Crafting Precision: Query Enhancements in MongoDB 7.0

Writing performant queries is the holy grail for any developer working with databases. It’s akin to an art form – balancing speed, efficiency, and functionality in one seamless package. At MongoDB .local Chicago, we announced the General Availability of MongoDB 7.0, the latest version of our database which includes a number of new features targeted at making the application development experience streamlined and boosting developer productivity. Constructing efficient queries frequently entails navigating complex optimization and index management processes. With MongoDB 7.0, this process has been significantly streamlined.

Introducing compound wildcard indexes

Now, imagine writing less code and utilizing fewer system resources to get the results you need faster. With MongoDB 7.0, Compound Wildcard Indexes make this dream a reality. Compound wildcard indexes support creating compound indexes on a combination of any field and all sub-fields in an embedded sub-document. This supports performant queries against a combination of an unknown or arbitrary field and an always-present field.

For example, consider a collection named customer_attributes with a diverse schema. Not every customer has every attribute filled out. Compound wildcard indexes can now support queries on Region or Tier and any of the sub-fields under Attributes, thus embracing MongoDB’s schema flexibility.

db.customer_attributes.createIndex({"Region": 1, "Attributes.$**": 1});

This index could support a query like this, efficiently and easily:

db.customer_attributes.find({"Region": "North America", "Attributes.CSM": "John Doe"});

Bitwise operators: The power in your pipeline

Before MongoDB 7.0, developers can leverage faster computations with Bitwise Operators in the Aggregation Pipeline. While this has been available for some time with find and update queries, MongoDB 7.0 allows you to compute common bitwise operators within aggregations, right on the database, significantly enhancing query performance, especially for application-driven and operational analytics.

Let's take an example of filtering documents based on binary data flags:

{
  $match: {
    $expr: {
      $eq: [{$bitAnd: ["$userFlags", 4]}, 4]
    }
  }
}

This filter checks whether the fourth bit of the userFlags field is set, a common scenario in permission systems.

Percentile operators: Unlocking efficient data analysis

Calculating percentiles is a cornerstone of data analysis. With MongoDB 7.0, Approximate Percentile Operator enables developers to perform these calculations directly on the database server, significantly enhancing efficiency and reducing complexity.

For example, to find the 90th percentile of response times in a weblogs collection:

{
  $group: {
    _id: null,
    responseTime90thPercentile: {
             $percentile: { input: "$responseTime", p: [0.9], 
                            method: 'approximate'}
    }
  }
}

Role-based data access with $$USER_ROLES

The new $$USER_ROLES variable introduces an elegant way to deliver user-specific views right from your queries, offering an enhanced layer of security and data privacy. No more separate views for each user role – now it’s seamless and simplified.

Consider a sales application, where account data can be seen by everyone but sensitive information like contract details can only be seen by specific managers. By leveraging the new variable and setting up a view as below, you can ensure only managers with that specific role can view the data:

pipeline = [
  {
     $set: {
        "contractDetails": {
           $cond: {
              if: { $in: [ 
                      "csmApp",
                      '$$USER_ROLES.role'
                      ] },
              then: "$contractDetails",
              else: "$$REMOVE"
           }
        }
     }
  }
]


db.createView('accounts_view', 'accounts', pipeline ) 

This ensures that users can only view the data tied to their account, based on their role privileges.

Conclusion: Elevating the developer experience

MongoDB 7.0’s new query features are more than just a set of tools; they represent a profound upgrade in the developer experience. With these features, you're not just writing queries - you are sculpting your application’s data interaction in the most efficient way, right from the start.

From Compound Wildcard Indexes to new operators in the aggregation pipeline, MongoDB 7.0 empowers developers to write cleaner, faster, and more intuitive queries. With less time spent on query optimization, you can focus on what truly matters: building amazing applications.