MongoDB與SQL數(shù)據(jù)庫(kù)的核心差異在於數(shù)據(jù)建模方式。 1. MongoDB採(cǎi)用文檔模型，以類(lèi)似JSON的BSON格式存儲(chǔ)數(shù)據(jù)，支持嵌套結(jié)構(gòu)和動(dòng)態(tài)模式，而SQL數(shù)據(jù)庫(kù)使用固定模式的表結(jié)構(gòu)，需嚴(yán)格定義行列關(guān)係。 2. MongoDB無(wú)需預(yù)定義模式即可添加字段，適應(yīng)性強(qiáng)，適合敏捷開(kāi)發(fā)；而SQL數(shù)據(jù)庫(kù)修改結(jié)構(gòu)常需執(zhí)行ALTER TABLE操作。 3. MongoDB推薦將關(guān)聯(lián)數(shù)據(jù)嵌入文檔內(nèi)以避免JOIN操作，也可通過(guò)引用實(shí)現(xiàn)關(guān)係管理，但不支持自動(dòng)JOIN，需手動(dòng)處理或使用$lookup。 4. MongoDB查詢語(yǔ)法基於JSON鍵值對(duì)，如db.users.find({ age: { $gt: 30 } })，較SQL更靈活但學(xué)習(xí)曲線較高?？傮w而言，MongoDB適合處理層次化、動(dòng)態(tài)變化的數(shù)據(jù)，SQL數(shù)據(jù)庫(kù)則適合需要強(qiáng)一致性和復(fù)雜事務(wù)的場(chǎng)景。

MongoDB's document model is fundamentally different from the relational model used in SQL databases. These differences affect how data is structured, stored, and accessed. If you're familiar with SQL databases like MySQL or PostgreSQL, switching to MongoDB requires a shift in thinking—especially when it comes to modeling your data.

1. Data Structure: Documents vs. Tables

In SQL databases, data is organized into tables with rows and columns. Each row represents a record, and each column defines a specific attribute. This structure is rigid—every row must conform to the same schema.

MongoDB, on the other hand, uses a document-based model , where data is stored as JSON-like documents (technically BSON). A single document can contain nested fields, arrays, and even sub-documents. There's no strict schema enforcement by default, meaning different documents in the same collection (similar to a table) can have different structures.

For example:

• In SQL, user data might be split across users , addresses , and orders tables.
• In MongoDB, a user document could include their address and order history directly inside it, avoiding joins.

This flexibility makes MongoDB well-suited for handling hierarchical or evolving data.

2. Schema Flexibility and Evolution

SQL databases require predefined schemas. If you want to add a new column, you often need to run an ALTER TABLE command, which can lock the table and impact performance during large migrations.

MongoDB allows for schema flexibility . You can insert new fields into some documents without affecting others. This makes it easier to adapt to changing requirements, especially in agile development environments.

However, this flexibility doesn't mean there's no schema at all. Applications typically enforce a logical schema on the application side or through validation rules in MongoDB itself.

3. Relationships and Joins

In relational databases, normalization is common practice. Data is split into multiple tables to avoid redundancy, and relationships are managed using foreign keys. Queries often use JOINs to combine data from multiple tables.

MongoDB encourages embedding related data within a single document when appropriate. For instance, instead of storing comments in a separate collection and joining them later, you might store them as an array inside a blog post document.

That said, MongoDB also supports referencing—similar to foreign keys—when embedding isn't practical. But unlike SQL, these references don't support automatic joins. You either fetch related data manually or use $lookup in aggregation pipelines, which isn't as performant as native SQL joins.

So, while MongoDB gives you more control over how to manage relationships, it also shifts some of the complexity to the application layer.

4. Query Syntax and Language

SQL databases use Structured Query Language (SQL), which is powerful and standardized. It includes commands like SELECT , JOIN , GROUP BY , etc., that work across most relational systems.

MongoDB uses a JSON-style query syntax . Instead of writing SQL statements, you pass in key-value pairs or expressions to filter and manipulate data. For example:

 db.users.find({ age: { $gt: 30 } })

This retrieves users older than 30. While MongoDB's query language is expressive and flexible, especially for working with nested data, it takes time to get used to if you're coming from SQL.

Also, MongoDB doesn't support transactions across collections out of the box (though multi-document ACID transactions are available in replica sets and sharded clusters starting from version 4.0).

Conclusion

The main difference between MongoDB and SQL databases boils down to how they model data. MongoDB's document model offers flexibility, avoids complex joins, and works well for hierarchical data. SQL databases provide strong consistency, mature tooling, and are ideal for highly structured, transactional data.

Depending on your use case, one may be better than the other—or you might find value in using both together.

基本上就這些。

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