It’s common for Salesforce Connect queries of external data to have a large result set that’s broken into smaller batches or pages. You decide whether to have the paging behavior controlled by the external system (server-driven) or by the SQL adapter for Salesforce Connect (client-driven).
| Available in: both Salesforce Classic and Lightning Experience (not for high-data-volume external objects) |
Available in: Developer Edition Available for an extra cost in: Enterprise, Performance, and Unlimited Editions |
To align with Snowflake’s recommendations, the Salesforce Connect SQL adapter uses server-driven paging by default, and Snowflake determines the page sizes and batch boundaries. With server-driven paging enabled, Salesforce ignores the requested batch sizes, including the batch size specified in REST and SOAP API calls and scope in Batch Apex. If you plan to use these features with external objects from Snowflake, refer to the Client-Driven Pagination section.
Server-driven pagination optimizes the external system’s performance and improves the load times for external objects in your org. Also, the external data can change while your users or the Lightning Platform are paging through the result set. Typically, server-driven paging adjusts batch boundaries to accommodate changing external data more effectively than client-driven paging.
The Server Driven Pagination field on the external data source specifies whether to
use client-driven or server-driven paging. When server-driven paging is disabled on
a Snowflake external data source, the requests use the $limit and $offset system query
options to page through the result set.
Client-driven paging is useful if you plan to write Apex code with custom SOQL queries or Apex SOQL for-loops against data from a Snowflake external data source. With these types of queries, client-driven pagination ensures that Snowflake returns only page sizes of up to 2,000 rows.
If your implementation requires iterating over large datasets in Apex on a row-by-row basis, we recommend that you limit the datasets by adding filters to your query. Snowflake can contain billions of rows, and large queries can consume significant resources.
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