⚡ WMS Performance Excellence
Systematische Optimierungsstrategien für maximale Effizienz des Jungheinrich WMS bei Georg Fischer
📈 PERFORMANCE-ZIELE
Ziel: Systemantwortzeit < 2 Sekunden, 99.9% Verfügbarkeit, 0% Datenverlust
Performance-Support: +41 81 770 5555 (Performance Team)
### Performance-Baseline
#### **Aktuelle Performance-Kennzahlen**
```yaml
performance_baselines:
response_times:
login: "< 3 Sekunden"
order_creation: "< 5 Sekunden"
inventory_lookup: "< 2 Sekunden"
report_generation: "< 30 Sekunden"
throughput:
concurrent_users: "100 gleichzeitige Benutzer"
transactions_per_minute: "500 TPS"
pick_operations: "120 Picks/Stunde/Operator"
availability:
uptime_target: "99.9%"
planned_downtime: "< 4 Stunden/Monat"
mttr: "< 2 Stunden"
mtbf: "> 720 Stunden"
```
#### **Performance-KPIs Dashboard**
```yaml
kpi_metriken:
system_metrics:
cpu_utilization:
target: "< 70%"
warning: "70-85%"
critical: "> 85%"
memory_usage:
target: "< 75%"
warning: "75-90%"
critical: "> 90%"
disk_io:
target: "< 50 IOPS/GB"
warning: "50-80 IOPS/GB"
critical: "> 80 IOPS/GB"
application_metrics:
session_count:
target: "< 80 aktive Sessions"
warning: "80-100 Sessions"
critical: "> 100 Sessions"
queue_depth:
target: "< 10 Nachrichten"
warning: "10-50 Nachrichten"
critical: "> 50 Nachrichten"
```
### Bottleneck-Analyse
#### **Systematische Performance-Diagnose**
```yaml
bottleneck_analysis:
step_1_overview:
tool: "Windows Performance Monitor"
metrics: ["CPU", "Memory", "Disk", "Network"]
duration: "15 Minuten Peak-Zeit-Monitoring"
step_2_database:
tool: "SQL Server Management Studio"
queries: ["sp_who2", "sys.dm_exec_requests", "sys.dm_os_wait_stats"]
focus: "Blocking, Wait Stats, Long-running Queries"
step_3_application:
tool: "Application Event Logs"
search: "WMS-spezifische Fehlermeldungen"
patterns: "Timeout-Errors, Memory-Exceptions"
step_4_network:
tool: "Network Performance Monitor"
tests: ["Latency zu DB-Server", "Bandwidth-Auslastung"]
baselines: "< 5ms Latency, < 70% Bandwidth"
```
#### **Common Performance Issues**
```yaml
haeufige_probleme:
slow_login:
symptom: "Anmeldung dauert > 10 Sekunden"
ursachen:
- "Active Directory Latency"
- "Benutzerrechte-Validierung langsam"
- "Session-Store überlastet"
solutions:
- "AD-Connection-Pool optimieren"
- "Lokale Benutzerrechte-Cache"
- "Session-Store auf SSD migrieren"
slow_reporting:
symptom: "Berichte-Generierung > 60 Sekunden"
ursachen:
- "Fehlende Datenbank-Indizes"
- "Große Datenmengen ohne Paginierung"
- "Unoptimierte SQL-Queries"
solutions:
- "Index-Analyse und -Optimierung"
- "Report-Paginierung implementieren"
- "Query-Execution-Plan analysieren"
high_memory_usage:
symptom: "Arbeitsspeicher-Verbrauch > 90%"
ursachen:
- "Memory Leaks in Anwendung"
- "Zu große Cache-Größen"
- "Unzureichende Garbage Collection"
solutions:
- "Memory-Profiling durchführen"
- "Cache-Strategien überprüfen"
- "GC-Parameter anpassen"
```
### Hardware-Optimierung
#### **Server-Konfiguration**
```yaml
server_optimization:
cpu_optimization:
cores: "Minimum 8 Cores für WMS-Server"
clock_speed: "Minimum 2.4 GHz"
hyperthreading: "Aktiviert für Multi-Threading"
power_plan: "High Performance"
memory_optimization:
total_ram: "32 GB für Produktionsserver"
allocation:
os_reserved: "4 GB"
sql_server: "16 GB"
wms_application: "8 GB"
cache_buffer: "4 GB"
storage_optimization:
system_drive: "SSD für OS und Programme"
database_files: "Separate SSD/NVMe für DB-Files"
log_files: "Separate SSD für Transaction Logs"
backup_storage: "Schnelle NAS für Backup-Files"
```
#### **Netzwerk-Optimierung**
```yaml
network_optimization:
lan_configuration:
bandwidth: "Gigabit Ethernet minimum"
switch_ports: "Dedicated Ports für kritische Server"
vlan_segmentation: "Separate VLANs für WMS-Traffic"
wan_optimization:
compression: "Aktiviert für SAP-Verbindungen"
caching: "Lokaler Cache für häufige Abfragen"
qos: "Priorisierung von WMS-Traffic"
wireless_optimization:
standard: "WiFi 6 (802.11ax) für Mobile Terminals"
coverage: "Vollständige Lager-Abdeckung"
handoff: "Nahtloser Roaming zwischen Access Points"
```
## Datenbank-Optimierung {#database-optimization}
### SQL Server Performance Tuning
#### **Index-Optimierung**
```sql
-- Index-Analyse und -Optimierung
-- 1. Fehlende Indizes identifizieren
SELECT
migs.avg_total_user_cost * (migs.user_seeks + migs.user_scans) as improvement_measure,
'CREATE INDEX IX_' +
REPLACE(REPLACE(REPLACE(mid.statement, '[', ''), ']', ''), '.', '_') + '_' +
CAST(ROW_NUMBER() OVER (ORDER BY migs.avg_total_user_cost DESC) AS VARCHAR(3)) +
' ON ' + mid.statement +
' (' + ISNULL(mid.equality_columns, '') +
CASE WHEN mid.inequality_columns IS NOT NULL
THEN (CASE WHEN mid.equality_columns IS NOT NULL THEN ',' ELSE '' END) + mid.inequality_columns
ELSE '' END + ')' +
CASE WHEN mid.included_columns IS NOT NULL
THEN ' INCLUDE (' + mid.included_columns + ')'
ELSE '' END as create_index_statement,
migs.user_seeks,
migs.user_scans,
migs.avg_total_user_cost
FROM sys.dm_db_missing_index_groups mig
INNER JOIN sys.dm_db_missing_index_group_stats migs ON migs.group_handle = mig.index_group_handle
INNER JOIN sys.dm_db_missing_index_details mid ON mig.index_handle = mid.index_handle
WHERE migs.avg_total_user_cost * (migs.user_seeks + migs.user_scans) > 10
ORDER BY improvement_measure DESC;
-- 2. Fragmentierte Indizes identifizieren
SELECT
OBJECT_NAME(ips.object_id) as table_name,
i.name as index_name,
ips.avg_fragmentation_in_percent,
ips.page_count,
CASE
WHEN ips.avg_fragmentation_in_percent > 30 THEN 'REBUILD'
WHEN ips.avg_fragmentation_in_percent > 10 THEN 'REORGANIZE'
ELSE 'NO ACTION'
END as recommendation
FROM sys.dm_db_index_physical_stats(DB_ID(), NULL, NULL, NULL, 'SAMPLED') ips
INNER JOIN sys.indexes i ON ips.object_id = i.object_id AND ips.index_id = i.index_id
WHERE ips.avg_fragmentation_in_percent > 10
AND ips.page_count > 100
ORDER BY ips.avg_fragmentation_in_percent DESC;
```
#### **Query-Optimierung**
```sql
-- Langsame Queries identifizieren
SELECT TOP 10
qs.total_elapsed_time / qs.execution_count / 1000 as avg_duration_ms,
qs.execution_count,
qs.total_logical_reads / qs.execution_count as avg_logical_reads,
SUBSTRING(st.text, (qs.statement_start_offset/2)+1,
((CASE qs.statement_end_offset
WHEN -1 THEN DATALENGTH(st.text)
ELSE qs.statement_end_offset
END - qs.statement_start_offset)/2) + 1) as statement_text,
qp.query_plan
FROM sys.dm_exec_query_stats qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) st
CROSS APPLY sys.dm_exec_query_plan(qs.plan_handle) qp
WHERE qs.total_elapsed_time / qs.execution_count > 1000000 -- > 1 Sekunde
ORDER BY avg_duration_ms DESC;
-- Blocking-Situationen analysieren
SELECT
blocking.session_id as blocking_spid,
blocked.session_id as blocked_spid,
blocking_sql.text as blocking_sql,
blocked_sql.text as blocked_sql,
w.wait_type,
w.wait_time_ms,
w.resource_description
FROM sys.dm_exec_sessions blocking
INNER JOIN sys.dm_exec_sessions blocked ON blocking.session_id = blocked.blocking_session_id
CROSS APPLY sys.dm_exec_sql_text(blocking.most_recent_sql_handle) blocking_sql
CROSS APPLY sys.dm_exec_sql_text(blocked.most_recent_sql_handle) blocked_sql
LEFT JOIN sys.dm_os_waiting_tasks w ON blocked.session_id = w.session_id
WHERE blocking.session_id != blocked.session_id;
```
### Database Configuration
#### **SQL Server Einstellungen**
```yaml
sql_server_configuration:
memory_settings:
max_server_memory: "16384 MB" # 16 GB für 32 GB Server
min_server_memory: "8192 MB" # 8 GB Minimum
optimize_for_ad_hoc: "1" # Plan Cache optimieren
io_settings:
max_degree_of_parallelism: "4" # Anzahl CPU Cores / 2
cost_threshold_for_parallelism: "50"
page_verify: "CHECKSUM"
tempdb_optimization:
data_files: "4" # Ein File pro CPU Core
initial_size: "1024 MB"
growth: "256 MB"
separate_drive: "T:\\"
maintenance_settings:
auto_update_statistics: "ON"
auto_create_statistics: "ON"
backup_compression: "ON"
recovery_model: "FULL"
```
#### **Wartungsplan**
```sql
-- Automatisierter Wartungsplan
-- 1. Index-Wartung (Sonntags 02:00)
DECLARE @sql NVARCHAR(MAX) = '';
SELECT @sql = @sql +
CASE
WHEN avg_fragmentation_in_percent > 30
THEN 'ALTER INDEX ' + i.name + ' ON ' + SCHEMA_NAME(t.schema_id) + '.' + t.name + ' REBUILD;' + CHAR(13)
WHEN avg_fragmentation_in_percent > 10
THEN 'ALTER INDEX ' + i.name + ' ON ' + SCHEMA_NAME(t.schema_id) + '.' + t.name + ' REORGANIZE;' + CHAR(13)
END
FROM sys.dm_db_index_physical_stats(DB_ID(), NULL, NULL, NULL, 'SAMPLED') ips
INNER JOIN sys.indexes i ON ips.object_id = i.object_id AND ips.index_id = i.index_id
INNER JOIN sys.tables t ON i.object_id = t.object_id
WHERE ips.avg_fragmentation_in_percent > 10
AND ips.page_count > 100;
EXEC sp_executesql @sql;
-- 2. Statistiken aktualisieren
EXEC sp_updatestats;
-- 3. Database Integrity Check
DBCC CHECKDB('WMS_GF_PROD') WITH NO_INFOMSGS;
```
### Connection Pooling
#### **Verbindungspool-Optimierung**
```yaml
connection_pooling:
pool_configuration:
min_pool_size: "5"
max_pool_size: "100"
connection_timeout: "30"
command_timeout: "300"
connection_string_optimization: |
Server=WMSDB01;Database=WMS_GF_PROD;
Integrated Security=true;
Connection Timeout=30;
Command Timeout=300;
Max Pool Size=100;
Min Pool Size=5;
Pooling=true;
Connection Lifetime=600;
monitoring:
pool_performance_counters:
- "Current # pooled connections"
- "Current # connection pools"
- "Peak # pooled connections"
- "Total # failed connections"
```
## Anwendungs-Tuning {#application-tuning}
### .NET Application Optimization
#### **Memory Management**
```yaml
memory_optimization:
garbage_collection:
gc_mode: "Server GC für Multi-Core-Server"
concurrent_gc: "Aktiviert für bessere Responsiveness"
gc_heap_size: "Nicht mehr als 70% des verfügbaren RAM"
memory_profiling:
tools: ["PerfView", "JetBrains dotMemory", "ANTS Memory Profiler"]
focus_areas:
- "Memory Leaks in Event Handlers"
- "Große Object Heap (LOH) Fragmentierung"
- "Unmanaged Memory Leaks"
caching_strategy:
application_cache:
size_limit: "512 MB"
expiry_policy: "Sliding 30 Minuten"
items: ["Benutzerberechtigungen", "Materialstamm", "Lagerplätze"]
session_cache:
provider: "SQL Server"
timeout: "30 Minuten"
compression: "Aktiviert für große Sessions"
```
#### **Code-Optimierungen**
```csharp
// Beispiel: Optimierte Datenbank-Abfrage
public async Task> GetInventoryOptimizedAsync(string location)
{
// 1. Connection Pooling nutzen
using var connection = new SqlConnection(connectionString);
// 2. Prepared Statements für bessere Performance
const string sql = @"
SELECT i.ItemId, i.ItemNumber, i.Quantity, i.Location
FROM Inventory i WITH (NOLOCK)
INNER JOIN Items it ON i.ItemId = it.Id
WHERE i.Location = @Location
AND i.IsActive = 1
ORDER BY i.ItemNumber";
// 3. Async-Operationen für Non-Blocking IO
var result = await connection.QueryAsync(sql, new { Location = location });
// 4. Materialized List für bessere Memory-Effizienz
return result.ToList();
}
// Beispiel: Caching-Pattern
private static readonly MemoryCache _cache = new MemoryCache(new MemoryCacheOptions
{
SizeLimit = 1000
});
public async Task GetMaterialAsync(string materialNumber)
{
string cacheKey = $"material_{materialNumber}";
if (_cache.TryGetValue(cacheKey, out MaterialMaster cachedMaterial))
{
return cachedMaterial;
}
var material = await _repository.GetMaterialAsync(materialNumber);
var cacheOptions = new MemoryCacheEntryOptions
{
AbsoluteExpirationRelativeToNow = TimeSpan.FromMinutes(30),
Size = 1
};
_cache.Set(cacheKey, material, cacheOptions);
return material;
}
```
### Web Application Tuning
#### **IIS-Optimierung**
```yaml
iis_optimization:
application_pool:
process_model:
idle_timeout: "0" # Keine automatische Recycling
max_processes: "1" # Single Process für bessere Memory-Effizienz
ping_enabled: "false"
recycling_conditions:
memory_limit: "1.5 GB"
time_based: "Täglich um 03:00"
request_limit: "100000 Requests"
compression:
static_compression: "Aktiviert"
dynamic_compression: "Aktiviert für .aspx/.ashx"
compression_level: "9" # Maximum
caching:
output_caching:
reports: "5 Minuten"
static_data: "60 Minuten"
user_specific: "Deaktiviert"
kernel_caching: "Aktiviert für statische Inhalte"
```
#### **Session State Optimization**
```xml
```
### Client-Side Optimization
#### **Browser-Performance**
```yaml
client_optimization:
javascript_optimization:
minification: "Aktiviert für Produktionsumgebung"
bundling: "CSS/JS-Files kombinieren"
async_loading: "Nicht-kritische Scripts async laden"
resource_optimization:
image_compression: "WebP-Format wo möglich"
css_optimization: "Critical CSS inline"
font_loading: "Web Font Loader verwenden"
caching_headers:
static_resources: "Cache-Control: max-age=31536000"
dynamic_content: "Cache-Control: no-cache"
api_responses: "ETag-Header für Conditional Requests"
```
## Infrastruktur {#infrastructure}
### Server-Infrastruktur
#### **Virtualisierung-Optimierung**
```yaml
virtualization_tuning:
vmware_settings:
cpu_allocation:
reservation: "50% der zugewiesenen vCPUs"
limit: "Unlimited"
shares: "High für WMS-VMs"
memory_allocation:
reservation: "100% für Produktions-VMs"
ballooning: "Deaktiviert"
transparent_page_sharing: "Deaktiviert"
storage_optimization:
disk_type: "Thick Provisioned Eager Zeroed"
multipathing: "Aktiviert für Storage-Arrays"
queue_depth: "32 für High-Performance"
hyper_v_settings:
dynamic_memory: "Deaktiviert für WMS-VMs"
integration_services: "Alle aktuellen Services installiert"
numa_spanning: "Deaktiviert für bessere Performance"
```
#### **Load Balancing**
```yaml
load_balancing:
web_tier:
algorithm: "Least Connections"
health_checks: "HTTP 200 auf /health-endpoint"
session_affinity: "Aktiviert für Session-State"
application_tier:
clustering: "Active/Active für WMS-Services"
failover_time: "< 30 Sekunden"
data_replication: "Sync zwischen Cluster-Nodes"
database_tier:
always_on: "Availability Groups konfiguriert"
read_replicas: "Read-Only für Reporting"
automatic_failover: "Bei Primary-Ausfall"
```
### Storage-Optimierung
#### **SAN/NAS-Konfiguration**
```yaml
storage_optimization:
disk_configuration:
database_files:
raid_level: "RAID 10"
disk_type: "SSD/NVMe"
stripe_size: "64KB"
log_files:
raid_level: "RAID 1"
disk_type: "High-Speed SSD"
dedicated_spindles: "Separate von Data Files"
backup_storage:
raid_level: "RAID 6"
disk_type: "High-Capacity SATA"
deduplication: "Aktiviert"
io_optimization:
queue_depth: "32-64 für SSD"
io_scheduler: "NOOP für SSD"
read_ahead: "128KB für Sequential Reads"
```
#### **Backup-Performance**
```yaml
backup_optimization:
backup_strategy:
compression: "Hardware-Compression bevorzugt"
parallelization: "4 parallele Backup-Streams"
network_optimization: "Dedicated Backup-VLAN"
restore_optimization:
staging_area: "High-Speed Storage für Restores"
parallel_restore: "Mehrere Files gleichzeitig"
verification: "Async Backup-Verification"
```
### Netzwerk-Infrastruktur
#### **Bandbreiten-Management**
```yaml
network_optimization:
qos_configuration:
wms_traffic: "Höchste Priorität (DSCP 46)"
sap_integration: "Hohe Priorität (DSCP 34)"
backup_traffic: "Niedrige Priorität (DSCP 10)"
bandwidth_allocation:
wms_operations: "60% der verfügbaren Bandbreite"
integration_traffic: "25% für SAP/EDI"
management_traffic: "15% für Administration"
link_aggregation:
server_uplinks: "LACP für redundante Verbindungen"
inter_switch: "Port Channels für Hochlast-Verbindungen"
wan_links: "ECMP für Load Distribution"
```
#### **Monitoring & Alerting**
```yaml
infrastructure_monitoring:
server_monitoring:
cpu_threshold: "Warning > 70%, Critical > 85%"
memory_threshold: "Warning > 80%, Critical > 90%"
disk_space: "Warning < 20%, Critical < 10%"
network_monitoring:
latency_threshold: "Warning > 10ms, Critical > 20ms"
packet_loss: "Warning > 0.1%, Critical > 1%"
bandwidth_utilization: "Warning > 70%, Critical > 85%"
storage_monitoring:
iops_threshold: "Warning > 80% of max IOPS"
response_time: "Warning > 10ms, Critical > 20ms"
queue_depth: "Warning > 16, Critical > 32"
```
## Performance-Monitoring {#monitoring}
### Monitoring-Framework
#### **Überwachungs-Architektur**
```yaml
monitoring_architecture:
data_collection:
agents:
windows_perfmon: "System-Metriken sammeln"
sql_server_agent: "Database-Performance"
application_insights: "Application Performance Monitoring"
metrics_storage:
short_term: "InfluxDB für Real-time Metrics"
long_term: "SQL Server für Historical Data"
retention: "Real-time: 7 Tage, Historical: 2 Jahre"
visualization:
dashboards: "Grafana für Executive Dashboards"
alerting: "Email + SMS + Slack Integration"
reporting: "Wöchentliche Performance-Reports"
```
#### **Key Performance Indicators**
```yaml
performance_kpis:
system_metrics:
availability:
calculation: "(Uptime / Total Time) * 100"
target: "> 99.9%"
measurement: "Minutenweise Überwachung"
response_time:
calculation: "95th Percentile Response Time"
target: "< 2 Sekunden"
measurement: "Kontinuierliche Messung"
throughput:
calculation: "Successful Transactions / Time"
target: "> 500 TPS"
measurement: "5-Minuten-Durchschnitt"
business_metrics:
picks_per_hour:
calculation: "Picks / Worked Hours"
target: "> 120 Picks/Stunde"
measurement: "Schichtweise Aggregation"
error_rate:
calculation: "(Failed Operations / Total Operations) * 100"
target: "< 0.1%"
measurement: "Tägliche Berechnung"
```
### Real-Time Performance Monitoring
#### **Live-Dashboard Konfiguration**
```yaml
dashboard_configuration:
executive_dashboard:
refresh_interval: "30 Sekunden"
widgets:
- system_health_summary
- current_active_users
- transaction_throughput
- error_rate_trend
- sla_compliance_gauge
technical_dashboard:
refresh_interval: "10 Sekunden"
widgets:
- cpu_utilization_graph
- memory_usage_trend
- disk_io_metrics
- network_latency_map
- database_performance
operational_dashboard:
refresh_interval: "60 Sekunden"
widgets:
- warehouse_productivity
- pick_accuracy_rates
- inventory_accuracy
- order_fulfillment_times
```
#### **Alerting-Strategie**
```yaml
alerting_framework:
severity_levels:
critical:
response_time: "5 Minuten"
notification: "SMS + Phone Call + Email"
escalation: "Nach 15 Minuten Auto-Escalation"
high:
response_time: "15 Minuten"
notification: "Email + Slack"
escalation: "Nach 1 Stunde Manager-Notification"
medium:
response_time: "1 Stunde"
notification: "Email"
escalation: "Nach 4 Stunden Team Lead"
low:
response_time: "4 Stunden"
notification: "Daily Digest Email"
escalation: "Wöchentlicher Review"
alert_correlation:
suppression: "Ähnliche Alerts innerhalb 5 Minuten gruppieren"
root_cause: "Automatische Root-Cause-Analyse"
dependencies: "Alert-Dependencies berücksichtigen"
```
### Performance Reports
#### **Automatisierte Berichterstattung**
```yaml
performance_reporting:
daily_reports:
recipients: ["operations-team@gf.com", "it-team@gf.com"]
content:
- "24h System Health Summary"
- "Peak Usage Times"
- "Error Summary"
- "Performance Trends"
weekly_reports:
recipients: ["management@gf.com", "operations-manager@gf.com"]
content:
- "SLA Compliance Summary"
- "Capacity Planning Metrics"
- "Performance Improvement Opportunities"
- "Cost-Benefit Analysis"
monthly_reports:
recipients: ["executives@gf.com", "board@gf.com"]
content:
- "Strategic Performance Overview"
- "ROI Analysis"
- "Technology Roadmap Updates"
- "Business Impact Assessment"
```
#### **Performance Benchmarking**
```sql
-- Performance Baseline Queries
-- 1. Response Time Trends
SELECT
DATE(timestamp) as date,
operation_type,
AVG(response_time_ms) as avg_response_time,
PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY response_time_ms) as p95_response_time,
COUNT(*) as operation_count
FROM performance_metrics
WHERE timestamp >= DATEADD(day, -30, GETDATE())
GROUP BY DATE(timestamp), operation_type
ORDER BY date DESC, operation_type;
-- 2. System Resource Utilization
SELECT
DATEPART(hour, timestamp) as hour_of_day,
AVG(cpu_percent) as avg_cpu,
AVG(memory_percent) as avg_memory,
AVG(disk_io_percent) as avg_disk_io
FROM system_metrics
WHERE timestamp >= DATEADD(day, -7, GETDATE())
GROUP BY DATEPART(hour, timestamp)
ORDER BY hour_of_day;
-- 3. Database Performance Metrics
SELECT
database_name,
AVG(logical_reads) as avg_logical_reads,
AVG(physical_reads) as avg_physical_reads,
AVG(duration_ms) as avg_duration,
COUNT(*) as query_count
FROM query_performance_stats
WHERE execution_time >= DATEADD(day, -1, GETDATE())
GROUP BY database_name
ORDER BY avg_duration DESC;
```
## Best Practices
### Performance-Optimierungs-Checklist
#### **Regelmäßige Wartungsaufgaben**
```yaml
maintenance_checklist:
taegliche_aufgaben:
- "System Health Dashboard überprüfen"
- "Error Logs auf neue Probleme prüfen"
- "Backup Success Status validieren"
- "Performance Alerts reviewen"
woechentliche_aufgaben:
- "Index Fragmentation Analysis"
- "Query Performance Review"
- "Capacity Planning Update"
- "Security Patch Assessment"
monatliche_aufgaben:
- "Comprehensive Performance Review"
- "Hardware Utilization Analysis"
- "Cost-Benefit Analysis Update"
- "Technology Roadmap Review"
quartalsweise_aufgaben:
- "Performance Baseline Update"
- "Disaster Recovery Testing"
- "Security Audit"
- "Business Continuity Plan Review"
```
#### **Proaktive Optimierung**
```yaml
proactive_optimization:
capacity_planning:
growth_projection: "20% jährliches Wachstum"
resource_forecasting: "6 Monate im Voraus"
performance_modeling: "Load-Testing für neue Features"
continuous_improvement:
performance_reviews: "Monatliche Team-Reviews"
optimization_sprints: "Quartalsweise Verbesserungs-Sprints"
technology_evaluation: "Halbjährliche Tool-Evaluierung"
knowledge_management:
documentation: "Performance-Playbooks aktuell halten"
training: "Regelmäßige Performance-Schulungen"
best_practices: "Lessons Learned dokumentieren"
```
## Support & Eskalation
### Performance Support Team
```yaml
support_struktur:
level_1_monitoring:
team: "Operations Center"
telefon: "+41 81 770 5555"
email: "performance-monitoring@georgfischer.com"
verfuegbarkeit: "24/7"
level_2_analysis:
team: "Performance Engineering"
telefon: "+41 81 770 5566"
email: "performance-engineering@georgfischer.com"
verfuegbarkeit: "Mo-Fr 07:00-19:00"
level_3_architecture:
team: "Principal Engineers"
telefon: "+41 81 770 5577"
email: "principal-engineers@georgfischer.com"
verfuegbarkeit: "Nach Vereinbarung"
```
## Quick Reference
### Performance-Troubleshooting
| Symptom | Wahrscheinliche Ursache | Sofort-Maßnahme |
|---------|------------------------|------------------|
| **Langsame Anmeldung** | AD-Latency oder Cache-Problem | Connection Pool prüfen |
| **Hohe CPU-Last** | Ineffiziente Queries oder Indexprobleme | Top Queries analysieren |
| **Memory-Lecks** | Application Memory-Management | Memory Profiling starten |
| **Disk I/O-Bottleneck** | Index Fragmentation | Index Rebuild ausführen |
| **Netzwerk-Latency** | WAN-Verbindung oder Switch-Problem | Network Monitoring aktivieren |
### Performance-Targets
- **Response Time:** < 2 Sekunden (95th Percentile)
- **Availability:** > 99.9% Uptime
- **Throughput:** > 500 Transaktionen/Minute
- **Error Rate:** < 0.1%
- **Resource Utilization:** < 70% CPU/Memory
---
*Diese Performance-Dokumentation wird kontinuierlich aktualisiert basierend auf aktuellen Monitoring-Daten und Best Practices. Letzte Aktualisierung: März 2025*