Is MongoDB Holding Your Scraped Data Back? Why We Moved On and What We Learned

In the fast-paced world of data solutions, choosing the right storage technology is critical. A few years ago, MongoDB was the go-to choice for many, including us, for storing scraped data. Its promise of flexibility and ease of use for JSON-style documents was incredibly appealing. For a time, it served us well. However, as our platform grew and the demands of our clients evolved, we began to see the cracks in this approach. We made the difficult decision to move away from MongoDB for our primary scraped data storage. This might come as a surprise to some, as MongoDB remains a popular database. In this post, we’ll explain the challenges we faced and why, in 2026, a different approach is necessary for large-scale web scraping operations.

The Early Days: Why MongoDB Seemed Like the Perfect Fit

When we first started, our needs were simple. We were scraping data for a handful of projects, and the data structure was often unpredictable. MongoDB’s schema-less nature was a significant advantage. We could store nested, JSON-serializable records without defining a rigid schema upfront. This flexibility allowed us to get up and running quickly and adapt to changes in the data we were collecting. Accessing, querying, and downloading this data was straightforward, making MongoDB a convenient and seemingly efficient solution.

The Growing Pains: Where MongoDB Fell Short

As our platform matured, so did the complexity and scale of our operations. We were no longer just a simple repository for scraped data. Our clients’ needs grew to include more sophisticated querying, bulk operations, and near real-time data access. It was at this point that the limitations of MongoDB for our specific use case became apparent.

Locking and Performance Bottlenecks

One of the most significant challenges we encountered was with MongoDB’s locking mechanism. Our platform handles a high volume of short, quick queries from our web crawlers. Concurrently, we also have a smaller number of long-running, intensive queries, such as exporting large datasets, applying complex filters, and performing bulk deletions. This created a perfect storm for lock contention.

In earlier versions of MongoDB, a single database-wide lock meant that a long-running query could block all other operations. This caused a cascade of problems:

• Short queries timed out: The constant stream of quick queries from our crawlers would get stuck waiting for longer tasks to complete, leading to timeouts and retries.
• Web server paralysis: Our web server threads would become completely blocked while waiting for responses from MongoDB, making our entire website and API unresponsive.
• Cascading failures: Eventually, the entire system would grind to a halt, impacting both data collection and our clients’ ability to access their data.

While newer versions of MongoDB have made significant improvements in this area with more granular locking, the fundamental issue of handling mixed workloads with vastly different performance characteristics remained a challenge for us.

The Hidden Costs of Inefficient Space Management

Another area where we faced difficulties was with MongoDB’s management of disk space. When documents are deleted, MongoDB doesn’t automatically reclaim that space. Instead, it attempts to reuse it for new documents. However, this often leads to data fragmentation, where the data is scattered across the disk, leading to slower query performance. Defragmenting the data required significant downtime, which was not feasible for our 24/7 operations.

Furthermore, the lack of built-in compression was a significant drawback. Scraped data, which is often text-heavy, can be highly compressed. Storing uncompressed data meant our storage costs were higher than they needed to be. While we could have compressed the data on the application side, this would have made it impossible to query the data directly, defeating one of the key purposes of using a database.

The way MongoDB stores data, with field names repeated in every single document, also contributes to storage inefficiency, especially in collections where the schema is relatively stable.

Pagination Problems at Scale

A common task for our users is to download their scraped data, often paginating through millions of items. This proved to be incredibly slow with MongoDB. To retrieve a page of results deep into a large collection, MongoDB has to traverse the index from the very beginning to find the starting point. This “offset” based pagination becomes progressively slower as you move further into the dataset. Some of our clients, frustrated with the slow downloads, would resort to sending multiple concurrent requests, which only exacerbated the server load and lock contention issues.

Memory Management Challenges

With terabytes of data per node, we rely on the database being able to keep the most frequently accessed data in memory to ensure fast query performance. However, MongoDB gave us very little control over how data was placed and managed in memory. Frequently accessed data could be scattered across a wide area of the disk, making it difficult to keep it all in memory. There was also no way to prevent large, one-time scans of infrequently accessed data from pushing our “hot” data out of the memory cache. Once the frequently accessed data was no longer in memory, the database became I/O bound, and performance plummeted, leading to more lock contention.

Thinking Beyond MongoDB: The 2026 Data Solutions Landscape

The data solutions landscape has evolved significantly since we first adopted MongoDB. For companies dealing with large-scale web scraping in 2026, a more nuanced and specialized approach to data storage is required. The “one-size-fits-all” database solution is no longer the most effective strategy. Instead, modern data architectures often involve a combination of technologies, each chosen for its specific strengths.

For handling the vast and varied nature of scraped data, many are now turning to a combination of data lakes and more specialized databases. A data lake, often built on cloud storage like Amazon S3 or Google Cloud Storage, can store raw, unstructured data in a cost-effective and scalable manner. From there, the data can be processed and loaded into different databases or analytics engines depending on the use case.

The Rise of Hybrid Approaches

A popular and effective approach is to use a combination of different database technologies. For example, you might use a powerful relational database like PostgreSQL for structured data and its excellent support for JSONB, which allows for efficient querying of semi-structured data. For search-heavy applications, a dedicated search engine like Elasticsearch is often the best choice, offering powerful full-text search capabilities that far exceed what general-purpose databases can offer.

This hybrid approach allows you to leverage the best tool for each specific job, rather than trying to force a single database to handle every workload. To learn more about modern data architectures for web scraping, you can explore resources like Zyte’s guide to architecting web scraping solutions.

Actionable Takeaways for Your Data Strategy

Our journey away from MongoDB for scraped data storage has taught us several valuable lessons that can help you make more informed decisions about your own data infrastructure:

• Understand your workloads: Don’t just choose a database because it’s popular. Carefully analyze the types of queries you’ll be running and the performance characteristics of your data.
• Plan for scale from the beginning: Even if you’re starting small, think about how your data will grow and how your access patterns might change over time.
• Don’t be afraid to use multiple tools: A modern data architecture often involves a combination of different technologies. Choose the right tool for each job.
• Prioritize data quality and governance: As you scale, having a clear strategy for managing data quality and ensuring compliance becomes increasingly important.

For more insights into the evolving world of data extraction and management, consider exploring this in-depth article on advanced web scraping techniques.

At Hir Infotech, we have over a decade of experience in helping businesses of all sizes navigate the complexities of data extraction and management. Our team of experts can help you design and implement a data solution that is tailored to your specific needs, ensuring that you can unlock the full potential of your data.

Frequently Asked Questions (FAQs)

1. Is MongoDB still a good choice for any type of project in 2026?

Absolutely. MongoDB continues to be an excellent choice for many applications, particularly those that benefit from its flexible schema and ease of development. It excels in use cases like content management systems, real-time analytics, and applications with rapidly evolving data models. The key is to match the tool to the specific requirements of the project.

2. What are the most popular alternatives to MongoDB for storing scraped data?

The best alternative depends on your specific needs. For structured and semi-structured data, PostgreSQL with its powerful JSONB support is a strong contender. For search-heavy applications, Elasticsearch is the industry standard. For extremely large datasets, a data lake approach using cloud storage like Amazon S3 or Google Cloud Storage, combined with a query engine like Presto or Spark, is a common choice.

3. How has the rise of AI impacted data storage choices for web scraping?

AI has significantly increased the demand for high-quality, well-structured data for training models. This has led to a greater emphasis on data pipelines that can clean, transform, and enrich scraped data before it is stored. Databases that can handle vector embeddings for similarity search, such as PostgreSQL with the pgvector extension, are also becoming increasingly popular.

4. What is the biggest mistake companies make when choosing a database for scraped data?

One of the most common mistakes is choosing a database based on hype rather than a thorough understanding of their specific requirements. It’s crucial to consider factors like data structure, query patterns, scalability needs, and the expertise of your team before making a decision.

5. How can I ensure my data storage solution is cost-effective?

Cost-effectiveness comes from choosing a solution that is well-suited to your needs. This includes considering factors like storage efficiency (compression, data types), query performance (to minimize compute costs), and the operational overhead of managing the system. Cloud-native solutions and serverless databases can also offer significant cost advantages.

6. What are the key considerations for data compliance and governance with scraped data?

Data compliance and governance are critical. You need to ensure you have the legal right to scrape and store the data, and you must comply with regulations like GDPR and CCPA. Your data storage solution should have robust security features, including encryption at rest and in transit, access controls, and auditing capabilities.

7. How can Hir Infotech help my company with its data solution needs?

Hir Infotech provides end-to-end data solutions, from web scraping and data extraction to data processing and storage. Our team of experts can help you design and implement a custom data architecture that is scalable, reliable, and cost-effective, allowing you to focus on deriving insights from your data.

Ready to Future-Proof Your Data Strategy?

Choosing the right data storage solution is a critical decision that can have a long-lasting impact on your business. If you’re struggling with the limitations of your current system or are planning a new web scraping project, we can help. Contact Hir Infotech today for a free consultation and let our experts design a data solution that will empower your business to thrive in the data-driven landscape of 2026 and beyond.

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