amberjs 1.17.0

// 自动根因分析系统
//
// 该模块利用因果推断、变更关联分析和知识图谱来快速定位系统故障的根本原因。

use serde::{Deserialize, Serialize};
use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use std::time::{Duration, SystemTime};
use tokio::sync::RwLock;

/// 事件类型
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum IncidentType {
    ServiceOutage,     // 服务中断
    HighLatency,       // 高延迟
    HighMemoryUsage,   // 高内存使用
    DiskFull,          // 磁盘满
    NetworkIssue,      // 网络问题
    DatabaseIssue,     // 数据库问题
    DeploymentFailure, // 部署失败
    SecurityIncident,  // 安全事件
}
/// 变更类型
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum ChangeType {
    Configuration,  // 配置变更
    Code,           // 代码变更
    Database,       // 数据库变更
    Infrastructure, // 基础设施变更
    Deployment,     // 部署变更
    Production,     // 生产环境变更
}
/// 事件严重程度
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum IncidentSeverity {
    Low,
    Medium,
    High,
    Critical,
}
/// 事件状态
#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)]
pub enum IncidentStatus {
    Open,
    Investigating,
    Identified,
    Resolving,
    Resolved,
    Closed,
}
/// 事件实体
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Incident {
    pub id: String,
    pub incident_type: IncidentType,
    pub severity: IncidentSeverity,
    pub status: IncidentStatus,
    pub title: String,
    pub description: String,
    pub timestamp: SystemTime,
    pub affected_services: Vec<String>,
    pub affected_components: Vec<String>,
    pub symptoms: Vec<String>,
    pub reporter: String,
    pub assignee: Option<String>,
}
/// 变更记录
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Change {
    pub id: String,
    pub change_type: ChangeType,
    pub title: String,
    pub description: String,
    pub timestamp: SystemTime,
    pub implemented_by: String,
    pub approved_by: Option<String>,
    pub affected_services: Vec<String>,
    pub affected_components: Vec<String>,
    pub risk_level: f64, // 0.0 - 1.0
    pub rollback_plan: Option<String>,
}
/// 因果关系类型
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum CausalityType {
    Causes,         // 直接导致
    ContributesTo,  // 促成
    CorrelatesWith, // 相关
    Precedes,       // 先于
    Blocks,         // 阻塞
}
/// 因果关系
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Causality {
    pub cause_id: String,
    pub effect_id: String,
    pub causality_type: CausalityType,
    pub strength: f64,   // 0.0 - 1.0
    pub confidence: f64, // 0.0 - 1.0
    pub evidence: Vec<String>,
}
/// 根因分析结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RootCauseAnalysis {
    pub incident_id: String,
    pub root_causes: Vec<RootCause>,
    pub contributing_factors: Vec<ContributingFactor>,
    pub confidence_score: f64,
    pub analysis_timestamp: SystemTime,
    pub recommendations: Vec<String>,
    pub related_changes: Vec<Change>,
}
/// 根因
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RootCause {
    pub cause_id: String,
    pub cause_type: String,
    pub description: String,
    pub evidence: Vec<String>,
    pub confidence: f64,
    pub impact_score: f64,
}
/// 促成因素
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ContributingFactor {
    pub factor_id: String,
    pub description: String,
    pub contribution_level: f64,
    pub related_components: Vec<String>,
}
/// 变更影响分析结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ChangeImpactAnalysis {
    pub change_id: String,
    pub risk_level: f64,
    pub potential_impacts: Vec<Impact>,
    pub mitigation_strategies: Vec<String>,
    pub rollback_strategy: Option<String>,
}
/// 影响
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Impact {
    pub impact_type: IncidentType,
    pub affected_services: Vec<String>,
    pub probability: f64,
    pub severity: IncidentSeverity,
}
/// 事件收集器
#[derive(Debug)]
pub struct EventCollector {
    incidents: Arc<RwLock<Vec<Incident>>>,
    changes: Arc<RwLock<Vec<Change>>>,
}
impl EventCollector {
    pub fn new() -> Self {
        Self {
            incidents: Arc::new(RwLock::new(Vec::new())),
            changes: Arc::new(RwLock::new(Vec::new())),
        }
    }
    /// 收集事件
    pub async fn collect_incident(
        &self,
        incident: Incident,
    ) -> Result<(), Box<dyn std::error::Error>> {
        let mut incidents = self.incidents.write().await;
        incidents.push(incident);
        Ok(())
    }
    /// 收集变更
    pub async fn collect_change(&self, change: Change) -> Result<(), Box<dyn std::error::Error>> {
        let mut changes = self.changes.write().await;
        changes.push(change);
        Ok(())
    }
    /// 获取相关事件
    pub async fn get_related_events(
        &self,
        service: &str,
        timeframe: Duration,
    ) -> Result<(Vec<Incident>, Vec<Change>), Box<dyn std::error::Error>> {
        let cutoff_time: _ = SystemTime::now() - timeframe;
        let service_str: _ = service.to_string();
        let incidents: _ = self.incidents.read().await;
        let changes: _ = self.changes.read().await;
        let related_incidents: Vec<Incident> = incidents
            .iter()
            .filter(|i| {
                i.timestamp > cutoff_time
                    && (i.affected_services.contains(&service_str)
                        || i.affected_components.contains(&service_str))
            })
            .cloned()
            .collect();
        let related_changes: Vec<Change> = changes
            .iter()
            .filter(|c| {
                c.timestamp > cutoff_time
                    && (c.affected_services.contains(&service_str)
                        || c.affected_components.contains(&service_str))
            })
            .cloned()
            .collect();
        Ok((related_incidents, related_changes))
    }
}
impl Default for EventCollector {
    fn default() -> Self {
        Self::new()
    }
}
/// 变更关联器
#[derive(Debug)]
pub struct ChangeCorrelator {
    event_collector: Arc<EventCollector>,
}
impl ChangeCorrelator {
    pub fn new(event_collector: Arc<EventCollector>) -> Self {
        Self { event_collector }
    }
    /// 关联变更和事件
    pub async fn correlate_changes_with_incidents(
        &self,
        timeframe: Duration,
    ) -> Result<Vec<(Change, Incident)>, Box<dyn std::error::Error>> {
        let (incidents, changes) = self.get_all_events().await?;
        let mut correlations = Vec::new();
        for change in &changes {
            for incident in &incidents {
                // 检查时间和组件重叠
                if self.has_temporal_correlation(change, incident, &timeframe)?
                    && self.has_component_overlap(change, incident)
                {
                    correlations.push((change.clone(), incident.clone()));
                }
            }
        }
        Ok(correlations)
    }
    /// 查找可疑变更
    pub async fn find_suspect_changes(
        &self,
        incident: &Incident,
        timeframe: Duration,
    ) -> Result<Vec<Change>, Box<dyn std::error::Error>> {
        let (incidents, changes) = self.get_all_events().await?;
        let mut suspect_changes = Vec::new();
        for change in &changes {
            // 检查变更是否在事件发生前
            if change.timestamp < incident.timestamp
                && incident
                    .timestamp
                    .duration_since(change.timestamp)
                    .unwrap_or_default()
                    < timeframe
                && self.has_component_overlap(change, incident)
            {
                // 计算可疑度分数
                let suspicion_score: _ = self.calculate_suspicion_score(change, incident);
                if suspicion_score > 0.5 {
                    suspect_changes.push(change.clone());
                }
            }
        }
        Ok(suspect_changes)
    }
    fn has_temporal_correlation(
        &self,
        change: &Change,
        incident: &Incident,
        timeframe: &Duration,
    ) -> Result<bool, Box<dyn std::error::Error>> {
        let time_diff: _ = if change.timestamp < incident.timestamp {
            incident
                .timestamp
                .duration_since(change.timestamp)
                .unwrap_or_default()
        } else {
            change
                .timestamp
                .duration_since(incident.timestamp)
                .unwrap_or_default()
        };
        Ok(time_diff <= *timeframe)
    }
    fn has_component_overlap(&self, change: &Change, incident: &Incident) -> bool {
        let change_components: HashSet<&str> = change
            .affected_services
            .iter()
            .chain(change.affected_components.iter())
            .map(|s| s.as_str())
            .collect();
        let incident_components: HashSet<&str> = incident
            .affected_services
            .iter()
            .chain(incident.affected_components.iter())
            .map(|s| s.as_str())
            .collect();
        !change_components.is_disjoint(&incident_components)
    }
    fn calculate_suspicion_score(&self, change: &Change, incident: &Incident) -> f64 {
        let mut score = 0.0;
        // 基于变更类型打分
        score += match change.change_type {
            ChangeType::Configuration => 0.3,
            ChangeType::Code => 0.4,
            ChangeType::Database => 0.5,
            ChangeType::Infrastructure => 0.6,
            ChangeType::Deployment => 0.4,
            ChangeType::Production => 0.7,
        };
        // 基于风险级别加分
        score += change.risk_level * 0.3;
        // 基于时间接近度加分
        let time_diff: _ = incident
            .timestamp
            .duration_since(change.timestamp)
            .unwrap_or_default();
        if time_diff.as_secs() < 3600 {
            // 1小时内
            score += 0.2;
        } else if time_diff.as_secs() < 86400 {
            // 1天内
            score += 0.1;
        }
        score.min(1.0)
    }
    async fn get_all_events(
        &self,
    ) -> Result<(Vec<Incident>, Vec<Change>), Box<dyn std::error::Error>> {
        let incidents: _ = self.event_collector.incidents.read().await;
        let changes: _ = self.event_collector.changes.read().await;
        Ok((incidents.clone(), changes.clone()))
    }
}
/// 因果推断引擎
#[derive(Debug)]
pub struct CausalInferenceEngine {
    causal_graph: Arc<RwLock<HashMap<String, Vec<Causality>>>>,
}
impl CausalInferenceEngine {
    pub fn new() -> Self {
        Self {
            causal_graph: Arc::new(RwLock::new(HashMap::new())),
        }
    }
    /// 构建因果图
    pub async fn build_causal_graph(
        &self,
        events: &[Incident],
    ) -> Result<(), Box<dyn std::error::Error>> {
        let mut graph = self.causal_graph.write().await;
        // 清除现有图
        graph.clear();
        // 分析事件间的因果关系
        for (i, event1) in events.iter().enumerate() {
            for event2 in events.iter().skip(i + 1) {
                if let Some(causality) = self.infer_causality(event1, event2)? {
                    graph
                        .entry(event1.id.clone())
                        .or_insert_with(Vec::new)
                        .push(causality);
                }
            }
        }
        Ok(())
    }
    /// 推断因果关系
    fn infer_causality(
        &self,
        event1: &Incident,
        event2: &Incident,
    ) -> Result<Option<Causality>, Box<dyn std::error::Error>> {
        // 检查时间顺序
        if event1.timestamp >= event2.timestamp {
            return Ok(None);
        }
        let time_diff: _ = event2
            .timestamp
            .duration_since(event1.timestamp)
            .unwrap_or_default();
        // 检查组件重叠
        let overlap: _ = self.calculate_component_overlap(event1, event2);
        // 如果有时间关系和组件重叠，推断因果关系
        if time_diff.as_secs() < 3600 && overlap > 0.3 {
            // 1小时内且有组件重叠
            Ok(Some(Causality {
                cause_id: event1.id.clone(),
                effect_id: event2.id.clone(),
                causality_type: CausalityType::Causes,
                strength: overlap,
                confidence: (1.0 - (time_diff.as_secs() as f64 / 3600.0)).min(1.0),
                evidence: vec![
                    format!("时间间隔: {} 秒", time_diff.as_secs()),
                    format!("组件重叠度: {:.2}", overlap),
                ],
            }))
        } else {
            Ok(None)
        }
    }
    fn calculate_component_overlap(&self, event1: &Incident, event2: &Incident) -> f64 {
        let components1: HashSet<&str> = event1
            .affected_services
            .iter()
            .chain(event1.affected_components.iter())
            .map(|s| s.as_str())
            .collect();
        let components2: HashSet<&str> = event2
            .affected_services
            .iter()
            .chain(event2.affected_components.iter())
            .map(|s| s.as_str())
            .collect();
        let intersection_size: _ = components1.intersection(&components2).count();
        let union_size: _ = components1.union(&components2).count();
        if union_size == 0 {
            0.0
        } else {
            intersection_size as f64 / union_size as f64
        }
    }
    /// 推断根因
    pub async fn infer_root_causes(
        &self,
        incident: &Incident,
    ) -> Result<Vec<RootCause>, Box<dyn std::error::Error>> {
        let graph: _ = self.causal_graph.read().await;
        // 查找可能导致此事件的原因
        let mut root_causes = Vec::new();
        for (cause_id, causalities) in graph.iter() {
            for causality in causalities {
                if causality.effect_id == incident.id {
                    root_causes.push(RootCause {
                        cause_id: cause_id.clone(),
                        cause_type: "event".to_string(),
                        description: format!("Event {} contributed to incident", cause_id),
                        evidence: causality.evidence.clone(),
                        confidence: causality.confidence,
                        impact_score: causality.strength,
                    });
                }
            }
        }
        Ok(root_causes)
    }
}
impl Default for CausalInferenceEngine {
    fn default() -> Self {
        Self::new()
    }
}
/// 根因分析器主类
#[derive(Debug)]
pub struct RootCauseAnalyzer {
    event_collector: Arc<EventCollector>,
    causal_inference_engine: Arc<CausalInferenceEngine>,
    change_correlator: Arc<ChangeCorrelator>,
}
impl RootCauseAnalyzer {
    pub fn new() -> Self {
        let event_collector: _ = Arc::new(EventCollector::new());
        Self {
            event_collector: event_collector.clone(),
            causal_inference_engine: Arc::new(CausalInferenceEngine::new()),
            change_correlator: Arc::new(ChangeCorrelator::new(event_collector)),
        }
    }
    /// 分析根因
    pub async fn analyze_root_cause(
        &self,
        incident: &Incident,
    ) -> Result<RootCauseAnalysis, Box<dyn std::error::Error>> {
        // 收集相关事件
        let timeframe: _ = Duration::from_secs(86400); // 24小时
        let (related_incidents, related_changes) = self
            .event_collector
            .get_related_events(&incident.affected_services[0], timeframe)
            .await?;
        // 构建因果图
        let all_events: _ = vec![incident.clone()]
            .into_iter()
            .chain(related_incidents.into_iter())
            .collect::<Vec<_>>();
        self.causal_inference_engine
            .build_causal_graph(&all_events)
            .await?;
        // 推断根因
        let root_causes: _ = self
            .causal_inference_engine
            .infer_root_causes(incident)
            .await?;
        // 查找促成因素
        let contributing_factors: _ = self
            .identify_contributing_factors(incident, &related_changes)
            .await?;
        // 计算置信度
        let confidence_score: _ =
            self.calculate_confidence_score(&root_causes, &contributing_factors)?;
        // 关联变更
        let correlated_changes: _ = self
            .change_correlator
            .find_suspect_changes(incident, timeframe)
            .await?;
        // 生成建议
        let recommendations: _ =
            self.generate_recommendations(&root_causes, &contributing_factors)?;
        Ok(RootCauseAnalysis {
            incident_id: incident.id.clone(),
            root_causes,
            contributing_factors,
            confidence_score,
            analysis_timestamp: SystemTime::now(),
            recommendations,
            related_changes: correlated_changes,
        })
    }
    /// 分析变更影响
    pub async fn analyze_change_impact(
        &self,
        change: &Change,
    ) -> Result<ChangeImpactAnalysis, Box<dyn std::error::Error>> {
        let mut potential_impacts = Vec::new();
        // 基于变更类型推断潜在影响
        match change.change_type {
            ChangeType::Configuration => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::ServiceOutage,
                    affected_services: change.affected_services.clone(),
                    probability: 0.3,
                    severity: IncidentSeverity::Medium,
                });
            }
            ChangeType::Code => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::HighLatency,
                    affected_services: change.affected_services.clone(),
                    probability: 0.4,
                    severity: IncidentSeverity::Medium,
                });
            }
            ChangeType::Database => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::DatabaseIssue,
                    affected_services: change.affected_services.clone(),
                    probability: 0.5,
                    severity: IncidentSeverity::High,
                });
            }
            ChangeType::Infrastructure => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::ServiceOutage,
                    affected_services: change.affected_services.clone(),
                    probability: 0.6,
                    severity: IncidentSeverity::High,
                });
            }
            ChangeType::Deployment => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::DeploymentFailure,
                    affected_services: change.affected_services.clone(),
                    probability: 0.4,
                    severity: IncidentSeverity::Medium,
                });
            }
            ChangeType::Production => {
                potential_impacts.push(Impact {
                    impact_type: IncidentType::ServiceOutage,
                    affected_services: change.affected_services.clone(),
                    probability: 0.7,
                    severity: IncidentSeverity::Critical,
                });
            }
        }
        let mitigation_strategies: _ = vec![
            "在非生产环境充分测试".to_string(),
            "准备快速回滚方案".to_string(),
            "分阶段部署".to_string(),
            "监控关键指标".to_string(),
        ];
        Ok(ChangeImpactAnalysis {
            change_id: change.id.clone(),
            risk_level: change.risk_level,
            potential_impacts,
            mitigation_strategies,
            rollback_strategy: change.rollback_plan.clone(),
        })
    }
    /// 识别促成因素
    async fn identify_contributing_factors(
        &self,
        incident: &Incident,
        changes: &[Change],
    ) -> Result<Vec<ContributingFactor>, Box<dyn std::error::Error>> {
        let mut factors = Vec::new();
        // 检查最近的变更作为促成因素
        for change in changes {
            if change.timestamp > incident.timestamp - Duration::from_secs(7200) {
                // 2小时内
                factors.push(ContributingFactor {
                    factor_id: change.id.clone(),
                    description: format!("Recent change: {}", change.title),
                    contribution_level: 0.5,
                    related_components: change.affected_services.clone(),
                });
            }
        }
        Ok(factors)
    }
    /// 计算置信度分数
    fn calculate_confidence_score(
        &self,
        root_causes: &[RootCause],
        contributing_factors: &[ContributingFactor],
    ) -> Result<f64, Box<dyn std::error::Error>> {
        if root_causes.is_empty() {
            return Ok(0.0);
        }
        let avg_confidence: f64 =
            root_causes.iter().map(|rc| rc.confidence).sum::<f64>() / root_causes.len() as f64;
        let factor_bonus: _ = (contributing_factors.len() as f64 * 0.1).min(0.3);
        Ok((avg_confidence + factor_bonus).min(1.0))
    }
    /// 生成建议
    fn generate_recommendations(
        &self,
        root_causes: &[RootCause],
        contributing_factors: &[ContributingFactor],
    ) -> Result<Vec<String>, Box<dyn std::error::Error>> {
        let mut recommendations = Vec::new();
        for root_cause in root_causes {
            if root_cause.cause_type == "event" {
                recommendations.push(format!(
                    "Investigate root cause event: {}",
                    root_cause.cause_id
                ));
            }
        }
        for factor in contributing_factors {
            recommendations.push(format!(
                "Address contributing factor: {}",
                factor.description
            ));
        }
        // 通用建议
        recommendations.push("Implement monitoring and alerting".to_string());
        recommendations.push("Review and update incident response procedures".to_string());
        recommendations.push("Consider chaos engineering practices".to_string());
        Ok(recommendations)
    }
}
impl Default for RootCauseAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}