C＃中的表達樹是什麼，在通常使用的情況下（例如，由ORMS）？

表達式樹在C#中用於將代碼表示為數據。它們通過構建描述代碼操作的樹結構而非直接執行代碼，使開發者能夠分析、修改或運行時生成新代碼。其核心組件包括參數表達式、二元表達式和lambda表達式。常見用途是LINQ to SQL和ORM（如Entity Framework），其中表達式樹使C# LINQ查詢可被翻譯為SQL語句。其他用途包括動態過濾與查詢、序列化或腳本系統、模擬框架及依賴注入容器。然而，在不需要檢查或轉換邏輯的情況下，使用普通函數或lambda表達式更為合適。 1. 構建動態查詢；2. 翻譯成其他形式（如SQL）；3. 實現動態行為與規則引擎；4. 避免在高性能路徑中使用以減少開銷。

Expression trees in C# are a way to represent code as data. Instead of directly executing code, you build a tree structure that describes what the code does. This lets you analyze, modify, or even generate new code at runtime.

They're especially useful when you need to inspect or translate logic into another form — like how ORMs turn C# LINQ queries into SQL statements.

How Expression Trees Work

An expression tree is made up of nodes that represent operations like method calls, mathematical operations, or property accesses. You can create them manually, but more commonly, they're built automatically from lambda expressions.

For example:

 Expression<Func<int, int>> square = x => x * x;

Here, square isn't just a delegate — it's an expression tree describing the operation. You can traverse and inspect this tree, which opens up possibilities for dynamic behavior.

Some key components:

• Parameter expressions : Represent inputs (like x above)
• Binary expressions : Represent operations like multiplication or addition
• Lambda expressions : Wrap everything together as a callable unit

This structure gives you visibility into the code's logic without running it immediately.

Common Use: LINQ to SQL and ORMs

One of the most well-known uses of expression trees is in LINQ providers like Entity Framework or other ORMs.

When you write:

 var results = db.Users.Where(u => u.Age > 25);

The Where method doesn't execute the filter right away. Instead, it receives an expression tree representing the condition u.Age > 25 . The ORM inspects this tree and translates it into SQL:

 SELECT * FROM Users WHERE Age > 25

This translation is only possible because the logic is in an expression tree rather than compiled IL code. If it were a regular delegate, the ORM couldn't see inside it.

Other scenarios where ORMs use expression trees include:

• Building dynamic queries based on user input
• Validating query structures before execution
• Implementing custom query extensions

Beyond ORMs: Other Practical Uses

While ORMs are the most visible users, expression trees come in handy in other areas too.

Dynamic filtering and querying
You can build filters on the fly, like for UI-driven search tools where users pick fields and conditions. Expression trees let you assemble these into executable logic dynamically.

Serialization or scripting systems
If you want to allow users to define rules (eg, "if X > Y then do Z"), expression trees can help parse and execute those safely.

Mocking frameworks and DI containers
Some libraries use expression trees to understand dependencies or intercept method calls without hardcoding behaviors.

A simple example would be creating a filter function based on configuration:

 var param = Expression.Parameter(typeof(User), "u");
var property = Expression.Property(param, "Age");
var value = Expression.Constant(25);
var body = Expression.GreaterThan(property, value);
var lambda = Expression.Lambda<Func<User, bool>>(body, param);

Func<User, bool> filter = lambda.Compile();
var filteredUsers = allUsers.Where(filter);

This builds a filtering function dynamically instead of writing it by hand.

When Not to Use Them

Despite their power, expression trees aren't always the best choice.

They add complexity, especially if you're building and manipulating them manually. For most apps, using delegates or LINQ-to-Objects is simpler and faster.

Also, compiling expression trees at runtime has performance overhead. While not prohibitive, it's something to consider in hot paths or high-performance code.

So unless you need to inspect or transform logic, stick with regular functions or lambdas.

Not every app will need expression trees, but when you do — like when building a query system or dynamic rule engine — they become indispensable. They bridge the gap between code and data, letting you work with logic as something you can examine and reshape.

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