面试题与答案

MongoDB is a NoSQL database that provides high performance, high availability, and easy scalability. It stores data in flexible, JSON-like documents called BSON.

A MongoDB document is a JSON-like data structure composed of field-value pairs. Fields may contain other documents, arrays, and arrays of documents.

BSON is a binary representation of JSON-like documents that MongoDB uses to store data. It adds support for data types like Date and Binary that are not natively supported in JSON.

NoSQL databases are databases that provide a mechanism for storage and retrieval of data that is modeled in ways other than the tabular relations used in relational databases.

A collection in MongoDB is a group of MongoDB documents. It is equivalent to an RDBMS table.

ObjectId is a 12-byte identifier typically employed as the primary key in a MongoDB document. It consists of a timestamp, machine identifier, process ID, and a random incrementing value.

The `findOne` method in MongoDB is used to retrieve a single document that satisfies the specified query criteria. It returns the first document that matches the query.

Indexing is the process of creating an index (like in a book) to improve the performance of queries. Indexes are used to quickly locate and access the data.

Sharding is the process of distributing data across multiple servers. It is MongoDB's approach to meeting the demands of data growth.

Replication is the process of synchronizing data across multiple servers. It provides redundancy and increases data availability.

The profiler is a MongoDB feature that tracks queries executed against the database. It helps in analyzing and optimizing query performance.

MongoDB provides the `aggregate` method to perform aggregation operations, such as filtering, grouping, sorting, and projecting data.

GridFS is a specification for storing and retrieving large files in MongoDB. It divides a file into chunks and stores each chunk as a separate document.

The TTL index is a special type of index that automatically deletes documents from a collection after a specified amount of time has passed.

MongoDB achieves high availability through replica sets, where data is replicated across multiple servers. If one server goes down, another can serve the data.

WiredTiger is the default storage engine in MongoDB. It provides features like compression, document-level locking, and support for multiple storage engines in the same MongoDB deployment.

The Journal in MongoDB is a write-ahead log that helps in recovering data in case of a server crash or failure. It ensures data durability and consistency.

Data migration in MongoDB involves transferring data from one database or collection to another. It can be done using tools like `mongodump` and `mongorestore`.

You can create an index in MongoDB using the `createIndex` method. For example, `db.collection.createIndex({ field: 1 })` creates an ascending index on the specified field.

The `mongorestore` command is used to restore MongoDB backups created with `mongodump`. It imports BSON data into a MongoDB database.

MongoDB sacrifices ACID properties for performance and scalability. However, it supports multi-document transactions in recent versions to some extent.

The `explain` method provides information on the query execution plan, helping in optimizing and understanding how a query is executed.

Covered queries are queries where all the fields in the query are part of an index, and the index itself can fulfill the query requirements, avoiding the need to fetch data from the actual documents.

The `$lookup` stage is used in MongoDB aggregation to perform a left outer join between documents from the source and target collections based on a specified condition.

The `mongostat` command provides a real-time view of various MongoDB statistics, such as insert and query rates, memory usage, and more.

MongoDB supports multi-document transactions starting from version 4.0. Transactions allow you to perform multiple operations on one or more documents and ensure atomicity and consistency.

The Aggregation Pipeline is a framework in MongoDB for data aggregation. It allows you to process data and transform it using a sequence of stages, such as `$match`, `$group`, and `$project`.