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 commonly used for real-time analytics, log and event data collection, and processing.
When working with AWS Kinesis, you might encounter the ThrottlingException. This error typically manifests when your application attempts to make requests at a rate higher than what is allowed by AWS Kinesis.
ThrottlingException errors in your application logs.The ThrottlingException occurs when the rate of requests exceeds the limits set by AWS Kinesis. AWS imposes these limits to ensure fair usage and to maintain the performance and reliability of the service for all users. Each Kinesis stream has a set number of shards, and each shard has its own limits on the number of transactions per second (TPS) and data throughput.
To resolve the ThrottlingException, you need to implement strategies to manage the request rate and ensure it stays within the allowed limits.
Exponential backoff is a common 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 the service and increases the chances of successful requests.
try {
// Your AWS Kinesis request logic
} catch (ThrottlingException e) {
// Implement exponential backoff
int retryCount = 0;
int maxRetries = 5;
while (retryCount < maxRetries) {
Thread.sleep((long) Math.pow(2, retryCount) * 1000);
retryCount++;
// Retry the request
}
}
Use AWS CloudWatch to monitor the metrics of your Kinesis stream, such as IncomingBytes and IncomingRecords. If you consistently hit the limits, consider increasing the number of shards in your stream to distribute the load more effectively.
For more information, refer to the AWS Kinesis Monitoring with CloudWatch documentation.
Review your data processing logic to ensure that it is efficient and that you are not making unnecessary requests. Batch multiple records into a single request where possible to reduce the number of transactions.
By understanding the limits of AWS Kinesis and implementing strategies such as exponential backoff and proper monitoring, you can effectively manage and resolve ThrottlingException errors. For further reading, check out the AWS Kinesis Service Limits documentation.
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