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Exercise 1: Using expression operators to filter input documents

MongoDB 301: Aggregation Working with Arrays in the Aggregation Pipeline Exercise 1: Using expression operators to filter input documents

In this exercise, you’ll use the Aggregation Editor to create an aggregate statement and add the first stage, which will filter the collection’s documents based on the specified conditions. To match the correct documents, the stage will use the $or and the $and Boolean expression operators, as well as the $in array expression operator.

The exercises in this section are based on the sales database and the customers collection. The first section in this course demonstrated how to create the database and import the collection. Refer to that section for details on how to set them up if you haven’t done so already.

To use expression operators to filter input documents

  1. Launch Studio 3T and connect to MongoDB Atlas.
  2. In the Connection Tree, expand the sales database node and, if necessary, expand the Collections node. 
  3. Right-click the customers collection node, and then click Open Aggregation Editor. Studio 3T adds the Aggregation tab to the main window. The tab displays the Aggregation Editor, with the editor’s Pipeline tab active. At this point, the aggregation pipeline is empty.
  4. On the Aggregation Editor toolbar, click the Add button (large plus sign). The Aggregation Editor adds a tab to the right of the Pipeline tab and makes the new tab active. The tab is named 1: $match because it is the first stage and because Studio 3T uses $match as its default operator when creating a new stage. You’ll be working with the $match operator for this exercise.
  5. In the editor window, delete the placeholder text and curly braces with the following code:
{ $and: [
  { interests: { $in: [ "Software" ] } },
  { $or: [
    { interests: { $in: [ "Web Design" ] } },
    { package: "Premium" } ]
  } ]
}

The expression is similar to the one you saw in the introductory material. The $and operator defines two conditions. Both conditions must both evaluate to true for a document to be included in the stage’s output. The first condition uses the $in operator to verify whether the interests array includes the term Software. If the term exists, the condition will evaluate to true.

The second condition starts with the $or operator, which defines its own two conditions. The first of these uses the $in operator to check whether the interests array includes the term Web Design. The second condition verifies whether the package value equals Premium. If the array meets either of these two conditions, the second condition of the $and operator evaluates to true.

If both the $and conditions evaluate to true, the document will be included in the stage’s output.

  1. In the Stage Input pane, click the Execute button. Studio 3T executes the pipeline up to but not including the current stage and returns the data to that pane. This is the data that is used as input for the current stage.
  2. In the StageOutput pane, click the Execute button. This will execute the pipeline up to and including the $match stage. The following figure shows part of the results, as they appear in Table View.

Because this is the first stage in the pipeline, the input data includes all 1,000 documents in the customers collection. However, the output data includes only 37 documents. These are the documents that meet both conditions defined by the $and operator.

At this point in the course, you should be well familiar with the concepts of the Stage Input and Stage Output panes. You do not need to execute both panes with each stage, but it’s generally a good practice to compare the before-and-after results. In some cases, however, it might be enough to run only the Stage Output pane—or sometimes neither pane at all.

  1. Click Save on the Aggregation Editor toolbar.
  2. When the Save Aggregation Script dialog box appears, name the script count_interests.js, navigate to the folder where you want to save the script, and click Save.
  3. Leave the Aggregation tab open and the existing statement in place for the next exercise.

If you’re not ready to move on to the next exercise, you can save the statement for now and close Studio 3T. You can then reopen the count_interests.js file in the Aggregation Editor when you’re ready to move onto the next exercise.

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  • Course Home Expand All
    Building a Basic Aggregation
    4 Topics | 1 Quiz
    Exercise 1: Filtering the documents in the aggregation pipeline
    Exercise 2: Grouping the documents in the aggregation pipeline
    Exercise 3: Sorting the documents in the aggregation pipeline
    Exercise 4: Adding processing options to the aggregation
    Building a Basic Aggregation: Test your skills
    Introducing the Aggregation Editor
    4 Topics | 1 Quiz
    Exercise 1: Importing an aggregate statement into the Aggregation Editor
    Exercise 2: Replace a field in the aggregation pipeline
    Exercise 3: Reorder the fields in the aggregation pipeline
    Exercise 4: Changing the sort order in the aggregation pipeline
    Introducing the Aggregation Editor: Test your skills
    Working with Arrays in the Aggregation Pipeline
    5 Topics | 1 Quiz
    Exercise 1: Using expression operators to filter input documents
    Exercise 2: Unwinding an array to create individual documents
    Exercise 3: Grouping array values and generating a document count for each group
    Exercise 4: Writing pipeline results to a new collection
    Working with Arrays in the Aggregation Pipeline: Test your skills
    MongoDB 301 Mid-Course Feedback
    Adding Lookup Data to the Aggregation Pipeline
    4 Topics | 1 Quiz
    Exercise 1: Adding lookup data to the aggregation pipeline
    Exercise 2: Converting string values in one of the lookup fields to integers
    Exercise 3: Adding a computed ratio field based on the converted lookup field
    Exercise 4: Limiting the number of returned documents
    Adding Lookup Data to the Aggregation Pipeline: Test your skills
    Working with Reschema for MongoDB
    4 Topics | 1 Quiz
    Exercise 1: Setting up a reschema unit that includes lookup data
    Exercise 2: Defining a target collection in the reschema unit
    Exercise 3: Adding and scheduling a task to create the target collection
    Exercise 4: Running an aggregate statement against the target collection
    Working with Reschema for MongoDB: Test your skills
    Reporting with Studio 3T Aggregations
    3 Topics | 1 Quiz
    Exercise 1: Creating a view based on an aggregation query
    Exercise 2: Exporting a collection as a .csv file for use by a third-party tool
    Exercise 3: Visualizing collection data in MongoDB Charts
    Reporting with Studio 3T Aggregations: Test your skills
    Course Extras
    Return to MongoDB 301: Aggregation
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