使用Rust与eBPF构建一个深度可观测的Puppeteer自动化框架

端到端（E2E）测试的脆弱性是出了名的。一个依赖 Puppeteer 的自动化套件，其失败可能源于前端代码变更、网络抖动、CDN问题，甚至是底层系统资源的竞争。当一个 page.click() 超时，我们得到的通常只有一个无法提供上下文的堆栈跟踪。问题究竟出在哪里？是DOM加载慢，还是某个关键API请求被阻塞，亦或是系统调用层面出现了异常？传统的日志和应用层指标无法穿透这层迷雾。

我开始构思一个方案：能否在完全不侵入 Puppeteer 脚本和目标应用的前提下，为每一次自动化任务建立一个从浏览器行为到内核系统调用的完整画像？这个想法的核心是需要一个高性能的编排器和一个强大的底层探针。Rust 的安全并发与零成本抽象使其成为编排器的不二之选。而 eBPF，作为内核中的一个安全沙箱，正是那个完美的、非侵入式的底层探針。

为了管理和分析这些复杂的、跨越多个生命周期的自动化任务，我决定引入一个意想不到的模型：Kanban。我们不构建一个UI，而是将Kanban作为一种状态机和分析框架。每个Puppeteer任务都是一张“卡片”，它会流经待处理 (Pending)、浏览器初始化 (Spawning)、脚本执行 (Running)、数据采集 (Tracing)、分析 (Analyzing)和完成 (Done)等列。eBPF采集到的底层数据将作为这张“卡片”流转过程中的关键度量，帮助我们识别整个自动化流水线中的瓶颈。

框架核心设计：Rust编排器与Kanban状态机

我们的目标是构建一个名为 Spectra 的后台服务。它接收任务请求，管理一个Puppeteer进程池，并为每个任务动态加载、挂载eBPF探针，最终将高层事件（Puppeteer操作）与底层踪迹（eBPF事件）关联起来。

首先，定义项目的结构和依赖。

Cargo.toml:

[package]
name = "spectra"
version = "0.1.0"
edition = "2021"

[dependencies]
tokio = { version = "1", features = ["full"] }
aya = { version = "0.11" }
aya-log = "0.1"
anyhow = "1"
log = "0.4"
env_logger = "0.9"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
uuid = { version = "1", features = ["v4"] }

# Our eBPF probes crate
spectra-ebpf = { path = "spectra-ebpf", features = ["user"] }

# A simple way to call out to a JS runner for Puppeteer
# In a real-world scenario, you might use a more robust IPC mechanism or a Rust Puppeteer library
std_ext = "1.0"

整个系统的核心是围绕Task和其状态流转（我们的Kanban模型）来设计的。

src/main.rs:

use anyhow::Result;
use log::{info, warn};
use std::collections::HashMap;
use tokio::sync::mpsc;
use uuid::Uuid;

// Kanban "columns" as states
#[derive(Debug, Clone, PartialEq, Eq)]
pub enum TaskState {
    Pending,
    Spawning,
    Running(u32), // Contains the browser's PID
    Tracing,
    Analyzing,
    Completed,
    Failed(String),
}

// A "card" on our Kanban board
#[derive(Debug, Clone)]
pub struct Task {
    id: Uuid,
    url: String,
    state: TaskState,
    // We will store correlated events here
    events: Vec<String>,
}

impl Task {
    fn new(url: String) -> Self {
        Self {
            id: Uuid::new_v4(),
            url,
            state: TaskState::Pending,
            events: Vec::new(),
        }
    }
}

// Events that drive the state machine
#[derive(Debug)]
pub enum SystemEvent {
    TaskSubmitted(Task),
    BrowserSpawned { task_id: Uuid, pid: u32 },
    PuppeteerScriptFinished { task_id: Uuid, success: bool, output: String },
    KernelTraceReceived { pid: u32, trace_data: String },
    TaskFailed { task_id: Uuid, reason: String },
}


#[tokio::main]
async fn main() -> Result<()> {
    env_logger::init();

    let (tx, mut rx) = mpsc::channel::<SystemEvent>(128);
    let mut tasks = HashMap::<Uuid, Task>::new();

    // Spawn a task submitter for demonstration
    let tx_clone = tx.clone();
    tokio::spawn(async move {
        let task = Task::new("https://example.com".to_string());
        info!("Submitting new task: {}", task.id);
        tx_clone.send(SystemEvent::TaskSubmitted(task)).await.unwrap();
    });
    
    info!("Spectra orchestrator started. Waiting for events...");

    // This is our main event loop, acting as the Kanban board manager
    while let Some(event) = rx.recv().await {
        match event {
            SystemEvent::TaskSubmitted(mut task) => {
                info!("[Task {}] State: Pending -> Spawning", task.id);
                task.state = TaskState::Spawning;
                let task_id = task.id;
                tasks.insert(task_id, task);

                let tx_clone = tx.clone();
                tokio::spawn(async move {
                    // In a real system, this would manage a pool of Puppeteer workers
                    // Here we just spawn a node process
                    // The script `puppeteer_runner.js` must print "PID: <pid>" on its first line
                    let mut cmd = tokio::process::Command::new("node");
                    cmd.arg("puppeteer_runner.js").arg("https://example.com");
                    
                    // Using std_ext to get PID and stdout/stderr easily
                    match std_ext::spawn_command_with_output("node", &["puppeteer_runner.js", "https://example.com"]) {
                        Ok((pid, output_result)) => {
                             tx_clone.send(SystemEvent::BrowserSpawned { task_id, pid }).await.unwrap();
                             let output = output_result.unwrap(); // Wait for process to finish
                             let stderr_string = String::from_utf8_lossy(&output.stderr).to_string();
                             
                             tx_clone.send(SystemEvent::PuppeteerScriptFinished {
                                 task_id,
                                 success: output.status.success(),
                                 output: if output.status.success() { "Success".to_string() } else { stderr_string },
                             }).await.unwrap();
                        }
                        Err(e) => {
                             tx_clone.send(SystemEvent::TaskFailed { task_id, reason: e.to_string() }).await.unwrap();
                        }
                    }
                });
            }
            SystemEvent::BrowserSpawned { task_id, pid } => {
                if let Some(task) = tasks.get_mut(&task_id) {
                    info!("[Task {}] Browser spawned with PID: {}. State: Spawning -> Running/Tracing", task_id, pid);
                    task.state = TaskState::Running(pid);
                    
                    // THIS IS THE CRITICAL STEP: Attach eBPF probes
                    // We'll implement this part next
                }
            }
            SystemEvent::PuppeteerScriptFinished { task_id, success, output } => {
                 if let Some(task) = tasks.get_mut(&task_id) {
                    if success {
                        info!("[Task {}] Script finished. State: Running -> Completed", task_id);
                        task.state = TaskState::Completed;
                    } else {
                        warn!("[Task {}] Script failed: {}. State: Running -> Failed", task_id, output);
                        task.state = TaskState::Failed(output);
                    }
                    info!("[Task {}] Final state: {:?}", task_id, task.state);
                 }
            }
            // ... other event handlers
            _ => {}
        }
    }

    Ok(())
}

这是一个基础的骨架，但它清晰地展示了基于事件驱动和状态机的Kanban流程。任务从提交开始，流转到Spawning状态，此时我们启动一个外部进程来运行Puppeteer脚本。

eBPF探针：深入内核捕获网络事件

现在，最酷的部分来了。我们将创建一个eBPF程序来监控由浏览器进程发起的connect系统调用。这将告诉我们浏览器尝试连接的每一个IP地址和端口，这是调试网络问题的金矿。我们使用aya-rs生态来完成这件事。

spectra-ebpf/src/main.rs:

```rust
#