In April 2022, Amazon Managed Streaming for Apache Kafka (Amazon MSK) launched an exciting new capability, Amazon MSK Serverless. Amazon MSK is a fully managed service for Apache Kafka that makes it easier for developers to build and run highly available, secure, and scalable applications based on Apache Kafka. With MSK Serverless, developers can run their applications without having to provision, configure, or optimize their Apache Kafka clusters. MSK Serverless automatically provisions and scales compute and storage resources, so developers have access to on-demand streaming capacity and storage.
Over the remainder of 2022, the team collected customer feedback and worked backward from customer requirements to add new capabilities that made MSK Serverless even better. In this post, we discuss a few of these enhancements in detail and provide an example use case.
Higher default quota for partitions in a cluster
Data in Apache Kafka is written to topics, which can be partitioned into multiple log files called partitions. When a producer application writes data to a topic, it is appended to one of these partitions. MSK Serverless launched with a maximum quota of 120 partitions per cluster. However, our customers told us that they needed more partitions per cluster for a variety of use cases, ranging from change data capture (CDC) to faster real-time data processing.
In December 2022, we increased the default quota for partitions for MSK Serverless clusters. With the increased quota, you can create up to 2,400 partitions per cluster. The 20-fold increase in the number of partitions you can have per cluster lets you create more topics per cluster and have more applications consume data in parallel. You can also implement better isolation of data with fine-grained access control. More partitions are particularly useful for CDC use cases where each table in the database has hundreds of unqiue keys, which are each mapped to a unique partition. With more partitions, you can use MSK Serverless for capturing changes in larger databases with lots of tables and hundreds of keys. Note that the 2,400 limit only applies to leader partitions. MSK Serverless creates two replicas of each partition by default at no additional cost that don’t count towards this limit.
Unlimited data retention duration
The data you produce to your topics can be retained in Apache Kafka for a configurable duration, depending on how long you need to access data using Apache Kafka consumer APIs. Typically, customers retain data for short periods of time, ranging from a few hours to a few days. Previously, MSK Serverless limited data retention to a maximum of 24 hours (1 day), which is sufficient for most popular Apache Kafka use cases. However, some use cases require customers to retain data for longer, such as retaining data for audit purposes or maintaining application recovery SLAs.
Now, with the increase in the data retention duration quota, you can retain data for as long as you need in your MSK Serverless clusters. Longer data retention is particularly useful for use cases where your consumer applications need quick access to older data. For instance, in the case of a failure, the application may need to access data from the start of the topic to reconstruct its state. Because you can now retain data in your topics for longer durations, you can restore your application’s state by accessing older data using Kafka’s consumer API, making it easier to recover from such failures. After the application recovers, you can configure your application to start consuming the data from the earliest timestamp you need to reestablish your application’s state. Note that you can only retain up to 250 GB of data per partition. As long as your partition doesn’t reach 250 GB in size, you may retain it for as long as you wish. You may create more partitions if you need more storage for a given topic.
These new quotas are available in all Regions where MSK Serverless is available. For more information, navigate to the MSK Serverless tab on the Amazon MSK pricing page and choose the Region drop-down menu.
You can also request an increase to the maximum number of partitions quota by contacting AWS Support if you need more than 2,400 partitions in a cluster. The quotas for more partitions and longer retention are applied to both existing and new clusters.
Getting started: Create a topic with 1,000 partitions and 7-day retention
In this section, we demonstrate how to create a topic in MSK Serverless, specify the number of partitions, and set its retention duration.
As a prerequisite, you must have an MSK Serverless cluster and an Apache Kafka client. Refer to Getting started using MSK Serverless clusters for step-by-step instructions.
On your client machine, access kafka_2.12-2.8.1/bin and run the following export command (replace the ‘my-endpoint’ with the bootstrap server string of your MSK Serverless cluster):
Run the following command to create a topic called msk-sample-topic with 1,000 partitions and 7-day data retention (604,800,000 milliseconds):
(Optional) Run the following command to view the details of the topic you created in step 2 above:
You will see the following result:
To avoid incurring charges on the AWS resources created in this post, delete the MSK Serverless cluster and the Amazon Elastic Compute Cloud (Amazon EC2) instance for your client machine.
On the Amazon MSK console, select the MSK Serverless cluster you used for this solution.
Choose Actions, then choose Delete.
On the Amazon EC2 console, select the instance that you created for your Apache Kafka client machine.
Choose Instance state, then choose Terminate instance.
This post demonstrated how to create an MSK Serverless cluster topic with 1,000 partitions and 7-day retention. With the new quota increases, you can create up to 2,400 partitions per cluster and retain data for as long as you need. If you have comments or feedback, please feel free to leave them in the comments.
About the author
Usama Naseem is a Senior Product Manager for Amazon MSK and focuses on MSK Serverless. Previously, he held product management roles for AWS Lambda and Amazon Fresh. He is passionate about giving customers the tools to build real-time applications in the cloud. Outside of work, he continues to be under the delusion that he will be the best squash player in the world one day.