探索：優雅地實現異步方法的並行化

接上篇通過一個示例形象地理解C# async await 非並行異步、並行異步、並行異步的併發量控制

前些天寫了兩篇關於C# async await異步的博客，
第一篇博客看的人多，點贊評論也多，我想應該都看懂了，比較簡單。
第二篇博客看的人少，點讚的也少，沒有評論。

我很納悶，第二篇博客纔是重點，如此吊炸天的代碼，居然沒人評論。
這個代碼，就是.NET圈的頂級大佬也沒有寫過，爲什麼這麼說，這就要說到C# async await的語法糖：
沒有語法糖，代碼一樣寫，java8沒有語法糖，一樣寫出很厲害的代碼。但有了C# async await語法糖，普通的水平一般的業務程序員，哪怕是菜B，也能寫出高吞吐高性能的代碼，這就是意義！
所以我說頂級大佬沒寫過，因爲他們水平高，腦力好，手段多，自然不需要這麼寫。但普通程序員要那樣寫代碼，麻煩不說，BUG頻出。
標題我用了"探索"這個詞，有沒有更好的實踐，讓小白們都會寫的並行異步的實踐？

ElasticSearch的性能

代碼的實用價值，是查詢es。
最近發現es的性能非常好！先給大家看個控制檯輸出的截圖。服務我是部署在服務器上的，真實環境，不是自己電腦。

379次es查詢，僅需0.185秒，當然耗時會有波動，零點幾秒都是正常的，超過1秒也有可能。
es最怕的是什麼，是慢查詢，是條件複雜的查詢，是範圍查詢。
我的策略是多次精確查詢，這樣可以利用es極高的吞吐能力。

並行異步

既然查詢次數多，單線程或者說同步肯定是不行的，必須並行。
並行代碼，python能寫嗎？java能寫嗎？肯定能啊！
但我前同事寫的python多次查詢es寫的就是同步代碼，爲什麼不併行呢？並行肯定可以寫，但是能不寫就不寫，爲什麼？因爲寫起來複雜，不好寫。你以爲自己技術好，腦力好沒問題，但別人呢？
重點是什麼？不僅要寫並行代碼，還要寫的簡單，不破壞代碼原有邏輯結構。

異步方法

大家都會寫的，用async await就行了，很簡單，放個我寫的，代碼主要是在雙循環中多次異步請求(大致看一下先跳過)：

/// <summary>
/// xxx查詢
/// </summary>
public async Task<List<AccompanyInfo>> Query2(string strStartTime, string strEndTime, int kpCountThreshold, int countThreshold, int distanceThreshold, int timeThreshold, List<PeopleCluster> peopleClusterList)
{
    List<AccompanyInfo> resultList = new List<AccompanyInfo>();
    Stopwatch sw = Stopwatch.StartNew();

    //創建字典
    Dictionary<string, PeopleCluster> clusterIdPeopleDict = new Dictionary<string, PeopleCluster>();
    foreach (PeopleCluster peopleCluster in peopleClusterList)
    {
        foreach (string clusterId in peopleCluster.ClusterIds)
        {
            if (!clusterIdPeopleDict.ContainsKey(clusterId))
            {
                clusterIdPeopleDict.Add(clusterId, peopleCluster);
            }
        }
    }

    int queryCount = 0;
    Dictionary<string, AccompanyInfo> dict = new Dictionary<string, AccompanyInfo>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        List<PeopleFeatureInfo> peopleFeatureList = await ServiceFactory.Get<PeopleFeatureQueryService>().Query(strStartTime, strEndTime, people1);
        queryCount++;
        foreach (PeopleFeatureInfo peopleFeatureInfo1 in peopleFeatureList)
        {
            DateTime capturedTime = DateTime.ParseExact(peopleFeatureInfo1.captured_time, "yyyyMMddHHmmss", CultureInfo.InvariantCulture);
            string strStartTime2 = capturedTime.AddSeconds(-timeThreshold).ToString("yyyyMMddHHmmss");
            string strEndTime2 = capturedTime.AddSeconds(timeThreshold).ToString("yyyyMMddHHmmss");
            List<PeopleFeatureInfo> peopleFeatureList2 = await ServiceFactory.Get<PeopleFeatureQueryService>().QueryExcludeSelf(strStartTime2, strEndTime2, people1);
            queryCount++;
            if (peopleFeatureList2.Count > 0)
            {
                foreach (PeopleFeatureInfo peopleFeatureInfo2 in peopleFeatureList2)
                {
                    string key = null;
                    PeopleCluster people2 = null;
                    string people2ClusterId = null;
                    if (clusterIdPeopleDict.ContainsKey(peopleFeatureInfo2.cluster_id.ToString()))
                    {
                        people2 = clusterIdPeopleDict[peopleFeatureInfo2.cluster_id.ToString()];
                        key = $"{string.Join(",", people1.ClusterIds)}_{string.Join(",", people2.ClusterIds)}";
                    }
                    else
                    {
                        people2ClusterId = peopleFeatureInfo2.cluster_id.ToString();
                        key = $"{string.Join(",", people1.ClusterIds)}_{string.Join(",", people2ClusterId)}";
                    }

                    double distance = LngLatUtil.CalcDistance(peopleFeatureInfo1.Longitude, peopleFeatureInfo1.Latitude, peopleFeatureInfo2.Longitude, peopleFeatureInfo2.Latitude);
                    if (distance > distanceThreshold) continue;

                    AccompanyInfo accompanyInfo;
                    if (dict.ContainsKey(key))
                    {
                        accompanyInfo = dict[key];
                    }
                    else
                    {
                        accompanyInfo = new AccompanyInfo();
                        dict.Add(key, accompanyInfo);
                    }

                    accompanyInfo.People1 = people1;
                    if (people2 != null)
                    {
                        accompanyInfo.People2 = people2;
                    }
                    else
                    {
                        accompanyInfo.ClusterId2 = people2ClusterId;
                    }

                    AccompanyItem accompanyItem = new AccompanyItem();
                    accompanyItem.Info1 = peopleFeatureInfo1;
                    accompanyItem.Info2 = peopleFeatureInfo2;
                    accompanyInfo.List.Add(accompanyItem);

                    accompanyInfo.Count++;

                    resultList.Add(accompanyInfo);
                }
            }
        }
    }

    resultList = resultList.FindAll(a => (a.People2 != null && a.Count >= kpCountThreshold) || a.Count >= countThreshold);

    //去重
    int beforeDistinctCount = resultList.Count;
    resultList = resultList.DistinctBy(a =>
    {
        string str1 = string.Join(",", a.People1.ClusterIds);
        string str2 = a.People2 != null ? string.Join(",", a.People2.ClusterIds) : string.Empty;
        string str3 = a.ClusterId2 ?? string.Empty;
        StringBuilder sb = new StringBuilder();
        foreach (AccompanyItem item in a.List)
        {
            var info2 = item.Info2;
            sb.Append($"{info2.camera_id},{info2.captured_time},{info2.cluster_id}");
        }
        return $"{str1}_{str2}_{str3}_{sb}";
    }).ToList();

    sw.Stop();
    string msg = $"xxx查詢，耗時：{sw.Elapsed.TotalSeconds:0.000} 秒，查詢次數：{queryCount}，去重：{beforeDistinctCount}-->{resultList.Count}";
    Console.WriteLine(msg);
    LogUtil.Info(msg);

    return resultList;
}

異步方法的並行化

上述代碼是沒有問題的，但也有問題。就是在雙循環中多次請求，雖然用了async await，但不是並行，耗時會很長，如何優化？請看如下代碼：

/// <summary>
/// xxx查詢
/// </summary>
public async Task<List<AccompanyInfo>> Query(string strStartTime, string strEndTime, int kpCountThreshold, int countThreshold, int distanceThreshold, int timeThreshold, List<PeopleCluster> peopleClusterList)
{
    List<AccompanyInfo> resultList = new List<AccompanyInfo>();
    Stopwatch sw = Stopwatch.StartNew();

    //創建字典
    Dictionary<string, PeopleCluster> clusterIdPeopleDict = new Dictionary<string, PeopleCluster>();
    foreach (PeopleCluster peopleCluster in peopleClusterList)
    {
        foreach (string clusterId in peopleCluster.ClusterIds)
        {
            if (!clusterIdPeopleDict.ContainsKey(clusterId))
            {
                clusterIdPeopleDict.Add(clusterId, peopleCluster);
            }
        }
    }

    //組織第一層循環task
    Dictionary<PeopleCluster, Task<List<PeopleFeatureInfo>>> tasks1 = new Dictionary<PeopleCluster, Task<List<PeopleFeatureInfo>>>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        var task1 = ServiceFactory.Get<PeopleFeatureQueryService>().Query(strStartTime, strEndTime, people1);
        tasks1.Add(people1, task1);
    }

    //計算第一層循環task並緩存結果，組織第二層循環task
    Dictionary<string, Task<List<PeopleFeatureInfo>>> tasks2 = new Dictionary<string, Task<List<PeopleFeatureInfo>>>();
    Dictionary<PeopleCluster, List<PeopleFeatureInfo>> cache1 = new Dictionary<PeopleCluster, List<PeopleFeatureInfo>>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        List<PeopleFeatureInfo> peopleFeatureList = await tasks1[people1];
        cache1.Add(people1, peopleFeatureList);
        foreach (PeopleFeatureInfo peopleFeatureInfo1 in peopleFeatureList)
        {
            DateTime capturedTime = DateTime.ParseExact(peopleFeatureInfo1.captured_time, "yyyyMMddHHmmss", CultureInfo.InvariantCulture);
            string strStartTime2 = capturedTime.AddSeconds(-timeThreshold).ToString("yyyyMMddHHmmss");
            string strEndTime2 = capturedTime.AddSeconds(timeThreshold).ToString("yyyyMMddHHmmss");
            var task2 = ServiceFactory.Get<PeopleFeatureQueryService>().QueryExcludeSelf(strStartTime2, strEndTime2, people1);
            string task2Key = $"{strStartTime2}_{strEndTime2}_{string.Join(",", people1.ClusterIds)}";
            tasks2.TryAdd(task2Key, task2);
        }
    }

    //讀取第一層循環task緩存結果，計算第二層循環task
    Dictionary<string, AccompanyInfo> dict = new Dictionary<string, AccompanyInfo>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        List<PeopleFeatureInfo> peopleFeatureList = cache1[people1];
        foreach (PeopleFeatureInfo peopleFeatureInfo1 in peopleFeatureList)
        {
            DateTime capturedTime = DateTime.ParseExact(peopleFeatureInfo1.captured_time, "yyyyMMddHHmmss", CultureInfo.InvariantCulture);
            string strStartTime2 = capturedTime.AddSeconds(-timeThreshold).ToString("yyyyMMddHHmmss");
            string strEndTime2 = capturedTime.AddSeconds(timeThreshold).ToString("yyyyMMddHHmmss");
            string task2Key = $"{strStartTime2}_{strEndTime2}_{string.Join(",", people1.ClusterIds)}";
            List<PeopleFeatureInfo> peopleFeatureList2 = await tasks2[task2Key];
            if (peopleFeatureList2.Count > 0)
            {
                foreach (PeopleFeatureInfo peopleFeatureInfo2 in peopleFeatureList2)
                {
                    string key = null;
                    PeopleCluster people2 = null;
                    string people2ClusterId = null;
                    if (clusterIdPeopleDict.ContainsKey(peopleFeatureInfo2.cluster_id.ToString()))
                    {
                        people2 = clusterIdPeopleDict[peopleFeatureInfo2.cluster_id.ToString()];
                        key = $"{string.Join(",", people1.ClusterIds)}_{string.Join(",", people2.ClusterIds)}";
                    }
                    else
                    {
                        people2ClusterId = peopleFeatureInfo2.cluster_id.ToString();
                        key = $"{string.Join(",", people1.ClusterIds)}_{string.Join(",", people2ClusterId)}";
                    }

                    double distance = LngLatUtil.CalcDistance(peopleFeatureInfo1.Longitude, peopleFeatureInfo1.Latitude, peopleFeatureInfo2.Longitude, peopleFeatureInfo2.Latitude);
                    if (distance > distanceThreshold) continue;

                    AccompanyInfo accompanyInfo;
                    if (dict.ContainsKey(key))
                    {
                        accompanyInfo = dict[key];
                    }
                    else
                    {
                        accompanyInfo = new AccompanyInfo();
                        dict.Add(key, accompanyInfo);
                    }

                    accompanyInfo.People1 = people1;
                    if (people2 != null)
                    {
                        accompanyInfo.People2 = people2;
                    }
                    else
                    {
                        accompanyInfo.ClusterId2 = people2ClusterId;
                    }

                    AccompanyItem accompanyItem = new AccompanyItem();
                    accompanyItem.Info1 = peopleFeatureInfo1;
                    accompanyItem.Info2 = peopleFeatureInfo2;
                    accompanyInfo.List.Add(accompanyItem);

                    accompanyInfo.Count++;

                    resultList.Add(accompanyInfo);
                }
            }
        }
    }

    resultList = resultList.FindAll(a => (a.People2 != null && a.Count >= kpCountThreshold) || a.Count >= countThreshold);

    //去重
    int beforeDistinctCount = resultList.Count;
    resultList = resultList.DistinctBy(a =>
    {
        string str1 = string.Join(",", a.People1.ClusterIds);
        string str2 = a.People2 != null ? string.Join(",", a.People2.ClusterIds) : string.Empty;
        string str3 = a.ClusterId2 ?? string.Empty;
        StringBuilder sb = new StringBuilder();
        foreach (AccompanyItem item in a.List)
        {
            var info2 = item.Info2;
            sb.Append($"{info2.camera_id},{info2.captured_time},{info2.cluster_id}");
        }
        return $"{str1}_{str2}_{str3}_{sb}";
    }).ToList();

    //排序
    foreach (AccompanyInfo item in resultList)
    {
        item.List.Sort((a, b) => -string.Compare(a.Info1.captured_time, b.Info1.captured_time));
    }

    sw.Stop();
    string msg = $"xxx查詢，耗時：{sw.Elapsed.TotalSeconds:0.000} 秒，查詢次數：{tasks1.Count + tasks2.Count}，去重：{beforeDistinctCount}-->{resultList.Count}";
    Console.WriteLine(msg);
    LogUtil.Info(msg);

    return resultList;
}

上述代碼說明

爲了使異步並行化，雙循環要寫三遍。第一遍只需寫第一層循環，省了第二層。第二遍沒有數據處理的那層子循環。第三遍是最全的。
和普通的異步相比，第一、二遍雙循環是多出來的，第三遍雙循環代碼結構和普通的異步代碼結構是一樣的。
寫的時候，可以先寫好普通的異步方法，然後再改造成並行化的異步方法。

你爲什麼說.NET圈的頂級大佬沒有寫過？

不吹個牛，博客沒人看，沒人點贊啊？！
牛B的是C#，由於C#語法糖，把牛B的代碼寫簡單了，纔是真的牛B。
我倒是希望有大佬寫個更好的實踐，把我這種寫法淘汰掉，因爲這是我能想到的最容易控制的寫法了。
並行代碼，很多人都會寫，java、python也能寫，但問題是，比較菜的普通業務程序員，如何無腦寫這種並行代碼？
最差的寫法，例如java的CompletableFuture，結合業務代碼，寫法就很複雜了。真的沒法無腦寫。
其次的寫法，例如：

List<PeopleFeatureInfo>[] listArray = await Task.WhenAll(tasks2.Values);

在雙循環體中，怎麼拿結果？肯定能寫，但又要思考怎麼寫了不是？
而我的寫法，在雙循環體中是可以直接拿結果的：

List<PeopleFeatureInfo> list = await tasks2[task2Key];

只放C#代碼沒有說服力，我一個同事python寫的很6，他寫的挖掘代碼很多都是並行，放一段代碼：

def get_es_multiprocess(index_list, people_list, core_percent, rev_clusterid_idcard_dict):
    '''
    多進程讀取es數據，轉爲整個數據幀，按時間排序
    :return: 規模較大的數據幀
    '''
    col_list = ["cluster_id", "camera_id", "captured_time"]
    pool = Pool(processes=int(mp.cpu_count() * core_percent))
    input_list = [(i, people_list, col_list) for i in index_list]
    res = pool.map(get_es, input_list)
    if not res:
        return None
    pool.close()
    pool.join()
    df_all = pd.DataFrame(columns=col_list+['longitude', 'latitude'])
    for df in res:
        df_all = pd.concat([df_all, df])
    # 這裏強制轉換爲字符串！
    df_all['cluster_id_'] = df_all['cluster_id'].apply(lambda x: rev_clusterid_idcard_dict[str(x)])
    del df_all['cluster_id']
    df_all.rename(columns={'cluster_id_': 'cluster_id'}, inplace=True)
    df_all.sort_values(by='captured_time', inplace=True)
    print('=' * 100)
    print('整個數據（聚類前）：')
    print(df_all.info())
    cluster_id_list = [(i, df) for i, df in df_all.groupby(['cluster_id'])]
    cluster_id_list_split = [j for j in func(cluster_id_list, 1000000)]
    # todo 縮小數據集,用於調試！
    data_all = df_all.iloc[:, :]
    return data_all, cluster_id_list_split

上述python代碼解析

核心代碼：

res = pool.map(get_es, input_list)
pool.join()
...省略

其中get_es是查詢es的方法，他寫的應該不是異步方法，不過這個不是重點
2. res是查詢結果，通過並行的方式一把查出來，放到res中，然後把結果再解出來
3. 注意，這只是單循環，想想雙層循環怎麼寫
4. pool.join()是阻塞當前線程的，這個不好
5. 同事註釋中寫的是"多進程"，是寫錯了嗎？實際是多線程？還是就是多進程？
6. 當然，python是有async await異步寫法的，應該不比C#差，只是同事沒有用，可能是因爲他用的python版本不夠新
7. python代碼，字符串太多，字符串是最不好維護的。C#中的字符串裏面都是強類型。

把腦力活變成體力活

照葫蘆畫瓢，把腦力活變成體力活，我又寫了一個方法(業務邏輯不重要，看並行異步的使用)：

/// <summary>
/// xxx查詢
/// </summary>
public async Task<List<SameVehicleInfo>> Query(string strStartTime, string strEndTime, int kpCountThreshold, int timeThreshold, List<PeopleCluster> peopleClusterList)
{
    List<SameVehicleInfo> resultList = new List<SameVehicleInfo>();
    Stopwatch sw = Stopwatch.StartNew();

    //組織第一層循環task，查xxx
    Dictionary<PeopleCluster, Task<List<PeopleFeatureInfo>>> tasks1 = new Dictionary<PeopleCluster, Task<List<PeopleFeatureInfo>>>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        var task1 = ServiceFactory.Get<PeopleFeatureQueryService>().Query(strStartTime, strEndTime, people1);
        tasks1.Add(people1, task1);
    }

    //計算第一層循環task並緩存結果，組織第二層循環task，精確搜xxx
    Dictionary<string, Task<List<MotorVehicleInfo>>> tasks2 = new Dictionary<string, Task<List<MotorVehicleInfo>>>();
    Dictionary<PeopleCluster, List<PeopleFeatureInfo>> cache1 = new Dictionary<PeopleCluster, List<PeopleFeatureInfo>>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        List<PeopleFeatureInfo> peopleFeatureList = await tasks1[people1];
        cache1.Add(people1, peopleFeatureList);
        foreach (PeopleFeatureInfo peopleFeatureInfo1 in peopleFeatureList)
        {
            string task2Key = $"{peopleFeatureInfo1.camera_id}_{peopleFeatureInfo1.captured_time}";
            var task2 = ServiceFactory.Get<MotorVehicleQueryService>().QueryExact(peopleFeatureInfo1.camera_id, peopleFeatureInfo1.captured_time);
            tasks2.TryAdd(task2Key, task2);
        }
    }

    //讀取第一層循環task緩存結果，計算第二層循環task
    Dictionary<PersonVehicleKey, PersonVehicleInfo> dictPersonVehicle = new Dictionary<PersonVehicleKey, PersonVehicleInfo>();
    foreach (PeopleCluster people1 in peopleClusterList)
    {
        List<PeopleFeatureInfo> peopleFeatureList = cache1[people1];
        foreach (PeopleFeatureInfo peopleFeatureInfo1 in peopleFeatureList)
        {
            string task2Key = $"{peopleFeatureInfo1.camera_id}_{peopleFeatureInfo1.captured_time}";
            List<MotorVehicleInfo> motorVehicleList = await tasks2[task2Key];
            motorVehicleList = motorVehicleList.DistinctBy(a => a.plate_no).ToList();
            foreach (MotorVehicleInfo motorVehicleInfo in motorVehicleList)
            {
                PersonVehicleKey key = new PersonVehicleKey(people1, motorVehicleInfo.plate_no);
                PersonVehicleInfo personVehicleInfo;
                if (dictPersonVehicle.ContainsKey(key))
                {
                    personVehicleInfo = dictPersonVehicle[key];
                }
                else
                {
                    personVehicleInfo = new PersonVehicleInfo()
                    {
                        People = people1,
                        PlateNo = motorVehicleInfo.plate_no,
                        List = new List<PeopleFeatureInfo>()
                    };
                    dictPersonVehicle.Add(key, personVehicleInfo);
                }
                personVehicleInfo.List.Add(peopleFeatureInfo1);
            }
        }
    }

    //比對xxx
    List<PersonVehicleKey> keys = dictPersonVehicle.Keys.ToList();
    Dictionary<string, SameVehicleInfo> dict = new Dictionary<string, SameVehicleInfo>();
    for (int i = 0; i < keys.Count - 1; i++)
    {
        for (int j = i + 1; j < keys.Count; j++)
        {
            var key1 = keys[i];
            var key2 = keys[j];
            var personVehicle1 = dictPersonVehicle[key1];
            var personVehicle2 = dictPersonVehicle[key2];
            if (key1.PlateNo == key2.PlateNo)
            {
                foreach (PeopleFeatureInfo peopleFeature1 in personVehicle1.List)
                {
                    double minTimeDiff = double.MaxValue;
                    int minIndex = -1;
                    for (int k = 0; k < personVehicle2.List.Count; k++)
                    {
                        PeopleFeatureInfo peopleFeature2 = personVehicle2.List[k];
                        DateTime capturedTime1 = DateTime.ParseExact(peopleFeature1.captured_time, "yyyyMMddHHmmss", CultureInfo.InvariantCulture);
                        DateTime capturedTime2 = DateTime.ParseExact(peopleFeature2.captured_time, "yyyyMMddHHmmss", CultureInfo.InvariantCulture);
                        var timeDiff = Math.Abs(capturedTime2.Subtract(capturedTime1).TotalSeconds);
                        if (timeDiff < minTimeDiff)
                        {
                            minTimeDiff = timeDiff;
                            minIndex = k;
                        }
                    }
                    if (minIndex >= 0 && minTimeDiff <= timeThreshold * 60)
                    {
                        PeopleCluster people1 = key1.People;
                        PeopleCluster people2 = key2.People;
                        PeopleFeatureInfo peopleFeatureInfo2 = personVehicle2.List[minIndex];

                        string key = $"{string.Join(",", people1.ClusterIds)}_{string.Join(",", people2.ClusterIds)}"; ;

                        SameVehicleInfo accompanyInfo;
                        if (dict.ContainsKey(key))
                        {
                            accompanyInfo = dict[key];
                        }
                        else
                        {
                            accompanyInfo = new SameVehicleInfo();
                            dict.Add(key, accompanyInfo);
                        }

                        accompanyInfo.People1 = people1;
                        accompanyInfo.People2 = people2;

                        SameVehicleItem accompanyItem = new SameVehicleItem();
                        accompanyItem.Info1 = peopleFeature1;
                        accompanyItem.Info2 = peopleFeatureInfo2;
                        accompanyInfo.List.Add(accompanyItem);

                        accompanyInfo.Count++;

                        resultList.Add(accompanyInfo);
                    }
                }
            }
        }
    }

    resultList = resultList.FindAll(a => a.Count >= kpCountThreshold);

    //篩選，排除xxx
    resultList = resultList.FindAll(a =>
    {
        if (string.Join(",", a.People1.ClusterIds) == string.Join(",", a.People2.ClusterIds))
        {
            return false;
        }
        return true;
    });

    //去重
    int beforeDistinctCount = resultList.Count;
    resultList = resultList.DistinctBy(a =>
    {
        string str1 = string.Join(",", a.People1.ClusterIds);
        string str2 = string.Join(",", a.People2.ClusterIds);
        StringBuilder sb = new StringBuilder();
        foreach (SameVehicleItem item in a.List)
        {
            var info2 = item.Info2;
            sb.Append($"{info2.camera_id},{info2.captured_time},{info2.cluster_id}");
        }
        return $"{str1}_{str2}_{sb}";
    }).ToList();

    //排序
    foreach (SameVehicleInfo item in resultList)
    {
        item.List.Sort((a, b) => -string.Compare(a.Info1.captured_time, b.Info1.captured_time));
    }

    sw.Stop();
    string msg = $"xxx查詢，耗時：{sw.Elapsed.TotalSeconds:0.000} 秒，查詢次數：{tasks1.Count + tasks2.Count}，去重：{beforeDistinctCount}-->{resultList.Count}";
    Console.WriteLine(msg);
    LogUtil.Info(msg);

    return resultList;
}

未完，待補充

XXX

我們開發的低代碼平臺很牛B，C#：我就是低代碼！
我們開發的平臺很牛B，支持寫腳本、自定義腳本，C#：我就是腳本！
我們用spark、flink分佈式，性能牛B，C#：並行異步性能吊炸天，內存給大些，單機就可以。C#：我的並行異步的性能，能把es幹掛，只要不是計算密集型，只要內存夠，不用spark、flink，單機簡單啊，只要es是集羣就行了。

探索：優雅地實現異步方法的並行化

ElasticSearch的性能

並行異步

異步方法

異步方法的並行化

上述代碼說明

你爲什麼說.NET圈的頂級大佬沒有寫過？

上述python代碼解析

把腦力活變成體力活

未完，待補充

XXX

京東面試：如何進行JVM調優？

美團一面：項目中有 10000 個 if else 如何優化？想了半天，被問懵了！

Python 將PowerPoint (PPT/PPTX) 轉爲HTML

SQL優化-20231016

java netty 實現 websocket 服務端和客戶端雙向通信實現心跳和斷線重連完整示例

Cesium 飛行路線編輯

js等待某個變量準備就緒

Cesium 根據飛機航線計算飛機的Heading(偏航角)、Pitch(俯仰角)、Roll(翻滾角)

JS日期時間處理庫 Day.js

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

linux以太網驅動總結