# 1. 环境准备
# MongoDB版本：4.4+
# 操作系统：Oracle Linux 9.3
# 监控工具：Prometheus + Grafana

# 2. 步骤一：安装Node
Exporter
$ wget https://github.com/prometheus/node_exporter/releases/download/v1.3.1/node_exporter-1.3.1.linux-amd64.tar.gz
$ tar -xzf node_exporter-1.3.1.linux-amd64.tar.gz
$ cd node_exporter-1.3.1.linux-amd64
$ ./node_exporter

# 3. 步骤二：配置Prometheus
$ vi /etc/prometheus/prometheus.yml
scrape_configs:
– job_name: ‘node’
static_configs:
– targets: [‘localhost:9100’]
– job_name: ‘mongodb’
static_configs:
– targets: [‘localhost:9216’]

# 4. 步骤三：启动Prometheus
$ systemctl start prometheus

# 5. 步骤四：配置Grafana
# 访问Grafana UI：http://localhost:3000
# 添加Prometheus数据源
# 导入系统监控面板（ID: 1860）
# 导入MongoDB监控面板（ID: 7353）

# 6. 步骤五：设置告警
# 在Grafana中设置系统资源告警
# CPU使用率 > 80%
# 内存使用率 > 85%
# 磁盘使用率 > 90%
# MongoDB连接数 > 5000

# 7. 步骤六：监控脚本
#!/bin/bash
# system_monitor.sh
# from:www.itpux.com.qq113257174.wx:itpux-com
# web: `http://www.fgedu.net.cn`

CPU_USAGE=$(top -bn1 | grep “Cpu(s)” | sed “s/.*, *\([0-9.]*\)%* id.*/\1/” | awk ‘{print 100 – $1}’)
MEMORY_USAGE=$(free | grep Mem | awk ‘{print $3/$2 * 100.0}’)
DISK_USAGE=$(df -h | grep /dev/vda1 | awk ‘{print $5}’ | sed ‘s/%//’)

if (( $(echo “$CPU_USAGE > 80” | bc -l) )); then
echo “CPU usage is too high: $CPU_USAGE%”
# 发送告警
fi

if (( $(echo “$MEMORY_USAGE > 85” | bc -l) )); then
echo “Memory usage is too high: $MEMORY_USAGE%”
# 发送告警
fi

if (( $DISK_USAGE > 90 )); then
echo “Disk usage is too high: $DISK_USAGE%”
# 发送告警
fi

# 1. 环境准备
# MongoDB版本：4.4+
# 监控工具：MongoDB Exporter + Prometheus + Grafana

# 2. 步骤一：安装MongoDB
Exporter
$ wget https://github.com/percona/mongodb_exporter/releases/download/v0.20.0/mongodb_exporter-0.20.0.linux-amd64.tar.gz
$ tar -xzf mongodb_exporter-0.20.0.linux-amd64.tar.gz
$ cd mongodb_exporter-0.20.0.linux-amd64
$ ./mongodb_exporter –mongodb.uri=”mongodb://fgedu:fgedu123@192.168.1.100:27017/admin”

# 3. 步骤二：配置Prometheus
$ vi /etc/prometheus/prometheus.yml
scrape_configs:
– job_name: ‘mongodb’
static_configs:
– targets: [‘localhost:9216’]

# 4. 步骤三：启动Prometheus
$ systemctl start prometheus

# 5. 步骤四：配置Grafana
# 访问Grafana UI：http://localhost:3000
# 添加Prometheus数据源
# 导入MongoDB监控面板（ID: 7353）

# 6. 步骤五：设置性能告警
# 查询响应时间 > 100ms
# 操作数 > 1000/sec
# 连接数 > 5000
# 复制延迟 > 10s

# 7. 步骤六：性能监控脚本
#!/bin/bash
# mongodb_perf_monitor.sh
# from:www.itpux.com.qq113257174.wx:itpux-com
# web: `http://www.fgedu.net.cn`

# 获取查询性能
QUERY_TIME=$(mongosh –host 192.168.1.100 –port 27017 –username fgedu –password fgedu123 –authenticationDatabase admin –eval “db.fgedu_users.find({ age: { \$gt: 25 } }).explain(‘executionStats’).executionStats.executionTimeMillis”)

# 获取操作数
OPERATIONS=$(mongostat –host 192.168.1.100 –port 27017 –username fgedu –password fgedu123 –authenticationDatabase admin –rowcount 1 | tail -n 1 | awk ‘{print $2+$3+$4+$5}’)

# 获取连接数
CONNECTIONS=$(mongosh –host 192.168.1.100 –port 27017 –username fgedu –password fgedu123 –authenticationDatabase admin –eval “db.serverStatus().connections.current”)

if (( $QUERY_TIME > 100 )); then
echo “Query time is too high: $QUERY_TIME ms”
# 发送告警
fi

if (( $OPERATIONS > 1000 )); then
echo “Operations per second is too high: $OPERATIONS”
# 发送告警
fi

if (( $CONNECTIONS > 5000 )); then
echo “Connections is too high: $CONNECTIONS”
# 发送告警
fi

# 1. 环境准备
# MongoDB版本：4.4+
# 数据库：fgedudb
# 集合：fgedu_users（包含100万条记录）

# 2. 步骤一：分析慢查询
# 启用慢查询日志
$ vi /mongodb/app/mongodb.conf
systemLog:
logAppend: true
logPath: /mongodb/log/mongodb.log
logComponentVerbosity:
command: 1

# 重启MongoDB
$ systemctl restart mongodb

# 3. 步骤二：分析查询性能
fgedudb> db.fgedu_users.find({ age: { $gt: 25 }, name: /^fgedu/ }).explain()

# 4. 步骤三：创建索引
fgedudb> db.fgedu_users.createIndex({ age: 1, name: 1 })

# 5. 步骤四：验证索引效果
fgedudb> db.fgedu_users.find({ age: { $gt: 25 }, name: /^fgedu/ }).explain(“executionStats”)

# 6. 步骤五：优化查询语句
# 原查询
fgedudb> db.fgedu_users.find({ age: { $gt: 25 }, name: /^fgedu/ })

# 优化后查询（使用投影）
fgedudb> db.fgedu_users.find({ age: { $gt: 25 }, name: /^fgedu/ }, { name: 1, email: 1, _id: 0 })

# 7. 步骤六：监控查询性能
# 使用MongoDB Atlas或Grafana监控查询性能
# 记录优化前后的查询响应时间

# 8. 步骤七：定期维护索引
# 重建索引
fgedudb> db.fgedu_users.reIndex()

# 分析索引使用情况
fgedudb> db.fgedu_users.aggregate([
{ $indexStats: { } }
])