Group Data with the Bucket Pattern

Query for Data with the Bucket Pattern

In the updated schema, each document contains data for a single page in the application. You can use the _id and count field to determine how to return and update data.

To query for data on the appropriate page, use a regex query to return data for a specified customerId, and use skip to return to the data for the correct page. The regex query on _id uses the default _id index, which results in performant queries without the need for an additional index.

The following query returns data for the first page of trades for customer 123:

db.trades.find( { "_id": /^123_/ } ).sort( { _id: 1 } ).limit(1)

To return data for later pages, specify a skip value of one less than the page you want to show data for. For example, to show data for page 10, run the following query:

db.trades.find( { "_id": /^123_/ } ).sort( { _id: 1 } ).skip(9).limit(1)

Insert Data with the Bucket Pattern

Now that the schema uses the bucket pattern, update your application logic to insert new trades into the correct bucket. Use an update command to insert the trade into the bucket with the appropriate customerId and bucket.

The following command inserts a new trade for customerId: 123:

db.trades.updateOne( { "_id": /^123_/, "count": { $lt: 10 } },
   {
      "$push": {
         "history": {
         "type": "buy",
         "ticker": "MSFT",
         "qty": 42,
         "date": ISODate("2023-11-02T11:43:10")
       }
    },
    "$inc": { "count": 1 },
    "$setOnInsert": { "_id": "123_1698939791", "customerId": 123 }
   },
   { upsert: true }
)

The application displays 10 trades per page. The update filter searches for a document for customerId: 123 where the count is less than 10, meaning that bucket does not contain a full page of data.

• If there is a document that matches "_id": /^123_/ and its count is less than 10, the update command pushes the new trade into the matched document's history array.

• If there is not a matching document, the update command inserts a new document with the new trade (because upsert is true). The _id field of the new document is a concatenation of the customerId and the time in seconds since the unix epoch of the trade.

The logic for update commands avoids unbounded arrays by ensuring that no history array contains more than 10 documents.

After you run the update operation, the trades collection has the following documents:

[
  {
    _id: '123_1698349623',
    customerId: 123,
    count: 3,
    history: [
      {
        type: 'buy',
        ticker: 'MDB',
        qty: 419,
        date: ISODate("2023-10-26T15:47:03.434Z")
      },
      {
        type: 'sell',
        ticker: 'MDB',
        qty: 29,
        date: ISODate("2023-10-30T09:32:57.765Z")
      },
      {
        type: 'buy',
        ticker: 'MSFT',
        qty: 42,
        date: ISODate("2023-11-02T11:43:10.000Z")
      }
    ]
  },
  {
    _id: '456_1698765362',
    customerId: 456,
    count: 1,
    history: [
      {
        type: 'buy',
        ticker: 'GOOG',
        quantity: 50,
        date: ISODate("2023-10-31T11:16:02.120Z")
      }
    ]
  }
]