-changes to organise all the built-in functions into more better way)
- [ ] Improve docs
- [ ] Add more code comments and visibilities
- [ ] Make github layout code into two left -> right
- [X] Put more examples
- [ ] Setup readthedocs
- [ ] Add code conduct, issue template into git project.
This project uses other awesome projects:
Please send a PR to keep the list growing, if you may please add your handle and company.