How To Use the MongoDB Shell

Technical writer

MongoDB

19.12.2024

Reading time: 8 min

MongoDB Shell is an adaptive JavaScript and Node.js REPL (Read-Eval-Print Loop) environment for dealing with MongoDB deployments. It is an important utility for querying data, administering databases, and running various operations. It provides an attractive view for controlling databases, whether you're using them on a remote server or MongoDB locally.

This article will demonstrate the step-by-step guidelines for utilizing the MongoDB shell.

Step 1: Installation of MongoDB Shell

Before starting operations, confirm that users have installed it on the system. If not, first, download the appropriate version via the official site and consider the installation instructions for the system.

Windows

Visit the official page, pick the Windows version, and start downloading.

macOS

On the official page, choose the macOS version and begin the downloading process. Alternatively, use Homebrew by executing the commands:

brew tap mongodb/brew
brew install mongosh

Linux

Consider the guidelines provided on the website for the Linux OS. For example, on a Debian-based operating system, follow these steps:

Add the GPG key for the MongoDB repository:

curl -fsSL https://pgp.mongodb.com/server-7.0.asc |  sudo gpg -o /usr/share/keyrings/mongodb-server-7.0.gpg --dearmor

Add the MongoDB repository to your system:

echo "deb [ arch=amd64,arm64 signed-by=/usr/share/keyrings/mongodb-server-7.0.gpg ] https://repo.mongodb.org/apt/ubuntu jammy/mongodb-org/7.0 multiverse" | sudo tee /etc/apt/sources.list.d/mongodb-org-7.0.list

Update the package list:

sudo apt update

Install MongoDB:

sudo apt install mongodb-org -y

Upon the successful installation, authenticate the installed version:

mongod --version

Once the installation is done, enable, start, and check the services of it:

sudo systemctl enable mongod
sudo systemctl start mongod
sudo systemctl status mongod

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Step 2: Establish a Connection to an Instance

Once the installation is done, you can establish connections to its instance. If it is local, enter mongosh. It establishes the connection to the instance by default. It is running at port 27017 on localhost:

mongosh

The following syntax is utilized to establish a connection to the remote server:

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

Modify <username>, <password>, <cluster-url>, and <db> with original credentials and connection details.

Step 3: Navigation to the Terminal

Once connected, you can work with their DBs. Before it, the interface greets a message:

The assigned DB on a freshly launched shell instance is called a test. It is safe to use for experimenting.

Useful operations are given below:

Exploring all Databases

First, run show dbs, which gives a view of every database that is available:

show dbs

Accessing the Current Database

The majority of commands operate on a database or a collection that is contained within the database. In order to accomplish this, the object db represents the presently selected database and is available:

db

Switching to the Database

Enter the use command with the DB name, e.g., new_db, to create or switch to the specified one:

use new_db

Replace new_db with the actual name of the DB.

Insertion of an Object

First, enter a name as an object into the student_data in the existing new_db:

db.student_data.insertOne({name: 'School has 500 students'})

Users can also insert multiple documents into the student_data, each containing a name field with specified values. This allows for batch insertion of data into the collection:

db.student_data.insertMany([
{name: 'School has 500 students'},
{name: 'School has 600 students'}
])

Viewing Collections

Once the input is inserted, display the existing collections:

show collections

If you have not done any task, leave the interface by executing:

exit

You have to understand the basic shell operations.

Step 4: Operations Using CRUD

When working with the shell, CRUD (Create, Read, Update, Delete) operations are essential. Let’s perform some basic ones:

Creating/Inserting Data

When inserting new information into the collection, employ the insertOne function. Let us create a new one and fill it with the required information, including name, age, and city:

db.collection.insertOne({ name: "Harry", age: 45, city: "Sydney" })

Finding/Reading Data

You can query documents that are associated with the collection by employing the find function. For instance, all entries with an age greater than 25 are retrieved:

db.collection.find({ age: { $gt: 25 } }) //  Condition where age is greater than 25

Modifying Data

Use the updateOne or updateMany functions to make changes to documents that exist. For instance, the age with Harry's name is updated to 50 on the existing one:

db.collection.updateOne({ name: "Harry" }, { $set: { age: 50 } })

Deleting Data

Use the deleteOne or deleteMany methods to eliminate entries from the collection that are available. This command removes one document from the available ones with the value John in the name field:

db.collection.deleteOne({ name: "John" })

Step 5: Advanced Usage

In this step, carry out complex operations via the advanced functionalities. By offering debugging, profiling, and performance optimization tools, the shell helps you find bottlenecks and optimize your setup.

Aggregation Pipeline

With the help of pipelines, readers can compute records and get calculated results. By using pipelines, create complex analytics and transformations that will assist you in extracting insightful information from your raw. For executing a pipeline of stages, utilize the aggregate function. Here’s a basic code example:

db.collection.aggregate([
    { $match: { status: "A" } },
    { $group: { _id: "$city", total: { $sum: "$amount" } } },
    { $sort: { total: -1 } }
])

In the script, the pipeline filters documents with status A, groups them by city, sums the amounts, and sorts the results in descending order.

One strong tool for data analysis and transformation is the aggregation pipeline. It is composed of several stages, each of which changes the stream of documents. These stages include:

$project: Selects or excludes specific fields.

$match: Applying a query condition to filter them.

$group: Merges input by a field and calculates aggregations.

$sort: Arrange entries according to a given field.

$limit: Restricts the amount of records.

$skip: Skips a specified number of records.

MapReduce

An effective paradigm for computing huge datasets. For executing MapReduce jobs, utilize the mapReduce command:

// Sample collection data
db.collection.insertMany([
    { name: "Harry", age: 25, salary: 5000 },
    { name: "Buttler", age: 30, salary: 6000 },
    { name: "Morgan", age: 35, salary: 7000 }
]);

// Map function
var mapFunction = function() {
    emit(this.age, this.salary);
};

// Reduce function
var reduceFunction = function(keyAge, valuesSalaries) {
    return Array.sum(valuesSalaries);
};

// Run MapReduce
db.runCommand({
    mapreduce: "collection",
    map: mapFunction,
    reduce: reduceFunction,
    out: "results"
});

db.results.find().forEach(printjson); // Output the results

In this example, the mapFunction emits the age as the key and the salary as the value.
The reduceFunction sums the salaries for each age group.
The results are stored in a new collection called results, and the final output is printed using db.results.find().forEach(printjson).

The output is given below:

Projection

Utilize the projection operator to specify which fields should be included or excluded from the result set. It retrieves all info associated with the particular query, e.g., displaying only the name and age fields. This offers anyone the ability to visualize specific results while excluding others. Let’s project only name and age fields:

db.collection.find({}, { name: 1, age: 1 })

Sorting

Sort the results employing the sort function. It retrieves every document belonging to the set. Then, sort them in descending sequence based on the age. It gives a view with the highest age values first. Let’s sort by age in descending order:

db.collection.find().sort({ age: -1 })

Limiting

Users limit the entries of results via the limit function. For instance, obtain the initial three documents linked to the collection. It is useful for previewing a small subset of information without fetching the entire list. Let’s limit to 3 results:

db.collection.find().limit(3)

Skipping

Skip an entry is done via the skip function. For instance, skipping the initial two documents that linked to the collection. It is beneficial for paginating results or ignoring an entry of initial documents. Let’s skip the first 2 results:

db.collection.find().skip(2)

Step 6: Scripting with Shell

Scripts for task automation can be written by users within the shell. To achieve it, save the script in the .js file and run the mongosh. It is beneficial for executing repetitive tasks efficiently, e.g., data seeding or batch updates:

mongosh script.js

By mastering the MongoDB command, you gain valuable insights into data.

Conclusion

With the help of the immersive MongoDB shell interface, you conduct repetitive management tasks like writing, reading, and manipulating. In addition, query existing collections, add new objects to the DB, and carry out management administrative tasks. From simple CRUD tasks to complex aggregations and scripting, users can efficiently utilize the interface to carry out a variety of activities.

By executing scripts, readers efficiently perform repetitive tasks. This tutorial has covered the installation, configuration, and managerial tasks for managing the DBs, their collections, and their users.

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MongoDB

19.12.2024

Reading time: 8 min

Similar

MongoDB

How To Use the MongoDB Shell

Step 1: Installation of MongoDB Shell

Windows

macOS

Linux

Step 2: Establish a Connection to an Instance

Step 3: Navigation to the Terminal

Exploring all Databases

Accessing the Current Database

Switching to the Database

Insertion of an Object

Viewing Collections

Step 4: Operations Using CRUD

Creating/Inserting Data

Finding/Reading Data

Modifying Data

Deleting Data

Step 5: Advanced Usage

Aggregation Pipeline

MapReduce

Projection

Sorting

Limiting

Skipping

Step 6: Scripting with Shell

Conclusion

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