/mongodb-fundamental

Primary LanguageJavaScript

Introduction

MongoDB is a leading open-source document-oriented NoSQL database management system. It provides a flexible and scalable solution for storing and managing data. > google, facebook, eBay etc.

noSQL = not only SQL and non-relational databases --> real-time-data-processing, high perfomance, flexible data model, Flexible, schemaless. its not following traditional database.

Scalability- ওয়েব অ্যাপ্লিকেশনের ক্ষেত্রে, স্কেলেবিলিটি নিশ্চিত করে যে অ্যাপ্লিকেশনটি বেশি সংখ্যক ব্যবহারকারী এবং ডেটা ম্যানেজ করতে পারবে। Flexibility - user এর need এবং condition অনুযায়ী website adapt করতে পারে

Why MongoDB ?

  • scalable hight perfomance and open source.
  • document oriented database
  • cost-Effective solution.
  • rich ecosystem of Tools, Documentation and Community.

MongoDB vs Traditional Database

  • Data Model:

    • MongoDB: Document-oriented data model with flexible schemas.
    • Traditional Database: Relational data model with fixed schemas.

  • Query Language:

    • MongoDB: Query language similar to JSON with support for rich query expressions.
    • Traditional Database: SQL (Structured Query Language) for querying relational data.

  • Scalability:

    • MongoDB: Designed for horizontal scalability across multiple servers or nodes.
    • Traditional Database: Often scaled vertically by adding more resources to a single server.

  • Flexibility:

    • MongoDB: Schema-flexible or schema-less, allowing for dynamic changes in data structure.
    • Traditional Database: Schema-driven, requiring predefined schemas for data storage.

  • Performance:

    • MongoDB: Optimized for high-performance read and write operations, especially for unstructured data.
    • Traditional Database: Offers predictable performance for transactional workloads with ACID properties.

Use Cases

  • MongoDB:

    • Well-suited for applications with flexible data models, rapid development cycles, and scalability requirements.
    • Use cases include content management systems, real-time analytics, and IoT platforms.

  • Traditional Database:

    • Suitable for applications with structured data, complex transactions, and strong data consistency requirements.
    • Use cases include financial systems, ERP systems, and CRM applications.

Scalability

  • MongoDB:
    • Horizontally scalable, distributing data across multiple servers or nodes to handle growing data volumes and traffic.
  • Traditional Database:
    • Often scaled vertically by adding more resources (CPU, memory) to a single server.

Flexibility

  • MongoDB:
    • Schema-flexible or schema-less, allowing for dynamic changes in data structure without requiring schema migrations.
  • Traditional Database:
    • Schema-driven, requiring predefined schemas for data storage. Changes to the schema may require alterations to the database structure.

Performance

  • MongoDB:
    • Optimized for high-performance read and write operations, especially for unstructured data.
  • Traditional Database:
    • Offers predictable performance for transactional workloads with ACID properties.

Data Model

  • MongoDB:

    • Document-oriented data model storing data in flexible, JSON-like documents.

  • Traditional Database:

    • Relational data model organizing data into tables with predefined schemas consisting of rows and columns.

Query Language

  • MongoDB:

    • Uses a query language similar to JSON with support for rich query expressions and operators.

  • Traditional Database:

    • Uses SQL (Structured Query Language) for querying relational data.

Community and Support

  • MongoDB:

    • Active community and strong support from MongoDB Inc. with regular updates, documentation, and online resources.

  • Traditional Database:

    • Established community and support from various vendors with extensive documentation and resources.

    MongoDB Features

    • JSON Like Documents (BSON)
    • indexing
    • Aggregate Framework
    • Security Featured
    • free atlas database
    • Mongodb Compass (GUI)

Where MongoDB is good choice

  • E-commerce website
  • social media website
  • gaming application
  • web application
  • mobile application
  • real-time application

Types of Databases

  1. SQL Database:

    • Uses Structured Query Language (SQL) and follows a relational database model.
    • Examples: MySQL, PostgreSQL, Oracle, Microsoft SQL, etc.

  2. NoSQL Database:

    • Non-relational databases that store and access data differently, often without rows and columns.
    • Examples: MongoDB, Redis, RavenDB, CouchDB, etc.

Manging database and collection --> we can create database or collection and check databse, delete db or collection

--> show dbs / show databases --> show collection --> db.createCollection("name") --> use --> for create database --> db.dropDatabase() --> for delete databse --> db..drop() --> for delete collection

mongosh -> clear -> showdbs

insert document in mongodb

--> db.getCollection("name").insertOne({data}) --> to create single document --> db.getCollection("name").insertMany([{data}]) --> to create multiple document ---> db.getCollection("name").findOne() ---> to show collection document. ---> db.getCollection("name").findOne({gender: "Male}, {gender: 1}) ---> field ke filtering korte parbo -> here is 1 mean true. ---> db.getCollection("name").find({gender: "Male}, {gender: 1, name:1}) ---> field ke filtering korte parbo -> here is 1 mean true. ---> db.getCollection("name").find({gender: "Male}).project({name: 1, gender: 1, email: 1}) ---> field filter and only find() kaj kore

Comprison Operator

comparison operators are used to compare values in documents.

$eq: Matches values that are equal to a specified value. $ne: Matches all values that are not equal to a specified value. $gt: Matches values that are greater than a specified value. $gte: Matches values that are greater than or equal to a specified value. $lt: Matches values that are less than a specified value. $lte: Matches values that are less than or equal to a specified value. $in: Matches any of the values specified in an array. $nin: Matches none of the values specified in an array.

for example :

db.data.find({gender:"Female",age: {$gt: 18,$lte:30}}, {age: 1, name: 1, gender: 1}).sort({age:1}) > comma ke bole empicit and;

db.data.find({gender:"Female",age: {$in:[18, 20, 22, 24, 26]}, interests:{$in:["Cooking", "Reading"]}}, {age: 1, name: 1, gender: 1, interests:1}).sort({age:1})

db.collectionName.find({'filename': ${operator: value}}) db.produtcts.find({'price': ${$eq: 600}}) db.category.find({'price': ${$in:[100, 300, 700]}})

Cursor Methods

count() sort() limit() skip()

for example : db.collectionName.find({'filename': ${operator: value}}).method() db.produtcts.find({'price': ${$gt: 256}}).count() &rarr this mean how much document have this query db.prods.find({'price': ${$gt: 256}}).limit(10).skip(5) &rarr Limit the number of documents returned by the cursor and skip 5 document db.category.find({'price': ${$gt:100}}).sort({price: 1}) &rarr sort(1) means assending and sort(-1) means desacending

Logical Operator

$and: Joins query clauses with a logical AND. $or: Joins query clauses with a logical OR. $not: Inverts the effect of a query expression. $nor: Joins query clauses with a logical NOR.

{$and: {[condition1, condition2]}, ...}