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Imagine your software as a well-oiled machine, and filters as your friendly helpers, making tasks easier and boosting performance. However, if these helpers aren’t designed just right, they might bring unexpected challenges to the table.
Navigating with Filters
Filters provide easier access and a concise approach for your user interface. You can navigate your software to find exactly what you’re looking for, even a layperson can use them without knowing much about them. Sometimes while searching for data in a system, you may get typing errors that lead to the exact content not being shown. With filters, data can be pulled from a server in fractions of a second, reducing manual inputs & man-made errors.
Simple Solution for Manufacturing Industry with UI Filters
In one of our recent projects with a leading material manufacturing company, we encountered a significant challenge: their search and filtering tools were incredibly slow at finding the right data. Imagine workers waiting for 15 to 20 seconds just for a screen to load— everyone was getting impatient.
So, we got to work. First off, we looked closely at how their filter for the Bill of Materials (BOM) was set up. We found that it was pulling way too much data from the server—around 16 to 18 megabytes! It was quite time-consuming for workers to retrieve information on materials, especially considering the limited 2-3 filters available. We took a closer look at their existing system and found that these search and filtering tools were responsible for serious delays.
To address this, we enhanced the filter functionality. We prioritized the UI filters that were most frequently used, such as the recently accessed materials filter. We also added additional options like storage location, material plant, and material category for easy-to-get data for the workers. This way, users could quickly access specific data without the hassle of searching through numerous pages.
Even though the searching experience had been revamped, the server was still sending a huge amount of data.
So, we dug into the data itself. We discovered that the system was sending quite a bit more information than it needed to. We trimmed it down to just the essentials.
And you know what? The download sizes dropped from 16 to 18 megabytes to just 8 to 10 kilobytes. And the best part? Screen loading times went from a painful 20 seconds down to a speedy 1 to 2 seconds.
In conclusion, our efforts resulted in the successful implementation of BOM filters, providing workers with the necessary information without the need to search through every page and enduring lengthy 20-second delays. Our user-friendly approach ensured that even those less familiar with the system could easily navigate the filters. Additionally, our application was optimized to retrieve material data simply by typing in material names and information, further enhancing efficiency and user experience.
Potential Pitfalls:
To be sure that filters have been correctly implemented, we must keep in mind 2 crucial aspects:
- In the front-end, if we are calling APIs to filter data, remember to pass on only the necessary data. This will help get a quicker response from the server.
- Make sure that there is a proper balance between the use of server data and static files.
Ignoring these points may lead to sluggish responses and underperformance in the application.
Possible Solutions
Addressing performance issues related to filters in large datasets requires a combination of thoughtful design, optimization techniques, and technical considerations. Here are some strategies to reduce the performance impact:
Lazy Loading:
Implement lazy loading, where the UI only loads the data required for the current view. This can significantly reduce initial loading times and improve overall responsiveness.
Pagination:
Implement pagination to divide the dataset into smaller chunks. Instead of loading the entire dataset at once, load data progressively as the user navigates through pages. Ensure that you have clearly visible, easy to use pagination controls on the pages.
Server-Side Filtering:
Conduct filtering operations on the server side rather than on the client side. This ensures that only the necessary data is sent to the client, reducing the amount of data transferred over the network.
Caching:
Implement caching mechanisms to store frequently accessed or recently filtered data. This can help us avoid unnecessary computations and database queries if caching is implemented on the backend side.
Indexing:
Optimize your database with proper indexing on columns frequently used for filtering. Indexing can significantly improve the speed of filtering operations.
Asynchronous Requests:
Use asynchronous requests to fetch data in the background while allowing the user to continue interacting with the UI. This can prevent the UI from becoming unresponsive during data retrieval.
Optimized Query Execution:
Try to optimize the database queries which is used for filtering to ensure they execute efficiently. Analyze and profile queries to identify and address any bottlenecks.
Conditional Rendering:
Use conditional rendering to display only the relevant portions of the UI based on the applied filters. This helps us to minimize the amount of data that needs to be processed and displayed.
Optimize Frontend Code:
Always look for and optimize client-side code to ensure that it efficiently handles large datasets. By applying this kind of strategy, not only can we enhance the performance of our UI, but also provide users with a smoother and more responsive experience.
In the last five years, we at CoReCo Technologies, have worked with 60+ various size businesses from across the globe, from various industries and have been part of 110+ such success stories. We applied the latest technologies for adding value to our customers’ businesses through our commitment to excellence.
For more details about such case studies, visit us at www.corecotechnologies.com and if you would like to convert this virtual conversation into a real collaboration, please write to [email protected].
