Understanding and Solving the Issue of Creating Multiple Records with the Same Name in .NET Web API

Introduction

In this article, we will delve into a common problem faced by developers when working with .NET Web APIs. The issue is related to creating multiple records with the same name in a database using an HTTP PUT request. We will explore the root cause of this problem and discuss several solutions to prevent it.

Understanding the Problem

The problem arises when two or more concurrent requests are sent to the Web API to create or update a record with the same name. For example, if a user clicks on a button twice in quick succession, the Web API may receive two separate requests to create a new record with the same name. In this scenario, both requests may reach the database at the same time and result in multiple records being created.

Understanding the Current Implementation

Let’s take a closer look at the current implementation:

[HttpPut]
public async Task<IActionResult> Put(string name)
{
    // ...
}

In this example, the Put method is marked as asynchronous and takes a single string parameter name. The method checks if the name parameter is null and returns a Bad Request response if it is. Otherwise, it retrieves a view from the database using LINQ to SQL and updates its NumOfViews property.

If the view does not exist in the database, a new view is created with the specified name and updated NumOfViews value. The method then returns an Ok response with the updated view object.

Understanding the Potential Issues

There are several potential issues with this implementation:

If multiple requests are sent concurrently to create or update records with the same name, they may overwrite each other’s changes.
This can lead to inconsistent data in the database and make it challenging to track updates.

Solution 1: Using Transactions

One way to prevent double clicks on the front-end is to use transactions. A transaction ensures that either all operations within the transaction are completed successfully or none of them are committed, maintaining consistency across multiple database records.

Here’s an example using Dapper and System.Transactions:

using System.Transactions;

...

using (var transactionScope = new TransactionScope())
{
    DoYourDapperWork();
    transactionScope.Complete();
}

In this code snippet, we create a new TransactionScope object to manage the transaction. The DoYourDapperWork() method contains the operations that are part of the transaction.

Solution 2: Using Locking Mechanisms

Another approach is to use locking mechanisms on the database table or column level. This can prevent multiple requests from updating the same record simultaneously.

For instance, if we’re using SQL Server, we can use the UPDATE statement with a lock hint:

ALTER TABLE Views ADD CONSTRAINT FK_View_PageName FOREIGN KEY (PageName) REFERENCES Pages(PageName)
ON UPDATE NO ACTION ON DELETE NO ACTION

-- Create or update view
UPDATE Views WITH (ROWLOCK)
SET NumOfViews = @NumOfViews
WHERE PageName = @PageName

In the above code snippet, we use the ROWLOCK hint to acquire an exclusive lock on the row being updated. This prevents other transactions from updating the same record until the current transaction is committed.

Solution 3: Using Double-Checked Locking

We can also implement a double-checked locking mechanism in our Web API controller:

[HttpPut]
public async Task<IActionResult> Put(string name)
{
    // ...
    lock (lockObject)
    {
        if (view != null)
        {
            view.NumOfViews++;

            await _context.SaveChangesAsync();
                return Ok(new { view, status = 200 });
        }
        else
        {
            //not exist in db create a new one
            View newView = new View
            {
                PageName = name,
                NumOfViews = 1
            };

            view = await _viewHelper.CreateAsync(newView);

            return CreatedAtAction("Put",new { status=201, view});
        }
    }
}

In the above code snippet, we use a lock statement to acquire an exclusive lock on the row being updated. The lockObject is a shared resource that’s used by all threads in the application.

Solution 4: Using Caching

Another approach is to cache the results of expensive database operations or calculate them on the client-side using JavaScript:

[HttpPut]
public async Task<IActionResult> Put(string name)
{
    // ...
    var cachedView = _viewCache.Get(name);

    if (cachedView == null)
    {
        view = await _context.Views
            .FirstOrDefaultAsync(v =&gt; v.PageName.ToLower() == name.ToLower());

        if (view != null)
        {
            view.NumOfViews++;

            await _context.SaveChangesAsync();
                return Ok(new { view, status = 200 });
        }
        else
        {
            //not exist in db create a new one
            View newView = new View
            {
                PageName = name,
                NumOfViews = 1
            };

            view = await _viewHelper.CreateAsync(newView);

            return CreatedAtAction("Put",new { status=201, view});
        }
    }

    return Ok(cachedView);
}

In the above code snippet, we use a cache to store the results of expensive database operations. If the result is already cached, we can simply return it instead of re-running the query.

Conclusion

Preventing double clicks on the front-end requires careful consideration of the underlying database schema and locking mechanisms. By understanding these concepts and implementing strategies like transactions, locking, double-checked locking, or caching, developers can ensure data consistency across multiple requests.

While there are no silver bullets in software development, using a combination of these approaches can help mitigate issues related to concurrent updates on shared resources.

Last modified on 2024-02-17