SSRS: How can I run dataset1 first in datasource1 then run dataset2 in datasource2

Question

I have 2 data source(db1, db2) and 2 dataset. 2 dataset are store procedure from each data source.
Dataset1 must run first to create a table for dataset 2 to update and show (dataset 1 will show result too).
Cause the data of the table must base on some table in DB1, the store procedure will create a table to db2 by using link server.
I have search online and tried "single transaction" in data source, but it show error in data set 1 with no detail.
Is there anyway to do it? cause I want to generate an excel with 2 sheet for this result.

Answer 1

Check out this this post .

The default behavior of SSRS is to run the dataset at the same time. They are run in the order in which they are presented in your rdl (top down when looking at it in the report data area). Changing the behavior of a single data source with multiple datasets is as simple as clicking on a checkbox in data source dialog.

With multiple datsources it is a little bit more tricky!

Here is the explanation from the MSDN Blog posted above:

Serializing dataset executions when using multiple data source:

Note that datasets using different data sources will still be executed in parallel; only datasets of the same data source are serialized when using the single transaction setting. If you need to chain dataset executions across different data sources, there are still other options to consider.

For example, if the source databases of your data sources all reside on the same SQL Server instance, you could use just one data source to connect (with single transaction turned on) and then use the three-part name (catalog.schema.object_name) to execute queries or invoke stored procedures in different databases.

Another option to consider is the linked server feature of SQL Server, and then use the four-part name (linked_server_name.catalog.schema.object_name) to execute queries. However, make sure to carefully read the documentation on linked servers to understand its performance and connection credential implications.

This is an interesting question and while I think there might be another way of doing it, it would take a bit of time and playing around with your datasets and more information on your setup of the datasources.

Hope this helps though.