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How to Run Atlas Search Queries Using Materialized Views

On this page

  • Create the purchaseOrders Collection
  • Create the updateMonthlySales Function
  • Create the updateMonthlyPurchaseOrders Function
  • Create Scheduled Triggers
  • Create an Atlas Search Index on the Materialized View
  • Run a Query on the Materialized View

This tutorial describes how to create an index and run queries against the sample_supplies.sales collection from the sample dataset and a new sample_supplies.purchaseOrders collection using a combination of the following features:

  • on-demand materialized views

  • Atlas App Services scheduled triggers

An on-demand materialized view is a collection that you create and update using a $merge aggregation pipeline stage. You can create an Atlas Search index on the materialized view and then run queries on the materialized view using the $search aggregation pipeline stage.

This tutorial takes you through the following steps:

  1. Create a collection named purchaseOrders in the sample_supplies database.

  2. Create an App Services function named updateMonthlySales in the App Services UI to initialize the monthlyPhoneTransactions materialized view using data from the sample sample_supplies.sales collection on your Atlas cluster.

  3. Create an App Services function named updateMonthlyPurchaseOrders in the App Services UI to update the monthlyPhoneTransactions materialized view using data from the sample_supplies.purchaseOrders collection that you created on your Atlas cluster.

  4. Schedule the following functions to update the monthlyPhoneTransactions materialized view on a periodic basis using App Services scheduled triggers:

    1. updateMonthlySales

    2. updateMonthlyPurchaseOrders

  5. Create an Atlas Search index on the monthlyPhoneTransactions materialized view.

  6. Run a query on the monthlyPhoneTransactions materialized view.

Before you begin, ensure that your Atlas cluster meets the requirements described in the Prerequisites.

To create an Atlas Search index, you must have Project Data Access Admin or higher access to the project.

To create an App Services function and trigger, you must have Project Owner or higher access to the project.

1
  1. Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

  2. Use the sample_supplies database:

    use sample_supplies
2

Add the purchaseOrders collection with new phone purchase order data from January of 2018. Run the following commands:

db.purchaseOrders.insertMany( [
{
saleDate: ISODate("2018-01-23T21:06:49.506Z"),
items: [
{
name: 'printer paper',
tags: [ 'office', 'stationary' ],
price: Decimal128("40.01"),
quantity: 2
},
{
name: 'notepad',
tags: [ 'office', 'writing', 'school' ],
price: Decimal128("35.29"),
quantity: 2
},
{
name: 'pens',
tags: [ 'writing', 'office', 'school', 'stationary' ],
price: Decimal128("56.12"),
quantity: 5
},
{
name: 'backpack',
tags: [ 'school', 'travel', 'kids' ],
price: Decimal128("77.71"),
quantity: 2
},
{
name: 'notepad',
tags: [ 'office', 'writing', 'school' ],
price: Decimal128("18.47"),
quantity: 2
},
{
name: 'envelopes',
tags: [ 'stationary', 'office', 'general' ],
price: Decimal128("19.95"),
quantity: 8
},
{
name: 'envelopes',
tags: [ 'stationary', 'office', 'general' ],
price: Decimal128("8.08"),
quantity: 3
},
{
name: 'binder',
tags: [ 'school', 'general', 'organization' ],
price: Decimal128("14.16"),
quantity: 3
}
],
storeLocation: 'Denver',
customer: {
gender: 'M',
age: 42,
email: 'cauho@witwuta.sv',
satisfaction: 4
},
couponUsed: true,
purchaseMethod: 'Phone'
}
])
db.purchaseOrders.insertMany( [
{
saleDate: ISODate("2018-01-25T10:01:02.918Z"),
items: [
{
name: 'envelopes',
tags: [ 'stationary', 'office', 'general' ],
price: Decimal128("8.05"),
quantity: 10
},
{
name: 'binder',
tags: [ 'school', 'general', 'organization' ],
price: Decimal128("28.31"),
quantity: 9
},
{
name: 'notepad',
tags: [ 'office', 'writing', 'school' ],
price: Decimal128("20.95"),
quantity: 3
},
{
name: 'laptop',
tags: [ 'electronics', 'school', 'office' ],
price: Decimal128("866.5"),
quantity: 4
},
{
name: 'notepad',
tags: [ 'office', 'writing', 'school' ],
price: Decimal128("33.09"),
quantity: 4
},
{
name: 'printer paper',
tags: [ 'office', 'stationary' ],
price: Decimal128("37.55"),
quantity: 1
},
{
name: 'backpack',
tags: [ 'school', 'travel', 'kids' ],
price: Decimal128("83.28"),
quantity: 2
},
{
name: 'pens',
tags: [ 'writing', 'office', 'school', 'stationary' ],
price: Decimal128("42.9"),
quantity: 4
},
{
name: 'envelopes',
tags: [ 'stationary', 'office', 'general' ],
price: Decimal128("16.68"),
quantity: 2
}
],
storeLocation: 'Seattle',
customer: { gender: 'M', age: 50, email: 'keecade@hem.uy', satisfaction: 5 },
couponUsed: false,
purchaseMethod: 'Phone'
}
])
3

Query the purchaseOrders collection to confirm the new purchase order entries.

db.purchaseOrders.find().sort( {saleDate: -1} )

The two query results reflect that the purchase order data ends in January of 2018.

Create the updateMonthlySales function in the App Services UI.

The updateMonthlySales function defines a monthlyPhoneTransactions materialized view that contains cumulative monthly sales information. The function updates monthly sales information for sales conducted over the phone.

The following example defines the function:

exports = function(){
var pipeline = [
{ $match: {purchaseMethod: "Phone"} },
{ $unwind: {path: "$items"}},
{ $group: {
_id: { $dateToString:
{ format: "%Y-%m", date: "$saleDate" } },
sales_quantity: { $sum: "$items.quantity"},
sales_price: { $sum: "$items.price"}
}},
{ $set: { sales_price: { $toDouble: "$sales_price"}}},
{ $merge: { into: "monthlyPhoneTransactions", whenMatched: "replace" } }
]
var monthlyPhoneTransactions = context.services.get("mongodb-atlas").db("sample_supplies").collection("sales");
return monthlyPhoneTransactions.aggregate(pipeline);
};

The function uses the following aggregation pipeline stages to update monthlyPhoneTransactions:

  • The $match stage filters the data to process only those sales that were completed over the Phone.

  • The $group stage groups the sales information by the year-month. This stage outputs documents that have the form:

    { "_id" : "<YYYY-mm>", "sales_quantity" : <num>, "sales_amount" : <NumberDecimal> }
  • The $set stage changes the data type of the sales_price field to double. Atlas Search $search operators don't support the Decimal128 data type. Changing the sales_price field's data type allows you to query this field using Atlas Search indexes.

  • The $merge stage writes the output to the monthlyPhoneTransactions collection.

    Based on the _id field, (the default for unsharded output collections), the stage checks if the document in the aggregation results matches an existing document in the collection:

    • When Atlas Search finds a match (that is, a document with the same year-month already exists in the collection), Atlas Search replaces the existing document with the document from the aggregation results as specified in the stage.

    • When Atlas Search doesn't find a match, Atlas Search inserts the document from the aggregation results into the collection as specified in the stage. This is the default behavior when there is no match for the field.

1

To define a new server-side function from the UI, you must first create an App Services App:

  1. If you have not already done so, click the App Services tab.

  2. Create the app:

    • If you are creating your first App Services App in the project, you will be shown an option to start without a template (Build your own App). Select the Build your own App option.

    • If you have already created at least one App Services App in the project, click Create a New App.

  3. In the Name field, enter Sales-App as the name of the function.

  4. Under the Link your Database field, select the Use an existing MongoDB Atlas Data Source option.

  5. From the dropdown, select the Atlas cluster you created in the Prerequisites.

  6. Click Create App Service.

2

To define a new server-side function from the UI:

  1. Click Functions in the left navigation menu.

  2. Click Create New Function.

  3. Enter updateMonthlySales as the name of the function.

  4. Under Authentication, select System.

3
  1. Click the Function Editor tab.

  2. Add the javascript code to the exports function. At minimum, the code must assign a function to the global variable exports:

    exports = function(){
    var pipeline = [
    { $match: {purchaseMethod: "Phone"} },
    { $unwind: {path: "$items"}},
    { $group: {
    _id: { $dateToString:{ format: "%Y-%m", date: "$saleDate" } },
    sales_quantity: { $sum: "$items.quantity"},
    sales_price: { $sum: "$items.price"}
    }
    },
    { $set: { sales_price: { $toDouble: "$sales_price"}}},
    { $merge: { into: "monthlyPhoneTransactions", whenMatched: "replace" } }
    ]
    var monthlyPhoneTransactions = context.services.get("mongodb-atlas").db("sample_supplies").collection("sales");
    return monthlyPhoneTransactions.aggregate(pipeline);
    };
  3. Click the Run button in the lower right-hand corner of the Function Editor to create the monthlyPhoneTransactions materialized view.

    The Result tab at the bottom of the Function Editor should indicate success without any errors.

  4. Click Save Draft.

4
  1. Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

  2. Use the sample_supplies database:

    use sample_supplies
  3. Query the sales collection. Note that the last sale in sales occurs in December of 2017:

    db.sales.find().sort( {saleDate: -1} )
  4. Confirm that the materialized view has been created in your sample_supplies database:

    show collections

    The command lists your collections, including the newly created monthlyPhoneTransactions materialized view.

  5. Query the monthlyPhoneTransactions materialized view:

    db.monthlyPhoneTransactions.find().sort( { _id: -1} )

    The monthlyPhoneTransactions materialized view shows the newly added data. The top result reflects that the most recent transaction took place in December 2017.

Create the updateMonthlyPurchaseOrders function in the App Services UI.

The updateMonthlyPurchaseOrders function adds cumulative monthly purchase order information to the monthlyPhoneTransactions materialized view. The function updates the monthly purchase order information for purchase orders conducted over the phone.

The following example defines the function:

exports = function(){
var pipeline = [
{ $match: {purchaseMethod: "Phone"} },
{ $unwind: {path: "$items"}},
{ $group: {
_id: { $dateToString:
{ format: "%Y-%m", date: "$saleDate" } },
sales_quantity: { $sum: "$items.quantity"},
sales_price: { $sum: "$items.price"}
}},
{ $set: { sales_price: { $toDouble: "$sales_price"}}},
{ $merge: { into: "monthlyPhoneTransactions", whenMatched: "replace" } }
]
var monthlyPhoneTransactions = context.services.get("mongodb-atlas").db("sample_supplies").collection("purchaseOrders");
return monthlyPhoneTransactions.aggregate(pipeline);
};

The updateMonthlyPurchaseOrders function uses the same aggregation pipeline stages to update monthlyPhoneTransactions as the updateMonthlySales function.

1

To define a new server-side function from the UI:

  1. Return to your App Services App.

  2. Click Functions in the left navigation menu.

  3. Click Create New Function.

  4. Enter updateMonthlyPurchaseOrders as the name of the function.

  5. Under Authentication, select System.

2
  1. Click the Function Editor tab.

  2. Add the javascript code to the exports function. At minimum, the code must assign a function to the global variable exports:

    exports = function(){
    var pipeline = [
    { $match: {purchaseMethod: "Phone"} },
    { $unwind: {path: "$items"}},
    { $group: {
    _id: { $dateToString:{ format: "%Y-%m", date: "$saleDate" } },
    sales_quantity: { $sum: "$items.quantity"},
    sales_price: { $sum: "$items.price"}
    }
    },
    { $set: { sales_price: { $toDouble: "$sales_price"}}},
    { $merge: { into: "monthlyPhoneTransactions", whenMatched: "replace" } }
    ]
    var monthlyPhoneTransactions = context.services.get("mongodb-atlas").db("sample_supplies").collection("purchaseOrders");
    return monthlyPhoneTransactions.aggregate(pipeline);
    };
  3. Click the Run button in the lower right-hand corner of the Function Editor to update the monthlyPhoneTransactions materialized view.

    The Result tab at the bottom of the Function Editor should indicate success without any errors.

    The updateMonthlyPurchaseOrders function refreshes the monthlyPhoneTransactions materialized view with the January 2018 purchase order data.

  4. Click Save Draft.

3
  1. Return to the mongosh and query the monthlyPhoneTransactions collection to confirm the update:

    db.monthlyPhoneTransactions.find().sort( { _id: -1} )

    The monthlyPhoneTransactions materialized view shows the newly added data. The top result reflects that the most recent transaction took place in January 2018.

Schedule the App Services functions created in the previous step to run once a day to keep the materialized view up-to-date.

1
2
3
UI Field Name
Configuration
Trigger Type
Select Scheduled.
Name
Specify updateMonthlySales.
Schedule Type
  1. Select Basic.

  2. For Repeat once by, select Day of the Month and set the value to your preferred date.

    Note

    Alternatively, for testing purposes, set Repeat once by dropdown to a more frequent occurrence, such as Minute or Hour

Select An Event Type
Select Function.
Function
Select updateMonthlySales.
4
5
6
7
UI Field Name
Configuration
Trigger Type
Select Scheduled.
Name
Specify updateMonthlyPurchaseOrders.
Schedule Type
  1. Select Basic.

  2. For Repeat once by, select Day of the Month and set the value to your preferred date.

    Note

    Alternatively, for testing purposes, set Repeat once by dropdown to a more frequent occurrence, such as Minute or Hour

Select An Event Type
Select Function.
Function
Select updateMonthlyPurchaseOrders.
8
9

Create an Atlas Search index on the monthlyPhoneTransactions collection.

1

Click Database in the top-left corner of Atlas to navigate to the Clusters page for your project.

2
3
4
5
6
  1. In the Index Name field, enter monthlyPhoneTransactions.

  2. In the Database and Collection section, find the sample_supplies database, and select the monthlyPhoneTransactions collection.

  3. Click Next.

7
8
9

A modal window appears to let you know your index is building. Click the Close button.

10

The newly created index appears on the Atlas Search tab. While the index is building, the Status field reads Build in Progress. When the index is finished building, the Status field reads Active.

Note

Larger collections take longer to index. You will receive an email notification when your index is finished building.

Run a query against the newly updated and indexed monthlyPhoneTransactions collection.

1

Open mongosh in a terminal window and connect to your cluster. For detailed instructions on connecting, see Connect via mongosh.

2

Run the following command at mongosh prompt:

use sample_supplies
3

The following query counts the number of months in monthlyPhoneTransactions with total sales greater than or equal to 10000 dollars:

db.monthlyPhoneTransactions.aggregate([
{
$search: {
"index": "monthlySalesIndex",
"range": {
"gt": 10000,
"path": ["sales_price"]
}
}
},
{
$count: 'months_w_over_10000'
},
])

The above query returns 4, indicating that only 4 months out of all the months in the monthlyPhoneTransactions materialized view had total sales greater than or equal to 10000 dollars. This result reflects data from both the sample_supplies.sales and sample_supplies.purchaseOrders collections.

For complete aggregation pipeline documentation, see the MongoDB Server Manual.

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