Consistent vs eventually consistent reads in DynamoDB - Hands-On Comparison

Consistent vs Eventually Consistent Reads in DynamoDB

📖 Scenario: You are building a simple inventory system for a small online store using DynamoDB. You want to understand how to read data with different consistency models to ensure your application behaves correctly.

🎯 Goal: Learn how to perform consistent and eventually consistent reads on a DynamoDB table by writing queries with the correct parameters.

📋 What You'll Learn

Create a DynamoDB table data structure with sample items

Add a configuration variable to select read consistency

Write a query to read an item using the selected consistency

Complete the query with the correct parameter to enforce consistency

💡 Why This Matters

🌍 Real World

Understanding consistent and eventually consistent reads helps developers build applications that balance performance and data accuracy when using DynamoDB.

💼 Career

Many cloud and backend developer roles require knowledge of NoSQL databases like DynamoDB and how to manage data consistency for scalable applications.

1

Create DynamoDB table data with sample items

Create a variable called inventory that represents a DynamoDB table with these exact items: {'ProductID': 'P100', 'Name': 'T-shirt', 'Stock': 50} and {'ProductID': 'P101', 'Name': 'Jeans', 'Stock': 30}.

DynamoDB

# Create the inventory table data with two items
# Your code here

Hint

Use a list of dictionaries to represent the table items exactly as shown.

2

Add a configuration variable for read consistency

Create a variable called consistent_read and set it to True to indicate you want a strongly consistent read.

DynamoDB

inventory = [
    {'ProductID': 'P100', 'Name': 'T-shirt', 'Stock': 50},
    {'ProductID': 'P101', 'Name': 'Jeans', 'Stock': 30}
]
# Set consistent_read to True for strong consistency
# Your code here

Hint

Set consistent_read exactly to True.

3

Write a query to read an item using the consistency setting

Write a function called get_item that takes product_id and returns the item from inventory with matching 'ProductID'. Use the variable consistent_read to simulate the read consistency (no actual delay needed).

DynamoDB

inventory = [
    {'ProductID': 'P100', 'Name': 'T-shirt', 'Stock': 50},
    {'ProductID': 'P101', 'Name': 'Jeans', 'Stock': 30}
]
consistent_read = True

# Define get_item function to find item by ProductID
# Your code here

Hint

Use a for loop to find the item with matching ProductID.

4

Complete the query with the consistency parameter

Add a variable called read_params as a dictionary with the key 'ConsistentRead' set to the value of consistent_read. This simulates passing the consistency option to DynamoDB's get_item method.

DynamoDB

inventory = [
    {'ProductID': 'P100', 'Name': 'T-shirt', 'Stock': 50},
    {'ProductID': 'P101', 'Name': 'Jeans', 'Stock': 30}
]
consistent_read = True

def get_item(product_id):
    for item in inventory:
        if item['ProductID'] == product_id:
            return item
    return None

# Add read_params dictionary with 'ConsistentRead' key
# Your code here

Hint

Create read_params dictionary with the key exactly as 'ConsistentRead' and value consistent_read.

Practice

1. What is the main difference between consistent reads and eventually consistent reads in DynamoDB?

easy

A. Eventually consistent reads always return the latest data, consistent reads may return older data.

B. Consistent reads are faster than eventually consistent reads.

C. Consistent reads always return the latest data, eventually consistent reads may return older data.

D. There is no difference; both return the same data at the same speed.

Solution

1. Step 1: Understand consistent reads

   Consistent reads always return the most up-to-date data from DynamoDB.

2. Step 2: Understand eventually consistent reads

   Eventually consistent reads may return data that is slightly out of date but are faster and cheaper.

3. Final Answer:

   Consistent reads always return the latest data, eventually consistent reads may return older data. -> Option C

4. Quick Check:

   Consistent = latest data, Eventually consistent = possibly older data [OK]

Hint: Remember: consistent = latest, eventually consistent = maybe older [OK]

Common Mistakes:

• Thinking eventually consistent reads are always faster but not cheaper
• Confusing which read type returns the latest data
• Assuming both read types always return the same data

2. Which of the following is the correct way to specify a consistent read in a DynamoDB GetItem request using AWS SDK?

easy

A. "ConsistentRead": true

B. "ConsistentRead": false

C. "Consistent": true

D. "ReadConsistency": "strong"

Solution

1. Step 1: Recall DynamoDB GetItem syntax

   The parameter to request a consistent read is exactly "ConsistentRead" set to true or false.

2. Step 2: Identify correct syntax

   Only "ConsistentRead": true is valid; other options are incorrect parameter names or values.

3. Final Answer:

   "ConsistentRead": true -> Option A

4. Quick Check:

   Use "ConsistentRead": true for consistent reads [OK]

Hint: Look for exact parameter name "ConsistentRead" set to true [OK]

Common Mistakes:

• Using incorrect parameter names like "Consistent" or "ReadConsistency"
• Setting ConsistentRead to false to get consistent reads
• Confusing boolean values with strings

3. Consider this DynamoDB query snippet using AWS SDK for JavaScript:

const params = {
  TableName: "Users",
  Key: { "UserId": "123" },
  ConsistentRead: false
};
const data = await dynamoDb.get(params).promise();
console.log(data.Item.lastLogin);

What can you say about the freshness of the lastLogin value printed?

medium

A. It is guaranteed to be the most recent lastLogin value.

B. It will cause a runtime error because ConsistentRead must be true.

C. It will always be null because ConsistentRead is false.

D. It may be an older lastLogin value due to eventual consistency.

Solution

1. Step 1: Check ConsistentRead parameter

   ConsistentRead is set to false, meaning the read is eventually consistent.

2. Step 2: Understand impact on data freshness

   Eventually consistent reads may return stale data, so lastLogin might be older than the latest update.

3. Final Answer:

   It may be an older lastLogin value due to eventual consistency. -> Option D

4. Quick Check:

   ConsistentRead false = possibly stale data [OK]

Hint: ConsistentRead false means data may be stale [OK]

Common Mistakes:

• Assuming ConsistentRead false always returns latest data
• Thinking ConsistentRead false causes errors
• Believing data will be null if not consistent

4. You wrote this DynamoDB query but always get stale data even after updates:

const params = {
  TableName: "Orders",
  Key: { "OrderId": "789" },
  ConsistentRead: "true"
};
const result = await dynamoDb.get(params).promise();

What is the problem in this code?

medium

A. ConsistentRead must be omitted to get consistent reads.

B. ConsistentRead should be a boolean, not a string.

C. The Key attribute name is incorrect.

D. TableName should be lowercase.

Solution

1. Step 1: Check ConsistentRead data type

   ConsistentRead must be a boolean (true/false), not a string "true".

2. Step 2: Understand impact of wrong type

   Passing a string may cause DynamoDB to ignore the parameter, resulting in eventually consistent reads and stale data.

3. Final Answer:

   ConsistentRead should be a boolean, not a string. -> Option B

4. Quick Check:

   ConsistentRead must be boolean true/false [OK]

Hint: Use boolean true, not string "true" for ConsistentRead [OK]

Common Mistakes:

• Passing ConsistentRead as string instead of boolean
• Changing Key attribute name unnecessarily
• Thinking TableName case matters for consistency

5. You have a high-traffic app that shows user profiles updated frequently. You want to minimize latency but ensure users see their latest profile changes immediately after saving. Which read strategy should you use in DynamoDB?

hard

A. Use consistent reads only for profile fetches immediately after updates, eventually consistent otherwise.

B. Use eventually consistent reads everywhere to reduce cost and latency.

C. Use consistent reads for all profile fetches regardless of timing.

D. Use eventually consistent reads only for profile fetches immediately after updates.

Solution

1. Step 1: Understand trade-offs

   Consistent reads ensure latest data but cost more and have higher latency; eventually consistent reads are cheaper and faster but may be stale.

2. Step 2: Apply strategy for user experience

   Use consistent reads right after updates to show fresh data, then eventually consistent reads for normal fetches to save cost and improve speed.

3. Final Answer:

   Use consistent reads only for profile fetches immediately after updates, eventually consistent otherwise. -> Option A

4. Quick Check:

   Consistent reads after update, eventually consistent otherwise [OK]

Hint: Use consistent reads only when fresh data is critical [OK]

Common Mistakes:

• Using consistent reads everywhere causing high cost and latency
• Using eventually consistent reads immediately after updates causing stale data
• Ignoring cost and latency trade-offs