siringa
(meaning syringe
in Italian) is a minimalist, idiomatic dependency injection/inversion of control library
for Python, implemented in Hy, a Lisp dialect for Python.
To get started, take a look to the documentation, API, tutorial and examples.
- Simple, idiomatic and versatile programmatic API.
- Annotation based dependency injection that is PEP 3017 and PEP 0484 friendly.
- First-class decorator driven dependency injection and registering.
- Ability to create multiple dependency containers.
- Hierarchical dependency containers based on inheritance.
- Dependency inference based on pattern-matching techniques.
- First-class support for dependency mocking for better testing.
- Highly tolerant: any type of object can be injected, including
str
,generators
,coroutines
... - Detects cyclic dependencies (work in progress).
- Small and (almost) dependency-free library.
- Works with CPython 3+ (because of Hy <> CPython AST compatibility)
- Code instrumentation should be non-intrusive and idiomatic.
- Explicitness over implicitness: dependencies and injections much be explicitly defined.
- Python idiomatic: embrace decorators and type annotations.
- Minimalism: less enables more.
- Uniformity: there is only one way to declare and consume dependencies.
- Predictability: developer intentions must persist based on explicitly defined intention.
- Domain agnostic: do not enforce any domain-specific pattern.
Using pip
package manager:
pip install --upgrade siringa
Or install the latest sources from Github:
pip install -e git+git://github.com/h2non/siringa.git#egg=siringa
import siringa
siringa
embraces type hints/arguments annotation Python syntax for
dependency inference and pattern matching.
@siringa.inject
def task(x, y, logger: '!Logger'):
logger.info('task called with arguments: {}, {}'.format(x, y))
return x * y
You can optionally annotate dependencies via siringa
type annotations:
from siringa import A
@siringa.inject
def task(x, y, logger: A('Logger')):
logger.info('task called with arguments: {}, {}'.format(x, y))
return x * y
Finally, for a DRYer approach you can simply annotate dependencies with !
annotation flag.
In this case, the argument name expression will be used for dependency inference.
from siringa import A
@siringa.inject
def task(x, y, Logger: '!'):
Logger.info('task called with arguments: {}, {}'.format(x, y))
return x * y
siringa
allows you to rely on decorators for idiomatic dependencies registering.
Dependency name is dynamically inferred at registration time based on class
or function
name.
@siringa.register
class Logger(object):
logger = logging.getLogger('siringa')
@staticmethod
def info(msg, *args, **kw):
logger.info(msg, *args, **kw)
However, you can define a custom dependency name by simply passing a string
as first argument:
@siringa.register('MyCustomLogger')
class Logger(object):
...
Finally, you can register dependencies with a traditional function call, such as:
class Logger(object):
pass
siringa.register('MyCustomLogger', Logger)
class compute(x, y):
return x * y
siringa.register('multiply', compute)
siringa
wraps callable object in the transparent and frictionless way abstracting things for developers.
You can invoke or instantiate any dependency injection instrumented object
as you do traditionally in raw Python code and siringa
will do the rest
inferring and pattern-matching required dependencies accordingly for you.
Below is an example of how simple it is:
# Call our previously declared function in this tutorial.
# Here, siringa will transparently inject required dependencies accordingly,
# respecting the invokation arguments and order.
task(2, 2) # => 4
Let's demostrate this with a featured example:
import siringa
@siringa.register
def mul(x, y):
return x * y
@siringa.register
def mul2(x, mul: '!mul'):
return mul(x, 2)
@siringa.register
def pow2(x):
return x ** 2
@siringa.inject
def compute(x, pow: '!pow2', mul: '!mul2'):
return pow(mul(x))
compute(2) # => 16
You can also use the invocation API in case that the target object was not properly instrumented as dependency:
@siringa.register
def mul2(x):
return x * 2
# Note that the function was not instrumented yet!
def compute(x, mul: '!mul2'):
return mul(x)
siringa.invoke(compute, 2)
siringa
provides a built-in global dependency container for usability purposes,
but you can create as much containers as you want.
In the siringa
idioms, this means creating a new dependency layer which provides its
own container and dependency injection API, pretty much as the global package API.
You can create a new dependencies layer
such as:
layer = siringa.Layer('app')
# Then you can use the standard API
layer.register('print', print)
# Then you can use the standard API
@layer.inject
def mul2(x, print: '!'):
print('Argument:', x)
return x * 2
mul2(x)
A dependency layer can inherit from a parent dependency layer.
This is particularly useful in order to create a hierarchy of dependency layers where you can consume and inject dependencies from a parent container.
parent = siringa.Layer('parent')
child = siringa.Layer('child', parent)
# Register a sample dependency within parent
@parent.register
def mul2(x):
return x * 2
# Verify that the dependency is injectable from child layer
parent.is_injectable('mul2') # True
child.is_injectable('mul2') # True
@child.inject
def compute(x, mul: '!mul2'):
return mul(x)
compute(2) # => 2
siringa
allows you to define mocks for dependencies, which is particularly useful during testing:
@siringa.register
class DB(object):
def query(self, sql):
return ['john', 'mike']
@siringa.mock('DB')
class DBMock(object):
def query(self, sql):
return ['foo', 'bar']
@siringa.inject
def run(sql, db: '!DB'):
return db().query(sql)
# Test mock call
assert run('SELECT name FROM foo') == ['foo', 'bar']
# Once done, clear all the mocks
siringa.unregister_mock('DB')
# Or alternatively clear all the registed mocks within the container
siringa.clear_mocks()
# Test read call
assert run('SELECT name FROM foo') == ['john', 'mike']