fixed db seed error

[dwnoble]

This pull request updates the database seeding logic and its corresponding test to add two new fields, name and value, to the seeded Node table entries. The changes ensure that these fields are populated with appropriate values during the seeding process and that the tests verify their correctness.

[gemini-code-assist]

Code Review

This pull request updates the seed_database function in spanner_client.py to include name and value columns when inserting seed nodes into the Spanner Node table. Corresponding unit tests in spanner_client_test.py were updated to verify these new fields. The review feedback suggests refactoring the candidates dictionary using a comprehension to reduce string redundancy and improving the test assertions by using a loop for better readability and efficiency.

[gemini-code-assist]

The candidates dictionary contains a lot of repeated strings. To improve maintainability and reduce redundancy, you could generate this dictionary programmatically. This would make it easier to add or modify fields in the future.

By defining the unique parts (like the types) separately and then building the full dictionary with a comprehension, the code becomes cleaner and less error-prone for future modifications.

Suggested change

	candidates = {
	"StatisticalVariable": ["StatisticalVariable", ["Class"], spanner.COMMIT_TIMESTAMP],
	"StatVarGroup": ["StatVarGroup", ["Class"], spanner.COMMIT_TIMESTAMP],
	"StatVarObservation": ["StatVarObservation", ["Class"], spanner.COMMIT_TIMESTAMP],
	"Topic": ["Topic", ["Class"], spanner.COMMIT_TIMESTAMP],
	"c/g/Root": ["c/g/Root", ["StatVarGroup"], spanner.COMMIT_TIMESTAMP],
	"StatisticalVariable": ["StatisticalVariable", "StatisticalVariable", "StatisticalVariable", ["Class"], spanner.COMMIT_TIMESTAMP],
	"StatVarGroup": ["StatVarGroup", "StatVarGroup", "StatVarGroup", ["Class"], spanner.COMMIT_TIMESTAMP],
	"StatVarObservation": ["StatVarObservation", "StatVarObservation", "StatVarObservation", ["Class"], spanner.COMMIT_TIMESTAMP],
	"Topic": ["Topic", "Topic", "Topic", ["Class"], spanner.COMMIT_TIMESTAMP],
	"c/g/Root": ["c/g/Root", "c/g/Root", "c/g/Root", ["StatVarGroup"], spanner.COMMIT_TIMESTAMP],
	}
	base_nodes = {
	"StatisticalVariable": ["Class"],
	"StatVarGroup": ["Class"],
	"StatVarObservation": ["Class"],
	"Topic": ["Class"],
	"c/g/Root": ["StatVarGroup"],
	}
	candidates = {
	subj: [subj, subj, subj, types, spanner.COMMIT_TIMESTAMP]
	for subj, types in base_nodes.items()
	}

[gemini-code-assist]

These assertions can be made more concise and robust by iterating through the values and checking the properties for each row within a loop. This makes the test easier to read and extend if more fields need to be checked against the subject_id in the future. It also avoids iterating over the list of values multiple times.

Suggested change

	actual_subjects = [val[0] for val in kwargs['values']]
	actual_names = [val[1] for val in kwargs['values']]
	actual_values = [val[2] for val in kwargs['values']]
	self.assertEqual(actual_subjects, expected_subjects)
	self.assertEqual(actual_names, expected_subjects)
	self.assertEqual(actual_values, expected_subjects)
	actual_subjects = []
	for val in kwargs['values']:
	subject_id, name, value, _, _ = val
	self.assertEqual(name, subject_id)
	self.assertEqual(value, subject_id)
	actual_subjects.append(subject_id)
	self.assertEqual(actual_subjects, expected_subjects)