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Fastest & Most Efficient DataTable Row Selection Methods in VB.NET (Single/Multiple, Simple/Complex Conditions)

test code 21 Reputation points
2026-02-05T13:24:21.8333333+00:00

We are using VB.NET with System.Data.DataTable objects that contain large volumes of data. We are evaluating which approach is the fastest and most efficient for selecting rows in specific scenarios.

  1. Single row
  2. Multiple rows
  3. Single row with simple condition
  4. Single row with complex condition
  5. Multiple rows with simple condition
  6. Multiple rows with complex condition

Which method is best for each case? Rows.Find(), DataTable.Select(), Manual Loops (For Each), LINQ, DataView.RowFilter? Any other methods?

Any performance benchmarks or timing comparisons for these methods would be helpful.

Developer technologies | .NET | Other
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  1. Jack Dang (WICLOUD CORPORATION) 14,955 Reputation points Microsoft External Staff Moderator
    2026-02-06T06:42:40.0833333+00:00

    Hi @test code ,

    Thanks for reaching out.

    For large DataTable objects in VB.NET, here’s a practical approach for the scenarios you mentioned:

    1. Single row: Use Rows.Find() if your table has a primary key, it’s the fastest.
    2. Multiple rows: DataTable.Select() or DataView.RowFilter work well for simple conditions.
    3. Single row with simple condition: DataTable.Select() or LINQ is clean and efficient.
    4. Single row with complex condition: LINQ is usually the most flexible for complex rules.
    5. Multiple rows with simple condition: Select() or RowFilter are easy and perform well.
    6. Multiple rows with complex condition: LINQ is recommended, especially if combining multiple rules or doing transformations.

    Tip: Manual loops (For Each) work for any case but are generally slower, so use them only if you need custom logic that other methods can’t handle. Indexing your primary key and minimizing full-table scans will give the biggest speed boost.

    Hope this helps! If my answer was helpful - kindly follow the instructions here so others with the same problem can benefit as well.


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