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. Avoid Unwinding & Regrouping Documents Just To Process Array Elements of the Pipeline Performance Considerations chapter.MongoDB Compass is a GUI for MongoDB. If you need to perform more complex array content filtering rules, you can use the approach described in section 2. Instead, you could use an $unset stage later in the top-level pipeline to project out these unwanted array elements. The presence of an embedded pipeline in the $lookup stage provides an opportunity to filter out three unwanted fields brought in from the second collection. In this instance, because the $expr operator performs an equality comparison specifically (as opposed to a range comparison), the aggregation runtime can employ an appropriate index for this match. You use an embedded pipeline inside the $lookup stage to match the bind variables with fields in the second collection's records. With a let parameter, you bind multiple fields from the first collection into variables ready to be used in the joining process. To perform a join of two or more fields between the two collections, you need to use a let parameter rather than specifying the localField and foreignField parameters used in a single field join. Two documents should be returned, representing the two products that had one or more orders in 2020, with the orders embedded in an array against each product, as shown below: [ĭescription: 'Good value laptop for students',ĭescription: 'Luxury mixer turning good cakes into great', Only show products that have at least one orderĮxecute the aggregation using the defined pipeline and also view its explain plan: db.products.aggregate(pipeline) ĭb.products.explain("executionStats").aggregate(pipeline) Exclude some unwanted fields from the right side of the join Part 2- // Create index for the orders collectionĭb.orders.createIndex(, "description": "Hose + nosels + winder for tidy storage", "description": "Luxury mixer turning good cakes into great", "description": "Classic post-apocalyptic novel", "description": "Good value laptop for students", "description": "Great for watching movies", Insert 6 records into the products collection Part 1- db = db.getSiblingDB("book-multi-one-to-many") Sample Data Populationĭrop any old version of the database (if it exists) and then populate new products and orders collections with documents spanning 2019-2021 (the database commands have been split in two to enable your clipboard to hold all the text - ensure you copy and execute each of the two sections): However, in this example, it has been deemed beneficial to show both of these aspects in one place. The requirement to perform a 1:many join does not mandate the need to join by multiple fields on each side of the join. Rather than joining on a single field like product_id (which doesn't exist in this data set), you need to use two common fields to join ( product_name and product_variation). There is a 1:many relationship between both collections, based on a match of two fields on each side. To achieve this, you need to take a shop's products collection and join each product record to all its orders stored in an orders collection. You want to generate a report to list all the orders made for each product in 2020. Summarising Arrays For First, Last, Min, Max & Average Advanced Use Of Expressions For Array Processing Embrace Composability For Increased Productivity
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