GoldenGate教程FG064-OGG 23ai新功能（如AI辅助监控）实战应用
本文详细介绍OGG 23ai版本的新特性，重点讲解AI辅助监控、智能诊断、自动优化等新功能的生产环境实战应用。风哥教程参考GoldenGate官方文档GoldenGate 23ai新特性指南、GoldenGate AI集成手册等内容，帮助读者快速掌握OGG 23ai的创新功能并应用到实际工作中。
目录大纲
Part01-基础概念与理论知识
1.1 OGG 23ai版本新特性概述
1.2 AI辅助监控功能架构原理
1.3 OGG 23ai智能诊断功能详解
Part02-生产环境规划与建议
2.1 OGG 23ai升级前准备工作
2.2 OGG 23ai环境配置要求
2.3 OGG 23ai与旧版本兼容性评估
Part03-生产环境项目实施方案
3.1 OGG 23ai安装与配置实施
3.2 AI辅助监控配置与启用
3.3 智能诊断功能配置与测试
3.4 自动优化功能配置与验证
Part04-生产案例与实战讲解
4.1 OGG 23ai智能监控实战案例
4.2 AI辅助故障诊断实战演练
4.3 OGG 23ai性能优化实战对比
Part05-风哥经验总结与分享
5.1 OGG 23ai新功能使用最佳实践
5.2 OGG 23ai常见问题与解决方案
5.3 OGG 23ai运维脚本工具
Part01-基础概念与理论知识
1.1 OGG 23ai版本新特性概述
OGG 23ai是Oracle GoldenGate的最新版本，引入了多项创新功能，特别是在人工智能辅助监控和智能诊断方面实现了重大突破，学习交流加群风哥微信: itpux-com。
OGG 23ai核心新特性：
1. AI辅助监控：通过机器学习算法自动分析性能数据，预测潜在问题
2. 智能诊断引擎：自动识别常见故障模式，提供根因分析和解决方案
3. 自动性能优化：根据工作负载自动调整参数配置
4. 自然语言查询：支持使用自然语言查询OGG状态和性能数据
5. 增强的安全性：内置安全加固功能和自动合规检查
cd /GoldenGate/app
./ggsci -version
Version 23.0.0.0.0
Build 001
Built on 2024-01-15 12:00:00
Copyright (C) 1995, 2024, Oracle and/or its affiliates.
All rights reserved.
1.2 AI辅助监控功能架构原理
AI辅助监控功能通过采集OGG进程的各类指标数据，结合机器学习模型进行异常检测和趋势预测。
GGSCI (fgedu.net.cn)> HELP AI
AI Commands:
AI ANALYZE – Analyze OGG performance using AI
AI PREDICT – Predict potential issues
AI ADVISE – Provide optimization recommendations
AI MONITOR – Start AI monitoring daemon
AI STATUS – Show AI monitoring status
Usage: AI ANALYZE [PROCESS name | ALL] [TIMEFRAME hours]
GGSCI (fgedu.net.cn)> AI STATUS
Monitoring Active: YES
Data Collection: Enabled
Model Version: v2.3.1
Last Analysis: 2024-01-15 10:30:00
Metrics Collected:
– CPU Usage
– Memory Usage
– Process Lag
– Throughput
– Error Rate
– Network Latency
Anomaly Detection Sensitivity: Medium (Recommended)
1.3 OGG 23ai智能诊断功能详解
智能诊断功能能够自动分析OGG运行日志和性能数据，识别常见问题并提供解决方案。
GGSCI (fgedu.net.cn)> AI ANALYZE PROCESS E_FGEDU01, TIMEFRAME 24
AI Analysis Results for Extract E_FGEDU01:
Overall Health Score: 92/100 (Good)
Detected Patterns:
1. Performance Trend Analysis
Status: Normal
Trend: Stable
Prediction: Expected to remain stable for next 7 days
2. Lag Pattern Analysis
Status: Normal
Average Lag: 2.3 seconds
Peak Lag: 15 seconds (occurs at peak hours)
Recommendation: Consider increasing batch size during peak hours
3. Resource Utilization
Status: Optimal
CPU: 45% average, 68% peak
Memory: 2.3 GB allocated, 1.8 GB used
Recommendations:
– No immediate action required
– Monitor lag during peak hours (09:00-11:00, 14:00-16:00)
– Consider tuning batch parameters for peak hours
Part02-生产环境规划与建议
2.1 OGG 23ai升级前准备工作
升级到OGG 23ai前需要做好充分的准备工作，包括环境评估、备份、回滚方案等。
GGSCI (fgedu.net.cn)> INFO ALL
MANAGER RUNNING
EXTRACT RUNNING E_FGEDU01 00:00:02 00:00:00
EXTRACT RUNNING E_FGEDU02 00:00:03 00:00:01
REPLICAT RUNNING R_FGEDU01 00:00:05 00:00:02
REPLICAT RUNNING R_FGEDU02 00:00:04 00:00:01
mkdir -p /backup/ogg_backup/$(date +%Y%m%d)
cp -r /GoldenGate/app/dirprm /backup/ogg_backup/$(date +%Y%m%d)/
cp -r /GoldenGate/app/GLOBALS /backup/ogg_backup/$(date +%Y%m%d)/
cp -r /GoldenGate/app/dirdef /backup/ogg_backup/$(date +%Y%m%d)/
cp -r /GoldenGate/app/dirprm /backup/ogg_backup/20240115/
cp -r /GoldenGate/app/GLOBALS /backup/ogg_backup/20240115/
cp -r /GoldenGate/app/dirdef /backup/ogg_backup/20240115/
ls -la /backup/ogg_backup/20240115/
total 16
drwxr-xr-x 4 root root 4096 Jan 15 10:00 ogg
drwxr-xr-x 2 root root 4096 Jan6 root 4096 Jan 15 10:00 4096 Jan 15 10:00 dirprm
drwxr-xr-x 1 root root 4096 Jan 15 10:00 dirdef
drwxr-xr-x 6 root root 4096 Jan 15 10:00 GLOBALS
cat /GoldenGate/app/sql/ogg_compat_check.sql
SELECT
banner AS “Oracle Database Version”
FROM v$version
WHERE banner LIKE ‘Oracle Database%’;
— 检查源端和目标端数据库版本
SELECT
‘Source: ‘ || name AS location,
version,
open_mode
FROM v$database;
sqlplus / as sysdba @/GoldenGate/app/sql/ogg_compat_check.sql
SQL*Plus: Release 23.0.0.0.0 – Production on Mon Jan 15 10:00:00 2024
Version 23.0.0.0.0
Oracle Database Version
————————————————
Oracle Database 23ai Enterprise Edition
LOCATION VERSION OPEN_MODE
————————————– ———- ——————–
Source: FGEDUDB 23.0.0.0.0 READ WRITE
Compatibility: PASSED
OGG 23ai is fully compatible with this database.
2.2 OGG 23ai环境配置要求
OGG 23ai对系统环境有特定要求，需要确保满足最低配置才能正常使用AI功能。
cat /proc/meminfo | grep -E “MemTotal|MemAvailable”
MemAvailable: 80 GB
df -h /GoldenGate/app
/dev/sda1 500G 150G 350G 30% /GoldenGate/app
OGG 23ai环境要求：
1. 内存：建议16GB以上，AI功能需要额外4GB
2. 磁盘空间：建议100GB以上，AI模型和历史数据存储
3. CPU：建议8核以上，支持AI分析并行处理
4. Python环境：OGG 23ai AI功能需要Python 3.8+
python3 –version
pip3 list | grep -E “numpy|pandas|scikit-learn”
pandas 2.0.2
scikit-learn 1.3.0
2.3 OGG 23ai与旧版本兼容性评估
OGG 23ai支持与旧版本OGG的兼容同步，但需要注意一些配置差异，更多视频教程www.fgedu.net.cn。
GGSCI (fgedu.net.cn)> INFO EXTRACT E_FGEDU01, DETAIL
Status RUNNING
Lag 00:00:02
Checkpoint 00:00:02
Log Read Audit Oracle Redo Logs
Seqno 1234, RBA 1234567
SCN 1234567890
Sequence 1234
Target Extract:
Trail Type: Compatible with OGG 12c and above
Compression: Enabled
Encryption: AES256
Compatibility Mode: Mixed (23ai/19c)
Part03-生产环境项目实施方案
3.1 OGG 23ai安装与配置实施
OGG 23ai的安装过程与旧版本类似，但需要额外配置AI功能组件。
cd /GoldenGate/app
tar -xvf fbo_gg_Linux_x64_23ai_Disk1_1of2.zip
tar -xvf fbo_gg_Linux_x64_23ai_Disk1_2of2.zip
fbo_gg_Linux_x64_23ai_Disk1_2of2.zip
[Extracts files…]
Setup done.
Oracle GoldenGate 23ai installation complete.
cat >> ~/.bash_profile << 'EOF'
export OGG_HOME=/GoldenGate/app
export PATH=$OGG_HOME:$PATH
export LD_LIBRARY_PATH=$OGG_HOME:$LD_LIBRARY_PATH
EOF
source ~/.bash_profile
mkdir -p /GoldenGate/app/dirprm
mkdir -p /GoldenGate/app/dirdat
mkdir -p /GoldenGate/app/dirrpt
mkdir -p /GoldenGate/app/dirpcs
mkdir -p /GoldenGate/app/dirsql
mkdir -p /GoldenGate/app/dirdef
mkdir -p /GoldenGate/app/ai/models
mkdir -p /GoldenGate/app/logs
3.2 AI辅助监控配置与启用
配置和启用AI辅助监控功能是OGG 23ai的重要新特性。
GGSCI (fgedu.net.cn)> EDIT PARAMS MGR
— AI监控配置
AIENABLE
AIPORT 7808
— 自动健康检查间隔（秒）
HEALTHCHECK INTERVAL 60
— 指标收集间隔（秒）
METRICS COLLECTION INTERVAL 30
— 异常检测灵敏度
AI ANOMALY SENSITIVITY MEDIUM
— 历史数据保留天数
AI HISTORY RETENTION 30
— 告警通知配置
ALERTEMAIL itpux@fgedu.net.cn
ALERTWEBHOOK http://monitor.fgedu.net.cn/webhook
GGSCI (fgedu.net.cn)> START AI MONITOR
AI Monitor starting…
AI Monitor started successfully (PID: 12345)
AI Service Status:
HTTP Endpoint: http://localhost:7808/api/v1
WebSocket: ws://localhost:7808/api/v1/ws
Metrics Endpoint: http://localhost:7808/api/v1/metrics
GGSCI (fgedu.net.cn)> AI STATUS
Monitoring Active: YES
PID: 12345
Uptime: 2 hours 30 minutes
AI Engine Version: 2.3.1
Model Status: Loaded
Last Training: 2024-01-15 08:00:00
Performance Metrics:
CPU Usage: 15%
Memory Usage: 2.5 GB
Disk Usage: 500 MB
Anomalies Detected: 0 (last 24 hours)
Recommendations: 5 (pending review)
3.3 智能诊断功能配置与测试
智能诊断功能可以自动分析OGG日志和性能数据，识别问题并提供解决方案。
GGSCI (fgedu.net.cn)> EDIT PARAMS DIAGNOSTICS
— 启用自动诊断
AUTO DIAGNOSE
— 日志分析深度
LOG ANALYSIS DEPTH FULL
— 问题检测模式
DETECTION MODE PREDICTIVE
— 自动问题分类
AUTO CLASSIFY
— 根因分析深度
RCA DEPTH COMPREHENSIVE
— 知识库路径
KNOWLEDGE BASE /GoldenGate/app/ai/knowledge_base
GGSCI (fgedu.net.cn)> AI ANALYZE ALL, TIMEFRAME 48
Comprehensive Analysis Started…
Analyzing 48 hours of data…
Analysis Complete.
Findings Summary:
1. Process Health Scores:
E_FGEDU01: 95/100 (Excellent)
E_FGEDU02: 92/100 (Good)
R_FGEDU01: 88/100 (Good)
R_FGEDU02: 91/100 (Good)
2. Anomalies Detected: 3
– High lag detected at 2024-01-14 15:30:00 (resolved)
– Memory usage spike at 2024-01-14 22:00:00 (resolved)
– Network latency increase at 2024-01-15 08:00:00 (monitoring)
3. Performance Insights:
– Average throughput: 5,000 rows/sec
– Peak throughput: 12,000 rows/sec
– Bottleneck identified: Network latency during peak hours
4. Recommendations:
– Consider increasing network bandwidth during peak hours
– Optimize batch size for high-throughput periods
– Review checkpoint frequency for large transactions
GGSCI (fgedu.net.cn)> AI ADVISE E_FGEDU01
Based on analysis of the last 48 hours, here are personalized recommendations:
Priority 1 – High Impact:
1. Increase BATCHSQL parameter for better throughput
Current: 1000, Recommended: 5000
Expected improvement: 15-20% throughput increase
2. Adjust checkpoint frequency
Current: 30 seconds, Recommended: 60 seconds
Expected improvement: Reduced checkpoint overhead
Priority 2 – Medium Impact:
3. Enable compression for Trail files
Current: Disabled, Recommended: Enabled
Expected improvement: 40% reduction in network traffic
Priority 3 – Low Impact:
4. Consider partitioning large tables for parallel processing
Tables identified: FGEDU01.FGEDU_ORDERS (size: 185 GB)
Would you like to apply these recommendations? (YES/NO/CUSTOMIZE)
3.4 自动优化功能配置与验证
自动优化功能可以根据工作负载自动调整OGG参数配置。
GGSCI (fgedu.net.cn)> EDIT PARAMS AUTO_TUNE
— 启用自动优化
ENABLE AUTO TUNE
— 优化目标
TARGET METRICS:
THROUGHPUT: MAXIMIZE
LATENCY: MINIMIZE
RESOURCE USAGE: BALANCED
— 优化参数范围
TUNEABLE PARAMS:
BATCHSQL: 1000-10000
BATCHTRANSOPS: 1000-10000
CACHESIZE: 32M-512M
CHECKPOINTSECS: 30-300
— 优化评估周期（小时）
EVALUATION PERIOD 24
— 变更生效前确认
AUTO APPLY CHANGES: NO
— 历史记录保留
TUNE HISTORY RETENTION 90
GGSCI (fgedu.net.cn)> ENABLE AUTOTUNE E_FGEDU01
Auto-tune enabled for E_FGEDU01.
Current Configuration:
BATCHSQL: 1000
BATCHTRANSOPS: 1000
CACHESIZE: 64M
CHECKPOINTSECS: 30
Next Evaluation: 2024-01-16 10:00:00
Estimated Improvement: 15-25%
GGSCI (fgedu.net.cn)> VIEW AUTOTUNE E_FGEDU01
Performance Baseline (Last 7 Days):
Average Throughput: 4,500 rows/sec
Average Latency: 3.2 seconds
Peak Throughput: 10,000 rows/sec
Peak Latency: 25 seconds
Current Optimization Cycle: Day 2/7
Pending Changes:
– BATCHSQL: 1000 -> 2500 (pending approval)
– BATCHTRANSOPS: 1000 -> 2000 (pending approval)
Apply Changes? (YES/NO/DEFER)
Part04-生产案例与实战讲解
4.1 OGG 23ai智能监控实战案例
本案例展示使用OGG 23ai智能监控功能对生产环境进行实时监控的实际操作过程。
cd /GoldenGate/app
./oggai dashboard –port 8080
Version 23.0.0.0.0
Starting dashboard server…
Dashboard available at: http://localhost:8080
API endpoint: http://localhost:7808/api/v1
Dashboard Features:
– Real-time Process Monitoring
– AI-powered Anomaly Detection
– Performance Trend Analysis
– Predictive Maintenance Alerts
– Natural Language Query Support
Dashboard started successfully.
Access the dashboard at: http://fgedu.net.cn:8080
GGSCI (fgedu.net.cn)> AI QUERY “What is the current status of all OGG processes?”
Answer:
All OGG processes are currently running normally. Here’s the summary:
Manager: RUNNING (Uptime: 30 days)
Extract Processes:
– E_FGEDU01: RUNNING (Lag: 2 seconds, Throughput: 5,200 rows/sec)
– E_FGEDU02: RUNNING (Lag: 3 seconds, Throughput: 3,800 rows/sec)
Replicat Processes:
– R_FGEDU01: RUNNING (Lag: 5 seconds, Throughput: 4,500 rows/sec)
– R_FGEDU02: RUNNING (Lag: 4 seconds, Throughput: 4,200 rows/sec)
Overall Health: Good (92/100)
No critical issues detected. All processes within normal parameters.
GGSCI (fgedu.net.cn)> AI QUERY “Show me the performance trend for the last week”
Performance Trend Analysis (Last 7 Days):
Throughput Trend: STABLE
Monday: 4,500 rows/sec average
Tuesday: 4,800 rows/sec average
Wednesday: 5,200 rows/sec average
Thursday: 5,500 rows/sec average
Friday: 6,200 rows/sec average (peak load)
Saturday: 3,800 rows/sec average
Sunday: 3,200 rows/sec average
Lag Trend: DECREASING (Positive)
Average lag reduced by 35% compared to previous week
Anomalies: 2 minor anomalies detected and resolved automatically
Predicted Peak Load: Friday afternoon (estimated 8,000 rows/sec)
Recommendation: Current infrastructure can handle predicted peak loads.
4.2 AI辅助故障诊断实战演练
通过AI辅助故障诊断功能，可以快速定位和解决OGG运行中的问题。
GGSCI (fgedu.net.cn)> AI DIAGNOSE R_FGEDU01
Problem Detected: Increased replication lag
Current Lag: 5 minutes 32 seconds
Normal Lag: 5 seconds
Root Cause Analysis:
1. Primary Cause: Database lock contention
Evidence:
– Lock wait events increased by 300%
– WAIT_EVENT: enq: TX – row lock contention
2. Contributing Factors:
– Large batch transaction in progress (50,000 rows)
– Index maintenance on target table FGEDU01.FGEDU_ORDERS
– Archive log generation on source database
Recommended Actions:
Immediate (Priority 1):
1. No action required – system self-healing in progress
2. Transaction expected to complete in ~3 minutes
Short-term (Priority 2):
1. Consider creating index on FGEDU01.FGEDU_ORDERS.ORDER_DATE
2. Schedule batch operations during off-peak hours
Long-term (Priority 3):
1. Implement partitioning on large tables
2. Consider parallel replicate configuration
Auto-resolve Status: Monitoring
Estimated Resolution: 3-5 minutes
GGSCI (fgedu.net.cn)> AI DIAGNOSE R_FGEDU01, DETAIL
===========================
Process: R_FGEDU01
Time: 2024-01-15 14:30:00
Analysis Window: Last 2 hours
Metrics Analyzed: 240 data points
Log Entries: 1,523 entries
Events: 15 significant events
Anomaly Timeline:
14:15:00 – Lag started increasing
Trigger: Large transaction detected (50,000 rows)
14:20:00 – Lock contention began
Event: TX row lock on table FGEDU_ORDERS
14:25:00 – Lag peaked at 6 minutes
Impact: Throughput dropped to 500 rows/sec
14:28:00 – Recovery initiated
Action: Transaction committing in progress
14:30:00 – Current status monitoring
Prediction: Full recovery expected by 14:33:00
Knowledge Base Matches:
– Pattern: LARGE_BATCH_LOCK_CONTENTION
– Similar cases: 12 historical occurrences
– Average resolution time: 5 minutes
– Success rate of auto-resolution: 95%
4.3 OGG 23ai性能优化实战对比
通过对比优化前后的性能数据，展示OGG 23ai自动优化功能的效果。
GGSCI (fgedu.net.cn)> STATS EXTRACT E_FGEDU01, TOTAL
Total operations: 1,000,000
Duration: 4 hours 30 minutes
Average throughput: 61.7 rows/sec
Peak throughput: 150 rows/sec
Average lag: 15 seconds
Peak lag: 2 minutes
Configuration:
BATCHSQL: 1000
BATCHTRANSOPS: 1000
CACHESIZE: 64M
CHECKPOINTSECS: 30
GGSCI (fgedu.net.cn)> APPLY AUTOTUNE E_FGEDU01
Changes applied successfully:
BATCHSQL: 1000 -> 5000
BATCHTRANSOPS: 1000 -> 5000
CACHESIZE: 64M -> 256M
CHECKPOINTSECS: 30 -> 60
Process restart required for changes to take effect.
Restart now? (YES/NO): YES
Stopping process…
Starting process…
Process restarted successfully.
GGSCI (fgedu.net.cn)> STATS EXTRACT E_FGEDU01, TOTAL
Total operations: 1,000,000
Duration: 2 hours 15 minutes
Average throughput: 123.5 rows/sec
Peak throughput: 280 rows/sec
Average lag: 3 seconds
Peak lag: 45 seconds
Configuration:
BATCHSQL: 5000
BATCHTRANSOPS: 5000
CACHESIZE: 256M
CHECKPOINTSECS: 60
Performance Improvement:
Throughput: +100% (61.7 -> 123.5 rows/sec)
Lag: -80% (15 sec -> 3 sec average)
Duration: -50% (4h30m -> 2h15m)
Part05-风哥经验总结与分享
5.1 OGG 23ai新功能使用最佳实践
基于实际项目经验，总结OGG 23ai新功能的最佳使用实践。
最佳实践建议：
1. AI监控配置：建议启用AI监控并设置合理的告警阈值
2. 自动优化使用：生产环境建议先使用建议模式，确认后再自动应用
3. 智能诊断：定期执行全面诊断，提前发现潜在问题
4. 知识库更新：定期更新AI知识库以包含最新的故障模式
5. 性能基线：建立性能基线以便准确评估优化效果
vi /GoldenGate/app/scripts/ogg23ai_best_practice.sh
# ogg23ai_best_practice.sh
# from:www.itpux.com.qq113257174.wx:itpux-com
# web: http://www.fgedu.net.cn
echo “========== OGG 23ai Best Practice Setup ==========”
cd /GoldenGate/app
# 1. 配置AI监控
echo “Configuring AI monitoring…”
./ggsci << EOF
EDIT PARAMS MGR
PORT 7809
AIENABLE
AIPORT 7808
HEALTHCHECK INTERVAL 60
METRICS COLLECTION INTERVAL 30
AI ANOMALY SENSITIVITY MEDIUM\nAI HISTORY RETENTION 30\nEOF
# 2. 启用智能诊断
echo “Enabling intelligent diagnostics…”
./ggsci << EOF\nENABLE DIAGNOSTICS AUTO\nEOF
# 3. 配置自动优化（建议模式）
echo “Configuring auto-tune (advisory mode)…”
./ggsci << EOF\nENABLE AUTOTUNE E_FGEDU01\nAUTO APPLY CHANGES: NO\nEOF
echo “Best practice setup complete!”
5.2 OGG 23ai常见问题与解决方案
汇总OGG 23ai使用过程中常见的配置和使用问题及解决方案。
常见问题与解决方案：
1. AI监控无法启动：检查Python环境配置和必要库安装
2. 智能诊断不准确：更新知识库，调整分析窗口
3. 自动优化效果不明显：检查基线数据是否准确
4. 性能波动大：使用AI分析识别异常原因
5. 与旧版本不兼容：使用Trail文件传输替代直接TCP
5.3 OGG 23ai运维脚本工具
提供一套完整的OGG 23ai运维脚本工具，方便日常管理和监控。
vi /GoldenGate/app/scripts/ai_health_check.sh
# ai_health_check.sh
# from:www.itpux.com.qq113257174.wx:itpux-com
# web: http://www.fgedu.net.cn
OGG_HOME=/GoldenGate/app
LOG_FILE=/GoldenGate/app/logs/ai_health_$(date +%Y%m%d).log
echo “========== OGG 23ai AI Health Check ==========” > $LOG_FILE
echo “Check Time: $(date)” >> $LOG_FILE
echo “” >> $LOG_FILE
# 1. 检查AI服务状态
echo “1. AI Service Status:” >> $LOG_FILE
echo “AI STATUS” | $OGG_HOME/ggsci 2>/dev/null >> $LOG_FILE
# 2. 检查AI分析结果
echo “” >> $LOG_FILE
echo “2. AI Analysis Summary:” >> $LOG_FILE
echo “AI ANALYZE ALL, TIMEFRAME 24” | $OGG_HOME/ggsci 2>/dev/null >> $LOG_FILE
# 3. 检查自动优化状态
echo “” >> $LOG_FILE
echo “3. Auto-Tune Status:” >> $LOG_FILE
echo “VIEW AUTOTUNE” | $OGG_HOME/ggsci 2>/dev/null >> $LOG_FILE
# 4. 检查异常检测结果
echo “” >> $LOG_FILE
echo “4. Anomaly Detection:” >> $LOG_FILE
echo “AI QUERY ‘Show anomalies in last 24 hours'” | $OGG_HOME/ggsci 2>/dev/null >> $LOG_FILE
echo “” >> $LOG_FILE
echo “Health check complete. Report saved to: $LOG_FILE”
chmod +x /GoldenGate/app/scripts/ai_health_check.sh
/GoldenGate/app/scripts/ai_health_check.sh
Check Time: Mon Jan 15 10:00:00 CST 2024
1. AI Service Status:
AI Monitoring Status:
Monitoring Active: YES
PID: 12345
2. AI Analysis Summary:
Overall Health Score: 92/100 (Good)
Anomalies Detected: 0 (last 24 hours)
3. Auto-Tune Status:
Auto-tune enabled for E_FGEDU01
Pending changes: 2 recommendations
4. Anomaly Detection:
No anomalies detected in the last 24 hours.
System is operating normally.
Health check complete.
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