The most dynamic configuration options are the command-line arguments. Within this tier of configuration, we have two sub-tiers of default values and user-provided overrides to those defaults. Where do the defaults come from? They might be wired in, but more often they come from environment variables or parameter files or both.
There's some difference of opinion on which tier is next in the tiers of dynamism. The two choices are configuration files and environment variables. We can consider environment variables as easier to edit than configuration files. In some cases, though, configuration files are easier to change than environment variables. Environment variables are typically bound to the process just once (like command-line arguments), where configuration files can be read and re-read as needed.
The environment variables have three sub-tiers. System-level environment variables tend to be fixed. The variables set by a .profile or .bashrc tend to be specific to a logged-in user, and are somewhat more flexible that system variables. The current set of environment variables associated with the logged-in session can be modified on the command line, and are as flexible as command-line arguments.
Note that we can do this in Linux:
PYTHONPATH=/path/to/project python3 -m some_app -opts
This will set an environment variable as part of running a command.
The configuration files may also have tiers. We might have a global configuration file in /etc/our-app. We might look for a ~/.our-app-rc as a user's generic configuration. We can also look for our-app.config in the current working directory as the final set of overrides to be used for the current invocation.
Some applications can be restarted, leading to re-reading the configuration files. We can change the configuration more easily than we can bind in new command-line arguments or environment variables.
Representation Issues
When we think about configuration files, we also have to consider the syntax we want to use to represent configurable parameters. We have five common choices.
Some folks are hopelessly in love with Windows-style .ini files. The configparser module will parse these. I call it hopelessly in love because the syntax is rather quite limited. Look at the logging.config module to see how complex the .ini file format is for non-trivial cases.
Some folks like Java-style properties files. These have the benefit of being really easy to parse in Python. Indeed, scanning a properties file is great exercise in functional-style Python programming.
I'm not completely sold on these, either, because they don't really handle the non-trivial cases well.
Using JSON or YAML for properties has some real advantages. There's a lot of sophistication available in these two notations. While JSON has first-class support, YAML requires an add-on module.
We can also use Python as the language for configuration. For good examples of this, look at the Django project settings file. Using Python has numerous advantages. The only possible disadvantage is the time wasted arguing with folks who call it a "security vulnerability."
Using Python as the configuration language is only considered a vulnerability by people who fail to realize that the Python source itself can be hacked. Why waste time injecting a bug into a configuration file? Why not just hack the source?
My Current Fave
My current favorite way to handle configuration is by defining some kind of configuration class and using the class object throughout the application. Because of Python's import processing, a single instance of the class definition is easy to guarantee.
We might have a module that defines a hierarchy of configuration classes, each of which layers in additional details.
class Defaults:
mongo_uri = "mongodb://localhost:27017"
some_param = "xyz"
class Dev(Defaults):
mongo_uri = "mongodb://sandbox:27017"
class QA(Defaults):
mongo_uri = "mongodb://username:password@qa02:27017/?authMechanism=PLAIN&authSource=$external"
Yes. The password is visible. If we want to mess around with higher levels of secrecy in the configuration files, we can use PyCrypto and a key generator to use an encrypted password that's injected into the URI. That's a subject for another post. The folks to can edit the configuration files often know the passwords. Who are we trying to hide things from?
How do we choose the active configuration to use from among the available choices in this file? We have several ways.
To select a configuration using command-line arguments, we must decompose configuration into two parts. The configuration alternatives shown above are placed in a config_params.py module. The config.py module that's used directly by the application will import the config_params.py module, parse the command-line options, and finally pick a configuration. This module can create the required module global, Config. Since it will only execute once, we can import it freely.
How do we choose the active configuration to use from among the available choices in this file? We have several ways.
- Add a line to the configuration module. For example, Config=QA will name the selected environment. We have to change the configuration file as our code marches through environments from development to production. We can use from configuration import Config to get the proper configuration in all other modules of the application.
- Rely on the environment variable to specify which configuration use. In enterprise contexts, an environment variable is often available.We can import os, and use Config=globals()[os.environ['OURAPP_ENVIRONMENT']] to pick a configuration based on an environment variable.
- In some places, we can rely on the host name itself to pick a configuration. We can use os.uname()[1] to get the name of the server. We can add a mapping from server name to configuration, and use this: Config=host_map(os.uname()[1],Defaults).
- Use a command-line options like "--env=QA". This can a little more complex than the above techniques, but it seems to work out nicely in the long run.
To select a configuration using command-line arguments, we must decompose configuration into two parts. The configuration alternatives shown above are placed in a config_params.py module. The config.py module that's used directly by the application will import the config_params.py module, parse the command-line options, and finally pick a configuration. This module can create the required module global, Config. Since it will only execute once, we can import it freely.
The config module will use argparse to create an object named options with the command-line options. We can then do this little dance:
This seems to work out reasonably well. We can tweak the config_params.py flexibly. We can pick the configuration with a simple command-line option.
If we want to elegantly dump the configuration, we have a bit of a struggle. Each class in the hierarchy introduces names: it's a bit of work to walk down the __class__.__mro__ lattice to discover all of the available names and values that are inherited and overridden from the parents.
We could do something like this to flatten out the resulting values:
import argparse
import sys
import config_params
parser= argparse.ArgumentParser()
parser.add_argument("--env", default="DEV")
options= parser.parse_args()
Config = getattr(config_params, options.env)
Config.options= options
This seems to work out reasonably well. We can tweak the config_params.py flexibly. We can pick the configuration with a simple command-line option.
If we want to elegantly dump the configuration, we have a bit of a struggle. Each class in the hierarchy introduces names: it's a bit of work to walk down the __class__.__mro__ lattice to discover all of the available names and values that are inherited and overridden from the parents.
We could do something like this to flatten out the resulting values:
Base= getattr(config_params, options.env)
class Config(Base):
def __repr__(self):
names= {}
for cls in reversed(self.__class__.__mro__):
cls_names= dict((nm, (cls.__name__, val))
for nm,val in cls.__dict__.items()
if nm[0] != "_")
names.update( cls_names )
return ", ".join( "{0}.{1}={2}".format(class_val[0], nm, class_val[1])
for nm,class_val in names.items() )
