Here‘s What Makes Apache Kafka So Fast

Apache Kafka has taken the world of data infrastructure by storm. Originally developed at LinkedIn and open sourced in 2011, Kafka is now used by over 80% of the Fortune 100 for everything from collecting user activity data and logs to building real-time data pipelines and streaming applications.

There are many reasons for Kafka‘s meteoric rise, but one of the most frequently cited is its speed. Kafka is often orders of magnitude faster than traditional message-oriented middleware, enabling it to handle the firehose of data generated by modern businesses. But what exactly makes Kafka so fast? In this post, we‘ll take a detailed look at Kafka‘s architecture and design principles to understand how it achieves such impressive performance.

Kafka‘s Distributed Architecture

At a high level, Kafka is a distributed streaming platform that enables you to publish, store, and process streams of records in real-time. Kafka runs as a cluster of servers called brokers, with data organized into categories called topics. Each topic is split into a number of partitions, which are distributed across the brokers in the cluster.

This distributed architecture is fundamental to Kafka‘s speed. By spreading data and processing across many servers, Kafka can handle massive throughputs beyond what any single server could manage. In practice, Kafka is often run on clusters of hundreds or even thousands of servers, processing trillions of messages per day.

Kafka‘s architecture is in stark contrast to traditional message queues like RabbitMQ or ActiveMQ, which typically run on a single server. While these systems can achieve high throughputs, they are fundamentally limited by the resources of a single machine. Kafka‘s distributed design allows it to scale horizontally to handle arbitrary data volumes.

Replication and Fault Tolerance

Kafka‘s distributed architecture also enables it to be highly fault-tolerant. Every partition in Kafka is replicated across a configurable number of servers, with one server acting as the leader and the others as followers. When data is published to a partition, it is written to the leader and then synchronously replicated to the followers.

This replication protocol ensures that data is not lost even if servers fail. If the leader for a partition goes down, one of the in-sync followers will automatically be promoted to leader and resume serving clients. Kafka‘s replication is designed to be fast, with followers able to keep up with leaders even under high write throughputs.

Replication also allows Kafka to perform maintenance and upgrades without downtime. Individual brokers can be taken offline for maintenance while the rest of the cluster continues serving traffic. This enables Kafka to provide the high availability required for mission-critical data pipelines.

Optimized Storage and I/O

Another key factor in Kafka‘s speed is its storage layer. Kafka stores all data on disk in a simple, append-only log format. When a producer publishes a message to a partition, the broker simply appends it to the end of the partition file. Consumers read messages from partitions by specifying an offset and reading sequentially from there.

This simple storage layout is incredibly efficient. By always writing to the end of the log, Kafka avoids expensive disk seeks and random I/O. Sequential disk I/O can be orders of magnitude faster than random access, especially on spinning hard drives.

Furthermore, since Kafka is storing data in files, it can take advantage of the operating system‘s page cache. Modern operating systems automatically keep recently accessed disk data in memory to speed up future reads. Since Kafka is reading and writing sequentially, the OS can intelligently preload data into the cache, allowing Kafka to serve many requests from memory without touching disk.

The combination of sequential I/O and filesystem caching enables Kafka to achieve performance close to the limits of the underlying hardware. In benchmarks, a single Kafka broker can achieve sustained write throughputs of over 1 GB/sec and read throughputs of over 3 GB/sec on commodity hardware.

Efficient Message Transfer

In addition to optimized storage, Kafka is also designed to efficiently move data between clients and servers. Kafka uses a binary protocol over TCP for communication between producers, brokers, and consumers. This binary protocol is carefully designed to minimize processing and data copying.

When a producer sends a batch of messages to a partition, the broker receives the data into pagecache without any intermediate buffers or copying. Similarly, when a consumer fetches data from a partition, the data is sent directly from the broker‘s pagecache to the consumer‘s TCP socket using the sendfile system call. This "zero-copy" transfer minimizes the number of times data is copied in memory, reducing CPU overhead and latency.

Kafka‘s protocol is also designed to enable efficient batch processing. Producers can accumulate messages in memory and send them to brokers in large batches. Batching amortizes the overhead of the network roundtrip and disk write across many messages, increasing overall throughput. Consumers can also fetch large batches of messages from brokers, reducing the number of network requests required.

Stateful Stream Processing

Kafka‘s performance benefits extend beyond just publish-subscribe messaging. With the introduction of the Kafka Streams library, Kafka is now also a powerful platform for stateful stream processing.

Kafka Streams allows you to build applications that consume data from Kafka, perform complex transformations and aggregations, and then produce results back to Kafka. Importantly, Kafka Streams is built for speed and scale. It leverages the same partitioning model as Kafka itself, allowing processing to be distributed across a cluster of machines. State is stored locally on each machine and can be processed alongside the incoming stream data without expensive network calls.

This architecture allows Kafka Streams to achieve very high throughputs and low latencies for stateful processing. In benchmarks, a single Kafka Streams application can process over 1 million events per second with millisecond latencies, outperforming traditional stream processing systems like Storm and Spark Streaming.

Kafka in the Big Data Ecosystem

Kafka‘s performance and scalability have made it a key component of the modern big data ecosystem. Kafka is often used as the "backbone" for data pipelines, ingesting data from various sources and then distributing it to downstream systems for further processing and analysis.

For example, many companies use Kafka to collect user activity data and system logs, which are then processed in real-time by Kafka Streams applications or loaded into data warehouses and Hadoop for batch analytics. Kafka integrates seamlessly with big data tools like Spark, Flink, and Presto, enabling complex data flows spanning real-time and batch processing.

Kafka has also become a popular choice for microservice architectures. Its high throughput and low latency make it well-suited for propagating events between services, while its durability guarantees ensure that no data is lost if services fail. Many companies are now building their entire data infrastructures around Kafka, using it as a real-time data bus connecting disparate systems and applications.

Operating Kafka at Scale

While Kafka‘s performance is impressive, achieving that performance in production requires careful operations and monitoring. Kafka is a complex system with many moving parts, and there are numerous configuration parameters that can impact performance.

One common challenge is provisioning the right amount of hardware for a Kafka cluster. Kafka‘s performance is ultimately limited by the speed of the underlying disks and networks. To achieve high throughputs, it‘s important to use fast disks (SSDs are recommended) and to have sufficient network bandwidth between brokers and clients.

Another key consideration is data retention. Kafka brokers retain messages on disk until a configurable retention period has passed, at which point old segments are deleted. Retaining data for longer periods can put pressure on disk space and I/O, impacting performance. It‘s important to carefully tune retention settings based on the use case and available hardware.

Monitoring is also critical for operating Kafka at scale. Kafka exposes a wealth of metrics that can be used to track the health and performance of the cluster, including message rates, request latencies, and disk usage. Tools like Prometheus and Grafana are commonly used to collect and visualize these metrics, enabling operators to spot issues and bottlenecks.

The Future of Kafka Performance

Looking forward, there are several developments in the Kafka roadmap that could further improve its performance and scalability. One major effort is the Kafka Improvement Proposals (KIPs) focused on the controller, which is responsible for managing the state of the cluster. Improving the scalability of the controller will enable Kafka to handle even larger clusters and higher metadata workloads.

Another area of development is tiered storage. Currently, Kafka brokers store all data on local disk, which can limit capacity and require expensive storage hardware. Tiered storage will allow Kafka to offload older data to cheaper, remote storage tiers like S3 or HDFS, while still maintaining fast access to recent data. This will enable Kafka to store vastly larger datasets at lower cost.

There are also ongoing efforts to improve Kafka‘s replication protocol and make it more efficient. For example, KIP-227 introduces incremental replication, which allows followers to fetch only the missing data from leaders, rather than full replicas. This can significantly reduce network overhead and accelerate recovery times.

Conclusion

Apache Kafka‘s speed and scalability have made it a critical component of modern data infrastructure. By combining a distributed architecture, optimized storage and I/O, efficient message transfer, and stateful stream processing, Kafka is able to handle immense data volumes with ease.

But Kafka‘s influence extends beyond just its raw performance. Its design has fundamentally shifted how we think about data pipelines and real-time processing. Kafka‘s success has inspired a wave of new streaming platforms and tools, all striving to provide the same combination of speed, scalability, and fault-tolerance.

As data volumes continue to grow and real-time processing becomes the norm, Kafka‘s importance will only continue to rise. While operating Kafka at scale comes with its own set of challenges, the benefits – in terms of improved insights, faster response times, and reduced downtime – are immense.

If you‘re building data infrastructure today, Kafka is impossible to ignore. Its performance and design have set a new standard for what‘s possible with data streaming. And with a thriving community and active development, Kafka is well-positioned to continue leading the charge in the fast data revolution.