Best Practices¶

Setting up Keystone¶

Get your development environment set up according to Setting up a Development Environment. It is recommended that you install Keystone into a virtualenv.

Configuring Keystone¶

Keystone requires a configuration file. There is a sample configuration file that can be used to get started:

$ cp etc/keystone.conf.sample etc/keystone.conf

The defaults are enough to get you going, but you can make any changes if needed.

Running Keystone¶

To run the Keystone Admin and API server instances, use:

$ uwsgi --http 127.0.0.1:35357 --wsgi-file $(which keystone-wsgi-admin)

This runs Keystone with the configuration the etc/ directory of the project. See Configuring Keystone for details on how Keystone is configured. By default, Keystone is configured with SQL backends.

Initializing Keystone¶

Before using keystone, it is necessary to create the database tables and ensures the database schemas are up to date, perform the following:

$ keystone-manage db_sync

If the above commands result in a KeyError, or they fail on a .pyc file with the message, You can only have one Python script per version, then it is possible that there are out-of-date compiled Python bytecode files in the Keystone directory tree that are causing problems. This can occur if you have previously installed and ran older versions of Keystone. These out-of-date files can be easily removed by running a command like the following from the Keystone root project directory:

$ find . -name "*.pyc" -delete

Initial Sample Data¶

There is an included script which is helpful in setting up some initial sample data for use with keystone:

$ ADMIN_PASSWORD=s3cr3t tools/sample_data.sh

Once run, you can see the sample data that has been created by using the python-openstackclient command-line interface:

$ export OS_USERNAME=admin
$ export OS_PASSWORD=s3cr3t
$ export OS_PROJECT_NAME=admin
$ export OS_USER_DOMAIN_ID=default
$ export OS_PROJECT_DOMAIN_ID=default
$ export OS_IDENTITY_API_VERSION=3
$ export OS_AUTH_URL=http://localhost:5000/v3
$ openstack user list

The python-openstackclient can be installed using the following:

$ pip install python-openstackclient

Interacting with Keystone¶

You can also interact with keystone through its REST API. There is a Python keystone client library python-keystoneclient which interacts exclusively through the REST API, and a command-line interface python-openstackclient command-line interface.

Building the Documentation¶

The documentation is generated with Sphinx using the tox command. To create HTML docs and man pages:

$ tox -e docs

The results are in the doc/build/html and doc/build/man directories respectively.

Generating a new Sample Config File¶

Keystone’s sample configuration file etc/keystone.conf.sample is automatically generated based upon all of the options available within Keystone. These options are sourced from the many files around Keystone as well as some external libraries.

The sample configuration file will be updated as the end of the development cycle approaches. Developers should NOT generate the config file and propose it as part of their patches, this will cause unnecessary conflicts.

To generate a new sample configuration to see what it looks like, run:

$ tox -egenconfig -r

The tox command will place an updated sample config in etc/keystone.conf.sample.

If there is a new external library (e.g. oslo.messaging) that utilizes the oslo.config package for configuration, it can be added to the list of libraries found in config-generator/keystone.conf.

Release Notes¶

The Keystone team uses reno to generate release notes. These are important user-facing documents that must be included when a user-facing change is performed. A release note should be included in the same patch the work is being performed.

For more information on how and when to create release notes, see the project-team-guide.

Testing Keystone¶

Running Tests¶

Before running tests, you should have tox installed and available in your environment (in addition to the other external dependencies in Setting up a Development Environment):

$ pip install tox

Note

You may need to perform both the above operation and the next inside a python virtualenv, or prefix the above command with sudo, depending on your preference.

To execute the full suite of tests maintained within Keystone, simply run:

$ tox

This iterates over multiple configuration variations, and uses external projects to do light integration testing to verify the Identity API against other projects.

Note

The first time you run tox, it will take additional time to build virtualenvs. You can later use the -r option with tox to rebuild your virtualenv in a similar manner.

To run tests for one or more specific test environments (for example, the most common configuration of Python 2.7 and PEP-8), list the environments with the -e option, separated by spaces:

$ tox -e py27,pep8

See tox.ini for the full list of available test environments.

Running with PDB¶

Using PDB breakpoints with tox and testr normally doesn’t work since the tests just fail with a BdbQuit exception rather than stopping at the breakpoint.

To run with PDB breakpoints during testing, use the debug tox environment rather than py27. Here’s an example, passing the name of a test since you’ll normally only want to run the test that hits your breakpoint:

$ tox -e debug keystone.tests.unit.test_auth.AuthWithToken.test_belongs_to

For reference, the debug tox environment implements the instructions here: https://wiki.openstack.org/wiki/Testr#Debugging_.28pdb.29_Tests

Disabling Stream Capture¶

The stdout, stderr and log messages generated during a test are captured and in the event of a test failure those streams will be printed to the terminal along with the traceback. The data is discarded for passing tests.

Each stream has an environment variable that can be used to force captured data to be discarded even if the test fails: OS_STDOUT_CAPTURE for stdout, OS_STDERR_CAPTURE for stderr and OS_LOG_CAPTURE for logging. If the value of the environment variable is not one of (True, true, 1, yes) the stream will be discarded. All three variables default to 1.

For example, to discard logging data during a test run:

$ OS_LOG_CAPTURE=0 tox -e py27

Test Structure¶

Not all of the tests in the keystone/tests/unit directory are strictly unit tests. Keystone intentionally includes tests that run the service locally and drives the entire configuration to achieve basic functional testing.

For the functional tests, an in-memory key-value store or in-memory SQLite database is used to keep the tests fast.

Within the tests directory, the general structure of the backend tests is a basic set of tests represented under a test class, and then subclasses of those tests under other classes with different configurations to drive different backends through the APIs.

For example, test_backend.py has a sequence of tests under the class IdentityTests that will work with the default drivers as configured in this project’s etc/ directory. test_backend_sql.py subclasses those tests, changing the configuration by overriding with configuration files stored in the tests/unit/config_files directory aimed at enabling the SQL backend for the Identity module.

keystone.tests.unit.test_v2_keystoneclient.ClientDrivenTestCase uses the installed python-keystoneclient, verifying it against a temporarily running local keystone instance to explicitly verify basic functional testing across the API.

Testing Schema Migrations¶

The application of schema migrations can be tested using SQLAlchemy Migrate’s built-in test runner, one migration at a time.

Warning

This may leave your database in an inconsistent state; attempt this in non-production environments only!

This is useful for testing the next migration in sequence (both forward & backward) in a database under version control:

$ python keystone/common/sql/migrate_repo/manage.py test \
--url=sqlite:///test.db \
--repository=keystone/common/sql/migrate_repo/

This command references to a SQLite database (test.db) to be used. Depending on the migration, this command alone does not make assertions as to the integrity of your data during migration.

Writing Tests¶

To add tests covering all drivers, update the base test class in test_backend.py.

Note

The structure of backend testing is in transition, migrating from having all classes in a single file (test_backend.py) to one where there is a directory structure to reduce the size of the test files. See:

keystone.tests.unit.backend.role

keystone.tests.unit.backend.domain_config

To add new drivers, subclass the test_backend.py (look towards test_backend_sql.py or test_backend_kvs.py for examples) and update the configuration of the test class in setUp().

Further Testing¶

devstack is the best way to quickly deploy Keystone with the rest of the OpenStack universe and should be critical step in your development workflow!

You may also be interested in either the OpenStack Continuous Integration Infrastructure or the OpenStack Integration Testing Project.

LDAP Tests¶

LDAP has a fake backend that performs rudimentary operations. If you are building more significant LDAP functionality, you should test against a live LDAP server. Devstack has an option to set up a directory server for Keystone to use. Add ldap to the ENABLED_SERVICES environment variable, and set environment variables KEYSTONE_IDENTITY_BACKEND=ldap and KEYSTONE_CLEAR_LDAP=yes in your localrc file.

The unit tests can be run against a live server with keystone/tests/unit/test_ldap_livetest.py and keystone/tests/unit/test_ldap_pool_livetest.py. The default password is test but if you have installed devstack with a different LDAP password, modify the file keystone/tests/unit/config_files/backend_liveldap.conf and keystone/tests/unit/config_files/backend_pool_liveldap.conf to reflect your password.

Note

To run the live tests you need to set the environment variable ENABLE_LDAP_LIVE_TEST to a non-negative value.

“Work in progress” Tests¶

Work in progress (WIP) tests are very useful in a variety of situations including:

During a TDD process they can be used to add tests to a review while they are not yet working and will not cause test failures. (They should be removed before the final merge.)
Often bug reports include small snippets of code to show broken behaviors. Some of these can be converted into WIP tests that can later be worked on by a developer. This allows us to take code that can be used to catch bug regressions and commit it before any code is written.

The keystone.tests.unit.utils.wip() decorator can be used to mark a test as WIP. A WIP test will always be run. If the test fails then a TestSkipped exception is raised because we expect the test to fail. We do not pass the test in this case so that it doesn’t count toward the number of successfully run tests. If the test passes an AssertionError exception is raised so that the developer knows they made the test pass. This is a reminder to remove the decorator.

The wip() decorator requires that the author provides a message. This message is important because it will tell other developers why this test is marked as a work in progress. Reviewers will require that these messages are descriptive and accurate.

Note

The wip() decorator is not a replacement for skipping tests.

@wip('waiting on bug #000000')
def test():
    pass

Note

Another strategy is to not use the wip decorator and instead show how the code currently incorrectly works. Which strategy is chosen is up to the developer.

Developing `doctor` checks¶

As noted in the section above, keystone’s management CLI provides various tools for administrating OpenStack Identity. One of those tools is called keystone-manage doctor and it is responsible for performing health checks about the deployment. If keystone-manage doctor detects a symptom, it will provide the operator with suggestions to improve the overall health of the deployment. This section is dedicated to documenting how to write symptoms for doctor.

The doctor tool consists of a list of symptoms. Each symptom is something that we can check against, and provide a warning for if we detect a misconfiguration. The doctor module is located in keystone.cmd.doctor. The current checks are based heavily on inspecting configuration values. As a result, many of the submodules within the doctor module are named after the configuration section for the symptoms they check. For example, if we want to ensure the keystone.conf [DEFAULT] max_token_size option is properly configured for whatever keystone.conf [token] provider is set to, we can place that symptom in a module called keystone.cmd.doctor.tokens. The symptom will be loaded by importing the doctor module, which is done when keystone-manage doctor is invoked from the command line. When adding new symptoms, it’s important to remember to add new modules to the SYMPTOM_MODULES list in keystone.cmd.doctor.__init__. Doing that will ensure doctor discovers properly named symptoms when executed.

Now that we know symptoms are organized according to configuration sections, and how to add them, how exactly do we write a new symptom? doctor will automatically discover new symptoms by inspecting the methods of each symptom module (i.e. SYMPTOM_MODULES). If a method declaration starts with def symptom_ it is considered a symptom that doctor should check for, and it should be run. The naming of the symptom, or method name, is extremely important since doctor will use it to describe what it’s doing to whoever runs doctor. In addition to a well named method, we also need to provide a complete documentation string for the method. If doctor detects a symptom, it will use the method’s documentation string as feedback to the operator. It should describe why the check is being done, why it was triggered, and possible solutions to cure the symptom. For examples of this, see the existing symptoms in any of doctor‘s symptom modules.

The last step is evaluating the logic within the symptom. As previously stated, doctor will check for a symptom if methods within specific symptom modules make a specific naming convention. In order for doctor to suggest feedback, it needs to know whether or not the symptom is actually present. We accomplish this by making all symptoms return True when a symptom is present. When a symptom evaluates to False, doctor will move along to the next symptom in the list since. If the deployment isn’t suffering for a specific symptom, doctor should not suggest any actions related to that symptom (i.e. if you have your cholesterol under control, why would a physician recommend cholesterol medication if you don’t need it).

To summarize:

Symptoms should live in modules named according to the most relevant configuration section they apply to. This ensure we keep our symptoms organized, grouped, and easy to find.
When writing symptoms for a new section, remember to add the module name to the SYMPTOM_MODULES list in keystone.cmd.doctor.__init__.
Remember to use a good name for the symptom method signature and to prepend it with symptom_ in order for it to be discovered automatically by doctor.
Symptoms have to evaluate to True in order to provide feedback to operators.
Symptoms should have very thorough documentation strings that describe the symptom, side-effects of the symptom, and ways to remedy it.

For examples, feel free to run doctor locally using keystone-manage and inspect the existing symptoms.

Database Migrations¶

Starting with Newton, keystone supports upgrading both with and without downtime. In order to support this, there are three separate migration repositories (all under keystone/common/sql/) that match the three phases of an upgrade (schema expansion, data migration, and schema contraction):

expand_repo: For additive schema modifications and triggers to ensure data is kept in sync between the old and new schema until the point when there are no keystone instances running old code.
data_migration_repo: To ensure new tables/columns are fully populated with data from the old schema.
contract_repo: Run after all old code versions have been upgraded to running the new code, so remove any old schema columns/tables that are not used by the new version of the code. Drop any triggers added in the expand phase.

All migrations are required to have a migration script in each of these repos, each with the same version number (which is indicated by the first three digits of the name of the script, e.g. 003_add_X_table.py). If there is no work to do in a specific phase, then include a no-op migration to simply pass (in fact the 001 migration in each of these repositories is a no-op migration, so that can be used as a template).

Note

Since rolling upgrade support was added part way through the Newton cycle, some migrations had already been added to the legacy repository (keystone/common/sql/migrate_repo). This repository is now closed and no new migrations should be added (except for backporting of previous placeholders).

In order to support rolling upgrades, where two releases of keystone briefly operate side-by-side using the same database without downtime, each phase of the migration must adhere to following constraints:

These triggers should be removed in the contract phase. There are further restrictions as to what can and cannot be included in migration scripts in each phase:

Expand phase:

Only additive schema changes are allowed, such as new columns, tables, indices, and triggers.

Data insertion, modification, and removal is not allowed.

Triggers must be created to keep data in sync between the previous release and the next release. Data written by the previous release must be readable by both the previous release and the next release. Data written by the next release must be readable by both the next release and the previous release.

In cases it is not possible for triggers to maintain data integrity across multiple schemas, writing data should be forbidden using triggers.

Data Migration phase:

Data is allowed to be inserted, updated, and deleted.

No schema changes are allowed.

Contract phase:

Only contractive schema changes are allowed, such as dropping or altering columns, tables, indices, and triggers.

Data insertion, modification, and removal is not allowed.

Triggers created during the expand phase must be dropped.

For more information on writing individual migration scripts refer to SQLAlchemy-migrate.

Filtering responsibilities between controllers and drivers¶

Keystone supports the specification of filtering on list queries as part of the v3 identity API. By default these queries are satisfied in the controller class when a controller calls the wrap_collection method at the end of a list_{entity} method. However, to enable optimum performance, any driver can implement some or all of the specified filters (for example, by adding filtering to the generated SQL statements to generate the list).

The communication of the filter details between the controller level and its drivers is handled by the passing of a reference to a Hints object, which is a list of dicts describing the filters. A driver that satisfies a filter must delete the filter from the Hints object so that when it is returned to the controller level, it knows to only execute any unsatisfied filters.

The contract for a driver for list_{entity} methods is therefore:

It MUST return a list of entities of the specified type
It MAY either just return all such entities, or alternatively reduce the list by filtering for one or more of the specified filters in the passed Hints reference, and removing any such satisfied filters. An exception to this is that for identity drivers that support domains, then they should at least support filtering by domain_id.

Entity list truncation by drivers¶

Keystone supports the ability for a deployment to restrict the number of entries returned from list_{entity} methods, typically to prevent poorly formed searches (e.g. without sufficient filters) from becoming a performance issue.

These limits are set in the configuration file, either for a specific driver or across all drivers. These limits are read at the Manager level and passed into individual drivers as part of the Hints list object. A driver should try and honor any such limit if possible, but if it is unable to do so then it may ignore it (and the truncation of the returned list of entities will happen at the controller level).

Identity entity ID management between controllers and drivers¶

Keystone supports the option of having domain-specific backends for the identity driver (i.e. for user and group storage), allowing, for example, a different LDAP server for each domain. To ensure that Keystone can determine to which backend it should route an API call, starting with Juno, the identity manager will, provided that domain-specific backends are enabled, build on-the-fly a persistent mapping table between Keystone Public IDs that are presented to the controller and the domain that holds the entity, along with whatever local ID is understood by the driver. This hides, for instance, the LDAP specifics of whatever ID is being used.

To ensure backward compatibility, the default configuration of either a single SQL or LDAP backend for Identity will not use the mapping table, meaning that public facing IDs will be the unchanged. If keeping these IDs the same for the default LDAP backend is not required, then setting the configuration variable backward_compatible_ids to False will enable the mapping for the default LDAP driver, hence hiding the LDAP specifics of the IDs being used.

Translated responses¶

The Keystone server can provide error responses translated into the language in the Accept-Language header of the request. In order to test this in your development environment, there’s a couple of things you need to do.

Build the message files. Run the following command in your keystone directory:

$ python setup.py compile_catalog

This will generate .mo files like keystone/locale/[lang]/LC_MESSAGES/[lang].mo

When running Keystone, set the KEYSTONE_LOCALEDIR environment variable to the keystone/locale directory. For example:

$ KEYSTONE_LOCALEDIR=/opt/stack/keystone/keystone/locale uwsgi --http 127.0.0.1:35357 --wsgi-file $(which keystone-wsgi-admin)

Now you can get a translated error response:

$ curl -s -H "Accept-Language: zh" http://localhost:5000/notapath | python -mjson.tool
{
    "error": {
        "code": 404,
        "message": "\u627e\u4e0d\u5230\u8cc7\u6e90\u3002",
        "title": "Not Found"
    }
}

Caching Layer¶

The caching layer is designed to be applied to any manager object within Keystone via the use of the on_arguments decorator provided in the keystone.common.cache module. This decorator leverages dogpile.cache caching system to provide a flexible caching backend.

It is recommended that each of the managers have an independent toggle within the config file to enable caching. The easiest method to utilize the toggle within the configuration file is to define a caching boolean option within that manager’s configuration section (e.g. identity). Once that option is defined you can pass function to the on_arguments decorator with the named argument should_cache_fn. In the keystone.common.cache module, there is a function called should_cache_fn, which will provide a reference, to a function, that will consult the global cache enabled option as well as the specific manager’s caching enable toggle.

Note

If a section-specific boolean option is not defined in the config section specified when calling should_cache_fn, the returned function reference will default to enabling caching for that manager.

Example use of cache and should_cache_fn (in this example, token is the manager):

from keystone.common import cache
SHOULD_CACHE = cache.should_cache_fn('token')

@cache.on_arguments(should_cache_fn=SHOULD_CACHE)
def cacheable_function(arg1, arg2, arg3):
    ...
    return some_value

With the above example, each call to the cacheable_function would check to see if the arguments passed to it matched a currently valid cached item. If the return value was cached, the caching layer would return the cached value; if the return value was not cached, the caching layer would call the function, pass the value to the SHOULD_CACHE function reference, which would then determine if caching was globally enabled and enabled for the token manager. If either caching toggle is disabled, the value is returned but not cached.

It is recommended that each of the managers have an independent configurable time-to-live (TTL). If a configurable TTL has been defined for the manager configuration section, it is possible to pass it to the cache.on_arguments decorator with the named-argument expiration_time. For consistency, it is recommended that this option be called cache_time and default to None. If the expiration_time argument passed to the decorator is set to None, the expiration time will be set to the global default (expiration_time option in the [cache] configuration section.

Example of using a section specific cache_time (in this example, identity is the manager):

from keystone.common import cache
SHOULD_CACHE = cache.should_cache_fn('identity')

@cache.on_arguments(should_cache_fn=SHOULD_CACHE,
                    expiration_time=CONF.identity.cache_time)
def cachable_function(arg1, arg2, arg3):
    ...
    return some_value

For cache invalidation, the on_arguments decorator will add an invalidate method (attribute) to your decorated function. To invalidate the cache, you pass the same arguments to the invalidate method as you would the normal function.

Example (using the above cacheable_function):

def invalidate_cache(arg1, arg2, arg3):
    cacheable_function.invalidate(arg1, arg2, arg3)

Warning

The on_arguments decorator does not accept keyword-arguments/named arguments. An exception will be raised if keyword arguments are passed to a caching-decorated function.

Note

In all cases methods work the same as functions except if you are attempting to invalidate the cache on a decorated bound-method, you need to pass self to the invalidate method as the first argument before the arguments.

dogpile.cache based Key-Value-Store (KVS)¶

The dogpile.cache based KVS system has been designed to allow for flexible stores for the backend of the KVS system. The implementation allows for the use of any normal dogpile.cache cache backends to be used as a store. All interfacing to the KVS system happens via the KeyValueStore object located at keystone.common.kvs.KeyValueStore.

To utilize the KVS system an instantiation of the KeyValueStore class is needed. To acquire a KeyValueStore instantiation use the keystone.common.kvs.get_key_value_store factory function. This factory will either create a new KeyValueStore object or retrieve the already instantiated KeyValueStore object by the name passed as an argument. The object must be configured before use. The KVS object will only be retrievable with the get_key_value_store function while there is an active reference outside of the registry. Once all references have been removed the object is gone (the registry uses a weakref to match the object to the name).

Example Instantiation and Configuration:

kvs_store = kvs.get_key_value_store('TestKVSRegion')
kvs_store.configure('openstack.kvs.Memory', ...)

Any keyword arguments passed to the configure method that are not defined as part of the KeyValueStore object configuration are passed to the backend for further configuration (e.g. memcached servers, lock_timeout, etc).

The memcached backend uses the Keystone manager mechanism to support the use of any of the provided memcached backends (bmemcached, pylibmc, and basic memcached). By default the memcached backend is used. Currently the Memcache URLs come from the servers option in the [memcache] configuration section of the Keystone config.

The following is an example showing how to configure the KVS system to use a KeyValueStore object named “TestKVSRegion” and a specific Memcached driver:

kvs_store = kvs.get_key_value_store('TestKVSRegion')
kvs_store.configure('openstack.kvs.Memcached', memcached_backend='Memcached')

The memcached backend supports a mechanism to supply an explicit TTL (in seconds) to all keys set via the KVS object. This is accomplished by passing the argument memcached_expire_time as a keyword argument to the configure method. Passing the memcache_expire_time argument will cause the time argument to be added to all set and set_multi calls performed by the memcached client. memcached_expire_time is an argument exclusive to the memcached dogpile backend, and will be ignored if passed to another backend:

kvs_store.configure('openstack.kvs.Memcached', memcached_backend='Memcached',
                    memcached_expire_time=86400)

If an explicit TTL is configured via the memcached_expire_time argument, it is possible to exempt specific keys from receiving the TTL by passing the argument no_expiry_keys (list) as a keyword argument to the configure method. no_expiry_keys should be supported by all OpenStack-specific dogpile backends (memcached) that have the ability to set an explicit TTL:

kvs_store.configure('openstack.kvs.Memcached', memcached_backend='Memcached',
                memcached_expire_time=86400, no_expiry_keys=['key', 'second_key', ...])

Note

For the non-expiring keys functionality to work, the backend must support the ability for the region to set the key_mangler on it and have the attribute raw_no_expiry_keys. In most cases, support for setting the key_mangler on the backend is handled by allowing the region object to set the key_mangler attribute on the backend.

The raw_no_expiry_keys attribute is expected to be used to hold the values of the keyword argument no_expiry_keys prior to hashing. It is the responsibility of the backend to use these raw values to determine if a key should be exempt from expiring and not set the TTL on the non-expiring keys when the set or set_multi methods are called.

Typically the key will be hashed by the region using its key_mangler method before being passed to the backend to set the value in the KeyValueStore. This means that in most cases, the backend will need to either pre-compute the hashed versions of the keys (when the key_mangler is set) and store a cached copy, or hash each item in the raw_no_expiry_keys attribute on each call to .set() and .set_multi(). The memcached backend handles this hashing and caching of the keys by utilizing an @property method for the .key_mangler attribute on the backend and utilizing the associated .settr() method to front-load the hashing work at attribute set time.

Once a KVS object has been instantiated the method of interacting is the same as most memcache implementations:

kvs_store = kvs.get_key_value_store('TestKVSRegion')
kvs_store.configure(...)
# Set a Value
kvs_store.set(<Key>, <Value>)
# Retrieve a value:
retrieved_value = kvs_store.get(<key>)
# Delete a key/value pair:
kvs_store.delete(<key>)
# multi-get:
kvs_store.get_multi([<key>, <key>, ...])
# multi-set:
kvs_store.set_multi(dict(<key>=<value>, <key>=<value>, ...))
# multi-delete
kvs_store.delete_multi([<key>, <key>, ...])

There is a global configuration option to be aware of (that can be set in the [kvs] section of the Keystone configuration file): enable_key_mangler can be set top false, disabling the use of key_manglers (modification of the key when saving to the backend to help prevent collisions or exceeding key size limits with memcached).

Note

The enable_key_mangler option in the [kvs] section of the Keystone configuration file is not the same option (and does not affect the cache-layer key manglers) from the option in the [cache] section of the configuration file. Similarly the [cache] section options relating to key manglers has no bearing on the [kvs] objects.

Warning

Setting the enable_key_mangler option to False can have detrimental effects on the KeyValueStore backend. It is recommended that this value is not set to False except for debugging issues with the dogpile.cache backend itself.

Any backends that are to be used with the KeyValueStore system need to be registered with dogpile. For in-tree/provided backends, the registration should occur in keystone/common/kvs/__init__.py. For backends that are developed out of tree, the location should be added to the backends option in the [kvs] section of the Keystone configuration:

[kvs]
backends = backend_module1.backend_class1,backend_module2.backend_class2

All registered backends will receive the “short name” of “openstack.kvs.<class name>” for use in the configure method on the KeyValueStore object. The <class name> of a backend must be globally unique.

dogpile.cache based MongoDB (NoSQL) backend¶

The dogpile.cache based MongoDB backend implementation allows for various MongoDB configurations, e.g., standalone, a replica set, sharded replicas, with or without SSL, use of TTL type collections, etc.

Example of typical configuration for MongoDB backend:

from dogpile.cache import region

arguments = {
    'db_hosts': 'localhost:27017',
    'db_name': 'ks_cache',
    'cache_collection': 'cache',
    'username': 'test_user',
    'password': 'test_password',

    # optional arguments
    'son_manipulator': 'my_son_manipulator_impl'
}

region.make_region().configure('keystone.cache.mongo',
                               arguments=arguments)

The optional son_manipulator is used to manipulate custom data type while its saved in or retrieved from MongoDB. If the dogpile cached values contain built-in data types and no custom classes, then the provided implementation class is sufficient. For further details, refer http://api.mongodb.org/python/current/examples/custom_type.html#automatic-encoding-and-decoding

Similar to other backends, this backend can be added via Keystone configuration in keystone.conf:

[cache]
# Global cache functionality toggle.
enabled = True

# Referring to specific cache backend
backend = keystone.cache.mongo

# Backend specific configuration arguments
backend_argument = db_hosts:localhost:27017
backend_argument = db_name:ks_cache
backend_argument = cache_collection:cache
backend_argument = username:test_user
backend_argument = password:test_password

This backend is registered in keystone.common.cache.core module. So, its usage is similar to other dogpile caching backends as it implements the same dogpile APIs.

OpenStack

Best Practices¶

Setting up Keystone¶

Configuring Keystone¶

Running Keystone¶

Initializing Keystone¶

Initial Sample Data¶

Interacting with Keystone¶

Building the Documentation¶

Generating a new Sample Config File¶

Release Notes¶

Testing Keystone¶

Running Tests¶

Running with PDB¶

Disabling Stream Capture¶

Test Structure¶

Testing Schema Migrations¶

Writing Tests¶

Further Testing¶

LDAP Tests¶

“Work in progress” Tests¶

Developing `doctor` checks¶

Database Migrations¶

Filtering responsibilities between controllers and drivers¶

Entity list truncation by drivers¶

Identity entity ID management between controllers and drivers¶

Translated responses¶

Caching Layer¶

dogpile.cache based Key-Value-Store (KVS)¶

dogpile.cache based MongoDB (NoSQL) backend¶

Table Of Contents

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Project Source

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OpenStack

Best Practices¶

Setting up Keystone¶

Configuring Keystone¶

Running Keystone¶

Initializing Keystone¶

Initial Sample Data¶

Interacting with Keystone¶

Building the Documentation¶

Generating a new Sample Config File¶

Release Notes¶

Testing Keystone¶

Running Tests¶

Running with PDB¶

Disabling Stream Capture¶

Test Structure¶

Testing Schema Migrations¶

Writing Tests¶

Further Testing¶

LDAP Tests¶

“Work in progress” Tests¶

Developing doctor checks¶

Database Migrations¶

Filtering responsibilities between controllers and drivers¶

Entity list truncation by drivers¶

Identity entity ID management between controllers and drivers¶

Translated responses¶

Caching Layer¶

dogpile.cache based Key-Value-Store (KVS)¶

dogpile.cache based MongoDB (NoSQL) backend¶

Table Of Contents

Previous topic

Next topic

Project Source

This Page

Quick search

Developing `doctor` checks¶