PyStaticConfiguration 0.7.0

Simple readers

The most direct method for reading config values is through the readers interface. These readers will return the value from the configuration namespace after passing them through a validator.

import staticconf

# read an int
max_cycles = staticconf.read_int('max_cycles')
start_id = staticconf.read_int('poller.init.start_id', default=0)

# start_date will be a datetime.date
start_date = staticconf.read_date('start_date')

# matcher will be a regex object
matcher = staticconf.read_regex('matcher_pattern')

If you’ve loaded your config into a namespace (using the namespace kwarg), you’ll need to make sure you’re reading your values from that namespace. This is done through a NamespaceReaders object, or using the namespace kwarg on the reader function.

import staticconf

# From a namespace, using kwarg
max_cycles = staticconf.read_int('max_cycles', namespace='iteration')

# Using a namespace reader
config = staticconf.NamespaceReaders('iteration')
max_cycles = config.read_int('max_cycles')
ratio = config.read_float('ratio')

Readers accept the following kwargs:

config_key

string configuration key

default

if no default is given, the key must be present in the configuration. Raises ConfigurationError on missing key.

namespace

get the value from this namespace instead of DEFAULT.

Schemas

Configuration schemas can be created to group configuration values for classes together. Configuration schemas are created using the staticconf.schema module. These schemas can be instantiated at import time, and values can be retrieved from them by accessing the attributes of the schema object.

from staticconf import schema

class SomethingUsefulSchema(schema.Schema):

    # namespace is optional, and will default to DEFAULT
    namespace = 'useful_namespace'

    # This path is prepended to each attribute, so the below schema will
    # expect values at useful.max_value, useful.ratio, etc
    config_path = 'useful'

    max_value = schema.int(default=100)
    ratio     = schema.float()
    msg       = schema.any(config_key='msg_string', default="Welcome")



config = SomethingUsefulSchema()
print config.msg

Schema accessors accept the following kwargs:

config_key

string configuration key

default

if no default is given, the key must be present in the configuration. Raises ConfigurationError on missing key.

help

a help string describing the purpose of the config value. See staticconf.view_help().

Proxy getters

The getters interface follows the same naming convention, but returns a ValueProxy instead of the raw value. This has a few advantages over the readers interface

• these calls can be made at import time, so all expected configuration values are known when the configuration is read.

• when a config is reloaded the proxies will refer to the new value

Note: ValueProxy objects do not work with c-modules. If you’re passing a value into a c-module, make sure to pass in proxy.value which is the underlying raw value.

import staticconf

# Returns a ValueProxy which can be used just like an int
max_cycles = staticconf.get_int('max_cycles')
print "Half of max_cycles", max_cycles / 2

# Using a NamespaceGetters object to retrieve from a namespace
config = staticconf.NamespaceGetters('special')
ratio = config.get_float('ratio')

Getters accept the following kwargs:

config_key

string configuration key

default

if no default is given, the key must be present in the configuration. Raises ConfigurationError on missing key.

help

a help string describing the purpose of the config value. See staticconf.view_help().

namespace

get the value from this namespace instead of DEFAULT.

Testing

MockConfiguration is a context manager provided in staticconf.testing. It patches the configuration namespace while inside the context.

import staticconf.testing

config = {
    ...
}
with staticconf.testing.MockConfiguration(config, namespace='special'):
    # Run your tests.
    ...

Reloading configuration

The ConfigurationWatcher and ReloadCallbackChain objects are provided as part of the staticconf.config module to reload configurations.

ConfigurationWatcher.reload_if_changed() will check if the file has been modified since the last reload, and reload the configuration when it has.

ReloadCallbackChain is provided to add post-reload callbacks. For most cases you should be able to create a custom validator to build types from your configuration data. If that is not possible, this class can be used to call arbitrary methods after the config is reloaded.

import staticconf
from staticconf import config

def build_configuration(filename, namespace):
    config_loader = partial(staticconf.YamlConfiguration,
                            filename, namespace=namespace)
    reloader = config.ReloadCallbackChain(namespace)
    return config.ConfigurationWatcher(
        config_loader, filename, min_interval=2, reloader=reloader)

config_watcher = build_configuration('config.yaml', 'my_namespace')

# Load the initial configuration
config_watcher.config_loader()

# Do some work
for item in work:
    config_watcher.reload_if_changed()
    ...

ConfigFacade

A ConfigFacade wraps up the ConfigurationWatcher and ReloadCallbackChain in a nicer interface for the most common case.

import staticconf

watcher = staticconf.ConfigFacade.load(
    'config.yaml', # Filename or list of filenames to watch
    'my_namespace',
    staticconf.YamlConfiguration, # Callable which takes the filename
    min_interval=3 # Wait at least 3 seconds before checking modified time
)

watcher.add_callback(do_this_after_reload)
watcher.reload_if_changed()

Building configuration loaders

staticconf.loader.build_loader can be used to create new configuration loaders. It takes a single argument which is a function. The function can accept any arguments, but must return a dictionary of configuration values.

from staticconf import loader

def load_from_db(table_name, conn):
    """Load configuration from a database table."""
    ....
    return dict((row.field, row.value) for row in cursor.fetchall())

DBConfiguration = loader.build_loader(load_from_db)

# Now lets use it
DBConfiguration('config_table', conn, namespace='special')

Building custom getters or readers

Both staticconf.getters and staticconf.readers provide a similar mechanism for creating a function to retrieve values from the configuration from a validation function. A validation function should handle all exceptions and raise a ValidationError if there is a problem. It should return the constructed value.

First create a validation function

def validate_currency(value):
    try:
        # Assume a tuple or a list
        name, decimal_points = value
        return Currency(name, decimal_points)
    except Exception, e:
        raise ValidationErrror(...)

Example of a getter

from staticconf import getters

# A getter without a default namespace
get_currency = getters.build_getter(validate_currency)

# A getter with a default namespace
get_currency = getters.build_getter(validate_currency, getter_namespace='special')

# Use the getter like any other staticconf getter
usd = get_currency('currencies.usd', namespace='money_stuff')

Example of a reader

from staticconf import readers

read_currency = readers.build_reader(validate_currency)

Building custom schema types

Building custom types for a schema is the same idea. Using the validate_currency() example from above:

from staticconf import schema

currency = schema.build_value_type(validate_currency)

class PaymentSchema(object):

    error_msg = schema.string()
    usd = currency()
    cdn = currency()

# And use it
config = PaymentSchema()
print config.usd

Disable Flatten

You can call loaders with the kwargs flatten=False.

Example:

YamlConfiguration(filename, flatten=False)

The disadvantage with this approach is that the entire config file will preserve its nested structure, so you lose out of the ability to easily merge and override configuration files.

Custom Reader

The second option is to represent a dict structures using lists of values (either a list of pairs or a list of dicts). This list can then be converted into a dict mapping using a custom getter/reader.

Below are some examples on how this is done. The readers interface is used as an example, but the same can be done for the getters and schema interface by replacing readers.build_reader() with getters.build_getter() and schema.build_value_type().

Create a reader which translates a list of dicts into a mapping

from staticconf import validation, readers

def build_map_from_key_value(item):
    return item['key'], item['value']

read_mapping = readers.build_reader(
    validation.build_map_type_validator(build_map_from_key_value))

my_mapping = read_mapping('config_key_of_a_list_of_dicts')

Create a reader which translates a list of pairs into a mapping

from staticconf import validation, readers

read_mapping = readers.build_reader(
    validation.build_map_type_validator(tuple))

my_mapping = read_mapping('config_key_of_a_list_of_pairs')

Create a reader from translates a list of complex dicts into a mapping

from staticconf import validation, readers

def build_map_from_dicts(item):
    return item.pop('name'), item

read_mapping = readers.build_reader(
    validation.build_map_type_validator(build_map_from_dicts))

my_mapping = read_mapping('config_key_of_a_list_of_dicts')