Scaling Kafka: Unraveling the Mystery of Topic Capacity

Published August 2023

Apache Kafka is the backbone of many modern data architectures due to its ability to handle real-time data streams efficiently. One of the most commonly asked questions in the Kafka community is, “How many topics can Kafka support?”

In this blog post, we will dive deep into this question of topic capacity and explore how Kafka is optimized differently when it has a few topics versus many topics.

Kafka's Topic Architecture

Before we discuss topic capacity, let’s briefly understand Kafka’s topic architecture.

Kafka organizes data into topics, where each topic is a logical stream of records. Producers publish records to a topic, and consumers subscribe to these topics to consume the records.

Figure depicting Kafka’s architecture. Figure originally published in the article Does Kafka Guarantee Ordering?

Topics are divided into partitions, which are the fundamental units of parallelism and scalability in Kafka. Each partition is a linearly ordered sequence of records, and they allow Kafka to distribute data across multiple brokers, providing fault tolerance and horizontal scalability.

We have a series of posts going into more detail about these different Kafka components: What is a Kafka partition?, What is a Kafka topic?, and Kafka Definitions. Check those articles out to learn more about each Kafka component.

How many topics can Kafka support?

Kafka itself imposes no hard limits on the number of topics. Instead, practical limitations arise from how the system is deployed and maintained.

Capacity Considerations for Kafka Topics

Determining the maximum number of topics a Kafka implementation can support depends on several factors:

hardware resources
cluster configuration
rate of data ingestion and consumption

Let’s dive a little deeper into these factors for determining the number of topics a Kafka implementation can support:

Metadata Overhead.
Each topic and partition in Kafka requires some metadata to be stored in memory. While this metadata is relatively small for individual topics, having an excessively large number of topics can lead to significant overhead on the cluster’s metadata storage. Metadata for Kafka is stored in ZooKeeper.

Memory Allocation.
Memory is a critical resource for Kafka brokers. As the number of topics increases, the memory required to manage and maintain metadata also grows. If the memory is insufficient, it may lead to performance degradation or even out-of-memory errors.

Consumer Management.
Consumers that subscribe to topics need to keep track of their offset (the position of the last record consumed) for each partition. With numerous topics and partitions, managing Kafka consumer offsets becomes more complex, especially when consumers join or leave consumer groups.

Topic Partitions.
Creating a large number of partitions for each topic can affect overall performance. While more partitions increase parallelism and throughput, excessive partition count can lead to higher overhead and increased disk usage.

Optimizations for Few Kafka Topics

When dealing with a relatively small number of topics, Kafka’s overhead is minimal, and the system operates efficiently. Engineers managing Kafka implementations with a few topics can focus on optimizing other aspects of the infrastructure.

Partitions per Topic: With fewer topics, you might be able to get away with fewer partitions per topic. Fewer partitions mean less metadata overhead and simplified consumer offset management. Check out our formula for calculating the number of Kafka partitions needed for your implementation.
Memory Allocation: While memory remains essential, the memory footprint will be smaller with fewer topics. As a result, you can allocate more memory to individual brokers or use smaller, cost-effective instances.
Resource Allocation: In a small-topic scenario, you can allocate more resources, such as CPU and disk, to individual brokers, optimizing their performance.
Consumer Groups: Managing consumer groups becomes relatively simpler when dealing with a smaller number of topics, and you can design consumer groups that align better with your use cases. Read more about the role of Kafka consumer groups for event scaling.

Optimizations for Many Kafka Topics

As Kafka implementations grow and handle a large number of topics, engineering efforts need to shift towards addressing the challenges associated with this scale.

Hardware Scaling: With numerous topics, the cluster’s hardware must scale to accommodate the increased metadata overhead and consumer offset management. Adding more memory to brokers becomes crucial.
Topic Partitioning Strategy: Careful consideration of the topic partitioning strategy is necessary. You may need to use a composite key approach to ensure even data distribution across partitions.
Monitoring and Alerting: Robust monitoring and alerting systems become critical to identify and mitigate potential issues promptly. Learn about monitoring Kafka with Elasticsearch or OpenSearch. Our managed Kafka services include 24×7 monitoring and a weekly 7-point assessment of Kafka performance.
Kafka Connect and Streams Optimization: For many topics, it’s essential to optimize Kafka Connect and Kafka Streams applications to handle the increased load efficiently.
Tiered Storage: Implementing tiered storage can help manage disk usage and reduce costs when dealing with a large volume of data. Read about saving money on data storage costs using tiered storage and other methods.

Key Facts on Kafka Topic Limits

Kafka’s ability to support topics is highly scalable and flexible, with no inherent hard limits. The number of topics Kafka can handle effectively depends on various factors, including hardware resources, cluster configuration, and usage patterns.

For smaller implementations with a limited number of topics, focus on optimizing other aspects of the infrastructure, such as memory allocation and resource allocation.

As Kafka implementations grow and handle a larger number of topics, engineering efforts must prioritize hardware scaling, topic partitioning strategies, and robust monitoring and alerting systems.