对具有不同列的目标使用多播转换
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 ?
如果你对这篇内容有疑问,欢迎到本站社区发帖提问 参与讨论,获取更多帮助,或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱,如果其他用户或者作者回复了您的评论,将不能在第一时间通知您!
发布评论
评论(2)
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
并且目标有这些列。
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:
Source
有两列 Header 和 Value< /强>。Destination 1
具有 Header 和 Value 两列。它们被相应地映射。Destination 2
仅包含 Header 列。它被相应地映射。希望有帮助。
屏幕截图 #1:
屏幕截图 #2:
屏幕截图 #3:
屏幕截图 #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
and destinations have these columns.
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:
Source
has two columns Header and Value.Destination 1
has both columns Header and Value. They are mapped accordingly.Destination 2
has only column Header. It is mapped accordingly.Hope that helps.
Screenshot #1:
Screenshot #2:
Screenshot #3:
Screenshot #4:
@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.