大型电商测试基础架构案例

/**
 * 全链路压测脚本（JMeter Java Sampler）
 * 模拟：浏览商品 → 加购物车 → 下单 → 支付
 */
public class FullLinkPressureTest extends AbstractJavaSamplerClient {
 
    @Override
    public SampleResult runTest(JavaSamplerContext context) {
        SampleResult result = new SampleResult();
        result.sampleStart();
 
        try {
            String userId = generatePressureTestUserId();  // 压测用户 ID
            String token = login(userId);
 
            // 1. 浏览商品详情
            String skuId = randomSkuId();
            HttpResponse detailResp = httpGet("/api/skus/" + skuId, token, true);
            assertSuccess(detailResp);
 
            // 2. 加入购物车
            HttpResponse cartResp = httpPost("/api/cart/add",
                jsonBody(Map.of("skuId", skuId, "qty", 1)), token, true);
            assertSuccess(cartResp);
 
            // 3. 创建订单
            HttpResponse orderResp = httpPost("/api/orders",
                jsonBody(Map.of("items", List.of(Map.of("skuId", skuId, "qty", 1)),
                    "addressId", "test-addr-001")), token, true);
            assertSuccess(orderResp);
 
            String orderId = parseJson(orderResp.getBody(), "id");
 
            // 4. 支付（模拟支付回调）
            HttpResponse payResp = httpPost("/api/orders/" + orderId + "/pay",
                jsonBody(Map.of("method", "MOCK")), token, true);
            assertSuccess(payResp);
 
            result.setSuccessful(true);
            result.setResponseCode("200");
        } catch (Exception e) {
            result.setSuccessful(false);
            result.setResponseCode("500");
            result.setResponseMessage(e.getMessage());
        } finally {
            result.sampleEnd();
        }
        return result;
    }
 
    /**
     * 压测请求统一添加压测标记 Header
     */
    private HttpResponse httpGet(String path, String token, boolean isPressure) {
        HttpRequest req = HttpRequest.get(BASE_URL + path)
            .header("Authorization", "Bearer " + token);
        if (isPressure) {
            req.header("X-Pressure-Test", "true")
               .header("X-Pressure-User", generatePressureTestUserId());
        }
        return req.execute();
    }
}

# Grafana 仪表盘核心指标面板配置（简化）
dashboards:
  - name: "电商线上质量监控"
    panels:
      - title: "核心链路成功率"
        targets:
          - expr: "sum(rate(http_requests_total{status=~'2..',route=~'order|pay|cart'}[1m])) / sum(rate(http_requests_total{route=~'order|pay|cart'}[1m])) * 100"
        thresholds:
          - value: 99.9
            color: green
          - value: 99.0
            color: yellow
          - value: 95.0
            color: red
 
      - title: "P99 响应延迟（按服务）"
        targets:
          - expr: "histogram_quantile(0.99, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))"
 
      - title: "线上缺陷逃逸率趋势"
        targets:
          - expr: "prod_defects_escape / total_defects * 100"
 
      - title: "全链路压测水位"
        targets:
          - expr: "sum(rate(pressure_test_requests_total[1m])) by (link)"
 
      - title: "金丝雀版本 vs 稳定版本对比"
        targets:
          - expr: "rate(http_errors_total{version='canary'}[5m]) / rate(http_requests_total{version='canary'}[5m])"
          - expr: "rate(http_errors_total{version='stable'}[5m]) / rate(http_requests_total{version='stable'}[5m])"

"""
质量度量周报自动生成
每周一自动汇总上周质量数据，推送至管理群
"""
import datetime
from dataclasses import dataclass
 
@dataclass
class QualityReport:
    week: str
    # CI 效率
    ci_success_rate: float       # CI 成功率
    avg_ci_duration: int         # 平均 CI 耗时（分钟）
    # 测试覆盖
    unit_coverage: float         # 单元测试覆盖率
    api_coverage: float          # API 覆盖率
    e2e_coverage: float          # E2E 路径覆盖率
    # 缺陷质量
    defects_found: int           # 发现缺陷数
    defects_escaped: int         # 逃逸缺陷数
    escape_rate: float           # 逃逸率
    avg_mttr: int                # 平均修复时长（小时）
    # 性能基线
    p99_latency: dict            # 各服务 P99 延迟
    error_rate: float            # 线上错误率
    # 发布效率
    deploy_count: int            # 发布次数
    rollback_count: int          # 回滚次数
    rollback_rate: float         # 回滚率
 
def generate_weekly_report():
    today = datetime.date.today()
    monday = today - datetime.timedelta(days=today.weekday())
    last_monday = monday - datetime.timedelta(days=7)
 
    report = QualityReport(
        week=f"{last_monday} ~ {sunday}",
        ci_success_rate=query_ci_success_rate(last_monday, sunday),
        avg_ci_duration=query_avg_ci_duration(last_monday, sunday),
        unit_coverage=query_coverage("unit", last_monday, sunday),
        api_coverage=query_coverage("api", last_monday, sunday),
        e2e_coverage=query_coverage("e2e", last_monday, sunday),
        defects_found=query_defects("found", last_monday, sunday),
        defects_escaped=query_defects("escaped", last_monday, sunday),
        escape_rate=calculate_escape_rate(last_monday, sunday),
        avg_mttr=query_mttr(last_monday, sunday),
        p99_latency=query_p99_by_service(last_monday, sunday),
        error_rate=query_error_rate(last_monday, sunday),
        deploy_count=query_deploy_count(last_monday, sunday),
        rollback_count=query_rollback_count(last_monday, sunday),
        rollback_rate=calculate_rollback_rate(last_monday, sunday),
    )
 
    send_to_slack(format_report(report))
    save_to_influxdb(report)