DolphinDB告警分析：告警统计与趋势

七夜zippoe2026-07-13 8:27

目录

    • 摘要
    • 一、告警分析概述
      • [1.1 分析目标](#1.1 分析目标)
      • [1.2 分析维度](#1.2 分析维度)
      • [1.3 分析指标](#1.3 分析指标)
    • 二、告警统计
      • [2.1 按时间统计](#2.1 按时间统计)
      • [2.2 按设备统计](#2.2 按设备统计)
      • [2.3 按类型统计](#2.3 按类型统计)
      • [2.4 按级别统计](#2.4 按级别统计)
    • 三、趋势分析
      • [3.1 告警趋势](#3.1 告警趋势)
      • [3.2 同比环比](#3.2 同比环比)
      • [3.3 预测分析](#3.3 预测分析)
    • 四、根因分析
      • [4.1 告警关联分析](#4.1 告警关联分析)
      • [4.2 根因识别](#4.2 根因识别)
      • [4.3 影响范围分析](#4.3 影响范围分析)
    • 五、效果评估
      • [5.1 响应时间分析](#5.1 响应时间分析)
      • [5.2 处理时间分析](#5.2 处理时间分析)
      • [5.3 告警质量评估](#5.3 告警质量评估)
    • 六、告警报告
      • [6.1 日报](#6.1 日报)
      • [6.2 周报](#6.2 周报)
      • [6.3 月报](#6.3 月报)
    • 七、实战案例
      • [7.1 完整告警分析系统](#7.1 完整告警分析系统)
    • 八、总结
    • 参考资料

摘要

本文深入讲解DolphinDB告警分析技术。从告警统计到趋势分析，从根因定位到效果评估，从告警报告到持续改进，全面介绍告警分析的核心方法。通过丰富的代码示例，帮助读者掌握告警统计与趋势分析的核心技能。

一、告警分析概述

1.1 分析目标

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告警统计
优化告警
趋势分析
根因定位

1.2 分析维度

维度 说明
时间维度 按时间统计告警
设备维度 按设备统计告警
类型维度 按告警类型统计
级别维度 按告警级别统计

1.3 分析指标

指标 计算方式
告警数量 告警总数
告警率 告警数/设备数
响应时间 平均响应时间
处理时间 平均处理时间

二、告警统计

2.1 按时间统计

python 复制代码 
// 按小时统计
def hourlyAlertStats(date) {
    return select bar(alert_time, 1h) as hour,
                  count(*) as alert_count,
                  count(distinct device_id) as device_count
           from alert_log
           where date(alert_time) = date
           group by bar(alert_time, 1h)
}

// 按天统计
def dailyAlertStats(startDate, endDate) {
    return select date(alert_time) as date,
                  count(*) as alert_count,
                  sum(iif(level = 1, 1, 0)) as critical_count,
                  sum(iif(level = 2, 1, 0)) as warning_count
           from alert_log
           where date(alert_time) between startDate and endDate
           group by date(alert_time)
}

// 按周统计
def weeklyAlertStats(weeks = 4) {
    return select week(alert_time) as week,
                  count(*) as alert_count
           from alert_log
           where alert_time > now() - weeks * 7 * 86400000
           group by week(alert_time)
}

2.2 按设备统计

python 复制代码 
// 按设备统计
def deviceAlertStats(startTime, endTime) {
    return select device_id,
                  count(*) as alert_count,
                  avg(level) as avg_level,
                  min(alert_time) as first_alert,
                  max(alert_time) as last_alert
           from alert_log
           where alert_time between startTime and endTime
           group by device_id
           order by alert_count desc
}

// Top N告警设备
def topAlertDevices(limit = 10) {
    return select device_id, count(*) as alert_count
           from alert_log
           where alert_time > now() - 86400000
           group by device_id
           order by alert_count desc
           limit limit
}

2.3 按类型统计

python 复制代码 
// 按告警类型统计
def typeAlertStats(startTime, endTime) {
    return select rule_id,
                  count(*) as alert_count,
                  avg(value) as avg_value,
                  max(value) as max_value
           from alert_log
           where alert_time between startTime and endTime
           group by rule_id
           order by alert_count desc
}

2.4 按级别统计

python 复制代码 
// 按级别统计
def levelAlertStats(startTime, endTime) {
    return select level,
                  count(*) as alert_count,
                  count(*) * 100.0 / (select count(*) from alert_log 
                                      where alert_time between startTime and endTime) as percentage
           from alert_log
           where alert_time between startTime and endTime
           group by level
}

三、趋势分析

3.1 告警趋势

python 复制代码 
// 告警趋势
def alertTrend(days = 7) {
    return select date(alert_time) as date,
                  count(*) as alert_count,
                  mavg(count(*), 7) over (order by date(alert_time)) as moving_avg
           from alert_log
           where alert_time > now() - days * 86400000
           group by date(alert_time)
}

3.2 同比环比

python 复制代码 
// 同比分析
def yearOverYear(metric) {
    now = now()
    current = getAlertCount(now - 86400000, now)
    lastYear = getAlertCount(now - 365*86400000, now - 364*86400000)
    
    return dict(STRING, ANY, [
        ["current", current],
        ["lastYear", lastYear],
        ["change", (current - lastYear) * 100.0 / lastYear]
    ])
}

// 环比分析
def monthOverMonth() {
    now = now()
    current = getAlertCount(now - 86400000, now)
    lastMonth = getAlertCount(now - 60*86400000, now - 59*86400000)
    
    return dict(STRING, ANY, [
        ["current", current],
        ["lastMonth", lastMonth],
        ["change", (current - lastMonth) * 100.0 / lastMonth]
    ])
}

def getAlertCount(startTime, endTime) {
    return exec count(*) from alert_log 
           where alert_time between startTime and endTime
}

3.3 预测分析

python 复制代码 
// 告警预测
def predictAlerts(days = 7) {
    // 获取历史数据
    data = select date(alert_time) as date, count(*) as count
           from alert_log
           where alert_time > now() - 30 * 86400000
           group by date(alert_time)
           order by date
    
    if (data.rows() < 7) {
        return 0
    }
    
    // 简单移动平均预测
    return avg(data.count[-7:])
}

四、根因分析

4.1 告警关联分析

python 复制代码 
// 告警关联分析
def correlationAnalysis(timeWindow = 300) {
    alerts = select * from alert_log
             where alert_time > now() - timeWindow * 1000
             order by alert_time
    
    correlations = dict(STRING, INT)
    
    for (i in 0..alerts.rows() - 1) {
        for (j in i + 1..alerts.rows() - 1) {
            if (alerts[j].alert_time - alerts[i].alert_time < 60000) {
                key = alerts[i].rule_id + "_" + alerts[j].rule_id
                correlations[key] = correlations.get(key, 0) + 1
            }
        }
    }
    
    return correlations
}

4.2 根因识别

python 复制代码 
// 识别根因
def identifyRootCause(timeWindow = 600) {
    alerts = select * from alert_log
             where alert_time > now() - timeWindow * 1000
             order by alert_time
    
    rootCauses = array(STRING, 0)
    
    for (alert in alerts) {
        // 检查是否触发其他告警
        subsequentAlerts = select count(*) from alerts
                          where alert_time > alert.alert_time
                          and alert_time < alert.alert_time + 60000
        
        if (subsequentAlerts > 2) {
            rootCauses.append!(alert.alert_id)
        }
    }
    
    return rootCauses
}

4.3 影响范围分析

python 复制代码 
// 影响范围分析
def impactAnalysis(alertId) {
    alert = select * from alert_log where alert_id = alertId
    
    if (alert.rows() == 0) {
        return null
    }
    
    // 查找后续告警
    subsequent = select * from alert_log
                 where alert_time > alert.alert_time[0]
                 and alert_time < alert.alert_time[0] + 600000
    
    return dict(STRING, ANY, [
        ["sourceAlert", alert[0]],
        ["affectedDevices", distinct(subsequent.device_id)],
        ["affectedCount", subsequent.rows()]
    ])
}

五、效果评估

5.1 响应时间分析

python 复制代码 
// 响应时间统计
def responseTimeStats(startTime, endTime) {
    return select avg(ack_time - alert_time) as avg_response_time,
                  min(ack_time - alert_time) as min_response_time,
                  max(ack_time - alert_time) as max_response_time
           from alert_log
           where alert_time between startTime and endTime
           and ack_time is not null
}

5.2 处理时间分析

python 复制代码 
// 处理时间统计
def resolutionTimeStats(startTime, endTime) {
    return select avg(resolve_time - alert_time) as avg_resolution_time,
                  min(resolve_time - alert_time) as min_resolution_time,
                  max(resolve_time - alert_time) as max_resolution_time
           from alert_log
           where alert_time between startTime and endTime
           and resolve_time is not null
}

5.3 告警质量评估

python 复制代码 
// 告警质量评估
def alertQualityAssessment() {
    total = exec count(*) from alert_log where alert_time > now() - 86400000
    acknowledged = exec count(*) from alert_log 
                   where alert_time > now() - 86400000 and status != "new"
    resolved = exec count(*) from alert_log 
               where alert_time > now() - 86400000 and status = "resolved"
    
    return dict(STRING, ANY, [
        ["total", total],
        ["acknowledged", acknowledged],
        ["resolved", resolved],
        ["ackRate", acknowledged * 100.0 / total],
        ["resolveRate", resolved * 100.0 / total]
    ])
}

六、告警报告

6.1 日报

python 复制代码 
// 生成日报
def generateDailyReport(date) {
    stats = dailyAlertStats(date, date)
    topDevices = topAlertDevices(10)
    quality = alertQualityAssessment()
    
    return dict(STRING, ANY, [
        ["date", date],
        ["stats", stats],
        ["topDevices", topDevices],
        ["quality", quality]
    ])
}

6.2 周报

python 复制代码 
// 生成周报
def generateWeeklyReport(weekStart) {
    weekEnd = weekStart + 6
    
    stats = dailyAlertStats(weekStart, weekEnd)
    trend = alertTrend(7)
    topTypes = typeAlertStats(weekStart, weekEnd)
    
    return dict(STRING, ANY, [
        ["weekStart", weekStart],
        ["weekEnd", weekEnd],
        ["stats", stats],
        ["trend", trend],
        ["topTypes", topTypes]
    ])
}

6.3 月报

python 复制代码 
// 生成月报
def generateMonthlyReport(month) {
    stats = levelAlertStats(month[0], month[-1])
    devices = deviceAlertStats(month[0], month[-1])
    yoy = yearOverYear("alerts")
    
    return dict(STRING, ANY, [
        ["month", month],
        ["stats", stats],
        ["devices", devices],
        ["yoy", yoy]
    ])
}

七、实战案例

7.1 完整告警分析系统

python 复制代码 
// ========== 告警分析系统 ==========

// 1. 创建告警表
share table(1:0, 
    `alert_id`rule_id`device_id`alert_time`metric`value`threshold`level`status`ack_time`resolve_time,
    [STRING, STRING, SYMBOL, TIMESTAMP, STRING, DOUBLE, DOUBLE, INT, STRING, TIMESTAMP, TIMESTAMP]) as alert_log

// 2. 统计接口
def getAlertDashboard() {
    now = now()
    
    return dict(STRING, ANY, [
        ["today", exec count(*) from alert_log where date(alert_time) = date(now)],
        ["week", exec count(*) from alert_log where alert_time > now - 7*86400000],
        ["month", exec count(*) from alert_log where alert_time > now - 30*86400000],
        ["critical", exec count(*) from alert_log where level = 1 and status = "new"],
        ["warning", exec count(*) from alert_log where level = 2 and status = "new"]
    ])
}

addFunctionView(getAlertDashboard)

// 3. 趋势接口
def getAlertTrend(days = 7) {
    return select date(alert_time) as date, count(*) as count
           from alert_log
           where alert_time > now() - days * 86400000
           group by date(alert_time)
}

addFunctionView(getAlertTrend)

// 4. Top设备
def getTopDevices(limit = 10) {
    return select device_id, count(*) as alert_count
           from alert_log
           where alert_time > now() - 86400000
           group by device_id
           order by alert_count desc
           limit limit
}

addFunctionView(getTopDevices)

print("告警分析系统启动完成")

八、总结

本文详细介绍了DolphinDB告警分析：

  1. 告警统计：时间、设备、类型、级别统计
  2. 趋势分析：告警趋势、同比环比、预测分析
  3. 根因分析：关联分析、根因识别、影响范围
  4. 效果评估：响应时间、处理时间、质量评估
  5. 告警报告：日报、周报、月报

思考题：

  1. 如何提高告警分析的准确性？
  2. 如何设计有效的告警报告？
  3. 如何实现告警的持续改进？

参考资料

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