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Source code for galaxy.managers.jobs
import json
import logging
import typing
from datetime import (
date,
datetime,
)
from boltons.iterutils import remap
from pydantic import (
BaseModel,
Field,
)
from sqlalchemy import (
and_,
false,
func,
or_,
)
from sqlalchemy.orm import aliased
from sqlalchemy.sql import select
from galaxy import model
from galaxy.exceptions import (
AdminRequiredException,
ItemAccessibilityException,
ObjectNotFound,
RequestParameterInvalidException,
)
from galaxy.job_metrics import (
RawMetric,
Safety,
)
from galaxy.managers.collections import DatasetCollectionManager
from galaxy.managers.datasets import DatasetManager
from galaxy.managers.hdas import HDAManager
from galaxy.managers.lddas import LDDAManager
from galaxy.model.index_filter_util import (
raw_text_column_filter,
text_column_filter,
)
from galaxy.model.scoped_session import galaxy_scoped_session
from galaxy.schema.schema import (
JobIndexQueryPayload,
JobIndexSortByEnum,
)
from galaxy.security.idencoding import IdEncodingHelper
from galaxy.structured_app import StructuredApp
from galaxy.util import (
defaultdict,
ExecutionTimer,
listify,
)
from galaxy.util.search import (
FilteredTerm,
parse_filters_structured,
RawTextTerm,
)
log = logging.getLogger(__name__)
[docs]class JobLock(BaseModel):
active: bool = Field(title="Job lock status", description="If active, jobs will not dispatch")
[docs]def get_path_key(path_tuple):
path_key = ""
tuple_elements = len(path_tuple)
for i, p in enumerate(path_tuple):
if isinstance(p, int):
sep = "_"
else:
sep = "|"
if i == (tuple_elements - 2) and p == "values":
# dataset inputs are always wrapped in lists. To avoid 'rep_factorName_0|rep_factorLevel_2|countsFile|values_0',
# we remove the last 2 items of the path tuple (values and list index)
return path_key
if path_key:
path_key = f"{path_key}{sep}{p}"
else:
path_key = p
return path_key
[docs]class JobManager:
[docs] def __init__(self, app: StructuredApp):
self.app = app
self.dataset_manager = DatasetManager(app)
[docs] def index_query(self, trans, payload: JobIndexQueryPayload):
is_admin = trans.user_is_admin
user_details = payload.user_details
decoded_user_id = payload.user_id
if is_admin:
if decoded_user_id is not None:
query = trans.sa_session.query(model.Job).filter(model.Job.user_id == decoded_user_id)
else:
query = trans.sa_session.query(model.Job)
if user_details:
query = query.outerjoin(model.Job.user)
else:
if user_details:
raise AdminRequiredException("Only admins can index the jobs with user details enabled")
if decoded_user_id is not None and decoded_user_id != trans.user.id:
raise AdminRequiredException("Only admins can index the jobs of others")
query = trans.sa_session.query(model.Job).filter(model.Job.user_id == trans.user.id)
def build_and_apply_filters(query, objects, filter_func):
if objects is not None:
if isinstance(objects, (str, date, datetime)):
query = query.filter(filter_func(objects))
elif isinstance(objects, list):
t = []
for obj in objects:
t.append(filter_func(obj))
query = query.filter(or_(*t))
return query
query = build_and_apply_filters(query, payload.states, lambda s: model.Job.state == s)
query = build_and_apply_filters(query, payload.tool_ids, lambda t: model.Job.tool_id == t)
query = build_and_apply_filters(query, payload.tool_ids_like, lambda t: model.Job.tool_id.like(t))
query = build_and_apply_filters(query, payload.date_range_min, lambda dmin: model.Job.update_time >= dmin)
query = build_and_apply_filters(query, payload.date_range_max, lambda dmax: model.Job.update_time <= dmax)
history_id = payload.history_id
workflow_id = payload.workflow_id
invocation_id = payload.invocation_id
if history_id is not None:
query = query.filter(model.Job.history_id == history_id)
if workflow_id or invocation_id:
if workflow_id is not None:
wfi_step = (
trans.sa_session.query(model.WorkflowInvocationStep)
.join(model.WorkflowInvocation)
.join(model.Workflow)
.filter(
model.Workflow.stored_workflow_id == workflow_id,
)
.subquery()
)
elif invocation_id is not None:
wfi_step = (
trans.sa_session.query(model.WorkflowInvocationStep)
.filter(model.WorkflowInvocationStep.workflow_invocation_id == invocation_id)
.subquery()
)
query1 = query.join(wfi_step)
query2 = query.join(model.ImplicitCollectionJobsJobAssociation).join(
wfi_step,
model.ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id
== wfi_step.c.implicit_collection_jobs_id,
)
query = query1.union(query2)
search = payload.search
if search:
search_filters = {
"tool": "tool",
"t": "tool",
}
if user_details:
search_filters.update(
{
"user": "user",
"u": "user",
}
)
if is_admin:
search_filters.update(
{
"runner": "runner",
"r": "runner",
"handler": "handler",
"h": "handler",
}
)
parsed_search = parse_filters_structured(search, search_filters)
for term in parsed_search.terms:
if isinstance(term, FilteredTerm):
key = term.filter
if key == "user":
query = query.filter(text_column_filter(model.User.email, term))
elif key == "tool":
query = query.filter(text_column_filter(model.Job.tool_id, term))
elif key == "handler":
query = query.filter(text_column_filter(model.Job.handler, term))
elif key == "runner":
query = query.filter(text_column_filter(model.Job.job_runner_name, term))
elif isinstance(term, RawTextTerm):
columns = [model.Job.tool_id]
if user_details:
columns.append(model.User.email)
if is_admin:
columns.append(model.Job.handler)
columns.append(model.Job.job_runner_name)
query = query.filter(raw_text_column_filter(columns, term))
if payload.order_by == JobIndexSortByEnum.create_time:
order_by = model.Job.create_time.desc()
else:
order_by = model.Job.update_time.desc()
query = query.order_by(order_by)
query = query.offset(payload.offset)
query = query.limit(payload.limit)
return query
[docs] def update_job_lock(self, job_lock: JobLock):
self.app.queue_worker.send_control_task(
"admin_job_lock", kwargs={"job_lock": job_lock.active}, get_response=True
)
return self.job_lock()
[docs] def get_accessible_job(self, trans, decoded_job_id):
job = trans.sa_session.query(trans.app.model.Job).filter(trans.app.model.Job.id == decoded_job_id).first()
if job is None:
raise ObjectNotFound()
belongs_to_user = (
(job.user_id == trans.user.id)
if job.user_id and trans.user
else (job.session_id == trans.get_galaxy_session().id)
)
if not trans.user_is_admin and not belongs_to_user:
# Check access granted via output datasets.
if not job.output_datasets:
raise ItemAccessibilityException("Job has no output datasets.")
for data_assoc in job.output_datasets:
if not self.dataset_manager.is_accessible(data_assoc.dataset.dataset, trans.user):
raise ItemAccessibilityException("You are not allowed to rerun this job.")
trans.sa_session.refresh(job)
return job
[docs] def stop(self, job, message=None):
if not job.finished:
job.mark_deleted(self.app.config.track_jobs_in_database)
self.app.model.session.flush()
self.app.job_manager.stop(job, message=message)
return True
else:
return False
[docs]class JobSearch:
"""Search for jobs using tool inputs or other jobs"""
[docs] def __init__(
self,
sa_session: galaxy_scoped_session,
hda_manager: HDAManager,
dataset_collection_manager: DatasetCollectionManager,
ldda_manager: LDDAManager,
id_encoding_helper: IdEncodingHelper,
):
self.sa_session = sa_session
self.hda_manager = hda_manager
self.dataset_collection_manager = dataset_collection_manager
self.ldda_manager = ldda_manager
self.decode_id = id_encoding_helper.decode_id
[docs] def by_tool_input(self, trans, tool_id, tool_version, param=None, param_dump=None, job_state="ok"):
"""Search for jobs producing same results using the 'inputs' part of a tool POST."""
user = trans.user
input_data = defaultdict(list)
def populate_input_data_input_id(path, key, value):
"""Traverses expanded incoming using remap and collects input_ids and input_data."""
if key == "id":
path_key = get_path_key(path[:-2])
current_case = param_dump
for p in path:
current_case = current_case[p]
src = current_case["src"]
current_case = param
for i, p in enumerate(path):
if p == "values" and i == len(path) - 2:
continue
if isinstance(current_case, (list, dict)):
current_case = current_case[p]
identifier = getattr(current_case, "element_identifier", None)
input_data[path_key].append(
{
"src": src,
"id": value,
"identifier": identifier,
}
)
return key, "__id_wildcard__"
return key, value
wildcard_param_dump = remap(param_dump, visit=populate_input_data_input_id)
return self.__search(
tool_id=tool_id,
tool_version=tool_version,
user=user,
input_data=input_data,
job_state=job_state,
param_dump=param_dump,
wildcard_param_dump=wildcard_param_dump,
)
def __search(
self, tool_id, tool_version, user, input_data, job_state=None, param_dump=None, wildcard_param_dump=None
):
search_timer = ExecutionTimer()
def replace_dataset_ids(path, key, value):
"""Exchanges dataset_ids (HDA, LDA, HDCA, not Dataset) in param_dump with dataset ids used in job."""
if key == "id":
current_case = param_dump
for p in path:
current_case = current_case[p]
src = current_case["src"]
value = job_input_ids[src][value]
return key, value
return key, value
job_conditions = [
and_(
model.Job.tool_id == tool_id,
model.Job.user == user,
model.Job.copied_from_job_id.is_(None), # Always pick original job
)
]
if tool_version:
job_conditions.append(model.Job.tool_version == str(tool_version))
if job_state is None:
job_conditions.append(
model.Job.state.in_(
[
model.Job.states.NEW,
model.Job.states.QUEUED,
model.Job.states.WAITING,
model.Job.states.RUNNING,
model.Job.states.OK,
]
)
)
else:
if isinstance(job_state, str):
job_conditions.append(model.Job.state == job_state)
elif isinstance(job_state, list):
o = []
for s in job_state:
o.append(model.Job.state == s)
job_conditions.append(or_(*o))
for k, v in wildcard_param_dump.items():
wildcard_value = None
if v == {"__class__": "RuntimeValue"}:
# TODO: verify this is always None. e.g. run with runtime input input
v = None
elif k.endswith("|__identifier__"):
# We've taken care of this while constructing the conditions based on ``input_data`` above
continue
elif k == "chromInfo" and "?.len" in v:
continue
wildcard_value = '"%?.len"'
if not wildcard_value:
value_dump = json.dumps(v, sort_keys=True)
wildcard_value = value_dump.replace('"id": "__id_wildcard__"', '"id": %')
a = aliased(model.JobParameter)
if value_dump == wildcard_value:
job_conditions.append(
and_(
model.Job.id == a.job_id,
a.name == k,
a.value == value_dump,
)
)
else:
job_conditions.append(and_(model.Job.id == a.job_id, a.name == k, a.value.like(wildcard_value)))
job_conditions.append(
and_(
model.Job.any_output_dataset_collection_instances_deleted == false(),
model.Job.any_output_dataset_deleted == false(),
)
)
subq = self.sa_session.query(model.Job.id).filter(*job_conditions).subquery()
data_conditions = []
# We now build the query filters that relate to the input datasets
# that this job uses. We keep track of the requested dataset id in `requested_ids`,
# the type (hda, hdca or lda) in `data_types`
# and the ids that have been used in the job that has already been run in `used_ids`.
requested_ids = []
data_types = []
used_ids = []
for k, input_list in input_data.items():
# k will be matched against the JobParameter.name column. This can be prefixed depending on whethter
# the input is in a repeat, or not (section and conditional)
k = {k, k.split("|")[-1]}
for type_values in input_list:
t = type_values["src"]
v = type_values["id"]
requested_ids.append(v)
data_types.append(t)
identifier = type_values["identifier"]
if t == "hda":
a = aliased(model.JobToInputDatasetAssociation)
b = aliased(model.HistoryDatasetAssociation)
c = aliased(model.HistoryDatasetAssociation)
d = aliased(model.JobParameter)
e = aliased(model.HistoryDatasetAssociationHistory)
stmt = select([model.HistoryDatasetAssociation.id]).where(
model.HistoryDatasetAssociation.id == e.history_dataset_association_id
)
name_condition = []
if identifier:
data_conditions.append(
and_(
model.Job.id == d.job_id,
d.name.in_({f"{_}|__identifier__" for _ in k}),
d.value == json.dumps(identifier),
)
)
else:
stmt = stmt.where(e.name == c.name)
name_condition.append(b.name == c.name)
stmt = (
stmt.where(
e.extension == c.extension,
)
.where(
a.dataset_version == e.version,
)
.where(
e._metadata == c._metadata,
)
)
data_conditions.append(
and_(
a.name.in_(k),
a.dataset_id == b.id, # b is the HDA used for the job
c.dataset_id == b.dataset_id,
c.id == v, # c is the requested job input HDA
# We need to make sure that the job we are looking for has been run with identical inputs.
# Here we deal with 3 requirements:
# - the jobs' input dataset (=b) version is 0, meaning the job's input dataset is not yet ready
# - b's update_time is older than the job create time, meaning no changes occurred
# - the job has a dataset_version recorded, and that versions' metadata matches c's metadata.
or_(
and_(
or_(a.dataset_version.in_([0, b.version]), b.update_time < model.Job.create_time),
b.extension == c.extension,
b.metadata == c.metadata,
*name_condition,
),
b.id.in_(stmt),
),
or_(b.deleted == false(), c.deleted == false()),
)
)
used_ids.append(a.dataset_id)
elif t == "ldda":
a = aliased(model.JobToInputLibraryDatasetAssociation)
data_conditions.append(and_(model.Job.id == a.job_id, a.name.in_(k), a.ldda_id == v))
used_ids.append(a.ldda_id)
elif t == "hdca":
a = aliased(model.JobToInputDatasetCollectionAssociation)
b = aliased(model.HistoryDatasetCollectionAssociation)
c = aliased(model.HistoryDatasetCollectionAssociation)
data_conditions.append(
and_(
model.Job.id == a.job_id,
a.name.in_(k),
b.id == a.dataset_collection_id,
c.id == v,
b.name == c.name,
or_(
and_(b.deleted == false(), b.id == v),
and_(
or_(
c.copied_from_history_dataset_collection_association_id == b.id,
b.copied_from_history_dataset_collection_association_id == c.id,
),
c.deleted == false(),
),
),
)
)
used_ids.append(a.dataset_collection_id)
elif t == "dce":
a = aliased(model.JobToInputDatasetCollectionElementAssociation)
b = aliased(model.DatasetCollectionElement)
c = aliased(model.DatasetCollectionElement)
data_conditions.append(
and_(
model.Job.id == a.job_id,
a.name.in_(k),
a.dataset_collection_element_id == b.id,
b.element_identifier == c.element_identifier,
c.child_collection_id == b.child_collection_id,
c.id == v,
)
)
used_ids.append(a.dataset_collection_element_id)
else:
return []
query = (
self.sa_session.query(model.Job.id, *used_ids)
.join(subq, model.Job.id == subq.c.id)
.filter(*data_conditions)
.group_by(model.Job.id, *used_ids)
.order_by(model.Job.id.desc())
)
for job in query:
# We found a job that is equal in terms of tool_id, user, state and input datasets,
# but to be able to verify that the parameters match we need to modify all instances of
# dataset_ids (HDA, LDDA, HDCA) in the incoming param_dump to point to those used by the
# possibly equivalent job, which may have been run on copies of the original input data.
job_input_ids = {}
if len(job) > 1:
# We do have datasets to check
job_id, current_jobs_data_ids = job[0], job[1:]
job_parameter_conditions = [model.Job.id == job_id]
for src, requested_id, used_id in zip(data_types, requested_ids, current_jobs_data_ids):
if src not in job_input_ids:
job_input_ids[src] = {requested_id: used_id}
else:
job_input_ids[src][requested_id] = used_id
new_param_dump = remap(param_dump, visit=replace_dataset_ids)
# new_param_dump has its dataset ids remapped to those used by the job.
# We now ask if the remapped job parameters match the current job.
for k, v in new_param_dump.items():
if v == {"__class__": "RuntimeValue"}:
# TODO: verify this is always None. e.g. run with runtime input input
v = None
elif k.endswith("|__identifier__"):
# We've taken care of this while constructing the conditions based on ``input_data`` above
continue
elif k == "chromInfo" and "?.len" in v:
continue
wildcard_value = '"%?.len"'
if not wildcard_value:
wildcard_value = json.dumps(v, sort_keys=True).replace('"id": "__id_wildcard__"', '"id": %')
a = aliased(model.JobParameter)
job_parameter_conditions.append(
and_(model.Job.id == a.job_id, a.name == k, a.value == json.dumps(v, sort_keys=True))
)
else:
job_parameter_conditions = [model.Job.id == job]
query = self.sa_session.query(model.Job).filter(*job_parameter_conditions)
job = query.first()
if job is None:
continue
n_parameters = 0
# Verify that equivalent jobs had the same number of job parameters
# We skip chrominfo, dbkey, __workflow_invocation_uuid__ and identifer
# parameter as these are not passed along when expanding tool parameters
# and they can differ without affecting the resulting dataset.
for parameter in job.parameters:
if parameter.name.startswith("__"):
continue
if parameter.name in {"chromInfo", "dbkey"} or parameter.name.endswith("|__identifier__"):
continue
n_parameters += 1
if not n_parameters == sum(
1
for k in param_dump
if not k.startswith("__") and not k.endswith("|__identifier__") and k not in {"chromInfo", "dbkey"}
):
continue
log.info("Found equivalent job %s", search_timer)
return job
log.info("No equivalent jobs found %s", search_timer)
return None
[docs]def view_show_job(trans, job, full: bool) -> typing.Dict:
is_admin = trans.user_is_admin
job_dict = trans.app.security.encode_all_ids(job.to_dict("element", system_details=is_admin), True)
if trans.app.config.expose_dataset_path and "command_line" not in job_dict:
job_dict["command_line"] = job.command_line
if full:
job_dict.update(
dict(
tool_stdout=job.tool_stdout,
tool_stderr=job.tool_stderr,
job_stdout=job.job_stdout,
job_stderr=job.job_stderr,
stderr=job.stderr,
stdout=job.stdout,
job_messages=job.job_messages,
dependencies=job.dependencies,
)
)
if is_admin:
job_dict["user_email"] = job.get_user_email()
job_dict["job_metrics"] = summarize_job_metrics(trans, job)
return job_dict
[docs]def invocation_job_source_iter(sa_session, invocation_id):
# TODO: Handle subworkflows.
join = model.WorkflowInvocationStep.table.join(model.WorkflowInvocation)
statement = (
select(
[
model.WorkflowInvocationStep.job_id,
model.WorkflowInvocationStep.implicit_collection_jobs_id,
model.WorkflowInvocationStep.state,
]
)
.select_from(join)
.where(model.WorkflowInvocation.id == invocation_id)
)
for row in sa_session.execute(statement):
if row[0]:
yield ("Job", row[0], row[2])
if row[1]:
yield ("ImplicitCollectionJobs", row[1], row[2])
[docs]def fetch_job_states(sa_session, job_source_ids, job_source_types):
assert len(job_source_ids) == len(job_source_types)
job_ids = set()
implicit_collection_job_ids = set()
workflow_invocations_job_sources = {}
workflow_invocation_states = (
{}
) # should be set before we walk step states to be conservative on whether things are done expanding yet
for job_source_id, job_source_type in zip(job_source_ids, job_source_types):
if job_source_type == "Job":
job_ids.add(job_source_id)
elif job_source_type == "ImplicitCollectionJobs":
implicit_collection_job_ids.add(job_source_id)
elif job_source_type == "WorkflowInvocation":
invocation_state = sa_session.query(model.WorkflowInvocation).get(job_source_id).state
workflow_invocation_states[job_source_id] = invocation_state
workflow_invocation_job_sources = []
for (
invocation_step_source_type,
invocation_step_source_id,
invocation_step_state,
) in invocation_job_source_iter(sa_session, job_source_id):
workflow_invocation_job_sources.append(
(invocation_step_source_type, invocation_step_source_id, invocation_step_state)
)
if invocation_step_source_type == "Job":
job_ids.add(invocation_step_source_id)
elif invocation_step_source_type == "ImplicitCollectionJobs":
implicit_collection_job_ids.add(invocation_step_source_id)
workflow_invocations_job_sources[job_source_id] = workflow_invocation_job_sources
else:
raise RequestParameterInvalidException(f"Invalid job source type {job_source_type} found.")
job_summaries = {}
implicit_collection_jobs_summaries = {}
for job_id in job_ids:
job_summaries[job_id] = summarize_jobs_to_dict(sa_session, sa_session.query(model.Job).get(job_id))
for implicit_collection_jobs_id in implicit_collection_job_ids:
implicit_collection_jobs_summaries[implicit_collection_jobs_id] = summarize_jobs_to_dict(
sa_session, sa_session.query(model.ImplicitCollectionJobs).get(implicit_collection_jobs_id)
)
rval = []
for job_source_id, job_source_type in zip(job_source_ids, job_source_types):
if job_source_type == "Job":
rval.append(job_summaries[job_source_id])
elif job_source_type == "ImplicitCollectionJobs":
rval.append(implicit_collection_jobs_summaries[job_source_id])
else:
invocation_state = workflow_invocation_states[job_source_id]
invocation_job_summaries = []
invocation_implicit_collection_job_summaries = []
invocation_step_states = []
for (
invocation_step_source_type,
invocation_step_source_id,
invocation_step_state,
) in workflow_invocations_job_sources[job_source_id]:
invocation_step_states.append(invocation_step_state)
if invocation_step_source_type == "Job":
invocation_job_summaries.append(job_summaries[invocation_step_source_id])
else:
invocation_implicit_collection_job_summaries.append(
implicit_collection_jobs_summaries[invocation_step_source_id]
)
rval.append(
summarize_invocation_jobs(
job_source_id,
invocation_job_summaries,
invocation_implicit_collection_job_summaries,
invocation_state,
invocation_step_states,
)
)
return rval
[docs]def summarize_invocation_jobs(
invocation_id, job_summaries, implicit_collection_job_summaries, invocation_state, invocation_step_states
):
states = {}
if invocation_state == "scheduled":
all_scheduled = True
for invocation_step_state in invocation_step_states:
all_scheduled = all_scheduled and invocation_step_state == "scheduled"
if all_scheduled:
populated_state = "ok"
else:
populated_state = "new"
elif invocation_state in ["cancelled", "failed"]:
populated_state = "failed"
else:
# call new, ready => new
populated_state = "new"
def merge_states(component_states):
for key, value in component_states.items():
if key not in states:
states[key] = value
else:
states[key] += value
for job_summary in job_summaries:
merge_states(job_summary["states"])
for implicit_collection_job_summary in implicit_collection_job_summaries:
# 'new' (un-populated collections might not yet have a states entry)
if "states" in implicit_collection_job_summary:
merge_states(implicit_collection_job_summary["states"])
component_populated_state = implicit_collection_job_summary["populated_state"]
if component_populated_state == "failed":
populated_state = "failed"
elif component_populated_state == "new" and populated_state != "failed":
populated_state = "new"
rval = {
"id": invocation_id,
"model": "WorkflowInvocation",
"states": states,
"populated_state": populated_state,
}
return rval
[docs]def summarize_jobs_to_dict(sa_session, jobs_source):
"""Produce a summary of jobs for job summary endpoints.
:type jobs_source: a Job or ImplicitCollectionJobs or None
:param jobs_source: the object to summarize
:rtype: dict
:returns: dictionary containing job summary information
"""
rval = None
if jobs_source is None:
pass
elif isinstance(jobs_source, model.Job):
rval = {
"populated_state": "ok",
"states": {jobs_source.state: 1},
"model": "Job",
"id": jobs_source.id,
}
else:
populated_state = jobs_source.populated_state
rval = {
"id": jobs_source.id,
"populated_state": populated_state,
"model": "ImplicitCollectionJobs",
}
if populated_state == "ok":
# produce state summary...
states = {}
join = model.ImplicitCollectionJobs.table.join(
model.ImplicitCollectionJobsJobAssociation.table.join(model.Job)
)
statement = (
select([model.Job.state, func.count("*")])
.select_from(join)
.where(model.ImplicitCollectionJobs.id == jobs_source.id)
.group_by(model.Job.state)
)
for row in sa_session.execute(statement):
states[row[0]] = row[1]
rval["states"] = states
return rval
[docs]def summarize_job_metrics(trans, job):
"""Produce a dict-ified version of job metrics ready for tabular rendering.
Precondition: the caller has verified the job is accessible to the user
represented by the trans parameter.
"""
safety_level = Safety.SAFE
if trans.user_is_admin:
safety_level = Safety.UNSAFE
elif trans.app.config.expose_potentially_sensitive_job_metrics:
safety_level = Safety.POTENTIALLY_SENSITVE
raw_metrics = [
RawMetric(
m.metric_name,
m.metric_value,
m.plugin,
)
for m in job.metrics
]
dictifiable_metrics = trans.app.job_metrics.dictifiable_metrics(raw_metrics, safety_level)
return [d.dict() for d in dictifiable_metrics]
[docs]def summarize_destination_params(trans, job):
"""Produce a dict-ified version of job destination parameters ready for tabular rendering.
Precondition: the caller has verified the job is accessible to the user
represented by the trans parameter.
"""
destination_params = {
"Runner": job.job_runner_name,
"Runner Job ID": job.job_runner_external_id,
"Handler": job.handler,
}
job_destination_params = job.destination_params
if job_destination_params:
destination_params.update(job_destination_params)
return destination_params
[docs]def summarize_job_parameters(trans, job):
"""Produce a dict-ified version of job parameters ready for tabular rendering.
Precondition: the caller has verified the job is accessible to the user
represented by the trans parameter.
"""
def inputs_recursive(input_params, param_values, depth=1, upgrade_messages=None):
if upgrade_messages is None:
upgrade_messages = {}
rval = []
for input in input_params.values():
if input.name in param_values:
if input.type == "repeat":
for i in range(len(param_values[input.name])):
rval.extend(inputs_recursive(input.inputs, param_values[input.name][i], depth=depth + 1))
elif input.type == "section":
# Get the value of the current Section parameter
rval.append(dict(text=input.name, depth=depth))
rval.extend(
inputs_recursive(
input.inputs,
param_values[input.name],
depth=depth + 1,
upgrade_messages=upgrade_messages.get(input.name),
)
)
elif input.type == "conditional":
try:
current_case = param_values[input.name]["__current_case__"]
is_valid = True
except Exception:
current_case = None
is_valid = False
if is_valid:
rval.append(
dict(text=input.test_param.label, depth=depth, value=input.cases[current_case].value)
)
rval.extend(
inputs_recursive(
input.cases[current_case].inputs,
param_values[input.name],
depth=depth + 1,
upgrade_messages=upgrade_messages.get(input.name),
)
)
else:
rval.append(
dict(
text=input.name,
depth=depth,
notes="The previously used value is no longer valid.",
error=True,
)
)
elif input.type == "upload_dataset":
rval.append(
dict(
text=input.group_title(param_values),
depth=depth,
value=f"{len(param_values[input.name])} uploaded datasets",
)
)
elif input.type == "data":
value = []
for element in listify(param_values[input.name]):
encoded_id = trans.security.encode_id(element.id)
if isinstance(element, model.HistoryDatasetAssociation):
hda = element
value.append({"src": "hda", "id": encoded_id, "hid": hda.hid, "name": hda.name})
elif isinstance(element, model.DatasetCollectionElement):
value.append({"src": "dce", "id": encoded_id, "name": element.element_identifier})
elif isinstance(element, model.HistoryDatasetCollectionAssociation):
value.append({"src": "hdca", "id": encoded_id, "hid": element.hid, "name": element.name})
else:
raise Exception(
f"Unhandled data input parameter type encountered {element.__class__.__name__}"
)
rval.append(dict(text=input.label, depth=depth, value=value))
elif input.visible:
if hasattr(input, "label") and input.label:
label = input.label
else:
# value for label not required, fallback to input name (same as tool panel)
label = input.name
rval.append(
dict(
text=label,
depth=depth,
value=input.value_to_display_text(param_values[input.name]),
notes=upgrade_messages.get(input.name, ""),
)
)
else:
# Parameter does not have a stored value.
# Get parameter label.
if input.type == "conditional":
label = input.test_param.label
elif input.type == "repeat":
label = input.label()
else:
label = input.label or input.name
rval.append(
dict(text=label, depth=depth, notes="not used (parameter was added after this job was run)")
)
return rval
# Load the tool
app = trans.app
toolbox = app.toolbox
tool = toolbox.get_tool(job.tool_id, job.tool_version)
params_objects = None
parameters = []
upgrade_messages = {}
has_parameter_errors = False
# Load parameter objects, if a parameter type has changed, it's possible for the value to no longer be valid
if tool:
try:
params_objects = job.get_param_values(app, ignore_errors=False)
except Exception:
params_objects = job.get_param_values(app, ignore_errors=True)
# use different param_objects in the following line, since we want to display original values as much as possible
upgrade_messages = tool.check_and_update_param_values(
job.get_param_values(app, ignore_errors=True), trans, update_values=False
)
has_parameter_errors = True
parameters = inputs_recursive(tool.inputs, params_objects, depth=1, upgrade_messages=upgrade_messages)
else:
has_parameter_errors = True
return {
"parameters": parameters,
"has_parameter_errors": has_parameter_errors,
"outputs": summarize_job_outputs(job=job, tool=tool, params=params_objects, security=trans.security),
}
[docs]def get_output_name(tool, output, params):
try:
return tool.tool_action.get_output_name(
output,
tool=tool,
params=params,
)
except Exception:
pass
[docs]def summarize_job_outputs(job: model.Job, tool, params, security):
outputs = defaultdict(list)
output_labels = {}
possible_outputs = (
("hda", "dataset_id", job.output_datasets),
("ldda", "ldda_id", job.output_library_datasets),
("hdca", "dataset_collection_id", job.output_dataset_collection_instances),
)
for src, attribute, output_associations in possible_outputs:
for output_association in output_associations:
output_name = output_association.name
if output_name not in output_labels and tool:
tool_output = tool.output_collections if src == "hdca" else tool.outputs
output_labels[output_name] = get_output_name(
tool=tool, output=tool_output.get(output_name), params=params
)
label = output_labels.get(output_name)
outputs[output_name].append(
{
"label": label,
"value": {"src": src, "id": security.encode_id(getattr(output_association, attribute))},
}
)
return outputs