Crawling IKEA with Rust & MongoDB: Extracting Furniture Prices, Product Availability, and Customer Ratings for Retail Research

In the ever-evolving world of retail, understanding market trends and consumer preferences is crucial for businesses to stay competitive. One way to gain insights into these trends is by analyzing data from major retailers like IKEA. This article explores how to use Rust, a systems programming language, and MongoDB, a NoSQL database, to crawl IKEA’s website and extract valuable information such as furniture prices, product availability, and customer ratings. By leveraging these technologies, businesses can make informed decisions and enhance their retail strategies.

Why Choose Rust for Web Crawling?

Rust is gaining popularity among developers for its performance, safety, and concurrency features. When it comes to web crawling, these attributes make Rust an excellent choice. Rust’s memory safety ensures that the crawler runs efficiently without memory leaks, while its concurrency model allows for handling multiple requests simultaneously, speeding up the data extraction process.

Moreover, Rust’s ecosystem includes libraries like `reqwest` for making HTTP requests and `scraper` for parsing HTML documents. These libraries simplify the process of building a web crawler, allowing developers to focus on extracting the necessary data rather than dealing with low-level details.

Setting Up the Rust Environment

Before diving into the code, it’s essential to set up the Rust environment. Start by installing Rust using `rustup`, the recommended installer for the Rust programming language. Once installed, you can create a new Rust project using Cargo, Rust’s package manager and build system.

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Create a new Rust project
cargo new ikea_crawler
cd ikea_crawler

Next, add the necessary dependencies to your `Cargo.toml` file. For this project, you’ll need `reqwest` for HTTP requests, `scraper` for HTML parsing, and `tokio` for asynchronous programming.

[dependencies]
reqwest = { version = "0.11", features = ["json"] }
scraper = "0.12"
tokio = { version = "1", features = ["full"] }

Building the Web Crawler

With the environment set up, you can start building the web crawler. The first step is to make an HTTP request to IKEA’s website and retrieve the HTML content of the page. Use the `reqwest` library to send a GET request and handle the response.

use reqwest;
use scraper::{Html, Selector};
use tokio;

#[tokio::main]
async fn main() -> Result<(), Box> {
    let url = "https://www.ikea.com/us/en/cat/furniture-fu001/";
    let response = reqwest::get(url).await?.text().await?;

    let document = Html::parse_document(&response);
    let selector = Selector::parse(".product-compact").unwrap();

    for element in document.select(&selector) {
        let name_selector = Selector::parse(".product-compact__name").unwrap();
        let price_selector = Selector::parse(".product-compact__price").unwrap();

        let name = element.select(&name_selector).next().unwrap().inner_html();
        let price = element.select(&price_selector).next().unwrap().inner_html();

        println!("Product: {}, Price: {}", name, price);
    }

    Ok(())
}

This code snippet demonstrates how to extract product names and prices from IKEA’s furniture category page. The `scraper` library is used to parse the HTML document and select elements based on CSS selectors. By iterating over the selected elements, you can extract the desired information.

Integrating MongoDB for Data Storage

Once the data is extracted, it’s essential to store it in a database for further analysis. MongoDB, a NoSQL database, is well-suited for this task due to its flexibility and scalability. To interact with MongoDB in Rust, you can use the `mongodb` crate, which provides a high-level API for database operations.

Start by adding the `mongodb` dependency to your `Cargo.toml` file.

[dependencies]
mongodb = "2.0"

Next, establish a connection to the MongoDB server and insert the extracted data into a collection.

use mongodb::{bson::doc, options::ClientOptions, Client};

async fn store_data(name: &str, price: &str) -> Result<(), Box> {
    let client_options = ClientOptions::parse("mongodb://localhost:27017").await?;
    let client = Client::with_options(client_options)?;

    let database = client.database("ikea_data");
    let collection = database.collection("furniture");

    let document = doc! {
        "name": name,
        "price": price,
    };

    collection.insert_one(document, None).await?;
    Ok(())
}

This function connects to a MongoDB server running on `localhost` and inserts a document containing the product name and price into the `furniture` collection. By storing the data in MongoDB, you can easily query and analyze it to gain insights into IKEA’s product offerings.

Analyzing the Extracted Data

With the data stored in MongoDB, you can perform various analyses to extract valuable insights. For instance, you can calculate the average price of furniture items, identify the most popular products based on customer ratings, or track changes in product availability over time.

MongoDB’s aggregation framework allows you to perform complex queries and transformations on the data. For example, you can use the `$group` stage to calculate the average price of products in each category.

db.furniture.aggregate([
    {
        $group: {
            _id: "$category",
            averagePrice: { $avg: "$price" }
        }
    }
])

This aggregation query groups the documents by category and calculates the average price for each group. By analyzing the results, you can identify pricing trends and make informed decisions about product pricing strategies.

Conclusion

In conclusion, crawling IKEA’s website using Rust and MongoDB provides a powerful approach to extracting and analyzing retail data. Rust’s performance and safety features make it an ideal choice for building efficient web crawlers, while MongoDB’s flexibility and scalability enable seamless data storage and analysis. By leveraging these technologies

Crawling IKEA with Rust & MongoDB: Extracting Furniture Prices, Product Availability, and Customer Ratings for Retail Research