Optimized LINQ Queries in .NET?

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LINQ (Language Integrated Query) has revolutionized data manipulation and querying in C#, offering developers a powerful and intuitive way to interact with data sources. However, using the full potential of LINQ requires more than just writing queries – it demands a keen understanding of its intricacies and nuances, particularly regarding performance optimization.

In this blog, we will explore tips and techniques designed to enhance the efficiency and performance of your C# applications and how to optimize LINQ queries. NET.

What is LINQ?

LINQ (Language-Integrated Query) is a simple and convenient language for querying a data source. LINQ to SQL is a data access technology in a DBMS(MS-SQL).

This is a powerful tool for working with data. Queries are constructed through a declarative language, which the platform converts into SQL queries and sends to the database server for execution. 

However, LINQ queries are not converted to optimally written SQL queries that an experienced DBA could write optimized SQL queries:

  • Optimal connections (JOIN) and filtering of results (WHERE)
  • Many nuances in the use of compounds and group conditions
  • Many variations in replacing IN conditions with EXISTS and NOT IN, <> with EXISTS
  • Intermediate caching of results through temporary tables, CTE, and table variables
  • Using a clause (OPTION) with instructions and table hints WITH (…)
  • The use of indexed views as one of the means to get rid of excessive readings of data in samples

What Are the Performance Bottlenecks?

The main performance bottlenecks of the resulting SQL queries when compiling LINQ queries are:

  • Consolidation of the entire data selection mechanism in one request
  • Duplication of identical blocks of code, which ultimately leads to multiple extra readings of data
  • Groups of multicomponent conditions (logical “and” and “or”) — AND and OR, combining under challenging conditions, leads to the fact that the optimizer, having suitable non-clustered indexes, by the necessary fields, eventually starts to scan by the clustered index (INDEX SCAN) by condition group.
  • The deep location of subqueries makes it very problematic to parse SQL statements and query plans from developers and DBAs.

LINQ Optimization Tips and Techniques

You can use the tips and techniques below to optimize LINQ:

Use AsNoTracking() for Read-Only Operations

When you’re querying data for read-only purposes (like displaying data on a webpage), use the AsNoTracking() method to turn off change tracking. This can improve performance by avoiding the overhead of tracking changes.

var result = dbContext.Entity

             .AsNoTracking()

             .Where(/* conditions */)

             .ToList();

Projection for Selecting Only Required Columns

Project only the necessary columns instead of selecting all columns from a table. This reduces the amount of data transferred and can improve query performance.

var result = dbContext.Entity

             .Select(e => new { e.Property1, e.Property2 })

             .ToList()

Avoid Using Wildcard Characters in Filtering

Be specific in your filtering conditions, and avoid using wildcard characters like % unless necessary. Wildcards can lead to slower queries.

var result = dbContext.Entity

             .Where(e => e.Name.StartsWith(“John”))

             .ToList();

Use Indexes

Ensure your database tables have appropriate indexes on columns used in WHERE clauses or JOIN conditions. Indexes can significantly speed up query performance.

Optimize JOIN Operations

Use the Join method wisely, and be aware of the different types of joins (inner, outer, etc.). Consider using navigation properties or foreign key relationships to simplify join operations.

Batching with Take and Skip

For large datasets, consider using Take and Skip to implement paging. This helps in fetching a smaller subset of data, improving performance.

var result = dbContext.YourEntity

             .Skip(50)

             .Take(10)

             .ToList(); // Example for paging

Execute Complex Operations in the Database

Move complex operations to the database side using stored procedures or views. This can reduce the data transfer between the database and the application.

Avoid Using Count() in Large Collections

If you need to check if there are any elements, use Any() instead of Count(). Any() stops iterating when it finds the first matching element, while Count() iterates through the entire collection.

var hasElements = dbContext.Entity.Any(e => e.SomeCondition);

Consider Lazy Loading

If you are working with related entities, be cautious about lazy loading. It can lead to the N+1 query problem. Use Include or projections to fetch related data efficiently.

var result = dbContext.Entity.Include(e => e.RelatedEntity).ToList();

Profile and Monitor Queries

Use tools like SQL Server Profiler or EF Profiler to monitor and profile the generated SQL queries. This helps identify areas for optimization.

Always profile your queries and understand the underlying database structure to make informed decisions about query optimizations.

.NET Offers a New Dimension of Performance

Another significant difference between the pre-releases of .NET 7 and itself is the performance improvements and enhancements. .NET 7 is designed to deliver faster and more efficient performance with more excellent reliability, and hundreds of performance improvements contribute to its retrieval speed.

Let’s take the essential LINQ helpers to evaluate the performance factor between .NET 7 and .NET 6
Consider taking, Min(), Max(), Average(), Count(), and Sum() methods.

|        Method |  Runtime |             Mean | Rank | Allocated |

|————– |——— |—————–:|—–:|———-:|

|           Min | .NET 6.0 |   656,027.606 ns |    2 |      41 B |

|           Min | .NET 7.0 |    10,243.608 ns |    1 |         – |

|               |          |                  |      |           |

|           Max | .NET 6.0 |   660,551.869 ns |    2 |      41 B |

|           Max | .NET 7.0 |    10,294.283 ns |    1 |         – |

|               |          |                  |      |           |

|       Average | .NET 6.0 |   561,299.193 ns |    2 |      41 B |

|       Average | .NET 7.0 |    13,850.906 ns |    1 |         – |

|               |          |                  |      |           |

|           Sum | .NET 6.0 |   579,482.666 ns |    2 |      41 B |

|           Sum | .NET 7.0 |    34,728.896 ns |    1 |         – |

|               |          |                  |      |           |

|         Count | .NET 6.0 |   569,091.900 ns |    2 |      41 B |

|         Count | .NET 7.0 |    34,944.719 ns |    1 |         – |

Conclusion

Optimizing LINQ queries is crucial to enhancing overall application performance. By following these optimization techniques and addressing performance bottlenecks, developers can ensure that LINQ queries translate into efficient and well-performing SQL queries. This improves application responsiveness and contributes to a smoother user experience.

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