01b4ec3f531e07b8c4a32a13288c963ad8b4b843,server/website/website/db/base/parser.py,BaseParser,convert_dbms_metrics,#BaseParser#Any#Any#Any#,188

Before Change

        //         if len(metrics) != len(self.numeric_metric_catalog_):
        //             raise Exception("The number of metrics should be equal!")
        metric_data = {}
        for name, metadata in list(self.numeric_metric_catalog_.items()):
            value = metrics[name]
            if metadata.metric_type == MetricType.COUNTER:
                converted = self.convert_integer(value, metadata)
                metric_data[name] = float(converted) / observation_time
            elif metadata.metric_type == MetricType.STATISTICS:
                converted = self.convert_integer(value, metadata)
                metric_data[name] = float(converted)
            else:
                raise Exception(
                    "Unknown metric type for {}: {}".format(name, metadata.metric_type))

        if target_objective is not None and self.target_metric(target_objective) not in metric_data:
            raise Exception("Cannot find objective function")

        if target_objective is not None:
            metric_data[target_objective] = metric_data[self.target_metric(target_objective)]
        else:
            // default
            metric_data["throughput_txn_per_sec"] = \
                metric_data[self.target_metric(target_objective)]

        return metric_data

    def extract_valid_variables(self, variables, catalog, default_value=None):

After Change

            value = metrics[name]

            if metadata.vartype == VarType.INTEGER:
                converted = float(self.convert_integer(value, metadata))
            elif metadata.vartype == VarType.REAL:
                converted = self.convert_real(value, metadata)
            else:
                raise ValueError(
                    ("Found non-numeric metric "{}" in the numeric "
                     "metric catalog: value={}, type={}").format(
                         name, value, VarType.name(metadata.vartype)))

            if metadata.metric_type == MetricType.COUNTER:
                assert isinstance(converted, float)
                base_metric_data[name] = converted
                metric_data[name] = converted / observation_time
            elif metadata.metric_type == MetricType.STATISTICS:
                assert isinstance(converted, float)
                base_metric_data[name] = converted
                metric_data[name] = converted
            else:
                raise ValueError(
                    "Unknown metric type for {}: {}".format(name, metadata.metric_type))

Instances