Get Start with Amazon DocumentDB

Know How Guide and Hands on Guide for AWS

Get Start with Amazon DocumentDB

Prerequisites

  1. Create a security group

Port 27017 is the default port for Amazon DocumentDB.

  1. Create an Amazon DocumentDB cluster

  2. Install the mongo shell on EC2 or Cloud 9 environment ```bash

    install the mongo shell

    echo -e “[mongodb-org-4.0] \nname=MongoDB Repository\nbaseurl=https://repo.mongodb.org/yum/amazon/2013.03/mongodb-org/4.0/x86_64/\ngpgcheck=1 \nenabled=1 \ngpgkey=https://www.mongodb.org/static/pgp/server-4.0.asc” | sudo tee /etc/yum.repos.d/mongodb-org-4.0.repo

sudo yum install -y mongodb-org-shell

To encrypt data in transit, download the public key for Amazon DocumentDB

Global region

wget https://s3.amazonaws.com/rds-downloads/rds-combined-ca-bundle.pem

China region

wget https://s3.cn-north-1.amazonaws.com.cn/rds-downloads/rds-combined-ca-cn-bundle.pem


## Quick start use
1. Connect to your Amazon DocumentDB cluster

    Amazon DocumentDB (with MongoDB compatibility) clusters are deployed within an Amazon VPC. They can be accessed directly by Amazon EC2 instances or other AWS services that are deployed in the same Amazon VPC. Additionally, Amazon DocumentDB can be accessed in different VPCs in the same AWS Region or other Regions via `VPC peering`.

- Cross region connection
![Cross region connection](/aws-is-how/database/documentdb/images/cross-region-connection.png)

- Inter-region peering
![Inter-region peering](/aws-is-how/database/documentdb/images/VPC-peering.png)

- Application VPC Route Table
![Inter-region peering-Application TB](/aws-is-how/database/documentdb/images/requester-rt.png)

- DocumentDB VPC Route Table
![Inter-region peering-DB TB](/aws-is-how/database/documentdb/images/db-rt.png)

    If your application) access your Amazon DocumentDB resources from outside the cluster's VPC. In that case, you can use `SSH tunneling` (also known as port forwarding) to access your Amazon DocumentDB resources. 

    https://docs.aws.amazon.com/documentdb/latest/developerguide/connect-from-outside-a-vpc.html

2. Get the conntion info
![DocDB-Connect](/aws-is-how/database/documentdb/images/DocDB-Connect.png)

```bash
mongo --ssl --host <docDB_endpoint>:27017 --sslCAFile rds-combined-ca-cn-bundle.pem --username dbadmin --password <insertYourPassword>

When you enter your password and your prompt becomes `rs0:PRIMARY>` prompt, you are successfully connected to your Amazon DocumentDB cluster.
  1. Insert and query data ```bash

    insert a single document

    s0:PRIMARY> db.collection.insert({“hello”:”DocumentDB”}) WriteResult({ “nInserted” : 1 })

read one document

rs0:PRIMARY> db.collection.findOne() { “_id” : ObjectId(“5fc72948c2e9c52076de75fb”), “hello” : “DocumentDB” }

insert a few entries into a collection

rs0:PRIMARY> db.profiles.insertMany([ … { “_id” : 1, “name” : “Matt”, “status”: “active”, “level”: 12, “score”:202}, … { “_id” : 2, “name” : “Frank”, “status”: “inactive”, “level”: 2, “score”:9}, … { “_id” : 3, “name” : “Karen”, “status”: “active”, “level”: 7, “score”:87}, … { “_id” : 4, “name” : “Katie”, “status”: “active”, “level”: 3, “score”:27} … ]) { “acknowledged” : true, “insertedIds” : [ 1, 2, 3, 4 ] }

return all the documents in the profiles collection

rs0:PRIMARY> db.profiles.find() { “_id” : 1, “name” : “Matt”, “status” : “active”, “level” : 12, “score” : 202 } { “_id” : 2, “name” : “Frank”, “status” : “inactive”, “level” : 2, “score” : 9 } { “_id” : 3, “name” : “Karen”, “status” : “active”, “level” : 7, “score” : 87 } { “_id” : 4, “name” : “Katie”, “status” : “active”, “level” : 3, “score” : 27 }

query for a single document using a filter

rs0:PRIMARY> db.profiles.find({name: “Katie”}) { “_id” : 4, “name” : “Katie”, “status” : “active”, “level” : 3, “score” : 27 }

find a profile and modify it using the findAndModify command

rs0:PRIMARY> db.profiles.findAndModify({ … query: { name: “Matt”, status: “active”}, … update: { $inc: { score: 10 } } … }) { “_id” : 1, “name” : “Matt”, “status” : “active”, “level” : 12, “score” : 202 }

verify that value has changed

rs0:PRIMARY> db.profiles.find({name: “Matt”}) { “_id” : 1, “name” : “Matt”, “status” : “active”, “level” : 12, “score” : 212 } rs0:PRIMARY>


## Aggregation query
1. Count the Number of Documents in a Collection
```bash
db.sales.insertMany([
  { "_id" : 1, "item" : "abc", "price" : 10, "quantity" : NumberInt("2"), "date" : ISODate("2014-03-01T08:00:00Z") },
  { "_id" : 2, "item" : "jkl", "price" : 20, "quantity" : NumberInt("1"), "date" : ISODate("2014-03-01T09:00:00Z") },
  { "_id" : 3, "item" : "xyz", "price" : 5, "quantity" : NumberInt( "10"), "date" : ISODate("2014-03-15T09:00:00Z") },
  { "_id" : 4, "item" : "xyz", "price" : 5, "quantity" :  NumberInt("20") , "date" : ISODate("2014-04-04T11:21:39.736Z") },
  { "_id" : 5, "item" : "abc", "price" : 10, "quantity" : NumberInt("10") , "date" : ISODate("2014-04-04T21:23:13.331Z") },
  { "_id" : 6, "item" : "def", "price" : 7.5, "quantity": NumberInt("5" ) , "date" : ISODate("2015-06-04T05:08:13Z") },
  { "_id" : 7, "item" : "def", "price" : 7.5, "quantity": NumberInt("10") , "date" : ISODate("2015-09-10T08:43:00Z") },
  { "_id" : 8, "item" : "abc", "price" : 10, "quantity" : NumberInt("5" ) , "date" : ISODate("2016-02-06T20:20:13Z") },
])

db.sales.aggregate( [ { $group : { _id : "$item" } } ] )

db.sales.aggregate( [
  {
    $group: {
       _id: "$item",
       count: { $sum: 1 }
    }
  }
] )
  1. Group by Item Having 
    db.sales.aggregate(
  [
 // First Stage
 {
   $group :
     {
       _id : "$item",
       totalSaleAmount: { $sum: { $multiply: [ "$price", "$quantity" ] } }
     }
  },
  // Second Stage
  {
    $match: { "totalSaleAmount": { $gte: 100 } }
  }
]
 )
  2. Calculate Count, Sum, and Average ```bash

    Group by Day of the Year

    db.sales.aggregate([ // First Stage { $match : { “date”: { $gte: new ISODate(“2014-01-01”), $lt: new ISODate(“2015-01-01”) } } }, // Second Stage { $group : { _id : { $dateToString: { format: “%Y-%m-%d”, date: “$date” } }, totalSaleAmount: { $sum: { $multiply: [ “$price”, “$quantity” ] } }, averageQuantity: { $avg: “$quantity” }, count: { $sum: 1 } } }, // Third Stage { $sort : { totalSaleAmount: -1 } } ])

Group by null: calculating the total sale amount, average quantity, and count of all documents in the collection

db.sales.aggregate([ { $group : { _id : null, totalSaleAmount: { $sum: { $multiply: [ “$price”, “$quantity” ] } }, averageQuantity: { $avg: “$quantity” }, count: { $sum: 1 } } } ])


4. Pivot Data
```bash
db.books.insertMany([
  { "_id" : 8751, "title" : "The Banquet", "author" : "Dante", "copies" : 2 },
  { "_id" : 8752, "title" : "Divine Comedy", "author" : "Dante", "copies" : 1 },
  { "_id" : 8645, "title" : "Eclogues", "author" : "Dante", "copies" : 2 },
  { "_id" : 7000, "title" : "The Odyssey", "author" : "Homer", "copies" : 10 },
  { "_id" : 7020, "title" : "Iliad", "author" : "Homer", "copies" : 10 }
])

# Group title by author
db.books.aggregate([
   { $group : { _id : "$author", books: { $push: "$title" } } }
 ])

# Group Documents by author
db.books.aggregate([
   // First Stage
   {
     $group : { _id : "$author", books: { $push: {"title": "$title", "copies": "$copies"} } }
   },
   // Second Stage
   {
     $addFields:
       {
         totalCopies : { $sum: "$books.copies" }
       }
   }
 ])

Reference

Amazon DocumentDB Official guide

Amazon DocumentDB Quick Start Using AWS CloudFormation

group (aggregation)