"""
This submodule contains factory/configuration methods for integrating the SDK with services
other than LaunchDarkly.
"""
from ldclient.feature_store import CacheConfig
from ldclient.feature_store_helpers import CachingStoreWrapper
from ldclient.impl.integrations.consul.consul_feature_store import _ConsulFeatureStoreCore
from ldclient.impl.integrations.dynamodb.dynamodb_feature_store import _DynamoDBFeatureStoreCore
from ldclient.impl.integrations.files.file_data_source import _FileDataSource
from ldclient.impl.integrations.redis.redis_feature_store import _RedisFeatureStoreCore
[docs]class Consul(object):
"""Provides factory methods for integrations between the LaunchDarkly SDK and Consul.
"""
"""The key prefix that is used if you do not specify one."""
DEFAULT_PREFIX = "launchdarkly"
[docs] @staticmethod
def new_feature_store(host=None,
port=None,
prefix=None,
consul_opts=None,
caching=CacheConfig.default()):
"""Creates a Consul-backed implementation of :class:`ldclient.interfaces.FeatureStore`.
For more details about how and why you can use a persistent feature store, see the
`SDK reference guide <https://docs.launchdarkly.com/v2.0/docs/using-a-persistent-feature-store>`_.
To use this method, you must first install the ``python-consul`` package. Then, put the object
returned by this method into the ``feature_store`` property of your client configuration
(:class:`ldclient.config.Config`).
::
from ldclient.integrations import Consul
store = Consul.new_feature_store()
config = Config(feature_store=store)
Note that ``python-consul`` is not available for Python 3.3 or 3.4, so this feature cannot be
used in those Python versions.
:param string host: hostname of the Consul server (uses ``localhost`` if omitted)
:param int port: port of the Consul server (uses 8500 if omitted)
:param string prefix: a namespace prefix to be prepended to all Consul keys
:param dict consul_opts: optional parameters for configuring the Consul client, if you need
to set any of them besides host and port, as defined in the
`python-consul API <https://python-consul.readthedocs.io/en/latest/#consul>`_
:param CacheConfig caching: specifies whether local caching should be enabled and if so,
sets the cache properties; defaults to :func:`ldclient.feature_store.CacheConfig.default()`
"""
core = _ConsulFeatureStoreCore(host, port, prefix, consul_opts)
return CachingStoreWrapper(core, caching)
[docs]class DynamoDB(object):
"""Provides factory methods for integrations between the LaunchDarkly SDK and DynamoDB.
"""
[docs] @staticmethod
def new_feature_store(table_name,
prefix=None,
dynamodb_opts={},
caching=CacheConfig.default()):
"""Creates a DynamoDB-backed implementation of :class:`ldclient.interfaces.FeatureStore`.
For more details about how and why you can use a persistent feature store, see the
`SDK reference guide <https://docs.launchdarkly.com/v2.0/docs/using-a-persistent-feature-store>`_.
To use this method, you must first install the ``boto3`` package containing the AWS SDK gems.
Then, put the object returned by this method into the ``feature_store`` property of your
client configuration (:class:`ldclient.config.Config`).
::
from ldclient.integrations import DynamoDB
store = DynamoDB.new_feature_store("my-table-name")
config = Config(feature_store=store)
Note that the DynamoDB table must already exist; the LaunchDarkly SDK does not create the table
automatically, because it has no way of knowing what additional properties (such as permissions
and throughput) you would want it to have. The table must have a partition key called
"namespace" and a sort key called "key", both with a string type.
By default, the DynamoDB client will try to get your AWS credentials and region name from
environment variables and/or local configuration files, as described in the AWS SDK documentation.
You may also pass configuration settings in ``dynamodb_opts``.
:param string table_name: the name of an existing DynamoDB table
:param string prefix: an optional namespace prefix to be prepended to all DynamoDB keys
:param dict dynamodb_opts: optional parameters for configuring the DynamoDB client, as defined in
the `boto3 API <https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session.client>`_
:param CacheConfig caching: specifies whether local caching should be enabled and if so,
sets the cache properties; defaults to :func:`ldclient.feature_store.CacheConfig.default()`
"""
core = _DynamoDBFeatureStoreCore(table_name, prefix, dynamodb_opts)
return CachingStoreWrapper(core, caching)
[docs]class Redis(object):
"""Provides factory methods for integrations between the LaunchDarkly SDK and Redis.
"""
DEFAULT_URL = 'redis://localhost:6379/0'
DEFAULT_PREFIX = 'launchdarkly'
DEFAULT_MAX_CONNECTIONS = 16
[docs] @staticmethod
def new_feature_store(url='redis://localhost:6379/0',
prefix='launchdarkly',
max_connections=16,
caching=CacheConfig.default()):
"""Creates a Redis-backed implementation of :class:`ldclient.interfaces.FeatureStore`.
For more details about how and why you can use a persistent feature store, see the
`SDK reference guide <https://docs.launchdarkly.com/v2.0/docs/using-a-persistent-feature-store>`_.
To use this method, you must first install the ``redis`` package. Then, put the object
returned by this method into the ``feature_store`` property of your client configuration
(:class:`ldclient.config.Config`).
::
from ldclient.integrations import Redis
store = Redis.new_feature_store()
config = Config(feature_store=store)
:param string url: the URL of the Redis host; defaults to ``DEFAULT_URL``
:param string prefix: a namespace prefix to be prepended to all Redis keys; defaults to
``DEFAULT_PREFIX``
:param int max_connections: the maximum number of Redis connections to keep in the
connection pool; defaults to ``DEFAULT_MAX_CONNECTIONS``
:param CacheConfig caching: specifies whether local caching should be enabled and if so,
sets the cache properties; defaults to :func:`ldclient.feature_store.CacheConfig.default()`
"""
core = _RedisFeatureStoreCore(url, prefix, max_connections)
wrapper = CachingStoreWrapper(core, caching)
wrapper.core = core # exposed for testing
return wrapper
[docs]class Files(object):
"""Provides factory methods for integrations with filesystem data.
"""
[docs] @staticmethod
def new_data_source(paths, auto_update=False, poll_interval=1, force_polling=False):
"""Provides a way to use local files as a source of feature flag state. This would typically be
used in a test environment, to operate using a predetermined feature flag state without an
actual LaunchDarkly connection.
To use this component, call ``new_data_source``, specifying the file path(s) of your data file(s)
in the ``paths`` parameter; then put the value returned by this method into the ``update_processor_class``
property of your LaunchDarkly client configuration (:class:`ldclient.config.Config`).
::
from ldclient.integrations import Files
data_source = Files.new_data_source(paths=[ myFilePath ])
config = Config(update_processor_class=data_source)
This will cause the client not to connect to LaunchDarkly to get feature flags. The
client may still make network connections to send analytics events, unless you have disabled
this in your configuration with ``send_events`` or ``offline``.
The format of the data files is described in the SDK Reference Guide on
`Reading flags from a file <https://docs.launchdarkly.com/v2.0/docs/reading-flags-from-a-file>`_.
Note that in order to use YAML, you will need to install the ``pyyaml`` package.
If the data source encounters any error in any file-- malformed content, a missing file, or a
duplicate key-- it will not load flags from any of the files.
:param array paths: the paths of the source files for loading flag data. These may be absolute paths
or relative to the current working directory. Files will be parsed as JSON unless the ``pyyaml``
package is installed, in which case YAML is also allowed.
:param bool auto_update: (default: false) True if the data source should watch for changes to the source file(s)
and reload flags whenever there is a change. The default implementation of this feature is based on
polling the filesystem, which may not perform well; if you install the ``watchdog`` package, its
native file watching mechanism will be used instead. Note that auto-updating will only work if all
of the files you specified have valid directory paths at startup time.
:param float poll_interval: (default: 1) the minimum interval, in seconds, between checks for file
modifications-- used only if ``auto_update`` is true, and if the native file-watching mechanism from
``watchdog`` is not being used.
:param bool force_polling: (default: false) True if the data source should implement auto-update via
polling the filesystem even if a native mechanism is available. This is mainly for SDK testing.
:return: an object (actually a lambda) to be stored in the ``update_processor_class`` configuration property
"""
return lambda config, store, ready : _FileDataSource(store, ready, paths, auto_update, poll_interval, force_polling)