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ThrottlingException in AWS Scheduler: How to Tackle API Rate Limits AWS Scheduler API call Process response here Apply backoff strategy

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By Exception Decoded 4 min read

Are you using AWS Scheduler to automate your tasks and facing issues with throttling? ThrottlingException is a common error that occurs when you exceed the limits set by the AWS Scheduler API. In this article, we will dive deep into ThrottlingException, understand its causes, and explore ways to efficiently handle it in your AWS Scheduler workflows.

What is ThrottlingException?

When a certain API call rate is exceeded, AWS Scheduler responds with a ThrottlingException. This exception indicates that the API call was throttled and the request rate should be reduced to avoid overwhelming the service. Throttling is implemented for various reasons, such as controlling costs, ensuring fair resource allocation, or maintaining system stability.

Common Causes of ThrottlingException

ThrottlingException can occur due to multiple reasons, and it’s vital to understand these causes to effectively manage throttling in your AWS Scheduler implementation.

  1. API Rate Limits: AWS Scheduler enforces API rate limits to prevent abuse and ensure fair usage. Each API has its own limits, such as the maximum number of API requests per second (RPS) or per minute (RPM). Exceeding these limits triggers the ThrottlingException.

  2. Burst Capacity: Burst capacity is the number of API requests that can be handled in a short period, surpassing the average rate limits. If the burst capacity is surpassed, ThrottlingException may occur even if the overall rate remains within the defined limits.

  3. Concurrent API Calls: Simultaneous API calls from multiple sources can lead to ThrottlingException. While each source may comply with individual rate limits, the combined rate might exceed the overall limits. It’s crucial to ensure coordination and synchronization to avoid overloading the API.

Mitigating ThrottlingException

Now that we understand the common causes of ThrottlingException, let’s discuss some strategies to tackle this issue and ensure a smooth experience with AWS Scheduler.

1. Backoff Strategy

When you receive a ThrottlingException, implementing a backoff strategy helps reduce the request rate and prevent further API throttling. A backoff strategy involves gradually increasing the wait time between retries to reduce the number of requests and respect API rate limits.

In Python, for example, you can use the time.sleep() function to introduce a delay between retries. Here’s a code snippet demonstrating how you can implement a basic backoff strategy:

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import time

def handle_throttling_exception():
    retries = 0
    while retries < 5:
        try:
            response = scheduler_client.start_job_run()
        except scheduler_model.ThrottlingException as e:
            wait_time = 2 ** retries
            time.sleep(wait_time)
            retries += 1

You can modify the backoff strategy to suit your specific requirements. Make sure to consider the exponential factor, maximum retry attempts, and any additional conditions based on your application and API usage patterns.

2. Asynchronous Processing

Efficiently managing ThrottlingException often involves reducing the number of requests sent to the AWS Scheduler API. One way to achieve this is by employing asynchronous processing.

Instead of making individual API requests for every task or job, group them together and send batch requests. With AWS Lambda or AWS Step Functions, you can execute multiple tasks in parallel and reduce the overall API call rate.

Additionally, consider using the AWS SDK’s BatchWriteJobRequest to submit a batch of jobs in a single API call. This reduces the number of separate API requests, potentially minimizing the chances of ThrottlingException.

3. Monitoring and Adjusting Rate Limits

To better manage ThrottlingException, monitoring and adjusting your API rate limits are crucial. AWS provides CloudWatch service, which allows you to collect and analyze metrics related to AWS Scheduler API usage.

By monitoring metrics like ThrottledRequests or RequestCount, you can gain insights into API call rates, identify any potential bottlenecks, and adjust your rate limits accordingly. Keep an eye on your service’s consumption patterns and adjust your application’s request rate to align with API rate limits.

4. Implementing Retries with Exponential Backoff

While implementing the backoff strategy, it is important to follow the concept of exponential backoff. Exponential backoff involves increasing the wait time exponentially with each retry attempt. It helps to avoid overwhelming the service with repeated requests immediately after ThrottlingException.

Here’s an example of implementing exponential backoff in Java:

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import com.amazonaws.AmazonServiceException;
import com.amazonaws.retry.PredefinedRetryPolicies;
import com.amazonaws.retry.RetryPolicy;
import com.amazonaws.services.scheduler.model.SchedulerException;

RetryPolicy backoffRetryPolicy = PredefinedRetryPolicies.getDefaultRetryPolicyWithCustomMaxRetries(5);
backoffRetryPolicy.setBackoffStrategy(PredefinedRetryPolicies.EXPONENTIAL_BACKOFF_RETRYING);
schedulerClient.setRetryPolicy(backoffRetryPolicy);

try {
    // AWS Scheduler API call
    schedulerClient.startJobRun();
    // Process response here
} catch (AmazonServiceException e) {
    if (e instanceof SchedulerException && "ThrottlingException".equals(((SchedulerException) e).getErrorCode())) {
        // ThrottlingException occurred, implement exponential backoff logic here
    }
}

Implementing exponential backoff ensures that your application gracefully retries after encountering a ThrottlingException, reducing the chances of API overload while respecting the AWS Scheduler API rate limits.

Conclusion

In this article, we explored ThrottlingException in AWS Scheduler and discussed its causes and possible solutions. By implementing a backoff strategy, employing asynchronous processing, monitoring rate limits, and following exponential backoff techniques, you can effectively handle ThrottlingException and ensure a smooth experience with AWS Scheduler.

Remember, understanding the overall architecture of your application and AWS Scheduler workflows is essential for optimizing resource utilization, reducing errors, and maintaining a scalable and resilient system.

Now armed with these strategies, you can confidently tackle ThrottlingException in AWS Scheduler and streamline your automated tasks.

Happy scheduling!

References:

AWS, AWS Scheduler
aws scheduler com.amazonaws.services.scheduler.model
This post is licensed under CC BY 4.0 by the author.