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[17.01] Order processing evaluation of workflow invocations when scheduling.

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xref https://github.com/galaxyproject/galaxy/pull/3820#issuecomment-289725795

Also optimize the query to just pull out workflow invocation IDs per the suggestion of @nsoranzo [here](https://github.com/galaxyproject/galaxy/pull/3830#pullrequestreview-29473700).
This commit is contained in:
John Chilton
2017-03-28 13:31:07 -04:00
parent a2b5c41de0
commit 5d3250fac0
2 changed files with 22 additions and 7 deletions
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lib/galaxy/model/__init__.py
+3 -3
View File
@@ -3999,11 +3999,11 @@ class WorkflowInvocation( object, Dictifiable ):
and_conditions.append( WorkflowInvocation.handler == handler )
query = sa_session.query(
WorkflowInvocation
).filter( and_( *and_conditions ) )
WorkflowInvocation.id
).filter( and_( *and_conditions ) ).order_by( WorkflowInvocation.table.c.id.asc() )
# Immediately just load all ids into memory so time slicing logic
# is relatively intutitive.
return [wi.id for wi in query.all()]
return [wid for wid in query.all()]
def to_dict( self, view='collection', value_mapper=None, step_details=False ):
rval = super( WorkflowInvocation, self ).to_dict( view=view, value_mapper=value_mapper )
test/api/test_workflows.py
+19 -4
View File
@@ -1597,11 +1597,19 @@ test_data:
history_id = self.dataset_populator.new_history()
hda1 = self.dataset_populator.new_dataset( history_id, content="1 2 3" )
hda2 = self.dataset_populator.new_dataset( history_id, content="4 5 6" )
hda3 = self.dataset_populator.new_dataset( history_id, content="7 8 9" )
hda4 = self.dataset_populator.new_dataset( history_id, content="10 11 12" )
parameters = {
"0": { "input": { "batch": True, "values": [ { "id" : hda1.get( "id" ), "hid": hda1.get( "hid" ), "src": "hda" },
{ "id" : hda2.get( "id" ), "hid": hda2.get( "hid" ), "src": "hda" },
{ "id" : hda3.get( "id" ), "hid": hda2.get( "hid" ), "src": "hda" },
{ "id" : hda4.get( "id" ), "hid": hda2.get( "hid" ), "src": "hda" } ] } },
"1": { "input": { "batch": False, "values": [ { "id" : hda1.get( "id" ), "hid": hda1.get( "hid" ), "src": "hda" } ] }, "exp": "2" } }
workflow_request = {
"history_id" : history_id,
"batch" : True,
"parameters_normalized": True,
"parameters" : dumps( { "0": { "input": { "batch": True, "values": [ { "id" : hda1.get( "id" ), "hid": hda1.get( "hid" ), "src": "hda" }, { "id" : hda2.get( "id" ), "hid": hda2.get( "hid" ), "src": "hda" } ] } }, "1": { "input": { "batch": False, "values": [ { "id" : hda1.get( "id" ), "hid": hda1.get( "hid" ), "src": "hda" } ] }, "exp": "2" } } )
"parameters" : dumps( parameters ),
}
invocation_response = self._post( "workflows/%s/usage" % workflow_id, data=workflow_request )
self._assert_status_code_is( invocation_response, 200 )
@@ -1609,9 +1617,16 @@ test_data:
self.dataset_populator.wait_for_history( history_id, assert_ok=True )
r1 = "1 2 3\t1\n1 2 3\t2\n"
r2 = "4 5 6\t1\n1 2 3\t2\n"
t1 = self.dataset_populator.get_history_dataset_content( history_id, hid=5 )
t2 = self.dataset_populator.get_history_dataset_content( history_id, hid=8 )
assert ( r1 == t1 and r2 == t2 ) or ( r1 == t2 and r2 == t1 )
r3 = "7 8 9\t1\n1 2 3\t2\n"
r4 = "10 11 12\t1\n1 2 3\t2\n"
t1 = self.dataset_populator.get_history_dataset_content( history_id, hid=7 )
t2 = self.dataset_populator.get_history_dataset_content( history_id, hid=10 )
t3 = self.dataset_populator.get_history_dataset_content( history_id, hid=13 )
t4 = self.dataset_populator.get_history_dataset_content( history_id, hid=16 )
self.assertEqual(r1, t1)
self.assertEqual(r2, t2)
self.assertEqual(r3, t3)
self.assertEqual(r4, t4)
@skip_without_tool( "validation_default" )
def test_parameter_substitution_sanitization( self ):