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This document is for an in-development version of Galaxy. You can alternatively view this page in the latest release if it exists or view the top of the latest release's documentation.
Source code for galaxy.managers.jobs
import json
import logging
from datetime import (
date,
datetime,
)
from pathlib import Path
from typing import (
Any,
cast,
Dict,
Iterable,
List,
Optional,
Set,
Union,
)
import sqlalchemy
from boltons.iterutils import remap
from pydantic import (
BaseModel,
Field,
)
from sqlalchemy import (
and_,
false,
func,
null,
or_,
true,
)
from sqlalchemy.orm import aliased
from sqlalchemy.sql import select
from typing_extensions import TypedDict
from galaxy import model
from galaxy.exceptions import (
ConfigDoesNotAllowException,
ItemAccessibilityException,
ObjectNotFound,
RequestParameterInvalidException,
RequestParameterMissingException,
)
from galaxy.job_metrics import (
RawMetric,
Safety,
)
from galaxy.managers.collections import DatasetCollectionManager
from galaxy.managers.context import ProvidesUserContext
from galaxy.managers.datasets import DatasetManager
from galaxy.managers.hdas import HDAManager
from galaxy.managers.lddas import LDDAManager
from galaxy.model import (
ImplicitCollectionJobs,
ImplicitCollectionJobsJobAssociation,
Job,
JobMetricNumeric,
JobParameter,
User,
Workflow,
WorkflowInvocation,
WorkflowInvocationStep,
WorkflowStep,
YIELD_PER_ROWS,
)
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.tools._types import (
ToolStateDumpedToJsonInternalT,
ToolStateJobInstancePopulatedT,
)
from galaxy.util import (
defaultdict,
ExecutionTimer,
listify,
string_as_bool_or_none,
)
from galaxy.util.search import (
FilteredTerm,
parse_filters_structured,
RawTextTerm,
)
log = logging.getLogger(__name__)
JobStateT = str
JobStatesT = Union[JobStateT, Iterable[JobStateT]]
STDOUT_LOCATION = "outputs/tool_stdout"
STDERR_LOCATION = "outputs/tool_stderr"
[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: ProvidesUserContext, payload: JobIndexQueryPayload) -> sqlalchemy.engine.ScalarResult:
"""The caller is responsible for security checks on the resulting job if
history_id, invocation_id, or implicit_collection_jobs_id is set.
Otherwise this will only return the user's jobs or all jobs if the requesting
user is acting as an admin.
"""
is_admin = trans.user_is_admin
user_details = payload.user_details
decoded_user_id = payload.user_id
history_id = payload.history_id
workflow_id = payload.workflow_id
invocation_id = payload.invocation_id
implicit_collection_jobs_id = payload.implicit_collection_jobs_id
search = payload.search
order_by = payload.order_by
def build_and_apply_filters(stmt, objects, filter_func):
if objects is not None:
if isinstance(objects, (str, date, datetime)):
stmt = stmt.where(filter_func(objects))
elif isinstance(objects, list):
t = []
for obj in objects:
t.append(filter_func(obj))
stmt = stmt.where(or_(*t))
return stmt
def add_workflow_jobs():
wfi_step = select(WorkflowInvocationStep)
if workflow_id is not None:
wfi_step = (
wfi_step.join(WorkflowInvocation).join(Workflow).where(Workflow.stored_workflow_id == workflow_id)
)
elif invocation_id is not None:
wfi_step = wfi_step.where(WorkflowInvocationStep.workflow_invocation_id == invocation_id)
wfi_step_sq = wfi_step.subquery()
stmt1 = stmt.join(wfi_step_sq)
stmt2 = stmt.join(ImplicitCollectionJobsJobAssociation).join(
wfi_step_sq,
ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id
== wfi_step_sq.c.implicit_collection_jobs_id,
)
# Ensure the result is models, not tuples
sq = stmt1.union(stmt2).subquery()
# SQLite won't recognize Job.foo as a valid column for the ORDER BY clause due to the UNION clause, so we'll use the subquery `columns` collection (`sq.c`).
# Ref: https://github.com/galaxyproject/galaxy/pull/16852#issuecomment-1804676322
return select(aliased(Job, sq)), sq.c
def add_search_criteria(stmt):
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",
}
)
assert search
parsed_search = parse_filters_structured(search, search_filters)
for term in parsed_search.terms:
if isinstance(term, FilteredTerm):
key = term.filter
if key == "user":
stmt = stmt.where(text_column_filter(User.email, term))
elif key == "tool":
stmt = stmt.where(text_column_filter(Job.tool_id, term))
elif key == "handler":
stmt = stmt.where(text_column_filter(Job.handler, term))
elif key == "runner":
stmt = stmt.where(text_column_filter(Job.job_runner_name, term))
elif isinstance(term, RawTextTerm):
columns: List = [Job.tool_id]
if user_details:
columns.append(User.email)
if is_admin:
columns.append(Job.handler)
columns.append(Job.job_runner_name)
stmt = stmt.filter(raw_text_column_filter(columns, term))
return stmt
stmt = select(Job)
if is_admin:
if decoded_user_id is not None:
stmt = stmt.where(Job.user_id == decoded_user_id)
if user_details:
stmt = stmt.outerjoin(Job.user)
else:
if history_id is None and invocation_id is None and implicit_collection_jobs_id is None:
# If we're not filtering on history, invocation or collection we filter the jobs owned by the current user
if trans.user:
stmt = stmt.where(Job.user_id == trans.user.id)
elif trans.galaxy_session:
stmt = stmt.where(Job.session_id == trans.galaxy_session.id)
else:
raise RequestParameterMissingException("A session is required to list jobs for anonymous users")
stmt = build_and_apply_filters(stmt, payload.states, lambda s: model.Job.state == s)
stmt = build_and_apply_filters(stmt, payload.tool_ids, lambda t: model.Job.tool_id == t)
stmt = build_and_apply_filters(stmt, payload.tool_ids_like, lambda t: model.Job.tool_id.like(t))
stmt = build_and_apply_filters(stmt, payload.date_range_min, lambda dmin: model.Job.update_time >= dmin)
stmt = build_and_apply_filters(stmt, payload.date_range_max, lambda dmax: model.Job.update_time <= dmax)
if history_id is not None:
stmt = stmt.where(Job.history_id == history_id)
order_by_columns = Job
if workflow_id or invocation_id:
stmt, order_by_columns = add_workflow_jobs()
elif implicit_collection_jobs_id:
stmt = (
stmt.join(ImplicitCollectionJobsJobAssociation, ImplicitCollectionJobsJobAssociation.job_id == Job.id)
.join(
ImplicitCollectionJobs,
ImplicitCollectionJobs.id == ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id,
)
.where(ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id == implicit_collection_jobs_id)
)
if search:
stmt = add_search_criteria(stmt)
if order_by == JobIndexSortByEnum.create_time:
stmt = stmt.order_by(order_by_columns.create_time.desc())
else:
stmt = stmt.order_by(order_by_columns.update_time.desc())
stmt = stmt.offset(payload.offset)
stmt = stmt.limit(payload.limit)
return trans.sa_session.scalars(stmt)
[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: ProvidesUserContext, decoded_job_id) -> Job:
job = trans.sa_session.get(Job, decoded_job_id)
if job is None:
raise ObjectNotFound()
belongs_to_user = False
if trans.user:
belongs_to_user = job.user_id == trans.user.id
elif trans.galaxy_session:
belongs_to_user = job.session_id == trans.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 get_job_console_output(
self, trans, job, stdout_position=-1, stdout_length=0, stderr_position=-1, stderr_length=0
):
if job is None:
raise ObjectNotFound()
# Check job destination params to see if stdout reporting is enabled
dest_params = job.destination_params
if not string_as_bool_or_none(dest_params.get("live_tool_output_reporting", False)):
raise ConfigDoesNotAllowException()
# If stdout_length and stdout_position are good values, then load standard out and add it to status
console_output = {}
console_output["state"] = job.state
if job.state == job.states.RUNNING:
working_directory = trans.app.object_store.get_filename(
job, base_dir="job_work", dir_only=True, obj_dir=True
)
if stdout_length > -1 and stdout_position > -1:
try:
stdout_path = Path(working_directory) / STDOUT_LOCATION
stdout_file = open(stdout_path)
stdout_file.seek(stdout_position)
console_output["stdout"] = stdout_file.read(stdout_length)
except Exception as e:
log.error("Could not read STDOUT: %s", e)
console_output["stdout"] = ""
if stderr_length > -1 and stderr_position > -1:
try:
stderr_path = Path(working_directory) / STDERR_LOCATION
stderr_file = open(stderr_path)
stderr_file.seek(stderr_position)
console_output["stderr"] = stderr_file.read(stderr_length)
except Exception as e:
log.error("Could not read STDERR: %s", e)
console_output["stderr"] = ""
else:
console_output["stdout"] = job.tool_stdout
console_output["stderr"] = job.tool_stderr
return console_output
[docs]
def stop(self, job, message=None):
if not job.finished:
job.mark_deleted(self.app.config.track_jobs_in_database, message)
session = self.app.model.session
session.commit()
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,
user: User,
tool_id: str,
tool_version: Optional[str],
param: ToolStateJobInstancePopulatedT,
param_dump: ToolStateDumpedToJsonInternalT,
job_state: Optional[JobStatesT] = (Job.states.OK,),
require_name_match: bool = True,
):
"""Search for jobs producing same results using the 'inputs' part of a tool POST."""
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.get("src")
if src is None:
# just a parameter named id.
# TODO: dispatch on tool parameter type instead of values,
# maybe with tool state variant
return key, value
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,
require_name_match=require_name_match,
)
def __search(
self,
tool_id: str,
tool_version: Optional[str],
user: model.User,
input_data,
job_state: Optional[JobStatesT],
param_dump: ToolStateDumpedToJsonInternalT,
wildcard_param_dump=None,
require_name_match: bool = True,
):
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.get("src")
if src is None:
# just a parameter named id.
# same workaround as in populate_input_data_input_id
return key, value
value = job_input_ids[src][value]
return key, value
return key, value
stmt = self._build_job_query(tool_id, user.id, tool_version, job_state, wildcard_param_dump)
data_conditions: List = []
# We now build the stmt 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: List = []
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":
stmt = self._build_stmt_for_hda(
stmt, data_conditions, used_ids, k, v, identifier, require_name_match=require_name_match
)
elif t == "ldda":
stmt = self._build_stmt_for_ldda(stmt, data_conditions, used_ids, k, v)
elif t == "hdca":
stmt = self._build_stmt_for_hdca(stmt, data_conditions, used_ids, k, v)
elif t == "dce":
stmt = self._build_stmt_for_dce(stmt, data_conditions, used_ids, k, v)
else:
log.error("Unknown input data type %s", t)
return None
stmt = stmt.where(*data_conditions).group_by(model.Job.id, *used_ids).order_by(model.Job.id.desc())
for job in self.sa_session.execute(stmt):
# 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
elif k == "__when_value__":
continue
a = aliased(model.JobParameter)
job_parameter_conditions.append(
and_(
model.Job.id == a.job_id,
a.name == k,
a.value == (None if v is None else json.dumps(v, sort_keys=True)),
)
)
else:
job_parameter_conditions = [model.Job.id == job[0]]
job = get_job(self.sa_session, *job_parameter_conditions)
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
def _build_job_query(self, tool_id: str, user_id: int, tool_version: Optional[str], job_state, wildcard_param_dump):
"""Build subquery that selects a job with correct job parameters."""
stmt = (
select(model.Job.id)
.join(model.History, model.Job.history_id == model.History.id)
.where(
and_(
model.Job.tool_id == tool_id,
or_(
model.Job.user_id == user_id,
model.History.published == true(),
),
model.Job.copied_from_job_id.is_(None), # Always pick original job
)
)
)
if tool_version:
stmt = stmt.where(Job.tool_version == str(tool_version))
if job_state is None:
job_states: Set[str] = {
Job.states.NEW,
Job.states.QUEUED,
Job.states.WAITING,
Job.states.RUNNING,
Job.states.OK,
}
else:
if isinstance(job_state, str):
job_states = {job_state}
else:
job_states = {*job_state}
if wildcard_param_dump.get("__when_value__") is False:
job_states = {Job.states.SKIPPED}
stmt = stmt.where(Job.state.in_(job_states))
# exclude jobs with deleted outputs
stmt = stmt.where(
and_(
model.Job.any_output_dataset_collection_instances_deleted == false(),
model.Job.any_output_dataset_deleted == false(),
)
)
for k, v in wildcard_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
elif k == "__when_value__":
# TODO: really need to separate this.
continue
value_dump = None if v is None else json.dumps(v, sort_keys=True)
wildcard_value = value_dump.replace('"id": "__id_wildcard__"', '"id": %') if value_dump else None
a = aliased(JobParameter)
if value_dump == wildcard_value:
# No wildcard needed, use exact match
stmt = stmt.join(a).where(
and_(
Job.id == a.job_id,
a.name == k,
a.value == value_dump,
)
)
else:
stmt = stmt.join(a).where(
and_(
Job.id == a.job_id,
a.name == k,
a.value.like(wildcard_value),
)
)
return stmt
def _build_stmt_for_hda(self, stmt, data_conditions, used_ids, k, v, identifier, require_name_match=True):
a = aliased(model.JobToInputDatasetAssociation)
b = aliased(model.HistoryDatasetAssociation)
c = aliased(model.HistoryDatasetAssociation)
d = aliased(model.JobParameter)
e = aliased(model.HistoryDatasetAssociationHistory)
stmt = stmt.add_columns(a.dataset_id)
used_ids.append(a.dataset_id)
stmt = stmt.join(a, a.job_id == model.Job.id)
hda_stmt = select(model.HistoryDatasetAssociation.id).where(
model.HistoryDatasetAssociation.id == e.history_dataset_association_id
)
# b is the HDA used for the job
stmt = stmt.join(b, a.dataset_id == b.id).join(c, c.dataset_id == b.dataset_id)
name_condition = []
if identifier:
stmt = stmt.join(d)
data_conditions.append(
and_(
d.name.in_({f"{_}|__identifier__" for _ in k}),
d.value == json.dumps(identifier),
)
)
elif require_name_match:
hda_stmt = hda_stmt.where(e.name == c.name)
name_condition.append(b.name == c.name)
hda_stmt = (
hda_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),
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_(hda_stmt),
),
or_(b.deleted == false(), c.deleted == false()),
)
)
return stmt
def _build_stmt_for_ldda(self, stmt, data_conditions, used_ids, k, v):
a = aliased(model.JobToInputLibraryDatasetAssociation)
stmt = stmt.add_columns(a.ldda_id)
stmt = stmt.join(a, a.job_id == model.Job.id)
data_conditions.append(and_(a.name.in_(k), a.ldda_id == v))
used_ids.append(a.ldda_id)
return stmt
def _build_stmt_for_hdca(self, stmt, data_conditions, used_ids, k, v, require_name_match=True):
# Determine depth based on collection_type of the HDCA we're matching against
collection_type = self.sa_session.scalar(
select(model.DatasetCollection.collection_type)
.select_from(model.HistoryDatasetCollectionAssociation)
.join(model.DatasetCollection)
.where(model.HistoryDatasetCollectionAssociation.id == v)
)
depth = collection_type.count(":") if collection_type else 0
a = aliased(model.JobToInputDatasetCollectionAssociation)
hdca_input = aliased(model.HistoryDatasetCollectionAssociation)
root_collection = aliased(model.DatasetCollection)
dce_left = [aliased(model.DatasetCollectionElement) for _ in range(depth + 1)]
dce_right = [aliased(model.DatasetCollectionElement) for _ in range(depth + 1)]
hda_left = aliased(model.HistoryDatasetAssociation)
hda_right = aliased(model.HistoryDatasetAssociation)
# Start joins from job → input HDCA → its collection → DCE
stmt = stmt.add_columns(a.dataset_collection_id)
stmt = stmt.join(a, a.job_id == model.Job.id)
stmt = stmt.join(hdca_input, hdca_input.id == a.dataset_collection_id)
stmt = stmt.join(root_collection, root_collection.id == hdca_input.collection_id)
stmt = stmt.join(dce_left[0], dce_left[0].dataset_collection_id == root_collection.id)
# Join to target HDCA (v), then to its collection and its first-level DCE
hdca_target = aliased(model.HistoryDatasetCollectionAssociation)
target_collection = aliased(model.DatasetCollection)
stmt = stmt.join(hdca_target, hdca_target.id == v)
stmt = stmt.join(target_collection, target_collection.id == hdca_target.collection_id)
stmt = stmt.join(dce_right[0], dce_right[0].dataset_collection_id == target_collection.id)
# Parallel walk the structure
for i in range(1, depth + 1):
stmt = (
stmt.join(dce_left[i], dce_left[i].dataset_collection_id == dce_left[i - 1].child_collection_id)
.join(dce_right[i], dce_right[i].dataset_collection_id == dce_right[i - 1].child_collection_id)
.filter(dce_left[i].element_identifier == dce_right[i].element_identifier)
)
# Compare leaf-level HDAs
leaf_left = dce_left[-1]
leaf_right = dce_right[-1]
stmt = stmt.outerjoin(hda_left, hda_left.id == leaf_left.hda_id)
stmt = stmt.outerjoin(hda_right, hda_right.id == leaf_right.hda_id)
data_conditions.append(
and_(
a.name.in_(k),
hda_left.dataset_id == hda_right.dataset_id,
)
)
used_ids.append(a.dataset_collection_id) # input-side HDCA
return stmt
def _build_stmt_for_dce(self, stmt, data_conditions, used_ids, k, v):
dce = self.sa_session.get_one(model.DatasetCollectionElement, v)
if dce.child_collection:
depth = dce.child_collection.collection_type.count(":") + 1
else:
depth = 0
a = aliased(model.JobToInputDatasetCollectionElementAssociation)
dce_left = [aliased(model.DatasetCollectionElement) for _ in range(depth + 1)]
dce_right = [aliased(model.DatasetCollectionElement) for _ in range(depth + 1)]
hda_left = aliased(model.HistoryDatasetAssociation)
hda_right = aliased(model.HistoryDatasetAssociation)
# Base joins
stmt = stmt.add_columns(a.dataset_collection_element_id)
stmt = stmt.join(a, a.job_id == model.Job.id)
stmt = stmt.join(dce_left[0], dce_left[0].id == a.dataset_collection_element_id)
stmt = stmt.join(dce_right[0], dce_right[0].id == v)
# Parallel walk the collection structure
for i in range(1, depth + 1):
stmt = (
stmt.join(dce_left[i], dce_left[i].dataset_collection_id == dce_left[i - 1].child_collection_id)
.join(dce_right[i], dce_right[i].dataset_collection_id == dce_right[i - 1].child_collection_id)
.filter(dce_left[i].element_identifier == dce_right[i].element_identifier)
)
# Compare dataset_ids at the leaf level
leaf_left = dce_left[-1]
leaf_right = dce_right[-1]
stmt = stmt.outerjoin(hda_left, hda_left.id == leaf_left.hda_id)
stmt = stmt.outerjoin(hda_right, hda_right.id == leaf_right.hda_id)
data_conditions.append(and_(a.name.in_(k), hda_left.dataset_id == hda_right.dataset_id))
used_ids.append(a.dataset_collection_element_id)
return stmt
[docs]
def view_show_job(trans, job: Job, full: bool) -> Dict:
is_admin = trans.user_is_admin
job_dict = job.to_dict("element", system_details=is_admin)
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 get_job_metrics_for_invocation(sa_session: galaxy_scoped_session, invocation_id: int):
single_job_stmnt = (
select(WorkflowStep.order_index, Job.id, Job.tool_id, WorkflowStep.label, JobMetricNumeric)
.join(Job, JobMetricNumeric.job_id == Job.id)
.join(
WorkflowInvocationStep,
and_(
WorkflowInvocationStep.workflow_invocation_id == invocation_id, WorkflowInvocationStep.job_id == Job.id
),
)
.join(WorkflowStep, WorkflowStep.id == WorkflowInvocationStep.workflow_step_id)
)
collection_job_stmnt = (
select(WorkflowStep.order_index, Job.id, Job.tool_id, WorkflowStep.label, JobMetricNumeric)
.join(Job, JobMetricNumeric.job_id == Job.id)
.join(ImplicitCollectionJobsJobAssociation, Job.id == ImplicitCollectionJobsJobAssociation.job_id)
.join(
ImplicitCollectionJobs,
ImplicitCollectionJobs.id == ImplicitCollectionJobsJobAssociation.implicit_collection_jobs_id,
)
.join(
WorkflowInvocationStep,
and_(
WorkflowInvocationStep.workflow_invocation_id == invocation_id,
WorkflowInvocationStep.implicit_collection_jobs_id == ImplicitCollectionJobs.id,
),
)
.join(WorkflowStep, WorkflowStep.id == WorkflowInvocationStep.workflow_step_id)
)
# should be sa_session.execute(single_job_stmnt.union(collection_job_stmnt)).all() but that returns
# columns instead of the job metrics ORM instance.
return sorted(
(*sa_session.execute(single_job_stmnt).all(), *sa_session.execute(collection_job_stmnt).all()),
key=lambda row: row[0],
)
[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.get(model.WorkflowInvocation, 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.get(Job, 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.get(model.ImplicitCollectionJobs, 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) -> Optional[JobsSummary]:
"""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: Optional[JobsSummary] = 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",
"states": {},
}
if populated_state == "ok":
# produce state summary...
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):
rval["states"][row[0]] = row[1]
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.
"""
return summarize_metrics(trans, job.metrics)
[docs]
def summarize_metrics(trans: ProvidesUserContext, job_metrics):
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,
}
if job_destination_params := job.destination_params:
destination_params.update(job_destination_params)
return destination_params
[docs]
def summarize_job_parameters(trans: ProvidesUserContext, job: 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.
"""
# More client logic here than is ideal but it is hard to reason about
# tool parameter types on the client relative to the server.
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:
input_value = param_values[input.name]
if input.type == "repeat":
for i in range(len(input_value)):
rval.extend(inputs_recursive(input.inputs, input_value[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,
input_value,
depth=depth + 1,
upgrade_messages=upgrade_messages.get(input.name),
)
)
elif input.type == "conditional":
try:
current_case = input_value["__current_case__"]
is_valid = True
except Exception:
current_case = None
is_valid = False
if is_valid:
rval.append(
dict(
text=input.test_param.label or input.test_param.name,
depth=depth,
value=input.cases[current_case].value,
)
)
rval.extend(
inputs_recursive(
input.cases[current_case].inputs,
input_value,
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(input_value)} uploaded datasets",
)
)
elif (
input.type == "data"
or input.type == "data_collection"
or isinstance(input_value, model.HistoryDatasetAssociation)
):
value: List[Union[Dict[str, Any], None]] = []
for element in listify(input_value):
if isinstance(element, model.HistoryDatasetAssociation):
hda = element
value.append({"src": "hda", "id": element.id, "hid": hda.hid, "name": hda.name})
elif isinstance(element, model.DatasetCollectionElement):
value.append({"src": "dce", "id": element.id, "name": element.element_identifier})
elif isinstance(element, model.HistoryDatasetCollectionAssociation):
value.append({"src": "hdca", "id": element.id, "hid": element.hid, "name": element.name})
elif element is None:
value.append(None)
else:
raise Exception(
f"Unhandled data input parameter type encountered {element.__class__.__name__}"
)
rval.append(dict(text=input.label or input.name, 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(input_value),
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
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_uuid = None
if dynamic_tool := job.dynamic_tool:
tool_uuid = dynamic_tool.uuid
tool = toolbox.get_tool(job.tool_id, job.tool_version, tool_uuid=tool_uuid, user=trans.user)
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),
}
[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):
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:
output_associations = cast(List, output_associations) # during iteration, mypy sees it as object
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": getattr(output_association, attribute)},
}
)
return outputs
[docs]
def get_jobs_to_check_at_startup(session: galaxy_scoped_session, track_jobs_in_database: bool, config):
if track_jobs_in_database:
in_list = (Job.states.QUEUED, Job.states.RUNNING, Job.states.STOPPED)
else:
in_list = (Job.states.NEW, Job.states.QUEUED, Job.states.RUNNING)
stmt = (
select(Job)
.execution_options(yield_per=YIELD_PER_ROWS)
.filter(Job.state.in_(in_list) & (Job.handler == config.server_name))
)
if config.user_activation_on:
# Filter out the jobs of inactive users.
stmt = stmt.outerjoin(User).filter(or_((Job.user_id == null()), (User.active == true())))
return session.scalars(stmt).all()
[docs]
def get_job(session: galaxy_scoped_session, *where_clauses):
stmt = select(Job).where(*where_clauses).limit(1)
return session.scalars(stmt).first()