Key-value and document store model in DynamoDB - Mini Project: Build & Apply

Create a Simple Product Catalog in DynamoDB

📖 Scenario: You are building a simple product catalog for an online store. Each product has a unique ID, a name, and a price. You will use DynamoDB, a key-value and document store database, to store and manage this data.

🎯 Goal: Create a DynamoDB table and add product items using the key-value and document store model. You will set up the table, configure a price threshold, query products above that price, and finally add a tag to the table for identification.

📋 What You'll Learn

Create a DynamoDB table named Products with ProductID as the primary key.

Add three products with exact details: ProductID, Name, and Price.

Create a variable price_threshold set to 100.

Query the table to find products with Price greater than price_threshold.

Add a tag Environment with value Test to the Products table.

💡 Why This Matters

🌍 Real World

Online stores and applications use DynamoDB to store product catalogs because it handles key-value and document data efficiently and scales well.

💼 Career

Knowing how to create tables, add items, query with filters, and tag resources is essential for cloud database management roles and backend development.

1

Create the DynamoDB table and add products

Create a DynamoDB table named Products with ProductID as the primary key. Add these exact product items: {'ProductID': 'P001', 'Name': 'Laptop', 'Price': 1200}, {'ProductID': 'P002', 'Name': 'Mouse', 'Price': 25}, and {'ProductID': 'P003', 'Name': 'Keyboard', 'Price': 75}.

DynamoDB

# Create the Products table and add the three product items
# Your code here

Hint

Use boto3.resource('dynamodb') to get the DynamoDB resource. Use Table('Products') to reference the table. Use put_item to add each product with the exact keys and values.

2

Set the price threshold

Create a variable called price_threshold and set it to 100.

DynamoDB

import boto3

dynamodb = boto3.resource('dynamodb')

Products = dynamodb.Table('Products')

Products.put_item(Item={'ProductID': 'P001', 'Name': 'Laptop', 'Price': 1200})
Products.put_item(Item={'ProductID': 'P002', 'Name': 'Mouse', 'Price': 25})
Products.put_item(Item={'ProductID': 'P003', 'Name': 'Keyboard', 'Price': 75})

# Create price_threshold variable
# Your code here

Hint

Just create a variable named price_threshold and assign it the value 100.

3

Query products with price above threshold

Use the scan method on the Products table with a filter expression to get items where Price is greater than price_threshold. Store the result in a variable called expensive_products.

DynamoDB

import boto3
from boto3.dynamodb.conditions import Attr

dynamodb = boto3.resource('dynamodb')

Products = dynamodb.Table('Products')

Products.put_item(Item={'ProductID': 'P001', 'Name': 'Laptop', 'Price': 1200})
Products.put_item(Item={'ProductID': 'P002', 'Name': 'Mouse', 'Price': 25})
Products.put_item(Item={'ProductID': 'P003', 'Name': 'Keyboard', 'Price': 75})

price_threshold = 100

# Query products with Price > price_threshold
# Your code here

Hint

Use from boto3.dynamodb.conditions import Attr and then use FilterExpression=Attr('Price').gt(price_threshold) inside scan().

4

Add a tag to the DynamoDB table

Add a tag to the Products table with key Environment and value Test using the DynamoDB client.

DynamoDB

import boto3
from boto3.dynamodb.conditions import Attr

dynamodb = boto3.resource('dynamodb')

Products = dynamodb.Table('Products')

Products.put_item(Item={'ProductID': 'P001', 'Name': 'Laptop', 'Price': 1200})
Products.put_item(Item={'ProductID': 'P002', 'Name': 'Mouse', 'Price': 25})
Products.put_item(Item={'ProductID': 'P003', 'Name': 'Keyboard', 'Price': 75})

price_threshold = 100

expensive_products = Products.scan(FilterExpression=Attr('Price').gt(price_threshold))

# Add tag to Products table
# Your code here

Hint

Create a DynamoDB client with boto3.client('dynamodb'). Use tag_resource with ResourceArn=Products.table_arn and the tag list.

Practice

1. What is the main feature of a key-value store in DynamoDB?

easy

A. It uses a unique key to quickly find data.

B. It requires complex joins between tables.

C. It stores data only in fixed columns.

D. It only supports relational data models.

Solution

1. Step 1: Understand key-value store basics

   A key-value store uses a unique key to store and retrieve data quickly without complex relations.

2. Step 2: Compare options with key-value features

   Options A, C, and D describe relational or fixed schema models, not key-value stores.

3. Final Answer:

   It uses a unique key to quickly find data. -> Option A

4. Quick Check:

   Key-value store = unique key fast access [OK]

Hint: Key-value means unique key for fast lookup [OK]

Common Mistakes:

• Confusing key-value with relational databases
• Thinking key-value needs fixed columns
• Assuming key-value supports joins

2. Which of the following is the correct way to define a composite primary key in a DynamoDB table?

easy

A. PrimaryKey: { PartitionKey: 'UserId', SortKey: 'Timestamp' }

B. PrimaryKey = UserId, SortKey = Timestamp

C. PrimaryKey: { PartitionKey: 'UserId' }

D. PrimaryKey: UserId, ForeignKey: Timestamp

Solution

1. Step 1: Recall DynamoDB primary key syntax

   DynamoDB primary key can be simple (partition key) or composite (partition + sort key) defined as an object with keys PartitionKey and SortKey.

2. Step 2: Check each option's syntax

   PrimaryKey: { PartitionKey: 'UserId', SortKey: 'Timestamp' } correctly shows both PartitionKey and SortKey in an object. PrimaryKey: { PartitionKey: 'UserId' } misses SortKey, B uses wrong assignment syntax, D uses ForeignKey which is invalid in DynamoDB.

3. Final Answer:

   PrimaryKey: { PartitionKey: 'UserId', SortKey: 'Timestamp' } -> Option A

4. Quick Check:

   Primary key = PartitionKey + SortKey object [OK]

Hint: Primary key uses PartitionKey and optional SortKey keys [OK]

Common Mistakes:

• Using equal sign instead of colon in definitions
• Confusing foreign key with sort key
• Omitting sort key when needed

3. Given a DynamoDB table with items: {UserId: '123', Name: 'Alice'}, {UserId: '456', Name: 'Bob'}, what will the query return if you request the item with UserId '123'?

medium

A. [{UserId: '456', Name: 'Bob'}]

B. Error: Item not found

C. []

D. [{UserId: '123', Name: 'Alice'}]

Solution

1. Step 1: Understand query by primary key

   Querying by UserId '123' returns the item with that key if it exists.

2. Step 2: Match UserId '123' in given items

   The item {UserId: '123', Name: 'Alice'} matches the query.

3. Final Answer:

   [{UserId: '123', Name: 'Alice'}] -> Option D

4. Quick Check:

   Query by key returns matching item [OK]

Hint: Query by key returns exact matching item [OK]

Common Mistakes:

• Expecting multiple items for a unique key
• Confusing query result with scan result
• Assuming error if item not found instead of empty

4. You wrote this DynamoDB query but it returns no results: Table.query(KeyConditionExpression='UserId = :uid', ExpressionAttributeValues={':uid': '789'}). What is the likely problem?

medium

A. KeyConditionExpression must use '==' instead of '='.

B. ExpressionAttributeValues should not use colons in keys.

C. The key name 'UserId' is incorrect or missing in the table.

D. Query requires a ScanIndexForward parameter.

Solution

1. Step 1: Check key name correctness

   If the key name 'UserId' does not exist or is misspelled in the table schema, the query returns no results.

2. Step 2: Validate syntax and parameters

   Using ':' in ExpressionAttributeValues keys is correct. '=' is valid in KeyConditionExpression. ScanIndexForward is optional for sorting, not required for results.

3. Final Answer:

   The key name 'UserId' is incorrect or missing in the table. -> Option C

4. Quick Check:

   Correct key name needed for query success [OK]

Hint: Check key names match table schema exactly [OK]

Common Mistakes:

• Removing colons from ExpressionAttributeValues keys
• Using '==' instead of '=' in expressions
• Adding unnecessary parameters

5. You want to store user profiles with flexible nested data like addresses and preferences in DynamoDB. Which model best fits this need?

hard

A. Use a relational model with multiple tables and foreign keys.

B. Use a document store model to save JSON-like nested objects.

C. Use a key-value store with only simple string values.

D. Use a graph database to store nested user data.

Solution

1. Step 1: Identify data flexibility needs

   Nested and flexible data like addresses and preferences require a document model that supports JSON-like structures.

2. Step 2: Match model to DynamoDB capabilities

   DynamoDB supports document store model allowing nested objects. Relational and graph models are not native to DynamoDB. Simple key-value stores do not support nested data well.

3. Final Answer:

   Use a document store model to save JSON-like nested objects. -> Option B

4. Quick Check:

   Nested data = document store model [OK]

Hint: Nested JSON data fits document store model best [OK]

Common Mistakes:

• Choosing relational model for flexible nested data
• Assuming key-value stores handle nested objects well
• Confusing graph databases with DynamoDB