MySQL vs. MongoDB for 1000 Reads: Which Database Performs Better?

MySQL and MongoDB Performance Debate: A 1000 Reads Comparison

Background:

MongoDB has gained significant attention as a document-based database, prompting a comparison to the well-established relational database MySQL. This article investigates the performance differences between these two systems when faced with 1000 read operations.

Methodology:

A table named "posts" was created in MySQL with 20 million records and indexed on the "id" field. The same data was also loaded into a MongoDB collection. A custom PHP script was used to perform random reads from both databases concurrently.

Results:

Surprisingly, the results indicated that MongoDB exhibited only a marginal speed advantage over MySQL. The query execution time for 1000 reads was roughly 1.1 times faster in MongoDB.

Possible Explanations:

This unexpected finding contradicts the perception of MongoDB's superior performance for read-intensive operations. Here are some potential explanations:

• Normalized vs. Denormalized Data: MySQL used a normalized schema, while MongoDB stored related data in a single document. In this scenario, MongoDB's denormalized approach offered no substantial performance benefits.
• IO Efficiency: MySQL had to perform multiple index lookups and data reads from 20 different tables. In contrast, MongoDB performed a single index lookup and retrieved a single document, resulting in significantly lower IO operations.
• Memory Usage: The 20 tables in MySQL potentially consumed more memory for indexing and storing data, while MongoDB consolidated all the data into a single collection, reducing memory overhead.

Conclusion:

While MongoDB offers certain advantages in specific use cases (e.g., when dealing with unstructured or highly interconnected data), it does not necessarily eclipse MySQL for all read-intensive workloads. The choice between these databases should depend on the specific data structure and query patterns of the application.

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