MongoDB Developer's Toolkit: CRUD Mastery with Node.js, Java, Python

Indexing

• MONGODB INTRODUCTION

  • Introduction
  • What is MongoDB
  • Key Features of MongoDB:

• GETTING STARTED WITH MONGODB ATLAS

  • Steps to Get Started:

• MONGODB AND THE DOCUMENT MODEL

  • Overview
  • Databases, Collections, and Documents

• CONNECTING TO A MONGODB DATABASE

  • MongoDB Connection Strings: A Comprehensive Guide

  • CONNECTING TO A MONGODB DATABASE

  • 2. Connecting to a MongoDB Atlas Cluster with Compass

  • 3. Connecting from Applications

• CRUD OPERATIONS

  • 1. INSERT AND FIND
  • 2. REPLACE, UPDATE AND DELETE
  • 3. MODIFYING QUERY RESULTS

• MONGODB AGGREGATION

  • 1. Aggregation Pipeline

  • 2. Key Stages

  • Using $match and $group Stages in MongoDB Aggregation Pipeline

  • Using $sort and $limit Stages in a MongoDB Aggregation Pipeline

  • Using $project, $count and $set Stages in a MongoDB Aggregation Pipeline

  • Using $out Stage in a MongoDB Aggregation Pipeline

• MONGODB INDEXES

  • Why Use Indexes?

  • Types of Indexes

  • Creating Indexes

  • Viewing Indexes

  • Dropping Indexes

  • Best Practices

  • Creating a Single Field Index in MongoDB

  • Creating a Multikey Index in MongoDB

  • Working with Compound Indexes in MongoDB

  • Deleting MongoDB Indexes

  • Hiding and Showing MongoDB Indexes

• MONGODB ATLAS SEARCH

  • Key Features

  • Getting Started with Atlas Search

  • Example Use Case: JobApp

  • Conclusion

• INTRODUCTION TO DATA MODELING IN MONGODB

  • Introduction to MongoDB Data Modeling

  • Introduction to Data Modeling

  • Types of Data Relationships

  • Modeling Data Relationships

  • Scaling a Data Model

  • Using Atlas Tools for Schema Help

• MONGODB TRANSACTIONS

  • Introduction to ACID Transactions

  • Using Transactions in MongoDB

• Java Developer Path

MONGODB INTRODUCTION

Introduction

In today's digital age, where data reigns supreme, the need for robust, scalable, and flexible database solutions is paramount. MongoDB, a leading NoSQL database, has emerged as a popular choice among developers and enterprises alike. In this guide, we'll explore the fundamentals of MongoDB and show you how to get started with MongoDB Atlas, the Developer Data Platform, to kickstart your database journey.

What is MongoDB

MongoDB is a document-oriented NoSQL database that provides high performance, scalability, and flexibility for managing modern, unstructured data. Unlike traditional relational databases, MongoDB stores data in flexible, JSON-like documents, making it ideal for handling diverse and evolving data structures.

Key Features of MongoDB:

• Flexible Schema: Store heterogeneous data without predefined schemas, enabling agile development and easy data modeling.
• High Scalability: Distributed architecture allows for seamless horizontal scaling across multiple nodes.
• Rich Query Language: Powerful query language supports rich queries, indexing, and aggregation pipelines.
• Document-Based Storage: Stores data in BSON (Binary JSON) format for efficient storage and retrieval of complex data structures.
• Replication and Sharding: Supports automatic replication and sharding for fault tolerance, data redundancy, and horizontal scaling.

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GETTING STARTED WITH MONGODB ATLAS

MongoDB Atlas is a fully managed cloud database service provided by MongoDB, designed to simplify database deployment, management, and scaling. With MongoDB Atlas, developers can focus on building applications without worrying about infrastructure management.

Steps to Get Started:

1. Sign Up for MongoDB Atlas:

   • Go to the MongoDB website.
   • Sign up for a free account. MongoDB offers a free tier with generous usage limits, making it easy for developers to get started without any upfront costs.

2. Create a Cluster:

   • After signing up, log in to the MongoDB Atlas dashboard.
   • Click on "Build a New Cluster" and follow the prompts to configure your cluster. Choose your cloud provider, region, and cluster tier based on your requirements.

3. Connect to Your Cluster:

   • Once your cluster is created, MongoDB Atlas provides a connection string.
   • Use this connection string to connect your application to the database. You can connect using:
     • MongoDB Shell: For command-line interactions.
     • Drivers: For various programming languages (e.g., Node.js, Python, Java).
     • MongoDB Compass: A graphical user interface for managing your database.

4. Manage Your Cluster:

   • MongoDB Atlas offers tools for monitoring, backups, and security.
   • Monitor your cluster's performance, set up automated backups, and configure access controls to secure your data.

5. Scale Your Cluster:

   • As your application grows, you can easily scale your cluster.
   • Upgrade instance sizes, add additional shards for horizontal scaling, or enable auto-scaling to automatically adjust resources based on demand.

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MONGODB AND THE DOCUMENT MODEL

Overview

MongoDB is a general-purpose database used for a variety of use cases and is part of the Atlas developer data platform. The MongoDB document model organizes data into documents, collections, and databases. This readme provides an overview of these concepts and how to use MongoDB and Atlas Data Explorer.

Databases, Collections, and Documents

• Database

  A container for collections, similar to a schema in relational databases
  

• Collection

  A group of MongoDB documents, analogous to a table in relational databases.
  

• Document

  A record in a collection, stored in BSON format.
  

  • The MongoDB Document Model

  • BSON

    Binary JSON format used to store MongoDB documents, which can contain nested data structures.

  • Document Structure

    The values in a document can be any data type, including strings, objects, arrays, booleans, nulls, dates, ObjectIds, and more. Here's the syntax for a MongoDB document, followed by an example:

  • Syntax

    {
    "key": value,
    "key": value,
    "key": value
    }
    

  • Example

    {
     "_id": ObjectId("507f1f77bcf86cd799439011"),
     "name": "John Doe",
     "age": 30,
     "email": "john.doe@example.com",
     "address": {
                 "street": "123 Main St",
                 "city": "Anytown",
                 "zip": "12345"
              },
     "hobbies": ["reading", "travelling", "coding"]
    }

  • Field

    A key-value pair within a document.
    

Key Features

• Data Organization: Data is organized into documents, collections, and databases.
• BSON: Documents are stored in BSON, supporting a wide range of data types including JSON data types, dates, numbers, and ObjectIds.
• _id Field: Every document requires an _id field, acting as a primary key or unique identifier. MongoDB generates one automatically if not provided.
• Flexible Schema: Documents with different structures can be stored in the same collection.

Summary

• A Database contains multiple Collections.
• Each Collection contains multiple Documents.
• Each Document contains multiple Fields.

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CONNECTING TO A MONGODB DATABASE

MongoDB Connection Strings: A Comprehensive Guide

MongoDB's connection strings encapsulate all the necessary information required to connect to a MongoDB database instance. Understanding these connection strings is crucial for establishing a successful connection to your MongoDB server. Below is a detailed breakdown of the components of a MongoDB connection string:

Example of a MongoDB Connection String

Here is an example of a MongoDB connection string with all components included:

mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority

Breakdown of the Example

• Protocol: mongodb+srv://
• Username: <username>
• Password: <password>
• Hostname: <cluster-url>
• Database Name: <dbname>
• Options: retryWrites=true&w=majority

Example in Context

For a MongoDB cluster hosted on MongoDB Atlas, the connection string might look like this:

mongodb+srv://admin:admin123@cluster0.mongodb.net/myDatabase?retryWrites=true&w=majority

In this example:

• The protocol is mongodb+srv://.
• The username is admin.
• The password is admin123.
• The cluster URL is cluster0.mongodb.net.
• The database name is myDatabase.
• The options include retryWrites=true and w=majority.

Components of a MongoDB Connection String

1. Protocol

   • The protocol specifies the communication protocol used to connect to the MongoDB server.
   • Common protocols include:
     • mongodb:// for unencrypted connections.
     • mongodb+srv:// for connections using DNS seed list discovery.

2. Hostname and Port ( CLUSTER URL)

   • The hostname and port components denote the address and port number of the MongoDB server.

3. Authentication Credentials

   • Authentication credentials include the username and password required to authenticate against the MongoDB server.
   • These credentials are essential for securing access to your database.
   • Example: mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority

4. Database Name

   • The database name specifies the name of the database to which you want to connect.
   • If the database does not exist, MongoDB will create it upon connection.

5. Options

   • Additional options can be included in the connection string to customize the connection.
   • Common options include:
     • retryWrites=true to enable automatic retries of certain write operations.
     • w=majority to set the write concern to "majority".

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CONNECTING TO A MONGODB DATABASE

Introduction

MongoDB Atlas, a cloud-based database service, offers developers a robust platform for managing their data. Establishing a connection to your MongoDB Atlas cluster is a crucial first step. This guide explores two methods for connecting to a MongoDB Atlas cluster: using the MongoDB shell and MongoDB Compass. Additionally, it covers connecting from Applications.

1. Connecting to a MongoDB Atlas Cluster with the Shell

Steps:

1. Locate Connection String:
   • Navigate to your MongoDB Atlas dashboard.
   • Select your cluster and click "Connect."
   • Choose "Connect Your Application" and select "Mongo Shell."
   • Copy the connection string provided.
2. Open Terminal:
   • Launch your terminal or command prompt.
   • Paste the copied connection string, replacing <password> with your MongoDB Atlas password.
3. Connect to Cluster:
   • Execute the command to connect to your MongoDB Atlas cluster:

     mongo "mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority"

   • Enter your MongoDB Atlas username and password when prompted.
4. Authentication:
   • Once authenticated, you are connected to your MongoDB Atlas cluster and can execute MongoDB commands and queries.

2. Connecting to a MongoDB Atlas Cluster with Compass

Steps:

1. Download and Install Compass:
   • Download MongoDB Compass from the MongoDB website.
   • Follow the installation instructions.
2. Locate Connection String:
   • In the MongoDB Atlas dashboard, select your cluster and click "Connect."
   • Choose "Connect Your Application" and select "MongoDB Compass."
   • Copy the connection string provided.
3. Launch Compass:
   • Open MongoDB Compass and click on the "New Connection" button.
4. Paste Connection String:
   • In the connection dialog, paste the copied connection string.
5. Connect:
   • Click "Connect" and enter your MongoDB Atlas username and password if prompted.

3. Connecting from Applications

MongoDB Atlas supports various programming languages and frameworks. Below are examples for Node.js, Python, and Java:

Node.js (Using the MongoDB Node.js Driver)

const { MongoClient } = require('mongodb');

// MongoDB connection string
const uri = 'mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority';

// Connect to MongoDB Atlas
async function connectToAtlas() {
  const client = new MongoClient(uri, { useNewUrlParser: true, useUnifiedTopology: true });
  try {
    await client.connect();
    console.log('Connected to MongoDB Atlas');
    // Perform database operations here
  } catch (error) {
    console.error('Error connecting to MongoDB Atlas:', error);
  } finally {
    await client.close();
    console.log('Disconnected from MongoDB Atlas');
  }
}

connectToAtlas();

Python (Using the PyMongo Driver)

from pymongo import MongoClient

# MongoDB connection string
uri = "mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority"

# Connect to MongoDB Atlas
def connect_to_atlas():
    client = MongoClient(uri)
    try:
        print("Connected to MongoDB Atlas")
        # Perform database operations here
    except Exception as e:
        print("Error connecting to MongoDB Atlas:", e)
    finally:
        client.close()
        print("Disconnected from MongoDB Atlas")

connect_to_atlas()

Java (Using the MongoDB Java Driver)

import com.mongodb.client.MongoClients;
import com.mongodb.client.MongoClient;
import com.mongodb.MongoClientSettings;
import com.mongodb.ConnectionString;

public class Main {
    public static void main(String[] args) {
        // MongoDB's connection string
        String uri = "mongodb+srv://<username>:<password>@<cluster-url>/<dbname>?retryWrites=true&w=majority";

        // Connect to MongoDB Atlas
        try (MongoClient client = MongoClients.create(new ConnectionString(uri))) {
            System.out.println("Connected to MongoDB Atlas");
            // Perform database operations here
        } catch (Exception e) {
            System.err.println("Error connecting to MongoDB Atlas: " + e);
        }
    }
}

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CRUD OPERATIONS

1. INSERT AND FIND

When you insert a document in a collection, it returns insertedId.

• insertOne()

   db.<collection>.insertOne({<document>})
  

  Example

  db.Sales.insertOne(
     { 
     'item': 'abc',   // key : value
     'price': 10, 
     'quantity': 2, 
     'date': new Date('2014-03-01T08:00:00Z') 
     }
  )

  You can see the insertedId in the terminal output.

  

• insertMany()

  db.<collection>.insertMany([{<document>},{<document>},{<document>}])
  

  Example

  db.Sales.insertMany(
  [
     { 'item': 'jkl', 'price': 20, 'quantity': 1, 'date': new Date('2014-03-01T09:00:00Z') },
     { 'item': 'xyz', 'price': 5, 'quantity': 10, 'date': new Date('2014-03-15T09:00:00Z') },
     { 'item': 'xyz', 'price': 5, 'quantity': 20, 'date': new Date('2014-04-04T11:21:39.736Z') },
     { 'item': 'abc', 'price': 10, 'quantity': 10, 'date': new Date('2014-04-04T21:23:13.331Z') },
     { 'item': 'def', 'price': 7.5, 'quantity': 5, 'date': new Date('2015-06-04T05:08:13Z') },
     { 'item': 'def', 'price': 7.5, 'quantity': 10, 'date': new Date('2015-09-10T08:43:00Z') },
     { 'item': 'abc', 'price': 10, 'quantity': 5, 'date': new Date('2016-02-06T20:20:13Z') },
  ]
  );

  You can see the insertedIds in the terminal output.

  

• find()

   db.<collection>.find()
  

  It returns all the documents in the collection

  Example

  db.sales.find()

  

• findOne()

  db.<collection>.findOne()
  

  It returns the first document in the collection.

  Example

  db.sales.findOne()

  

• find({query})

  db.<collection>.find({query})
  

  It returns the documents that match the query.

  Example

  db.sales.find({item: 'xyz'})

  There is 2 documents with same query.

  

• findOne({query})

  db.<collection>.findOne({query})
  

  It returns the first document that match the query.

  Example

  db.sales.findOne({item: 'xyz'})

  

• find({ $in operator })

  db.<collection>.find({<field>:{$in:[<value>,<value>,<value>]}})
  

  It returns the documents that match the value in the array.

  Example

  db.sales.find({price:{$in:[10,5,20]}})

  

• find({comparison operator})

  • $gt : stands for greater than.

  • $lt : stands for less than.

  • $gte : stands for greater than or equal.

  • $lte : stands for less than or equal.

  • $ne : stands for not equal.

    db.<collection>.find({<field>:{$(comparison operator):<value>}})
    

    Example

    db.movies.find({"imdb.rating":{$gt:7}})

    

      db.<collection>.find({
      <field>:{$(comparison operator):<value>},
      <field:{$(comparison operator):<value>}
      })
    

    Example

       db.movies.find({
       "imdb.rating":{$gt:7},
       "tomatoes.viewer.rating":{$gt:4},
       })

    

• find({query in Array})

   db.<collection>.find({<field>:{
    $elemMatch:{$eq:<value>}
   }})
  

  It returns the documents that match the value in the array.

  Example

  db.movies.find({cast:{
    $elemMatch:{$eq:"John Bowers"}
  }})

  

• find({logical operator})

  • $and

  • $or

     db.<collection>.find({
      $(logical operator):[
        {<expression>},
        {<expression>},
      ]
     })
    

    $and Example

    db.movies.find({
      $and:[
        {"imdb.rating":{$gte:6.9}},
        {runtime:65}
        ]
    })

    

    $or Example

    db.movies.find(
      $or:[
        {runtime:65},
        {directors:{
          $elemMatch:{$eq:"Winsor McCay"}
        }}
      ]
    )

    

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2. REPLACE, UPDATE AND DELETE

• replaceOne(filter , replacement)

  • filter: The filter is used to find the document to be replaced.

  • replacement: The replacement document is used to replace the document found by the filter.

  • options: The options are used to specify the behavior of the replaceOne() method.

  • Upsert Option: When upsert is set to true, if no document matches the filter, MongoDB will insert the replacement document as a new document. If a document matches the filter, it will be replaced with the replacement document.

    db.<collection>.replaceOne(filter, replacement, options)
    

  Example

  db.sales.replaceOne(
     {_id:new ObjectId("6682e1a165332a71e20576f5")},
     {
      "item": "abc",
      "price": 15,
      "quantity": 5,
      "date": {
        "$date": "2014-03-01T08:00:00Z"
        }
     }
     {
      upsert: true
     }
  )

  

• updateOne(filter, update, options)

    db.<collection>.updateOne(filter, update, options)
  

  Example

  db.movies.updateOne(
    {title:"The Matrix"},
    {$set:{title:"The Matrix Reloaded"}}
  )

  

  db.movies.updateOne({
    {title:"The Matrix Reloaded"},
    {
      $push:{
        genres:"Horror"
      }
    }
  })

  

• findAndModify(query,update,options)

  db.<collection>.findAndModify(query,update,options)
  

  Example

  db.movies.findAndModify(
    {
      query:{title:"The Matrix Reloaded"},
      update:{
        $inc:{"imdb.rating":1}
      },
      new:true
    }
  )

  

  Rating increased by 1.

  

• updateMany(filter,update)

   db.<collection>.updateMany(filter,update)
  

  Example

  db.movies.updateMany(
    {year:1999},
    {
      $inc:{"imdb.rating":-1}
    }
  )

  

• deleteOne(filter)

  db.<collection>.deleteOne(filter)
  

  Example

  db.sales.deleteOne({price:15})

  

• deleteMany(filter)

  It will delete all the document that matches the filter.

  db.<collection>.deleteMany(filter)
  

  Example

  db.sales.deleteMany({price:5})

  

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3. MODIFYING QUERY RESULTS

• Sorting and Limiting Query Result

  • limit(n)

     db.<collection>.find().limit(n)
    

    Example

    db.sales.find().limit(2)

    

  • sort({field:n})

    n=1 Ascending. n=-1 Descending.

       db.<collection>.find().sort({filed:n})
    

    Example

    db.sales.find().sort({item:1})

    

    db.movies.find({"imdb.rating":{
      $ge:6
    }},{"imdb.rating":-1}).sort({"imdb.rating":-1})

    

• Returning Specific Data from a Query

  • Getting single field only along with _id.

      db.<collection>.find().project({field:1})
    

  • • 1 mean include and 0 means exclude.

  Example

  db.movies.find().projection({cast:1});

  

  • Getting more than 1 field along with _id.

       db.<collection>.find().projection({filed1:1,field2:1})
    

  Example

  db.movies.find().projection({cast:1,title:1})

  

  • Excluding the desired filed.

    Example

    db.movies.find().projection({cast:0,title:0})

    

• Counting Documents in MongoDB Documents

  • countDocuments({filter})

      db.<collection>.countDocuments()
    

    Example

    db.movies.countDocuments()

    

    Example

    db.movies.countDocuments("imdb.rating":{
      $gte: 8
    })

    

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MONGODB AGGREGATION

MongoDB Aggregation Framework is a powerful tool for performing data processing and analysis directly within the database. It allows you to transform and combine data from multiple documents, providing a way to conduct complex operations and analytics without the need to pull data into your application. Here's an introduction to its key concepts:

1. Aggregation Pipeline

The core of MongoDB’s aggregation framework is the aggregation pipeline. This is a series of stages that process documents. Each stage transforms the documents as they pass through the pipeline. The stages are executed in sequence, and the output of one stage is passed as input to the next stage.

2. Key Stages

• $match: Filters documents to pass only those that match the specified conditions.
• $group: Groups documents by a specified identifier and can calculate aggregates, such as sums, averages, and counts.
• $project: Reshapes documents, including or excluding fields, adding new fields, or computing new fields.
• $sort: Sorts documents by a specified field.
• $limit: Limits the number of documents passing through the pipeline.
• $skip: Skips a specified number of documents.
• $unwind: Deconstructs an array field from the input documents to output a document for each element.
• $lookup: Performs a left outer join to another collection to filter in documents from the “joined” collection.

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Using $match and $group Stages in MongoDB Aggregation Pipeline

Example 1: $match

Suppose you want to find all theaters in the city of Mansfield.

db.theaters.aggregate([
  {
    $match: {
      "location.address.city": "Mansfield"
    }
  }
])

Example 2: $group

Let's say you want to group theaters by state and count the number of theaters in each state.

db.theaters.aggregate([
  {
    $group: {
      _id: "$location.address.state",
      theaterCount: { $count: {} }
    }
  }
])

Combined Example: $match and $group

You might also want to find the count of theaters in each city within Texas (TX).

db.theaters.aggregate([
  {
    $match: {
      "location.address.state": "TX"
    }
  },
  {
    $group: {
      _id: "$location.address.city",
      theaterCount: { $count: {} }
    }
  }
])

Breakdown of the Stages

1. $match Stage:

   • Filters documents to pass only those that match the specified condition.
   • In the first example, it filters theaters located in Mansfield.
   • In the combined example, it filters theaters located in the state of Texas.

2. $group Stage:

   • Groups the documents by a specified identifier (e.g., city or state).
   • Computes aggregate values such as counts, sums, averages, etc.
   • In the second example, it groups theaters by state and counts them.
   • In the combined example, it groups theaters by city within Texas and counts them.

Using $sort and $limit Stages in a MongoDB Aggregation Pipeline

Example: $sort

Suppose you want to sort theaters by their theaterId in ascending order.

db.theaters.aggregate([
  {
    $sort: {
      theaterId: 1
    }
  }
])

In this example, the $sort stage sorts the documents by the theaterId field in ascending order (1 for ascending, -1 for descending).

Example: $limit

If you want to limit the results to the first 5 theaters, you can use the $limit stage.

db.theaters.aggregate([
  {
    $limit: 5
  }
])

Combined Example: $sort and $limit

Let's combine $sort and $limit to sort the theaters by theaterId in descending order and limit the results to the top 3 theaters.

db.theaters.aggregate([
  {
    $sort: {
      theaterId: -1
    }
  },
  {
    $limit: 3
  }
])

Breakdown of the Stages

1. $sort Stage:

   • Sorts the documents by the specified field(s).
   • In the combined example, it sorts the theaters by theaterId in descending order.

2. $limit Stage:

   • Limits the number of documents passing through the pipeline.
   • In the combined example, it limits the results to the top 3 theaters after sorting.

Using $project, $count and $set Stages in a MongoDB Aggregation Pipeline

Example: $project

Suppose you want to project only the theaterId and location.address.city fields from the documents.

db.theaters.aggregate([
  {
    $project: {
      _id: 0,
      theaterId: 1,
      city: "$location.address.city"
    }
  }
])

In this example, the $project stage reshapes the documents to include only the theaterId and city fields.

Example: $count

If you want to count the total number of theaters, you can use the $count stage.

db.theaters.aggregate([
  {
    $count: "totalTheaters"
  }
])

Example: $set

Let's say you want to add a new field state that duplicates the value of location.address.state.

db.theaters.aggregate([
  {
    $set: {
      state: "$location.address.state"
    }
  }
])

Combined Example: $match, $group, $project, and $set

Let's combine several stages: filter theaters in Texas ($match), group by city and count the number of theaters in each city ($group), project the results to include the city and count ($project), and then use $set to create a field "fullAddress" containing the street, city, state, and zip code separated by commas.

db.theaters.aggregate([
  {
    $match: {
      "location.address.state": "TX"
    }
  },
  {
    $group: {
      _id: {
        city: "$location.address.city",
        street: "$location.address.street1",
        state: "$location.address.state",
        zipcode: "$location.address.zipcode"
      },
      theaterCount: { $sum: 1 }
    }
  },
  {
    $set: {
      fullAddress: {
        $concat: [
          "$_id.street", ", ",
          "$_id.city", ", ",
          "$_id.state", ", ",
          "$_id.zipcode"
        ]
      }
    }
  },
  {
    $project: {
      _id: 0,
      theaterCount: 1,
      fullAddress: 1
    }
  }
])

Breakdown of the Stages

1. $match Stage:

   • Filters documents to pass only those that match the specified condition (theaters in Texas).

2. $group Stage:

   • Groups the documents by city and counts the number of theaters in each city.

3. $project Stage:

   • Reshapes the documents to include only the city and theaterCount fields, and excludes the _id field.

4. $set Stage:

   • Adds a new field label with the value "Number of Theaters".

5. $count Stage:

   • Although not included in the combined example, it can be used to count the total number of documents passing through the pipeline.

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Using $out Stage in a MongoDB Aggregation Pipeline

The $out stage in MongoDB’s aggregation pipeline writes the results of the aggregation to a specified collection. This stage must be the last stage in the pipeline.

Here’s an example of how to modify the previous pipeline to use the $out stage to write the results to a new collection called texas_theaters_summary:

db.theaters.aggregate([
  {
    $match: {
      "location.address.state": "TX"
    }
  },
  {
    $group: {
      _id: {
        city: "$location.address.city",
        street: "$location.address.street1",
        state: "$location.address.state",
        zipcode: "$location.address.zipcode"
      },
      theaterCount: { $sum: 1 }
    }
  },
  {
    $set: {
      fullAddress: {
        $concat: [
          "$_id.street", ", ",
          "$_id.city", ", ",
          "$_id.state", ", ",
          "$_id.zipcode"
        ]
      }
    }
  },
  {
    $project: {
      _id: 0,
      theaterCount: 1,
      fullAddress: 1
    }
  },
  {
    $out: "texas_theaters_summary"
  }
])

Breakdown of the Stages with $out

1. $match Stage:

   • Filters documents to pass only those that match the specified condition (theaters in Texas).

2. $group Stage:

   • Groups the documents by a compound key of city, street, state, and zipcode and counts the number of theaters in each group.

3. $set Stage:

   • Adds a new field fullAddress that concatenates the street, city, state, and zipcode fields separated by commas.

4. $project Stage:

   • Reshapes the documents to include only the theaterCount and fullAddress fields, excluding the _id field.

5. $out Stage:

   • Writes the results of the aggregation pipeline to the specified collection texas_theaters_summary.

This pipeline processes the documents and writes the results to the texas_theaters_summary collection, which will contain documents like this:

{
  "theaterCount": 1,
  "fullAddress": "2041 Highway 287 N, Mansfield, TX, 76063"
}

The $out stage will overwrite the texas_theaters_summary collection if it already exists. If it does not exist, MongoDB will create it.

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MONGODB INDEXES

Indexes in MongoDB are special data structures that store a small portion of the collection's data set in an easy-to-traverse form. The index stores the value of a specific field or set of fields, ordered by the value of the field as specified in the index.

Why Use Indexes?

• Improve Query Performance: Indexes support the efficient execution of queries by providing a faster path to data retrieval.
• Reduce Query Execution Time: Indexes allow the database to quickly locate the required data without scanning the entire collection.

Types of Indexes

1. Single Field Index: Index on a single field.

   db.collection.createIndex({ field: 1 })  // Ascending order
   db.collection.createIndex({ field: -1 }) // Descending order

2. Compound Index: Index on multiple fields.

   db.collection.createIndex({ field1: 1, field2: -1 })

3. Multikey Index: Index on an array field, allowing queries to search for array elements.

   db.collection.createIndex({ arrayField: 1 })

4. Text Index: Index for text search on string content.

   db.collection.createIndex({ field: "text" })

5. Geospatial Index: Index for spatial data queries.

   db.collection.createIndex({ location: "2dsphere" })

6. Hashed Index: Index based on a hash of the field's value.

   db.collection.createIndex({ field: "hashed" })

Creating Indexes

To create an index, use the createIndex method on the collection. For example:

db.users.createIndex({ username: 1 })

Viewing Indexes

To view the indexes on a collection, use the getIndexes method:

db.collection.getIndexes()

Dropping Indexes

To drop an index, use the dropIndex method:

db.collection.dropIndex("indexName")

Best Practices

1. Index Fields Used in Queries: Index the fields that are frequently used in query filters, sorts, and join operations.
2. Limit Number of Indexes: While indexes improve read performance, they can degrade write performance as the database must update the index each time a document is written.
3. Monitor Index Usage: Use MongoDB tools like explain() to understand how queries use indexes and optimize accordingly.

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Creating a Single Field Index in MongoDB

To create a single field index on the theaterId field in the given MongoDB document, you can use the createIndex method. Here's how you can do it:

db.collection.createIndex({ theaterId: 1 })

This command will create an ascending index on the theaterId field. If you want to create a descending index, you can use -1 instead of 1:

db.collection.createIndex({ theaterId: -1 })

Example in MongoDB Shell

db.theaters.createIndex({ theaterId: 1 })

Explanation

• db.theaters specifies the collection name, which you should replace with your actual collection name if it's different.
• createIndex is the method to create the index.
• { theaterId: 1 } specifies that you are creating an ascending index on the theaterId field.

Creating an Index on the location.geo Field

If you want to create a geospatial index on the location.geo field to support geospatial queries, you can use the following command:

db.theaters.createIndex({ "location.geo": "2dsphere" })

This command creates a 2dsphere index, which supports queries that calculate geometries on an Earth-like sphere.

Creating a Multikey Index in MongoDB

In MongoDB, a multikey index is an index that can be created on fields that contain arrays. When you create a multikey index, MongoDB indexes each element of the array, allowing queries to efficiently filter and sort documents based on array values.

Creating a Multikey Index

Suppose you have a collection of documents representing movies, and each document has a genres field that is an array of genres. Here’s an example document:

{
  "_id": ObjectId("59a47286cfa9a3a73e51e74d"),
  "title": "Inception",
  "director": "Christopher Nolan",
  "genres": ["Action", "Sci-Fi", "Thriller"]
}

To create a multikey index on the genres field, you would use the createIndex method:

db.movies.createIndex({ genres: 1 })

Use Case Example

1. Querying for Movies of a Specific Genre

Suppose you want to find all movies that belong to the "Action" genre. With a multikey index on the genres field, MongoDB can efficiently retrieve these documents.

db.movies.find({ genres: "Action" })

2. Querying for Movies of Multiple Genres

You can also query for movies that belong to multiple genres. MongoDB will use the multikey index to optimize this query.

db.movies.find({ genres: { $all: ["Action", "Sci-Fi"] } })

Example Scenario: JobApp

Let's consider your JobApp, where job listings might have an array of required skills:

{
  "_id": ObjectId("59a47286cfa9a3a73e51e74d"),
  "title": "Software Developer",
  "company": "TechCorp",
  "location": "New York",
  "skills": ["JavaScript", "React", "Node.js"]
}

To create a multikey index on the skills field:

db.jobs.createIndex({ skills: 1 })

Use Case Example for JobApp

1. Querying for Jobs Requiring a Specific Skill

If you want to find all job listings that require "React":

db.jobs.find({ skills: "React" })

2. Querying for Jobs Requiring Multiple Skills

If you want to find job listings that require both "JavaScript" and "Node.js":

db.jobs.find({ skills: { $all: ["JavaScript", "Node.js"] } })

Benefits of Multikey Indexes

• Efficient Array Queries: Allows efficient querying of documents with array fields.
• Improved Query Performance: Enhances performance for queries that filter on array elements.

Best Practices

• Limit Array Size: Large arrays can increase the index size and impact performance. Try to limit the size of arrays in your documents.
• Monitor Index Usage: Use MongoDB's explain method to analyze query execution and ensure indexes are used effectively.

Creating multikey indexes on array fields in your MongoDB collections will optimize queries that involve array elements, improving the overall performance and responsiveness of your application.

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Working with Compound Indexes in MongoDB

Compound indexes in MongoDB allow you to create an index on multiple fields within a document. These indexes can improve the performance of queries that filter and sort by multiple fields.

Creating a Compound Index

To create a compound index, you use the createIndex method and specify multiple fields in the index key specification. The order of the fields in the index is significant as it affects the types of queries that can utilize the index.

Example Document

Consider a collection of job listings with documents like this:

{
  "_id": ObjectId("59a47286cfa9a3a73e51e74d"),
  "title": "Software Developer",
  "company": "TechCorp",
  "location": "New York",
  "postedDate": new Date("2023-06-15"),
  "salary": 120000
}

Creating a Compound Index

Suppose you frequently query this collection by location and salary. You can create a compound index on these fields:

db.jobs.createIndex({ location: 1, salary: -1 })

• location: 1 specifies an ascending order for the location field.
• salary: -1 specifies a descending order for the salary field.

Use Case Examples

1. Query Filtering by Location and Sorting by Salary

   With the compound index in place, this query can efficiently filter jobs by location and sort the results by salary in descending order:

   db.jobs.find({ location: "New York" }).sort({ salary: -1 })

2. Query Filtering by Location and Salary Range

   The compound index also improves performance for queries that filter by location and a range of salary values:

   db.jobs.find({ location: "New York", salary: { $gt: 100000, $lt: 150000 } })

3. Query Filtering by Location Only

   The compound index can still be used for queries that filter by location only:

   db.jobs.find({ location: "New York" })

Understanding Index Prefix

MongoDB can use the index for queries that include a prefix of the indexed fields. In the compound index { location: 1, salary: -1 }, the following queries can use the index:

• Queries that filter by location and sort by salary.
• Queries that filter by location.
• Queries that filter by location and salary.

However, queries that only filter by salary cannot use this index efficiently.

Querying with explain

To understand how MongoDB uses the index, you can use the explain method:

db.jobs.find({ location: "New York", salary: { $gt: 100000 } }).explain("executionStats")

The explain output will show whether the index is being used and how many documents are being scanned.

Best Practices

• Order Matters: The order of fields in a compound index is significant. Place the most selective field first (the field that reduces the number of matching documents the most).
• Index Only Required Fields: Limit the number of fields in the compound index to what is necessary for your queries.
• Monitor Index Performance: Use MongoDB's monitoring tools to ensure that indexes are improving query performance as expected.

Summary

Compound indexes in MongoDB provide a powerful way to optimize queries that filter and sort by multiple fields. By carefully selecting and ordering the fields in a compound index, you can significantly improve query performance and ensure efficient data retrieval for your application.

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Deleting MongoDB Indexes

Deleting indexes in MongoDB is a straightforward process. You can delete indexes to optimize performance, manage storage, or if they are no longer needed for your query patterns.

Deleting an Index

1. Delete a Specific Index

To delete a specific index, use the dropIndex method and provide the name of the index you want to remove.

db.collection.dropIndex("indexName")

For example, if you have an index on the title field in a jobs collection:

db.jobs.dropIndex("title_1")

2. Delete Multiple Indexes

You can delete multiple indexes at once by providing an array of index names to the dropIndexes method.

db.collection.dropIndexes(["indexName1", "indexName2"])

3. Delete All Indexes

To delete all indexes on a collection except for the _id index, use the dropIndexes method without any arguments.

db.collection.dropIndexes()

For example:

db.jobs.dropIndexes()

Finding Index Names

To determine which indexes exist on a collection, use the getIndexes method:

db.collection.getIndexes()

For example, to list all indexes on the jobs collection:

db.jobs.getIndexes()

This command will return an array of index specifications, including the index names.

Example Scenario

Consider the jobs collection with the following indexes:

db.jobs.createIndex({ title: 1 })
db.jobs.createIndex({ location: 1, salary: -1 })
db.jobs.createIndex({ company: 1 })

Viewing Indexes

db.jobs.getIndexes()

Output:

[
  { "v": 2, "key": { "_id": 1 }, "name": "_id_", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "title": 1 }, "name": "title_1", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "location": 1, "salary": -1 }, "name": "location_1_salary_-1", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "company": 1 }, "name": "company_1", "ns": "yourDatabase.jobs" }
]

Deleting a Specific Index

To delete the index on the title field:

db.jobs.dropIndex("title_1")

Deleting All Indexes

To delete all indexes except for the _id index:

db.jobs.dropIndexes()

Summary

• Specific Index: Use db.collection.dropIndex("indexName") to delete a specific index.
• Multiple Indexes: Use db.collection.dropIndexes(["indexName1", "indexName2"]) to delete multiple indexes.
• All Indexes: Use db.collection.dropIndexes() to delete all indexes except the _id index.
• Find Indexes: Use db.collection.getIndexes() to list all indexes on a collection.

Regularly review and manage your indexes to ensure they align with your query patterns and optimize your MongoDB performance.

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Hiding and Showing MongoDB Indexes

In MongoDB 4.4 and later, you have the ability to hide and unhide indexes. This feature allows you to test the impact of removing an index on your query performance without actually dropping the index. If you find that the performance is acceptable without the index, you can then drop it. If not, you can unhide it.

Hiding an Index

To hide an index, you use the hideIndex command.

Example

Suppose you have an index on the title field in the jobs collection:

db.jobs.createIndex({ title: 1 })

To hide this index:

db.jobs.hideIndex("title_1")

Showing an Index

To unhide (show) an index, you use the unhideIndex command.

Example

To unhide the previously hidden index on the title field:

db.jobs.unhideIndex("title_1")

Checking Index Visibility

You can check whether an index is hidden or not by using the getIndexes method. Hidden indexes will have a hidden field set to true.

Example

To list all indexes and their visibility status:

db.jobs.getIndexes()

Output:

[
  { "v": 2, "key": { "_id": 1 }, "name": "_id_", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "title": 1 }, "name": "title_1", "ns": "yourDatabase.jobs", "hidden": true },
  { "v": 2, "key": { "location": 1, "salary": -1 }, "name": "location_1_salary_-1", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "company": 1 }, "name": "company_1", "ns": "yourDatabase.jobs" }
]

In this example, the index on title is hidden.

Example Scenario

Assume you have a jobs collection with several indexes:

db.jobs.createIndex({ title: 1 })
db.jobs.createIndex({ location: 1, salary: -1 })
db.jobs.createIndex({ company: 1 })

Hiding an Index

To hide the index on location and salary:

db.jobs.hideIndex("location_1_salary_-1")

Checking Indexes

To verify the index status:

db.jobs.getIndexes()

Output

[
  { "v": 2, "key": { "_id": 1 }, "name": "_id_", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "title": 1 }, "name": "title_1", "ns": "yourDatabase.jobs" },
  { "v": 2, "key": { "location": 1, "salary": -1 }, "name": "location_1_salary_-1", "ns": "yourDatabase.jobs", "hidden": true },
  { "v": 2, "key": { "company": 1 }, "name": "company_1", "ns": "yourDatabase.jobs" }
]

Unhiding an Index

To unhide the location and salary index:

db.jobs.unhideIndex("location_1_salary_-1")

Summary

• Hide an Index: Use db.collection.hideIndex("indexName") to hide an index.
• Unhide an Index: Use db.collection.unhideIndex("indexName") to unhide an index.
• Check Index Visibility: Use db.collection.getIndexes() to list indexes and check their visibility status.

These commands provide a flexible way to manage your indexes and test the impact of their removal on query performance.

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MONGODB ATLAS SEARCH

MongoDB Atlas Search is a powerful, integrated full-text search solution that allows you to run sophisticated searches on your MongoDB data. It leverages the power of the Lucene search engine and is fully managed within MongoDB Atlas, the cloud-based MongoDB service.

Key Features

1. Full-Text Search: Provides capabilities like relevance scoring, text indexing, tokenization, and more.
2. Rich Query Language: Supports advanced search queries with operators like $search, $text, $and, $or, $regex, and many more.
3. Faceted Search: Allows you to categorize search results into groups.
4. Autocomplete: Provides real-time search suggestions as you type.

Getting Started with Atlas Search

To use Atlas Search, you need to have a MongoDB Atlas cluster. Here’s a step-by-step guide to enable and use Atlas Search:

Step 1: Create an Atlas Cluster

1. Sign up for MongoDB Atlas: If you haven't already, sign up at MongoDB Atlas.
2. Create a Cluster: Follow the prompts to create a new cluster. Choose your cloud provider, region, and cluster tier based on your needs.

Step 2: Enable Atlas Search

1. Access the Cluster: In the MongoDB Atlas UI, go to the cluster where you want to enable search.
2. Create Search Index:
   • Navigate to the "Collections" tab.
   • Select the database and collection you want to enable search for.
   • Click on the "Search Indexes" tab and then "Create Search Index".
   • Define your index by specifying the fields you want to include in the search index.

Step 3: Define Search Indexes

When creating a search index, you define which fields to index and how they should be indexed. Here’s an example JSON configuration for a simple search index:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "title": {
        "type": "string"
      },
      "description": {
        "type": "string"
      },
      "tags": {
        "type": "string"
      }
    }
  }
}

Step 4: Performing a Search Query

Once your search index is created, you can perform search queries using the $search stage in the aggregation pipeline.

Here’s an example using the MongoDB shell or a MongoDB driver:

db.collection.aggregate([
  {
    $search: {
      index: "default",
      text: {
        query: "software developer",
        path: ["title", "description"]
      }
    }
  }
])

This query searches for the phrase "software developer" in the title and description fields.

Example Use Case: JobApp

Consider your JobApp project, where you have a collection of job listings with fields like title, description, skills, and location. You want to enable full-text search on these fields.

1. Create a Search Index:

{
  "mappings": {
    "dynamic": true,
    "fields": {
      "title": {
        "type": "string"
      },
      "description": {
        "type": "string"
      },
      "skills": {
        "type": "string"
      },
      "location": {
        "type": "string"
      }
    }
  }
}

2. Search Query Example:

To search for job listings that mention "JavaScript developer" in the title or description:

db.jobs.aggregate([
  {
    $search: {
      index: "default",
      text: {
        query: "JavaScript developer",
        path: ["title", "description"]
      }
    }
  }
])

3. Using Autocomplete:

To provide real-time search suggestions as users type, use the autocomplete operator:

db.jobs.aggregate([
  {
    $search: {
      index: "default",
      autocomplete: {
        query: "soft",
        path: "title",
        fuzzy: {
          maxEdits: 2
        }
      }
    }
  }
])

Conclusion

MongoDB Atlas Search offers a robust, fully integrated search solution for your MongoDB data. By enabling full-text search, faceted search, and autocomplete capabilities, it enhances the user experience and allows for more sophisticated querying of your data. This makes it an excellent choice for applications like your JobApp, where searching and filtering through job listings are essential features.

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INTRODUCTION TO DATA MODELING IN MONGODB

Introduction to MongoDB Data Modeling

Data modeling in MongoDB is the process of creating a data structure that matches the requirements of the application, taking into account how the data will be queried and updated. Unlike traditional SQL databases, MongoDB uses a flexible schema design that allows for the storage of varied and complex data structures.

Introduction to Data Modeling

Data modeling involves defining the structure of data in a way that is optimized for the application’s requirements. It includes determining the relationships between different types of data and how they will be stored and accessed. In MongoDB, this involves using collections and documents instead of tables and rows.

Types of Data Relationships

1. One-to-One

A one-to-one relationship occurs when a single document is related to another single document. For example, a user profile and user settings might have a one-to-one relationship.

2. One-to-Many

A one-to-many relationship exists when a single document is related to multiple documents. For instance, a single customer can have multiple orders.

3. Many-to-Many

A many-to-many relationship is when multiple documents are related to multiple other documents. An example would be students and courses, where a student can enroll in multiple courses and a course can have multiple students.

Modeling Data Relationships

Embedding Data in Documents

Embedding data means storing related data within a single document. This approach is useful when you frequently need to retrieve the related data together. It improves read performance by reducing the need for joins.

Example:

{
  "title": "Post Title",
  "content": "Post Content",
  "comments": [
    {
      "author": "Commenter Name",
      "content": "Comment Content"
    }
  ]
}

Referencing Data in Documents

Referencing involves storing the related data in separate documents and using references (or links) between them. This approach is useful for maintaining data consistency and avoiding duplication.

Example:

// Post Document
{
  "title": "Post Title",
  "content": "Post Content",
  "comments": [
    ObjectId("comment_id_1"),
    ObjectId("comment_id_2")
  ]
}

// Comment Document
{
  "_id": ObjectId("comment_id_1"),
  "author": "Commenter Name",
  "content": "Comment Content",
  "postId": ObjectId("post_id")
}

Scaling a Data Model

As your application grows, you need to ensure that your data model can scale effectively. Considerations for scaling include:

• Sharding: Distributing data across multiple servers to handle large volumes of data and traffic.
• Indexing: Creating indexes to improve query performance.
• Data Partitioning: Dividing data into partitions to manage and query large datasets efficiently.

Using Atlas Tools for Schema Help

MongoDB Atlas provides several tools to help with schema design and optimization:

• Schema Analyzer: Analyzes your data and suggests schema improvements.
• Performance Advisor: Provides indexing suggestions based on your query patterns.
• Data Explorer: Allows you to visually explore your data and understand its structure.

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MONGODB TRANSACTIONS

Introduction to ACID Transactions

Introduction to ACID Transactions

ACID stands for Atomicity, Consistency, Isolation, and Durability, which are a set of properties that ensure reliable processing of database transactions.

• Atomicity: Ensures that all operations within a transaction are completed successfully; otherwise, the transaction is aborted.
• Consistency: Ensures that the database remains in a valid state before and after the transaction.
• Isolation: Ensures that the operations within a transaction are isolated from other concurrent transactions.
• Durability: Ensures that once a transaction is committed, it remains so, even in the event of a system failure.

ACID Transactions in MongoDB

MongoDB supports multi-document ACID transactions, which allow multiple operations to be executed within a single transaction. This ensures that the operations are executed in an all-or-nothing manner, maintaining the integrity of the database.

Using Transactions in MongoDB

MongoDB transactions can be used in a variety of scenarios, such as when multiple documents need to be updated atomically or when complex operations must be executed while maintaining database consistency.

Example of Using Transactions in MongoDB

const { MongoClient } = require('mongodb');

async function run() {
  const uri = "your_mongodb_connection_string";
  const client = new MongoClient(uri);

  try {
    await client.connect();
    const session = client.startSession();

    session.startTransaction();

    const usersCollection = client.db("exampleDB").collection("users");
    const ordersCollection = client.db("exampleDB").collection("orders");

    try {
      await usersCollection.updateOne(
        { _id: 1 },
        { $inc: { balance: -100 } },
        { session }
      );

      await ordersCollection.insertOne(
        {
          userId: 1,
          item: "item_name",
          price: 100
        },
        { session }
      );

      await session.commitTransaction();
      console.log("Transaction committed.");
    } catch (error) {
      await session.abortTransaction();
      console.error("Transaction aborted due to an error: ", error);
    } finally {
      session.endSession();
    }
  } finally {
    await client.close();
  }
}

run().catch(console.dir);

Key Steps in Using Transactions

1. Start a Session: Transactions are associated with sessions.
2. Start a Transaction: Begin the transaction using startTransaction().
3. Perform Operations: Execute the necessary operations within the transaction.
4. Commit or Abort: Depending on the outcome, either commit the transaction using commitTransaction() or abort it using abortTransaction().
5. End the Session: Clean up by ending the session.

Using transactions effectively ensures data integrity and consistency, particularly in scenarios involving multiple document updates or complex operations.

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