def run()

in table_class_evaluator.py [0:0]
45 lines of code
12 McCabe index (conditional complexity)

    def run(self):
        """Main program entry point"""
        table_names = []

        try:
            if self.args.table_name is not None:
                table_names = [self.args.table_name]
            else:
                table_names = self.table_utility.get_table_names()

            table_cost_estimates = self.table_utility.estimate_table_costs_for_region(table_names, self.args.region)

            if not table_cost_estimates:
                print("No table cost results returned.")
                exit(0)

            if self.args.estimates_only:
                print(json.dumps(table_cost_estimates, cls=DecimalEncoder, indent=2))
                exit(0)

            recommendations = []

            # evaluate tables costs for storage classes
            for table_estimate in table_cost_estimates:
                table_pricing_data = table_estimate[constants.PRICING_DATA]
                
                # skip on-demand tables
                if table_pricing_data[constants.BILLING_MODE] == constants.ON_DEMAND_BILLING:
                    continue

                table_class = table_pricing_data[constants.TABLE_CLASS]
                monthly_cost_estimates = table_estimate[constants.ESTIMATED_MONTHLY_COSTS]

                ia_cost_differential = (monthly_cost_estimates[constants.IA_MO_TOTAL_COST] 
                                        - monthly_cost_estimates[constants.STD_MO_TOTAL_COST])

                if ia_cost_differential < 0:
                    if table_class == constants.STD_TABLE_CLASS:
                        recommendation = {constants.RECOMMENDATION_TYPE: constants.TABLE_CLASS_CHANGE_RECOMMENDATION,
                                          constants.RECOMMENDED_TABLE_CLASS: constants.IA_TABLE_CLASS,
                                          constants.ESTIMATED_MO_SAVINGS: abs(ia_cost_differential),
                                          constants.ESTIMATE_DETAIL: table_estimate}
                        recommendations.append(recommendation)

                elif ia_cost_differential > 0:
                    if table_class == constants.IA_TABLE_CLASS:
                        recommendation = {constants.RECOMMENDATION_TYPE: constants.TABLE_CLASS_CHANGE_RECOMMENDATION,
                                          constants.RECOMMENDED_TABLE_CLASS: constants.STD_TABLE_CLASS,
                                          constants.ESTIMATED_MO_SAVINGS: ia_cost_differential,
                                          constants.ESTIMATE_DETAIL: table_estimate}
                        recommendations.append(recommendation)


            output = json.dumps(recommendations, cls=DecimalEncoder, indent=2)
            print(output)
            exit(0)
        except Exception as e:
            print(f"Table evaluation failed: {e}")
            import traceback
            traceback.print_exc()
            exit(0)