## 1. 环境准备
– 服务器：至少3台服务器，配置推荐：8核16G内存
– 操作系统：CentOS 7.9 / RHEL 7.9
– 网络：确保服务器之间网络畅通
– 存储：每个服务器至少500GB磁盘空间

## 2. 部署Prometheus和Grafana
$ cat > docker-compose.yml << EOF version: '3' services: prometheus: image: prom/prometheus:v2.35.0 ports: - "9090:9090" volumes: - ./prometheus.yml:/etc/prometheus/prometheus.yml - prometheus_data:/prometheus grafana: image: grafana/grafana:8.5.0 ports: - "3000:3000" volumes: - grafana_data:/var/lib/grafana volumes: prometheus_data: grafana_data: EOF $ cat> prometheus.yml << EOF global: scrape_interval: 15s scrape_configs: - job_name: 'tidb' static_configs: - targets: ['192.168.1.100:9090'] - job_name: 'tikv' static_configs: - targets: ['192.168.1.101:9091', '192.168.1.102:9091' ] - job_name: 'pd' static_configs: - targets: ['192.168.1.100:9092'] EOF $ docker-compose up -d ## 3. 部署ELK Stack $ cat> docker-compose-elk.yml << EOF version: '3' services: elasticsearch: image: elasticsearch:7.17.0 ports: - "9200:9200" environment: - discovery.type=single-node - ES_JAVA_OPTS=-Xms1g -Xmx1g volumes: - es_data:/usr/share/elasticsearch/data logstash: image: logstash:7.17.0 ports: - "5044:5044" volumes: - ./logstash/pipeline:/usr/share/logstash/pipeline kibana: image: kibana:7.17.0 ports: - "5601:5601" environment: - ELASTICSEARCH_HOSTS=http://elasticsearch:9200 volumes: es_data: EOF $ mkdir -p logstash/pipeline $ cat> logstash/pipeline/logstash.conf << EOF input { file { path=>
“/tidb/app/tidb-deploy/tidb-4000/log/tidb.log”
start_position => “beginning”
sincedb_path => “/dev/null”
type => “tidb”
}
file {
path => “/tidb/app/tidb-deploy/tikv-20160/log/tikv.log”
start_position => “beginning”
sincedb_path => “/dev/null”
type => “tikv”
}
file {
path => “/tidb/app/tidb-deploy/pd-2379/log/pd.log”
start_position => “beginning”
sincedb_path => “/dev/null”
type => “pd”
}
}

filter {
grok {
match => { “message” => “%{TIMESTAMP_ISO8601:timestamp} \[%{DATA:level}\]
\[%{DATA:component}\] \[%{DATA:file}\:%{NUMBER:line}\] %{GREEDYDATA:message}” }
}
date {
match => [ “timestamp”, “yyyy-MM-dd HH:mm:ss.SSS” ]
target => “@timestamp”
}
mutate {
add_field => { “cluster” => “fgedu-tidb-cluster” }
}
}

output {
elasticsearch {
hosts => [“elasticsearch:9200”]
index => “tidb-logs-%{type}-%{+YYYY.MM.dd}”
}
}
EOF

$ docker-compose -f docker-compose-elk.yml up -d

## 4. 部署机器学习服务
$ cat > docker-compose-ml.yml << EOF version: '3' services: jupyter: image: jupyter/tensorflow-notebook:latest ports: - "8888:8888" volumes: - jupyter_data:/home/jovyan/work mlflow: image: mlflow/mlflow:latest ports: - "5000:5000" volumes: - mlflow_data:/mlflow volumes: jupyter_data: mlflow_data: EOF $ docker-compose -f docker-compose-ml.yml up -d ## 5. 配置智能运维系统 $ cat> intelligent_ops.py << EOF #!/usr/bin/env python3 # intelligent_ops.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn import requests import json import time import numpy as np from sklearn.ensemble import IsolationForest # 收集监控数据 def collect_metrics(): url="http://192.168.1.100:9090/api/v1/query" queries=[ "avg(tidb_server_qps) by (instance)" , "avg(tikv_server_qps) by (instance)" , "avg(pd_server_qps) by (instance)" , "avg(node_cpu_seconds_total) by (instance)" , "avg(node_memory_MemUsed_bytes) by (instance)" , "avg(node_disk_io_time_seconds_total) by (instance)" ] metrics={} for query in queries: params={"query": query} response=requests.get(url, params=params) data=response.json() if "data" in data and "result" in data["data"]: for result in data["data"]["result"]: instance=result["metric"].get("instance", "unknown" ) value=float(result["value"][1]) metric_name=query.split("(")[1].split(")")[0] if instance not in metrics: metrics[instance]={} metrics[instance][metric_name]=value return metrics # 异常检测 def detect_anomalies(metrics): # 准备数据 X=[] instances=[] for instance, instance_metrics in metrics.items(): instance_data=[] for metric_name, value in instance_metrics.items(): instance_data.append(value) if instance_data: X.append(instance_data) instances.append(instance) if not X: return [] # 训练模型 model=IsolationForest(contamination=0.1) model.fit(X) # 检测异常 predictions=model.predict(X) anomalies=[] for i, prediction in enumerate(predictions): if prediction==-1: anomalies.append(instances[i]) return anomalies # 发送告警 def send_alert(anomalies): if not anomalies: return url="http://192.168.1.100:9093/api/v2/alerts" alerts=[] for instance in anomalies: alert={ "status" : "firing" , "labels" : { "alertname" : "InstanceAnomaly" , "severity" : "warning" , "instance" : instance }, "annotations" : { "summary" : f"Instance {instance} has anomaly", "description" : f"Instance {instance} has been detected as anomalous" }, "generatorURL" : "http://192.168.1.100:3000" } alerts.append(alert) data={"alerts": alerts} response=requests.post(url, json=data) print(f"Alert sent: {response.status_code}") # 主函数 def main(): while True: metrics=collect_metrics() anomalies=detect_anomalies(metrics) send_alert(anomalies) time.sleep(60) if __name__=="__main__" : main() EOF ## 6. 启动智能运维系统 $ chmod +x intelligent_ops.py $ nohup python3 intelligent_ops.py> intelligent_ops.log 2>&1 &

## 7. 访问智能运维系统
# Prometheus：http://192.168.1.100:9090
# Grafana：http://192.168.1.100:3000
# Kibana：http://192.168.1.100:5601
# Jupyter Notebook：http://192.168.1.100:8888
# MLflow：http://192.168.1.100:5000

## 1. 配置Prometheus告警规则
$ cat > prometheus_rules.yml << EOF groups: - name: tidb_alerts rules: - alert: TiDBHighQPS expr: avg(tidb_server_qps) by (instance)> 10000
for: 5m
labels:
severity: warning
annotations:
summary: “TiDB high QPS (instance {{ $labels.instance }})”
description: “TiDB QPS is high (> 10000) for 5 minutes”

– alert: TiKVHighIO
expr: avg(tikv_server_io_bytes_total) by (instance) > 1000000000
for: 5m
labels:
severity: warning
annotations:
summary: “TiKV high IO (instance {{ $labels.instance }})”
description: “TiKV IO is high (> 1GB/s) for 5 minutes”

– alert: PDHighCPU
expr: avg(node_cpu_seconds_total{job=”pd”}) by (instance) > 0.8
for: 5m
labels:
severity: warning
annotations:
summary: “PD high CPU (instance {{ $labels.instance }})”
description: “PD CPU usage is high (> 80%) for 5 minutes”
EOF

## 2. 更新Prometheus配置
$ cat > prometheus.yml << EOF global: scrape_interval: 15s rule_files: - "prometheus_rules.yml" scrape_configs: - job_name: 'tidb' static_configs: - targets: ['192.168.1.100:9090'] - job_name: 'tikv' static_configs: - targets: ['192.168.1.101:9091', '192.168.1.102:9091' ] - job_name: 'pd' static_configs: - targets: ['192.168.1.100:9092'] - job_name: 'node' static_configs: - targets: ['192.168.1.100:9100', '192.168.1.101:9100' , '192.168.1.102:9100' ] EOF ## 3. 配置Grafana仪表板 # 访问 http://192.168.1.100:3000 # 登录（默认用户名：admin，密码：admin） # 创建新的仪表板，添加面板，选择Prometheus数据源 # 配置面板查询，例如：avg(tidb_server_qps) by (instance) ## 4. 配置Kibana索引模式 # 访问 http://192.168.1.100:5601 # 登录（默认用户名：elastic，密码：changeme） # 创建索引模式 tidb-logs-* # 配置时间字段为 @timestamp ## 5. 配置机器学习模型 $ cat> anomaly_detection.py << EOF #!/usr/bin/env python3 # anomaly_detection.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn import pandas as pd import numpy as np from sklearn.ensemble import IsolationForest from sklearn.preprocessing import StandardScaler import joblib # 加载数据 def load_data(): # 模拟数据，实际应用中应从数据库或API获取 data=[] for i in range(1000): # 正常数据 data.append({ "timestamp" : pd.Timestamp.now() - pd.Timedelta(minutes=i), "qps" : np.random.normal(5000, 1000), "cpu" : np.random.normal(0.5, 0.1), "memory" : np.random.normal(0.6, 0.1), "io" : np.random.normal(500000000, 100000000) }) # 添加异常数据 for i in range(100): data.append({ "timestamp" : pd.Timestamp.now() - pd.Timedelta(minutes=1000 + i), "qps" : np.random.normal(15000, 1000), # 高QPS "cpu" : np.random.normal(0.9, 0.05), # 高CPU "memory" : np.random.normal(0.9, 0.05), # 高内存 "io" : np.random.normal(2000000000, 100000000) # 高IO }) df=pd.DataFrame(data) return df # 训练模型 def train_model(): df=load_data() # 提取特征 features=df[["qps", "cpu" , "memory" , "io" ]] # 标准化 scaler=StandardScaler() features_scaled=scaler.fit_transform(features) # 训练模型 model=IsolationForest(contamination=0.1, random_state=42) model.fit(features_scaled) # 保存模型和scaler joblib.dump(model, "anomaly_detection_model.joblib" ) joblib.dump(scaler, "scaler.joblib" ) print("Model trained and saved successfully") # 预测异常 def predict_anomaly(data): # 加载模型和scaler model=joblib.load("anomaly_detection_model.joblib") scaler=joblib.load("scaler.joblib") # 提取特征 features=data[["qps", "cpu" , "memory" , "io" ]] # 标准化 features_scaled=scaler.transform(features) # 预测 predictions=model.predict(features_scaled) # -1表示异常，1表示正常 data["anomaly"]=predictions return data if __name__=="__main__" : train_model() # 测试预测 test_data=load_data().tail(100) result=predict_anomaly(test_data) print("Anomaly detection results:") print(result[result["anomaly"]==-1]) EOF ## 6. 训练机器学习模型 $ python3 anomaly_detection.py ## 7. 配置智能运维系统集成 $ cat> intelligent_ops_integration.py << EOF #!/usr/bin/env python3 # intelligent_ops_integration.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn import requests import json import time import pandas as pd import joblib # 收集监控数据 def collect_metrics(): url="http://192.168.1.100:9090/api/v1/query" queries=[ "avg(tidb_server_qps) by (instance)" , "avg(node_cpu_seconds_total) by (instance)" , "avg(node_memory_MemUsed_bytes / node_memory_MemTotal_bytes) by (instance)" , "avg(node_disk_io_time_seconds_total) by (instance)" ] metrics={} for query in queries: params={"query": query} response=requests.get(url, params=params) data=response.json() if "data" in data and "result" in data["data"]: for result in data["data"]["result"]: instance=result["metric"].get("instance", "unknown" ) value=float(result["value"][1]) metric_name=query.split("(")[1].split(")")[0] if instance not in metrics: metrics[instance]={} metrics[instance][metric_name]=value return metrics # 预测异常 def predict_anomaly(metrics): # 加载模型和scaler model=joblib.load("anomaly_detection_model.joblib") scaler=joblib.load("scaler.joblib") # 准备数据 data=[] instances=[] for instance, instance_metrics in metrics.items(): qps=instance_metrics.get("tidb_server_qps", 0) cpu=instance_metrics.get("node_cpu_seconds_total", 0) memory=instance_metrics.get("node_memory_MemUsed_bytes / node_memory_MemTotal_bytes", 0) io=instance_metrics.get("node_disk_io_time_seconds_total", 0) data.append([qps, cpu, memory, io]) instances.append(instance) if not data: return [] # 标准化 data_scaled=scaler.transform(data) # 预测 predictions=model.predict(data_scaled) # 提取异常实例 anomalies=[] for i, prediction in enumerate(predictions): if prediction==-1: anomalies.append(instances[i]) return anomalies # 发送告警 def send_alert(anomalies): if not anomalies: return url="http://192.168.1.100:9093/api/v2/alerts" alerts=[] for instance in anomalies: alert={ "status" : "firing" , "labels" : { "alertname" : "InstanceAnomaly" , "severity" : "warning" , "instance" : instance }, "annotations" : { "summary" : f"Instance {instance} has anomaly", "description" : f"Instance {instance} has been detected as anomalous by machine learning model" }, "generatorURL" : "http://192.168.1.100:3000" } alerts.append(alert) data={"alerts": alerts} response=requests.post(url, json=data) print(f"Alert sent: {response.status_code}") # 主函数 def main(): while True: metrics=collect_metrics() anomalies=predict_anomaly(metrics) send_alert(anomalies) time.sleep(60) if __name__=="__main__" : main() EOF ## 8. 启动智能运维集成服务 $ chmod +x intelligent_ops_integration.py $ nohup python3 intelligent_ops_integration.py>
intelligent_ops_integration.log 2>&1 &

## 1. 环境准备
– 服务器：至少1台服务器，配置推荐：8核16G内存
– 操作系统：CentOS 7.9 / RHEL 7.9
– 网络：确保服务器之间网络畅通
– 存储：至少500GB磁盘空间

## 2. 安装依赖
$ pip3 install pandas numpy scikit-learn joblib requests flask

## 3. 部署异常检测服务
$ cat > anomaly_detection_service.py << EOF #!/usr/bin/env python3 # anomaly_detection_service.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn from flask import Flask, request, jsonify import pandas as pd import joblib import numpy as np app=Flask(__name__) # 加载模型和scaler model=joblib.load("anomaly_detection_model.joblib") scaler=joblib.load("scaler.joblib") @app.route('/detect', methods=['POST']) def detect_anomaly(): data=request.json if not data or "metrics" not in data: return jsonify({"error": "Invalid request data" }), 400 # 准备数据 metrics=data["metrics"] instances=[] features=[] for instance, instance_metrics in metrics.items(): qps=instance_metrics.get("qps", 0) cpu=instance_metrics.get("cpu", 0) memory=instance_metrics.get("memory", 0) io=instance_metrics.get("io", 0) features.append([qps, cpu, memory, io]) instances.append(instance) if not features: return jsonify({"anomalies": []}), 200 # 标准化 features_scaled=scaler.transform(features) # 预测 predictions=model.predict(features_scaled) # 提取异常实例 anomalies=[] for i, prediction in enumerate(predictions): if prediction==-1: anomalies.append(instances[i]) return jsonify({"anomalies": anomalies}), 200 if __name__=='__main__' : app.run(host='0.0.0.0' , port=5001) EOF ## 4. 启动异常检测服务 $ chmod +x anomaly_detection_service.py $ nohup python3 anomaly_detection_service.py> anomaly_detection_service.log 2>&1 &

## 5. 测试异常检测服务
$ curl -X POST http://192.168.1.100:5001/detect \
-H “Content-Type: application/json” \
-d ‘{
“metrics”: {
“192.168.1.100:9090”: {
“qps”: 15000,
“cpu”: 0.9,
“memory”: 0.9,
“io”: 2000000000
},
“192.168.1.101:9091”: {
“qps”: 5000,
“cpu”: 0.5,
“memory”: 0.6,
“io”: 500000000
}
}
}’

## 6. 输出示例
{
“anomalies”: [“192.168.1.100:9090”]
}

## 1. 配置异常检测规则
$ cat > anomaly_detection_config.py << EOF #!/usr/bin/env python3 # anomaly_detection_config.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn # 异常检测配置 config={ # 模型配置 "model" : { "type" : "IsolationForest" , "contamination" : 0.1, "random_state" : 42 }, # 特征配置 "features" : { "qps" : { "enabled" : True, "weight" : 1.0 }, "cpu" : { "enabled" : True, "weight" : 1.0 }, "memory" : { "enabled" : True, "weight" : 1.0 }, "io" : { "enabled" : True, "weight" : 1.0 } }, # 告警配置 "alert" : { "enabled" : True, "severity" : "warning" , "threshold" : 1, "cooldown" : 300 # 冷却时间（秒） }, # 数据配置 "data" : { "window" : 300, # 数据窗口（秒） "interval" : 60, # 采集间隔（秒） "history" : 86400 # 历史数据保留时间（秒） } } def get_config(): return config EOF ## 2. 配置异常检测客户端 $ cat> anomaly_detection_client.py << EOF #!/usr/bin/env python3 # anomaly_detection_client.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn import requests import json import time from anomaly_detection_config import get_config config=get_config() # 收集监控数据 def collect_metrics(): url="http://192.168.1.100:9090/api/v1/query" queries={ "qps" : "avg(tidb_server_qps) by (instance)" , "cpu" : "avg(node_cpu_seconds_total) by (instance)" , "memory" : "avg(node_memory_MemUsed_bytes / node_memory_MemTotal_bytes) by (instance)" , "io" : "avg(node_disk_io_time_seconds_total) by (instance)" } metrics={} for metric_name, query in queries.items(): params={"query": query} response=requests.get(url, params=params) data=response.json() if "data" in data and "result" in data["data"]: for result in data["data"]["result"]: instance=result["metric"].get("instance", "unknown" ) value=float(result["value"][1]) if instance not in metrics: metrics[instance]={} metrics[instance][metric_name]=value return metrics # 检测异常 def detect_anomaly(metrics): url="http://192.168.1.100:5001/detect" data={"metrics": metrics} response=requests.post(url, json=data) result=response.json() return result.get("anomalies", []) # 发送告警 def send_alert(anomalies): if not anomalies: return url="http://192.168.1.100:9093/api/v2/alerts" alerts=[] for instance in anomalies: alert={ "status" : "firing" , "labels" : { "alertname" : "InstanceAnomaly" , "severity" : config["alert"]["severity"], "instance" : instance }, "annotations" : { "summary" : f"Instance {instance} has anomaly", "description" : f"Instance {instance} has been detected as anomalous by machine learning model" }, "generatorURL" : "http://192.168.1.100:3000" } alerts.append(alert) data={"alerts": alerts} response=requests.post(url, json=data) print(f"Alert sent: {response.status_code}") # 主函数 def main(): while True: metrics=collect_metrics() anomalies=detect_anomaly(metrics) if len(anomalies)>=
config[“alert”][“threshold”]:
send_alert(anomalies)
time.sleep(config[“data”][“interval”])

if __name__ == “__main__”:
main()
EOF

## 3. 启动异常检测客户端
$ chmod +x anomaly_detection_client.py
$ nohup python3 anomaly_detection_client.py > anomaly_detection_client.log 2>&1 &

## 4. 配置异常检测可视化
$ cat > anomaly_detection_dashboard.py << EOF #!/usr/bin/env python3 # anomaly_detection_dashboard.py # from:www.itpux.com.qq113257174.wx:itpux-com # web: http://www.fgedu.net.cn from flask import Flask, render_template, jsonify import pandas as pd import joblib import numpy as np import requests app=Flask(__name__) # 加载模型和scaler model=joblib.load("anomaly_detection_model.joblib") scaler=joblib.load("scaler.joblib") # 收集监控数据 def collect_metrics(): url="http://192.168.1.100:9090/api/v1/query" queries={ "qps" : "avg(tidb_server_qps) by (instance)" , "cpu" : "avg(node_cpu_seconds_total) by (instance)" , "memory" : "avg(node_memory_MemUsed_bytes / node_memory_MemTotal_bytes) by (instance)" , "io" : "avg(node_disk_io_time_seconds_total) by (instance)" } metrics={} for metric_name, query in queries.items(): params={"query": query} response=requests.get(url, params=params) data=response.json() if "data" in data and "result" in data["data"]: for result in data["data"]["result"]: instance=result["metric"].get("instance", "unknown" ) value=float(result["value"][1]) if instance not in metrics: metrics[instance]={} metrics[instance][metric_name]=value return metrics # 检测异常 def detect_anomaly(metrics): # 准备数据 data=[] instances=[] for instance, instance_metrics in metrics.items(): qps=instance_metrics.get("qps", 0) cpu=instance_metrics.get("cpu", 0) memory=instance_metrics.get("memory", 0) io=instance_metrics.get("io", 0) data.append([qps, cpu, memory, io]) instances.append(instance) if not data: return [] # 标准化 data_scaled=scaler.transform(data) # 预测 predictions=model.predict(data_scaled) # 提取异常实例 anomalies=[] for i, prediction in enumerate(predictions): if prediction==-1: anomalies.append(instances[i]) return anomalies @app.route('/') def index(): return render_template('dashboard.html') @app.route('/api/metrics') def get_metrics(): metrics=collect_metrics() anomalies=detect_anomaly(metrics) result={ "metrics" : metrics, "anomalies" : anomalies } return jsonify(result) if __name__=='__main__' : app.run(host='0.0.0.0' , port=5002) EOF ## 5. 创建可视化模板 $ mkdir -p templates $ cat>
templates/dashboard.html << EOF