def run(self):
"""
Get the required data from the operation queue and launch the operation.
"""
# Try to get a spot to launch own operation.
LOCKS_QUEUE.get(True)
operation_id = self.operation_id
run_params = [TvbProfile.current.PYTHON_INTERPRETER_PATH, '-m', 'tvb.core.operation_async_launcher',
str(operation_id), TvbProfile.CURRENT_PROFILE_NAME]
# In the exceptional case where the user pressed stop while the Thread startup is done,
# We should no longer launch the operation.
if self.stopped() is False:
env = os.environ.copy()
env['PYTHONPATH'] = os.pathsep.join(sys.path)
# anything that was already in $PYTHONPATH should have been reproduced in sys.path
launched_process = Popen(run_params, stdout=PIPE, stderr=PIPE, env=env)
LOGGER.debug("Storing pid=%s for operation id=%s launched on local machine." % (operation_id,
launched_process.pid))
op_ident = model.OperationProcessIdentifier(operation_id, pid=launched_process.pid)
dao.store_entity(op_ident)
if self.stopped():
# In the exceptional case where the user pressed stop while the Thread startup is done.
# and stop_operation is concurrently asking about OperationProcessIdentity.
self.stop_pid(launched_process.pid)
subprocess_result = launched_process.communicate()
LOGGER.info("Finished with launch of operation %s" % operation_id)
returned = launched_process.wait()
if returned != 0 and not self.stopped():
# Process did not end as expected. (e.g. Segmentation fault)
workflow_service = WorkflowService()
operation = dao.get_operation_by_id(self.operation_id)
LOGGER.error("Operation suffered fatal failure! Exit code: %s Exit message: %s" % (returned,
subprocess_result))
workflow_service.persist_operation_state(operation, model.STATUS_ERROR,
"Operation failed unexpectedly! Please check the log files.")
burst_entity = dao.get_burst_for_operation_id(self.operation_id)
if burst_entity:
message = "Error in operation process! Possibly segmentation fault."
workflow_service.mark_burst_finished(burst_entity, error_message=message)
del launched_process
# Give back empty spot now that you finished your operation
CURRENT_ACTIVE_THREADS.remove(self)
LOCKS_QUEUE.put(1)
def run(self):
"""
Get the required data from the operation queue and launch the operation.
"""
#Try to get a spot to launch own operation.
LOCKS_QUEUE.get(True)
operation_id = self.operation_id
run_params = [TvbProfile.current.PYTHON_INTERPRETER_PATH, '-m', 'tvb.core.operation_async_launcher',
str(operation_id), TvbProfile.CURRENT_PROFILE_NAME]
# In the exceptional case where the user pressed stop while the Thread startup is done,
# We should no longer launch the operation.
if self.stopped() is False:
env = os.environ.copy()
env['PYTHONPATH'] = os.pathsep.join(sys.path)
# anything that was already in $PYTHONPATH should have been reproduced in sys.path
launched_process = Popen(run_params, stdout=PIPE, stderr=PIPE, env=env)
LOGGER.debug("Storing pid=%s for operation id=%s launched on local machine." % (operation_id,
launched_process.pid))
op_ident = model.OperationProcessIdentifier(operation_id, pid=launched_process.pid)
dao.store_entity(op_ident)
if self.stopped():
# In the exceptional case where the user pressed stop while the Thread startup is done.
# and stop_operation is concurrently asking about OperationProcessIdentity.
self.stop_pid(launched_process.pid)
subprocess_result = launched_process.communicate()
LOGGER.info("Finished with launch of operation %s" % operation_id)
returned = launched_process.wait()
if returned != 0 and not self.stopped():
# Process did not end as expected. (e.g. Segmentation fault)
workflow_service = WorkflowService()
operation = dao.get_operation_by_id(self.operation_id)
LOGGER.error("Operation suffered fatal failure! Exit code: %s Exit message: %s" % (returned,
subprocess_result))
workflow_service.persist_operation_state(operation, model.STATUS_ERROR,
"Operation failed unexpectedly! Please check the log files.")
burst_entity = dao.get_burst_for_operation_id(self.operation_id)
if burst_entity:
message = "Error in operation process! Possibly segmentation fault."
workflow_service.mark_burst_finished(burst_entity, error_message=message)
del launched_process
#Give back empty spot now that you finished your operation
CURRENT_ACTIVE_THREADS.remove(self)
LOCKS_QUEUE.put(1)
class BurstService:
"""
Service layer for Burst related entities.
"""
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
def build_portlet_interface(self, portlet_configuration, project_id):
"""
From a portlet_id and a project_id, first build the portlet
entity then get it's configurable interface.
:param portlet_configuration: a portlet configuration entity. It holds at the
least the portlet_id, and in case any default parameters were saved
they can be rebuilt from the analyzers // visualizer parameters
:param project_id: the id of the current project
:returns: the portlet interface will be of the following form::
[{'interface': adapter_interface,
'prefix': prefix_for_parameter_names,
'subalg': {algorithm_field_name: default_algorithm_value},
'algo_group': algorithm_group,
'alg_ui_name': displayname},
......]
A list of dictionaries for each adapter that makes up the portlet.
"""
portlet_entity = dao.get_portlet_by_id(portlet_configuration.portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration(
"No portlet entity located in database with id=%s. "
"Portlet configuration %s is not valid." % (portlet_configuration.portlet_id, portlet_configuration)
)
portlet_configurer = PortletConfigurer(portlet_entity)
portlet_interface = portlet_configurer.get_configurable_interface()
self.logger.debug("Created interface for portlet " + str([portlet_entity]))
for adapter_conf in portlet_interface:
interface = adapter_conf.interface
interface = FlowService().prepare_parameters(interface, project_id, adapter_conf.group.fk_category)
interface = ABCAdapter.prepare_param_names(interface, adapter_conf.prefix)
adapter_conf.interface = interface
portlet_configurer.update_default_values(portlet_interface, portlet_configuration)
portlet_configurer.prefix_adapters_parameters(portlet_interface)
return portlet_interface
@staticmethod
def update_portlet_configuration(portlet_configuration, submited_parameters):
"""
:param portlet_configuration: the portlet configuration that needs to be updated
:param submited_parameters: a list of parameters as submitted from the UI. This
is a dictionary in the form :
{'dynamic' : {name:value pairs}, 'static' : {name:value pairs}}
All names are prefixed with adapter specific generated prefix.
"""
portlet_entity = dao.get_portlet_by_id(portlet_configuration.portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
return portlet_configurer.update_portlet_configuration(portlet_configuration, submited_parameters)
@staticmethod
def new_burst_configuration(project_id):
"""
Return a new burst configuration entity with all the default values.
"""
burst_configuration = model.BurstConfiguration(project_id)
burst_configuration.selected_tab = 0
BurstService.set_default_portlets(burst_configuration)
return burst_configuration
@staticmethod
def set_default_portlets(burst_configuration):
"""
Sets the default portlets for the specified burst configuration.
The default portlets are specified in the __init__.py script from tvb root.
"""
for tab_idx, value in DEFAULT_PORTLETS.items():
for sel_idx, portlet_identifier in value.items():
portlet = BurstService.get_portlet_by_identifier(portlet_identifier)
if portlet is not None:
portlet_configuration = BurstService.new_portlet_configuration(
portlet.id, tab_idx, sel_idx, portlet.algorithm_identifier
)
burst_configuration.set_portlet(tab_idx, sel_idx, portlet_configuration)
@staticmethod
def _store_burst_config(burst_config):
"""
Store a burst configuration entity.
"""
burst_config.prepare_before_save()
saved_entity = dao.store_entity(burst_config)
return saved_entity.id
@staticmethod
def get_available_bursts(project_id):
"""
Return all the burst for the current project.
"""
bursts = dao.get_bursts_for_project(project_id, page_size=MAX_BURSTS_DISPLAYED) or []
for burst in bursts:
burst.prepare_after_load()
return bursts
@staticmethod
def populate_burst_disk_usage(bursts):
"""
Adds a disk_usage field to each burst object.
The disk usage is computed as the sum of the datatypes generated by a burst
"""
sizes = dao.compute_bursts_disk_size([b.id for b in bursts])
for b in bursts:
b.disk_size = format_bytes_human(sizes[b.id])
@staticmethod
def rename_burst(burst_id, new_name):
"""
Rename the burst given by burst_id, setting it's new name to
burst_name.
"""
burst = dao.get_burst_by_id(burst_id)
burst.name = new_name
dao.store_entity(burst)
def load_burst(self, burst_id):
"""
:param burst_id: the id of the burst that should be loaded
Having this input the method should:
- load the entity from the DB
- get all the workflow steps for the saved burst id
- go trough the visualization workflow steps to create the tab
configuration of the burst using the tab_index and index_in_tab
fields saved on each workflow_step
"""
burst = dao.get_burst_by_id(burst_id)
burst.prepare_after_load()
burst.reset_tabs()
burst_workflows = dao.get_workflows_for_burst(burst.id)
group_gid = None
if len(burst_workflows) == 1:
# A simple burst with no range parameters
burst = self.__populate_tabs_from_workflow(burst, burst_workflows[0])
elif len(burst_workflows) > 1:
# A burst workflow with a range of values, created multiple workflows and need
# to launch parameter space exploration with the resulted group
self.__populate_tabs_from_workflow(burst, burst_workflows[0])
executed_steps = dao.get_workflow_steps(burst_workflows[0].id)
operation = dao.get_operation_by_id(executed_steps[0].fk_operation)
if operation.operation_group:
workflow_group = dao.get_datatypegroup_by_op_group_id(operation.operation_group.id)
group_gid = workflow_group.gid
return burst, group_gid
@staticmethod
def __populate_tabs_from_workflow(burst_entity, workflow):
"""
Given a burst and a workflow populate the tabs of the burst with the PortletConfigurations
generated from the steps of the workflow.
"""
visualizers = dao.get_visualization_steps(workflow.id)
for entry in visualizers:
## For each visualize step, also load all of the analyze steps.
portlet_cfg = PortletConfiguration(entry.fk_portlet)
portlet_cfg.set_visualizer(entry)
analyzers = dao.get_workflow_steps_for_position(entry.fk_workflow, entry.tab_index, entry.index_in_tab)
portlet_cfg.set_analyzers(analyzers)
burst_entity.tabs[entry.tab_index].portlets[entry.index_in_tab] = portlet_cfg
return burst_entity
def load_tab_configuration(self, burst_entity, op_id):
"""
Given a burst entity and an operation id, find the workflow to which the op_id
belongs and the load the burst_entity's tab configuration with those workflow steps.
"""
originating_workflow = dao.get_workflow_for_operation_id(op_id)
burst_entity = self.__populate_tabs_from_workflow(burst_entity, originating_workflow)
return burst_entity
@staticmethod
def new_portlet_configuration(portlet_id, tab_nr=-1, index_in_tab=-1, portlet_name="Default"):
"""
Return a new portlet configuration entitiy with default parameters.
:param portlet_id: the id of the portlet for which a configuration will
be stored
:param tab_nr: the index of the currently selected tab
:param index_in_tab: the index from the currently selected tab
"""
portlet_entity = dao.get_portlet_by_id(portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration("No portlet entity located in database with id=%s." % portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
configuration = portlet_configurer.create_new_portlet_configuration(portlet_name)
for wf_step in configuration.analyzers:
wf_step.tab_index = tab_nr
wf_step.index_in_tab = index_in_tab
configuration.visualizer.tab_index = tab_nr
configuration.visualizer.index_in_tab = index_in_tab
return configuration
@staticmethod
def get_available_portlets():
"""
:returns: a list of all the available portlet entites
"""
return dao.get_available_portlets()
@staticmethod
def get_portlet_by_id(portlet_id):
"""
:returns: the portlet entity with the id =@portlet_id
"""
return dao.get_portlet_by_id(portlet_id)
@staticmethod
def get_portlet_by_identifier(portlet_identifier):
"""
:returns: the portlet entity with the algorithm identifier =@portlet_identifier
"""
return dao.get_portlet_by_identifier(portlet_identifier)
def launch_burst(self, burst_configuration, simulator_index, simulator_id, user_id, launch_mode=LAUNCH_NEW):
"""
Given a burst configuration and all the necessary data do the actual launch.
:param burst_configuration: BurstConfiguration
:param simulator_index: the position within the workflows step list that the simulator will take. This is needed
so that the rest of the portlet workflow steps know what steps do their dynamic parameters come from.
:param simulator_id: the id of the simulator adapter as stored in the DB. It's needed to load the simulator algo
group and category that are then passed to the launcher's prepare_operation method.
:param user_id: the id of the user that launched this burst
:param launch_mode: new/branch/continue
"""
## 1. Prepare BurstConfiguration entity
if launch_mode == LAUNCH_NEW:
## Fully new entity for new simulation
burst_config = burst_configuration.clone()
if burst_config.name is None:
new_id = dao.get_max_burst_id() + 1
burst_config.name = "simulation_" + str(new_id)
else:
## Branch or Continue simulation
burst_config = burst_configuration
simulation_state = dao.get_generic_entity(
SIMULATION_DATATYPE_MODULE + "." + SIMULATION_DATATYPE_CLASS, burst_config.id, "fk_parent_burst"
)
if simulation_state is None or len(simulation_state) < 1:
exc = BurstServiceException(
"Simulation State not found for %s, " "thus we are unable to branch from it!" % burst_config.name
)
self.logger.error(exc)
raise exc
simulation_state = simulation_state[0]
burst_config.update_simulation_parameter("simulation_state", simulation_state.gid)
burst_config = burst_configuration.clone()
count = dao.count_bursts_with_name(burst_config.name, burst_config.fk_project)
burst_config.name = burst_config.name + "_" + launch_mode + str(count)
## 2. Create Operations and do the actual launch
if launch_mode in [LAUNCH_NEW, LAUNCH_BRANCH]:
## New Burst entry in the history
burst_id = self._store_burst_config(burst_config)
thread = threading.Thread(
target=self._async_launch_and_prepare,
kwargs={
"burst_config": burst_config,
"simulator_index": simulator_index,
"simulator_id": simulator_id,
"user_id": user_id,
},
)
thread.start()
return burst_id, burst_config.name
else:
## Continue simulation
## TODO
return burst_config.id, burst_config.name
@transactional
def _prepare_operations(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare all required operations for burst launch.
"""
project_id = burst_config.fk_project
burst_id = burst_config.id
workflow_step_list = []
starting_index = simulator_index + 1
sim_algo = FlowService().get_algorithm_by_identifier(simulator_id)
metadata = {DataTypeMetaData.KEY_BURST: burst_id}
launch_data = burst_config.get_all_simulator_values()[0]
operations, group = self.operation_service.prepare_operations(
user_id, project_id, sim_algo, sim_algo.algo_group.group_category, metadata, **launch_data
)
group_launched = group is not None
if group_launched:
starting_index += 1
for tab in burst_config.tabs:
for portlet_cfg in tab.portlets:
### For each portlet configuration stored, update the step index ###
### and also change the dynamic parameters step indexes to point ###
### to the simulator outputs. ##
if portlet_cfg is not None:
analyzers = portlet_cfg.analyzers
visualizer = portlet_cfg.visualizer
for entry in analyzers:
entry.step_index = starting_index
self.workflow_service.set_dynamic_step_references(entry, simulator_index)
workflow_step_list.append(entry)
starting_index += 1
### Change the dynamic parameters to point to the last adapter from this portlet execution.
visualizer.step_visible = False
if len(workflow_step_list) > 0 and isinstance(workflow_step_list[-1], model.WorkflowStep):
self.workflow_service.set_dynamic_step_references(visualizer, workflow_step_list[-1].step_index)
else:
self.workflow_service.set_dynamic_step_references(visualizer, simulator_index)
### Only for a single operation have the step of visualization, otherwise is useless.
if not group_launched:
workflow_step_list.append(visualizer)
if group_launched:
### For a group of operations, make sure the metric for PSE view
### is also computed, immediately after the simulation.
metric_algo, metric_group = FlowService().get_algorithm_by_module_and_class(
MEASURE_METRICS_MODULE, MEASURE_METRICS_CLASS
)
_, metric_interface = FlowService().prepare_adapter(project_id, metric_group)
dynamics = {}
for entry in metric_interface:
# We have a select that should be the dataType and a select multiple with the
# required metric algorithms to be evaluated. Only dynamic parameter should be
# the select type.
if entry[ABCAdapter.KEY_TYPE] == "select":
dynamics[entry[ABCAdapter.KEY_NAME]] = {
WorkflowStepConfiguration.DATATYPE_INDEX_KEY: 0,
WorkflowStepConfiguration.STEP_INDEX_KEY: simulator_index,
}
metric_step = model.WorkflowStep(
algorithm_id=metric_algo.id, step_index=simulator_index + 1, static_param={}, dynamic_param=dynamics
)
metric_step.step_visible = False
workflow_step_list.insert(0, metric_step)
workflows = self.workflow_service.create_and_store_workflow(
project_id, burst_id, simulator_index, simulator_id, operations
)
self.operation_service.prepare_operations_for_workflowsteps(
workflow_step_list, workflows, user_id, burst_id, project_id, group, operations
)
operation_ids = [operation.id for operation in operations]
return operation_ids
def _async_launch_and_prepare(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config, simulator_index, simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" % (len(operation_ids)))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception, excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
self.logger.debug(
"Finished launching workflows. "
+ str(len(operation_ids) - wf_errs)
+ " were launched successfully, "
+ str(wf_errs)
+ " had error on pre-launch steps"
)
except Exception, excep:
self.logger.error(excep)
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
class BurstService():
"""
Service layer for Burst related entities.
"""
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
def build_portlet_interface(self, portlet_configuration, project_id):
"""
From a portlet_id and a project_id, first build the portlet
entity then get it's configurable interface.
:param portlet_configuration: a portlet configuration entity. It holds at the
least the portlet_id, and in case any default parameters were saved
they can be rebuilt from the analyzers // visualizer parameters
:param project_id: the id of the current project
:returns: the portlet interface will be of the following form::
[{'interface': adapter_interface,
'prefix': prefix_for_parameter_names,
'subalg': {algorithm_field_name: default_algorithm_value},
'algo_group': algorithm_group,
'alg_ui_name': displayname},
......]
A list of dictionaries for each adapter that makes up the portlet.
"""
portlet_entity = dao.get_portlet_by_id(
portlet_configuration.portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration(
"No portlet entity located in database with id=%s. "
"Portlet configuration %s is not valid." %
(portlet_configuration.portlet_id, portlet_configuration))
portlet_configurer = PortletConfigurer(portlet_entity)
portlet_interface = portlet_configurer.get_configurable_interface()
self.logger.debug("Created interface for portlet " +
str([portlet_entity]))
for adapter_conf in portlet_interface:
interface = adapter_conf.interface
interface = FlowService().prepare_parameters(
interface, project_id, adapter_conf.group.fk_category)
interface = ABCAdapter.prepare_param_names(interface,
adapter_conf.prefix)
adapter_conf.interface = interface
portlet_configurer.update_default_values(portlet_interface,
portlet_configuration)
portlet_configurer.prefix_adapters_parameters(portlet_interface)
return portlet_interface
@staticmethod
def update_portlet_configuration(portlet_configuration,
submited_parameters):
"""
:param portlet_configuration: the portlet configuration that needs to be updated
:param submited_parameters: a list of parameters as submitted from the UI. This
is a dictionary in the form :
{'dynamic' : {name:value pairs}, 'static' : {name:value pairs}}
All names are prefixed with adapter specific generated prefix.
"""
portlet_entity = dao.get_portlet_by_id(
portlet_configuration.portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
return portlet_configurer.update_portlet_configuration(
portlet_configuration, submited_parameters)
@staticmethod
def new_burst_configuration(project_id):
"""
Return a new burst configuration entity with all the default values.
"""
burst_configuration = model.BurstConfiguration(project_id)
burst_configuration.selected_tab = 0
BurstService.set_default_portlets(burst_configuration)
return burst_configuration
@staticmethod
def set_default_portlets(burst_configuration):
"""
Sets the default portlets for the specified burst configuration.
The default portlets are specified in the __init__.py script from tvb root.
"""
for tab_idx, value in DEFAULT_PORTLETS.items():
for sel_idx, portlet_identifier in value.items():
portlet = BurstService.get_portlet_by_identifier(
portlet_identifier)
if portlet is not None:
portlet_configuration = BurstService.new_portlet_configuration(
portlet.id, tab_idx, sel_idx,
portlet.algorithm_identifier)
burst_configuration.set_portlet(tab_idx, sel_idx,
portlet_configuration)
@staticmethod
def _store_burst_config(burst_config):
"""
Store a burst configuration entity.
"""
burst_config.prepare_before_save()
saved_entity = dao.store_entity(burst_config)
return saved_entity.id
@staticmethod
def get_available_bursts(project_id):
"""
Return all the burst for the current project.
"""
bursts = dao.get_bursts_for_project(
project_id, page_size=MAX_BURSTS_DISPLAYED) or []
for burst in bursts:
burst.prepare_after_load()
return bursts
@staticmethod
def populate_burst_disk_usage(bursts):
"""
Adds a disk_usage field to each burst object.
The disk usage is computed as the sum of the datatypes generated by a burst
"""
sizes = dao.compute_bursts_disk_size([b.id for b in bursts])
for b in bursts:
b.disk_size = format_bytes_human(sizes[b.id])
@staticmethod
def rename_burst(burst_id, new_name):
"""
Rename the burst given by burst_id, setting it's new name to
burst_name.
"""
burst = dao.get_burst_by_id(burst_id)
burst.name = new_name
dao.store_entity(burst)
def load_burst(self, burst_id):
"""
:param burst_id: the id of the burst that should be loaded
Having this input the method should:
- load the entity from the DB
- get all the workflow steps for the saved burst id
- go trough the visualization workflow steps to create the tab
configuration of the burst using the tab_index and index_in_tab
fields saved on each workflow_step
"""
burst = dao.get_burst_by_id(burst_id)
burst.prepare_after_load()
burst.reset_tabs()
burst_workflows = dao.get_workflows_for_burst(burst.id)
group_gid = None
if len(burst_workflows) == 1:
# A simple burst with no range parameters
burst = self.__populate_tabs_from_workflow(burst,
burst_workflows[0])
elif len(burst_workflows) > 1:
# A burst workflow with a range of values, created multiple workflows and need
# to launch parameter space exploration with the resulted group
self.__populate_tabs_from_workflow(burst, burst_workflows[0])
executed_steps = dao.get_workflow_steps(burst_workflows[0].id)
operation = dao.get_operation_by_id(executed_steps[0].fk_operation)
if operation.operation_group:
workflow_group = dao.get_datatypegroup_by_op_group_id(
operation.operation_group.id)
group_gid = workflow_group.gid
return burst, group_gid
@staticmethod
def __populate_tabs_from_workflow(burst_entity, workflow):
"""
Given a burst and a workflow populate the tabs of the burst with the PortletConfigurations
generated from the steps of the workflow.
"""
visualizers = dao.get_visualization_steps(workflow.id)
for entry in visualizers:
## For each visualize step, also load all of the analyze steps.
portlet_cfg = PortletConfiguration(entry.fk_portlet)
portlet_cfg.set_visualizer(entry)
analyzers = dao.get_workflow_steps_for_position(
entry.fk_workflow, entry.tab_index, entry.index_in_tab)
portlet_cfg.set_analyzers(analyzers)
burst_entity.tabs[entry.tab_index].portlets[
entry.index_in_tab] = portlet_cfg
return burst_entity
def load_tab_configuration(self, burst_entity, op_id):
"""
Given a burst entity and an operation id, find the workflow to which the op_id
belongs and the load the burst_entity's tab configuration with those workflow steps.
"""
originating_workflow = dao.get_workflow_for_operation_id(op_id)
burst_entity = self.__populate_tabs_from_workflow(
burst_entity, originating_workflow)
return burst_entity
@staticmethod
def new_portlet_configuration(portlet_id,
tab_nr=-1,
index_in_tab=-1,
portlet_name='Default'):
"""
Return a new portlet configuration entitiy with default parameters.
:param portlet_id: the id of the portlet for which a configuration will
be stored
:param tab_nr: the index of the currently selected tab
:param index_in_tab: the index from the currently selected tab
"""
portlet_entity = dao.get_portlet_by_id(portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration(
"No portlet entity located in database with id=%s." %
portlet_id)
portlet_configurer = PortletConfigurer(portlet_entity)
configuration = portlet_configurer.create_new_portlet_configuration(
portlet_name)
for wf_step in configuration.analyzers:
wf_step.tab_index = tab_nr
wf_step.index_in_tab = index_in_tab
configuration.visualizer.tab_index = tab_nr
configuration.visualizer.index_in_tab = index_in_tab
return configuration
@staticmethod
def get_available_portlets():
"""
:returns: a list of all the available portlet entites
"""
return dao.get_available_portlets()
@staticmethod
def get_portlet_by_id(portlet_id):
"""
:returns: the portlet entity with the id =@portlet_id
"""
return dao.get_portlet_by_id(portlet_id)
@staticmethod
def get_portlet_by_identifier(portlet_identifier):
"""
:returns: the portlet entity with the algorithm identifier =@portlet_identifier
"""
return dao.get_portlet_by_identifier(portlet_identifier)
def launch_burst(self,
burst_configuration,
simulator_index,
simulator_id,
user_id,
launch_mode=LAUNCH_NEW):
"""
Given a burst configuration and all the necessary data do the actual launch.
:param burst_configuration: BurstConfiguration
:param simulator_index: the position within the workflows step list that the simulator will take. This is needed
so that the rest of the portlet workflow steps know what steps do their dynamic parameters come from.
:param simulator_id: the id of the simulator adapter as stored in the DB. It's needed to load the simulator algo
group and category that are then passed to the launcher's prepare_operation method.
:param user_id: the id of the user that launched this burst
:param launch_mode: new/branch/continue
"""
## 1. Prepare BurstConfiguration entity
if launch_mode == LAUNCH_NEW:
## Fully new entity for new simulation
burst_config = burst_configuration.clone()
if burst_config.name is None:
new_id = dao.get_max_burst_id() + 1
burst_config.name = 'simulation_' + str(new_id)
else:
## Branch or Continue simulation
burst_config = burst_configuration
simulation_state = dao.get_generic_entity(
SIMULATION_DATATYPE_MODULE + "." + SIMULATION_DATATYPE_CLASS,
burst_config.id, "fk_parent_burst")
if simulation_state is None or len(simulation_state) < 1:
exc = BurstServiceException(
"Simulation State not found for %s, "
"thus we are unable to branch from it!" %
burst_config.name)
self.logger.error(exc)
raise exc
simulation_state = simulation_state[0]
burst_config.update_simulation_parameter("simulation_state",
simulation_state.gid)
burst_config = burst_configuration.clone()
count = dao.count_bursts_with_name(burst_config.name,
burst_config.fk_project)
burst_config.name = burst_config.name + "_" + launch_mode + str(
count)
## 2. Create Operations and do the actual launch
if launch_mode in [LAUNCH_NEW, LAUNCH_BRANCH]:
## New Burst entry in the history
burst_id = self._store_burst_config(burst_config)
thread = threading.Thread(target=self._async_launch_and_prepare,
kwargs={
'burst_config': burst_config,
'simulator_index': simulator_index,
'simulator_id': simulator_id,
'user_id': user_id
})
thread.start()
return burst_id, burst_config.name
else:
## Continue simulation
## TODO
return burst_config.id, burst_config.name
@transactional
def _prepare_operations(self, burst_config, simulator_index, simulator_id,
user_id):
"""
Prepare all required operations for burst launch.
"""
project_id = burst_config.fk_project
burst_id = burst_config.id
workflow_step_list = []
starting_index = simulator_index + 1
sim_algo = FlowService().get_algorithm_by_identifier(simulator_id)
metadata = {DataTypeMetaData.KEY_BURST: burst_id}
launch_data = burst_config.get_all_simulator_values()[0]
operations, group = self.operation_service.prepare_operations(
user_id, project_id, sim_algo, sim_algo.algo_group.group_category,
metadata, **launch_data)
group_launched = group is not None
if group_launched:
starting_index += 1
for tab in burst_config.tabs:
for portlet_cfg in tab.portlets:
### For each portlet configuration stored, update the step index ###
### and also change the dynamic parameters step indexes to point ###
### to the simulator outputs. ##
if portlet_cfg is not None:
analyzers = portlet_cfg.analyzers
visualizer = portlet_cfg.visualizer
for entry in analyzers:
entry.step_index = starting_index
self.workflow_service.set_dynamic_step_references(
entry, simulator_index)
workflow_step_list.append(entry)
starting_index += 1
### Change the dynamic parameters to point to the last adapter from this portlet execution.
visualizer.step_visible = False
if len(workflow_step_list) > 0 and isinstance(
workflow_step_list[-1], model.WorkflowStep):
self.workflow_service.set_dynamic_step_references(
visualizer, workflow_step_list[-1].step_index)
else:
self.workflow_service.set_dynamic_step_references(
visualizer, simulator_index)
### Only for a single operation have the step of visualization, otherwise is useless.
if not group_launched:
workflow_step_list.append(visualizer)
if group_launched:
### For a group of operations, make sure the metric for PSE view
### is also computed, immediately after the simulation.
metric_algo, metric_group = FlowService(
).get_algorithm_by_module_and_class(MEASURE_METRICS_MODULE,
MEASURE_METRICS_CLASS)
_, metric_interface = FlowService().prepare_adapter(
project_id, metric_group)
dynamics = {}
for entry in metric_interface:
# We have a select that should be the dataType and a select multiple with the
# required metric algorithms to be evaluated. Only dynamic parameter should be
# the select type.
if entry[ABCAdapter.KEY_TYPE] == 'select':
dynamics[entry[ABCAdapter.KEY_NAME]] = {
WorkflowStepConfiguration.DATATYPE_INDEX_KEY: 0,
WorkflowStepConfiguration.STEP_INDEX_KEY:
simulator_index
}
metric_step = model.WorkflowStep(algorithm_id=metric_algo.id,
step_index=simulator_index + 1,
static_param={},
dynamic_param=dynamics)
metric_step.step_visible = False
workflow_step_list.insert(0, metric_step)
workflows = self.workflow_service.create_and_store_workflow(
project_id, burst_id, simulator_index, simulator_id, operations)
self.operation_service.prepare_operations_for_workflowsteps(
workflow_step_list, workflows, user_id, burst_id, project_id,
group, operations)
operation_ids = [operation.id for operation in operations]
return operation_ids
def _async_launch_and_prepare(self, burst_config, simulator_index,
simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config,
simulator_index,
simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" %
(len(operation_ids, )))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception, excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(
burst_config, error_message=str(excep))
self.logger.debug("Finished launching workflows. " +
str(len(operation_ids) - wf_errs) +
" were launched successfully, " + str(wf_errs) +
" had error on pre-launch steps")
except Exception, excep:
self.logger.error(excep)
self.workflow_service.mark_burst_finished(burst_config,
error_message=str(excep))
class BurstService(object):
"""
Service layer for Burst related entities.
"""
def __init__(self):
self.operation_service = OperationService()
self.workflow_service = WorkflowService()
self.logger = get_logger(self.__class__.__module__)
self.cache_portlet_configurators = {}
def build_portlet_interface(self, portlet_configuration, project_id):
"""
From a portlet_id and a project_id, first build the portlet
entity then get it's configurable interface.
:param portlet_configuration: a portlet configuration entity. It holds at the
least the portlet_id, and in case any default parameters were saved
they can be rebuilt from the analyzers // visualizer parameters
:param project_id: the id of the current project
:returns: the portlet interface will be of the following form::
[{'interface': adapter_interface,
'prefix': prefix_for_parameter_names,
'subalg': {algorithm_field_name: default_algorithm_value},
'algo_group': algorithm_group,
'alg_ui_name': displayname},
......]
A list of dictionaries for each adapter that makes up the portlet.
"""
portlet_configurer = self._get_portlet_configurer(portlet_configuration.portlet_id)
portlet_interface = portlet_configurer.get_configurable_interface()
for adapter_conf in portlet_interface:
interface = adapter_conf.interface
itree_mngr = InputTreeManager()
interface = itree_mngr.fill_input_tree_with_options(interface, project_id,
adapter_conf.stored_adapter.fk_category)
adapter_conf.interface = itree_mngr.prepare_param_names(interface)
portlet_configurer.update_default_values(portlet_interface, portlet_configuration)
portlet_configurer.prefix_adapters_parameters(portlet_interface)
return portlet_interface
def _get_portlet_configurer(self, portlet_id):
if portlet_id not in self.cache_portlet_configurators:
portlet_entity = dao.get_portlet_by_id(portlet_id)
if portlet_entity is None:
raise InvalidPortletConfiguration("No portlet entity located in database with id=%s. " % portlet_id)
self.cache_portlet_configurators[portlet_id] = PortletConfigurer(portlet_entity)
self.logger.debug("Recently parsed portlet XML:" + str([portlet_entity]))
return self.cache_portlet_configurators[portlet_id]
def update_portlet_configuration(self, portlet_configuration, submited_parameters):
"""
:param portlet_configuration: the portlet configuration that needs to be updated
:param submited_parameters: a list of parameters as submitted from the UI. This
is a dictionary in the form :
{'dynamic' : {name:value pairs}, 'static' : {name:value pairs}}
All names are prefixed with adapter specific generated prefix.
"""
portlet_configurer = self._get_portlet_configurer(portlet_configuration.portlet_id)
return portlet_configurer.update_portlet_configuration(portlet_configuration, submited_parameters)
def new_burst_configuration(self, project_id):
"""
Return a new burst configuration entity with all the default values.
"""
burst_configuration = model.BurstConfiguration(project_id)
burst_configuration.selected_tab = 0
# Now set the default portlets for the specified burst configuration.
# The default portlets are specified in the __init__.py script from tvb root.
for tab_idx, value in DEFAULT_PORTLETS.items():
for sel_idx, portlet_identifier in value.items():
portlet = BurstService.get_portlet_by_identifier(portlet_identifier)
if portlet is not None:
portlet_configuration = self.new_portlet_configuration(portlet.id, tab_idx, sel_idx,
portlet.algorithm_identifier)
burst_configuration.set_portlet(tab_idx, sel_idx, portlet_configuration)
return burst_configuration
@staticmethod
def _store_burst_config(burst_config):
"""
Store a burst configuration entity.
"""
burst_config.prepare_before_save()
saved_entity = dao.store_entity(burst_config)
return saved_entity.id
@staticmethod
def get_available_bursts(project_id):
"""
Return all the burst for the current project.
"""
bursts = dao.get_bursts_for_project(project_id, page_size=MAX_BURSTS_DISPLAYED) or []
for burst in bursts:
burst.prepare_after_load()
return bursts
@staticmethod
def populate_burst_disk_usage(bursts):
"""
Adds a disk_usage field to each burst object.
The disk usage is computed as the sum of the datatypes generated by a burst
"""
sizes = dao.compute_bursts_disk_size([b.id for b in bursts])
for b in bursts:
b.disk_size = format_bytes_human(sizes[b.id])
@staticmethod
def rename_burst(burst_id, new_name):
"""
Rename the burst given by burst_id, setting it's new name to
burst_name.
"""
burst = dao.get_burst_by_id(burst_id)
burst.name = new_name
dao.store_entity(burst)
def load_burst(self, burst_id):
"""
:param burst_id: the id of the burst that should be loaded
Having this input the method should:
- load the entity from the DB
- get all the workflow steps for the saved burst id
- go trough the visualization workflow steps to create the tab
configuration of the burst using the tab_index and index_in_tab
fields saved on each workflow_step
"""
burst = dao.get_burst_by_id(burst_id)
burst.prepare_after_load()
burst.reset_tabs()
burst_workflows = dao.get_workflows_for_burst(burst.id)
group_gid = None
if len(burst_workflows) == 1:
# A simple burst with no range parameters
burst = self.__populate_tabs_from_workflow(burst, burst_workflows[0])
elif len(burst_workflows) > 1:
# A burst workflow with a range of values, created multiple workflows and need
# to launch parameter space exploration with the resulted group
self.__populate_tabs_from_workflow(burst, burst_workflows[0])
executed_steps = dao.get_workflow_steps(burst_workflows[0].id)
operation = dao.get_operation_by_id(executed_steps[0].fk_operation)
if operation.operation_group:
workflow_group = dao.get_datatypegroup_by_op_group_id(operation.operation_group.id)
group_gid = workflow_group.gid
return burst, group_gid
@staticmethod
def __populate_tabs_from_workflow(burst_entity, workflow):
"""
Given a burst and a workflow populate the tabs of the burst with the PortletConfigurations
generated from the steps of the workflow.
"""
visualizers = dao.get_visualization_steps(workflow.id)
for entry in visualizers:
## For each visualize step, also load all of the analyze steps.
portlet_cfg = PortletConfiguration(entry.fk_portlet)
portlet_cfg.set_visualizer(entry)
analyzers = dao.get_workflow_steps_for_position(entry.fk_workflow, entry.tab_index, entry.index_in_tab)
portlet_cfg.set_analyzers(analyzers)
burst_entity.tabs[entry.tab_index].portlets[entry.index_in_tab] = portlet_cfg
return burst_entity
def load_tab_configuration(self, burst_entity, op_id):
"""
Given a burst entity and an operation id, find the workflow to which the op_id
belongs and the load the burst_entity's tab configuration with those workflow steps.
"""
originating_workflow = dao.get_workflow_for_operation_id(op_id)
burst_entity = self.__populate_tabs_from_workflow(burst_entity, originating_workflow)
return burst_entity
def new_portlet_configuration(self, portlet_id, tab_nr=-1, index_in_tab=-1, portlet_name='Default'):
"""
Return a new portlet configuration entity with default parameters.
:param portlet_id: the id of the portlet for which a configuration will be stored
:param tab_nr: the index of the currently selected tab
:param index_in_tab: the index from the currently selected tab
"""
portlet_configurer = self._get_portlet_configurer(portlet_id)
configuration = portlet_configurer.create_new_portlet_configuration(portlet_name)
for wf_step in configuration.analyzers:
wf_step.tab_index = tab_nr
wf_step.index_in_tab = index_in_tab
configuration.visualizer.tab_index = tab_nr
configuration.visualizer.index_in_tab = index_in_tab
return configuration
@staticmethod
def get_available_portlets():
"""
:returns: a list of all the available portlet entites
"""
return dao.get_available_portlets()
@staticmethod
def get_portlet_by_id(portlet_id):
"""
:returns: the portlet entity with the id =@portlet_id
"""
return dao.get_portlet_by_id(portlet_id)
@staticmethod
def get_portlet_by_identifier(portlet_identifier):
"""
:returns: the portlet entity with the algorithm identifier =@portlet_identifier
"""
return dao.get_portlet_by_identifier(portlet_identifier)
def launch_burst(self, burst_configuration, simulator_index, simulator_id, user_id, launch_mode=LAUNCH_NEW):
"""
Given a burst configuration and all the necessary data do the actual launch.
:param burst_configuration: BurstConfiguration
:param simulator_index: the position within the workflows step list that the simulator will take. This is needed
so that the rest of the portlet workflow steps know what steps do their dynamic parameters come from.
:param simulator_id: the id of the simulator adapter as stored in the DB. It's needed to load the simulator algo
group and category that are then passed to the launcher's prepare_operation method.
:param user_id: the id of the user that launched this burst
:param launch_mode: new/branch/continue
"""
## 1. Prepare BurstConfiguration entity
if launch_mode == LAUNCH_NEW:
## Fully new entity for new simulation
burst_config = burst_configuration.clone()
if burst_config.name is None:
new_id = dao.get_max_burst_id() + 1
burst_config.name = 'simulation_' + str(new_id)
else:
## Branch or Continue simulation
burst_config = burst_configuration
simulation_state = dao.get_generic_entity(SIMULATION_DATATYPE_MODULE + "." + SIMULATION_DATATYPE_CLASS,
burst_config.id, "fk_parent_burst")
if simulation_state is None or len(simulation_state) < 1:
exc = BurstServiceException("Simulation State not found for %s, "
"thus we are unable to branch from it!" % burst_config.name)
self.logger.error(exc)
raise exc
simulation_state = simulation_state[0]
burst_config.update_simulation_parameter("simulation_state", simulation_state.gid)
burst_config = burst_configuration.clone()
count = dao.count_bursts_with_name(burst_config.name, burst_config.fk_project)
burst_config.name = burst_config.name + "_" + launch_mode + str(count)
## 2. Create Operations and do the actual launch
if launch_mode in [LAUNCH_NEW, LAUNCH_BRANCH]:
## New Burst entry in the history
burst_id = self._store_burst_config(burst_config)
thread = threading.Thread(target=self._async_launch_and_prepare,
kwargs={'burst_config': burst_config,
'simulator_index': simulator_index,
'simulator_id': simulator_id,
'user_id': user_id})
thread.start()
return burst_id, burst_config.name
else:
## Continue simulation
## TODO
return burst_config.id, burst_config.name
@transactional
def _prepare_operations(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare all required operations for burst launch.
"""
project_id = burst_config.fk_project
burst_id = burst_config.id
workflow_step_list = []
starting_index = simulator_index + 1
sim_algo = FlowService().get_algorithm_by_identifier(simulator_id)
metadata = {DataTypeMetaData.KEY_BURST: burst_id}
launch_data = burst_config.get_all_simulator_values()[0]
operations, group = self.operation_service.prepare_operations(user_id, project_id, sim_algo,
sim_algo.algorithm_category, metadata,
**launch_data)
group_launched = group is not None
if group_launched:
starting_index += 1
for tab in burst_config.tabs:
for portlet_cfg in tab.portlets:
### For each portlet configuration stored, update the step index ###
### and also change the dynamic parameters step indexes to point ###
### to the simulator outputs. ##
if portlet_cfg is not None:
analyzers = portlet_cfg.analyzers
visualizer = portlet_cfg.visualizer
for entry in analyzers:
entry.step_index = starting_index
self.workflow_service.set_dynamic_step_references(entry, simulator_index)
workflow_step_list.append(entry)
starting_index += 1
### Change the dynamic parameters to point to the last adapter from this portlet execution.
visualizer.step_visible = False
if len(workflow_step_list) > 0 and isinstance(workflow_step_list[-1], model.WorkflowStep):
self.workflow_service.set_dynamic_step_references(visualizer, workflow_step_list[-1].step_index)
else:
self.workflow_service.set_dynamic_step_references(visualizer, simulator_index)
### Only for a single operation have the step of visualization, otherwise is useless.
if not group_launched:
workflow_step_list.append(visualizer)
if group_launched:
### For a group of operations, make sure the metric for PSE view
### is also computed, immediately after the simulation.
metric_algo = FlowService().get_algorithm_by_module_and_class(MEASURE_METRICS_MODULE, MEASURE_METRICS_CLASS)
metric_interface = FlowService().prepare_adapter(project_id, metric_algo)
dynamics = {}
for entry in metric_interface:
# We have a select that should be the dataType and a select multiple with the
# required metric algorithms to be evaluated. Only dynamic parameter should be
# the select type.
if entry[KEY_TYPE] == TYPE_SELECT:
dynamics[entry[KEY_NAME]] = {WorkflowStepConfiguration.DATATYPE_INDEX_KEY: 0,
WorkflowStepConfiguration.STEP_INDEX_KEY: simulator_index}
metric_step = model.WorkflowStep(algorithm_id=metric_algo.id, step_index=simulator_index + 1,
static_param={}, dynamic_param=dynamics)
metric_step.step_visible = False
workflow_step_list.insert(0, metric_step)
workflows = self.workflow_service.create_and_store_workflow(project_id, burst_id, simulator_index,
simulator_id, operations)
self.operation_service.prepare_operations_for_workflowsteps(workflow_step_list, workflows, user_id,
burst_id, project_id, group, operations)
operation_ids = [operation.id for operation in operations]
return operation_ids
def _async_launch_and_prepare(self, burst_config, simulator_index, simulator_id, user_id):
"""
Prepare operations asynchronously.
"""
try:
operation_ids = self._prepare_operations(burst_config, simulator_index, simulator_id, user_id)
self.logger.debug("Starting a total of %s workflows" % (len(operation_ids, )))
wf_errs = 0
for operation_id in operation_ids:
try:
OperationService().launch_operation(operation_id, True)
except Exception as excep:
self.logger.error(excep)
wf_errs += 1
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
self.logger.debug("Finished launching workflows. " + str(len(operation_ids) - wf_errs) +
" were launched successfully, " + str(wf_errs) + " had error on pre-launch steps")
except Exception as excep:
self.logger.error(excep)
self.workflow_service.mark_burst_finished(burst_config, error_message=str(excep))
@staticmethod
def launch_visualization(visualization, frame_width=None, frame_height=None, is_preview=True):
"""
:param visualization: a visualization workflow step
"""
dynamic_params = visualization.dynamic_param
static_params = visualization.static_param
parameters_dict = static_params
current_project_id = 0
# Current operation id needed for export mechanism. So far just use ##
# the operation of the workflow_step from which the inputs are taken ####
for param in dynamic_params:
step_index = dynamic_params[param][WorkflowStepConfiguration.STEP_INDEX_KEY]
datatype_index = dynamic_params[param][WorkflowStepConfiguration.DATATYPE_INDEX_KEY]
referred_workflow_step = dao.get_workflow_step_by_step_index(visualization.fk_workflow, step_index)
referred_operation_id = referred_workflow_step.fk_operation
referred_operation = dao.get_operation_by_id(referred_operation_id)
current_project_id = referred_operation.fk_launched_in
if type(datatype_index) is IntType:
# Entry is the output of a previous step ##
datatypes = dao.get_results_for_operation(referred_operation_id)
parameters_dict[param] = datatypes[datatype_index].gid
else:
# Entry is the input of a previous step ###
parameters_dict[param] = json.loads(referred_operation.parameters)[datatype_index]
algorithm = dao.get_algorithm_by_id(visualization.fk_algorithm)
adapter_instance = ABCAdapter.build_adapter(algorithm)
adapter_instance.current_project_id = current_project_id
prepared_inputs = adapter_instance.prepare_ui_inputs(parameters_dict)
if frame_width is not None:
prepared_inputs[ABCDisplayer.PARAM_FIGURE_SIZE] = (frame_width, frame_height)
if is_preview:
result = adapter_instance.generate_preview(**prepared_inputs)
else:
result = adapter_instance.launch(**prepared_inputs)
return result, parameters_dict
def update_history_status(self, id_list):
"""
For each burst_id received in the id_list read new status from DB and return a list [id, new_status] pair.
"""
result = []
for b_id in id_list:
burst = dao.get_burst_by_id(b_id)
burst.prepare_after_load()
if burst is not None:
if burst.status == burst.BURST_RUNNING:
running_time = datetime.now() - burst.start_time
else:
running_time = burst.finish_time - burst.start_time
running_time = format_timedelta(running_time, most_significant2=False)
if burst.status == burst.BURST_ERROR:
msg = 'Check Operations page for error Message'
else:
msg = ''
result.append([burst.id, burst.status, burst.is_group, msg, running_time])
else:
self.logger.debug("Could not find burst with id=" + str(b_id) + ". Might have been deleted by user!!")
return result
def stop_burst(self, burst_entity):
"""
Stop all the entities for the current burst and set the burst status to canceled.
"""
burst_wfs = dao.get_workflows_for_burst(burst_entity.id)
any_stopped = False
for workflow in burst_wfs:
wf_steps = dao.get_workflow_steps(workflow.id)
for step in wf_steps:
if step.fk_operation is not None:
self.logger.debug("We will stop operation: %d" % step.fk_operation)
any_stopped = self.operation_service.stop_operation(step.fk_operation) or any_stopped
if any_stopped and burst_entity.status != burst_entity.BURST_CANCELED:
self.workflow_service.mark_burst_finished(burst_entity, model.BurstConfiguration.BURST_CANCELED)
return True
return False
@transactional
def cancel_or_remove_burst(self, burst_id):
"""
Cancel (if burst is still running) or Remove the burst given by burst_id.
:returns True when Remove operation was done and False when Cancel
"""
burst_entity = dao.get_burst_by_id(burst_id)
if burst_entity.status == burst_entity.BURST_RUNNING:
self.stop_burst(burst_entity)
return False
service = ProjectService()
## Remove each DataType in current burst.
## We can not leave all on cascade, because it won't work on SQLite for mapped dataTypes.
datatypes = dao.get_all_datatypes_in_burst(burst_id)
## Get operations linked to current burst before removing the burst or else
## the burst won't be there to identify operations any more.
remaining_ops = dao.get_operations_in_burst(burst_id)
# Remove burst first to delete work-flow steps which still hold foreign keys to operations.
correct = dao.remove_entity(burst_entity.__class__, burst_id)
if not correct:
raise RemoveDataTypeException("Could not remove Burst entity!")
for datatype in datatypes:
service.remove_datatype(burst_entity.fk_project, datatype.gid, False)
## Remove all Operations remained.
correct = True
remaining_op_groups = set()
project = dao.get_project_by_id(burst_entity.fk_project)
for oper in remaining_ops:
is_remaining = dao.get_generic_entity(oper.__class__, oper.id)
if len(is_remaining) == 0:
### Operation removed cascaded.
continue
if oper.fk_operation_group is not None and oper.fk_operation_group not in remaining_op_groups:
is_remaining = dao.get_generic_entity(model.OperationGroup, oper.fk_operation_group)
if len(is_remaining) > 0:
remaining_op_groups.add(oper.fk_operation_group)
correct = correct and dao.remove_entity(model.OperationGroup, oper.fk_operation_group)
correct = correct and dao.remove_entity(oper.__class__, oper.id)
service.structure_helper.remove_operation_data(project.name, oper.id)
if not correct:
raise RemoveDataTypeException("Could not remove Burst because a linked operation could not be dropped!!")
return True
@staticmethod
def get_portlet_status(portlet_cfg):
"""
Get the status of a portlet configuration.
"""
if portlet_cfg.analyzers:
for analyze_step in portlet_cfg.analyzers:
operation = dao.try_get_operation_by_id(analyze_step.fk_operation)
if operation is None:
return model.STATUS_ERROR, "Operation has been removed"
if operation.status != model.STATUS_FINISHED:
return operation.status, operation.additional_info or ''
else:
## Simulator is first step so now decide if we are waiting for input or output ##
visualizer = portlet_cfg.visualizer
wait_on_outputs = False
for entry in visualizer.dynamic_param:
if type(visualizer.dynamic_param[entry][WorkflowStepConfiguration.DATATYPE_INDEX_KEY]) == IntType:
wait_on_outputs = True
break
if wait_on_outputs:
simulator_step = dao.get_workflow_step_by_step_index(visualizer.fk_workflow, 0)
operation = dao.try_get_operation_by_id(simulator_step.fk_operation)
if operation is None:
error_msg = ("At least one simulation result was not found, it might have been removed. <br\>"
"You can copy and relaunch current simulation, if you are interested in having "
"your results re-computed.")
return model.STATUS_ERROR, error_msg
else:
return operation.status, operation.additional_info or ''
return model.STATUS_FINISHED, ''
