The MongoDB Atlas Sample Datasets

#Sample AirBnB Listings Dataset

This dataset consists of a single collection of AirBnB reviews and listings. There are indexes on the property type, room type, bed, name, and on the location fields as well as on the _id of the documents.

The data is a randomized subset of the original publicly available AirBnB dataset. It covers several different cities around the world. This dataset is used extensively in MongoDB University courses.

You can find more details on the Sample AirBnB Documentation page.

#Sample Analytics Dataset

This dataset consists of three collections of randomly generated financial services data. There are no additional indexes beyond the _id index on each collection. The collections represent accounts, transactions, and customers.

The transactions collection uses the Bucket Pattern to hold a set of transactions for a period. It was built for MongoDB's private training, specifically for the MongoDB for Data Analysis course.

The advantages in using this pattern are a reduction in index size when compared to storing each transaction in a single document. It can potentially simplify queries and it provides the ability to use pre-aggregated data in our documents.

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// transaction collection document example
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{
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"account_id": 794875,
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"transaction_count": 6,
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"bucket_start_date": {"$date": 693792000000},
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"bucket_end_date": {"$date": 1473120000000},
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"transactions": [
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   {
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      "date": {"$date": 1325030400000},
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      "amount": 1197,
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      "transaction_code": "buy",
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      "symbol": "nvda",
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      "price": "12.7330024299341033611199236474931240081787109375",
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      "total": "15241.40390863112172326054861"
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   },
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   {
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      "date": {"$date": 1465776000000},
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      "amount": 8797,
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      "transaction_code": "buy",
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      "symbol": "nvda",
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      "price": "46.53873172406391489630550495348870754241943359375",
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      "total": "409401.2229765902593427995271"
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   },
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   {
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      "date": {"$date": 1472601600000},
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      "amount": 6146,
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      "transaction_code": "sell",
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      "symbol": "ebay",
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      "price": "32.11600884852845894101847079582512378692626953125",
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      "total": "197384.9903830559086514995215"
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   },
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   {
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      "date": {"$date": 1101081600000},
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      "amount": 253,
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      "transaction_code": "buy",
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      "symbol": "amzn",
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      "price": "37.77441226157566944721111212857067584991455078125",
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      "total": "9556.926302178644370144411369"
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   },
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   {
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      "date": {"$date": 1022112000000},
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      "amount": 4521,
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      "transaction_code": "buy",
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      "symbol": "nvda",
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      "price": "10.763069758141103449133879621513187885284423828125",
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      "total": "48659.83837655592869353426977"
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   },
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   {
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      "date": {"$date": 936144000000},
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      "amount": 955,
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      "transaction_code": "buy",
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      "symbol": "csco",
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      "price": "27.992136535152877030441231909207999706268310546875",
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      "total": "26732.49039107099756407137647"
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   }
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]
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}

You can find more details on the Sample Analytics Documentation page.

#Sample Geospatial Dataset

This dataset consists of a single collection with information on shipwrecks. It has an additional index on the coordinates field (GeoJSON). This index is a Geospatial 2dsphere index. This dataset was created to help explore the possibility of geospatial queries within MongoDB.

The image below was created in MongoDB Charts and shows all of the shipwrecks on the eastern seaboard of North America.

You can find more details on the Sample Geospatial Documentation page.

#Sample Mflix Dataset

This dataset consists of five collections with information on movies, movie theatres, movie metadata, and user movie reviews and their ratings for specific movies. The data is a subset of the IMDB dataset. There are three additional indexes beyond _id: on the sessions collection on the user_id field, on the theatres collection on the location.geo field, and on the users collection on the email field. You can see this dataset used in this MongoDB Charts tutorial.

The Atlas Search Movies site uses this data and MongoDB's Atlas Search to provide a searchable movie catalog.

This dataset is the basis of our Atlas Search tutorial.

You can find more details on the Sample Mflix Documentation page.

#Sample Restaurants Dataset

This dataset consists of two collections with information on restaurants and neighbourhoods in New York. There are no additional indexes. This dataset is the basis of our Geospatial tutorial. The restaurant document only contains the location and the name for a given restaurant.

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// restaurants collection document example
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{
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   location: {
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      type: "Point",
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      coordinates: [-73.856077, 40.848447]
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   },
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   name: "Morris Park Bake Shop"
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}

In order to use the collections for geographical searching, we need to add an index, specifically a 2dsphere index. We can add this index and then search for all restaurants in a one-kilometer radius of a given location, with the results being sorted by those closest to those furthest away. The code below creates the index, then adds a helper variable to represent 1km, which our query then uses with the $nearSphere criteria to return the list of restaurants within 1km of that location.

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db.restaurants.createIndex({ location: "2dsphere" })
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var ONE_KILOMETER = 1000
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db.restaurants.find({ location: { $nearSphere: { $geometry: { type: "Point", coordinates: [ -73.93414657, 40.82302903 ] }, $maxDistance: ONE_KILOMETER } } })

You can find more details on the Sample Restaurants Documentation page.

#Sample Supply Store Dataset

This dataset consists of a single collection with information on mock sales data for a hypothetical office supplies company. There are no additional indexes. This is the second dataset used in the MongoDB Chart tutorials.

The sales collection uses the Extended Reference pattern to hold both the items sold and their details as well as information on the customer who purchased these items. This pattern includes frequently accessed fields in the main document to improve performance at the cost of additional data duplication.

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// sales collection document example
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{
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   "_id": {
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      "$oid": "5bd761dcae323e45a93ccfe8"
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   },
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   "saleDate": {
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      "$date": { "$numberLong": "1427144809506" }
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   },
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   "items": [
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      {
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         "name": "notepad",
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         "tags": [ "office", "writing", "school" ],
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         "price": { "$numberDecimal": "35.29" },
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         "quantity": { "$numberInt": "2" }
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      },
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      {
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         "name": "pens",
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         "tags": [ "writing", "office", "school", "stationary" ],
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         "price": { "$numberDecimal": "56.12" },
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         "quantity": { "$numberInt": "5" }
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      },
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      {
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         "name": "envelopes",
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         "tags": [ "stationary", "office", "general" ],
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         "price": { "$numberDecimal": "19.95" },
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         "quantity": { "$numberInt": "8" }
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      },
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      {
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         "name": "binder",
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         "tags": [ "school", "general", "organization" ],
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         "price": { "$numberDecimal": "14.16" },
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         "quantity": { "$numberInt": "3" }
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      }
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   ],
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   "storeLocation": "Denver",
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   "customer": {
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      "gender": "M",
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      "age": { "$numberInt": "42" },
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      "email": "cauho@witwuta.sv",
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      "satisfaction": { "$numberInt": "4" }
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   },
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   "couponUsed": true,
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   "purchaseMethod": "Online"
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}

You can find more details on the Sample Supply Store Documentation page.

#Sample Training Dataset

This dataset consists of nine collections with no additional indexes. It represents a selection of realistic data and is used in the MongoDB private training courses.

It includes a number of public, well-known data sources such as the OpenFlights, NYC's OpenData, and NYC's Citibike Data.

The routes collection uses the Extended Reference pattern to hold OpenFlights data on airline routes between airports. It references airline information in the airline sub document, which has details about the specific plane on the route. This is another example of improving performance at the cost of minor data duplication for fields that are likely to be frequently accessed.

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// routes collection document example
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{
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   "_id": {
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      "$oid": "56e9b39b732b6122f877fa5c"
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   },
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   "airline": {
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         "alias": "2G",
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         "iata": "CRG",
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         "id": 1654,
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         "name": "Cargoitalia"
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   },
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   "airplane": "A81",
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   "codeshare": "",
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   "dst_airport": "OVB",
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   "src_airport": "BTK",
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   "stops": 0
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}

You can find more details on the Sample Training Documentation page.

#Sample Weather Dataset

This dataset consists of a single collection with no additional indexes. It represents detailed weather reports from locations across the world. It holds geospatial data on the locations in the form of legacy coordinate pairs.

You can find more details on the Sample Weather Documentation page.

If you have ideas or suggestions for new datasets, we are always interested. Let us know on the developer community website.

#Downloading the Dataset for Use on Your Local Machine

It is also possible to download and explore these datasets on your own local machine. You can download the complete sample dataset via the wget command:

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wget https://atlas-education.s3.amazonaws.com/sampledata.archive

Note: You can also use the curl command:

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curl https://atlas-education.s3.amazonaws.com/sampledata.archive -o sampledata.archive

You should check you are running a local mongod instance or you should start a new mongod instance at this point. This mongod will be used in conjunction with mongorestore to unpack and host a local copy of the sample dataset. You can find more details on starting mongod instances on this documentation page.

This section assumes that you're connecting to a relatively straightforward setup, with a default authentication database and some authentication set up. (You should always create some users for authentication!)

If you don't provide any connection details to mongorestore, it will attempt to connect to MongoDB on your local machine, on port 27017 (which is MongoDB's default). This is the same as providing --host localhost:27017.

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mongorestore --archive=sampledata.archive

You can use a variety of tools to view your documents. You can use MongoDB Compass, the CLI, or the MongoDB Visual Studio Code (VSCode) plugin to interact with the documents in your collections. You can find out how to use MongoDB Playground for VSCode and integrate MongoDB into a Visual Studio Code environment.

If you find the sample data useful for building or helpful, let us know on the community forums!