How to Partition Datasets in SQL Server While Preserving Row Groupings скачать в хорошем качестве

How to Partition Datasets in SQL Server While Preserving Row Groupings

Discover effective ways to partition SQL Server datasets without breaking row groupings, ensuring optimal performance with multiple queries. --- This video is based on the question https://stackoverflow.com/q/62832249/ asked by the user 'TurgidWizard' ( https://stackoverflow.com/u/4362211/ ) and on the answer https://stackoverflow.com/a/62832764/ provided by the user 'Gordon Linoff' ( https://stackoverflow.com/u/1144035/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Sql Server: Partition Dataset While Preserving Row Groupings Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Understanding the Problem: Partitioning Datasets in SQL Server When working with large datasets in SQL Server, you may often face the challenge of efficiently processing the data, especially if you aim to utilize parallel processing to enhance performance. A particular scenario we encounter involves returning rows that are grouped based on crucial columns, such as Student_ID and Module_ID. The goal is to partition this dataset into segments of equal size while ensuring that each segment retains complete row groupings. This means we must avoid splitting groups across multiple partitions, as that will hinder further processing. The traditional method of using the ROW_NUMBER() function falls short here because it can lead to the unfortunate situation of a group being scattered between partitions. Let's delve into an effective solution that addresses this problem using SQL's ranking functions. Proposed Solution: Utilizing DENSE_RANK() To ensure groupings remain intact while efficiently partitioning data, we can leverage ranking functions such as RANK() or DENSE_RANK(). Here's a detailed breakdown of how to implement this: Step 1: Choose the Right Ranking Function RANK() This function generates a unique rank number for each distinct row within the partition of a result set. If two rows tie on rank, the next rank will skip numbers. DENSE_RANK() Similar to RANK(), however, it does not skip rank numbers when there are ties. Every unique value gets a sequential rank. Step 2: Implement the Ranking Function in Your Query Using either RANK() or DENSE_RANK(), you can assign an identifier to each grouping: [[See Video to Reveal this Text or Code Snippet]] Step 3: Filter the Groups Now that each group has a distinct identifier (GroupID), you can easily filter based on this identifier to select different sections of your data without breaking groupings. For example, if you only want groups up to a certain ID: [[See Video to Reveal this Text or Code Snippet]] Practical Application In the provided dataset example, let's say you wanted to achieve this result: First Query: [[See Video to Reveal this Text or Code Snippet]] Second Query: [[See Video to Reveal this Text or Code Snippet]] Conclusion By using the DENSE_RANK() function, you can effectively partition your results while maintaining the integrity of each grouping. This approach not only improves performance by allowing parallel processing but also ensures that your processing logic remains intact. This method provides a structured way to handle the requirement of partitioning datasets without the pitfalls typically associated with earlier techniques like ROW_NUMBER(). Implement these strategies in your SQL queries to achieve more efficient data handling!