Working with large database tables in high-traffic environments can be challenging, especially when performing updates. As a C# developer, you need strategies to optimize transactions, reduce timeouts, and maintain performance. Here’s how to approach this problem.

1. Optimize Transactions

Transactions are critical for data consistency but can become bottlenecks.

  • Keep Transactions Short Minimize the time a transaction remains open. Split large operations into smaller batches (e.g., update 1,000 rows at a time). 
using (var transaction = connection.BeginTransaction())  
  {  
      for (int i = 0; i < totalRecords; i += batchSize)  
      {  
          var batch = GetBatchData(i, batchSize);  
          connection.Execute("UPDATE LargeTable SET Column = @Value WHERE Id = @Id", batch, transaction);  
      }  
      transaction.Commit();  
  }
  • Use Read Committed Snapshot Isolation (RCSI) Enable RCSI in SQL Server to reduce locking and blocking. This allows readers to see a consistent snapshot of data without blocking writers.

2. Tune Queries and Indexes

Slow queries are a common cause of timeouts.

  • Optimize Indexes

    Ensure indexes exist on columns used in WHERE, JOIN, or ORDER BY clauses. Avoid over-indexing, as it slows down writes.

    • Use SQL Server Execution Plans to identify missing indexes.

  • Filter and Batch Updates


    Update only necessary columns and rows. Use WHERE clauses to target specific data.

UPDATE LargeTable  
  SET Status = 'Processed'  
  WHERE Status = 'Pending' AND CreatedDate < DATEADD(DAY, -1, GETDATE());

3. Handle Timeouts Gracefully

Timeouts often occur due to resource contention or long-running queries.

  • Increase Command Timeout Adjust the timeout for specific operations in C#: 
using (var command = new SqlCommand(query, connection))  
  {  
      command.CommandTimeout = 120; // 120 seconds  
  }
  • Implement Retry Logic Use libraries like Polly to retry transient errors (e.g., deadlocks): 
var retryPolicy = Policy  
      .Handle<SqlException>(ex => ex.Number == 1205) // Deadlock error code  
      .WaitAndRetry(3, retryAttempt => TimeSpan.FromSeconds(2));  

  retryPolicy.Execute(() => UpdateLargeTable());

4. Scale and Maintain Tables

  • Partition Large Tables

    Split tables into smaller partitions by date or category (e.g., monthly partitions). This reduces lock contention.

  • Archive Old Data

    Move historical data to an archive table to keep the main table lean.

  • Update Statistics Regularly

    Ensure the database optimizer has up-to-date statistics for efficient query plans.

5. Use Asynchronous Programming

For high-traffic apps, use async/await to avoid blocking threads:

public async Task UpdateRecordAsync(int id)  
{  
    using (var connection = new SqlConnection(connectionString))  
    {  
        await connection.ExecuteAsync(  
            "UPDATE LargeTable SET Column = @Value WHERE Id = @Id",  
            new { Value = "NewData", Id = id }  
        );  
    }  
}

Final Tips

  • Monitor Performance: Use tools like SQL Server Profiler or Application Insights to identify slow queries.
  • Test Under Load: Simulate high traffic to uncover bottlenecks before deployment.
  • Avoid Cursors: Use set-based operations instead of row-by-row processing.

By combining optimized code, smart database design, and proper error handling, you can maintain performance even when working with large tables in high-traffic C# applications.