{"id":3274,"date":"2025-11-17T18:11:01","date_gmt":"2025-11-17T18:11:01","guid":{"rendered":"https:\/\/xceed.com\/?p=3274"},"modified":"2026-02-16T18:16:26","modified_gmt":"2026-02-16T18:16:26","slug":"wpf-datagrid-performance-large-datasets","status":"publish","type":"post","link":"https:\/\/xceed.com\/fr\/blog\/uncategorized\/wpf-datagrid-performance-large-datasets\/","title":{"rendered":"Rendering 1 Million Rows in WPF Without Freezing the UI: A Practical Guide with Xceed DataGrid"},"content":{"rendered":"<p>Facing UI freezes with big data in your WPF apps? Discover how to achieve top-tier WPF DataGrid performance with large datasets using Xceed DataGrid. This guide shows you how to render 1 million rows without lag, using async data virtualization and best practices for enterprise-scale .NET applications.<br><\/p>\n\n\n\n<h1 class=\"wp-block-heading\" style=\"font-size:35px\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-color\">Why Naive WPF Grids Freeze with Large Datasets<\/mark><\/h1>\n\n\n\n<p>A typical WPF grid implementation often looks like this:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Bind directly to a <strong>List<\/strong> ou <strong>ObservableCollection<\/strong> containing all rows<\/li>\n\n\n\n<li>Let WPF create a visual element for each row<\/li>\n\n\n\n<li>Rely on default virtualization settings (or none at all)<\/li>\n<\/ul>\n\n\n\n<p>This works fine for a few thousand rows. But once you push into <strong>hundreds of thousands or millions of items<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>UI thread blocks<\/strong> while the grid creates and measures visual elements<\/li>\n\n\n\n<li><strong>Scrolling becomes jittery<\/strong> because too many containers are being recycled or recreated<\/li>\n\n\n\n<li><strong>Memory usage spikes<\/strong> because the grid tries to keep too many visuals alive<\/li>\n<\/ul>\n\n\n\n<p>The result: the grid feels slow, even if your data access is fast.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-f7e812387cb6c0514e9c2b2f8dc46d1e\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-color\">How Xceed DataGrid for WPF Handles 1 Million Rows<\/mark><\/h2>\n\n\n\n<p><strong>Xceed DataGrid pour WPF<\/strong> is designed for <strong>large, real-world datasets<\/strong>. The key difference is how it handles <strong>virtualization and async data loading<\/strong>:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Data virtualization:<\/strong> Only a small window of rows is realized at any time, even if the source contains 1M+ items<\/li>\n\n\n\n<li><strong>UI virtualization:<\/strong> Visual containers are reused aggressively, minimizing layout and rendering cost<\/li>\n\n\n\n<li><strong>Async loading:<\/strong> Data is fetched and prepared on background threads, keeping the UI responsive<\/li>\n<\/ul>\n\n\n\n<p>Practically, this means:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Initial grid load stays fast, even with huge datasets<\/li>\n\n\n\n<li>Scrolling remains smooth because you are not paying the cost of 1M visuals<\/li>\n\n\n\n<li>Memory footprint stays predictable and manageable<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\"><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-color\">Before\/After: Naive WPF Grid vs Xceed DataGrid<\/mark><\/h2>\n\n\n\n<p>Let\u2019s compare a simplified scenario.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Naive WPF Grid (Conceptual)<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Dataset:<\/strong> 1,000,000 rows in a List<\/li>\n\n\n\n<li><strong>Grille :<\/strong> Standard ItemsControl-style grid with minimal virtualization<\/li>\n<\/ul>\n\n\n\n<p>Observed behavior in a typical setup:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Initial load: several seconds before the UI becomes responsive<\/li>\n\n\n\n<li>Scrolling: noticeable stutter when paging through large jumps<\/li>\n\n\n\n<li>Memory: high usage due to many realized containers<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Xceed DataGrid for WPF with Async Virtualization<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Same dataset:<\/strong> 1,000,000 rows, but exposed through a virtualized source<\/li>\n\n\n\n<li><strong>Grille :<\/strong> Xceed DataGrid configured for data + UI virtualization<\/li>\n<\/ul>\n\n\n\n<p>Observed behavior in a typical setup:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Initial load: UI becomes usable much faster because only a small subset is realized<\/li>\n\n\n\n<li>Scrolling: smooth, even when jumping far in the dataset<\/li>\n\n\n\n<li>Memory: significantly lower because only visible rows are realized<\/li>\n<\/ul>\n\n\n\n<p>The rest of this article shows how to get there in your own project.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-a3c8b7ab3c5d5b56b69bf29e3d200514\">Setting Up Xceed DataGrid for WPF<\/h2>\n\n\n\n<p>First, install the DataGrid package via NuGet and reference it in your XAML.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">XAML: Basic Grid Setup with Virtualization<\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code data-no-translation=\"\">&lt;Window x:Class=\"MillionRowsDemo.MainWindow\"\n        xmlns=\"http:\/\/schemas.microsoft.com\/winfx\/2006\/xaml\/presentation\"\n        xmlns:x=\"http:\/\/schemas.microsoft.com\/winfx\/2006\/xaml\"\n        xmlns:xcdg=\"http:\/\/schemas.xceed.com\/wpf\/xaml\/datagrid\"\n        Title=\"1M Rows Demo\" Height=\"600\" Width=\"1000\"&gt;\n\n  &lt;Grid&gt;\n    &lt;xcdg:DataGridControl\n        x:Name=\"dataGrid\"\n        ItemsSource=\"{Binding Rows}\"\n        AutoCreateColumns=\"True\"\n        EnableColumnVirtualization=\"True\"\n        EnableRowVirtualization=\"True\"\n        VirtualizingPanel.ScrollUnit=\"Pixel\"\n        VirtualizingPanel.IsVirtualizing=\"True\"\n        VirtualizingPanel.IsVirtualizingWhenGrouping=\"True\"\n        UseLayoutRounding=\"True\" \/&gt;\n  &lt;\/Grid&gt;\n&lt;\/Window&gt;<\/code><\/pre>\n\n\n\n<p>Key points:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>EnableRowVirtualization<\/strong> et <strong>EnableColumnVirtualization<\/strong> keep the number of realized containers low<\/li>\n\n\n\n<li><strong>VirtualizingPanel<\/strong> settings ensure virtualization stays active even when grouping or scrolling heavily<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-0615025391a11875cfb36bff191575c2\">Generating 1 Million Rows in the ViewModel<\/h2>\n\n\n\n<p>For demonstration purposes, we can simulate a 1M-row dataset. In a real app, you might be streaming from a database or service.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code data-no-translation=\"\">public class MainViewModel\n{\n    public IList&lt;RowViewModel&gt; Rows { get; }\n\n    public MainViewModel()\n    {\n        const int rowCount = 1_000_000;\n        var rows = new List&lt;RowViewModel&gt;(rowCount);\n\n        for (int i = 0; i &lt; rowCount; i++)\n        {\n            rows.Add(new RowViewModel\n            {\n                Id = i,\n                Name = $\"Item {i}\",\n                Value = i % 1000,\n                Timestamp = DateTime.UtcNow.AddSeconds(-i)\n            });\n        }\n\n        Rows = rows;\n    }\n}\n\npublic class RowViewModel\n{\n    public int Id { get; set; }\n    public string Name { get; set; }\n    public int Value { get; set; }\n    public DateTime Timestamp { get; set; }\n}<\/code><\/pre>\n\n\n\n<p>Even with a simple in-memory list, Xceed DataGrid\u2019s virtualization will prevent the UI from trying to render all 1M rows at once.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-0f20b5b1621c1cf38560c2d20c0c5be3\">Introducing Async Data Virtualization<\/h2>\n\n\n\n<p>For truly large or remote datasets, you don\u2019t want to materialize all rows in memory. Instead, you can use a <strong>virtualized collection<\/strong> that loads pages of data on demand.<\/p>\n\n\n\n<p>Below is a conceptual example of a paged, async collection pattern:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code data-no-translation=\"\">public class AsyncVirtualizedCollection&lt;T&gt; : IList&lt;T&gt;\n{\n    \/\/ Implementation omitted for brevity.\n    \/\/ The important idea: items are loaded in pages on demand,\n    \/\/ often using async calls to a repository or service.\n}\n\npublic class MillionRowsViewModel\n{\n    public IList&lt;RowViewModel&gt; Rows { get; }\n\n    public MillionRowsViewModel(IDataSource dataSource)\n    {\n        Rows = new AsyncVirtualizedCollection&lt;RowViewModel&gt;(\n            pageIndex =&gt; dataSource.GetPageAsync(pageIndex),\n            pageSize: 200,\n            itemCount: 1_000_000);\n    }\n}<\/code><\/pre>\n\n\n\n<p>With this pattern:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The grid only requests the pages it needs for the current viewport<\/li>\n\n\n\n<li>Data is fetched asynchronously, keeping the UI thread responsive<\/li>\n\n\n\n<li>You avoid loading all 1M rows into memory at once<\/li>\n<\/ul>\n\n\n\n<p>Xceed DataGrid works very well with this style of virtualized source because it is built for <strong>data virtualization<\/strong> scenarios.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-5df06428771e1778df05fb58230f9d1e\">Simple Benchmark: Load Time, Memory, Scroll Smoothness<\/h2>\n\n\n\n<p>Every application is different, but you can run a simple benchmark comparing your current grid to Xceed DataGrid.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1. Measure Initial Load Time<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Start a stopwatch when you set <strong>ItemsSource<\/strong><\/li>\n\n\n\n<li>Stop it when the grid first becomes interactive<\/li>\n<\/ul>\n\n\n\n<pre class=\"wp-block-code\"><code data-no-translation=\"\">var stopwatch = Stopwatch.StartNew();\n\ndataGrid.ItemsSource = viewModel.Rows;\n\nstopwatch.Stop();\nDebug.WriteLine($\"Initial grid load: {stopwatch.ElapsedMilliseconds} ms\");<\/code><\/pre>\n\n\n\n<p>Compare:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Naive grid:<\/strong> Often multiple seconds with 1M rows<\/li>\n\n\n\n<li><strong>Xceed DataGrid:<\/strong> Typically much faster because it only realizes visible rows<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">2. Monitor Memory Usage<\/h3>\n\n\n\n<p>Use your usual profiling tools (Visual Studio Diagnostic Tools, dotMemory, etc.) and compare:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Baseline memory before binding the grid<\/li>\n\n\n\n<li>Memory after scrolling through a large portion of the dataset<\/li>\n<\/ul>\n\n\n\n<p>With Xceed DataGrid and proper virtualization, you should see:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Lower peak memory<\/li>\n\n\n\n<li>More stable memory usage over time<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">3. Evaluate Scroll Smoothness<\/h3>\n\n\n\n<p>This is subjective but critical for user experience:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Scroll slowly and quickly through the dataset<\/li>\n\n\n\n<li>Jump using PageUp\/PageDown and scrollbar dragging<\/li>\n<\/ul>\n\n\n\n<p>With Xceed DataGrid configured for virtualization, scrolling should remain <strong>smooth and responsive<\/strong>, even when the underlying dataset is very large.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-f6fb87521eae4b00eca6e53369e4b523\">Best Practices for Large Datasets in WPF with Xceed DataGrid<\/h2>\n\n\n\n<p>To get the best performance when rendering 1M+ rows:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Use data virtualization<\/strong> for remote or very large datasets<\/li>\n\n\n\n<li><strong>Keep row templates lightweight<\/strong>\u2014avoid heavy visuals or complex layouts in each cell<\/li>\n\n\n\n<li><strong>Enable row and column virtualization<\/strong> explicitly<\/li>\n\n\n\n<li><strong>Avoid unnecessary converters<\/strong> in hot paths; precompute values where possible<\/li>\n\n\n\n<li><strong>Profile early<\/strong> with realistic data volumes, not just sample data<\/li>\n<\/ul>\n\n\n\n<p>These practices, combined with Xceed DataGrid\u2019s virtualization engine, let you scale far beyond what a naive WPF grid can handle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-8ae6c5fc7d7761f535b219c5ddc04143\">Try This with Your Own Dataset<\/h2>\n\n\n\n<p>The best benchmark is your real application, with your real data and constraints.<\/p>\n\n\n\n<p><strong>Try Xceed DataGrid for WPF with your own dataset \u2013 start a 45-day trial:<\/strong><a target=\"_blank\" rel=\"noreferrer noopener\" href=\"https:\/\/xceed.com\/fr\/proces\/\">https:\/\/xceed.com\/trial\/<\/a><\/p>\n\n\n\n<p>If you want help reproducing these results or mapping this approach to your architecture, we\u2019re happy to collaborate.<\/p>\n\n\n\n<p><strong>Need help reproducing these results? Reach out:<\/strong><a target=\"_blank\" rel=\"noreferrer noopener\" href=\"https:\/\/xceed.com\/fr\/support\/\">https:\/\/xceed.com\/support\/<\/a><\/p>\n\n\n\n<h2 class=\"wp-block-heading has-primary-color has-text-color has-link-color wp-elements-61997020eca9ed832d9f0e9c40a127e6\">FAQ<\/h2>\n\n\n\n<p><strong>Q: Do I really need data virtualization if I already use UI virtualization?<\/strong>A: UI virtualization helps, but it only controls how many visual elements are created. With 1M+ rows, you also need <strong>data virtualization<\/strong> so you are not loading or holding all items in memory at once.<\/p>\n\n\n\n<p><strong>Q: Will grouping, sorting, and filtering still perform well with 1M rows?<\/strong>A: Yes, but you should be intentional. Xceed DataGrid is optimized for these operations, and you can combine them with server-side or paged operations when working with very large datasets.<\/p>\n\n\n\n<p><strong>Q: Can I start with an in-memory list and move to virtualization later?<\/strong>A: Absolutely. Many teams begin with an in-memory list for simplicity, then introduce a virtualized collection once they understand their real data volumes and performance requirements.<\/p>\n\n\n\n<p><strong>Q: What if my existing grid is already in production and struggling?<\/strong>A: You can introduce Xceed DataGrid incrementally\u2014start with a single screen or module, benchmark it against your current grid, and then roll out the change once you\u2019re confident in the performance gains.<\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>When your WPF app needs to render hundreds of thousands or even 1 million+ rows most grids start to choke. Scrolling stutters, the UI freezes, and users lose trust in the application.<\/p>\n<p>In this guide, we\u2019ll walk through how to render 1,000,000 rows in WPF without freezing the UI using Xceed DataGrid for WPF. We\u2019ll compare a naive WPF grid to Xceed DataGrid with async data virtualization, show a simple benchmark, and give you code you can drop into your own app.<\/p>","protected":false},"author":12,"featured_media":3275,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[141,60,1],"tags":[442,347,545,356,396,366,353,431,543,373,439,542,544,358,428],"class_list":["post-3274","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all","category-tutorials","category-uncategorized","tag-appdev","tag-asyncvirtualization","tag-bigdataui","tag-datagrid","tag-developerexperience","tag-developertools","tag-dotnet-2","tag-enterpriseapps","tag-highperformanceui","tag-net9","tag-performance-2","tag-scalability","tag-uxperformance","tag-wpf-2","tag-xceed-2"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>WPF DataGrid Performance with Large Datasets: 1 Million Rows with Xceed<\/title>\n<meta name=\"description\" content=\"Learn how to maximize WPF DataGrid performance with large datasets using Xceed DataGrid. 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