AI 集成的智能 Profiling 实践：从性能分析到优化闭环

go tool pprof -text <profile_data.pb.gz> > profiling.txt

      flat  flat%   sum%        cum   cum%      50ms  6.41%  6.41%       50ms  6.41%  internal/runtime/syscall.Syscall6      30ms  3.85% 10.26%      130ms 16.67%  runtime.scanobject      20ms  2.56% 30.77%      100ms 12.82%  runtime.mallocgc

def build_prompt(pprof_content, code_snippet):    messages = [        {            "role": "system",            "content": "You are a professional Go performance optimization expert skilled in analyzing pprof data to identify bottlenecks and provide actionable solutions."        },        {            "role": "user",            "content": f"""            Please help me analyze the following Go program's CPU profiling report and identify the performance bottlenecks.             Below is the CPU profiling data generated by the go tool pprof:            {pprof_content}            """        }    ]    if code_snippet:        messages.append({            "role": "user",            "content": f"""            **Here is the Go program code.**              Please do the following:            - Identify the problematic code segments based on the profiling report and code snippet.            - Clearly correlate each performance issue with the profiling result (e.g., function name, time percentage).            - Provide optimization suggestions with explanations.            - If possible, show an optimized code snippet with a brief explanation of why it improves performance.            **Code:**            ```go            {code_snippet}

        """    })return messages

      flat   flat%   sum%      cum   cum%     1.50s  13.33%  13.33%   1.50s  13.33%  runtime/internal/syscall.Syscall6     0.44s   3.91%  17.24%   1.54s  13.69%  runtime.mallocgc     0.41s   3.64%  24.71%   1.01s   8.98%  runtime.scanobject     0.18s   1.60%  29.87%   0.43s   3.82%  runtime.mapassign_faststr     0.07s   0.62%  49.16%   1.38s  12.27%  runtime.gcDrain     0.02s   0.18%  60.98%   1.10s   9.78%  example.com/bio.(*SubItemBIO).setSubItemBasicInfo     0.01s  0.089%  63.11%   1.59s  14.13%  database/sql.(*DB).queryDC     0.01s  0.089%  63.56%  1.07s  9.51%  database/sql.withLock         0      0%  67.47%   1.62s  14.40%  database/sql.(*DB).QueryContext         0      0%  67.47%   1.62s  14.40%  database/sql.(*DB).query         0      0%  67.47%   0.69s   6.13%  example.com/bio.(*SubItemBIO).getSubItemAlertInfos         0      0%  67.47%   0.65s   5.78%  example.com/bio.(*SubItemBIO).getSubItemAlertInfos.func1

func (o *SubItemBIO) setSubItemBasicInfo(records ...){  subItems := make([]*SubItem, 0)  for _, subItem := range records {      alertInfos, err := o.getSubItemAlertInfos(subItem)      if err != nil {          return nil, err      }      subItem.AlertInfos = alertInfos      subItems = append(subItems, modelToSubItem(subItem))  }  ...}func (o *SubItemBIO) getSubItemAlertInfos(subItem) {    result := make(map[string]string)     getAlertInfos := func(subItemID, subItem.ProductID, subItem.VariantID int64, alertType string) error {        luAlertInfoQuery := NewQueryBuilder().Build()        // fetch lu alert info        luAlertInfos, err := o.LuAlertInfoApi.FindAll(ctx, luAlertInfoQuery)        luAlertInfoIds := getLuAlertInfoIDs(luAlertInfos)        entityID := getEntityID(subItem.ProductID, subItem.VariantID, alertType)        alertInfoQuery := NewQueryBuilder(subItemID, luAlertInfoIds,entityID).Build()        // fetch alerts        alerts, err := o.AlertInfoApi.FindAll(ctx, alertInfoQuery)        luAlertIDToName := getLuAlertIDToName(luAlertInfos)        for _, alert := range alerts {            alertName := luAlertIDToName[alert.LuAlertID]            result[alertName] = alert        }    }   // Prduct alert infos   if err := getAlertInfos(subItemID, subItem.ProductID, subItem.VariantID, "Product"); err != nil {        ...     }    // Variant alert infos    if err := getAlertInfos(subItemID, subItem.ProductID, subItem.VariantID, "Variant"); err != nil {        ...    }}

      flat  flat%   sum%        cum   cum%      50ms  6.41%  6.41%       50ms  6.41%  internal/runtime/syscall.Syscall6      30ms  3.85% 10.26%      130ms 16.67%  runtime.scanobject      20ms  2.56% 30.77%      100ms 12.82%  runtime.mallocgc      10ms  1.28% 53.85%      300ms 38.46%  example.com/internal/proto/client.(*subItemClient).ListByItemID      10ms  1.28% 79.49%      140ms 17.95%  runtime.gcDrain         0     0%   100%      110ms 14.10%  runtime.schedule

// FetchOrderData 加载主订单数据并处理其下的任务结构func FetchOrderData(ctx context.Context, req RequestInput) error {    data, _ := OrderService.GetByExternalID(ctx, req)    return loadTasksConcurrently(ctx, data.ID)}// loadTasksConcurrently 并发加载所有任务及其下属条目func loadTasksConcurrently(ctx context.Context, orderID int64) error {    tasks, _ := TaskService.ListByOrderID(ctx, orderID)    for _, task := range tasks {        go func(t Task) {            loadItemsConcurrently(ctx, t.ID)        }(task)    }    return nil}// loadItemsConcurrently 并发加载条目并处理其子项信息func loadItemsConcurrently(ctx context.Context, taskID int64) {    items, _ := ItemService.ListByTaskID(ctx, taskID)    for _, item := range items {        go func(it Item) {            _ = SubItemService.ListByItemID(ctx, it.ID)        }(item)    }}