Creating DynamoDB Table using Boto3
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.
GitRepo
Step 2 - Create a new file calledBooksTableCreate.py and paste this content to create DynamoDB Table. After that Run thepython BooksTableCreate.py command to run this file.
BooksTableCreate.py
import boto3dynamodb = 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 calledbookdata.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 theBooksLoadData.py file and paste the following content. Finally, run thepython BooksLoadData.py command for execution.
BooksLoadData.py
import boto3import jsonimport decimaldynamodb = 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 anAddNewBook.py file and add the following content. After that run thepython AddNewBook.py command.
AddNewBook.py
import boto3import jsonimport decimalclass 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 = 2019response = 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 aReadBook.py file and add the following values. Run thepython ReadBook.py command.
ReadBook.py
import boto3import jsonimport decimalfrom boto3.dynamodb.conditions import Key, Attrfrom botocore.exceptions import ClientErrorclass 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 = 2023try: 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. CreateBooksQuery.py and add the following values. Run thepython BooksQuery.py command.
BooksQuery.py
import boto3import jsonimport decimalfrom boto3.dynamodb.conditions import Key, Attrclass 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 boto3dynamodb = boto3.resource('dynamodb', region_name='us-east-2')table = dynamodb.Table('Books')table.delete()
Thanks for reading the Article.
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