How to Fix MongoDB Slow Performance with 1 Million Records скачать в хорошем качестве

How to Fix MongoDB Slow Performance with 1 Million Records

Discover how to improve the performance of your MongoDB database when managing `1 million records` effectively. --- This video is based on the question https://stackoverflow.com/q/72035504/ asked by the user 'Kasper Hansen' ( https://stackoverflow.com/u/510731/ ) and on the answer https://stackoverflow.com/a/72035642/ provided by the user 'Alex' ( https://stackoverflow.com/u/4516913/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Mongodb with 1 million records is getting slow Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- How to Fix MongoDB Slow Performance with 1 Million Records MongoDB is a powerful NoSQL database that can handle large datasets effectively. However, as your dataset grows — in this case, to 1 million records — you may encounter performance issues. If you've been scraping supermarket prices, like our example with 35,000 products and their price histories, you might find that queries and updates start to slow down significantly. Let’s dive into understanding a common issue and how to solve it effectively. The Problem: Slow Query Response Times You’ve been observing that queries in your MongoDB database, such as counting records, are becoming increasingly sluggish: [[See Video to Reveal this Text or Code Snippet]] In your case, this query is taking several seconds to execute, which is acceptable for now. However, as your dataset scales up by 5 to 10 times, the performance will only worsen, leading to an unusable database. Understanding Your Current Setup Your current data document looks structured and organized: [[See Video to Reveal this Text or Code Snippet]] You’ve implemented an upsert operation based on the unique title field, and have created an index: [[See Video to Reveal this Text or Code Snippet]] Where Things Might Be Going Wrong 1. Indexing Issues The index you've created on title is helpful but may not be sufficient. You need to check if it's a unique index. This ensures that updates are faster since the database can quickly determine if a record exists and prevent unnecessary checks against similar records. 2. Lack of Additional Indexes While you have an index on title, it would be advantageous to create another index for the name field: [[See Video to Reveal this Text or Code Snippet]] Why Are Indexes Important? Indexes improve the speed of data retrieval operations at the cost of additional space and slower writes (inserts, updates, and deletes). In scenarios where you will frequently search for documents, indexes can drastically enhance performance. Steps to Improve MongoDB Performance A. Create Unique Index on Title Drop Existing Index: If your title index is not unique, drop it first: [[See Video to Reveal this Text or Code Snippet]] Create Unique Index: [[See Video to Reveal this Text or Code Snippet]] B. Adding Index on Name Create an index for the name field: [[See Video to Reveal this Text or Code Snippet]] C. Monitor Performance After Changes After applying these changes, run your queries again and monitor the response times. You should notice a significant improvement in speed when retrieving documents. Conclusion Handling 1 million records in MongoDB does not have to lead to slow query times. By implementing unique indexes and adding additional indexes, you can optimize your database structure for significant performance gains. Regularly monitor your database performance and adapt your indexing strategy as your data grows. Feel free to experiment with the outlined steps and share your results!