Scaling up your Kafka event streaming processing: what can go wrong?
So, your organization successfully introduced small-scale event streaming processing with Kafka. Now, teams company-wide are knocking down your Kafka architects’ door with ideas for new use cases. But going too fast, too soon is dangerous. Losing track of scale, ownership, and access can rapidly put your customer data and mission-critical applications at risk. So what’s the key to secure Kafka scale-up? Having the right tools in place to track, manage, and safeguard your Kafka expansion.
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Enterprise organizations know that Apache Kafka is the data-centric gold standard they need to run mission-critical applications. Logically, it’s very tempting to scale up Kafka usage quickly — but it’s vital to tread carefully.
Here, we’ll look at key issues organizations frequently experience when scaling up their Kafka operations, as well as how to set your organization up to leverage Kafka’s full potential.
If you’re nearer the beginning of your Kafka event streaming journey, you might like to check out our blog on avoiding easily made mistakes when getting started with Kafka data governance.
Common pitfalls to avoid when scaling up with Kafka
Let’s imagine the scenario: Your organization introduced small-scale event streaming processing with Kafka — and it went down brilliantly.
With such clear opportunities to leverage real-time data, accelerate time to market for applications, and evolve the service your organization offers its customers, teams from all over the company are just about knocking down your Kafka architects’ door with enthusiastic expansion ideas.
What could go wrong? Unfortunately, the answer is a lot, very quickly.
You can easily lose track of scale, ownership, and access
As you scale up, increasing numbers of teams will start working with the data you manage with Kafka. Without clear processes in place to track who’s creating all these new Kafka topics and accessing your ever-growing range of applications — and also why, where, and when they’re doing it — your Kafka architects will rapidly lose their grasp of who has created which topics and who has access to what.
Your Kafka owners will also lose their overview of the what, why, how, and when of who’s making mistakes in Kafka within your organization. So when something does go wrong, it’s incredibly difficult to trace it, fix it, and prevent it from happening again.
Your central Kafka team can become a bottleneck
Your internal platform team can only handle so many requests, questions, and tasks at once. Often, the workload acceleration that goes hand in hand with an organization scaling its Kafka instance overwhelms these teams, meaning they inadvertently become a high-pressure bottleneck that prevents your Kafka from scaling smoothly.
How to scale up your Kafka data streaming effectively
The potential for enterprise organizations to evolve their service with a successful Kafka scale-up is vast. Yes, it’s a significant challenge — but the key is setting your organization up with the right tools to succeed.
With Axual’s user-centric, easy to interpret interface, you can tick these key security, data governance, and compliance boxes for your Kafka landscape:
- Secure access by ensuring traffic to and from your Kafka platform is encrypted
- Make sure all applications connected to your Kafka ecosystem are authenticated
- Use the latest TLS versions for authorization
- Grant permissions and topic access on a strictly need-only basis
- Configure your cluster settings to reduce the impact of potential server outages
- Offer Kafka self-service for developers, reducing pressure on your central Kafka team
Kafka data governance peace of mind? Axual is here to help.
We exist to take the stress out of Kafka streaming, compliance and data governance — so that’s no more sleepless nights for your Kafka team!
For an in-depth take on securing your organization’s event streaming processing, why not read our whitepaper on mastering Kafka data governance and compliance? Or for a bite-size look at why it’s so easy to make data governance mistakes when working with Kafka, dive into our blog on the topic.
Answers to your questions about Axual’s All-in-one Kafka Platform
Are you curious about our All-in-one Kafka platform? Dive into our FAQs
for all the details you need, and find the answers to your burning questions.
Apache Kafka applications can encounter several scalability pitfalls, including: Network Round-Trips: Operations that involve waiting for network responses can severely limit throughput. It’s important to minimize waiting times by decoupling sending messages from confirmation checks and using asynchronous offset commits. Misinterpreting Processing Delays: Kafka may mistakenly identify a slow consumer as failed, leading to unnecessary disconnections. Properly configuring poll intervals and managing message processing can help avoid this issue. Idle Consumers: If consumers are idle and frequently sending fetch requests, it can strain resources and affect performance. Adjusting fetch wait times and reconsidering the number of consumer instances can alleviate this.
To enhance the performance of your Kafka application, consider adjusting the consumer configurations by using the max.poll.records and max.poll.interval.ms settings. This can help manage consumer behavior and reduce the likelihood of false failure detections. Additionally, increasing the fetch.max.wait.ms setting can minimize idle fetch requests from consumers. It's also important to evaluate the necessity of having a large number of consumer instances. Finally, limit the number of topics to the low thousands while ensuring that each topic has multiple partitions to effectively balance the load across brokers.
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