探索:優雅地實現非同步方法的並行化

0611163發表於2023-02-09

接上篇 透過一個示例形象地理解C# async await 非並行非同步、並行非同步、並行非同步的併發量控制

前些天寫了兩篇關於C# async await非同步的部落格,
第一篇部落格看的人多,點贊評論也多,我想應該都看懂了,比較簡單。
第二篇部落格看的人少,點讚的也少,沒有評論。

我很納悶,第二篇部落格才是重點,如此吊炸天的程式碼,居然沒人評論。
這個程式碼,就是.NET圈的頂級大佬也沒有寫過,為什麼這麼說,這就要說到C# async await的語法糖:
沒有語法糖,程式碼一樣寫,java8沒有語法糖,一樣寫出很厲害的程式碼。但有了C# async await語法糖,普通的水平一般的業務程式設計師,哪怕是菜B,也能寫出高吞吐高效能的程式碼,這就是意義!
所以我說頂級大佬沒寫過,因為他們水平高,腦力好,手段多,自然不需要這麼寫。但普通程式設計師要那樣寫程式碼,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非同步的實踐?

ElasticSearch的效能

程式碼的實用價值,是查詢es。
最近發現es的效能非常好!先給大家看個控制檯輸出的截圖。服務我是部署在伺服器上的,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。

並行非同步

既然查詢次數多,單執行緒或者說同步肯定是不行的,必須並行。
並行程式碼,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寫的就是同步程式碼,為什麼不併行呢?並行肯定可以寫,但是能不寫就不寫,為什麼?因為寫起來複雜,不好寫。你以為自己技術好,腦力好沒問題,但別人呢?
重點是什麼?不僅要寫並行程式碼,還要寫的簡單,不破壞程式碼原有邏輯結構。

非同步方法

大家都會寫的,用async await就行了,很簡單,放個我寫的,程式碼主要是在雙迴圈中多次非同步請求(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();

    //建立字典
    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;
}

非同步方法的並行化

上述程式碼是沒有問題的,但也有問題。就是在雙迴圈中多次請求,雖然用了async await,但不是並行,耗時會很長,如何最佳化?請看如下程式碼:

/// <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();

    //建立字典
    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);
            }
        }
    }

    //組織第一層迴圈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);
    }

    //計算第一層迴圈task並快取結果,組織第二層迴圈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);
        }
    }

    //讀取第一層迴圈task快取結果,計算第二層迴圈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;
}

上述程式碼說明

  1. 為了使非同步並行化,雙迴圈要寫三遍。第一遍只需寫第一層迴圈,省了第二層。第二遍沒有資料處理的那層子迴圈。第三遍是最全的。
  2. 和普通的非同步相比,第一、二遍雙迴圈是多出來的,第三遍雙迴圈程式碼結構和普通的非同步程式碼結構是一樣的。
  3. 寫的時候,可以先寫好普通的非同步方法,然後再改造成並行化的非同步方法。

你為什麼說.NET圈的頂級大佬沒有寫過?

  1. 不吹個牛,部落格沒人看,沒人點贊啊?!
  2. 牛B的是C#,由於C#語法糖,把牛B的程式碼寫簡單了,才是真的牛B。
  3. 我倒是希望有大佬寫個更好的實踐,把我這種寫法淘汰掉,因為這是我能想到的最容易控制的寫法了。
    並行程式碼,很多人都會寫,java、python也能寫,但問題是,比較菜的普通業務程式設計師,如何無腦寫這種並行程式碼?
    最差的寫法,例如java的CompletableFuture,結合業務程式碼,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);

在雙迴圈體中,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,在雙迴圈體中是可以直接拿結果的:

List<PeopleFeatureInfo> list = await tasks2[task2Key];
  1. 只放C#程式碼沒有說服力,我一個同事python寫的很6,他寫的挖掘程式碼很多都是並行,放一段程式碼:
def get_es_multiprocess(index_list, people_list, core_percent, rev_clusterid_idcard_dict):
    '''
    多程式讀取es資料,轉為整個資料幀,按時間排序
    :return: 規模較大的資料幀
    '''
    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])
    # 這裡強制轉換為字串!
    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('整個資料(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 縮小資料集,用於除錯!
    data_all = df_all.iloc[:, :]
    return data_all, cluster_id_list_split

上述python程式碼解析

  1. 核心程式碼:
res = pool.map(get_es, input_list)
pool.join()
...省略

其中get_es是查詢es的方法,他寫的應該不是非同步方法,不過這個不是重點
2. res是查詢結果,透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,這只是單迴圈,想想雙層迴圈怎麼寫
4. pool.join()是阻塞當前執行緒的,這個不好
5. 同事註釋中寫的是"多程式",是寫錯了嗎?實際是多執行緒?還是就是多程式?
6. 當然,python是有async await非同步寫法的,應該不比C#差,只是同事沒有用,可能是因為他用的python版本不夠新
7. python程式碼,字串太多,字串是最不好維護的。C#中的字串裡面都是強型別。

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,看並行非同步的使用):

/// <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();

    //組織第一層迴圈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);
    }

    //計算第一層迴圈task並快取結果,組織第二層迴圈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);
        }
    }

    //讀取第一層迴圈task快取結果,計算第二層迴圈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

  1. 我們開發的低程式碼平臺很牛B,C#:我就是低程式碼!
  2. 我們開發的平臺很牛B,支援寫指令碼、自定義指令碼,C#:我就是指令碼!
  3. 我們用spark、flink分散式,效能牛B,C#:並行非同步效能吊炸天,記憶體給大些,單機就可以。C#:我的並行非同步的效能,能把es幹掛,只要不是計算密集型,只要記憶體夠,不用spark、flink,單機簡單啊,只要es是叢集就行了。

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