def cleanup_job_execution(self, job_exe):
"""See :meth:`job.execution.job_exe_cleaner.JobExecutionCleaner.cleanup_job_execution`
"""
logger.info('Cleaning up a non-system job')
download_dir = get_job_exe_input_data_dir(job_exe.id)
download_work_dir = get_job_exe_input_work_dir(job_exe.id)
upload_dir = get_job_exe_output_data_dir(job_exe.id)
upload_work_dir = get_job_exe_output_work_dir(job_exe.id)
logger.info('Cleaning up download directory')
ScaleFile.objects.cleanup_download_dir(download_dir, download_work_dir)
logger.info('Cleaning up upload directories')
workspace_ids = job_exe.job.get_job_data().get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
logger.info('Cleaning up upload directory for workspace %s',
workspace.name)
ScaleFile.objects.cleanup_upload_dir(upload_dir, upload_work_dir,
workspace)
move_work_dir = os.path.join(upload_work_dir,
'move_source_file_in_workspace')
if os.path.exists(move_work_dir):
logger.info(
'Cleaning up work directory for moving parsed source files')
ScaleFile.objects.cleanup_move_dir(move_work_dir)
logger.info('Deleting %s', move_work_dir)
os.rmdir(move_work_dir)
delete_normal_job_exe_dir_tree(job_exe.id)
def test_successful(self, mock_file_call, mock_file_list_call, mock_store,
mock_isfile):
'''Tests calling JobData.store_output_data_files() successfully'''
def new_isfile(path):
return True
mock_isfile.side_effect = new_isfile
job_exe = MagicMock()
job_exe.id = 1
upload_dir = get_job_exe_output_data_dir(job_exe.id)
data = {
u'output_data': [{
u'name': u'Param1',
u'workspace_id': 1
}, {
u'name': u'Param2',
u'workspace_id': 2
}]
}
file_path_1 = os.path.join(upload_dir, u'path', u'1', u'my_file.txt')
file_path_2 = os.path.join(upload_dir, u'path', u'2', u'my_file_2.txt')
file_path_3 = os.path.join(upload_dir, u'path', u'3', u'my_file_3.txt')
data_files = {
u'Param1': (file_path_1, None),
u'Param2': [(file_path_2, u'text/plain'), (file_path_3, None)]
}
JobData(data).store_output_data_files(data_files, job_exe)
mock_file_call.assert_called_once_with(u'Param1', long(1))
self.assertEqual(u'Param2', mock_file_list_call.call_args[0][0])
self.assertSetEqual(set([long(3), long(2)]),
set(mock_file_list_call.call_args[0][1]))
def setup_job_dir(self, data_files, job_exe_id):
'''Sets up the directory structure for a job execution and downloads the given files
:param data_files: Dict with each file parameter name mapping to a bool indicating if the parameter accepts
multiple files (True) and a relative directory path
:type data_files: dict of str -> tuple(bool, str)
:param job_exe_id: The job execution ID
:type job_exe_id: int
:returns: Dict with each file parameter name mapping to a list of absolute file paths of the written files
:rtype: dict of str -> list of str
'''
download_dir = get_job_exe_input_data_dir(job_exe_id)
download_work_dir = get_job_exe_input_work_dir(job_exe_id)
upload_dir = get_job_exe_output_data_dir(job_exe_id)
upload_work_dir = get_job_exe_output_work_dir(job_exe_id)
# Download the job execution input files
self.retrieve_input_data_files(download_dir, download_work_dir,
data_files)
# Set up upload directories for output workspace
workspace_ids = self.get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
ScaleFile.objects.setup_upload_dir(upload_dir, upload_work_dir,
workspace)
def cleanup_job_execution(self, job_exe):
"""See :meth:`job.execution.job_exe_cleaner.JobExecutionCleaner.cleanup_job_execution`
"""
logger.info('Cleaning up a non-system job')
download_dir = get_job_exe_input_data_dir(job_exe.id)
download_work_dir = get_job_exe_input_work_dir(job_exe.id)
upload_dir = get_job_exe_output_data_dir(job_exe.id)
upload_work_dir = get_job_exe_output_work_dir(job_exe.id)
logger.info('Cleaning up download directory')
ScaleFile.objects.cleanup_download_dir(download_dir, download_work_dir)
logger.info('Cleaning up upload directories')
workspace_ids = job_exe.job.get_job_data().get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
logger.info('Cleaning up upload directory for workspace %s', workspace.name)
ScaleFile.objects.cleanup_upload_dir(upload_dir, upload_work_dir, workspace)
move_work_dir = os.path.join(upload_work_dir, 'move_source_file_in_workspace')
if os.path.exists(move_work_dir):
logger.info('Cleaning up work directory for moving parsed source files')
ScaleFile.objects.cleanup_move_dir(move_work_dir)
logger.info('Deleting %s', move_work_dir)
os.rmdir(move_work_dir)
delete_normal_job_exe_dir_tree(job_exe.id)
def test_file_in_command(self, mock_retrieve_call, mock_os_mkdir, mock_get_one_file, mock_setup_upload):
job_work_dir = "/test"
job_exe_id = 1
job_input_dir = get_job_exe_input_data_dir(job_exe_id)
job_output_dir = os.path.join(job_work_dir, u'outputs')
def new_retrieve(arg1, arg2, arg3):
return {u'file1_out': [input_file_path]}
input_file_path = os.path.join(job_input_dir, 'file1', 'foo.txt')
mock_retrieve_call.side_effect = new_retrieve
mock_get_one_file.side_effect = lambda(arg1): input_file_path
job_interface_dict, job_data_dict, job_environment_dict = self._get_simple_interface_data_env()
job_interface_dict[u'command_arguments'] = u'${file1}'
job_interface_dict[u'input_data'] = [{u'name' : u'file1', u'type' : u'file', 'required' : True}]
job_data_dict[u'input_data'].append({u'name': u'file1', u'file_id': 1})
job_data_dict[u'output_data'].append({u'name': u'file1_out', u'workspace_id': self.workspace.id})
job_interface = JobInterface(job_interface_dict)
job_data = JobData(job_data_dict)
job_environment = JobEnvironment(job_environment_dict)
job_interface.perform_pre_steps(job_data, job_environment, job_exe_id)
job_command_arguments = job_interface.fully_populate_command_argument(job_data, job_environment, job_exe_id)
self.assertEqual(job_command_arguments, input_file_path, u'expected a different command from pre_steps')
mock_setup_upload.assert_called_once_with(get_job_exe_output_data_dir(job_exe_id), get_job_exe_output_work_dir(job_exe_id), self.workspace)
def setup_job_dir(self, data_files, job_exe_id):
"""Sets up the directory structure for a job execution and downloads the given files
:param data_files: Dict with each file parameter name mapping to a bool indicating if the parameter accepts
multiple files (True) and a relative directory path
:type data_files: dict of str -> tuple(bool, str)
:param job_exe_id: The job execution ID
:type job_exe_id: int
:returns: Dict with each file parameter name mapping to a list of absolute file paths of the written files
:rtype: dict of str -> list of str
"""
download_dir = get_job_exe_input_data_dir(job_exe_id)
download_work_dir = get_job_exe_input_work_dir(job_exe_id)
upload_dir = get_job_exe_output_data_dir(job_exe_id)
upload_work_dir = get_job_exe_output_work_dir(job_exe_id)
# Download the job execution input files
self.retrieve_input_data_files(download_dir, download_work_dir, data_files)
# Set up upload directories for output workspace
workspace_ids = self.get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
ScaleFile.objects.setup_upload_dir(upload_dir, upload_work_dir, workspace)
# If the upload dir did not get created (e.g. no output files), make sure it gets created for results manifests
if not os.path.exists(upload_dir):
logger.info("Creating %s", upload_dir)
os.makedirs(upload_dir, mode=0755)
def handle(self, **options):
'''See :meth:`django.core.management.base.BaseCommand.handle`.
This method starts the command.
'''
exe_id = options.get('job_exe_id')
logger.info('Command starting: scale_pre_steps - Job Execution ID: %i', exe_id)
try:
node_work_dir = settings.NODE_WORK_DIR
job_exe = JobExecution.objects.get_job_exe_with_job_and_job_type(exe_id)
job_dir = file_system.get_job_exe_dir(exe_id, node_work_dir)
input_dir = file_system.get_job_exe_input_dir(exe_id, node_work_dir)
output_dir = file_system.get_job_exe_output_dir(exe_id, node_work_dir)
job_dirs = [job_dir, input_dir, output_dir]
for target_dir in job_dirs:
self._create_job_dir(exe_id, target_dir)
job_interface = job_exe.get_job_interface()
job_data = job_exe.job.get_job_data()
job_environment = job_exe.get_job_environment()
job_interface.perform_pre_steps(job_data, job_environment, exe_id)
command_args = job_interface.fully_populate_command_argument(job_data, job_environment, exe_id)
# This shouldn't be necessary once we have user namespaces in docker
self._chmod_job_dir(file_system.get_job_exe_output_data_dir(exe_id))
# Perform a force pull for docker jobs to get the latest version of the image before running
# TODO: Remove this hack in favor of the feature in Mesos 0.22.x, see MESOS-1886 for details
docker_image = job_exe.job.job_type.docker_image
if docker_image:
logger.info('Pulling latest docker image: %s', docker_image)
try:
subprocess.check_call(['sudo', 'docker', 'pull', docker_image])
except subprocess.CalledProcessError:
logger.exception('Docker pull returned unexpected exit code.')
except OSError:
logger.exception('OS unable to run docker pull command.')
logger.info('Executing job: %i -> %s', exe_id, ' '.join(command_args))
JobExecution.objects.pre_steps_command_arguments(exe_id, command_args)
except Exception as e:
logger.exception('Job Execution %i: Error performing pre-job steps', exe_id)
exit_code = -1
if isinstance(e, DatabaseError):
exit_code = DB_EXIT_CODE
elif isinstance(e, NfsError):
exit_code = NFS_EXIT_CODE
elif isinstance(e, IOError):
exit_code = IO_EXIT_CODE
sys.exit(exit_code)
logger.info('Command completed: scale_pre_steps')
def test_output_dir_in_command(self):
job_interface_dict, job_data_dict, job_environment_dict = self._get_simple_interface_data_env()
job_interface_dict['command_arguments'] = '${job_output_dir}'
job_interface = JobInterface(job_interface_dict)
job_data = JobData(job_data_dict)
job_environment = JobEnvironment(job_environment_dict)
job_exe_id = 1
job_output_dir = file_system.get_job_exe_output_data_dir(job_exe_id)
job_interface.perform_pre_steps(job_data, job_environment, 1)
job_command_arguments = job_interface.fully_populate_command_argument(job_data, job_environment, job_exe_id)
self.assertEqual(job_command_arguments, job_output_dir, 'expected a different command from pre_steps')
def handle(self, **options):
'''See :meth:`django.core.management.base.BaseCommand.handle`.
This method starts the command.
'''
job_exe_id = options.get('job_exe_id')
logger.info('Command starting: scale_pre_steps - Job Execution ID: %i', job_exe_id)
try:
job_exe = self._get_job_exe(job_exe_id)
file_system.create_job_exe_dir(job_exe_id)
file_system.create_normal_job_exe_dir_tree(job_exe_id)
job_interface = job_exe.get_job_interface()
job_data = job_exe.job.get_job_data()
job_environment = job_exe.get_job_environment()
job_interface.perform_pre_steps(job_data, job_environment, job_exe_id)
command_args = job_interface.fully_populate_command_argument(job_data, job_environment, job_exe_id)
# This shouldn't be necessary once we have user namespaces in docker
self._chmod_job_dir(file_system.get_job_exe_output_data_dir(job_exe_id))
# Perform a force pull for docker jobs to get the latest version of the image before running
# TODO: Remove this hack in favor of the feature in Mesos 0.22.x, see MESOS-1886 for details
docker_image = job_exe.job.job_type.docker_image
if docker_image:
logger.info('Pulling latest docker image: %s', docker_image)
try:
subprocess.check_call(['sudo', 'docker', 'pull', docker_image])
except subprocess.CalledProcessError:
logger.exception('Docker pull returned unexpected exit code.')
except OSError:
logger.exception('OS unable to run docker pull command.')
logger.info('Executing job: %i -> %s', job_exe_id, ' '.join(command_args))
self._populate_command_arguments(job_exe_id, command_args)
except Exception as ex:
logger.exception('Job Execution %i: Error performing pre-job steps', job_exe_id)
exit_code = -1
if isinstance(ex, DatabaseError):
exit_code = DB_EXIT_CODE
elif isinstance(ex, NfsError):
exit_code = NFS_EXIT_CODE
elif isinstance(ex, IOError):
exit_code = IO_EXIT_CODE
sys.exit(exit_code)
logger.info('Command completed: scale_pre_steps')
def test_output_dir_in_command(self):
job_interface_dict, job_data_dict, job_environment_dict = self._get_simple_interface_data_env(
)
job_interface_dict['command_arguments'] = '${job_output_dir}'
job_interface = JobInterface(job_interface_dict)
job_data = JobData(job_data_dict)
job_environment = JobEnvironment(job_environment_dict)
job_exe_id = 1
job_output_dir = file_system.get_job_exe_output_data_dir(job_exe_id)
job_interface.perform_pre_steps(job_data, job_environment, 1)
job_command_arguments = job_interface.fully_populate_command_argument(
job_data, job_environment, job_exe_id)
self.assertEqual(job_command_arguments, job_output_dir,
'expected a different command from pre_steps')
def test_file_in_command(self, mock_retrieve_call, mock_os_mkdir,
mock_get_one_file, mock_setup_upload):
job_exe_id = 1
job_input_dir = file_system.get_job_exe_input_data_dir(job_exe_id)
def new_retrieve(arg1, arg2, arg3):
return {
'file1_out': [input_file_path],
}
input_file_path = os.path.join(job_input_dir, 'file1', 'foo.txt')
mock_retrieve_call.side_effect = new_retrieve
mock_get_one_file.side_effect = lambda (arg1): input_file_path
job_interface_dict, job_data_dict, job_environment_dict = self._get_simple_interface_data_env(
)
job_interface_dict['command_arguments'] = '${file1}'
job_interface_dict['input_data'] = [{
'name': 'file1',
'type': 'file',
'required': True,
}]
job_data_dict['input_data'].append({
'name': 'file1',
'file_id': self.file.id,
})
job_data_dict['output_data'].append({
'name': 'file1_out',
'workspace_id': self.workspace.id,
})
job_interface = JobInterface(job_interface_dict)
job_data = JobData(job_data_dict)
job_environment = JobEnvironment(job_environment_dict)
job_interface.perform_pre_steps(job_data, job_environment, job_exe_id)
job_command_arguments = job_interface.fully_populate_command_argument(
job_data, job_environment, job_exe_id)
self.assertEqual(job_command_arguments, input_file_path,
'expected a different command from pre_steps')
mock_setup_upload.assert_called_once_with(
file_system.get_job_exe_output_data_dir(job_exe_id),
file_system.get_job_exe_output_work_dir(job_exe_id),
self.workspace)
def test_successful(self, mock_file_call, mock_file_list_call, mock_store, mock_isfile):
'''Tests calling JobData.store_output_data_files() successfully'''
def new_isfile(path):
return True
mock_isfile.side_effect = new_isfile
job_exe = MagicMock()
job_exe.id = 1
upload_dir = get_job_exe_output_data_dir(job_exe.id)
data = {u'output_data': [{u'name': u'Param1', u'workspace_id': 1},
{u'name': u'Param2', u'workspace_id': 2}]}
file_path_1 = os.path.join(upload_dir, u'path', u'1', u'my_file.txt')
file_path_2 = os.path.join(upload_dir, u'path', u'2', u'my_file_2.txt')
file_path_3 = os.path.join(upload_dir, u'path', u'3', u'my_file_3.txt')
data_files = {u'Param1': (file_path_1, None), u'Param2': [(file_path_2, u'text/plain'), (file_path_3, None)]}
JobData(data).store_output_data_files(data_files, job_exe)
mock_file_call.assert_called_once_with(u'Param1', long(1))
self.assertEqual(u'Param2', mock_file_list_call.call_args[0][0])
self.assertSetEqual(set([long(3), long(2)]), set(mock_file_list_call.call_args[0][1]))
def cleanup_job_execution(self, job_exe):
'''See :meth:`job.execution.job_exe_cleaner.JobExecutionCleaner.cleanup_job_execution`
'''
logger.info('Cleaning up a non-system job')
download_dir = get_job_exe_input_data_dir(job_exe.id)
download_work_dir = get_job_exe_input_work_dir(job_exe.id)
upload_dir = get_job_exe_output_data_dir(job_exe.id)
upload_work_dir = get_job_exe_output_work_dir(job_exe.id)
logger.info('Cleaning up download directory')
ScaleFile.objects.cleanup_download_dir(download_dir, download_work_dir)
logger.info('Cleaning up upload directories')
workspace_ids = job_exe.job.get_job_data().get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
logger.info('Cleaning up upload directory for workspace %s', workspace.name)
ScaleFile.objects.cleanup_upload_dir(upload_dir, upload_work_dir, workspace)
save_job_exe_metrics(job_exe)
def fully_populate_command_argument(self, job_data, job_environment,
job_exe_id):
'''Return a fully populated command arguments string. If pre-steps are necessary
(see are_pre_steps_needed), they should be run before this. populated with information
from the job_data, job_environment, job_input_dir, and job_output_dir.This gets the properties and
input_files from the job_data, the shared_resources from the job_environment, and ${input}
${output_dir} from the work_dir.
Throws a :class:`job.configuration.interface.exceptions.InvalidEnvironment` if the necessary
pre-steps have not been performed
:param job_data: The job data
:type job_data: :class:`job.configuration.data.job_data.JobData`
:param job_environment: The job environment
:type job_environment: :class:`job.configuration.environment.job_environment.JobEnvironment`
:param job_exe_id: The job execution ID
:type job_exe_id: int
'''
# TODO: don't ignore job_envirnoment
command_arguments = self.populate_command_argument_properties(job_data)
job_input_dir = get_job_exe_input_data_dir(job_exe_id)
job_output_dir = get_job_exe_output_data_dir(job_exe_id)
for input_data in self.definition['input_data']:
input_name = input_data['name']
input_type = input_data['type']
if input_type == 'file':
param_dir = os.path.join(job_input_dir, input_name)
file_path = self._get_one_file_from_directory(param_dir)
command_arguments = self._replace_command_parameter(
command_arguments, input_name, file_path)
elif input_type == 'files':
#TODO: verify folder exists
param_dir = os.path.join(job_input_dir, input_name)
command_arguments = self._replace_command_parameter(
command_arguments, input_name, param_dir)
command_arguments = self._replace_command_parameter(
command_arguments, 'job_output_dir', job_output_dir)
return command_arguments
def setup_job_dir(self, data_files, job_exe_id):
'''Sets up the directory structure for a job execution and downloads the given files
:param data_files: Dict with each file parameter name mapping to a bool indicating if the parameter accepts
multiple files (True) and a relative directory path
:type data_files: dict of str -> tuple(bool, str)
:param job_exe_id: The job execution ID
:type job_exe_id: int
:returns: Dict with each file parameter name mapping to a list of absolute file paths of the written files
:rtype: dict of str -> list of str
'''
download_dir = get_job_exe_input_data_dir(job_exe_id)
download_work_dir = get_job_exe_input_work_dir(job_exe_id)
upload_dir = get_job_exe_output_data_dir(job_exe_id)
upload_work_dir = get_job_exe_output_work_dir(job_exe_id)
# Download the job execution input files
self.retrieve_input_data_files(download_dir, download_work_dir, data_files)
# Set up upload directories for output workspace
workspace_ids = self.get_output_workspace_ids()
for workspace in Workspace.objects.filter(id__in=workspace_ids):
ScaleFile.objects.setup_upload_dir(upload_dir, upload_work_dir, workspace)
def fully_populate_command_argument(self, job_data, job_environment, job_exe_id):
'''Return a fully populated command arguments string. If pre-steps are necessary
(see are_pre_steps_needed), they should be run before this. populated with information
from the job_data, job_environment, job_input_dir, and job_output_dir.This gets the properties and
input_files from the job_data, the shared_resources from the job_environment, and ${input}
${output_dir} from the work_dir.
Throws a :class:`job.configuration.interface.exceptions.InvalidEnvironment` if the necessary
pre-steps have not been performed
:param job_data: The job data
:type job_data: :class:`job.configuration.data.job_data.JobData`
:param job_environment: The job environment
:type job_environment: :class:`job.configuration.environment.job_environment.JobEnvironment`
:param job_exe_id: The job execution ID
:type job_exe_id: int
'''
# TODO: don't ignore job_envirnoment
command_arguments = self.populate_command_argument_properties(job_data)
job_input_dir = get_job_exe_input_data_dir(job_exe_id)
job_output_dir = get_job_exe_output_data_dir(job_exe_id)
for input_data in self.definition['input_data']:
input_name = input_data['name']
input_type = input_data['type']
if input_type == 'file':
param_dir = os.path.join(job_input_dir, input_name)
file_path = self._get_one_file_from_directory(param_dir)
command_arguments = self._replace_command_parameter(command_arguments, input_name, file_path)
elif input_type == 'files':
#TODO: verify folder exists
param_dir = os.path.join(job_input_dir, input_name)
command_arguments = self._replace_command_parameter(command_arguments, input_name, param_dir)
command_arguments = self._replace_command_parameter(command_arguments, 'job_output_dir', job_output_dir)
return command_arguments
def test_files_in_command(self, mock_retrieve_call, mock_os_mkdir, mock_setup_upload):
def new_retrieve(arg1, arg2, arg3):
return {
'files1_out': ['/test/file1/foo.txt', '/test/file1/bar.txt'],
}
mock_retrieve_call.side_effect = new_retrieve
job_interface_dict, job_data_dict, job_environment_dict = self._get_simple_interface_data_env()
job_interface_dict['command_arguments'] = '${files1}'
job_interface_dict['input_data'] = [{
'name': 'files1',
'type': 'files',
'required': True,
}]
job_data_dict['input_data'].append({
'name': 'files1',
'file_ids': [1, 2, 3],
})
job_data_dict['output_data'].append({
'name': 'files1_out',
'workspace_id': self.workspace.id,
})
job_interface = JobInterface(job_interface_dict)
job_data = JobData(job_data_dict)
job_environment = JobEnvironment(job_environment_dict)
job_exe_id = 1
job_input_dir = file_system.get_job_exe_input_data_dir(job_exe_id)
job_interface.perform_pre_steps(job_data, job_environment, 1)
job_command_arguments = job_interface.fully_populate_command_argument(job_data, job_environment, job_exe_id)
expected_command_arguments = os.path.join(job_input_dir, 'files1')
self.assertEqual(job_command_arguments, expected_command_arguments,
'expected a different command from pre_steps')
mock_setup_upload.assert_called_once_with(file_system.get_job_exe_output_data_dir(job_exe_id),
file_system.get_job_exe_output_work_dir(job_exe_id), self.workspace)
def perform_post_steps(self, job_exe, job_data, stdoutAndStderr):
'''Stores the files and deletes any working directories
:param job_exe: The job execution model with related job and job_type fields
:type job_exe: :class:`job.models.JobExecution`
:param job_data: The job data
:type job_data: :class:`job.configuration.data.job_data.JobData`
:param stdoutAndStderr: the standard out from the job execution
:type stdoutAndStderr: str
:return: A tuple of the job results and the results manifest generated by the job execution
:rtype: (:class:`job.configuration.results.job_results.JobResults`,
:class:`job.configuration.results.results_manifest.results_manifest.ResultsManifest`)
'''
manifest_data = {}
job_output_dir = get_job_exe_output_data_dir(job_exe.id)
path_to_manifest_file = os.path.join(job_output_dir, 'results_manifest.json')
if os.path.exists(path_to_manifest_file):
logger.info('Opening results manifest...')
with open(path_to_manifest_file, 'r') as manifest_file:
manifest_data = json.loads(manifest_file.read())
logger.info('Results manifest:')
logger.info(manifest_data)
else:
logger.info('No results manifest found')
results_manifest = ResultsManifest(manifest_data)
stdout_files = self._get_artifacts_from_stdout(stdoutAndStderr)
results_manifest.add_files(stdout_files)
results_manifest.validate(self._output_file_manifest_dict)
files_to_store = {}
for manifest_file_entry in results_manifest.get_files():
param_name = manifest_file_entry['name']
media_type = None
output_data_item = self._get_output_data_item_by_name(param_name)
if output_data_item:
media_type = output_data_item.get('media_type')
if 'file' in manifest_file_entry:
file_entry = manifest_file_entry['file']
if 'geo_metadata' in file_entry:
files_to_store[param_name] = (file_entry['path'], media_type, file_entry['geo_metadata'])
else:
files_to_store[param_name] = (file_entry['path'], media_type)
elif 'files' in manifest_file_entry:
file_tuples = []
for file_entry in manifest_file_entry['files']:
if 'geo_metadata' in file_entry:
file_tuples.append((file_entry['path'], media_type, file_entry['geo_metadata']))
else:
file_tuples.append((file_entry['path'], media_type))
files_to_store[param_name] = file_tuples
job_data_parse_results = {} # parse results formatted for job_data
for parse_result in results_manifest.get_parse_results():
filename = parse_result['filename']
assert filename not in job_data_parse_results
geo_metadata = parse_result.get('geo_metadata', {})
geo_json = geo_metadata.get('geo_json', None)
data_started = geo_metadata.get('data_started', None)
data_ended = geo_metadata.get('data_ended', None)
data_types = parse_result.get('data_types', [])
new_workspace_path = parse_result.get('new_workspace_path', None)
work_dir = None
if new_workspace_path:
new_workspace_path = os.path.join(new_workspace_path, filename)
work_dir = os.path.join(get_job_exe_output_work_dir(job_exe.id), 'move_source_file_in_workspace')
job_data_parse_results[filename] = (geo_json, data_started, data_ended, data_types, new_workspace_path,
work_dir)
job_data.save_parse_results(job_data_parse_results)
return (job_data.store_output_data_files(files_to_store, job_exe), results_manifest)
def store_output_data_files(self, data_files, job_exe):
'''Stores the given data output files
:param data_files: Dict with each file parameter name mapping to a tuple of absolute local file path and media
type (media type is optionally None) for a single file parameter and a list of tuples for a multiple file
parameter
:type data_files: dict of str -> tuple(str, str) or list of tuple(str, str)
:param job_exe: The job execution model (with related job and job_type fields) that is storing the output data
files
:type job_exe: :class:`job.models.JobExecution`
:returns: The job results
:rtype: :class:`job.configuration.results.job_results.JobResults`
'''
upload_dir = get_job_exe_output_data_dir(job_exe.id)
work_dir = get_job_exe_output_work_dir(job_exe.id)
# Organize the data files
workspace_files = {
} # Workspace ID -> list of (absolute local file path, media type)
params_by_file_path = {
} # Absolute local file path -> output parameter name
for name in data_files:
file_output = self.data_outputs_by_name[name]
workspace_id = file_output[u'workspace_id']
if workspace_id in workspace_files:
workspace_file_list = workspace_files[workspace_id]
else:
workspace_file_list = []
workspace_files[workspace_id] = workspace_file_list
data_file_entry = data_files[name]
if isinstance(data_file_entry, list):
for file_tuple in data_file_entry:
file_path = os.path.normpath(file_tuple[0])
if not os.path.isfile(file_path):
raise Exception('%s is not a valid file' % file_path)
params_by_file_path[file_path] = name
# Adjust file path to be relative to upload_dir
if len(file_tuple) == 2:
new_tuple = (os.path.relpath(file_path, upload_dir),
file_tuple[1])
else:
new_tuple = (os.path.relpath(file_path, upload_dir),
file_tuple[1], file_tuple[2])
workspace_file_list.append(new_tuple)
else:
file_path = os.path.normpath(data_file_entry[0])
if not os.path.isfile(file_path):
raise Exception('%s is not a valid file' % file_path)
params_by_file_path[file_path] = name
# Adjust file path to be relative to upload_dir
if len(data_file_entry) == 2:
new_tuple = (os.path.relpath(file_path, upload_dir),
data_file_entry[1])
else:
new_tuple = (os.path.relpath(file_path, upload_dir),
data_file_entry[1], data_file_entry[2])
workspace_file_list.append(new_tuple)
data_file_store = DATA_FILE_STORE[u'DATA_FILE_STORE']
if not data_file_store:
raise Exception(u'No data file store found')
stored_files = data_file_store.store_files(upload_dir, work_dir,
workspace_files,
self.get_input_file_ids(),
job_exe)
# Organize results
param_file_ids = {
} # Output parameter name -> file ID or list of file IDs
for file_path in stored_files:
file_id = stored_files[file_path]
name = params_by_file_path[file_path]
if isinstance(data_files[name], list):
if name in param_file_ids:
file_id_list = param_file_ids[name]
else:
file_id_list = []
param_file_ids[name] = file_id_list
file_id_list.append(file_id)
else:
param_file_ids[name] = file_id
# Create job results
results = JobResults()
for name in param_file_ids:
param_entry = param_file_ids[name]
if isinstance(param_entry, list):
results.add_file_list_parameter(name, param_entry)
else:
results.add_file_parameter(name, param_entry)
return results
def store_output_data_files(self, data_files, job_exe):
"""Stores the given data output files
:param data_files: Dict with each file parameter name mapping to a tuple of absolute local file path and media
type (media type is optionally None) for a single file parameter and a list of tuples for a multiple file
parameter
:type data_files: dict of str -> tuple(str, str) or list of tuple(str, str)
:param job_exe: The job execution model (with related job and job_type fields) that is storing the output data
files
:type job_exe: :class:`job.models.JobExecution`
:returns: The job results
:rtype: :class:`job.configuration.results.job_results.JobResults`
"""
upload_dir = get_job_exe_output_data_dir(job_exe.id)
work_dir = get_job_exe_output_work_dir(job_exe.id)
# Organize the data files
workspace_files = {} # Workspace ID -> list of (absolute local file path, media type)
params_by_file_path = {} # Absolute local file path -> output parameter name
for name in data_files:
file_output = self.data_outputs_by_name[name]
workspace_id = file_output[u"workspace_id"]
if workspace_id in workspace_files:
workspace_file_list = workspace_files[workspace_id]
else:
workspace_file_list = []
workspace_files[workspace_id] = workspace_file_list
data_file_entry = data_files[name]
if isinstance(data_file_entry, list):
for file_tuple in data_file_entry:
file_path = os.path.normpath(file_tuple[0])
if not os.path.isfile(file_path):
raise Exception("%s is not a valid file" % file_path)
params_by_file_path[file_path] = name
# Adjust file path to be relative to upload_dir
if len(file_tuple) == 2:
new_tuple = (os.path.relpath(file_path, upload_dir), file_tuple[1])
else:
new_tuple = (os.path.relpath(file_path, upload_dir), file_tuple[1], file_tuple[2])
workspace_file_list.append(new_tuple)
else:
file_path = os.path.normpath(data_file_entry[0])
if not os.path.isfile(file_path):
raise Exception("%s is not a valid file" % file_path)
params_by_file_path[file_path] = name
# Adjust file path to be relative to upload_dir
if len(data_file_entry) == 2:
new_tuple = (os.path.relpath(file_path, upload_dir), data_file_entry[1])
else:
new_tuple = (os.path.relpath(file_path, upload_dir), data_file_entry[1], data_file_entry[2])
workspace_file_list.append(new_tuple)
data_file_store = DATA_FILE_STORE[u"DATA_FILE_STORE"]
if not data_file_store:
raise Exception(u"No data file store found")
stored_files = data_file_store.store_files(
upload_dir, work_dir, workspace_files, self.get_input_file_ids(), job_exe
)
# Organize results
param_file_ids = {} # Output parameter name -> file ID or list of file IDs
for file_path in stored_files:
file_id = stored_files[file_path]
name = params_by_file_path[file_path]
if isinstance(data_files[name], list):
if name in param_file_ids:
file_id_list = param_file_ids[name]
else:
file_id_list = []
param_file_ids[name] = file_id_list
file_id_list.append(file_id)
else:
param_file_ids[name] = file_id
# Create job results
results = JobResults()
for name in param_file_ids:
param_entry = param_file_ids[name]
if isinstance(param_entry, list):
results.add_file_list_parameter(name, param_entry)
else:
results.add_file_parameter(name, param_entry)
return results
def perform_post_steps(self, job_exe, job_data, stdoutAndStderr):
'''Stores the files and deletes any working directories
:param job_exe: The job execution model with related job and job_type fields
:type job_exe: :class:`job.models.JobExecution`
:param job_data: The job data
:type job_data: :class:`job.configuration.data.job_data.JobData`
:param stdoutAndStderr: the standard out from the job execution
:type stdoutAndStderr: str
:return: A tuple of the job results and the results manifest generated by the job execution
:rtype: (:class:`job.configuration.results.job_results.JobResults`,
:class:`job.configuration.results.results_manifest.results_manifest.ResultsManifest`)
'''
manifest_data = {}
job_output_dir = get_job_exe_output_data_dir(job_exe.id)
path_to_manifest_file = os.path.join(job_output_dir,
'results_manifest.json')
if os.path.exists(path_to_manifest_file):
logger.info('Opening results manifest...')
with open(path_to_manifest_file, 'r') as manifest_file:
manifest_data = json.loads(manifest_file.read())
logger.info('Results manifest:')
logger.info(manifest_data)
else:
logger.info('No results manifest found')
results_manifest = ResultsManifest(manifest_data)
stdout_files = self._get_artifacts_from_stdout(stdoutAndStderr)
results_manifest.add_files(stdout_files)
results_manifest.validate(self._output_file_manifest_dict)
files_to_store = {}
for manifest_file_entry in results_manifest.get_files():
param_name = manifest_file_entry['name']
media_type = None
output_data_item = self._get_output_data_item_by_name(param_name)
if output_data_item:
media_type = output_data_item.get('media_type')
if 'file' in manifest_file_entry:
file_entry = manifest_file_entry['file']
if 'geo_metadata' in file_entry:
files_to_store[param_name] = (file_entry['path'],
media_type,
file_entry['geo_metadata'])
else:
files_to_store[param_name] = (file_entry['path'],
media_type)
elif 'files' in manifest_file_entry:
file_tuples = []
for file_entry in manifest_file_entry['files']:
if 'geo_metadata' in file_entry:
file_tuples.append((file_entry['path'], media_type,
file_entry['geo_metadata']))
else:
file_tuples.append((file_entry['path'], media_type))
files_to_store[param_name] = file_tuples
job_data_parse_results = {} # parse results formatted for job_data
for parse_result in results_manifest.get_parse_results():
filename = parse_result['filename']
assert filename not in job_data_parse_results
geo_metadata = parse_result.get('geo_metadata', {})
geo_json = geo_metadata.get('geo_json', None)
data_started = geo_metadata.get('data_started', None)
data_ended = geo_metadata.get('data_ended', None)
data_types = parse_result.get('data_types', [])
new_workspace_path = parse_result.get('new_workspace_path', None)
work_dir = None
if new_workspace_path:
new_workspace_path = os.path.join(new_workspace_path, filename)
work_dir = os.path.join(
get_job_exe_output_work_dir(job_exe.id),
'move_source_file_in_workspace')
job_data_parse_results[filename] = (geo_json, data_started,
data_ended, data_types,
new_workspace_path, work_dir)
job_data.save_parse_results(job_data_parse_results)
return (job_data.store_output_data_files(files_to_store,
job_exe), results_manifest)
