Question

即使我使用Parallel.Foreach，我也有超过7分钟的完成代码。我迭代的“final_products”列表包含大约7000个产品。

 public void GenerateTreeFromAllFinalProducts()
    {
        XmlSerializer serializer = new XmlSerializer(typeof(ImageFeature<float>[]));
        DSTableAdapters.Products_UniqueTableAdapter pft = new DSTableAdapters.Products_UniqueTableAdapter();
        DSTableAdapters.Products_Unique_SURFTableAdapter pus = new DSTableAdapters.Products_Unique_SURFTableAdapter();
        DS.Products_UniqueDataTable final_products = pft.GetData();

        Stopwatch stopwatch = new Stopwatch();
        stopwatch.Start();

        Parallel.ForEach(final_products.AsParallel(), row =>
        {                 
            //Get SURF data for all images found similar to this image
            Types.Products_Unique_SURFRow surfData = GetDataByUniqueProductID(row.id);

            ImageFeature<float>[] row_features = (ImageFeature<float>[])serializer.Deserialize(new StringReader(Decompress(surfData.SURF)));
            if (row_features != null)
                flann.AddSurfDescriptors(row_features, row.id);                       
        });

        stopwatch.Stop();
        Console.WriteLine("Time elapsed: {0}", stopwatch.Elapsed);
    }

这是否正常，需要这么长时间，如果不能，我如何优化代码？

GetDataByUniqueProductID（row.id）是对我的数据库的一次调用，返回1行。

private static Types.Products_Unique_SURFRow GetDataByUniqueProductID(int rowid)
    {
        Types.Products_Unique_SURFRow ret = new Types.Products_Unique_SURFRow();

        string sqlText = "SET ROWCOUNT 1 SELECT SURF from Products_Unique_SURF WHERE unique_product_id =" + rowid;

        using (SqlConnection myConn = new SqlConnection(global::SCBot.Properties.Settings.Default.DataConnectionString))
        {
            myConn.Open();

            SqlCommand cmd = new SqlCommand(sqlText, myConn);
            try
            {
                cmd.CommandType = CommandType.Text;

                SqlDataReader reader = cmd.ExecuteReader();
                while (reader.Read())
                {
                    Types.Products_Unique_SURFRow row = new Types.Products_Unique_SURFRow();
                    row.SURF = Convert.ToString(reader["SURF"]);

                    ret = row;
                }
            }
            catch (Exception e)
            {
                MessageBox.Show(e.ToString());
            }
        }
        return ret;
    }

我的初始代码如下

 public void GenerateTreeFromAllFinalProducts()
    {
        XmlSerializer serializer = new XmlSerializer(typeof(ImageFeature<float>[]));
        DSTableAdapters.Products_UniqueTableAdapter pft = new DSTableAdapters.Products_UniqueTableAdapter();
        DSTableAdapters.Products_Unique_SURFTableAdapter pus = new DSTableAdapters.Products_Unique_SURFTableAdapter();
        DS.Products_UniqueDataTable final_products = pft.GetData();

        foreach (DS.Products_UniqueRow row in final_products)
        {
            //Get SURF data for all images found similar to this image
            List<DS.Products_Unique_SURFRow> surfData = pus.GetDataByUniqueProductID(row.id).ToList();

            foreach (DS.Products_Unique_SURFRow data in surfData)
            {
                ImageFeature<float>[] row_features = (ImageFeature<float>[])serializer.Deserialize(new StringReader(Decompress(data.SURF)));
                flann.AddSurfDescriptors(row_features, row.id);
            }
        }
    }

但这太慢了，这就是为什么我试图做一个Parallel.Foreach

Answer 1

您应该测量内部代码所花费的时间。并行不会加速内码。您可以单独测量它们，而不是嵌套方法Decompress / Deserialize。

编辑后：

每个线程都会创建一个新连接。我认为这是一个很好的例子，不使用并行加速。但改变算法会有所帮助。因为查询不同连接/线程上的单个行所花费的时间比查询它们所有（或选择）和单线程foreach循环它们要花费更多时间。

我的观点：

我将收集final_products enemration的所有id并使用stringbuilder构建一个字符串。

StringBuilder sb = new StringBuiler();
bool isFirst = true;

sb.Append("(");
foreach(var prod in final_products)
{
    if(isFirst)
        isFirst = false;
    else
        sb.Append(", ");

    sb.Append(prod.Id);
}
sb.Append(")");

string query = "SELECT SURF FROM Products_Unique_SURF WHERE Id in "+sb.ToString();

// execute the query

// foreach row, decompress, deserialize etc...

Parallel.Foreach是慢的。如何优化我的代码

1 个答案: