Refs #22608 -- Optimized migration optimizer
Moved list constants instantiation into optimizer's __init__.
This commit is contained in:
Markus Holtermann
Tim Graham
parent
391bb09bb0
commit
12bf42ae0d
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@ -15,65 +15,18 @@ class MigrationOptimizer(object):
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nothing.
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"""
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def optimize(self, operations, app_label=None):
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"""
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Main optimization entry point. Pass in a list of Operation instances,
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get out a new list of Operation instances.
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Unfortunately, due to the scope of the optimization (two combinable
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operations might be separated by several hundred others), this can't be
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done as a peephole optimization with checks/output implemented on
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the Operations themselves; instead, the optimizer looks at each
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individual operation and scans forwards in the list to see if there
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are any matches, stopping at boundaries - operations which can't
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be optimized over (RunSQL, operations on the same field/model, etc.)
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The inner loop is run until the starting list is the same as the result
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list, and then the result is returned. This means that operation
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optimization must be stable and always return an equal or shorter list.
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The app_label argument is optional, but if you pass it you'll get more
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efficient optimization.
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"""
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# Internal tracking variable for test assertions about # of loops
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self._iterations = 0
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while True:
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result = self.optimize_inner(operations, app_label)
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self._iterations += 1
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if result == operations:
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return result
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operations = result
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def optimize_inner(self, operations, app_label=None):
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"""
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Inner optimization loop.
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"""
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new_operations = []
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for i, operation in enumerate(operations):
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# Compare it to each operation after it
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for j, other in enumerate(operations[i + 1:]):
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result = self.reduce(operation, other, operations[i + 1:i + j + 1])
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if result is not None:
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# Optimize! Add result, then remaining others, then return
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new_operations.extend(result)
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new_operations.extend(operations[i + 1:i + 1 + j])
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new_operations.extend(operations[i + j + 2:])
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return new_operations
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if not self.can_optimize_through(operation, other, app_label):
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new_operations.append(operation)
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break
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else:
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new_operations.append(operation)
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return new_operations
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#### REDUCTION ####
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def reduce(self, operation, other, in_between=None):
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"""
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Either returns a list of zero, one or two operations,
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or None, meaning this pair cannot be optimized.
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"""
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submethods = [
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def __init__(self):
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self.model_level_operations = (
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migrations.CreateModel,
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migrations.AlterModelTable,
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migrations.AlterUniqueTogether,
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migrations.AlterIndexTogether,
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)
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self.field_level_operations = (
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migrations.AddField,
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migrations.AlterField,
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)
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self.reduce_methods = [
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(
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migrations.CreateModel,
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migrations.DeleteModel,
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@ -155,7 +108,66 @@ class MigrationOptimizer(object):
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self.reduce_rename_field_self,
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),
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]
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for ia, ib, om in submethods:
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def optimize(self, operations, app_label=None):
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"""
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Main optimization entry point. Pass in a list of Operation instances,
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get out a new list of Operation instances.
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Unfortunately, due to the scope of the optimization (two combinable
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operations might be separated by several hundred others), this can't be
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done as a peephole optimization with checks/output implemented on
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the Operations themselves; instead, the optimizer looks at each
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individual operation and scans forwards in the list to see if there
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are any matches, stopping at boundaries - operations which can't
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be optimized over (RunSQL, operations on the same field/model, etc.)
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The inner loop is run until the starting list is the same as the result
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list, and then the result is returned. This means that operation
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optimization must be stable and always return an equal or shorter list.
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The app_label argument is optional, but if you pass it you'll get more
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efficient optimization.
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"""
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# Internal tracking variable for test assertions about # of loops
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self._iterations = 0
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while True:
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result = self.optimize_inner(operations, app_label)
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self._iterations += 1
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if result == operations:
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return result
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operations = result
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def optimize_inner(self, operations, app_label=None):
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"""
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Inner optimization loop.
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"""
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new_operations = []
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for i, operation in enumerate(operations):
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# Compare it to each operation after it
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for j, other in enumerate(operations[i + 1:]):
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result = self.reduce(operation, other, operations[i + 1:i + j + 1])
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if result is not None:
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# Optimize! Add result, then remaining others, then return
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new_operations.extend(result)
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new_operations.extend(operations[i + 1:i + 1 + j])
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new_operations.extend(operations[i + j + 2:])
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return new_operations
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if not self.can_optimize_through(operation, other, app_label):
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new_operations.append(operation)
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break
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else:
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new_operations.append(operation)
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return new_operations
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# REDUCTION
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def reduce(self, operation, other, in_between=None):
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"""
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Either returns a list of zero, one or two operations,
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or None, meaning this pair cannot be optimized.
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"""
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for ia, ib, om in self.reduce_methods:
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if isinstance(operation, ia) and isinstance(other, ib):
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return om(operation, other, in_between or [])
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return None
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@ -336,7 +348,7 @@ class MigrationOptimizer(object):
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),
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]
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#### THROUGH CHECKS ####
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# THROUGH CHECKS
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def can_optimize_through(self, operation, other, app_label=None):
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"""
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@ -344,24 +356,14 @@ class MigrationOptimizer(object):
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the other side of 'other'. This is possible if, for example, they
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affect different models.
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"""
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MODEL_LEVEL_OPERATIONS = (
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migrations.CreateModel,
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migrations.AlterModelTable,
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migrations.AlterUniqueTogether,
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migrations.AlterIndexTogether,
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)
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FIELD_LEVEL_OPERATIONS = (
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migrations.AddField,
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migrations.AlterField,
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)
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# If it's a model level operation, let it through if there's
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# nothing that looks like a reference to us in 'other'.
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if isinstance(operation, MODEL_LEVEL_OPERATIONS):
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if isinstance(operation, self.model_level_operations):
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if not other.references_model(operation.name, app_label):
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return True
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# If it's field level, only let it through things that don't reference
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# the field (which includes not referencing the model)
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if isinstance(operation, FIELD_LEVEL_OPERATIONS):
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if isinstance(operation, self.field_level_operations):
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if not other.references_field(operation.model_name, operation.name, app_label):
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return True
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return False
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