đ Systematische Log-Analyse
Effiziente Fehlerprotokoll-Auswertung fĂŒr schnelle Problemlösung im Georg Fischer WMS
⥠LOG-ANALYSE PRIORITĂTEN
Ziel: Kritische Fehler innerhalb 15 Minuten identifiziert und eskaliert
Log-Hotline: +41 81 770 8888 (Technical Support)
### VerfĂŒgbare Log-Quellen
#### **WMS Application Logs**
```yaml
wms_logs:
location: "C:\\WMS\\Logs\\"
format: "YYYY-MM-DD_HH-mm-ss.log"
retention: "30 Tage lokal, 1 Jahr archiviert"
log_types:
application_log:
filename: "Application_{date}.log"
content: "Anwendungslogik, GeschÀftsprozesse"
level: "INFO, WARN, ERROR, FATAL"
system_log:
filename: "System_{date}.log"
content: "Systemereignisse, Ressourcen"
level: "DEBUG, INFO, WARN, ERROR"
integration_log:
filename: "Integration_{date}.log"
content: "SAP-Kommunikation, EDI, APIs"
level: "INFO, WARN, ERROR"
performance_log:
filename: "Performance_{date}.log"
content: "Performance-Metriken, Laufzeiten"
level: "INFO, WARN"
```
#### **Database Logs**
```yaml
sql_server_logs:
location: "C:\\Program Files\\Microsoft SQL Server\\MSSQL15.MSSQLSERVER\\MSSQL\\Log\\"
log_files:
errorlog:
description: "SQL Server Fehlerprotokoll"
rotation: "Wöchentlich oder bei 10MB"
content: "Startmeldungen, Fehler, Warnungen"
agent_log:
description: "SQL Server Agent Log"
content: "Job-AusfĂŒhrungen, Schedules"
retention: "4 Wochen"
backup_log:
description: "Backup/Restore Operationen"
content: "Backup-Status, Fehler, Wiederherstellungen"
```
#### **Windows Event Logs**
```yaml
windows_logs:
zugriff: "eventvwr.msc oder PowerShell"
relevant_logs:
application:
path: "Windows Logs > Application"
filter: "Source: WMS*, Jungheinrich*"
system:
path: "Windows Logs > System"
relevance: "Hardware-Fehler, Treiber-Probleme"
security:
path: "Windows Logs > Security"
relevance: "Anmeldungsfehler, Berechtigungsprobleme"
custom:
path: "Applications and Services Logs"
content: "Anwendungsspezifische Logs"
```
### Log-Zugriffs-Methoden
#### **Direkte Datei-Analyse**
```powershell
# PowerShell-Befehle fĂŒr Log-Zugriff
# Neueste Fehler anzeigen
Get-Content "C:\WMS\Logs\Application_$(Get-Date -Format 'yyyy-MM-dd').log" | Select-String "ERROR|FATAL" | Select-Object -Last 20
# Log-Dateien nach GröĂe sortieren
Get-ChildItem "C:\WMS\Logs\*.log" | Sort-Object Length -Descending | Select-Object Name, @{n='Size(MB)';e={[math]::Round($_.Length/1MB,2)}}
# Letzte 1000 Zeilen einer Log-Datei
Get-Content "C:\WMS\Logs\System_$(Get-Date -Format 'yyyy-MM-dd').log" -Tail 1000
# Fehler in einem bestimmten Zeitraum
$startTime = (Get-Date).AddHours(-2)
Get-WinEvent -FilterHashtable @{LogName='Application'; StartTime=$startTime; Level=2}
```
#### **Remote Log Access**
```yaml
remote_access:
rdp_connection:
server: "WMS-SRV01.georgfischer.local"
credentials: "Domain-Administrator-Account erforderlich"
powershell_remoting:
command: "Enter-PSSession -ComputerName WMS-SRV01"
requirements: "PowerShell Remoting aktiviert"
file_shares:
path: "\\\\WMS-SRV01\\WMS-Logs$"
permissions: "Leserecht fĂŒr IT-Support-Gruppe"
log_shipping:
destination: "Central Log Server"
frequency: "Real-time via Syslog"
retention: "1 Jahr zentral gespeichert"
```
## Analyse-Methoden {#analyse-methoden}
### Systematische Log-Analyse
#### **Fehler-Priorisierung**
```yaml
fehler_prioritaet:
kritisch:
keywords: ["FATAL", "OutOfMemoryException", "DatabaseConnectionLost", "SystemHalt"]
response_time: "Sofortige Bearbeitung"
escalation: "Automatische Benachrichtigung Management"
hoch:
keywords: ["ERROR", "Exception", "Failed", "Timeout", "AccessDenied"]
response_time: "15 Minuten"
escalation: "Team Lead nach 30 Minuten"
mittel:
keywords: ["WARN", "Warning", "Deprecated", "SlowQuery"]
response_time: "2 Stunden"
escalation: "Daily Review"
niedrig:
keywords: ["INFO", "Debug", "Trace"]
response_time: "Best Effort"
escalation: "Wöchentliche Analyse"
```
#### **Pattern Recognition**
```regex
# Wichtige Regex-Muster fĂŒr Log-Analyse
# Timestamp-Extraktion
^(\d{4}-\d{2}-\d{2}\s\d{2}:\d{2}:\d{2},\d{3})
# Exception-Erkennung
(Exception|Error).*?\n(\s+at\s+.*\n)*
# SQL-Fehler
SQL.*?(Error|Exception|Failed).*?Message:\s*(.*?)\n
# Performance-Probleme
(slow|timeout|performance).*?(\d+(?:\.\d+)?)\s*(ms|seconds|minutes)
# User-AktivitÀten
User\s+(\w+)\s+(login|logout|action)\s+(.+)
# System Resources
(Memory|CPU|Disk).*?(\d+(?:\.\d+)?)\s*%
```
#### **Korrelations-Analyse**
```yaml
korrelation_methoden:
zeitbasiert:
window: "±5 Minuten um Fehler-Timestamp"
sources: ["Application", "System", "Database", "Network"]
ziel: "Identifikation von Root-Cause"
benutzerbasiert:
tracking: "User-ID durch alle Log-Quellen"
scope: "Benutzer-spezifische Fehler-Muster"
transaktionsbasiert:
identifier: "Transaction-ID, Session-ID"
verfolgung: "Ende-zu-Ende Transaktions-Verfolgung"
systemkomponenten:
mapping: "Fehler zu betroffenen System-Komponenten"
impact_analysis: "Auswirkungsanalyse auf andere Komponenten"
```
### Analysewerkzeuge
#### **PowerShell-Analyse-Scripts**
```powershell
# Error-Summary-Script
function Get-WMSErrorSummary {
param(
[string]$LogPath = "C:\WMS\Logs",
[int]$Hours = 24
)
$StartTime = (Get-Date).AddHours(-$Hours)
$LogFiles = Get-ChildItem $LogPath -Filter "*.log"
$ErrorSummary = @{}
foreach ($LogFile in $LogFiles) {
$Content = Get-Content $LogFile.FullName
$Errors = $Content | Where-Object { $_ -match "ERROR|FATAL" -and $_ -match $StartTime.ToString("yyyy-MM-dd") }
foreach ($Error in $Errors) {
$ErrorType = ($Error -split "\s+")[3] # Annahme: 4. Element ist Error-Type
if ($ErrorSummary.ContainsKey($ErrorType)) {
$ErrorSummary[$ErrorType]++
} else {
$ErrorSummary[$ErrorType] = 1
}
}
}
return $ErrorSummary | Sort-Object Value -Descending
}
# Performance-Analysis
function Get-WMSPerformanceMetrics {
param(
[string]$LogPath = "C:\WMS\Logs\Performance_$(Get-Date -Format 'yyyy-MM-dd').log"
)
$Content = Get-Content $LogPath
$SlowQueries = $Content | Where-Object { $_ -match "slow" -and $_ -match "(\d+)ms" }
$Metrics = @{
'Total Slow Queries' = $SlowQueries.Count
'Slowest Query' = ($SlowQueries | ForEach-Object { [regex]::Match($_, "(\d+)ms").Groups[1].Value } | Measure-Object -Maximum).Maximum
'Average Response Time' = ($SlowQueries | ForEach-Object { [regex]::Match($_, "(\d+)ms").Groups[1].Value } | Measure-Object -Average).Average
}
return $Metrics
}
```
## Fehler-Kategorien {#fehler-kategorien}
### System-Kategorien
#### **Anwendungsfehler**
```yaml
application_errors:
business_logic_errors:
description: "Fehler in GeschÀftslogik-Verarbeitung"
examples:
- "InvalidOrderStatus: Order cannot be shipped in current status"
- "InsufficientInventory: Not enough stock for requested quantity"
- "ValidationError: Article number format invalid"
impact: "Funktionale EinschrÀnkungen"
resolution: "Code-Review und Business Rule Validation"
data_consistency_errors:
description: "Dateninkonsistenzen zwischen Systemen"
examples:
- "StockMismatch: WMS stock differs from SAP"
- "MasterDataOutOfSync: Article not found in local cache"
- "TimestampConflict: Concurrent modification detected"
impact: "DatenqualitÀtsprobleme"
resolution: "Daten-Synchronisation und Reconciliation"
integration_errors:
description: "Probleme bei System-Integration"
examples:
- "SAPConnectionTimeout: RFC call timed out"
- "EDITransmissionFailed: Message delivery failed"
- "APIAuthenticationError: Invalid credentials"
impact: "Unterbrochene GeschÀftsprozesse"
resolution: "Verbindungs-Diagnose und Credential-ĂberprĂŒfung"
```
#### **System-Level Fehler**
```yaml
system_errors:
resource_exhaustion:
memory_leaks:
pattern: "OutOfMemoryException|GC overhead limit exceeded"
symptoms: "Langsame Performance, System freezes"
diagnosis: "Memory Profiling, Heap Dumps"
disk_space:
pattern: "No space left on device|Disk full"
symptoms: "Log rotation failures, Database write errors"
diagnosis: "Disk usage analysis, cleanup procedures"
cpu_overload:
pattern: "High CPU usage|Thread pool exhausted"
symptoms: "Slow response times, timeouts"
diagnosis: "Performance counters, thread dumps"
network_issues:
connectivity:
pattern: "Connection refused|Network unreachable"
symptoms: "Integration failures, timeouts"
diagnosis: "Network connectivity tests, firewall rules"
dns_resolution:
pattern: "Name resolution failed|Unknown host"
symptoms: "Service discovery failures"
diagnosis: "DNS configuration check"
security_violations:
authentication_failures:
pattern: "Authentication failed|Invalid credentials"
symptoms: "User login problems"
diagnosis: "Credential validation, account status"
authorization_errors:
pattern: "Access denied|Insufficient privileges"
symptoms: "Feature access restrictions"
diagnosis: "Role and permission review"
```
### Error Classification Matrix
#### **Criticality vs. Frequency Matrix**
| HÀufigkeit \ KritikalitÀt | Niedrig | Mittel | Hoch | Kritisch |
|---------------------------|---------|--------|------|----------|
| **Sehr hÀufig (>100/Tag)** | Monitor | Investigate | Fix Immediately | Emergency Response |
| **HĂ€ufig (10-100/Tag)** | Log & Review | Investigate | High Priority Fix | Critical Fix |
| **Gelegentlich (1-10/Tag)** | Log | Review Weekly | Investigate | High Priority |
| **Selten (<1/Tag)** | Log | Monthly Review | Document | Investigate |
#### **Automatische Klassifizierung**
```yaml
auto_classification:
rules:
critical_keywords:
- "FATAL"
- "OutOfMemory"
- "DatabaseConnectionLost"
- "SystemHalt"
- "SecurityBreach"
high_priority_patterns:
- "ERROR.*Exception"
- "Failed.*Transaction"
- "Timeout.*Critical"
- "Integration.*Failed"
performance_indicators:
- "ResponseTime > 5000ms"
- "QueryTime > 30s"
- "CPU > 90%"
- "Memory > 85%"
machine_learning:
model_type: "Classification Tree"
features: ["error_text", "frequency", "time_pattern", "user_impact"]
training_data: "Historical incidents with known resolutions"
accuracy_target: "> 85% classification accuracy"
```
## Analysis Tools {#tools}
### Spezialisierte Log-Tools
#### **ELK Stack Integration**
```yaml
elk_stack:
elasticsearch:
version: "7.15+"
indices:
- "wms-application-logs-*"
- "wms-system-logs-*"
- "wms-performance-logs-*"
retention: "90 Tage fĂŒr hot data, 1 Jahr warm/cold"
logstash:
pipelines:
wms_application:
input: "filebeat"
filters:
- "grok pattern matching"
- "date parsing"
- "error classification"
output: "elasticsearch"
kibana:
dashboards:
operational_overview:
panels: ["Error Rate", "Response Times", "System Health"]
refresh: "30 seconds"
error_analysis:
panels: ["Error Distribution", "Error Trends", "Top Errors"]
timeframe: "Last 24 hours"
performance_monitoring:
panels: ["Transaction Times", "Resource Usage", "Throughput"]
alerts: "Threshold-based alerting"
```
#### **Splunk Configuration**
```yaml
splunk_setup:
indexes:
wms_main:
max_data_size: "500GB"
retention: "1 Jahr"
sourcetypes:
wms_application:
pattern: "*.log"
extraction_rules: "Custom field extraction"
searches:
error_trending:
query: 'index=wms_main sourcetype=wms_application level=ERROR | timechart count by error_type'
schedule: "Hourly"
performance_baseline:
query: 'index=wms_main sourcetype=wms_performance | stats avg(response_time) by operation'
schedule: "Daily"
alerts:
critical_errors:
search: 'index=wms_main level=FATAL'
trigger: "Real-time"
action: "Email + SMS notification"
performance_degradation:
search: 'index=wms_main sourcetype=wms_performance response_time>5000'
trigger: "Count > 10 in 15 minutes"
action: "Team notification"
```
#### **Custom Analysis Tools**
```csharp
// C# Log Analyzer Beispiel
public class WMSLogAnalyzer
{
public class LogEntry
{
public DateTime Timestamp { get; set; }
public string Level { get; set; }
public string Category { get; set; }
public string Message { get; set; }
public string StackTrace { get; set; }
}
public class AnalysisResult
{
public int TotalErrors { get; set; }
public Dictionary ErrorsByType { get; set; }
public List TopErrors { get; set; }
public TimeSpan AnalysisDuration { get; set; }
}
public static AnalysisResult AnalyzeLogs(string logPath, DateTime fromDate)
{
var result = new AnalysisResult
{
ErrorsByType = new Dictionary(),
TopErrors = new List()
};
var logFiles = Directory.GetFiles(logPath, "*.log")
.Where(f => File.GetCreationTime(f) >= fromDate);
foreach (var logFile in logFiles)
{
var lines = File.ReadAllLines(logFile);
foreach (var line in lines)
{
if (IsErrorLine(line))
{
result.TotalErrors++;
var errorType = ExtractErrorType(line);
if (result.ErrorsByType.ContainsKey(errorType))
result.ErrorsByType[errorType]++;
else
result.ErrorsByType[errorType] = 1;
}
}
}
result.TopErrors = result.ErrorsByType
.OrderByDescending(kvp => kvp.Value)
.Take(10)
.Select(kvp => $"{kvp.Key}: {kvp.Value} occurrences")
.ToList();
return result;
}
private static bool IsErrorLine(string line)
{
return line.Contains("ERROR") || line.Contains("FATAL") || line.Contains("Exception");
}
private static string ExtractErrorType(string line)
{
// Regex to extract error type from log line
var match = Regex.Match(line, @"(\w+(?:Exception|Error))");
return match.Success ? match.Groups[1].Value : "Unknown";
}
}
```
### Monitoring Dashboards
#### **Real-time Monitoring**
```yaml
real_time_dashboard:
metrics:
error_rate:
calculation: "Errors per minute"
threshold: "> 5 errors/minute"
alert: "Immediate notification"
response_time:
percentiles: ["50th", "95th", "99th"]
target: "95th percentile < 2 seconds"
system_health:
cpu_usage: "< 80%"
memory_usage: "< 85%"
disk_space: "< 90%"
visualizations:
error_heatmap:
type: "Heatmap"
dimensions: ["Time", "Error Type"]
color_scale: "Green to Red"
trend_analysis:
type: "Line Chart"
metrics: ["Error Count", "Response Time"]
timeframe: "Last 24 hours"
status_indicators:
type: "Status Lights"
systems: ["WMS Core", "Database", "SAP Integration", "Hardware"]
```
## Automatisierung {#automation}
### Automated Error Detection
#### **Event-Driven Processing**
```yaml
automated_detection:
log_watchers:
file_system_watcher:
path: "C:\\WMS\\Logs"
filter: "*.log"
event: "File Modified"
action: "Real-time parsing"
event_log_monitor:
source: "Windows Event Log"
levels: ["Error", "Critical"]
filter: "Source=WMS*"
pattern_matching:
regex_rules:
- pattern: "FATAL.*"
severity: "Critical"
action: "Immediate alert"
- pattern: "ERROR.*OutOfMemory"
severity: "High"
action: "Resource monitoring + alert"
- pattern: "WARN.*PerformanceDegradation"
severity: "Medium"
action: "Trend analysis"
machine_learning:
anomaly_detection:
algorithm: "Isolation Forest"
features: ["log_frequency", "error_patterns", "time_correlations"]
training_period: "30 Tage"
sensitivity: "95% confidence interval"
```
#### **Automated Response Actions**
```yaml
automated_responses:
immediate_actions:
critical_errors:
- "SMS alert to on-call engineer"
- "Create high-priority incident ticket"
- "Capture system state snapshot"
- "Initiate automated diagnostics"
memory_issues:
- "Trigger garbage collection"
- "Capture memory dump"
- "Monitor memory usage trend"
- "Prepare for service restart if needed"
integration_failures:
- "Retry failed operations"
- "Switch to backup connection"
- "Log detailed connection state"
- "Notify integration team"
escalation_procedures:
level_1_response:
timeframe: "5 minutes"
actions: ["Automated diagnostics", "Self-healing attempts"]
level_2_escalation:
timeframe: "15 minutes"
actions: ["Technical team notification", "Enhanced monitoring"]
level_3_escalation:
timeframe: "45 minutes"
actions: ["Management notification", "Vendor escalation"]
```
### Predictive Analytics
#### **Error Prediction Models**
```python
# Python Example: Error Prediction Model
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
class WMSErrorPredictor:
def __init__(self):
self.model = RandomForestClassifier(n_estimators=100, random_state=42)
self.features = [
'error_frequency_1h',
'error_frequency_6h',
'error_frequency_24h',
'cpu_usage_avg',
'memory_usage_avg',
'disk_io_rate',
'active_users',
'transaction_volume',
'hour_of_day',
'day_of_week'
]
def prepare_training_data(self, log_data):
# Feature engineering from log data
df = pd.DataFrame(log_data)
# Time-based features
df['hour_of_day'] = pd.to_datetime(df['timestamp']).dt.hour
df['day_of_week'] = pd.to_datetime(df['timestamp']).dt.dayofweek
# Rolling windows for error frequency
df['error_frequency_1h'] = df['error_count'].rolling('1H').sum()
df['error_frequency_6h'] = df['error_count'].rolling('6H').sum()
df['error_frequency_24h'] = df['error_count'].rolling('24H').sum()
# Target: Will there be a critical error in next hour?
df['critical_error_next_hour'] = df['critical_errors'].shift(-1) > 0
return df[self.features], df['critical_error_next_hour']
def train_model(self, training_data):
X, y = self.prepare_training_data(training_data)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
self.model.fit(X_train, y_train)
# Evaluate model
predictions = self.model.predict(X_test)
print(classification_report(y_test, predictions))
def predict_errors(self, current_metrics):
probability = self.model.predict_proba([current_metrics])[0][1]
return {
'critical_error_probability': probability,
'risk_level': 'High' if probability > 0.7 else 'Medium' if probability > 0.3 else 'Low',
'recommended_actions': self.get_recommendations(probability)
}
def get_recommendations(self, probability):
if probability > 0.7:
return [
"Increase monitoring frequency",
"Prepare incident response team",
"Consider preemptive service restart"
]
elif probability > 0.3:
return [
"Monitor system metrics closely",
"Review recent changes"
]
else:
return ["Continue normal operations"]
```
#### **Trend Analysis**
```yaml
trend_analysis:
metrics_tracked:
error_rates:
calculation: "Errors per hour by category"
trending: "7-day moving average"
prediction: "Next 24-hour error volume"
performance_degradation:
calculation: "Response time percentiles"
trending: "Week-over-week comparison"
prediction: "Performance threshold breach probability"
resource_utilization:
calculation: "CPU, Memory, Disk usage patterns"
trending: "Growth rate analysis"
prediction: "Resource exhaustion timeline"
alerting_thresholds:
trend_alerts:
error_rate_increase: "> 50% increase over 3-day average"
performance_decline: "> 20% response time increase"
resource_growth: "> 10% utilization increase per week"
predictive_maintenance:
schedule_optimization:
analysis: "Failure pattern correlation with maintenance schedules"
recommendation: "Optimal maintenance windows"
component_lifecycle:
tracking: "Error patterns by system component age"
prediction: "Component replacement recommendations"
```
## Support & Kontakte
### Error Analysis Support Team
```yaml
support_kontakte:
log_analysis_team:
team: "Technical Log Analysis"
telefon: "+41 81 770 8888"
email: "log-analysis@georgfischer.com"
verfuegbarkeit: "24/7 fĂŒr kritische Fehler"
system_monitoring:
team: "System Monitoring & Alerting"
telefon: "+41 81 770 8899"
email: "monitoring@georgfischer.com"
verfuegbarkeit: "Mo-Fr 06:00-22:00"
automation_team:
team: "Log Automation & Tools"
telefon: "+41 81 770 8877"
email: "automation@georgfischer.com"
verfuegbarkeit: "Mo-Fr 08:00-17:00"
emergency_escalation:
hotline: "+41 81 770 9999"
verfuegbarkeit: "24/7 fĂŒr System-kritische Störungen"
escalation: "Automatische Eskalation nach 20 Minuten"
```
## Quick Reference
### Critical Commands
```bash
# Wichtigste PowerShell-Befehle fĂŒr Log-Analyse
# Aktuelle Fehler anzeigen
Get-EventLog -LogName Application -EntryType Error -Newest 10
# WMS-Logs nach Fehlern durchsuchen
Select-String -Path "C:\WMS\Logs\*.log" -Pattern "ERROR|FATAL" | Select-Object -Last 20
# Systemressourcen prĂŒfen
Get-Counter "\Processor(_Total)\% Processor Time","\Memory\Available MBytes"
# Dienste-Status prĂŒfen
Get-Service | Where-Object {$_.Name -like "*WMS*"} | Select-Object Name, Status
```
### Error Severity Matrix
| Severity | Response Time | Escalation | Actions |
|----------|--------------|------------|----------|
| **FATAL** | Immediate | Automatic | Emergency response, system halt possible |
| **ERROR** | 15 minutes | After 30 min | Investigation required, potential impact |
| **WARN** | 2 hours | Daily review | Monitor trends, preventive measures |
| **INFO** | Best effort | Weekly review | Documentation and analysis |
---
*Diese Error Logs Dokumentation ermöglicht die systematische und effiziente Analyse von WMS-Fehlerprotokollen bei Georg Fischer. Kontinuierliche Updates gewÀhrleisten optimale Fehlerbehandlung. Letzte Aktualisierung: MÀrz 2025*