def prepare(self):
# STEP 1: Reconstruct database and workload based on the given design
d = Denormalizer(self.metadata_db, self.dataset_db, self.design)
d.process()
# STEP 2: Put indexs on the dataset_db based on the given design
self.setIndexes(self.dataset_db, self.design)
def prepare(self):
# STEP 1: Reconstruct database and workload based on the given design
d = Denormalizer(self.metadata_db, self.dataset_db, self.design)
d.process()
# STEP 2: Put indexs on the dataset_db based on the given design
self.setIndexes(self.dataset_db, self.design)
def testDenormalizer(self):
d = Design()
for col_name in self.col_names:
d.addCollection(col_name)
## FOR
op_list = self.printOperations()
col_list = self.printAllCollections()
d.setDenormalizationParent("koalas", "apples")
dn = Denormalizer(self.metadata_db, self.dataset_db, d)
dn.process()
new_op_list = self.printOperations()
new_col_list = self.printAllCollections()
self.assertTrue("koalas" not in new_op_list)
self.assertTrue("koalas" not in new_col_list)
def testDenormalizer(self):
d = Design()
for col_name in self.col_names:
d.addCollection(col_name)
## FOR
op_list = self.printOperations()
col_list = self.printAllCollections()
d.setDenormalizationParent("koalas", "apples")
dn = Denormalizer(self.metadata_db, self.dataset_db, d)
dn.process()
new_op_list = self.printOperations()
new_col_list = self.printAllCollections()
self.assertTrue("koalas" not in new_op_list)
self.assertTrue("koalas" not in new_col_list)