Boost Performance in ADO .NET Code - Limiting Data

Amit Lale · October 2, 2023

.NET   ADO.NET   C#   Performance

Boost ADO .NET Performance TIP 2: Limiting Data

Table of contents

Introduction

Continuing in the series of 10 Proven Ways to Boost Performance in ADO .NET Code, this article will explore the second tip: Limiting Data. In the world of software development, performance is a critical factor that can significantly impact user experience. When it comes to database operations, such as querying and updating data, limiting the amount of data retrieved can greatly enhance the performance of ADO .NET applications. We will explore the benefits of limiting data in .NET and provide examples to illustrate their effectiveness.

The Impact of Retrieving Unnecessary Data on Performance

When ADO .NET executes a query, the data that is returned by the query is stored in a buffer on the client side. The size of this buffer depends on the number and type of columns that are selected by the query, as well as on the number of rows that are returned. The larger the buffer, the more memory and network resources are consumed by the application.

Retrieving unnecessary data can cause several performance issues, such as:

  • Increased network traffic: The more data that is transferred from the server to the client, the more network bandwidth is used. This can result in slower response times and increased network congestion.

  • Increased memory consumption: The more data that is stored in the buffer, the more memory is allocated by the application. This can result in increased memory pressure and garbage collection overhead.

  • Increased CPU usage: The more data that is processed by the application, the more CPU cycles are consumed. This can result in increased CPU load and reduced responsiveness.

Therefore, it is important to limit the amount of data that is retrieved by the query to only what is necessary for the application logic.

The Techniques to Limit the Data Retrieved by the Query

One of the most effective techniques to limit the data retrieved by the query is to use SELECT statements with specific columns. This means that instead of selecting all columns from a table or a view, only select those columns that are actually needed by the application. For example, if an application only needs to display the name and email address of a customer, there is no need to select other columns such as phone number, address, or credit card information.

Using SELECT statements with specific columns can reduce the amount of data that is retrieved by the query in several ways:

  • Reduced buffer size: By selecting only specific columns, the buffer size on the client side is reduced. This means that less memory and network resources are used by the application.

  • Reduced processing time: By selecting only specific columns, the processing time on both the server and the client side is reduced. This means that less CPU cycles are used by both parties.

  • Reduced locking time: By selecting only specific columns, the locking time on the server side is reduced. This means that less concurrency issues and blocking scenarios are encountered.

To illustrate this technique, consider the following example:


-- Select all columns from Customers table

SELECT * FROM Customers

  

-- Select only specific columns from Customers table

SELECT CustomerName, CustomerEmail FROM Customers

The first query selects all columns from Customers table, which might include unnecessary or sensitive information. The second query selects only two columns from Customers table, which are relevant for displaying customer details. The second query will result in less data being retrieved by ADO .NET than the first query.

Another technique to limit the data retrieved by the query is to use WHERE clauses or other filtering criteria to limit the number of rows that are returned by the query. This means that instead of returning all rows from a table or a view, only return those rows that match certain conditions or criteria. For example, if an application only needs to display customers who have placed orders in a certain date range, there is no need to return customers who have not placed any orders or who have placed orders outside of that date range.

Using WHERE clauses or other filtering criteria can reduce the amount of data that is retrieved by the query in several ways:

  • Reduced buffer size: By returning only specific rows, the buffer size on the client side is reduced. This means that less memory and network resources are used by the application.

  • Reduced processing time: By returning only specific rows, the processing time on both the server and the client side is reduced. This means that less CPU cycles are used by both parties.

  • Reduced locking time: By returning only specific rows, the locking time on the server side is reduced. This means that less concurrency issues and blocking scenarios are encountered.

To illustrate this technique, consider the following example:


-- Select all rows from Orders table

SELECT * FROM Orders

  

-- Select only rows from Orders table that match a certain date range

SELECT * FROM Orders WHERE OrderDate BETWEEN '2023-01-01' AND '2023-01-31'

The first query selects all rows from Orders table, which might include irrelevant or outdated information. The second query selects only rows from Orders table that match a certain date range, which are relevant for displaying order details. The second query will result in less data being retrieved by ADO .NET than the first query.

The Common Pitfalls to Avoid When Writing SELECT Queries

While using SELECT statements with specific columns and WHERE clauses or other filtering criteria can improve the performance of ADO .NET applications, there are some common pitfalls that beginner or inexperienced developers might make while writing SELECT queries to retrieve the data. These pitfalls can negate the benefits of limiting the data retrieved by the query, or even worsen the performance of the application. Some of these pitfalls are:

  • Using SELECT * instead of specifying columns: Using SELECT * is a bad practice that should be avoided, as it returns all columns from a table or a view, regardless of whether they are needed or not. This can result in unnecessary data being retrieved by the query, which can increase the buffer size, the processing time, and the locking time.

  • Using unnecessary joins or subqueries: Using joins or subqueries can be useful for retrieving data from multiple tables or views, but they should be used with caution, as they can increase the complexity and the cost of the query. If a join or a subquery is not needed for the application logic, it should be removed from the query. This can result in less data being retrieved by the query, which can reduce the buffer size, the processing time, and the locking time.

  • Using functions or expressions on columns: Using functions or expressions on columns can be useful for transforming or manipulating data, but they should be used with caution, as they can affect the performance of the query. If a function or an expression is applied to a column, it can prevent the use of indexes on that column, which can result in a full table scan instead of an index seek. This can result in more data being retrieved by the query, which can increase the buffer size, the processing time, and the locking time.

To illustrate these pitfalls, consider the following examples:


-- Using SELECT * instead of specifying columns

SELECT * FROM Customers

  

-- Using unnecessary joins or subqueries

SELECT c.CustomerName, c.CustomerEmail, o.OrderID, o.OrderDate

FROM Customers c

JOIN (SELECT * FROM Orders) o ON c.CustomerID = o.CustomerID

  

-- Using functions or expressions on columns

SELECT CustomerName, CustomerEmail

FROM Customers

WHERE UPPER(CustomerName) LIKE '%JOHN%'

The first query uses SELECT * instead of specifying columns, which returns all columns from Customers table. The second query uses an unnecessary subquery to select all rows from Orders table, which joins with Customers table. The third query uses a function on CustomerName column, which converts it to uppercase. All these queries will result in more data being retrieved by ADO .NET than necessary.

A better way to write these queries would be:


-- Specifying columns

SELECT CustomerName, CustomerEmail FROM Customers

  

-- Removing unnecessary joins or subqueries

SELECT c.CustomerName, c.CustomerEmail, o.OrderID, o.OrderDate

FROM Customers c

JOIN Orders o ON c.CustomerID = o.CustomerID

  

-- Avoiding functions or expressions on columns

SELECT CustomerName, CustomerEmail

FROM Customers

WHERE CustomerName LIKE '%John%'

These queries will result in less data being retrieved by ADO .NET than before.

Conclusion

In conclusion, limiting the data retrieved by the query is one of the key techniques to improve ADO .NET performance. The application should limit the data by selecting only the required columns. The application should also retrieve limited number of rows. This can be done using appropriate filter clauses to the query.

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