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Mongo DB

Overview​

1. Database​

Definition: A MongoDB database is a container for collections, which are akin to tables in a relational database. Within a database, you can store multiple collections, and each collection contains documents. Here's a detailed explanation with production-grade code snippets, insights from well-regarded sources, and references to high-quality repositories.

Instance: A running MongoDB server. Database: A container for collections. Authentication: Mechanism to validate user identity.

2. Collections​

Definition: A collection in MongoDB is a group of documents. It is analogous to a table in a relational database but without a fixed schema. Collections are dynamic and can store documents with varying structures.

Document: A set of key-value pairs (akin to a row in a relational database). Capped Collections: Fixed-size collections that support high-throughput operations. TTL Indexes: Automatically remove documents after a certain period.

3. Documents​

Definition: A document in MongoDB is a set of key-value pairs, analogous to a row in a relational database but much more flexible. Documents are stored in BSON (Binary JSON) format, which allows for embedded documents and arrays.

BSON Format: Binary representation of JSON-like documents. Fields: Individual key-value pairs within a document. Embedded Documents: Documents within documents (denormalization). Arrays: Lists of values within a document.

4. Indexes​

Definition: Indexes in MongoDB are special data structures that store a small portion of the data set in an easy-to-traverse form. Indexes are crucial for efficient query execution and improving read performance. They support various types of queries and help MongoDB locate data without scanning the entire collection.

Single Field Index: Index on a single field. Compound Index: Index on multiple fields. Multikey Index: Indexes on arrays. Text Index: Index for text search. Geospatial Index: Index for geospatial queries. Hashed Index: Index for hashed values.

5. Aggregation Framework​

Definition: The Aggregation Framework in MongoDB is a powerful tool for data processing and analysis. It allows you to transform and aggregate data stored in MongoDB collections through a pipeline of stages, each performing a specific operation.

Pipeline: Sequence of operations to process documents. Stages: Individual operations in the pipeline. $match: Filters documents. $group: Groups documents by a specified expression. $project: Reshapes documents. $sort: Orders documents. $limit: Limits the number of documents. $skip: Skips a number of documents. $unwind: Deconstructs an array field. $lookup: Performs a left outer join with another collection.

6. Queries​

Definition: MongoDB provides a powerful query language that supports CRUD operations: Create, Read, Update, Delete. Queries can be enhanced with a variety of operators.

CRUD Operations: Create, Read, Update, Delete. Insert: Adds documents to a collection. Find: Retrieves documents. Update: Modifies documents. Delete: Removes documents. Query Operators: Comparison Operators: $eq, $ne, $gt, $gte, $lt, $lte, etc. Logical Operators: $and, $or, $not, $nor. Element Operators: $exists, $type. Evaluation Operators: $regex, $expr, $mod, etc.

7. Data Models​

Definition: Data models in MongoDB can be either normalized (references) or denormalized (embedded). The choice depends on the use case, with trade-offs in terms of performance and data integrity.

Normalization: Dividing data into different collections. Denormalization: Embedding data in a single document. Schema Design: Planning the structure of collections and documents.

8. Replication​

Definition: Replication in MongoDB provides high availability and data redundancy by replicating data across multiple servers.

Replica Set: A group of mongod instances that maintain the same data set. Primary: Receives all write operations. Secondary: Replicates data from the primary. Arbiter: Participates in elections but does not hold data.

9. Sharding​

Definition: Sharding in MongoDB enables horizontal scaling by distributing data across multiple servers.

Shard: A subset of data within a cluster. Shard Key: Field that determines the distribution of data. Config Server: Stores metadata and configuration settings. Query Router (mongos): Routes queries to the appropriate shard.

10. Transactions​

Definition: Transactions in MongoDB provide atomicity across multiple documents and collections, ensuring ACID properties.

Multi-Document Transactions: Ensure ACID properties across multiple documents. Atomic Operations: Single operation is atomic within a document.

11. Security​

Authentication: Validates user identity. Authorization: Controls access to resources. Encryption: Protects data at rest and in transit. Auditing: Tracks access and operations.

12. Backup and Restore​

mongodump: Utility for creating binary export of data. mongorestore: Utility for importing binary export data. Backup Strategies: Regular backups, snapshots, and continuous backup solutions.

13. Monitoring and Performance Tuning​

Monitoring Tools: MongoDB Atlas, Cloud Manager, Ops Manager. Performance Metrics: Latency, throughput, resource utilization. Indexes: Proper indexing to improve query performance. Sharding: Distributing data across multiple servers to balance load.

14. MongoDB Drivers​

Language-Specific Drivers: Libraries for various programming languages (Node.js, Python, Java, etc.). MongoDB Shell: Interactive JavaScript interface for MongoDB.

15. Utilities and Tools​

mongostat: Real-time status of a running mongod instance. mongotop: Tracks and reports the time a MongoDB instance spends reading and writing data. mongoimport: Imports data in JSON, CSV, or TSV format. mongoexport: Exports data to JSON or CSV format.