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👐 Build a compound index following the ESR rule

In this exercise, you will build a compound index following the ESR rule, compare the query explain plans before and after creating the index, and analyze them.

Explore the code

  1. Open the file server/src/indexing/borrowed-books-index.ts file in your GitHub Codespace.

  2. Examine the code to build a compound index on the borrowedBooks collection.

    /**
    * Create the index to support the following query:
    * issueDetails.find({
    * 'user._id': userID,
    * borrowDate: { $gte: date },
    * }, {
    * sort: { returnedDate: -1 }
    * })
    */
    await collections?.issueDetails?.createIndex({
    // Equality
    'user._id': 1,
    // Sort
    returnedDate: 1,
    // Range
    borrowDate: 1,
    });
    信息

    The index is created on the borrowedBooks collection to support the query that finds the issue details for a user with a specific userID, where the borrowDate is greater than or equal to a specific date, and sorts the results by returnedDate in descending order.

    This is compound index and it follows the ESR rule: Equality, Sort, and Range. This ensures optimal performance for the query.

  3. Execute the script to create the compound index.

    npx tsx src/indexing/borrowed-books-index.ts

    After a few seconds, you should see the following output:

    Connecting to MongoDB Atlas...
    Connected!

    BEFORE creating the index

    Winning plan stage: COLLSCAN
    No index used
    Total documents examined: 1284
    Number of documents returned: 3

    -----------------------------

    AFTER creating the index

    Winning plan stage: IXSCAN
    Index used: user._id_1_returnedDate_1_borrowDate_1
    Total documents examined: 3
    Number of documents returned: 3
    信息

    The script uses explain plans to compare the query plans before and after creating the index.

    Notice the difference in the winning plan stage and the number of documents examined before and after creating the index.

🦸‍♀️ Try different indexes

Modify the compound index by adding and removing fields, and observe the changes in the query explain plans.