DataTable数据批量写入数据库三种方法比较 以及方法介绍

最近在将excel中的文件导入到数据库中，用程序进行编写，由于数据量较大所以速度很慢，后来采用了SqlBulkCopy类，解决了速度的问题，我就insert语句,sqldataadapter.update(dataset,tablename);sqlbulkcopy.WriteToServer(datatable);三个方法的性能进行比较：

1、生成测试的datatable表，表结构如下：

UniqueID(主键，自动增长) | CompanyName | CompanyCode | Address | Owner | Memo

共6个字段。

SqlConnection sqlconnection = new SqlConnection(connectionString);
SqlDataAdapter sqldataadapter = new SqlDataAdapter("select * from Table_1 where 1=2", sqlconnection);
DataSet dataset = new DataSet();
sqldataadapter.Fill(dataset, "Table_1");
DataTable datatable = dataset.Tables[0];
//生成20000条记录

for (int i = 0; i < 20000; i++)
{
DataRow datarow = datatable.NewRow();
datarow["CompanyName"] = "companyname"+string.Format("{0:0000}",i);
datarow["CompanyCode"] = "companycode" + string.Format("{0:0000}", i);
datarow["Address"] = "address" + string.Format("{0:0000}", i);
datarow["Owner"] = "owner" + string.Format("{0:0000}", i);
datarow["Memo"] = "memo" + string.Format("{0:0000}", i);

datatable.Rows.Add(datarow);
}

2、使用sqlcommand.executenonquery()方法插入

foreach (DataRow datarow in datatable.Rows)
{
string sql = "INSERT INTO [Table_1]

([CompanyName],[CompanyCode],[Address],[Owner],[Memo])" +
"VALUES('" + datarow["CompanyName"].ToString() + "'" +
",'" + datarow["CompanyCode"].ToString() + "'" +
",'" + datarow["Address"].ToString() + "'" +
",'" + datarow["Owner"].ToString() + "'" +
",'" + datarow["Memo"].ToString() + "')";
using (SqlConnection sqlconn = new SqlConnection(connectionString))
{
sqlconn.Open();

SqlCommand sqlcommand = new SqlCommand(sql, sqlconn);
sqlcommand.ExecuteNonQuery();
sqlconn.Close();
}
}

插入20000条记录时间:00:00:29.7336000

3、使用sqldataadapter.update(dataset,tablename);

SqlCommand insertcommand = new SqlCommand("INSERT INTO [Table_1]([CompanyName],[CompanyCode],[Address],[Owner],[Memo])" +
"VALUES(@CompanyName, @CompanyCode,@Address,@Owner,@Memo)",new SqlConnection(connectionString));
insertcommand.Parameters.Add("@CompanyName", SqlDbType.NChar, 50, "CompanyName");
insertcommand.Parameters.Add("@CompanyCode", SqlDbType.NChar, 25, "CompanyCode");
insertcommand.Parameters.Add("@Address", SqlDbType.NChar, 255, "Address");
insertcommand.Parameters.Add("@Owner", SqlDbType.NChar, 25, "Owner");
insertcommand.Parameters.Add("@Memo", SqlDbType.NChar, 255, "Memo");
sqldataadapter.InsertCommand = insertcommand;

sqldataadapter.Update(dataset, "Table_1");

插入20000条记录时间:00:00:22.8938000

4、使用sqlbulkcopy.writetoserver(datatable)

SqlBulkCopy sqlbulkcopy = new SqlBulkCopy(connectionString, SqlBulkCopyOptions.UseInternalTransaction);
sqlbulkcopy.DestinationTableName = "Table_1";//数据库中的表名

sqlbulkcopy.WriteToServer(dataset.Tables[0]);

插入20000条记录时间:00:00:00.3276000

所以说速度是sqlbulkcopy最快，sqldataadapter.update()次之，sqlcommand.ExecuteNonQuery()最慢。

本文来自CSDN博客，转载请标明出处：http://blog.csdn.net/humozhi/archive/2009/03/31/4039868.aspx

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1.大批量数据操作

　　可以利用SqlBulkCopy类快速写入大批量数据，针对SQL Server的优化，可以写入DataRow数据，DataTable,DataReader

　　WriteToServer（DataTable）写入数据表

　　WriteToServer（DataRow[]）批次写入数据行

　　WriteToServer（DataTable ，DataRowState）按行状态写入数据库表

　　WriteToServer（IDataReader）写入DataReader对象

string connstr = "server=(local);database=northwind;integrated security=true;async=true";
// Fill up a DataSet
DataSet ds = new DataSet();
SqlConnection conn = new SqlConnection(connstr);
SqlDataAdapter dadp = new SqlDataAdapter("select * from customers", conn);
dadp.Fill(ds);
// Copy the Data to SqlServer
SqlBulkCopy bcp = new SqlBulkCopy(connstr);
bcp.DestinationTableName = "customers1";
bcp.WriteToServer(ds.Tables[0]);
2.多个动态的结果集

　　Multiple Active Result Sets（MARS）

　　这个只能在SQL Server 2005中使用

　　可以在一个Command对象上同时打开多个DataReader

string connstr = "server=(local);database=northwind;integrated security=true;async=true";
SqlConnection conn = new SqlConnection(connstr);
conn.Open();
SqlCommand cmd1 = new SqlCommand("select * from customers", conn);
SqlCommand cmd2 = new SqlCommand("select * from orders", conn);
SqlDataReader rdr1 = cmd1.ExecuteReader();
// next statement causes an error prior to SQL Server 2005
SqlDataReader rdr2 = cmd2.ExecuteReader();
// now you can reader from rdr1 and rdr2 at the same time.