MongoDB provides various query operators for document query operations. These operators are categorized into comparison, logical, element, array, bitwise, and other types. Comparison operators such as `$eq`, `$gt`, and `$in` are used for field value comparisons. Logical operators like `$and`, `$or`, and `$not` combine query conditions. Element operators such as `$exists` and `$type` check field existence and data types. Array operators like `$all`, `$elemMatch`, and `$size` handle array fields. Bitwise operators such as `$bitsAllSet` and `$bitsAnySet` perform bitwise evaluations. Evaluation operators like `$expr` and `$regex` support expressions and regular expression matching. Geospatial operators like `$geoWithin` and `$near` process spatial data. Projection operators such as `$elemMatch` and `$slice` control returned fields. Update operators like `$` are used for array updates. Aggregation pipeline operators such as `$match` and `$lookup` are used for aggregation operations. These operators provide powerful query capabilities to meet various data retrieval needs.
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MongoDB's `bulkWrite` method efficiently executes multiple write operations (including inserts, updates, deletions, etc.) in a single network request, significantly reducing network overhead and server load compared to processing operations individually. It is particularly suitable for scenarios requiring atomic processing of large-scale data. The method supports various operation types such as `insertOne`, `updateOne`, `replaceOne`, and `deleteMany`, and allows control over execution order via the `ordered` parameter. Practical applications include e-commerce inventory management and user data migration. For performance optimization, it is recommended to control batch size, establish appropriate indexes, and adjust write concern levels. Error handling differs between ordered and unordered operations. Special scenarios, such as mixed operations, atomicity, and batch array updates, also have specific handling techniques. Compared to `insertMany` or looping through individual operations, `bulkWrite` offers superior efficiency for batch processing. Best practices include setting reasonable batch sizes, implementing robust error handling, and designing retry mechanisms.
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MongoDB provides two document deletion methods, `deleteOne` and `deleteMany`, for deleting single or multiple documents that match specified conditions. `deleteOne` removes the first matching document and returns an object containing `deletedCount` and `acknowledged` properties. `deleteMany` deletes all matching documents and returns a similar object, though `deletedCount` may be greater than 1. The deletion criteria syntax is the same as queries, allowing the use of various operators. Deletion operations are atomic at the single-document level, but large-scale deletions may impact performance, making soft deletion a viable alternative. Deletion operations can be included in transactions, requiring proper error handling. Monitoring tools can track deletion activities. MongoDB offers fine-grained access control to restrict deletion conditions. For large-scale deletions, batch processing or off-peak execution is recommended. In a replica set environment, deletion operations propagate to all nodes, while in a sharded cluster, deletions are routed to the shards containing the target documents.
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MongoDB provides various document update methods, including `updateOne` to update the first matching document, `updateMany` to update all matching documents, and `replaceOne` to completely replace a matching document. These methods support a rich set of update operators, such as `$set` to assign field values, `$inc` to increment numerical values, etc. `updateOne` and `updateMany` are suitable for modifying partial fields, while `replaceOne` is used for entire document replacement. Update operations can be combined with options like `upsert` to insert a new document if no match is found. For performance, it is recommended to use indexes and batch operations. MongoDB 4.0 and above supports transactional updates. Update operations are atomic at the single-document level. In high-concurrency environments, optimistic concurrency control can be employed. Best practices include specifying query conditions, checking returned results, and testing in a production-like environment before deployment.
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MongoDB provides two core query methods for document retrieval: `find` and `findOne`. `find` returns all matching documents, while `findOne` returns the first matching document. Query conditions use JSON format and support various comparison and logical operators such as `$gt`, `$in`, `$or`, and `$and`. Nested document queries use dot notation, and array queries have special operators like `$all` and `$size`. Projection functionality allows control over returned fields. Pagination and sorting use `limit`, `skip`, and `sort` methods. Performance optimization includes index usage and query analysis. The aggregation framework is suitable for complex queries. Text search and geospatial queries have specialized support. Query optimization techniques include covered queries and avoiding `$where`. Queries in transactions must consider isolation levels. Batch queries can improve efficiency. Replica sets and sharded clusters have specific query considerations.
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MongoDB provides two document insertion methods, `insertOne` and `insertMany`, for inserting single or multiple documents. `insertOne` is used to insert a single document, which can automatically generate an `_id` field or be explicitly specified. It throws an error when violating unique index constraints. `insertMany` is used for batch insertion of multiple documents and is more efficient than calling `insertOne` multiple times. It supports the `ordered` parameter to control whether insertion is sequential. Write concern can specify the level of acknowledgment for successful write operations. In practical applications, it can be used for e-commerce product imports or user registration systems. Performance optimization recommendations include batch insertion, adjusting the `ordered` parameter, disabling indexes, and using bulk writes. Common issues include duplicate key errors, document size limits, and slow insertion speeds, with corresponding solutions provided.
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MongoDB collection operations include implicit and explicit creation methods. Explicit creation can use the `createCollection` method with options such as capped collection size and validation rules. Collections are deleted using the `drop` method, which permanently removes all data. Checking collections can be done via `getCollectionNames` or `listCollections`. Renaming collections uses the `renameCollection` method. Modifying collection options employs the `collMod` command. Statistical information is obtained through the `stats` method. Cloning and replication can be achieved using `aggregate` or loop insertion. Import and export utilize the `mongoimport`/`mongoexport` tools or programmatic approaches. Special operations include compression, converting capped collections, and rebuilding collections, among others.
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In MongoDB, database creation does not require explicit declaration; it is automatically generated upon the first write operation. The `use` command switches to or creates a database, with names being case-sensitive and limited to 64 characters. Reserved databases like `admin`, `local`, and `config` cannot be directly created. Naming restrictions include avoiding special characters. Listing all databases requires at least one collection to be displayed. Collections can be explicitly created, such as capped collections or those with validation rules. To delete a database, you must first switch to the target database; the operation is irreversible and requires write permissions. Disk space is not immediately freed after deletion. There are two methods for deleting collections. A batch operation example demonstrates the process of creating an e-commerce database, including collection creation, initial data insertion, and database deletion. Permission management involves user creation and role assignment. Special scenarios include handling deletion failures, checking active connections, and forced deletion (requiring admin privileges). Best practices for temporary databases include enabling auto-cleanup and setting expiration times.
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MongoDB uses JSON and BSON formats for data interaction and internal storage, respectively. JSON, as a lightweight data interchange format, is easy to read and write but has limitations such as limited data type support. BSON, a binary-encoded format, extends JSON by supporting additional data types like dates and binary data while optimizing storage efficiency. MongoDB drivers automatically handle the conversion between the two. BSON outperforms JSON in parsing speed and storage efficiency. In practice, attention should be paid to best practices like data type matching and query optimization, including proper use of ObjectId, handling time zones for dates, and leveraging advanced features like Decimal128 and geospatial data. When integrating with other systems, format conversion must be considered, and debugging should focus on common issues like type mismatches.
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MongoDB offers a variety of client tools to meet different scenario needs. The Mongo Shell is a built-in interactive JavaScript interface, suitable for quick testing, queries, and management tasks, supporting CRUD operations and complex scripts. MongoDB Compass is a graphical management tool that provides data browsing, query building, performance analysis, and index management. There are also official drivers for multiple programming languages, such as Node.js, Python, and Java, making it easy for developers to integrate into applications. Additionally, there are third-party tools like Robo 3T and NoSQLBooster, as well as performance optimization and security-related tools such as mongostat and mongodump. Depending on specific requirements, different tool combinations can be selected for use.
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