Compass Isolated: The Isolated edition of MongoDB compass doesn't start any network requests except to the MongoDB server to which MongoDB GUI connects.Compass Randomly: It is limited to read operation only with all update and delete capabilities removed.It includes all the features and capabilities that MongoDB provides. Compass: It is released as the full version of MongoDB Compass.Compass Community: This edition is designed for developing with MongoDB and includes a subset of the features of Compass.MongoDB GUI is available in the following four editions: The source repositories of MongoDB compass can be found on the following link of GitHub When we explore exploring our data in the visual environment, we can use Compass GUI to optimize performance, manage indexes, and implement document-validation.Īll the versions of MongoDB Compass are opensource (i.e., we can freely deploy and view the repositories of all MongoDB GUI versions). MongoDB allows users to analyze the content of their stored data without any prior knowledge of MongoDB query syntax. ask yourself if you made the right architecture decisions when you find yourself frequently using $lookup.MongoDB Compass is a GUI for MongoDB. That's a lot of $lookups.and starts to beg the question "WHY NOT JUST USE SQL". This will require one UGLY Mongo aggregation where you aggregate likes, comments, etc for a given post. Let's say you want to display a users most liked blog posts. If you use MongoDb for this use case, you can quickly find yourself in trouble (especially if each entity is represented by it's own collection!) This is a lot of RELATIONSHIPS to manage. A typical blog comes with Users, Posts, Comments, Likes, Followers, etc. To further illustrate, let's say you set up a blog using Mongo. Remember that one of the key advantages of MongoDb is faster performance with unstructured data.the irony. Remember that a $lookup operation comes with the cost of performing a JOIN operation on your collections. While it's good that $lookup exists in MongoDb, it goes against every reason why you would use MongoDB vs another relational datastore (MySQL). Remember that $lookup can be used for both equality checks (first example) as well as more complex sub-queries (second example).ĭepending on which type of query you run, $lookup will take slightly different parameters (let, pipeline vs localField, foreignField).įor more on the aggregation pipeline, be sure to check out this 5 minute guide to the MongoDB aggregation framework. You've now seen two examples of using $lookup in the aggregation pipeline. This query adds an additional condition for returning comments whose likes count is greater than the corresponding post's likes.īy running this query, we now know if any posts have comments with more likes than the post itself :). Like the first example, this query returns the comments whose postTitle matches the title field for each post. We reference fields in the foreign collection with a single $. Notice how we use $$ to refer to the variables we defined in let. The examples are based on this sample data. We'll provide an example of each scenario. $lookup supports both basic equality matches as well as uncorrelated sub-queries. $lookup works by returning documents from a "joined" collection as a sub-array of the original collection. $lookup allows you to perform joins on collections in the same database. Preface: New to the aggregation pipeline? Check out our 5 minute explanation before diving into these $lookup examples.Īlso be sure to understand indexing and it's performance implications before reviewing these examples.
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