"Unable to marshal response: Object of type datetime is not JSON serializable" in AWS Lambda: A Cognito User Management Headache
The Problem:
When trying to fetch all users from an AWS Cognito user pool using a Lambda function, you might encounter an error message: "Unable to marshal response: Object of type datetime is not JSON serializable". This frustrating error indicates that your Lambda function is attempting to return data containing a datetime object, which is not directly supported by JSON.
The Scenario:
Let's imagine you're building an application that requires retrieving a list of all users from your Cognito pool. You've set up an AWS Lambda function to handle this request, using the boto3 library to interact with Cognito. Your code might look something like this:
import boto3
def lambda_handler(event, context):
client = boto3.client('cognito-idp')
response = client.list_users(
UserPoolId='your-user-pool-id',
AttributesToGet=['email', 'phone_number', 'createdAt']
)
users = response['Users']
# Extract relevant data from users
user_data = [
{'email': user['Attributes'][0]['Value'],
'phone_number': user['Attributes'][1]['Value'],
'created_at': user['CreatedAt']}
for user in users
]
return {
'statusCode': 200,
'body': json.dumps(user_data)
}
Analysis:
The error arises because the CreatedAt attribute, a datetime object, is not inherently serializable to JSON. This means that when you try to convert the user_data list to JSON using json.dumps(), the process fails at the datetime object.
Understanding the Issue:
JSON, a popular data exchange format, relies on a limited set of data types. It supports strings, numbers, booleans, arrays, and objects, but not datetime objects. The Python datetime object, while commonly used to represent dates and times, is not directly understood by the JSON library.
Solutions:
To resolve this error, we must convert the CreatedAt datetime objects into a JSON-compatible format. Here are two common approaches:
1. Convert to String:
Convert the CreatedAt datetime objects to strings using the strftime() method. This provides a readable and easily parseable representation of the date and time information.
user_data = [
{'email': user['Attributes'][0]['Value'],
'phone_number': user['Attributes'][1]['Value'],
'created_at': user['CreatedAt'].strftime('%Y-%m-%d %H:%M:%S')}
for user in users
]
2. Use a Custom JSON Encoder:
Create a custom JSON encoder that handles the datetime object conversion. This approach provides greater control over the serialization process and can be useful if you need specific formatting or time zone adjustments.
import json
from datetime import datetime
class DateTimeEncoder(json.JSONEncoder):
def default(self, o):
if isinstance(o, datetime):
return o.isoformat()
return super().default(o)
user_data = [
{'email': user['Attributes'][0]['Value'],
'phone_number': user['Attributes'][1]['Value'],
'created_at': user['CreatedAt']}
for user in users
]
return {
'statusCode': 200,
'body': json.dumps(user_data, cls=DateTimeEncoder)
}
Conclusion:
By implementing one of the solutions above, you can effectively serialize datetime objects within your Lambda function and return them as JSON data. This ensures your API can seamlessly exchange information with applications that rely on JSON.
