CodePack

CodePack is a Python package to easily make, run, and manage workflows. You can write a task which is called Code with pure python code. By simply making linkages between the Codes, you can easily build a workflow which is called CodePack. The CodePack can be either executed or scheduled (via APScheduler) with a set of arguments which is called ArgPack. The Code, CodePack, and ArgPack support JSON serialization and deserialization, so they can be saved to a file or external storage and recycled to create different combinations.

CodePack is one of the good choices to represent flexible workflows. Business environments often require frequent changes to workflows to quickly meet volatile customer requirements. CodePack provides various plugins to configure the whole system. For example, file systems, MongoDB, and AWS S3 can be options for transferring data between Codes or storing serialized objects. You can easily setup the various components by modifying few lines in the configuration file or adding some OS environment variables.

Simple is the best ;) but sometimes... you may need helps from rich user interfaces and powerful utilities to troubleshoot complex workflows and expand your system. It is recommended to convert CodePacks into the DAGs used in Apache Airflow especially when you deal with workflows that are mostly static and slowly changing. CodePack is also planning to provide rich APIs and user interfaces, so please stay tuned!

The key features are:

• Easy: Designed to be easy to use with pure python code. There is nothing new to learn!
• Simple: Minimize the effort of configuring the entire system to your taste.
• Responsive: Get workflow results immediately or later by sync/async execution or scheduling right after making changes in the workflow and its arguments.
• Transformable: Convert almost everything into JSON, so you can easily extend its use to other solutions.

Requirements

CodePack is tested with Python 3.6, 3.7, 3.8, and 3.9.

Installation

Use pip install command to install CodePack into your Python environment. Check PyPI for more details.

$ pip install codepack

This will install CodePack with minimal dependencies. You can activate full features by using pip install codepack[all] command.

If you want to try sample apps provided in apps, you need to install additional packages.

$ pip install codepack[all] jupyter uvicorn[standard] fastapi

To run unit tests, use following commands.

$ pip install pytest pytest-cov mongomock
$ pytest --cov codepack tests

Example

1. Instantiate and run Code

Just wrap a normal python function with Code and use it the way you are used to it. A Code has its own id. If you do not specify an id when creating a Code, the given function name is automatically set to id.

from codepack import Code

def add3(a, b=1, c=2):
  return a + b + c

code = Code(add3)
result = code(3, b=5)  # result == 10

In order to improve readability of the Code, you can use type annotations and docstrings.

Several classes in typing module (e.g. Any, Optional, etc.) can be added to the annotations. However, Code does not understand user-defined and 3rd party modules. To use these modules in annotations, it is recommended to enclose them in quotation marks. For example, use 'pandas.core.frame.DataFrame' and 'numpy.ndarray' instead of DataFrame and ndarray.

The docstring in the function is stored in the description attribute in the Code instance. The longer, the better!

from typing import Optional, Any, Union

def my_function(a: str, b: list, c: Optional['my_module.MyClass'] = None, **kwargs: Any) -> Union[str, dict]:
  """ You can find this message in code.description. """
  if c is None:
    return dict()
  else:
    return str()

code = Code(my_function)
print(code)  # Code(id: my_function, function: my_function, params: (a: str, b: list, c: Union[ForwardRef('my_module.MyClass'), NoneType] = None, **kwargs: Any) -> Union[str, dict])
print(code.description)  # You can find this message in code.description.

If you want to attach a decorator to a Code, just pass it to the constructor of the Code. You may also use set_decorator function after creation of the Code instance. One thing to note is that you have to detach the decorator from the function before you pass the function and the decorator to the constructor of the Code. In other words, a function which is passed to a Code constructor should not include decorator.

@my_decorator
def decorated_add2(a: int, b: int) -> int:  # Do not pass this to the constructor!
  return a + b

def add2(a: int, b: int) -> int:  # Detach my_decorator
  return a + b

code1 = Code(add2, decorator=my_decorator)
code2 = Code(add2)
code2.set_decorator(decorator=my_decorator)  # code1 and code2 do same thing.

2. Link Codes

You can create links between Codes with >> operator and break links with // operator. The linkage represents the order of execution between Codes. If you want to transfer the result of a preceding Code to the following Code, use receive method and << operator to represent the data dependency.

code1 >> code2 >> code3
code3 >> [code4, code5]
code3 // code5

code2.receive('a') << code1
code4.receive('c') << code3

3. Make a CodePack

Tag any one of the connected Codes to a CodePack in code, and specify the Code to return the final result in subscribe. If you don't specify the subscription, the CodePack returns None.

from codepack import CodePack

codepack = CodePack('my_codepack', code=code1, subscribe=code4)

4. Set arguments and run CodePack

Don't bother yourself to remember all argument specifications in Codes. You can extract an empty ArgPack from the CodePack. ArgPack even understands the default keyword arguments in Codes. All you need to do is just fill out the blanks!

from codepack import ArgPack

argpack = codepack.make_argpack()

argpack['add2'](x=3, y=5)
argpack['add3'](a=3)  # receive c from add2
argpack['mul2'](x2=2)  # receive x1 from add3

result = codepack(argpack)  # subscribe mul2

5. Save and load

You can save Code, CodePack, and ArgPack into somewhere you specified in configuration. Each element has its own id, so it can be retrieved and reused by querying the id.

# Code
code.save()
code = Code.load('my_code')

# CodePack
codepack.save()
codepack = CodePack.load('my_codepack')

# ArgPack (1)
codepack.save_argpack(argpack)  # codepack.id == argpack.id
argpack = codepack.load_argpack('my_codepack')

# ArgPack (2)
argpack.save()
argpack = ArgPack.load('my_argpack')

6. JSON serialization and deserialization

Code, CodePack, and ArgPack are interchangeable with JSON. Use these guys everywhere!

code_json = code.to_json()
code_py = Code.from_json(code_json)

codepack_json = codepack.to_json()
codepack_py = CodePack.from_json(codepack_json)

argpack_json = argpack.to_json()
argpack_py = ArgPack.from_json(argpack_json)

7. Set configuration

There are many plugins to manage Codes, CodePacks, and ArgPacks. You can easily configure the plugins by using a configuration file or adding some OS environment variables. If you don't specify any configuration files, CodePack uses the default configuration file located at codepack/utils/config/default/default.ini. The default configuration file can be replaced with your custom configuration file by adding an OS environment variable: CODEPACK_CONFIG_PATH=/path/to/your-configuration-file.ini.

Let's say you want the following scenario:

• Save and load Codes through MongoDB.
• Exchange data between Codes through files.

Your configuration file should include:

[code]
source = mongodb
db = codepack
collection = codes

[delivery]
source = file
path = /data/delivery

[mongodb]
host = server1.ip,server2.ip,server3.ip
port = 27017
username = admin
password = ***
replicaSet = test

When the external service like MongoDB or AWS S3 is set to source, the connection information should also be included.

If it is too annoying to modify the configuration file, you can overwrite configuration values with OS environment variables: CODEPACK__<SECTION>__<KEY>=<VALUE>. For example, if the IP address of the MongoDB has changed to localhost, set CODEPACK__MONGODB__HOST=localhost.

The configuration is accessible from code via Config and Default classes.

from codepack import Config, Default
from codepack.interfaces import MongoDB

config = Config()
mongodb_config = config.get_config('mongodb')
mongodb = MongoDB(mongodb_config)
document = mongodb.test_db.test_collection.find_one({'_id': 'test'})

code_storage_service = Default.get_service('code', 'storage_service')
delivery_service = Default.get_service('delivery', 'delivery_service')
scheduler = Default.get_scheduler()
logger = Default.get_logger()

If the default configuration bothers you, pass default=False to get_config.

import os

os.environ['CODEPACK__SSH__CUSTOM_KEY'] = 'custom_value'
config.get_config('ssh')  # {'ssh_host': 'localhost', 'ssh_port': '22', 'custom_key': 'custom_value'}
config.get_config('ssh', default=False)  # {'custom_key': 'custom_value'}
os.environ.pop('CODEPACK__SSH__CUSTOM_KEY', None)
config.get_config('ssh', default=False)  # {}

8. To use in other machines...

A Code should be executable in isolated environments different from where it was created. In order to avoid the python package dependency issue, put import statements into the function.

def calc_numpy_array_sum(x):
  import numpy as np
  return np.sum(np.array(x))

This little trick is very important when setting up distributed systems where each Code in a CodePack runs on a different machine.

9. The other features

For more information on schedulers, asynchronous workers, APIs, and other features, please see Documentation and Github.

Source code

The source can be browsed at Github.

Contributing

Want to help CodePack? Feel free to use Issues and Discussions to unleash your imagination!

License

This project is licensed under the terms of the MIT license.