Efficient use of the MongoDB Query Language (MQL) can significantly enhance your experience and especially performance. This section will provide some best practices to help optimize your queries and your database’s performance.
Designing Your Schema According to Your Needs
Namespaces of type document have a flexible structure without a strict schema. How you structure your data can have a significant impact on your database’s performance and the complexity of your queries. Aim to structure your data in a way that caters to your application’s needs, considering the types of queries you will make and the types of updates your data will require. For example, for data that’s often accessed together, it might be beneficial to store it in a single document, taking advantage of the ability to nest data.
db.users.insert({
username: "john_doe",
profile: {
name: "John Doe",
email: "john@example.com",
age: 30
},
history: {
purchases: ["item1", "item2", "item3"],
lastLogin: new Date()
}
})
Creating Indexes for Query Optimization
Indexes are data structures that enhance the performance of your queries. Without indexes, Polypheny has to perform a collection scan, i.e., scan every document in a collection, to select those documents that match the query statement. With an index, Polypheny can limit its search to the index, which can result in significantly faster queries.
Be mindful about the fields you index. Indexing can speed up read queries, but it slows down write operations because every write operation now also has to update the index.
db.users.createIndex( { "profile.name": 1 } )
Using Projection to Limit Returned Data
When you query documents, you may not always need every field from each document. By using projection, you can specify the fields you want to return, thereby reducing the amount of data that has to send over the network, which can result in speedier queries.
db.users.find( {}, { username: 1, "profile.name": 1 } )
