How to Run Atlas Search Queries Using Materialized Views

In Atlas, go to the Triggers page for your project.

1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

3. In the sidebar, click Triggers under the Services heading.

   The Triggers page displays.

Click Add a Trigger to open the Trigger configuration page.

Enter configuration values for the Trigger.

Create the Function.

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

Paste the following code into 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 of 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 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.

Test the Function.

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 reflects the execution status of the function. Click Save Draft.

In Atlas, go to the Triggers page for your project.

1. If it's not already displayed, select the organization that contains your project from the Organizations menu in the navigation bar.

2. If it's not already displayed, select your project from the Projects menu in the navigation bar.

3. In the sidebar, click Triggers under the Services heading.

   The Triggers page displays.

Click Add a Trigger to open the Trigger configuration page.

Enter configuration values for the Trigger.

Create 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);
};

Test the Function.

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 reflects the execution status of the function.

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

Click Save.

Review and deploy the Sales-App application draft.

Use mongosh to query the monthlyPhoneTransactions collection to confirm the update:

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

{
  _id: '2018-01',
  sales_quantity: 66,
  sales_price: Decimal128("1407.10")
}

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

Review and deploy the Sales-App application draft.