98% Faster and More Efficient: The Power of MongoDB
From slow to lightning fast: How MongoDB has transformed the performance of our processes.
While MongoDB is often seen as the first choice for many developers, I believe it's important to evaluate project requirements before making a decision. Rather than blindly adopting a popular tool, understanding its strengths and weaknesses allows for more informed and effective use.
Finding the perfect match between the tool and the task is the key to success. As this article shows: MongoDB can be a powerful catalyst for achieving remarkable results when used judiciously.
The MSSQL Insertion Struggle
Our team had a significant performance bottleneck during the insertion of a large data set of 53 million rows into a MSSQL table. The table was struggling to keep up with rapid data ingestion from an external API, which contained over 30 fields (all varchar without indexes).
Fetching data from the API was efficient. However, when inserting data in batches of 10,000 records, the process ground to a halt. The slow insertion times were an obstacle to our ability to do the data processing and analysis promptly.
In total, the entire process took about 19 hours to complete. We experimented with various approaches, including adjusting bulk sizes and saving the data as CSV files before importing, but these methods only marginally improved performance, reducing the time to around 12 hours or more.
Optimizing Performance: Diagnosis and Tool Selection
Our primary objective was the efficient extraction of data from an API and its storage for later analysis. We wanted a solution that could accommodate evolving data without requiring frequent schema changes, given the potential for future changes to the API's data structure.
Finding the perfect match between the tool and the task is the key to success.
We chose MongoDB after much consideration. Its ability to store documents in JSON format, eliminating the need for rigid schemas, and its reputation for high-performance data storage were a perfect match for our requirements.
We integrated MongoDB into our Docker environment and started testing. The initial results were so unexpected that we had to ask ourselves what we had done wrong.
It took just 25 minutes to complete the entire process, from extraction to storage!
After checking and rechecking, we were able to confirm that all of the data was successfully inserted. When we realized the incredible performance gains we had achieved by moving to MongoDB, our disbelief turned to amazement.
The next step was to add multi-threading, which sped up the process even more, and we were able to get the whole thing done in less than 20 minutes.
The Power of MongoDB: A Real-World Example
When we first started using MSSQL to process a large data set of 53 million records, we struggled with slow insert times. After a careful evaluation, we migrated to MongoDB and saw an immediate and significant increase in performance.
We achieved a 98% reduction in processing time due to MongoDB's ability to handle unstructured data and its efficient data storage mechanisms. For organizations facing database performance bottlenecks, this case study demonstrates the transformative power of MongoDB.
Great post! Performance is important