62 lines
2.7 KiB
Python
62 lines
2.7 KiB
Python
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class MigrationOptimizer:
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"""
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Power the optimization process, where you provide a list of Operations
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and you are returned a list of equal or shorter length - operations
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are merged into one if possible.
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For example, a CreateModel and an AddField can be optimized into a
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new CreateModel, and CreateModel and DeleteModel can be optimized into
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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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"""Inner optimization loop."""
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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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in_between = operations[i + 1:i + j + 1]
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result = operation.reduce(other, in_between, app_label)
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if isinstance(result, list):
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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(in_between)
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new_operations.extend(operations[i + j + 2:])
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return new_operations
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if not result:
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# We can't optimize across `other`.
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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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