In a recent blog post by Craig White, the focus is on harnessing the full capabilities of the Power Apps Patch function for updating multiple rows in a data source. He highlights three methods for achieving this, promising an analysis of which is the fastest. He cites the strength of Power Fx in this context and sets the stage for a deep dive into optimizing data transactions.
The core of the post revolves around the Patch function, a key Power Fx function used in Power Apps. White explains the function’s ability to modify records, outlining how the syntax changes based on whether you're creating or editing data. For multiple records, he suggests the approach must shift for optimal performance.
White presents a practical scenario involving a collection of questions answered by several employees. He details the use of Power Apps to facilitate dynamic forms—a flexible method for data collection. This leads to a discussion on the various structures used in his demonstration, such as Dataverse tables, and the efficiency of different patch methods.
One of the methods explored is using ForAll combined with Patch. White points out that while this method is common, it tends to be slower, especially with a large number of rows. He shares his testing results, which indicate a significant time range for processing data, suggesting this might not always be the best choice for user experience.
Conversely, reversing the Patch and ForAll functions can lead to better results. He notes a single Patch transaction is considerably faster and more efficient. Crediting fellow Power Apps enthusiast Randy Hayes for this insight, White emphasizes the strength of correctly pairing ForAll with Patch.
Schema matching, another method for multiple row operations, also receives attention. This strategy involves creating a data structure that mirrors the target data source for efficient batch processing. White describes how this method can manage both creation and update operations seamlessly.
Next, the blog post touches on JSON and the Parse JSON function, shedding light on their applications in the context of Power Apps. With these functions, data can be stored and manipulated in JSON format, which may offer performance and flexibility benefits.
White’s experimentation reveals that the JSON/ParseJSON method is exceptionally fast and consistent across different data sizes. He provides a comprehensive speed test graph to demonstrate this point. However, the choice between JSON and traditional table storage should be based on specific application needs, as each has its own set of advantages and potential limitations.
In conclusion, the blog post does not endorse a one-size-fits-all solution; instead, White suggests choosing the right method based on factors like reporting needs, scalability, and the data's nature. For those interested in trying the methods themselves, White offers a downloadable solution for experimentation.
Power Apps offer multiple ways to handle data manipulation, particularly when dealing with numerous records. Whether you opt for ForAll with Patch, the schema matching technique, or the newly discussed JSON/ParseJSON method, each approach has its strengths. As technology progresses, Power Apps capabilities grow, providing developers with ever more efficient ways to process large datasets. With careful consideration of the context and requirements, these techniques can be powerful tools in a developer's arsenal, enabling fast and flexible app development.