Tools for creating and managing independent chunks of data.
This extension provides a base entity for storing arbitrary data. It can be used in a number of cases, especially, if you don't want yet to create a brand new model, database migrations and tables, but you have no other options.
ckanext-flakes gives you a set of actions for creating and managing small
dictionary-like objects(anything, that can be serialized into JSON). If you are
using it and want to add an extra action, feel free to create a PR or an issue
with your suggestion.
Scenario: user needs a todo list
Flakes created by any user are visible only to this user so flakes can be used as a storage for private data.
Flakes can have extras, that plays a role of tags. extras represented by a
dictionary and whenever user lists his flakes, he has an option to see only
flakes that contains particular data inside extras.
flake_create = tk.get_action("flakes_flake_create")
flake_list = tk.get_action("flakes_flake_create")
# create an urgent taks
flake_create(
{"user": "john"},
{"data": {"task": "feed the cat"}, "extras": {"when": "today", "type": "task"}}
)
# create a couple of tasks that can wait
flake_create(
{"user": "john"},
{"data": {"task": "buy food"}, "extras": {"when": "tomorrow", "type": "task"}}
)
flake_create(
{"user": "john"},
{"data": {"task": "update documentation"}, "extras": {"when": "tomorrow", "type": "task"}}
)
# list all the tasks
flake_list(
{"user": "john"},
{"extras": {"type": "task"}}
)
# list all the urgent tasks
flake_list(
{"user": "john"},
{"extras": {"type": "task", "when": "today"}}
)
# list all the tasks for tomorrow
flake_list(
{"user": "john"},
{"extras": {"type": "task", "when": "tomorrow"}}
)
Scenario: each user can set a theme of application and this theme will be applied only for the current user
Flakes are created for the user from the context. Flakes of the user A are
visible only to the user A, flakes of the user B exist in the different
namespace and are visible only to the user B.
Each flake can have a name. Name must be unique among the flakes of the user. But different users can use the same names for their flakes, because every user has its own namespace for flakes.
Flakes can be created either via flakes_flake_create action(accepts
optional name and raises exception if name is not unique) or
flakes_flake_override(requires a name and creates a new flake if name is not
taken or updates existing flake if name already used by some flake)
In order to get the flake use flakes_flake_show with the id of the flake or
flakes_flake_lookup with the name.
# set a theme for John
tk.get_action("flakes_flake_override")(
{"user": "john"},
{"name": "application:theme", "data": {"theme": "dark"}}
)
# set a theme for Mary
tk.get_action("flakes_flake_override")(
{"user": "mary"},
{"name": "application:theme", "data": {"theme": "light"}}
)
# get the value from the flake
john_theme = tk.get_action("flakes_flake_lookup")(
{"user": "john"},
{"name": "application:theme"}
)["data"]["theme"]
mary_theme = tk.get_action("flakes_flake_lookup")(
{"user": "mary"},
{"name": "application:theme"}
)["data"]["theme"]
assert john_theme == "dark"
assert mary_theme == "light"
Scenario: application requires global option, that can be changed in runtime
By default flakes are created in the "namespace" of the current user. Only the author can see and modify his own flakes.
Global values should not be owned by someone, so here we need "unowned" flake -
the flake that is not connected to the particular user. Only sysadmin can
create such flakes, so we are going to use ignore_auth=True attribute of the
context.
We'll use flakes_flake_override action, that accepts a name of the flake
and either updates existing flakes with this name or creates a new one if this
name is free. In this way we'll avoid duplicates of the global flake.
# create a flake
tk.get_action("flakes_flake_override")(
{"ignore_auth": True}, # only syadmin allowed to create unowned flakes with empty author id
{"name": "global:config:value", "data": {"value": 1}, "author_id": None}
)
# get the value from the flake
value = tk.get_action("flakes_flake_lookup")(
{"ignore_auth": True},
{"name": "global:config:value", "author_id": None}
)["data"]["value"]
These plugins implement basic features that can be used as a real-life example
of ckanext-flakes usage.
flakes_ratingUser can rate a package via API action. Add
ckanext.flakes_rating.show_package_widget = true to the config and default
widget will be added to the sidebar on dataset.read page.
flakes_feedbackUser can leave a feedback for package via API action. Add
ckanext.flakes_feedback.enable_views = true to the config and default page
will be added to navigation tabs on dataset.read page.
Requires python v3.7 or greater. Python v2 support doesn't require much effort, but it neither worth the time you'll spend on it.
Compatibility with core CKAN versions:
| CKAN version | Compatible? |
|---|---|
| 2.9 | yes |
| 2.10 | yes |
To install ckanext-flakes:
pip install ckanext-flakes
flakes to the ckan.plugins setting in your CKAN config file.ckan db upgrade -p flakes
# Any user can create a new flake.
# (optional, default: true)
ckanext.flakes.creation.allowed = false
# Any user can validate flake or plain data.
# (optional, default: false)
ckanext.flakes.validation.allowed = true
Provides ckanext.flakes.interfaces.IFlakes interface. Always use
inherit=True when implementing it, because it may change in the future.
Currently it provides the following hooks:
class IFlakes(Interface):
"""Extend functionality of ckanext-flakes"""
def get_flake_schemas(self) -> dict[str, dict[str, Any]]:
"""Register named validation schemas.
Used by `flakes_flake_validate` and `flakes_data_validate` actions.
Returns:
Mapping of names and corresponding validation schemas.
Example:
def get_flake_schemas(self) -> dict[str, dict[str, Any]]:
return {
"schema-that-requires-name": {"name": [not_missing]}
}
"""
return {}
def get_flake_factories(self) -> dict[str, Callable[[dict[str, Any]], dict[str, Any]]]:
"""Register named example factories.
Used by `flakes_data_example` action.
Returns:
Mapping of names and corresponding example factories.
Example:
def get_flake_factories(self) -> dict[str, dict[str, Any]]:
def factory(payload: dict[str, Any]):
return {"field": "value"}
return {
"test-factory": factory
}
"""
return {}
flakes_flake_createCreate a flake.
Args:
name (str, optional): name of the flake
data (dict): flake's data
parent_id (str, optional): ID of flake to extend
author_id (str, optional): author ID(can be set only by sysadmin)
extras (dict): flake's extra details
flakes_flake_showDisplay existing flake
Args:
id (str): ID of flake to display
expand (bool, optional): Extend flake using data from the parent flakes
flakes_flake_listDisplay all flakes of the user.
If extras dictionary passed, show only flakes that contains given extras. Example:
first_flake = Flake(extras={"xxx": {"yyy": "hello"}})
second_flake = Flake(extras={"xxx": {"yyy": "world"}})
flake_list(context, {"extras": {"xxx": {"yyy": "hello"}})
>>> first_flake
Args:
expand (bool, optional): Extend flake using data from the parent flakes
extras (dict, optional): Show only flakes whose extras contains passed dict
author_id (str, optional): author ID(can be set only by sysadmin)
flakes_flake_updateUpdate existing flake
Args:
id (str): ID of flake to update
data (dict): flake's data
parent_id (str, optional): ID of flake to extend
extras (dict): flake's extra details
flakes_flake_overrideUpdate existing flake by name or create a new one.
Args:
name (str): Name flake to override
data (dict): template itself
parent_id (str, optional): ID of flake to extend
author_id (str, optional): author ID(can be set only by sysadmin if flake does not exist)
extras (dict): flake's extra details
flakes_flake_deleteDelete existing flake
Args:
id (str): ID of flake to delete
flakes_flake_lookupDisplay flake using its name.
Args:
name (str): Name of the flake
expand (bool, optional): Extend flake using data from the parent flakes
author_id (str, optional): author ID(can be set only by sysadmin)
flakes_flake_validateValidate existing flake
Schemas must be registered via IFlakes interface.
Args:
id (str): ID of flake to validate
expand (bool, optional): Extend flake using data from the parent flakes
schema(str): validation schema for the flake's data
flakes_data_validateValidate arbitrary data against the named schema(registered via IFlakes).
Args:
data (dict): data that needs to be validated
schema(str): validation schema for the data
flakes_data_exampleGenerate an example of the flake's data using named factory(registered via IFlakes).
Factories must be registered via IFlakes interface.
Args:
factory(str): example factory
data (dict, optional): payload for the example factory
flakes_flake_materializeSend flake's data to API action.
Args:
id (str): ID of flake to materialize
expand (bool, optional): Extend flake using data from the parent flakes
remove (bool, optional): Remove flake after materialization
action (str): API action to use for materialization
flakes_flake_combineCombine data from multiple flakes
id argument specifies all the flakes that must be combined. All of the flakes
must exist, otherwise NotFound error raised. IDs at the start of the list have
higher priority(override matching keys). IDs at the end of the list have lower
priority(can be shadowed by former flakes).
expand must be a dict[str, bool]. Keys are IDs of the flakes, values are
expand flags for the corresponding flake.
Args:
id (list): IDs of flakes.
expand (dict, optional): Extend flake using data from the parent flakes
flakes_flake_mergeCombine multiple flakes and save the result.
Args:
id (list): IDs of flakes.
expand (dict, optional): Extend flake using data from the parent flakes
remove (bool, optional): Remove flakes after the operation.
destination (str, optional): Save data into the specified flake instead of a new one
flakes_data_patchPartially overrides data leaving other fields intact.
Args:
id (str): ID of flake
data (dict): patch for data
flakes_extras_patchPartially overrides extras leaving other fields intact.
Args:
id (str): ID of flake
extras (dict): patch for extras
To install ckanext-flakes for development, activate your CKAN virtualenv and do:
git clone https://github.com/DataShades/ckanext-flakes.git
cd ckanext-flakes
python setup.py develop
To run the tests, do:
pytest
FAQs
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