SDK or KPL/KCL for Kinesis Stream Integration?

Reading time: 5 minutes

Hello Data Enthusiasts!

Integrating Python with AWS Kinesis provides you with a powerful tool to process and analyze real-time streaming data.

When it comes to building a producer-consumer architecture for Kinesis, you have two primary approaches: using the AWS SDK for Python (boto3) and utilizing the Kinesis Producer Library (KPL) and Kinesis Consumer Library (KCL).

Each approach has its own set of advantages and considerations.

In this blog post, we’ll delve into the key differences between these methods and add some further considerations to help you make an informed choice based on your project’s requirements.

What you’ll see in this post:

AWS SDK for Python (boto3) Approach
Kinesis Producer Library (KPL) and Kinesis Consumer Library (KCL) Approach
Choosing the Right Approach for Kinesis Integration

AWS SDK for Python (boto3) Approach

SDK Advantages:

Flexibility and Familiarity: If you’re already experienced with using boto3 to interact with AWS services, this approach might feel more intuitive. It allows you to manage Kinesis streams just like any other AWS resource.
Fine-grained Control: boto3 offers you full control over how you interact with Kinesis streams. You can customize the way you send and receive records, handle retries, and manage exceptions.
Ecosystem Compatibility: Since boto3 is a popular AWS SDK for Python, it is well-supported and integrated into various Python libraries and frameworks. This makes it easier to incorporate Kinesis into existing projects.

SDK Considerations:

Development Overhead: Using boto3 for Kinesis integration might involve more development effort, as you’ll need to handle various aspects like record batching, error handling, and retries manually.
Scaling Challenges: While boto3 can handle scaling to some extent, if your application needs to handle high-throughput scenarios, managing scalability might become more complex.
Additional Considerations:
- You may need to write custom code to handle record batching, error handling, and retries.
- You may need to manage the dependencies of your application.
- You may need to monitor your application’s performance to ensure that it is able to handle the load.

Kinesis Producer Library (KPL) and Kinesis Consumer Library (KCL) Approach

The KCL is primarily a Java library. Support for languages other than Java, including Python, is provided using a multi-language interface called the MultiLangDaemon.

This daemon is Java-based and runs in the background when you are using a KCL language other than Java.

So, even if you’re developing in Python, you’ll still need Java installed on your system because of the MultiLangDaemon.

The KPL is also a Java-based library and it simplifies producer application development. It integrates seamlessly with the KCL to de-aggregate batched records on the consumer.

KPL/KCL Advantages:

Simplified Scaling: KPL abstracts away the complexities of record batching, retries, and handling network issues. It automatically scales based on the number of shards in the stream, making it suitable for high-throughput scenarios.
Built-in Partitioning: KPL intelligently partitions records across shards, optimizing the use of multiple shards in a stream. It also aggregates user records to increase payload size and improve throughput.
Enhanced Performance: KCL manages record processing in a distributed and fault-tolerant manner. It ensures that each record is processed by only one consumer in the consumer group, avoiding duplication.
Monitoring: Both KPL and KCL can be monitored using Amazon CloudWatch to provide visibility into producer and consumer performance
Buffering and Delays: One consideration to keep in mind when using the KPL is that it can incur an additional processing delay of up to RecordMaxBufferedTime within the library. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly.

KPL/KCL Considerations:

Learning Curve: While KPL and KCL offer streamlined integration, there might be a learning curve associated with these libraries, especially if you’re new to them.

Limited Customization: While KPL and KCL abstract away many complexities, they might limit your ability to customize certain behaviors according to your application’s unique requirements.
Additional Considerations:
- You may need to learn how to use the KPL and KCL.
- You may need to make changes to your application’s code to accommodate the KPL and KCL.
- You may need to monitor your application’s performance to ensure that it is able to handle the load.

Choosing the Right Approach for Kinesis Integration

The best approach for you will depend on your specific requirements.

If you require fine-grained control over interactions with Kinesis, then boto3 is a good option.

If you are already familiar with boto3 and want to leverage that knowledge, then boto3 is also a good option.

If your project is relatively small-scale and doesn’t require automatic scaling, then boto3 is a good option.

But, if you need to handle high-throughput scenarios and automatic scaling is essential, then KPL/KCL is a good option.

If you prefer a more hands-off approach to record batching, partitioning, and error handling, then KPL/KCL is a good option.

If your application requires robust fault tolerance and record deduplication, then KPL/KCL is a good option.

Ultimately, the best way to choose the right approach is to evaluate your specific requirements and weigh the advantages and considerations of each approach.

If you’re curious about diving into a more hands-on SDK Integration approach, I’ve prepared an in-depth blog post that focuses on it here!

I hope this helps and that’s all folks!!!