Sample data source

A variety of sample data is provided by the developers of MongoDB. They can be found here:
https://developer.mongodb.com/article/atlas-sample-datasets/

A link to directly download all available sample datasets (307 MB):
https://atlas-education.s3.amazonaws.com/sampledata.archive

Import data

1. Navigate to folder /home/ricky/Downloads.

2. Download sample data: wget https://atlas-education.s3.amazonaws.com/sampledata.archive

3. Import data by using mongorestore tool:
   mongorestore --archive=sampledata.archive

   Last line of output:
   433281 document(s) restored successfully. 0 document(s) failed to restore.

Explore sample data

Show all DBs:

> show dbs
admin               0.000GB
config              0.000GB
local               0.000GB
logbook             0.000GB
people              0.000GB
sample_airbnb       0.051GB
sample_analytics    0.009GB
sample_geospatial   0.001GB
sample_mflix        0.040GB
sample_restaurants  0.006GB
sample_supplies     0.001GB
sample_training     0.040GB
sample_weatherdata  0.002GB

Use DB sample_supplies, and show all collections:

> use sample_supplies
switched to db sample_supplies
> show collections
sales

Explore the collection sales:

// Count the number of documents in this collection.
> db.sales.count()
5000

// Find the first document in the collection.
> db.sales.findOne()
{
    "_id" : ObjectId("5bd761dcae323e45a93ccfe8"),
    "saleDate" : ISODate("2015-03-23T21:06:49.506Z"),
    "items" : [
        {
            "name" : "printer paper",
            "tags" : [
                "office",
                "stationary"
            ],
            "price" : NumberDecimal("40.01"),
            "quantity" : 2
        },
        {
            "name" : "notepad",
            "tags" : [
                "office",
                "writing",
                "school"
            ],
            "price" : NumberDecimal("35.29"),
            "quantity" : 2
        },
        {
            "name" : "pens",
            "tags" : [
                "writing",
                "office",
                "school",
                "stationary"
            ],
            "price" : NumberDecimal("56.12"),
            "quantity" : 5
        },
        {
            "name" : "backpack",
            "tags" : [
                "school",
                "travel",
                "kids"
            ],
            "price" : NumberDecimal("77.71"),
            "quantity" : 2
        },
        {
            "name" : "notepad",
            "tags" : [
                "office",
                "writing",
                "school"
            ],
            "price" : NumberDecimal("18.47"),
            "quantity" : 2
        },
        {
            "name" : "envelopes",
            "tags" : [
                "stationary",
                "office",
                "general"
            ],
            "price" : NumberDecimal("19.95"),
            "quantity" : 8
        },
        {
            "name" : "envelopes",
            "tags" : [
                "stationary",
                "office",
                "general"
            ],
            "price" : NumberDecimal("8.08"),
            "quantity" : 3
        },
        {
            "name" : "binder",
            "tags" : [
                "school",
                "general",
                "organization"
            ],
            "price" : NumberDecimal("14.16"),
            "quantity" : 3
        }
    ],
    "storeLocation" : "Denver",
    "customer" : {
        "gender" : "M",
        "age" : 42,
        "email" : "[email protected]",
        "satisfaction" : 4
    },
    "couponUsed" : true,
    "purchaseMethod" : "Online"
}

From the output, we can see the structure of the document:

• _id: ObjectId
• saleDate: ISODate
• items: An array of items
  • name: Item name
  • tags: Item tags
  • price: Price of the item, to 2 decimal places
  • quantity: An integer counting the quantity
• storeLocation: The name of the store location
• customer: An object recording the customer details
  • gender: “M” or “F”
  • age: Integer age
  • email: An email address
  • satisfaction: An integer of satisfaction score
• couponUsed: A boolean of whether a coupon is used
• purchaseMethod: The name of the purchase method

distinct() method

We can further explore the collection by using some query methods:

// Find all the distinct values of customer gender.
> db.sales.distinct("customer.gender")
[ "F", "M" ]

// Find all the distinct values of item tags.
> db.sales.distinct("items.tags");
[
    "electronics",
    "general",
    "kids",
    "office",
    "organization",
    "school",
    "stationary",
    "travel",
    "writing"
]

// Find all the item names
> db.sales.distinct("items.name");
[
    "backpack",
    "binder",
    "envelopes",
    "laptop",
    "notepad",
    "pens",
    "printer paper"
]

// Find all the purchase methods.
> db.sales.distinct("purchaseMethod");
[ "In store", "Online", "Phone" ]

// Find all the store locations.
> db.sales.distinct("storeLocation");
[ "Austin", "Denver", "London", "New York", "San Diego", "Seattle" ]

// Find the number of documents with store location "Austin".
> db.sales.count({
... storeLocation: "Austin"
... })
676