format tencent cpu ratio from 100 to 1

exporter.py

@@ -47,7 +47,8 @@ class TencentExporter(Exporter):
         return data_from_tencent
 
     def _transfer_to_prometheus_format(self, data):
-        # print(data)
+        # 做一步格式转换
+        self._format_data(data)
         metrics_to_export = []
         service_provider = "tencent"
 
@@ -71,15 +72,27 @@ class TencentExporter(Exporter):
                 instance_type = instance.get('type', 'master')
                 # 当前queryname，当前rds实例的因子，若没有配置，默认为1
                 rds_factor = instance.get(factor, 1)
-                for timestamp, value in timestamp_value_dict.items():
-                    avg_metric_family.add_metric([instance_name, instance_type, service_provider], value, timestamp)
+                for value in timestamp_value_dict.values():
+                    avg_metric_family.add_metric([instance_name, instance_type, service_provider], value)
                     with_factor_metric_family.add_metric(
                         ['{}*{}'.format(instance_name,rds_factor), instance_type, service_provider], 
-                        value*rds_factor, timestamp)
+                        value*rds_factor)
             metrics_to_export.extend([avg_metric_family, with_factor_metric_family])
 
         return metrics_to_export
 
+    def _format_data(self, data):
+        """ inplace change data
+        """
+        for label, instance_data_list in data.items():
+            # 腾讯云CPU利用率返回的是0-100(%), 转化成 0-1
+            if label == "CPUUseRate":
+                for instance in instance_data_list: 
+                    instance['Values'][:] = map(lambda v: v/100, instance['Values'])
+            # 腾讯云返回的是内存使用率，通过除以配置中记录的实例的总内存得到内存使用率
+            elif label == "MemoryUse":
+                pass
+
 
 class AliyunExporter(Exporter):
     def __init__(self):
@@ -132,11 +145,11 @@ class AliyunExporter(Exporter):
                 # 当前queryname，当前rds实例的因子，若没有配置，默认为1
                 rds_factor = rds.get(factor, 1)
                 avg_metric_family.add_metric([rds_name, rds_type, service_provider],
-                        point['Average']/100.0, point['timestamp']/1000)
+                        point['Average']/100.0)
                 max_metric_family.add_metric([rds_name, rds_type, service_provider],
-                        point['Maximum']/100.0, point['timestamp']/1000)
+                        point['Maximum']/100.0)
                 with_factor_metric_family.add_metric(['{}*{}'.format(rds_name,rds_factor), rds_type, service_provider],
-                        point['Average']*rds_factor/100.0, point['timestamp']/1000)
+                        point['Average']*rds_factor/100.0)
 
             metrics_to_export.extend([avg_metric_family, max_metric_family, with_factor_metric_family])