Abstract: A method provides deterministic row ordering for query result sets in a database system. A framework receives a query referencing a database table and generating a row index column in response. The framework generates the row index column that involves determining an order associated with source files underlying the referenced table and assigns each table row a unique index value based on the file order and row position within the files. The framework executes the received query to produce a result set ordered by the generated row index column. The framework determines row order based on analyzing underlying data file metadata and facilitates returning the result set with consistent, reliable ordering, enabling stable positional-based access semantics when querying distributed database tables.

Abstract: Examples provide a method for performing dynamic pivot queries employing PANDA-compatible semantics inside a cloud data platform workload. A SQL engine receives a pivot query specifying a pivot column for dynamically determining pivot values in a dynamic pivot operation. The SQL engine identifies, in the pivot query, a keyword or a subquery indicating dynamic determination of the pivot values. Based on identifying the keyword or the subquery indicating the dynamic determination of the pivot values, the SQL engine determines distinct pivot values for the pivot column by evaluating the keyword or executing the subquery. The evaluating of the keyword includes issuing a query to a data store to obtain the distinct pivot values for the pivot column. The SQL engine performs the dynamic pivot operation using the distinct pivot values for the pivot column.

Abstract: A method includes receiving instructions to perform order-dependent DataFrame operation on data. In response to receiving the instructions, the framework analyzes the instructions to identify the order-dependent DataFrame operation, and generates an executable query corresponding to the identified order-dependent DataFrame operation. The framework executes the generated executable query on the data stored in the first database in the cloud data platform, creates a row position column that generates for each row a unique integer identifier, reproducible across multiple sessions and/or queries for the same underlying data to make the data accessible via positional indexing, and assigns each row of the first database a unique row index value based on the row position column and the row position column order.

Abstract: Instead of processing a complex query having multiple interdependent levels of subqueries as-is, each level is processed to return results in subsets. Each level of subquery can be chunked or the results broken into subsets using pagination. The smaller subsets of the results are streamed back to the requestor and the next level of subquery to continue processing the complex query to substantially decrease the user's time to value when running a complex query by returning some immediate results for display which are refined and eventually converge on the full results as each chunked query runs.

Abstract: Instead of processing a complex query having multiple interdependent levels of subqueries as-is, each level is processed to return results in subsets. Each level of subquery can be chunked or the results broken into subsets using pagination. The smaller subsets of the results are streamed back to the requestor and the next level of subquery to continue processing the complex query to substantially decrease the user's time to value when running a complex query by returning some immediate results for display which are refined and eventually converge on the full results as each chunked query runs.