Simple Log Service:Anomaly comparison function

• Function expressions

  select anomaly_compare(long stamp, array[ feature_1, feature_2 ], long timePoint, long interval)
  select anomaly_compare(long stamp, array[ feature_1, feature_2 ], array[ feature1_name, feature2_name ], long timePoint, long interval)

• Input parameters

  Parameter	Description
  stamp	The UNIX timestamp of the data.
  array[features]	The metrics of the observation object at a specific point in time.
  array[featureNames]	The description of the metrics.
  timePoint	The UNIX timestamp of the time when the observed object changes.
  interval	The interval at which data is collected. For example, if data is collected every 10 seconds, the interval is 10.

• Function expressions

  select anomaly_compare(long stamp, array[ feature_1, feature_2 ], array[ feature1_name, feature2_name ], long version)

• Input parameters

  Parameter	Description
  stamp	The UNIX timestamp of the data.
  array[features]	The metrics of the observation object at a specific point in time.
  array[featureNames]	The description of the metrics.
  version	The version number of the time series. A value of 0 indicates the raw data. A value of 1 indicates the new data.

{
   "results" : [ {
     "attr" : "cpu",
     "anomalyScore" : 0.01106371634297909,
     "details" : {
       "left" : [ {
         "key" : "mean",
         "value" : 0.07002069952622482
       }, {
         "key" : "std",
         "value" : 0.1364542814430179
       }, {
         "key" : "median",
         "value" : 0.04467685956328345
       }, {
         "key" : "variance",
         "value" : 0.018619770924130346
       } ],
       "rightMetrics" : [ {
         "key" : "mean",
         "value" : 0.4472823405432968
       }, {
         "key" : "std",
         "value" : 0.22405908739288383
       }, {
         "key" : "median",
         "value" : 0.42513225830553775
       }, {
         "key" : "variance",
         "value" : 0.05020247464333195
       } ]
     }
   } ]
 }

• The mean, std, median, and variance methods are used for the statistics of a time series.
• If you specify the names of metrics, the names are included in the attr field. Otherwise, the prefix column_ is concatenated with the array subscript of a metric as the name of the metric, for example, column_0.
• The anomalyScore indicates the degree of difference of a feature metric. Value values: 0 to 1. If the value approaches 0, the degree of difference is low. If the value approaches 1, the degree of difference is high.