Amazon DynamoDB is a fully managed NoSQL database service offered by Amazon Web Services (AWS). It is a fast, flexible, and scalable database service that allows users to store and retrieve data in a highly available and durable manner.
Boto3 is a Python library that provides an easy-to-use interface for interacting with various AWS services, allowing developers to automate tasks and integrate AWS services into their Python applications.
Now we going to setup step by step.
Step 1 - Go to the AWS management console and create a Cloud9 environment and create one empty folder. You can also use this GitHub repo to clone files.
Git Repo
Step 2 - Create a new file called BooksTableCreate.py and paste this content to create DynamoDB Table. After that Run the python BooksTableCreate.py command to run this file.
BooksTableCreate.py
import boto3
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.create_table(
TableName='Books',
KeySchema=[
{
'AttributeName': 'year',
'KeyType': 'HASH' #Partition_key
},
{
'AttributeName': 'title',
'KeyType': 'RANGE' #Sort_key
}
],
AttributeDefinitions=[
{
'AttributeName': 'year',
'AttributeType': 'N'
},
{
'AttributeName': 'title',
'AttributeType': 'S'
},
],
ProvisionedThroughput={
'ReadCapacityUnits': 10,
'WriteCapacityUnits': 10
}
)
print("Table status:", table.table_status)
After that go to the AWS console and search DynamoDB. After that in DynamoDB Dashboard you can see your DyanmoDB tables are created.
Step 3 - Create a file called bookdata.json and paste the following content.
bookdata.json
[
{
"year": 2023,
"title": "DevSecOps",
"info": {
"release_date": "2023-01-03",
"rank": 2,
"authors": [
"Daniel Bruhl",
"Chris Hemsworth",
"Olivia Wilde"
]
}
},
{
"year": 2022,
"title": "IaaS Tools",
"info": {
"release_date": "2022-12-01",
"rank": 3,
"authors": [
"Daniel Bruhl",
"Chris Hemsworth",
"Olivia Wilde"
]
}
}
]
After that create the BooksLoadData.py file and paste the following content. Finally, run the python BooksLoadData.py command for execution.
BooksLoadData.py
import boto3
import json
import decimal
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('Books')
with open("bookdata.json") as json_file:
books = json.load(json_file, parse_float = decimal.Decimal)
for book in books:
year = int(book['year'])
title = book['title']
info = book['info']
print("Add Book:", year, title)
table.put_item(
Item={
'year': year,
'title': title,
'info': info,
}
)
Finally, go to the AWS management console and in the DynamoDB dashboard you can see your table added the following values.
Step 4 - Next, add new content to the table. Create an AddNewBook.py file and add the following content. After that run the python AddNewBook.py command.
AddNewBook.py
import boto3
import json
import decimal
class DecimalEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, decimal.Decimal):
if abs(o) % 1 > 0:
return float(o)
else:
return int(o)
return super(DecimalEncoder, self).default(o)
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('Books')
title = "Docker"
year = 2019
response = table.put_item(
Item={
'year': year,
'title': title,
'info': {
"release_date": "2029-12-01",
"rank": 5,
"authors": "Daniel Bruhl"
}
}
)
print("Add New Book:")
print(json.dumps(response, indent=4, cls=DecimalEncoder))
Next, go to the AWS management console and in the DynamoDB dashboard you can see your latest added value in your dashboard.
Step 5 - Next, get values from tables. Next, create a ReadBook.py file and add the following values. Run the python ReadBook.py command.
ReadBook.py
import boto3
import json
import decimal
from boto3.dynamodb.conditions import Key, Attr
from botocore.exceptions import ClientError
class DecimalEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, decimal.Decimal):
if o % 1 > 0:
return float(o)
else:
return int(o)
return super(DecimalEncoder, self).default(o)
dynamodb = boto3.resource("dynamodb", region_name='us-east-2')
table = dynamodb.Table('Books')
title = "DevSecOps"
year = 2023
try:
response = table.get_item(
Key={
'year': year,
'title': title
}
)
except ClientError as e:
print(e.response['Error']['Message'])
else:
item = response['Item']
print("GetBook")
print(json.dumps(item, indent=4, cls=DecimalEncoder))
Step 6 - Next, query the values. Create BooksQuery.py and add the following values. Run the python BooksQuery.py command.
BooksQuery.py
import boto3
import json
import decimal
from boto3.dynamodb.conditions import Key, Attr
class DecimalEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, decimal.Decimal):
if o % 1 > 0:
return float(o)
else:
return int(o)
return super(DecimalEncoder, self).default(o)
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('Books')
print("Books From 2023")
response = table.query(
KeyConditionExpression=Key('year').eq(2023)
)
for i in response['Items']:
print(i['year'], ":", i['title'])
Step 7 - Finally, delete the DyanamoDB table. Because it helps to save our costs. As well as delete Cloud9 Environment. Create DeleteTable.py and add the following values. Next, run the python DeleteTable.py command.
DeleteTable.py
import boto3
dynamodb = boto3.resource('dynamodb', region_name='us-east-2')
table = dynamodb.Table('Books')
table.delete()
Thanks for reading the Article.
Top comments (0)