How to Use Primary Constructors for Compact Classes in C#

Removing duplicates from a list in C# is a common task, especially when working with large datasets. C# provides multiple ways to achieve this efficiently, leveraging built-in collections and LINQ.

Using HashSet (Fastest for Unique Elements)

A HashSet<T> automatically removes duplicates since it only stores unique values. This is one of the fastest methods:

List<int> numbers = new List<int> { 1, 2, 2, 3, 4, 4, 5 };
numbers = new HashSet<int>(numbers).ToList();
Console.WriteLine(string.Join(", ", numbers)); // Output: 1, 2, 3, 4, 5

Using LINQ Distinct (Concise and Readable)

LINQ’s Distinct() method provides an elegant way to remove duplicates:

List<int> numbers = new List<int> { 1, 2, 2, 3, 4, 4, 5 };
numbers = numbers.Distinct().ToList();
Console.WriteLine(string.Join(", ", numbers)); // Output: 1, 2, 3, 4, 5

Removing Duplicates by Custom Property (For Complex Objects)

When working with objects, DistinctBy() from .NET 6+ simplifies duplicate removal based on a property:

using System.Linq;
using System.Collections.Generic;

class Person
{
    public string Name { get; set; }
    public int Age { get; set; }
}

List<Person> people = new List<Person>
{
    new Person { Name = "Alice", Age = 30 },
    new Person { Name = "Bob", Age = 25 },
    new Person { Name = "Alice", Age = 30 }
};

people = people.DistinctBy(p => p.Name).ToList();
Console.WriteLine(string.Join(", ", people.Select(p => p.Name))); // Output: Alice, Bob

For earlier .NET versions, use GroupBy():

people = people.GroupBy(p => p.Name).Select(g => g.First()).ToList();

Performance Considerations

• HashSet<T> is the fastest but only works for simple types.
• Distinct() is easy to use but slower than HashSet<T> for large lists.
• DistinctBy() (or GroupBy()) is useful for complex objects but may have performance trade-offs.

Conclusion

Choosing the best approach depends on the data type and use case. HashSet<T> is ideal for primitive types, Distinct() is simple and readable, and DistinctBy() (or GroupBy()) is effective for objects.

When working with relational databases, JOIN operations allow you to retrieve data from multiple tables based on a common column.

SQL Server supports different types of joins, each serving a specific purpose. Let’s break them down with examples.

1. INNER JOIN

The INNER JOIN returns only the rows where there is a match in both tables.

SELECT A.id, A.name, B.order_id
FROM Customers A
INNER JOIN Orders B ON A.id = B.customer_id;

• If a customer has no matching order, they won’t appear in the result.

2. LEFT JOIN (or LEFT OUTER JOIN)

The LEFT JOIN returns all rows from the left table (Customers), and only matching rows from the right table (Orders). If there’s no match, NULL values are returned for the right table columns.

SELECT A.id, A.name, B.order_id
FROM Customers A
LEFT JOIN Orders B ON A.id = B.customer_id;

• Customers without orders will still appear, but order_id will be NULL.

3. RIGHT JOIN (or RIGHT OUTER JOIN)

The RIGHT JOIN works the opposite of LEFT JOIN, returning all rows from the right table (Orders) and only matching rows from the left table (Customers).

SELECT A.id, A.name, B.order_id
FROM Customers A
RIGHT JOIN Orders B ON A.id = B.customer_id;

• Orders without a matching customer will still appear, but name will be NULL.

4. FULL JOIN (or FULL OUTER JOIN)

The FULL JOIN returns all records from both tables. If there’s no match, NULL values will be shown in the missing columns.

SELECT A.id, A.name, B.order_id
FROM Customers A
FULL JOIN Orders B ON A.id = B.customer_id;

• This ensures that all customers and all orders appear in the results, even if there’s no match.

Quick Summary:

Understanding these joins can help you extract data efficiently and ensure that your queries return the expected results. Happy querying!

When working with SQL Server, you may often need to count the number of unique values in a specific column. This is useful for analyzing data, detecting duplicates, and understanding dataset distributions.

Using COUNT(DISTINCT column_name)

To count the number of unique values in a column, SQL Server provides the COUNT(DISTINCT column_name) function. Here’s a simple example:

SELECT COUNT(DISTINCT column_name) AS distinct_count
FROM table_name;

This query will return the number of unique values in column_name.

Counting Distinct Values Across Multiple Columns

If you need to count distinct combinations of multiple columns, you can use a subquery:

SELECT COUNT(*) AS distinct_count
FROM (SELECT DISTINCT column1, column2 FROM table_name) AS subquery;

This approach ensures that only unique pairs of column1 and column2 are counted.

Why Use COUNT DISTINCT?

• Helps in identifying unique entries in a dataset.
• Useful for reporting and analytics.
• Efficient way to check for duplicates.

By leveraging COUNT(DISTINCT column_name), you can efficiently analyze your database and extract meaningful insights. Happy querying!