workshops/RI2021/ml_ops/lambdas/anomaly-alert-function/anomaly-alert-function.py [160:176]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        data = {}
        data ['Timestamp'] =[]
        data['metricName'] =[]
        data['dimensionName'] =[]
        data['dimensionValue'] =[]
        data['valueContribution'] =[]
        
        #Data collection for the CSV
        for i in event['impactedMetric']['dimensionContribution']:
            for a in i['dimensionValueContributions']:
                data['Timestamp'].append(start_time)
                data['dimensionName'].append(i['dimensionName'])
                data['dimensionValue'].append(a['dimensionValue'])
                data['valueContribution'].append(a['valueContribution'])
                data['metricName'].append(event['impactedMetric']['metricName'])
          
        df=pd.DataFrame(data=data)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



workshops/RI2021/ml_ops/lambdas/anomaly-alert-function/anomaly-alert-function.py [190:206]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        data = {}
        data ['Timestamp'] =[]
        data['metricName'] =[]
        data['dimensionName'] =[]
        data['dimensionValue'] =[]
        data['valueContribution'] =[]
        
        #Data collection for the CSV
        for i in event['impactedMetric']['dimensionContribution']:
            for a in i['dimensionValueContributions']:
                data['Timestamp'].append(start_time)
                data['dimensionName'].append(i['dimensionName'])
                data['dimensionValue'].append(a['dimensionValue'])
                data['valueContribution'].append(a['valueContribution'])
                data['metricName'].append(event['impactedMetric']['metricName'])
          
        df=pd.DataFrame(data=data)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -