Adventures in Machine Learning

Efficient Web Scraping: Avoiding Duplicates with MongoDB and Scrapy

Web Scraping with Scrapy and MongoDB is a popular way of extracting valuable data from the internet. With the help of Scrapy and MongoDB, users can easily extract data from sources like websites, APIs, and more, and then store that data in a database for later use.

In this article, we will discuss various topics related to web scraping and how it can be done using Scrapy and MongoDB.

Basic Web Scraping from StackOverflow

When we talk about web scraping, one of the most popular sources of information is StackOverflow. If you are looking to build a basic web scraper, this is a perfect place to start.

The primary keyword here is web scraper and StackOverflow. The first step is to set up a Scrapy project.

Scrapy is a Python-based web crawling framework that provides a simple way to extract the data you need from the internet. Once you have set up the project, you can start writing your spider to extract data from the StackOverflow website.

Crawling through Pagination with Scrapy

Many websites have pagination links that allow users to navigate the website and find the information they need. When it comes to web scraping, it’s important to crawl through these pagination links to capture all the data available on the website.

The primary keywords here are pagination links and callback. Scrapy provides a simple way to crawl through pagination links using a callback function.

A callback function is essentially a function that is called when Scrapy encounters certain events. By using this approach, you can ensure that every page of the website is scraped effectively.

Ethical Scraping Practices

Web scraping can be a powerful tool, but it’s important to use it ethically. Many websites have terms of use policies that outline the rules for accessing their data.

Additionally, many websites have a robots.txt file that outlines which parts of the website can be crawled and which parts are off-limits. The primary keywords here are terms of use policy, robots.txt, and flooding.

To scrape websites ethically, it’s important to read and follow the terms of use policy for each website, as well as respect the rules set out in the robots.txt file. Additionally, it’s important not to flood a website with requests, as this can cause the website to crash and could lead to legal trouble.

Collaboration is a powerful force in the world of software development.

When it comes to web scraping, one of the most valuable collaborations comes from the Python enthusiast, Gyrgy. Gyrgy is a software developer who has made a name for himself in the big data industry.

He is passionate about using Python for web scraping and has a Twitter handle that is dedicated to sharing his insights and techniques with others.

Using CrawlSpider for Extended Scraping

In some cases, a basic web scraper may not be enough to capture all the data from a website. This is where CrawlSpider comes in.

CrawlSpider is a tool provided by Scrapy that allows users to create a more extensive web scraper that can crawl through multiple pages of a website and extract relevant data. The primary keywords here are CrawlSpider and Scrapy project.

With CrawlSpider, users can easily crawl through multiple pages of a website, even if the structure of each page is different.

Code Repository Access

One of the benefits of using Scrapy for web scraping is that there are many existing spiders available that can be used for various projects. If you are new to web scraping, it’s a good idea to start by looking at the Scrapy documentation and exploring some of the existing spiders that are available.

The primary keywords here are existing Spider and Scrapy documentation. By accessing the code repository, users can easily find and customize existing spiders to meet their specific needs.


Web scraping with Scrapy and MongoDB is a powerful tool that can help extract valuable data from the internet. By following ethical scraping practices, and utilizing the tools provided by Scrapy, users can create effective scraping tools that can be used for a variety of projects.

With the help of collaboration from Python enthusiasts like Gyrgy, it’s easier than ever to get started with web scraping and create useful tools that can help make your business more efficient.

Avoiding Duplicate Questions with MongoDB

Web scraping is an efficient way of gathering data for analysis, research, and business optimization. However, one problem that arises with web scraping is the issue of duplicate data.

Duplicate data can pose a problem, especially when collecting data from several sources. In this article, we will discuss how to avoid duplicate questions with MongoDB, a popular document-based database, by implementing MongoDB Upsert and modifying the MongoDBPipeline.

Implementing MongoDB Upsert

MongoDB Upsert is a query that is used to insert data if the record does not exist, or update the record if it already exists. In other words, Upsert is a combination of a “Update” and “Insert” operation.

MongoDB Upsert is a great way to avoid duplicate data when scraping the web. The primary keyword here is MongoDB Upsert.

It’s a simple operation that can be performed using the MongoDB driver for any programming language that can interface with MongoDB. To implement MongoDB Upsert in a Scrapy project, you must first configure the project to connect to a MongoDB instance.

Then, you can define a function that inserts data into MongoDB. This function can be called whenever new data is scraped.

The function should employ the “Upsert” query to check if the record already exists and update it if it does. If it does not, then the record should be inserted.

The use of Upsert ensures that no duplicate data is inserted into the MongoDB database.

MongoDBPipeline Modification

In a Scrapy project, it is necessary to define a pipeline to handle the scraped data. A Pipeline is a set of functions that are called to process the scraped items.

The primary keywords here are pymongo.MongoClient, process_item, log.msg, and DropItem. A standard Scrapy pipeline might log the item, prepare it for storage, and then store it in a database.

However, to avoid duplicate data, it is important to modify the MongoDBPipeline and check if the item already exists in the database before storing it. To modify the MongoDBPipeline to avoid duplicate data, we need to use the pymongo.MongoClient to connect to MongoDB, and then use the process_item function to check if the item already exists in the database.

If the item exists, we can use the log.msg to log that the item has been dropped as a duplicate. And if the item does not exist, we can use the pymongo to insert the item into our database.

It is also important to use the DropItem function when dropping a duplicate item. The DropItem function raises an exception that can be caught later in the process, preventing the item from being sent to the rest of the pipeline.

This ensures that duplicate data is not stored in the database, saving disk space, and resources.


Web scraping can be an effective way to collect data from the web. However, duplicate data can pose a problem, especially when collecting data from several sources.

Using MongoDB Upsert and modifying the MongoDBPipeline can help avoid duplicate data when scraping the web. It is important to implement these strategies when web scraping to ensure that data is stored efficiently and without duplicates.

In conclusion, avoiding duplicate data is a crucial aspect of successful web scraping, and implementing MongoDB Upsert and modifying the MongoDBPipeline are two effective ways to do this. By employing MongoDB Upsert, query data can be inserted if not exist, or updated if it does.

Modifying the MongoDBPipeline allows for the checking of duplicate data and dropping duplicated items before they’re stored, preventing disk space and resource wastage. By utilizing these techniques, businesses and researchers can access accurate and trustworthy data while optimizing their scraping efforts.

Overall, efficient web scraping is key to optimizing the research, analysis, and business operations process.

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