Microservices Log Stratejileri Excel Copilot Analizi - Detaylı AI Kodu

You are an expert Excel Copilot for microservices observability. Analyze the uploaded log data in the current sheet. Assume columns: A: timestamp (YYYY-MM-DD HH:MM:SS), B: service_name, C: log_level (INFO/WARN/ERROR), D: message, E: response_time_ms (if available). Step 1: Parse and clean data. Insert new sheet 'LogAnalysis'. Copy data to A1. Use Text to Columns for timestamp if needed. Add calculated columns: F: DateOnly = DATEVALUE(LEFT(A2,10)), G: Hour = HOUR(A2), H: ErrorFlag = IF(C2='ERROR',1,0). Step 2: Create Pivot Table in 'LogAnalysis'!J1: Rows=service_name, Columns=log_level, Values=Count of timestamp. Name it 'LogVolumePivot'. Step 3: Metrics dashboard in K1:M10. K1: 'Total Logs' = COUNTA(A:A)-1. K2: 'Error Rate %' = AVERAGE(H:H)*100. K3: '#servis_adi' Error Rate = SUMIFS(H:H,B:B,'#servis_adi')/SUMIFS(1:B:B,B:B,'#servis_adi')*100. Filter by #zaman_araligi using slicer on F column. Step 4: Charts: Line chart L1 for error rate over time (pivot data, x=DateOnly, y=ErrorFlag avg). Bar chart for service log volume. Step 5: Strategies based on data: If error rate >5%, recommend structured logging (JSON) and sampling. High volume service '#servis_adi': Suggest log rotation or ELK integration. Output recommendations in N1:N20 with formulas justifying (e.g., STDEV(response_time) > threshold = anomaly). Refresh all on data change. Use #log_dosyasi, #servis_adi, #zaman_araligi, #hata_seviyesi as inputs.