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Kafka plays a huge role in modern data processing, powering everything from analytics to event-driven applications. As more teams rely on Kafka for an increasingly diverse range of tasks, they often ask it to handle wildly different workloads at the same time, like high-throughput real-time analytics running alongside resource-heavy batch jobs.
On paper, this flexibility sounds great. In reality, though, it creates some big challenges. In shared Kafka setups, these mixed workloads can clash. One job might suddenly spike in resource usage, slowing down or even disrupting others. This can lead to delays, performance issues, and sometimes even failures for critical tasks.
To manage these issues, organizations have traditionally gone one of two routes: they either set strict resource limits or spin up separate Kafka clusters for different workloads. Both approaches have trade-offs. Limits can be too inflexible, leaving some jobs underpowered. Separate clusters, on the other hand, add complexity and cost.
That’s where WarpStream comes in. Instead of forcing you to pick between cost and flexibility, WarpStream introduces an alternative architecture to manage workloads with a feature called Agent Groups. This approach isolates different tasks within the same Kafka cluster—without requiring extra configurations or duplicating data—making it more reliable and efficient.
In this post, we’ll dive into the noisy neighbor problem, explore traditional solutions like cluster quotas and mirrored clusters, and show how WarpStream’s solution compares to them.
In shared infrastructures like a Kafka cluster, workloads often compete for resources such as CPU, memory, network bandwidth, and disk I/O. The problem is, not all workloads share these resources equally. Some, like batch analytics jobs, can demand a lot all at once, leaving others—such as real-time analytics—struggling to keep up. This is what’s known as the “noisy neighbor” problem. When it happens, you might see higher latency, performance drops, or even failures in tasks that don’t get the resources they need.
Picture this: your Kafka cluster supports a mix of applications, from real-time Apache Flink jobs to batch analytics. The Flink jobs depend on steady, reliable access to Kafka for real-time data processing. Meanwhile, batch analytics jobs don’t have the same urgency but can still cause trouble. When a batch job kicks off, it might suddenly hog resources like network bandwidth, CPU, and memory—sometimes for short but intense periods. These spikes can overwhelm the system, leaving Flink jobs to deal with delays or even failures. That’s hardly ideal for a real-time pipeline!
In environments like these, resource contention can cause serious headaches. So how do you address the noisy neighbor problem? Let’s explore the most popular solutions.
One way to manage resources in Kafka is by setting quotas, which cap how much each workload can use on a per-broker basis. This can help prevent any individual workload from spiking and hogging resources like network and CPU. Kafka offers two types of quotas that, are specifically designed for handling noisy neighbors:
Quotas provide a powerful tool for controlling resource consumption and distribution, but actually configuring quotas in a useful way can be very challenging:
The second solution is to create separate Kafka clusters for different workloads (one for streaming, another for batch processing, etc.) and replicate data between them. This approach completely isolates workloads, eliminating noisy neighbor problems.
However, mirroring clusters comes with its own set of limitations:
To be clear, mirroring clusters isn’t something we advise against, it’s just not the most practical solution if your goal is to eliminate noisy neighbors in Kafka. The approach of setting up separate clusters for different workloads, such as one for real-time analytics and another for batch processing, does effectively isolate workloads and prevent interference, but it introduces several limitations that are not worth it at all.
Mirroring clusters is a critical operation for many other scenarios, like maintaining a backup cluster for disaster recovery or enabling cross-region data replication. That’s exactly why, to support these use cases, we recently launched a mirroring product called Orbit directly embedded within our agents. This product not only mirrors data across clusters but also preserves offsets, ensuring consistent metadata alignment for tools that rely on precise offsets between environments.
We’ve seen that the usual ways of dealing with noisy neighbors in Kafka clusters each have their drawbacks. Kafka Cluster Quotas can be too restrictive, while mirroring clusters often brings high costs and added complexity. So how do you tackle noisy neighbors without sacrificing performance or blowing your budget?
That’s where WarpStream comes in. WarpStream can completely isolate different workloads, even when they’re accessing the same Kafka topics and partitions. But how is that even possible? To answer that, we need to take a closer look at how WarpStream differs from other Kafka implementations. These differences are the key to WarpStream’s ability to eliminate noisy neighbors for good.
If you’re not familiar with it, WarpStream is a drop-in replacement for Apache Kafka that operates directly on object storage, such as S3, rather than traditional disk-based storage. This architectural shift fundamentally changes how Kafka operates and eliminates the need for the leader-follower replication model used in Kafka. In WarpStream, the system is entirely leaderless: any agent in the cluster can handle any read or write request independently by accessing object storage directly. This design removes the need for agents to replicate data between designated leaders and followers, reducing inter-agent traffic and eliminating dependencies between agents in the cluster.
The leaderless nature of WarpStream’s agents is a direct consequence of its shared storage architecture. In Kafka’s traditional shared nothing design, a leader is responsible for managing access to locally stored data and ensuring consistency across replicas. WarpStream, however, decouples storage from compute, relying on object storage for a centralized and consistent view of data. This eliminates the need for any specific agent to act as a leader. Instead, agents independently perform reads and writes by directly interacting with the shared storage while relying on the metadata layer for coordination. This approach simplifies operations and allows workloads to be dynamically distributed across all agents.
This disk- and leader-free architecture allows for what WarpStream calls Agent Groups. These are logical groupings of agents that isolate workloads effectively without needing intricate configurations. Unlike traditional Kafka, where brokers share resources and require network connections between them to sync up, WarpStream Agents in different groups don’t need to be connected. As long as each Agent Group has access to the same object storage buckets, they will be able to read and write the same topic and partitions. They can even operate independently in separate Virtual Private Clouds (VPCs) or Cloud Accounts.
This setup makes Agent Groups an ideal solution for managing noisy neighbors. Each group functions independently, allowing different workloads to coexist without interference. For example, if the group handling batch analytics is temporarily overloaded before auto-scaling kicks in due to a sudden surge in demand, it can scale up without impacting another group dedicated to real-time analytics. This targeted isolation ensures that resource-intensive workloads don’t disrupt other processes.
With Agent Groups, WarpStream provides a solution to the noisy neighbor problem, offering dynamic scalability, zero interference, and a more reliable Kafka environment that adapts to each workload’s demands.
WarpStream’s agent groups go beyond just isolating different workloads, it brings additional benefits to Kafka environments:
Consistent Data Without Duplication: Agent Groups ensure a consistent view of data across all workloads, without needing to duplicate it. You write data once into object storage (like S3), and every Agent Group reads from the same source. What’s more, offsets remain consistent across groups. If Group A reads data at a specific offset, Group B sees the exact same offset and data. This eliminates the hassle of offset mismatches that often happen with mirrored clusters or replicated offsets.
Non-Interfering Writes Across Groups: Mirrored Kafka clusters restrict simultaneous writes from different sources to the same topic-partition. WarpStream’s architecture, however, allows independent writes from different groups to the same topic-partition without interference. This is possible because WarpStream has no leader nodes, each agent operates independently. As a result, each Agent Group can write to shared data without creating bottlenecks or needing complex synchronization.
Seamless Multi-VPC Operations: WarpStream’s setup eliminates the need for complex VPC peering or separate clusters for isolated environments. Since Agent Groups are connected solely via object storage, they act as isolated units within a single logical cluster. This means you can deploy Agent Groups in different VPCs, as long as they all have access to the same object storage.
Dynamic Resource Scaling Without Static Quotas: Unlike traditional Kafka setups that rely on static quotas, WarpStream doesn’t need pre-configured resource limits. Scaling Agent Groups is straightforward: you can put autoscalers in front of each group to adjust resources based on real-time needs. Each group can independently scale up or down depending on workload characteristics, with no need for manual quota adjustments. If an Agent Group has a high processing demand, it will automatically scale, handling resource usage based on actual demand rather than predefined constraints.
Tailored Latency with Multiple Storage Backends: With Agent Groups, you can isolate workloads not to prevent noisy neighbors, but to match each workload’s latency requirements with the right storage backend. WarpStream offers options for lower-latency storage, making it easy to configure specific groups with faster backends. For instance, if a workload doesn’t have data in common with others and needs quicker access, you can configure it to use a low-latency backend like S3 Express One Zone. This flexibility allows each group to choose the storage class that best meets its performance needs, all within the same WarpStream cluster.
A typical setup might involve producers with low-latency requirements writing directly to an Agent Group configured with a low-latency storage backend. Consumers, on the other hand, can connect to any Agent Group and read data from both low-latency and standard-latency topics. As long as all Agent Groups have access to the necessary storage locations, they can seamlessly share data across workloads with different latency requirements.
Managing noisy neighbors in Kafka has always been a balancing act, forcing teams to choose between strict resource limits or complex, costly cluster setups. WarpStream changes that. By introducing Agent Groups, WarpStream isolates workloads within the same Kafka environment, enabling consistent performance, simplified operations, and seamless scalability, without sacrificing flexibility or blowing your budget.
With WarpStream, you can tackle noisy neighbor challenges head-on while unlocking additional benefits. Whether your workloads require multi-VPC deployments, the ability to scale on demand, or tailored latency for specific workloads, WarpStream adapts to your needs while keeping your infrastructure lean and cost-effective.
Check out our docs to learn more about Agent Groups. You can create a free WarpStream account or contact us if you have questions. All WarpStream accounts come with $400 in credits that never expire and no credit card is required to start.
