如何在使用 SSIS 包导入大型数据集时执行查找?

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问题描述

我有一个问题 - 我希望通过 SSIS (Microsoft ETL) 下载客户数据.对于我导入的每个客户记录，我必须对照目标数据库中的现有客户记录进行检查.如果记录存在，我必须更新记录，如果不存在，我必须插入它.最佳性能的策略是什么?我们说的是 20-3000 万行.

I have a problem - I wish to dowload customer data via SSIS (Microsoft ETL). For each customer record I import, I have to check against existing customer records in my target DB. If the record exists, I have to UPDATE the record, and if it doesn't I have to INSERT it. What is the strategy for the best performance? We're talking 20-30 million rows.

我想到了触发器(插入时触发 - 检查记录是否存在)、查找、游标，但我确定有更好的方法，可能使用 SSIS 中的数据流转换之一?

I thought of triggers (trigger on INSERT - check for existence of record), lookup, cursors, but I'm sure there's a better way, probably using one of the Data Flow Transformations in SSIS?

还是将其导入临时表然后在本地进行查找更好?

Or is it better I import this to a staging table and then do the lookup locally?

推荐答案

这里有一个可能的选项，您可以应用它来将数据加载到目标表中，无论它是否涉及插入/更新.下面给出的示例在 3 分钟内将包含 100 万行的文本文件加载到 SQL 表中.如果该记录在 SQL 表中不存在，那么它将被插入到目标表中，否则该记录将被插入到一个临时表中，然后该表将用于在存储过程的帮助下更新目标表.存储过程还将删除 SQL 表中可能存在但平面文件中不存在的任何记录.该示例使用 SSIS 2008 R2 和 SQL Server 2008 R2 后端.

Here is one possible option that you can apply to load data into destination table whether it involves insert/update. The example given below loads a text file containing 1 million rows into an SQL table in under 3 minutes. If the record doesn't exist in the SQL table then it will be inserted into destination table else the record will be inserted into a staging table which will then be used to update the destination table with the help of a stored procedure. The stored procedure will also delete any records that might be present in SQL table but not present in the flat file. The example uses SSIS 2008 R2 with SQL Server 2008 R2 backend.

分步过程:

在 SQL Server 数据库中，创建两个表，分别是 dbo.ItemInfo 和 dbo.Staging.脚本部分下提供了创建表查询.这些表的结构显示在屏幕截图 #1 中.ItemInfo 将保存实际数据，Staging 表将保存临时数据以比较和更新实际记录.这两个表中的 Id 列都是自动生成的唯一标识列.ItemInfo 表中的IsProcessed 列将用于识别和删除不再有效的记录.

In the SQL Server database, create two tables namely dbo.ItemInfo and dbo.Staging. Create table queries are available under Scripts section. Structure of these tables are shown in screenshot #1. ItemInfo will hold the actual data and Staging table will hold the staging data to compare and update the actual records. Id column in both these tables is an auto-generated unique identity column. IsProcessed column in the table ItemInfo will be used to identify and delete the records that are no longer valid.

创建一个 SSIS 包并创建 5 个变量，如屏幕截图 #2 所示.我为制表符分隔的文件使用了 .txt 扩展名，因此变量 FileExtension 中的值是 *.txt.FilePath 变量将在运行时赋值.FolderLocation 变量表示文件所在的位置.SQLPostLoad 和 SQLPreLoad 变量表示在预加载和后加载操作期间使用的存储过程.脚本部分下提供了这些存储过程的脚本.

Create an SSIS package and create 5 variables as shown in screenshot #2. I have used .txt extension for the tab delimited files and hence the value *.txt in the variable FileExtension. FilePath variable will be assigned with value during run-time. FolderLocation variable denotes where the files will be located. SQLPostLoad and SQLPreLoad variables denote the stored procedures used during the pre-load and post-load operations. Scripts for these stored procedures are provided under the Scripts section.

创建指向 SQL Server 数据库的 OLE DB 连接.创建平面文件连接，如屏幕截图 #3 和 #4 所示.平面文件连接列部分包含列级信息.屏幕截图 #5 显示列数据预览.

Create an OLE DB connection pointing to the SQL Server database. Create a flat file connection as shown in screenshots #3 and #4. Flat File Connection Columns section contains column level information. Screenshot #5 shows the columns data preview.

配置控制流任务，如屏幕截图 #6 所示.配置任务Pre Load、Post Load 和Loop Files，如截图#7 - #10.Pre Load 将截断临时表并将 ItemInfo 表中所有行的 IsProcessed 标志设置为 false.Post Load 将更新更改并删除数据库中未在文件中找到的行.请参阅这些任务中使用的存储过程以了解这些 Execute SQL 任务中正在执行的操作.

Configure the Control Flow Task as shown in screenshot #6. Configure the tasks Pre Load, Post Load and Loop Files as shown in screenshots #7 - #10. Pre Load will truncate staging table and set IsProcessed flag to false for all rows in ItemInfo table. Post Load will update the changes and will delete rows in database that are not found in the file. Refer the stored procedures used in those tasks to understand what is being done in these Execute SQL tasks.

双击 Load Items 数据流任务并按屏幕截图 #11 所示对其进行配置.Read File 是配置为使用平面文件连接的平面文件源.Row Count 是派生列转换，其配置显示在 screenshto #12 中.Check Exist 是一种查找转换，其配置显示在屏幕截图 #13 - #15 中.Lookup No Match Output 被重定向到左侧的 Destination Split.查找匹配输出被重定向到左侧的Staging Split.Destination Split 和 Staging Split 具有与屏幕截图 #16 中所示完全相同的配置.目的地和登台表有 9 个不同目的地的原因是为了提高包的性能.

Double-click on the Load Items data flow task and configure it as shown in screenshot #11. Read File is a flat file source configured to use the flat file connection. Row Count is derived column transformation and its configuration is shown in screenshto #12. Check Exist is a lookup transformation and its configurations are shown in screenshots #13 - #15. Lookup No Match Output is redirected to Destination Split on the left side. Lookup Match Output is redirected to Staging Split on the left side. Destination Split and Staging Split have the exact same configuration as shown in screenshot #16. The reason for 9 different destinations for both destination and staging table is to improve the performance of the package.

所有目标任务 0 - 8 都配置为将数据插入表 dbo.ItemInfo，如屏幕截图 #17 所示.所有暂存任务 0 - 8 都配置为将数据插入 dbo.Staging，如屏幕截图 #18 所示.

All the destination tasks 0 - 8 are configured to insert data into table dbo.ItemInfo as shown in screenshot #17. All the staging tasks 0 - 8 are configured to insert data into dbo.Staging as shown in screenshot #18.

在平面文件连接管理器上，将 ConnectionString 属性设置为使用变量 FilePath，如屏幕截图 #19 所示.这将使包能够在循环遍历文件夹中的每个文件时使用变量中设置的值.

On the Flat File connection manager, set the ConnectionString property to use the variable FilePath as shown in screenshot #19. This will enable the package to use the value set in the variable as it loops through each file in a folder.

测试场景:

Test results may vary from machine to machine.
In this scenario, file was located locally on the machine.
Files on network might perform slower.
This is provided just to give you an idea.
So, please take these results with grain of salt.

程序包是在配备至强单核 CPU 2.5GHz 和 3.00 GB RAM 的 64 位机器上执行的.

Package was executed on a 64-bit machine with Xeon single core CPU 2.5GHz and 3.00 GB RAM.

加载了一个包含 100 万行 的平面文件.包在大约 2 分 47 秒内执行.请参阅屏幕截图 #20 和 #21.

Loaded a flat file with 1 million rows. Package executed in about 2 mins 47 seconds. Refer screenshots #20 and #21.

使用测试查询部分下提供的查询来修改数据，以模拟在包的第二次运行期间更新、删除和创建新记录.

Used the queries provided under Test queries section to modify the data to simulate update, delete and creation of new records during the second run of the package.

在数据库中执行以下查询后加载包含 100 万行 的相同文件.包在大约 1 分 35 秒内执行.请参阅屏幕截图 #22 和 #23.请注意屏幕截图 #22 中重定向到目标和暂存表的行数.

Loaded the same file containing the 1 million rows after the following queries were executed in the database. Package executed in about 1 min 35 seconds. Refer screenshots #22 and #23. Please note the number of rows redirected to destination and staging table in screenshot #22.

以上答案是提供给这个 Stack Overflow 问题也是如此.

The above answer is copy of the answer provided to this Stack Overflow question as well.

希望有所帮助.

测试查询:.

--These records will be deleted during next run
--because item ids won't match with file data.
--(111111 row(s) affected)
UPDATE dbo.ItemInfo SET ItemId = 'DEL_' + ItemId WHERE Id % 9 IN (3)

--These records will be modified to their original item type of 'General'
--because that is the data present in the file.
--(222222 row(s) affected)
UPDATE dbo.ItemInfo SET ItemType = 'Testing' + ItemId WHERE Id % 9 IN (2,6)

--These records will be reloaded into the table from the file.
--(111111 row(s) affected)
DELETE FROM dbo.ItemInfo WHERE Id % 9 IN (5,9)

平面文件连接列.

Name        InputColumnWidth     DataType          OutputColumnWidth
----------  ----------------     ---------------   -----------------
Id          8                    string [DT_STR]   8
ItemId      11                   string [DT_STR]   11
ItemName    21                   string [DT_STR]   21
ItemType    9                    string [DT_STR]   9

脚本:(创建表和存储过程).

CREATE TABLE [dbo].[ItemInfo](
    [Id] [int] IDENTITY(1,1) NOT NULL,
    [ItemId] [varchar](255) NOT NULL,
    [ItemName] [varchar](255) NOT NULL,
    [ItemType] [varchar](255) NOT NULL,
    [IsProcessed] [bit] NULL,
    CONSTRAINT [PK_ItemInfo] PRIMARY KEY CLUSTERED ([Id] ASC),
    CONSTRAINT [UK_ItemInfo_ItemId] UNIQUE NONCLUSTERED ([ItemId] ASC)) ON [PRIMARY]
GO

CREATE TABLE [dbo].[Staging](
    [Id] [int] IDENTITY(1,1) NOT NULL,
    [ItemId] [varchar](255) NOT NULL,
    [ItemName] [varchar](255) NOT NULL,
    [ItemType] [varchar](255) NOT NULL,
 CONSTRAINT [PK_Staging] PRIMARY KEY CLUSTERED ([Id] ASC)) ON [PRIMARY]
GO

CREATE PROCEDURE [dbo].[PostLoad]
AS
BEGIN
    SET NOCOUNT ON;

    UPDATE      ITM
    SET         ITM.ItemName    = STG.ItemName
            ,   ITM.ItemType    = STG.ItemType
            ,   ITM.IsProcessed = 1
    FROM        dbo.ItemInfo    ITM
    INNER JOIN  dbo.Staging     STG
    ON          ITM.ItemId      = STG.ItemId;

    DELETE FROM dbo.ItemInfo
    WHERE       IsProcessed = 0;
END
GO

CREATE PROCEDURE [dbo].[PreLoad]
AS
BEGIN
    SET NOCOUNT ON;

    TRUNCATE TABLE dbo.Staging;

    UPDATE  dbo.ItemInfo
    SET     IsProcessed = 0;
END
GO

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