The way that you structure documents in Couchbase Server will influence how often retrieve them for their information, and will therefore influence application performance. Given identical document size for your entire data set, it takes more operations to retrieve two documents than it does one document; therefore there are scenarios where you can reduce the number of reads/write you perform on Couchbase Server if you perform the reads/writes on one document instead of many documents. In doing so, you improve application performance by structuring your documents in way that optimizes read/write times.

The following goes back to our beer application example and illustrates all the additional operations you would need to perform if you used separate documents. In this case, pretend our beer application has a 'leader board.' This board has all of the top 10 best selling beers that exist in our application. Imagine what this leader board document would look like:

{
    "board_id": 222
    "leader_board": "best selling"
    "top_sales" : [ "beer_id" : 75623,
                    "beer_id" : 98756,
                    "beer_id" : 2938,
                    "beer_id" : 49283,
                    "beer_id" : 204857,
                    "beer_id" : 12345,
                    "beer_id" : 23456,
                    "beer_id" : 56413,
                    "beer_id" : 24645,
                    "beer_id" : 34502
                  ],
     "updated": "2010-07-22 20:00:20"
}

In the example document above, we store a reference to a top-selling beer in the 'top_sales' array. A specific beer in that list of beers could look like this document:

{
    "beer_id" :  75623,
    "name" : "Pleny the Felder"
    "type" : "wheat",
    "aroma" : "wheaty",
    "category": "koelsch",
    "units_sold": 37011,
    "brewery" : ”brewery_Legacy_Brewing_Co”
}

If we use this approach, we need to 1) retrieve the leader board document from Couchbase Server, 2) go through each element in the 'top_sales' array and retrieve each beer from Couchbase Server, 3) get the 'units_sold' value from each beer document. Consider the alternative when we use a single leader board document with the relevant beer sales:

{
    "board_id": 222
    "leader_board": "best selling"
    "top_sales" : [ { "beer_id" : 75623, "units_sold": 37011, "name": "Pleny the Felder" },
                    { "beer_id" : 98756, "units_sold": 23002, "name": "Sub-Hoptimus" },
                    { "beer_id" : 2938, "units_sold": 23001, "name": "Speckled Hen" },
                    { "beer_id" : 49283, "units_sold": 11023, "name": "Happy Hops" },
                    { "beer_id" : 204857, "units_sold": 9856, "name": "Bruxulle Rouge" },
                    { "beer_id" : 12345, "units_sold": 7654, "name": "Plums Pilsner" },
                    { "beer_id" : 23456, "units_sold": 7112, "name": "Humble Amber Lager" },
                    { "beer_id" : 56413, "units_sold": 6723, "name": "Hermit Dopplebock" },
                    { "beer_id" : 24645, "units_sold": 6409, "name": "IAM Lambic" },
                    { "beer_id" : 34502, "units_sold": 5012, "name": "Inlaws Special Bitter" }
                  ],
    "updated": "2010-07-22 20:00:20"
}

In this case, we only need to perform a single request to get the leader board document from Couchbase Server. Then within our application logic, we can get each of leading beers from that document. Instead of eleven database requests, we have a single request, which is far less time- and resource- consuming as having multiple server requests. So when you creating or modifying document structures, keep in mind this approach.