How to Fix Throttling in DynamoDB: Causes and Solutions

Throttling in DynamoDB happens when your requests exceed the provisioned throughput limits. To fix it, you can increase your table's capacity, use exponential backoff retries, or switch to on-demand capacity mode to handle variable traffic smoothly.

Why This Happens

Throttling occurs when your application sends more read or write requests than your DynamoDB table's provisioned capacity allows. DynamoDB limits the number of requests per second to protect the service and ensure fair usage. If you exceed these limits, DynamoDB returns a ProvisionedThroughputExceededException, causing your requests to be rejected temporarily.

python

import boto3
from botocore.exceptions import ClientError

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('MyTable')

try:
    response = table.put_item(
        Item={'id': '123', 'data': 'example'}
    )
except ClientError as e:
    if e.response['Error']['Code'] == 'ProvisionedThroughputExceededException':
        print('Throttling error: Too many requests')

Output

Throttling error: Too many requests

The Fix

To fix throttling, increase your table's read/write capacity units or switch to on-demand capacity mode to automatically handle traffic spikes. Also, implement exponential backoff retries in your code to wait and retry requests after throttling errors instead of failing immediately.

python

import boto3
import time
from botocore.exceptions import ClientError

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('MyTable')

max_retries = 5
retry_delay = 0.1  # start with 100ms

for attempt in range(max_retries):
    try:
        response = table.put_item(
            Item={'id': '123', 'data': 'example'}
        )
        print('Write succeeded')
        break
    except ClientError as e:
        if e.response['Error']['Code'] == 'ProvisionedThroughputExceededException':
            print(f'Throttled, retrying in {retry_delay} seconds...')
            time.sleep(retry_delay)
            retry_delay *= 2  # exponential backoff
        else:
            raise

Output

Write succeeded

Prevention

To avoid throttling in the future, monitor your table's consumed capacity and adjust provisioned throughput accordingly. Use on-demand capacity mode if your traffic is unpredictable. Also, design your access patterns to distribute requests evenly across partition keys to prevent hot partitions.

• Enable CloudWatch alarms for throttling metrics.
• Use exponential backoff retries in all client code.
• Consider caching frequently read data.

Related Errors

Other errors similar to throttling include:

• ConditionalCheckFailedException: Happens when a condition in your write request fails.
• ProvisionedThroughputExceededException on Global Secondary Indexes: Requires separate capacity management.
• RequestLimitExceeded: Happens when you exceed account-level API request limits.

Fixes usually involve adjusting capacity, retry logic, or request patterns.

Key Takeaways

Throttling happens when request rates exceed your DynamoDB table's capacity limits.

Increase capacity or switch to on-demand mode to handle variable traffic.

Use exponential backoff retries to gracefully handle throttling errors.

Distribute requests evenly across partition keys to avoid hot partitions.

Monitor usage with CloudWatch and adjust capacity proactively.