AWS Kinesis is a platform on AWS to collect, process, and analyze real-time, streaming data. It allows developers to build applications that can continuously ingest and process large streams of data records in real-time. Kinesis is often used for real-time analytics, log and event data collection, and real-time data processing.
When working with AWS Kinesis, you might encounter the KMSThrottlingException. This error typically manifests when your application attempts to perform operations that require AWS Key Management Service (KMS) and the requests are throttled.
In your application logs or AWS CloudWatch, you may see error messages indicating a KMSThrottlingException. This suggests that the requests to AWS KMS are being throttled due to exceeding the allowed request rate.
The KMSThrottlingException occurs when the number of requests made to AWS KMS exceeds the rate limits set by AWS. KMS is used in Kinesis for encrypting data streams, and if your application makes too many requests in a short period, AWS KMS will throttle these requests to maintain service stability.
This issue often arises in high-throughput applications where multiple requests are made to encrypt or decrypt data using AWS KMS. If these requests exceed the KMS rate limits, throttling occurs.
To resolve the KMSThrottlingException, you can implement the following strategies:
Exponential backoff is a standard error-handling strategy for network applications in which the client increases the wait time between retries exponentially. This approach helps to reduce the load on AWS KMS and allows your requests to be processed successfully.
import boto3
import time
client = boto3.client('kinesis')
# Example of exponential backoff
for attempt in range(1, 6):
try:
# Your Kinesis operation here
response = client.put_record(
StreamName='your-stream-name',
Data=b'your-data',
PartitionKey='your-partition-key'
)
break
except client.exceptions.KMSThrottlingException as e:
print(f"Attempt {attempt}: Throttled, retrying...")
time.sleep(2 ** attempt)
Use AWS CloudWatch to monitor your Kinesis and KMS request rates. Identify patterns or spikes in usage that may lead to throttling. Consider optimizing your application to reduce unnecessary requests.
If your application consistently requires higher request rates, consider contacting AWS Support to discuss increasing your KMS limits. Ensure you have a valid use case and provide details about your application's requirements.
For more information on handling AWS KMS throttling, refer to the AWS KMS Limits documentation. Additionally, explore the AWS Kinesis Developer Guide for best practices in managing data streams.
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