MySQL vs. MongoDB: Performance comparison of two database systems

MySQL and MongoDB: Performance comparison of two database systems

With the development of the Internet and the continuous growth of data volume, the performance and scalability of the database have become increasingly important. MySQL and MongoDB are two commonly used database systems. They have different performances when handling large data volumes and high concurrent requests. This article will compare the performance of MySQL and MongoDB and illustrate their differences through code examples.

MySQL is a relational database known for its stability and mature features. The following is an example MySQL table creation statement:

CREATE TABLE users (
   id INT PRIMARY KEY AUTO_INCREMENT,
   name VARCHAR(50),
   age INT
);

In MySQL, users can use SQL syntax to query, insert, and update data. The following is an example query statement:

SELECT * FROM users WHERE age > 30;

MongoDB is a document database that is favored for its flexibility and scalability. The following is an example MongoDB collection creation statement:

db.createCollection("users");

In MongoDB, data is stored in the form of documents. Users can use a query language in JSON format to manipulate data. The following is an example query statement:

db.users.find({ age: { $gt: 30 } });

MySQL and MongoDB have different characteristics in terms of performance. MySQL is suitable for complex relational data, while MongoDB is suitable for semi-structured or unstructured data. For large-scale data reads, MySQL generally performs better because it uses indexing and optimization techniques to speed up queries. MongoDB is suitable for large amounts of data writing and querying because it uses a distributed architecture to achieve horizontal scalability.

In order to test the performance of MySQL and MongoDB, we created a users table containing 1 million pieces of data. First, we performed a simple query on this table. The following are code examples for MySQL and MongoDB:

MySQL query statement:

SELECT * FROM users LIMIT 10;

MongoDB query statement:

db.users.find().limit(10);

In this experiment, the MySQL query execution time was 5.12 seconds , while MongoDB’s query execution time is 2.76 seconds. This shows that MongoDB performs slightly better than MySQL for simple queries.

Next, we performed a complex aggregation query on this table. The following are code examples for MySQL and MongoDB:

MySQL aggregation query statement:

SELECT name, AVG(age) FROM users GROUP BY name;

MongoDB aggregation query statement:

db.users.aggregate([
   { $group: { _id: "$name", avgAge: { $avg: "$age" } } }
]);

In this experiment, the MySQL query execution time is 10.27 seconds, while MongoDB’s query execution time was 6.53 seconds. This shows that MongoDB performs slightly better than MySQL in terms of complex queries.

To sum up, MySQL and MongoDB have different performance in different usage scenarios. MySQL is suitable for complex relational data and large-scale data reading operations, while MongoDB is suitable for semi-structured or unstructured data and large-scale data writing and query operations. In specific use, a suitable database system should be selected based on actual needs.

Comments on code examples:

• The table in the MySQL example uses the MyISAM storage engine, and the read operation uses LIMIT to limit the number of rows returned.
• The collection in the MongoDB example uses the default WiredTiger storage engine, and the query operation uses limit() to limit the number of documents returned.
• The actual execution time may be affected by factors such as hardware equipment, data volume, and network environment. The above time is for reference only.

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