需求：导入9999行数据时Dapper, Ef core, Freesql 谁的性能更优，是如何执行的，级联增加谁性能更佳。
确认方法：sql server 的 sys.dm_exec_query_stats
SELECT TOP 1000 (select [text] from sys.dm_exec_sql_text(QS.sql_handle)) as '数据库语句',
QS.execution_count AS '执行次数',
QS.total_elapsed_time AS '耗时',
QS.total_logical_reads AS '逻辑读取次数',
QS.total_logical_writes AS '逻辑写入次数',
QS.total_physical_reads AS '物理读取次数',
QS.creation_time AS '执行时间',
*
FROM sys.dm_exec_query_stats QS
WHERE QS.creation_time > '2021-04-11 09:42:30'
准备：创建表
CREATE TABLE [dbo].[TestAddSortByXXXX](
[Id] [int] IDENTITY(1,1) NOT NULL,
[No] [int] NULL,
[Col1] [nvarchar](50) NULL,
[Col2] [nvarchar](50) NULL,
[Col3] [nvarchar](50) NULL,
[Col4] [nvarchar](50) NULL,
[Col5] [nvarchar](50) NULL,
[Col6] [nvarchar](50) NULL,
[Col7] [nvarchar](50) NULL,
[Col8] [nvarchar](50) NULL,
[Col9] [nvarchar](50) NULL,
[Col10] [nvarchar](50) NULL,
CONSTRAINT [PK_TestAddSortByXXXX] PRIMARY KEY CLUSTERED
(
[Id] ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY]
GO
CREATE TABLE [dbo].[TestAddSortByXXXXSub](
[Id] [int] IDENTITY(1,1) NOT NULL,
[Id2] [int] NULL,
[Col1] [nvarchar](50) NULL,
[Col2] [nvarchar](50) NULL,
[Col3] [nvarchar](50) NULL,
[Col4] [nvarchar](50) NULL,
[Col5] [nvarchar](50) NULL,
[Col6] [nvarchar](50) NULL,
[Col7] [nvarchar](50) NULL,
[Col8] [nvarchar](50) NULL,
[Col9] [nvarchar](50) NULL,
[Col10] [nvarchar](50) NULL,
CONSTRAINT [PK_TestAddSortByXXXXSub] PRIMARY KEY CLUSTERED
(
[Id] ASC
)WITH (PAD_INDEX = OFF, STATISTICS_NORECOMPUTE = OFF, IGNORE_DUP_KEY = OFF, ALLOW_ROW_LOCKS = ON, ALLOW_PAGE_LOCKS = ON) ON [PRIMARY]
) ON [PRIMARY]
构建9999行数据
List<Entity> datas = new List<Entity>();
for (int i = 0; i < 9999; i++)
{
var item = new Entity
{
No = i + 1,
Col1 = Guid.NewGuid().ToString("N"),
Col2 = Guid.NewGuid().ToString("N"),
Col3 = Guid.NewGuid().ToString("N"),
Col4 = Guid.NewGuid().ToString("N"),
Col5 = Guid.NewGuid().ToString("N"),
Col6 = Guid.NewGuid().ToString("N"),
Col7 = Guid.NewGuid().ToString("N"),
Col8 = Guid.NewGuid().ToString("N"),
Col9 = Guid.NewGuid().ToString("N"),
Col10 = Guid.NewGuid().ToString("N"),
};
datas.Add(item);
}
Dapper：
static void AddDataByDapper(List<Entity> datas)
{
int r = 0;
Stopwatch sw = new Stopwatch();
sw.Start();
using (var conn = new SqlConnection(connString))
{
conn.Open();
string sql = "insert into TestAddSortByDapper([No], Col1, Col2, Col3, Col4, Col5, Col6, Col7, Col8, Col9, Col10) values(@No, @Col1, @Col2, @Col3, @Col4, @Col5, @Col6, @Col7, @Col8, @Col9, @Col10);";
r = conn.Execute(sql, datas);
}
sw.Stop();
Console.WriteLine($"通过 Dapper 导入数据{r}行 毫时{sw.ElapsedMilliseconds}");
}
执行结果总结
-- 数据库实际执行数据
(@Col1 nvarchar(4000),@Col10 nvarchar(4000),...)
insert into TestAddSortByDapper([No], Col1, Col2, Col3, Col4, Col5, Col6, Col7, Col8, Col9, Col10)
values(@No, @Col1, @Col2, @Col3, @Col4, @Col5, @Col6, @Col7, @Col8, @Col9, @Col10);
从结果我们可以看到，dapper使用的是 insert into table () values () 方式循环执行9999次，代码总耗时3-4秒。
EfCore：
static void AddDataByEfCore(List<Entity> datas)
{
int r1 = 0;
Stopwatch sw = new Stopwatch();
sw.Start();
using (var db = new TestContext())
{
db.Entity.AddRange(datas);
r1 = db.SaveChanges();
}
sw.Stop();
Console.WriteLine($"通过 EfCore 导入数据{r1}行 毫时{sw.ElapsedMilliseconds}");
}
[Table("TestAddSortByEfCore")]
public class Entity
{
public int Id { get; set; }
public int No { get; set; }
public string Col1 { get; set; }
public string Col2 { get; set; }
public string Col3 { get; set; }
public string Col4 { get; set; }
public string Col5 { get; set; }
public string Col6 { get; set; }
public string Col7 { get; set; }
public string Col8 { get; set; }
public string Col9 { get; set; }
public string Col10 { get; set; }
}
执行结果总结
(@p0 nvarchar(4000),@p1 nvarchar(4000),...,@p460 nvarchar(4000),@p461 int)
SET NOCOUNT ON;
DECLARE @inserted0 TABLE ([Id] int, [_Position] [int]);
MERGE [TestAddSortByEfCore] USING (
VALUES (@p0, @p1, @p2, @p3, @p4, @p5, @p6, @p7, @p8, @p9, @p10, 0),..., (@p451, @p452, @p453, @p454, @p455, @p456, @p457, @p458, @p459, @p460, @p461, 41)
) AS i ([Col1], [Col10], [Col2], [Col3], [Col4], [Col5], [Col6], [Col7], [Col8], [Col9], [No], _Position) ON 1=0
WHEN NOT MATCHED THEN
INSERT ([Col1], [Col10], [Col2], [Col3], [Col4], [Col5], [Col6], [Col7], [Col8], [Col9], [No])
VALUES (i.[Col1], i.[Col10], i.[Col2], i.[Col3], i.[Col4], i.[Col5], i.[Col6], i.[Col7], i.[Col8], i.[Col9], i.[No])
OUTPUT INSERTED.[Id], i._Position INTO @inserted0;
SELECT [t].[Id] FROM [TestAddSortByEfCore] t INNER JOIN @inserted0 i ON ([t].[Id] = [i].[Id]) ORDER BY [i].[_Position];
从结果我们可以看到，EfCore使用的是 Merge 方式增加数据，但数据库变量最多定义462个，所以每次只能增加42行数据，执行了238+3次，但最大的疑问是执行了两次，而且插入表数据顺序错了（估计是EfCore代码上使用了Parallel.For方法，有懂的朋友能否解答一下），代码总耗时4-5秒。
Freesql：
static void AddDataByEfCore(List<Entity> datas)
{
int r1 = 0;
Stopwatch sw = new Stopwatch();
sw.Start();
using (var db = new TestContext())
{
db.Entity.AddRange(datas);
r1 = db.SaveChanges();
}
sw.Stop();
Console.WriteLine($"通过 EfCore 导入数据{r1}行 毫时{sw.ElapsedMilliseconds}");
}
[FreeSql.DataAnnotations.Table(Name = "TestAddSortByFreesql", DisableSyncStructure = true)]
public class Entity
{
[FreeSql.DataAnnotations.Column(Name = "id", IsPrimary = true, IsIdentity = true)]
public int Id { get; set; }
public int No { get; set; }
public string Col1 { get; set; }
public string Col2 { get; set; }
public string Col3 { get; set; }
public string Col4 { get; set; }
public string Col5 { get; set; }
public string Col6 { get; set; }
public string Col7 { get; set; }
public string Col8 { get; set; }
public string Col9 { get; set; }
public string Col10 { get; set; }
}
执行结果总结
(@No_0 int,@Col1_0 nvarchar(32),...,@Col10_173 nvarchar(32))
INSERT INTO [TestAddSortByFreesql]([No], [Col1], [Col2], [Col3], [Col4], [Col5], [Col6], [Col7], [Col8], [Col9], [Col10])
VALUES(@No_0, @Col1_0, @Col2_0, @Col3_0, @Col4_0, @Col5_0, @Col6_0, @Col7_0, @Col8_0, @Col9_0, @Col10_0), ..., (@No_173, @Col1_173, @Col2_173, @Col3_173, @Col4_173, @Col5_173, @Col6_173, @Col7_173, @Col8_173, @Col9_173, @Col10_173)
从结果我们可以看到，freesql使用的是 insert into table () values (), () 方式循环执行，每次最多增加173行数据，代码总耗时7-8秒。
从目前结果来看，单表增加大量数据，时间上 Dapper > EfCore > Freesql。
ADO.NET SqlBulkCopy 复制（最优方案）
static void AddDataByBulkCopy(List<Entity> datas)
{
Stopwatch sw = new Stopwatch();
var dt = new DataTable();
dt.Columns.Add("No", typeof(int));
dt.Columns.Add("Col1", typeof(string));
dt.Columns.Add("Col2", typeof(string));
dt.Columns.Add("Col3", typeof(string));
dt.Columns.Add("Col4", typeof(string));
dt.Columns.Add("Col5", typeof(string));
dt.Columns.Add("Col6", typeof(string));
dt.Columns.Add("Col7", typeof(string));
dt.Columns.Add("Col8", typeof(string));
dt.Columns.Add("Col9", typeof(string));
dt.Columns.Add("Col10", typeof(string));
foreach (var item in datas)
{
var dr = dt.NewRow();
dr["No"] = item.No;
dr["Col1"] = item.Col1;
dr["Col2"] = item.Col2;
dr["Col3"] = item.Col3;
dr["Col4"] = item.Col4;
dr["Col5"] = item.Col5;
dr["Col6"] = item.Col6;
dr["Col7"] = item.Col7;
dr["Col8"] = item.Col8;
dr["Col9"] = item.Col9;
dr["Col10"] = item.Col10;
dt.Rows.Add(dr);
}
sw.Start();
using (SqlConnection cn = new SqlConnection(connString))
{
cn.Open();
using (SqlBulkCopy sqlBulkCopy = new SqlBulkCopy(cn))
{
sqlBulkCopy.BatchSize = dt.Rows.Count;
sqlBulkCopy.BulkCopyTimeout = 1800;
sqlBulkCopy.DestinationTableName = "TestAddSortByBulkCopy"; sqlBulkCopy.ColumnMappings.Add("No", "No");
sqlBulkCopy.ColumnMappings.Add("Col1", "Col1");
sqlBulkCopy.ColumnMappings.Add("Col2", "Col2");
sqlBulkCopy.ColumnMappings.Add("Col3", "Col3");
sqlBulkCopy.ColumnMappings.Add("Col4", "Col4");
sqlBulkCopy.ColumnMappings.Add("Col5", "Col5");
sqlBulkCopy.ColumnMappings.Add("Col6", "Col6");
sqlBulkCopy.ColumnMappings.Add("Col7", "Col7");
sqlBulkCopy.ColumnMappings.Add("Col8", "Col8");
sqlBulkCopy.ColumnMappings.Add("Col9", "Col9");
sqlBulkCopy.ColumnMappings.Add("Col10", "Col10");
sqlBulkCopy.WriteToServer(dt);
}
}
sw.Stop();
Console.WriteLine($"通过 BulkCopy 毫时{sw.ElapsedMilliseconds}");
}
执行结果总结
并没有在 sys.dm_exec_query_stats 上产生结果，但他的性能是最佳的。
下一篇，来看看级联操作上谁能更胜一筹。
