Component definitions for Pip.Services in Python
Project description
Component definitions for Python
This module is a part of the Pip.Services polyglot microservices toolkit.
The Components module contains standard component definitions that can be used to build applications and services.
The module contains the following packages:
- Auth - authentication credential stores
- Build - basic factories for constructing objects
- Cache - distributed cache
- Config - configuration readers and managers, whose main task is to deliver configuration parameters to the application from wherever they are being stored
- Connect - connection discovery and configuration services
- Count - performance counters
- Info - context info implementations that manage the saving of process information and sending additional parameter sets
- Lock - distributed lock components
- Log - basic logging components that provide console and composite logging, as well as an interface for developing custom loggers
- Test - minimal set of test components to make testing easier
- Component - the root package
Use
Install the Python package as
pip install pip_services3_components
Example how to use Logging and Performance counters. Here we are going to use CompositeLogger and CompositeCounters components. They will pass through calls to loggers and counters that are set in references.
from pip_services3_commons.config import ConfigParams, IConfigurable
from pip_services3_commons.refer import IReferences, IReferenceable
from pip_services3_components.count import CompositeCounters
from pip_services3_components.log import CompositeLogger
class MyComponent(IConfigurable, IReferenceable):
__logger = CompositeLogger()
__counters = CompositeCounters()
def configure(self, config):
self.__logger.configure(config)
def set_references(self, references):
self.__logger.set_references(references)
self.__counters.set_references(references)
def my_method(self, correlation_id, param1):
try:
self.__logger.trace(correlation_id, "Executed method mycomponent.mymethod")
self.__counters.increment("mycomponent.mymethod.exec_count", 1)
timing = self.__counters.begin_timing("mycomponent.mymethod.exec_time")
# ...
timing.end_timing()
except Exception as ex:
self.__logger.error(correlation_id, ex, "Failed to execute mycomponent.mymethod")
self.__counters.increment("mycomponent.mymethod.error_count", 1)
Example how to get connection parameters and credentials using resolvers. The resolvers support "discovery_key" and "store_key" configuration parameters to retrieve configuration from discovery services and credential stores respectively.
from pip_services3_commons.config import ConfigParams, IConfigurable
from pip_services3_commons.refer import IReferences, IReferenceable
from pip_services3_commons.run import IOpenable
from pip_services3_components.auth import CredentialParams, CredentialResolver
from pip_services3_components.connect import ConnectionParams, ConnectionResolver
class MyComponent(IConfigurable, IReferenceable, IOpenable):
__connection_resolver = ConnectionResolver()
__credential_resolver = CredentialResolver()
def configure(self, config):
self.__connection_resolver.configure(config)
self.__credential_resolver.configure(config)
def set_references(self, references):
self.__connection_resolver.set_references(references)
self.__credential_resolver.set_references(references)
# ...
def open(self, correlation_id):
connection = self.__connection_resolver.resolve(correlation_id)
credential = self.__credential_resolver.lookup(correlation_id)
host = connection.get_post()
port = connection.get_port()
user = credential.get_username()
pas = credential.get_password()
# ...
# Using the component
my_component = MyComponent()
my_component.configure(ConfigParams.from_tuples(
'connection.host', 'localhost',
'connection.port', 1234,
'credential.username', 'anonymous',
'credential.password', 'pass123'
))
my_component.open(None)
Example how to use caching and locking. Here we assume that references are passed externally.
from pip_services3_commons.refer import Descriptor, References, IReferences, IReferenceable
from pip_services3_components.cache import ICache, MemoryCache
from pip_services3_components.lock.ILock import ILock
from pip_services3_components.lock.MemoryLock import MemoryLock
class MyComponent(IReferenceable):
__cache: ICache
__lock: ILock
def set_references(self, references: IReferences):
self.__cache = references.get_one_required(Descriptor("*", "cache", "*", "*", "1.0"))
self.__lock = references.get_one_required(Descriptor("*", "lock", "*", "*", "1.0"))
def my_method(self, correlation_id, param1):
# First check cache for result
result = self.__cache.retrieve(correlation_id, 'mykey')
# Lock..
self.__lock.acquire_lock(correlation_id, "mykey", 1000, 1000, )
# Do processing
# ...
# Store result to cache async
self.__cache.store(correlation_id, 'mykey', result, 3600000)
# Release lock async
self.__lock.release_lock(correlation_id, 'mykey')
return result
# Use the component
my_component = MyComponent()
my_component.set_references(References.from_tuples(
Descriptor("pip-services", "cache", "memory", "default", "1.0"), MemoryCache(),
Descriptor("pip-services", "lock", "memory", "default", "1.0"), MemoryLock(),
))
result = my_component.my_method(None, param1)
If you need to create components using their locators (descriptors) implement component factories similar to the example below.
from pip_services3_commons.refer import Descriptor
from pip_services3_components.build import Factory
class MyFactory(Factory):
my_component_descriptor = Descriptor("myservice", "mycomponent", "default", "*", "1.0")
def __init__(self):
super(MyFactory, self).__init__()
self.register_as_type(MyFactory.my_component_descriptor, MyFactory)
# Using the factory
my_factory = MyFactory()
my_component1 = my_factory.create(Descriptor("myservice", "mycomponent", "default", "myComponent1", "1.0"))
my_component2 = my_factory.create(Descriptor("myservice", "mycomponent", "default", "myComponent2", "1.0"))
...
Develop
For development you shall install the following prerequisites:
- Python 3.7+
- Visual Studio Code or another IDE of your choice
- Docker
Install dependencies:
pip install -r requirements.txt
Run automated tests:
python test.py
Generate API documentation:
./docgen.ps1
Before committing changes run dockerized build and test as:
./build.ps1
./test.ps1
./clear.ps1
Contacts
The initial implementation is done by Sergey Seroukhov. Pip.Services team is looking for volunteers to take ownership over Python implementation in the project.
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