Why not use Enable Client Side Query. Best practices?
In ArcGIS Experience Builder, "Enable Client Side Query" is a powerful feature, but there are scenarios where it might not be the best option. Here's a breakdown of why and when you might want to avoid using it, along with some best practices:
Why Not Use "Enable Client Side Query":
Large Datasets:
If you're working with large datasets, enabling client-side querying can result in poor performance because the entire dataset is downloaded to the client (i.e., the user's browser). This can lead to longer load times and excessive memory usage, especially if the dataset is extensive.
It can also put a strain on the client device, leading to performance issues such as slow interactions and crashes.
Device Constraints:
Users accessing the application on lower-powered devices (e.g., mobile phones, tablets, older computers) may experience issues with large data loads. This can result in a poor user experience as their device struggles to handle the large amounts of data.
Dynamic or Real-Time Data:
If your data is updated frequently, client-side queries may not be ideal because the user will only have access to the data loaded when the app was first accessed. Server-side queries ensure that users always receive the most up-to-date data without having to refresh or reload the entire dataset.
Security Concerns:
When the entire dataset is loaded client-side, all the data is potentially accessible to the user, even if certain parts should not be visible. This can lead to inadvertent data exposure, especially if you have sensitive data or need to limit what data specific users can see.
Best Practices for Queries in Experience Builder:
Use Server-Side Queries for Large Datasets:
For larger datasets or frequently updated data, server-side queries are generally more efficient. These queries only pull the necessary data based on the filters applied, resulting in better performance and reduced load on the client.
Leverage Client-Side Queries for Small, Static Datasets:
Client-side queries are beneficial for small datasets that don't change often. In this case, loading the data once to the client allows for quicker interactions without re-querying the server.
Pagination for Large Data:
When working with large datasets, consider using pagination (loading a portion of data at a time). This way, the user is not overwhelmed with a massive data load all at once, and the app remains responsive.
Optimize Data Loading:
Use filters, extents, and other constraints to limit the amount of data that needs to be loaded. You can use server-side queries to initially limit the scope of data, and then enable client-side queries for smaller, manageable datasets after the initial load.
Data Sensitivity:
Be mindful of what data should be exposed to the client. If you need to control access to certain data, use server-side queries with user authentication or data permissions to ensure only authorized data is shown.
Consider User Devices:
Think about the devices your audience will be using. For a mobile audience, server-side queries with optimized data loads are typically a better option to maintain good performance across all devices.
Caching and Preprocessing:
If applicable, you can cache common queries or preprocess data on the server to improve performance and minimize repeated heavy queries.
Conclusion:
Client-side queries are not always a bad option but are best suited for smaller, static datasets where performance won't degrade and users don't need real-time updates. Server-side queries are generally more scalable and efficient for large, dynamic datasets. Balancing the two based on the nature of your data and user requirements is key to building performant and user-friendly applications in Experience Builder.
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You said:
Why not larger static?
ChatGPT said:
ChatGPT
Even for larger static datasets, using client-side queries in ArcGIS Experience Builder can still introduce challenges. Here's why larger static datasets might not be ideal for client-side querying, even though they aren't changing frequently:
1. Performance Impact on Load Time:
Initial Load Time: When using client-side queries, the entire dataset is loaded into the user's browser at once. If the dataset is large, the time it takes to download all that data can result in a long initial load time. Users may experience a delay before they can start interacting with the app.
Processing Time: Once the dataset is loaded, the browser has to process and render it. For large datasets, this can lead to sluggish performance as the browser processes the data for display and interaction.
2. Browser and Device Limitations:
Memory Consumption: A large dataset can consume a significant amount of memory, which can cause issues on lower-end devices (like mobile phones or older computers). The browser may crash, or the app could become unresponsive if the dataset is too large for the device to handle.
CPU Usage: Processing large datasets client-side requires computational power. This can lead to increased CPU usage on the client machine, making the browser slow and the device hot, especially on mobile or older devices.
3. User Experience:
Responsiveness: The larger the dataset, the slower the app becomes in terms of interactions such as filtering, zooming, or panning the map. Even if the data is static, the user experience can degrade significantly as they try to interact with the loaded data.
Slow Searching and Filtering: If users are trying to search or apply filters, client-side queries on large datasets can result in long delays between user actions and results being displayed.
4. Data Transfer Limits:
Bandwidth Concerns: Even if the data is static, downloading a large dataset to the client may exceed certain network bandwidth limits. Users on slower internet connections may have an especially frustrating experience waiting for the data to load.
5. Browser Limitations:
Limits on Data Handling: Browsers have limits on how much data they can handle efficiently. Loading a very large dataset into memory can hit these limits, leading to errors, crashes, or warnings in the browser.
Compatibility: Different browsers handle large datasets in varying ways, which can introduce compatibility issues across different devices and browsers.
6. Updates and Data Distribution:
App Updates: Even if the dataset is static, any updates or modifications to the dataset or app require reloading the entire dataset when a user revisits the app. This means they might need to download the large dataset again, impacting the overall experience if updates are frequent.
CDN and Caching: Although using a Content Delivery Network (CDN) can help distribute the load, large files may still need to be cached on the client side, which requires storage and impacts performance.
Best Practices for Larger Static Datasets:
Use Server-Side Queries with Caching: Even for static datasets, it’s often more efficient to handle querying server-side and cache the results. This allows the server to deliver just the subset of data the user needs based on filters or extents, improving performance.
Paginate Data: If the dataset is large, consider paginating the data so that only a small portion of it is loaded at a time. This can reduce the memory footprint and improve responsiveness.
Use Extent or Filter-Based Loading: Instead of loading the entire dataset client-side, load only the data relevant to the current map extent or based on user filters. This way, you’re not overwhelming the browser with unnecessary data.
Conclusion:
Even for larger static datasets, it’s better to use server-side queries because they allow for more efficient performance, reduce load on the client’s browser, and improve the overall user experience. Loading large datasets directly into the client-side can cause significant performance issues and degrade the functionality of your application.
