Python API 

Contents

Python API

API for writing service templates 

TOSCA Field Specifiers

The follow are functions that are used as field specified when declaring attributes on TOSCA type. Use these if you need to specify TOSCA specific information about the field or if the TOSCA field type can’t be inferred from the Python’s attribute’s type. For example:

class MyNodeType(tosca.nodes.Root):
    a_tosca_property: str = Property(name="a-tosca-property", default=None, metadata={"foo": "bar"})

Note that these functions all take keyword-only parameters (this is needed for IDE integration).

tosca.Artifact(*, default=<dataclasses._MISSING_TYPE object>, factory=<dataclasses._MISSING_TYPE object>, name='', metadata=None)

Field specifier for declaring a TOSCA artifact.

Parameters

default (Any, optional) – Default value. Set to None if the artifact isn’t required. Defaults to MISSING.
factory (Callable, optional) – Factory function to initialize the artifact with a unique value per template. Defaults to MISSING.
name (str, optional) – TOSCA name of the field, overrides the artifact’s name when generating YAML. Defaults to “”.
metadata (Dict[str, str], optional) – Dictionary of metadata to associate with the artifact.

Return type

Any

tosca.Attribute(*, default=None, factory=<dataclasses._MISSING_TYPE object>, name='', constraints=None, metadata=None, title='', status='', options=None, init=False)

Field specifier for declaring a TOSCA attribute.

Parameters

default (Any, optional) – Default value. Set to None if the attribute isn’t required. Defaults to MISSING.
factory (Callable, optional) – Factory function to initialize the attribute with a unique value per template. Defaults to MISSING.
name (str, optional) – TOSCA name of the field, overrides the attribute’s name when generating YAML. Defaults to “”.
metadata (Dict[str, str], optional) – Dictionary of metadata to associate with the attribute.
constraints (List[DataConstraints], optional) – List of TOSCA property constraints to apply to the attribute.
status (str, optional) – TOSCA status of the attribute.
options (AttributeOptions, optional) – Typed metadata to apply.
init (Literal[False]) –

Return type

Any

tosca.Capability(*, default=<dataclasses._MISSING_TYPE object>, factory=<dataclasses._MISSING_TYPE object>, name='', metadata=None, valid_source_types=None)

Field specifier for declaring a TOSCA capability.

Parameters

default (Any, optional) – Default value. Set to None if the capability isn’t required. Defaults to MISSING.
factory (Callable, optional) – Factory function to initialize the capability with a unique value per template. Defaults to MISSING.
name (str, optional) – TOSCA name of the field, overrides the capability’s name when generating YAML. Defaults to “”.
metadata (Dict[str, str], optional) – Dictionary of metadata to associate with the capability.
valid_source_types (List[str], optional) – List of TOSCA type names to set as the capability’s valid_source_types

Return type

Any

tosca.Property(*, default=<dataclasses._MISSING_TYPE object>, factory=<dataclasses._MISSING_TYPE object>, name='', constraints=None, metadata=None, title='', status='', options=None, attribute=False)

Field specifier for declaring a TOSCA property.

Parameters

default (Any, optional) – Default value. Set to None if the property isn’t required. Defaults to MISSING.
factory (Callable, optional) – Factory function to initialize the property with a unique value per template. Defaults to MISSING.
name (str, optional) – TOSCA name of the field, overrides the property’s name when generating YAML. Defaults to “”.
metadata (Dict[str, str], optional) – Dictionary of metadata to associate with the property.
constraints (List[DataConstraints], optional) – List of TOSCA property constraints to apply to the property.
status (str, optional) – TOSCA status of the property.
options (PropertyOptions, optional) – Typed metadata to apply.
attribute (bool, optional) – Indicate that the property is also a TOSCA attribute. Defaults to False.
title (str) –

Return type

Any

tosca.Requirement(*, default=<dataclasses._MISSING_TYPE object>, factory=<dataclasses._MISSING_TYPE object>, name='', metadata=None, node_filter=None)

Field specifier for declaring a TOSCA requirement.

Parameters

default (Any, optional) – Default value. Set to None if the requirement isn’t required. Defaults to MISSING.
factory (Callable, optional) – Factory function to initialize the requirement with a unique value per template. Defaults to MISSING.
name (str, optional) – TOSCA name of the field, overrides the requirement’s name when generating YAML. Defaults to “”.
metadata (Dict[str, str], optional) – Dictionary of metadata to associate with the requirement.
node_filter (Dict[str, Any], optional) – The TOSCA node_filter for this requirement.

Return type

Any

tosca.operation(name='', apply_to=(), timeout=None, operation_host=None, environment=None, dependencies=None, outputs=None, entry_state=None, invoke=None)

Function decorator that marks a function or methods as a TOSCA operation.

Parameters

name (str, optional) – Name of the TOSCA operation. Defaults to the name of the method.
apply_to (Sequence[str], optional) – List of TOSCA operations to apply this method to. If omitted, match by the operation name.
timeout (float, optional) – Timeout for the operation (in seconds). Defaults to None.
operation_host (str, optional) – The name of host where this operation will be executed. Defaults to None.
environment (Dict[str, str], optional) – A map of environment variables to use while executing the operation. Defaults to None.
dependencies (List[Union[str, Dict[str, Any]]], optional) – List of artifacts this operation depends on. Defaults to None.
outputs (Dict[str, str], optional) – TOSCA outputs mapping. Defaults to None.
entry_state (str, optional) – Node state required to invoke this operation. Defaults to None.
invoke (str, optional) – Name of operation to delegate this operation to. Defaults to None.

Return type

Callable

This example marks a method a implementing the create and delete operations on the Standard TOSCA interface.

@operation(apply_to=["Standard.create", "Standard.delete"])
def default(self):
    return self.my_artifact.execute()

API for writing configurators 

class unfurl.configurator.Configurator(*args, **kw)

Base class for implementing Configurators. Subclasses should at least implement a run(), for example:

class MinimalConfigurator(Configurator):
    def run(self, task):
        assert self.can_run(task)
        return Status.ok

Parameters: args (tosca._tosca.ToscaInputs) –
Return type: None

attribute_output_metadata_key: Optional[str] = None

can_dry_run(task)

Returns whether this configurator can handle a dry-runs for the given task. (And should check TaskView.dry_run in during run().

Parameters: task (TaskView) – The task about to be run.
Returns: bool
Return type: bool

can_run(task)

Return whether or not the configurator can execute the given task depending on if this configurator support the requested action and parameters and given the current state of the target instance?

Parameters: task (TaskView) – The task that is about to be run.
Returns: Should return True or a message describing why the task couldn’t be run.
Return type: (bool or str)

check_digest(task, changeset)

Examine the previous ChangeRecord generated by the previous time this operation was performed on the target instance and return whether it should be rerun or not.

The default implementation recalculates the digest of input parameters that were accessed in the previous run.

Parameters

task (TaskView) – The task that might execute this operation.
changeset (ChangeRecordRecord) – The task that might execute this operation.

Returns

True if configuration’s digest has changed, False if it is the same.

Return type

bool

exclude_from_digest: Tuple[str, ...] = ()

classmethod get_dry_run(inputs, template)

Parameters: template (unfurl.spec.EntitySpec) –
Return type: bool

get_generator(task)

Parameters: task (unfurl.configurator.TaskView) –
Return type: Generator

render(task)

This method is called during the planning phase to give the configurator an opportunity to do early validation and error detection and generate any plan information or configuration files that the user may want to review before the running the deployment task.

Property access and writes will be tracked and used to establish dynamic dependencies between instances so the plan can be ordered properly. Any updates made to instances maybe reverted if it has dependencies on attributes that might be changed later in the plan, so this method should be idempotent.

Returns: The value returned here will subsequently be available as task.rendered
Parameters: task (unfurl.configurator.TaskView) –
Return type: Any

run(task)

Subclasses of Configurator need to implement this method. It should perform the operation specified in the ConfigurationSpec on the task.target. It can be either a generator that yields one or more JobRequest or TaskRequest or a regular function.

Parameters: task (TaskView) – The task currently running.
Yields: Optionally run can yield either a JobRequest, TaskRequest to run subtasks and finally a ConfiguratorResult when done
Returns: If run is not defined as a generator it must return either a Status, a bool or a ConfiguratorResult to indicate if the task succeeded and any changes to the target’s state.
Return type: Union[Generator, unfurl.configurator.ConfiguratorResult, unfurl.support.Status, bool]

save_digest(task)

Generate a compact, deterministic representation of the current configuration. This is saved in the job log and used by check_digest in subsequent jobs to determine if the configuration changed the operation needs to be re-run.

The default implementation calculates a SHA1 digest of the values of the inputs that where accessed while that task was run, with the exception of the input parameters listed in exclude_from_digest.

Parameters: task (TaskView) –
Returns: A dictionary whose keys are strings that start with “digest”
Return type: dict

classmethod set_config_spec_args(kw, template)

Parameters

kw (dict) –
template (unfurl.spec.EntitySpec) –

Return type

dict

short_name: Optional[str] = None: shortName can be used to customize the “short name” of the configurator as an alternative to using the full name (“module.class”) when setting the implementation on an operation. (Titlecase recommended)

should_run(task)

Does this configuration need to be run?

Parameters: task (unfurl.configurator.TaskView) –
Return type: Union[bool, unfurl.support.Priority]

class unfurl.configurator.ConfiguratorResult(success, modified, status=None, configChanged=None, result=None, outputs=None, exception=None)

Represents the result of a task that ran.

See TaskView.done() for more documentation.

Parameters

success (bool) –
modified (Optional[bool]) –
status (Optional[unfurl.support.Status]) –
configChanged (Optional[bool]) –
result (object) –
outputs (Optional[dict]) –
exception (Optional[unfurl.util.UnfurlTaskError]) –

Return type

None

class unfurl.configurator.JobRequest(resources, errors=None, update=None)

Yield this to run a child job.

Parameters

resources (List[unfurl.runtime.EntityInstance]) –
errors (Optional[Sequence[unfurl.util.UnfurlError]]) –

get_instance_specs()

property name

property root

set_error(msg)

Parameters: msg (str) –

property target

class unfurl.configurator.TaskRequest(configSpec, target, reason, persist=False, required=None, startState=None)

Yield this to run a child task. (see unfurl.configurator.TaskView.create_sub_task())

Parameters

configSpec (unfurl.planrequests.ConfigurationSpec) –
target (unfurl.runtime.EntityInstance) –
reason (str) –
persist (bool) –
required (Optional[bool]) –
startState (Optional[unfurl.support.NodeState]) –

property completed: bool

bool(x) -> bool

Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.

finish_workflow()

Return type: None

get_operation_artifacts()

property name

reassign_final_for_workflow()

Return type: Optional[unfurl.planrequests.TaskRequest]

class unfurl.configurator.TaskView(manifest, configSpec, target, reason=None, dependencies=None)

The interface presented to configurators.

The following public attributes are available:

Parameters

manifest (Manifest) –
configSpec (unfurl.planrequests.ConfigurationSpec) –
target (unfurl.runtime.EntityInstance) –
reason (Optional[str]) –
dependencies (Optional[List[Operational]]) –

Return type

None

target: The instance this task is operating on.

reason

The reason this operation was planned. See Reason

Type: str

cwd

Current working directory

Type: str

dry_run

Dry run only

Type: bool

verbose

Verbosity level set for this job (-1 error, 0 normal, 1 verbose, 2 debug)

Type: int

add_dependency(expr, expected=None, schema=None, name=None, required=True, wantList=False, target=None)

Parameters

expr (Union[str, collections.abc.Mapping]) –
expected (Optional[Union[unfurl.result.ResultsList, list, unfurl.result.Result]]) –
schema (Optional[collections.abc.Mapping]) –
name (Optional[str]) –
required (bool) –
wantList (bool) –
target (Optional[unfurl.runtime.EntityInstance]) –

Return type

unfurl.configurator.Dependency

add_message(message)

Parameters: message (object) –
Return type: None

apply_work_folders(*names)

Parameters: names (str) –
Return type: None

property connections: unfurl.configurator._ConnectionsMap

create_sub_task(operation=None, resource=None, inputs=None, persist=False, required=None)

Create a subtask that will be executed if yielded by unfurl.configurator.Configurator.run()

Parameters

operation (str) – The operation call (like interface.operation)
resource (NodeInstance) –
inputs (Optional[dict]) –
persist (bool) –
required (Optional[bool]) –

Returns

TaskRequest

Return type

Optional[unfurl.planrequests.TaskRequest]

discard_work_folders()

Return type: None

done(success=None, modified=None, status=None, result=None, outputs=None, captureException=None)

unfurl.configurator.Configurator.run() should call this method and return or yield its return value before terminating.

>>> yield task.done(True)

Parameters

success (bool) – indicates if this operation completed without an error.
modified (bool) – (optional) indicates whether the physical instance was modified by this operation.
status (Status) – (optional) should be set if the operation changed the operational status of the target instance. If not specified, the runtime will updated the instance status as needed, based the operation preformed and observed changes to the instance (attributes changed).
result (dict) – (optional) A dictionary that will be serialized as YAML into the changelog, can contain any useful data about these operation.
outputs (dict) – (optional) Operation outputs, as specified in the topology template.
captureException (Optional[object]) –

Returns

ConfiguratorResult

Return type

unfurl.configurator.ConfiguratorResult

property environ: Dict[str, str]

fail_work_folders()

Return type: None

static find_connection(ctx, target, relation='tosca.relationships.ConnectsTo')

Find a relationship that this task can use to connect to the given instance. First look for relationship between the task’s target instance and the given instance. If none is found, see if there a default connection of the given type.

Parameters

target (NodeInstance) – The instance to connect to.
relation (str, optional) – The relationship type. Defaults to tosca.relationships.ConnectsTo.
ctx (unfurl.eval.RefContext) –

Returns

The connection instance.

Return type

RelationshipInstance or None

find_instance(name)

Parameters: name (str) –
Return type: unfurl.runtime.NodeInstance

get_environment(addOnly, env=None)

Return a dictionary of environment variables applicable to this task.

Parameters

addOnly (bool) – If addOnly is False all variables in the current os environment will be included otherwise only variables added will be included.
env (Optional[dict]) –

Returns

dict:

Return type

dict

Variable sources (by order of preference, lowest to highest): 1. The ensemble’s environment 2. Variables set by the connections that are available to this operation. 3. Variables declared in the operation’s environment section.

get_settings()

Return type: dict

get_work_folder(location=None)

Parameters: location (Optional[str]) –
Return type: unfurl.projectpaths.WorkFolder

property inputs: unfurl.result.ResultsMap

Exposes inputs and task settings as expression variables, so they can be accessed like:

eval: $inputs::param

or in jinja2 templates:

query(query, dependency=False, name=None, required=False, wantList=False, resolveExternal=True, strict=True, vars=None, throw=False)

Parameters

query (Union[str, dict]) –
dependency (bool) –
name (Optional[str]) –
required (bool) –
wantList (bool) –
resolveExternal (bool) –
strict (bool) –
vars (Optional[dict]) –
throw (bool) –

Return type

Optional[Union[unfurl.result.ResultsList, unfurl.result.Result, List[unfurl.result.Result]]]

remove_dependency(name)

Parameters: name (str) –
Return type: Optional[unfurl.configurator.Dependency]

restore_envvars()

sensitive(value)

Mark the given value as sensitive. Sensitive values will be encrypted or redacted when outputed.

Returns: A copy of the value converted the appropriate subtype of unfurl.logs.sensitive value or the value itself if it can’t be converted.
Return type: sensitive
Parameters: value (object) –

set_envvars(): Update os.environ with the task’s environ and save the current one so it can be restored by restore_envvars

set_work_folder(location='operation', preserve=None, always_apply=False)

Parameters

location (str) –
preserve (Optional[bool]) –
always_apply (bool) –

Return type

unfurl.projectpaths.WorkFolder

update_instances(instances)

Notify Unfurl of new or changes to instances made while the configurator was running.

Operational status indicates if the instance currently exists or not. This will queue a new child job if needed.

To run the job based on the supplied spec immediately, yield the returned JobRequest.

- name:     aNewInstance
  template: aNodeTemplate
  parent:   HOST
  attributes:
     anAttribute: aValue
  readyState:
    local: ok
    state: state
- name:     SELF
  attributes:
      anAttribute: aNewValue

Parameters: instances (list or str) – Either a list or string that is parsed as YAML.
Return type: Tuple[Optional[unfurl.planrequests.JobRequest], List[unfurl.util.UnfurlTaskError]]

property vars: dict: A dictionary of the same variables that are available to expressions when evaluating inputs.

Internal classes supporting the runtime.

class unfurl.support.NodeState(value)

An enumeration.

configured = 5

configuring = 4

created = 3

creating = 2

deleted = 11

deleting = 10

error = 12

initial = 1

started = 7

starting = 6

stopped = 9

stopping = 8

class unfurl.support.Priority(value)

An enumeration.

critical = 3

ignore = 0

optional = 1

required = 2

class unfurl.support.Reason

add = 'add'

check = 'check'

degraded = 'degraded'

error = 'error'

force = 'force'

missing = 'missing'

prune = 'prune'

reconfigure = 'reconfigure'

run = 'run'

update = 'update'

upgrade = 'upgrade'

class unfurl.support.Status(value)

An enumeration.

absent = 5

property color

degraded = 2

error = 3

ok = 1

pending = 4

unknown = 0

class unfurl.result.ChangeRecord(jobId=None, startTime=None, taskId=0, previousId=None, parse=None)

A ChangeRecord represents a job or task in the change log file. It consists of a change ID and named attributes.

A change ID is an identifier with this sequence of 12 characters: - “A” serves as a format version identifier - 7 alphanumeric characters (0-9, A-Z, and a-z) encoding the date and time the job ran. - 4 hexadecimal digits encoding the task id

Parameters

jobId (Optional[str]) –
startTime (Optional[datetime.datetime]) –
taskId (int) –
previousId (Optional[str]) –
parse (Optional[str]) –

classmethod format_log(changeId, attributes)

format: changeidtkey=valuetkey=value

Parameters

changeId (str) –
attributes (dict) –

Return type

str

log(attributes=None)

changeidtkey=valuetkey=value

Parameters: attributes (Optional[dict]) –
Return type: str

Runtime module

This module defines the core model and implements the runtime operations of the model.

The state of the system is represented as a collection of Instances. Each instance have a status; attributes that describe its state; and a TOSCA template which describes its capabilities, relationships and available interfaces for configuring and interacting with it.

class unfurl.runtime.Operational

This is an abstract base class for Jobs, Resources, and Configurations all have a Status associated with them and all use the same algorithm to compute their status from their dependent resouces, tasks, and configurations

static aggregate_status(statuses, seen)

Returns: ok, degraded, pending or None

If there are no instances, return None If any required are not operational, return pending or error If any other are not operational or degraded, return degraded Otherwise return ok. (Instances with priority set to “ignore” are ignored.)

Parameters

statuses (Iterable[unfurl.runtime.Operational]) –
seen (Dict[int, unfurl.runtime.Operational]) –

Return type

Optional[unfurl.support.Status]

get_operational_dependencies()

Return an iterator of Operational object that this instance depends on to be operational.

Return type: Iterable[unfurl.runtime.Operational]

has_changed(changeset)

Whether or not this object changed since the give ChangeRecord.

Return type: bool

class unfurl.runtime.OperationalInstance(status=None, priority=None, manualOveride=None, lastStateChange=None, lastConfigChange=None, state=None)

A concrete implementation of Operational

Parameters

status (Optional[Union[OperationalInstance, int, str]]) –
priority (Optional[unfurl.support.Priority]) –
manualOveride (Optional[Union[OperationalInstance, int, str]]) –
lastStateChange (Optional[str]) –
lastConfigChange (Optional[str]) –
state (Optional[unfurl.support.NodeState]) –

Return type

None

get_operational_dependencies()

Return an iterator of Operational object that this instance depends on to be operational.

Return type: Iterable[unfurl.runtime.Operational]

property local_status: Optional[unfurl.support.Status]: The local_status property.

property manual_override_status: Optional[unfurl.support.Status]: The manualOverideStatus property.

property priority: Optional[unfurl.support.Priority]: The priority property.

property state: Optional[unfurl.support.NodeState]: The state property.

APIs for controlling Unfurl 

Localenv module

Classes for managing the local environment.

Repositories can optionally be organized into projects that have a local configuration.

By convention, the “home” project defines a localhost instance and adds it to its context.

class unfurl.localenv.LocalEnv(manifestPath=None, homePath=None, parent=None, project=None, can_be_empty=False, override_context=None, overrides=None, readonly=False)

This class represents the local environment that an ensemble runs in, including the local project it is part of and the home project.

Parameters

manifestPath (Optional[str]) –
homePath (Optional[str]) –
parent (Optional[unfurl.localenv.LocalEnv]) –
project (Optional[unfurl.localenv.Project]) –
can_be_empty (bool) –
override_context (Optional[str]) –
overrides (Optional[Dict[str, Any]]) –
readonly (Optional[bool]) –

Return type

None

find_git_repo(repoURL, revision=None)

Parameters

repoURL (str) –
revision (Optional[str]) –

Return type

Optional[unfurl.repo.GitRepo]

find_or_create_working_dir(repoURL, revision=None, basepath=None, checkout_args={})

Parameters

repoURL (str) –
revision (Optional[str]) –
basepath (Optional[str]) –
checkout_args (dict) –

Return type

Tuple[Optional[unfurl.repo.GitRepo], Optional[str], Optional[bool]]

find_path_in_repos(path, importLoader=None)

If the given path is part of the working directory of a git repository return that repository and a path relative to it

Parameters

path (str) –
importLoader (Optional[Any]) –

Return type

Tuple[Optional[unfurl.repo.GitRepo], Optional[str], Optional[str], Optional[bool]]

find_project(testPath, stopPath=None)

Walk parents looking for unfurl.yaml

Parameters

testPath (str) –
stopPath (Optional[str]) –

Return type

Optional[unfurl.localenv.Project]

get_context(context=None)

Return a new context that merges the given context with the local context.

Parameters: context (Optional[dict]) –
Return type: dict

get_external_manifest(location, skip_validation, safe_mode)

Parameters

location (dict) –
skip_validation (bool) –
safe_mode (bool) –

Return type

Optional[YamlManifest]

get_local_instance(name, context)

Parameters

name (str) –
context (dict) –

Return type

Tuple[unfurl.runtime.NodeInstance, dict]

get_manifest(path=None, skip_validation=False, safe_mode=None)

Parameters

path (Optional[str]) –
skip_validation (bool) –
safe_mode (Optional[bool]) –

Return type

YamlManifest

get_paths()

Return a list of directories for $PATH. Includes the directory the unfurl script is installed in and, if asdf is installed, appends a PATH list from the .toolversions found in the current project and the home project.

Return type: List[str]

get_project(path, homeProject)

Parameters

path (str) –
homeProject (Optional[unfurl.localenv.Project]) –

Return type

unfurl.localenv.Project

get_runtime()

Return type: Optional[str]

get_vault()

get_vault_password(vaultId='default')

Parameters: vaultId (str) –
Return type: Optional[str]

link_repo(base_path, name, url, revision)

Parameters

base_path (str) –
name (str) –
url (str) –

Return type

Tuple[str, str]

map_value(val, env_rules)

Evaluate using project home as a base dir.

Parameters

val (Any) –
env_rules (Optional[dict]) –

Return type

Any

parent: Optional[unfurl.localenv.LocalEnv] = None

project: Optional[unfurl.localenv.Project] = None

class unfurl.localenv.Project(path, homeProject=None, overrides=None, readonly=False)

A Unfurl project is a folder that contains at least a local configuration file (unfurl.yaml), one or more ensemble.yaml files which maybe optionally organized into one or more git repositories.

Parameters

path (str) –
homeProject (Optional[Project]) –
overrides (Optional[dict]) –
readonly (Optional[bool]) –

create_working_dir(gitUrl, ref=None)

Parameters

gitUrl (str) –
ref (Optional[str]) –

Return type

unfurl.repo.GitRepo

find_ensemble_by_name(name)

Parameters: name (str) –
Return type: Optional[dict]

find_ensemble_by_path(path)

Parameters: path (str) –
Return type: Optional[dict]

find_git_repo(repoURL, revision=None)

Parameters

repoURL (str) –
revision (Optional[str]) –

Return type

Optional[unfurl.repo.GitRepo]

find_git_repo_from_repository(repoSpec)

Parameters: repoSpec (toscaparser.repositories.Repository) –
Return type: Optional[unfurl.repo.GitRepo]

find_or_clone(repo)

Parameters: repo (unfurl.repo.GitRepo) –
Return type: unfurl.repo.GitRepo

find_or_create_working_dir(repoURL, revision=None)

Parameters

repoURL (str) –
revision (Optional[str]) –

Return type

Optional[unfurl.repo.GitRepo]

static find_path(testPath, stopPath=None)

Walk parents looking for unfurl.yaml

Parameters

testPath (str) –
stopPath (Optional[str]) –

Return type

Optional[str]

find_path_in_repos(path, importLoader=None)

If the given path is part of the working directory of a git repository return that repository and a path relative to it

Parameters

path (str) –
importLoader (Optional[Any]) –

Return type

Tuple[Optional[unfurl.repo.GitRepo], Optional[str], Optional[str], Optional[bool]]

static get_asdf_paths(projectRoot, asdfDataDir, toolVersions={})

Return type: List[str]

get_context(contextName, context=None)

Parameters

contextName (Optional[str]) –
context (Optional[dict]) –

Return type

dict

get_default_context()

Return type: Any

get_default_project_path(context_name)

Parameters: context_name (str) –
Return type: Optional[Any]

get_managed_project(location, localEnv)

Parameters

location (dict) –
localEnv (unfurl.localenv.LocalEnv) –

Return type

Optional[unfurl.localenv.Project]

get_manifest_path(localEnv, manifestPath, can_be_empty)

Parameters

localEnv (unfurl.localenv.LocalEnv) –
manifestPath (Optional[str]) –
can_be_empty (bool) –

Return type

Tuple[Optional[unfurl.localenv.Project], str, str]

static get_name_from_dir(projectRoot)

Parameters: projectRoot (str) –
Return type: str

get_relative_path(path)

Parameters: path (str) –
Return type: str

get_unique_path(name)

Parameters: name (str) –
Return type: str

get_vault_password(contextName=None, vaultId='default')

Parameters

contextName (Optional[str]) –
vaultId (str) –

Return type

Optional[str]

get_vault_passwords(contextName=None)

Parameters: contextName (Optional[str]) –
Return type: Iterable[Tuple[str, Union[str, bytes]]]

is_path_in_project(path)

Parameters: path (str) –
Return type: bool

load_yaml_include(yamlConfig, templatePath, baseDir, warnWhenNotFound=False, expanded=None, action=None)

This is called while the YAML config is being loaded. Returns (url or fullpath, parsed yaml)

Parameters

yamlConfig (unfurl.yamlloader.YamlConfig) –
templatePath (Union[str, dict]) –

make_vault_lib(contextName=None)

Parameters: contextName (Optional[str]) –
Return type: Optional[ansible.parsing.vault.VaultLib]

property name: str

static normalize_path(path)

Parameters: path (str) –
Return type: str

register_ensemble(manifestPath, *, project=None, managedBy=None, context=None)

Parameters

manifestPath (str) –
project (Optional[unfurl.localenv.Project]) –
managedBy (Optional[unfurl.localenv.Project]) –
context (Optional[str]) –

Return type

None

register_project(project, for_context=None, changed=False, save_project=True)

search_for_manifest(can_be_empty)

Parameters: can_be_empty (bool) –
Return type: Optional[str]

property venv: Optional[str]

Job module

A Job is generated by comparing a list of specs with the last known state of the system. Job runs tasks, each of which has a configuration spec that is executed on the running system Each task tracks and records its modifications to the system’s state

class unfurl.job.ConfigChange(parentJob=None, startTime=None, status=None, previousId=None, **kw)

Represents a configuration change made to the system. It has a operating status and a list of dependencies that contribute to its status. There are two kinds of dependencies:

Live resource attributes that the configuration’s inputs depend on.
Other configurations and resources it relies on to function properly.

Parameters

parentJob (Optional[Job]) –
startTime (Optional[datetime.datetime]) –
status (Optional[Union[unfurl.runtime.OperationalInstance, int, str]]) –
previousId (Optional[str]) –
kw (Any) –

Return type

None

get_operational_dependencies()

Return an iterator of Operational object that this instance depends on to be operational.

Return type: Iterable[unfurl.runtime.Operational]

class unfurl.job.Job(manifest, rootResource, jobOptions, previousId=None)

runs ConfigTasks and child Jobs

Parameters

manifest (YamlManifest) –
rootResource (unfurl.runtime.TopologyInstance) –
jobOptions (unfurl.job.JobOptions) –
previousId (Optional[str]) –

Return type

None

can_run_task(task)

Checked at runtime right before each task is run

validate inputs
check pre-conditions to see if it can be run
check task if it can be run

Parameters: task (unfurl.job.ConfigTask) –
Return type: Tuple[bool, str]

get_operational_dependencies()

Return an iterator of Operational object that this instance depends on to be operational.

Return type: Iterable[unfurl.job.ConfigTask]

run_task(task, depth=0)

During each task run: * Notification of metadata changes that reflect changes made to resources * Notification of add or removing dependency on a resource or properties of a resource * Notification of creation or deletion of a resource * Requests a resource with requested metadata, if it doesn’t exist, a task is run to make it so (e.g. add a dns entry, install a package).

Returns a task.

Parameters

task (unfurl.job.ConfigTask) –
depth (int) –

Return type

unfurl.job.ConfigTask

should_run_task(task)

Checked at runtime right before each task is run

Parameters: task (unfurl.job.ConfigTask) –
Return type: Tuple[bool, str]

class unfurl.job.JobOptions(**kw)

Options available to select which tasks are run, e.g. read-only

does the config apply to the operation? is it out of date? is it in a ok state?

Parameters: kw (Any) –
Return type: None

unfurl.job.run_job(manifestPath=None, _opts=None)

Loads the given Ensemble and creates and runs a job.

Parameters

manifestPath (str, optional) – If None, it will look for an ensemble in the current working directory.
_opts (dict, optional) – the names of the command line options for creating jobs.

Returns

The job that just ran or None if it couldn’t be created.

Return type

(Job)

Init module

This module implements creating and cloning project and ensembles as well Unfurl runtimes.

unfurl.init.clone(source, dest, ensemble_name='ensemble', **options)

Clone the source ensemble to dest. If dest isn’t in a project, create one. source can point to an ensemble_template, a service_template, an existing ensemble or a folder containing one of those. If it points to a project its default ensemble will be cloned.

Referenced Repositories will be cloned if a git repository or copied if a regular file folder, If the folders already exist they will be copied to new folder unless the git repositories have the same HEAD. but the local repository names will remain the same.

dest	result
Inside source project	new ensemble
missing or empty folder	clone project, new or cloned ensemble
another project	new or cloned ensemble with source as spec
non-empty folder	error

Parameters

source (str) –
dest (str) –
ensemble_name (str) –
options (Any) –

Return type

str

Utility classes and functions 

class unfurl.logs.sensitive: Base class for marking a value as sensitive. Depending on the context, sensitive values will either be encrypted or redacted when outputed.

exception unfurl.util.UnfurlError(message, saveStack=False, log=False)

Parameters

message (object) –
saveStack (bool) –
log (bool) –

Return type

None

exception unfurl.util.UnfurlTaskError(task, message, log=40, dependency=None)

Parameters

task (TaskView) –
message (object) –
log (int) –

unfurl.util.filter_env(rules, env=None, addOnly=False, sub=None)

Applies the given list of rules to a dictionary of environment variables and returns a new dictionary.

Parameters

rules (dict) – A dictionary of rules for adding, removing and filtering environment variables.
env (dict, optional) – The environment to apply the give rules to. If env is None it will be set to os.environ. Defaults to None.
addOnly (bool, optional) – If addOnly is False (the default) all variables in env will be included in the returned dict, otherwise only variables added by rules will be included
sub (Optional[MutableMapping]) –

Return type

Dict[str, str]

Rules applied in the order they are declared in the rules dictionary. The following examples show the different patterns for the rules:

foo : bar

Add foo=bar

+foo

Copy foo from the current environment

+foo : bar

Copy foo, or add foo=bar if it is not present

+!foo*

Copy all name from the current environment except those matching foo*

-!foo

Remove all names except for foo

^foo : /bar/bin

Treat foo like PATH and prepend /bar/bin:$foo

class unfurl.util.sensitive_bytes

Transparent wrapper class to mark bytes as sensitive

decode(*args, **kwargs)

Wrapper method to ensure type conversions maintain sensitive context

Parameters

args (List[object]) –
kwargs (collections.abc.Mapping) –

Return type

unfurl.util.sensitive_str

class unfurl.util.sensitive_dict: Transparent wrapper class to mark a dict as sensitive

class unfurl.util.sensitive_list(iterable=(), /): Transparent wrapper class to mark a list as sensitive

class unfurl.util.sensitive_str

Transparent wrapper class to mark a str as sensitive

encode(*args, **kwargs)

Wrapper method to ensure type conversions maintain sensitive context

Parameters

args (List[object]) –
kwargs (collections.abc.Mapping) –

Return type

unfurl.util.sensitive_bytes

Eval module

Public Api:

map_value - returns a copy of the given value resolving any embedded queries or template strings

Ref.resolve given an expression, returns a ResultList Ref.resolve_one given an expression, return value, none or a (regular) list Ref.is_ref return true if the given diction looks like a Ref

Internal:

eval_ref() given expression (string or dictionary) return list of Result Expr.resolve() given expression string, return list of Result Results._map_value same as map_value but with lazily evaluation

class unfurl.eval.Ref(exp, vars=None, trace=None)

A Ref objects describes a path to metadata associated with a resource.

Parameters

exp (Union[str, collections.abc.Mapping]) –
vars (dict) –
trace (Optional[int]) –

Return type

None

resolve(ctx, wantList=True, strict=None)

If wantList=True (default) returns a ResultList of matches Note that values in the list can be a list or None If wantList=False return resolve_one semantics If wantList=’result’ return a Result

Parameters

ctx (unfurl.eval.RefContext) –
wantList (Union[bool, str]) –
strict (Optional[bool]) –

Return type

Optional[Union[unfurl.result.ResultsList, unfurl.result.Result, List[unfurl.result.Result], Any]]

resolve_one(ctx, strict=None)

If no match return None If more than one match return a list of matches Otherwise return the match

Note: If you want to distinguish between None values and no match or between single match that is a list and a list of matches use resolve() which always returns a (possible empty) of matches

Parameters

ctx (unfurl.eval.RefContext) –
strict (Optional[bool]) –

Return type

Optional[Union[unfurl.result.ResultsList, unfurl.result.Result, List[unfurl.result.Result]]]

class unfurl.eval.RefContext(currentResource, vars=None, wantList=False, resolveExternal=False, trace=None, strict=None, task=None)

The context of the expression being evaluated.

Parameters

currentResource (ResourceRef) –
vars (Optional[dict]) –
wantList (Optional[Union[bool, str]]) –
resolveExternal (bool) –
trace (Optional[int]) –
strict (Optional[bool]) –
task (Optional[TaskView]) –

Return type

None

unfurl.eval.eval_ref(val, ctx, top=False)

val is assumed to be an expression, evaluate and return a list of Result

Parameters

val (Union[collections.abc.Mapping, str]) –
ctx (unfurl.eval.RefContext) –
top (bool) –

Return type

List[unfurl.result.Result]

unfurl.eval.map_value(value, resourceOrCxt, applyTemplates=True)

Resolves any expressions or template strings embedded in the given map or list.

Parameters

value (Any) –
resourceOrCxt (Union[unfurl.eval.RefContext, unfurl.result.ResourceRef]) –
applyTemplates (bool) –

Return type

Any

Python API

API for writing service templates

TOSCA Field Specifiers

API for writing configurators

Runtime module

APIs for controlling Unfurl

Localenv module

Job module

Init module

Utility classes and functions

Eval module

Python API 

API for writing service templates 

API for writing configurators 

APIs for controlling Unfurl 

Utility classes and functions 