Glossary
Kafka Capacity Planning
Learn how to properly size and plan Kafka infrastructure by understanding throughput, storage, and resource allocation for current and future workloads.
Kafka Capacity Planning
Capacity planning is one of the most critical tasks when deploying Apache Kafka in production. Underestimate your needs, and you risk performance degradation or outages. Overestimate, and you waste resources and budget. Getting it right requires understanding your workload characteristics and how Kafka's architecture translates into infrastructure requirements.
This guide walks through the essential aspects of Kafka capacity planning, from calculating throughput to sizing storage and network resources.
What is Kafka Capacity Planning?
Kafka capacity planning is the process of estimating the hardware and infrastructure resources needed to support your expected workload. This includes determining the number of brokers, amount of storage, network bandwidth, CPU, and memory required to handle your message throughput, retention policies, and consumer patterns.
Unlike traditional databases where capacity planning often focuses on transaction volume and query performance, Kafka's distributed log architecture requires considering factors like replication, partition count, message retention, and the continuous nature of streaming data.
Proper capacity planning ensures your Kafka cluster can handle peak loads, maintain low latency, and scale as your data streaming needs grow. It also directly impacts cost efficiency, especially in cloud environments where you pay for provisioned resources.
Key Resources to Consider
Kafka capacity planning involves four primary resource types, each serving different aspects of the system:
Disk Storage is typically the most critical resource. Kafka persists all messages to disk, and your storage needs depend on message throughput, average message size, retention period, and replication factor. Unlike in-memory systems, Kafka's durability guarantees mean you must provision enough disk space for the entire retention window.
Network Bandwidth becomes crucial when dealing with high-throughput scenarios. Network traffic includes producer writes, consumer reads, inter-broker replication, and potentially cross-datacenter replication. Each of these can saturate network interfaces if not properly sized.
CPU usage in Kafka is generally moderate but increases significantly when using compression, SSL/TLS encryption, or complex message transformations. Producers and consumers also consume CPU for serialization and network operations.
Memory is used for page cache (which Kafka leverages heavily for performance), in-flight requests, and internal buffers. While Kafka itself has a relatively small heap footprint, the operating system's page cache is critical for read performance.
Calculating Throughput Requirements
Start by understanding your workload characteristics. You need to know your peak message rate (messages per second), average message size, and desired retention period.
A simple formula for daily storage requirements is:
For example, if you're processing 1,000 messages per second with an average size of 1 KB and a replication factor of 3:
For a 7-day retention policy, you would need approximately 1.8 TB of storage capacity per topic. This is a simplified calculation—real-world scenarios require adding overhead for log segment metadata, indexes, and operational headroom (typically 20-30% extra capacity).
Don't forget to account for growth. If your message volume is expected to double in the next year, factor that into your initial provisioning to avoid frequent hardware additions.
Storage Planning and Disk I/O
Disk performance is as important as disk capacity. Kafka's sequential write pattern is disk-friendly, but you still need sufficient I/O throughput to handle your write rate, especially during peak loads or when consumers fall behind.
Modern SSDs are strongly recommended for production Kafka clusters. While Kafka's sequential access patterns work reasonably well with spinning disks, SSDs provide better latency consistency and can handle random read patterns when consumers read older messages.
Consider your replication factor carefully. A replication factor of 3 is common for production systems, providing fault tolerance while tripling your storage requirements. Each replica must be written to disk, so your disk I/O capacity must handle the replication overhead.
Log segment size also impacts performance. Kafka rolls log segments based on size or time. Smaller segments mean more files to manage but faster cleanup and compaction. Larger segments reduce file count but can slow maintenance operations.
Network and CPU Considerations
Network bandwidth requirements multiply quickly. Producer traffic is written to disk and replicated to other brokers. If you have a replication factor of 3, each message is written once by the producer but transmitted twice more for replication.
Consumer traffic adds to network usage. Each consumer group reads the full dataset, so multiple consumer groups multiply your read traffic. A common mistake is planning only for producer throughput and being surprised when adding consumer groups saturates network links.
CPU usage spikes when enabling compression. Compressing messages reduces network and storage requirements but increases CPU load on both producers and brokers. Benchmark your specific workload to understand the tradeoff. Similarly, enabling SSL/TLS encryption adds CPU overhead—typically 10-30% depending on cipher choice and hardware.
For high-throughput scenarios, consider using dedicated network interfaces for replication traffic to avoid contention with client traffic.
Monitoring and Scaling in Data Streaming
Effective capacity planning doesn't end at deployment. Continuous monitoring is essential to validate your estimates and detect when you're approaching limits.
Key metrics to track include:
Broker CPU and memory utilization: Sustained high CPU (>70%) indicates you may need more brokers or to reduce load
Disk usage and I/O wait: Approaching disk capacity or high I/O wait times signal storage constraints
Network throughput: Monitor bytes in/out per broker to identify network bottlenecks
Consumer lag: Growing lag indicates consumers can't keep up, which may require scaling
Platforms like Conduktor provide comprehensive monitoring dashboards that track these metrics across your cluster, making it easier to spot capacity issues before they impact production. Being able to visualize throughput trends, partition distribution, and resource utilization in one place helps teams make informed scaling decisions.
When scaling Kafka, you can scale vertically (larger machines) or horizontally (more brokers). Horizontal scaling is generally preferred as it improves fault tolerance and allows for better load distribution across partitions.
Common Pitfalls and Best Practices
Several common mistakes can derail capacity planning:
Underestimating replication overhead is frequent. Remember that replication multiplies your network and storage needs. A replication factor of 3 doesn't just triple storage—it also triples write network traffic.
Ignoring partition count can limit scalability. Since partitions are the unit of parallelism in Kafka, having too few partitions means you can't fully utilize multiple brokers or scale consumer throughput. However, too many partitions increase metadata overhead and recovery time.
Not planning for peaks is another common issue. Average load might be manageable, but if your system experiences traffic spikes (e.g., end-of-day processing), you need capacity for peak load, not average.
Best practices include:
Start with conservative estimates and monitor actual usage
Provision 20-30% headroom beyond calculated requirements
Use tiered storage for long-term retention to reduce primary storage costs
Test capacity under load before production deployment
Document your capacity assumptions and review them quarterly
Summary
Kafka capacity planning requires balancing multiple resources—storage, network, CPU, and memory—based on your throughput, retention, and reliability requirements. Start by calculating your storage needs from message rate, size, retention period, and replication factor. Factor in network bandwidth for both client traffic and replication, and don't overlook CPU requirements for compression and encryption.
Continuous monitoring is essential to validate your planning and detect when scaling is needed. By understanding these fundamentals and avoiding common pitfalls like underestimating replication overhead or ignoring peak loads, you can build a Kafka infrastructure that's both cost-effective and reliable.
Proper capacity planning isn't a one-time task—it's an ongoing process as your data streaming needs evolve. Regular review of your metrics and capacity assumptions ensures your Kafka cluster continues to meet your organization's requirements.
Sources and References
Apache Kafka Documentation - Operations Guide: https://kafka.apache.org/documentation/#operations
Confluent - Kafka Capacity Planning: https://docs.confluent.io/platform/current/kafka/deployment.html
Narkhede, Neha, Gwen Shapira, and Todd Palino. "Kafka: The Definitive Guide" (O'Reilly Media, 2017)
LinkedIn Engineering Blog - Kafka Capacity Planning at Scale: https://engineering.linkedin.com/kafka
AWS Big Data Blog - Best Practices for Amazon MSK Sizing: https://aws.amazon.com/blogs/big-data/