对具有不同列的目标使用多播转换

发布于 2024-11-11 07:18:13 字数 54 浏览 1 评论 0原文

组播转换任务有什么用?通过此任务,是否可以从单个源发送到两个目的地,而每个目的地具有不同的列?

What is the use of Multicast Transformation Task ? With this task, is it possible to send to two destinations from a single source, while each destination has different columns ?

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终难遇 2024-11-18 07:18:13

我假设您指的是数据流任务中的多播转换。如果是这样,是的,这是可能的。转换的目的是将数据从单个源传输到 n 个转换任务或目标

如果源有以下列

Source
Column 1
Column 2
Column 3

并且目标有这些列。

Destination 1   Destination 2
Column 1        Column 2
Column 3

两个目标都将能够看到源中可用的第 1 - 3 列。您必须将列相应地映射到相应的目的地。请参阅以下示例:

示例:

  1. 屏幕截图 #1 显示 Source 有两列 HeaderValue< /强>。
  2. 屏幕截图 #2 显示 Destination 1 具有 HeaderValue 两列。它们被相应地映射。
  3. 屏幕截图 #3 显示 Destination 2 仅包含 Header 列。它被相应地映射。
  4. 屏幕截图#4 显示示例包执行情况。

希望有帮助。

屏幕截图 #1:

1

屏幕截图 #2:

2

屏幕截图 #3:

3

屏幕截图 #4:

4

I assume that you are referring to Multicast Transformation inside the Data Flow task. If so, yes it is possible. The purpose of the transformation is to channel data from a single source to n number of Transformation tasks or Destinations.

If source has following columns

Source
Column 1
Column 2
Column 3

and destinations have these columns.

Destination 1   Destination 2
Column 1        Column 2
Column 3

Both destinations will be able to see Columns 1 - 3 that are available in Source. You have to map the columns accordingly in the respective destinations. Refer below example:

Example:

  1. Screenshot #1 shows that Source has two columns Header and Value.
  2. Screenshot #2 shows that Destination 1 has both columns Header and Value. They are mapped accordingly.
  3. Screenshot #3 shows that Destination 2 has only column Header. It is mapped accordingly.
  4. Screenshot #4 shows sample package execution.

Hope that helps.

Screenshot #1:

1

Screenshot #2:

2

Screenshot #3:

3

Screenshot #4:

4

枯叶蝶 2024-11-18 07:18:13

@Siva 很好地解释了如何操作。我将解决“多播转换任务有什么用?”问题。

让我举例说明我如何使用它或看到它如何使用。首先,我们喜欢将数据存储在仅包含原始未更改数据的临时表中(这使我们更容易研究数据问题,看看数据问题是否来自我们流程中的错误或客户端发送的错误数据) .) 同时我想将相同的数据发送到另一个用于转换数据的临时表。

有时我们使用多播来获取非规范化文件并将它们发送到规范化数据表。因此,姓名进入人员表,地址进入地址表,电话进入电话表。

多播可用于同时对同一源中的不同数据字段进行多种不同的转换,而不是一次进行一个转换,然后在合并连接中将所有修改后的数据重新组合在一起。因此,一条路径检查州以确保它们有效,或者将长名称转换为 2 个字符的缩写,另一条路径检查邮政编码并添加因数据来自 Excel 文件而丢失的前导零。然后,将清理后的地址数据与我们想要插入数据库的正确值放回一起。这可以加快清理速度,因为数据是同时清理的,而不是一次一步。

@Siva did a good job of explaining the how. I'm going to tackle the "What is the use of Multicast Transformation Task?" question.

Let me give you examples of how I have used it or seen it used. First, we like to store the data in a staging table that contains just the raw unchanged data (this makes it easier for us to research data issues to see if the data problem came from a bug in our process or bad data sent by the client.) and at the same time I want to send the same data to another staging table that will be used to transform the data.

Sometimes we use Mulitcast to take denormalized files and send them to normalized data tables. So the names go to the person table, the addresses go to the address table and the phones go to the phone table.

Multicast can be used to do several different transformations on different data fields in the same source at the same time rather than one at a time and then bring all the revised data back together in a Merge join. So one path checks the States to make sure they are valid or converts the long names to the 2 character abbreviations and another checks the zip codes and adds the leading zeros that got lost because the data came from an Excel file. Then the cleaned address data is put back together with the correct values we want for insertion to our database. This can speed up cleaning as data is being scrubbed simultaneously not one step at a time.

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