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)