Setting up a Document Database

On the Amazon EC2 management console, under Network & Security, choose Security groups.

Choose Create security group.

• For Security group name, enter demoDocDB.

• For Description, enter a description.

• For VPC, accept the usage of your default VPC

If necessary, open the AWS Cloud9 management console, navigate to Your environments, and choose DocumentDBCloud9.

Choose open IDE.

At the command prompt, create the repository file with the following code:

echo -e "[mongodb-org-3.6] \nname=MongoDB Repository\nbaseurl=https://repo.mongodb.org/yum/amazon/2013.03/mongodb-org/3.6/x86_64/\ngpgcheck=1 \nenabled=1 \ngpgkey=https://www.mongodb.org/static/pgp/server-3.6.asc" | sudo tee /etc/yum.repos.d/mongodb-org-3.6.repo

When it is complete, install the mongo shell with the following code:

sudo yum install -y mongodb-org-shell

To encrypt data in transit, download the CA certificate for Amazon DocumentDB.

You are now ready to connect to your Amazon DocumentDB cluster.

Now that you are connected to your cluster, you can run a few queries to get familiar with using a document database.

To insert a single document, enter the following code:

db.collection.insert({"hello":"DocumentDB"})

You get the following output:

WriteResult({ "nInserted" : 1 })

You can read the document that you wrote with the findOne() command (because it only returns a single document). See the following code:

db.collection.findOne()

You get the following output:

{ "_id" : ObjectId("5e401fe56056fda7321fbd67"), "hello" : "DocumentDB" }

To perform a few more queries, consider a gaming profiles use case. First, insert a few entries into a collection entitled profiles. See the following code:

db.profiles.insertMany([{ "_id" : 1, "name" : "Tim", "status": "active", "level": 12, "score":202},{ "_id" : 2, "name" : "Justin", "status": "inactive", "level": 2, "score":9},{ "_id" : 3, "name" : "Beth", "status": "active", "level": 7, "score":87},{ "_id" : 4, "name" : "Jesse", "status": "active", "level": 3, "score":27}])

You get the following output:

{ "acknowledged" : true, "insertedIds" : [ 1, 2, 3, 4 ] }

Use the find() command to return all the documents in the profiles collection. See the following code:

db.profiles.find()

You get the following output:

{ "_id" : 1, "name" : "Tim", "status" : "active", "level" : 12, "score" : 202 }{ "_id" : 2, "name" : "Justin", "status" : "inactive", "level" : 2, "score" : 9 }{ "_id" : 3, "name" : "Beth", "status" : "active", "level" : 7, "score" : 87 }{ "_id" : 4, "name" : "Jesse", "status" : "active", "level" : 3, "score" : 27 }

Use a query for a single document using a filter. See the following code.

db.profiles.find({name: "Jesse"})

You get the following output:

{ "_id" : 4, "name" : "Jesse", "status" : "active", "level" : 3, "score" : 27 }

A common use case in gaming is finding a profile for a given user and increment a value in the user’s profile. In this scenario, you want to run a promotion for the top active gamers. If the gamer fills out a survey, you increase their score by +10.

To do that, use the findAndModify command. In this use case, the user Tim received and completed a survey. To give Tim the credit to their score, enter the following code:

db.profiles.findAndModify({   query: { name: "Tim", status: "active"},   update: { $inc: { score: 10 } }})

You get the following output:

{      "_id" : 1,      "name" : "Tim",      "status" : "active",      "level" : 12,      "score" : 202}

You can verify the result with the following query:

db.profiles.find({name: "Tim"})

You get the following output:

{ "_id" : 1, "name" : "Tim", "status" : "active", "level" : 12, "score" : 212 }

When you complete the walkthrough, stop your Amazon DocumentDB cluster to reduce costs or delete the cluster altogether.

By default, after 30 minutes of inactivity, your AWS Cloud9 environment stops the underlying EC2 instance to help save costs.

This tutorial showed you how to get started with Amazon DocumentDB by creating an AWS Cloud9 environment.

You were able to install the mongo shell, create an Amazon DocumentDB cluster, connect to your cluster, and perform a few queries, seeing how easy it is to insert and query JSON documents within Amazon DocumentDB.

Amazon DocumentDB (with MongoDB compatibility) is a fast, scalable, highly available, and fully managed document database service that supports MongoDB workloads, and makes it easy to store, query, and index JSON data.