Redpanda’s throughput claims often seem too good to be true, especially when compared to battle-tested Kafka.
Here’s a look at a typical benchmark scenario: 100 topics, 10 partitions each, 1 producer, 1 consumer, producing 1KB messages at 100MB/s.
# Example producer command (conceptual, actual client libraries vary)
rpk topic produce my-topic --value "some data" --count 100000
# Example consumer command (conceptual)
rpk topic consume my-topic --group my-group --output-file output.log
The core difference often boils down to how each system handles I/O and network. Kafka, by default, relies heavily on the operating system’s page cache for buffering and uses a thread-per-broker model that can lead to context-switching overhead. Redpanda, on the other hand, is written in C++ with an event-driven, asynchronous architecture, designed to minimize OS interaction and maximize CPU utilization by avoiding traditional thread-per-connection models. It uses io_uring for highly efficient I/O and its own memory management to bypass the OS page cache where beneficial, aiming for predictable latency and higher raw throughput.
A key differentiator is Redpanda’s use of the seastar C++ framework. Seastar is built for high-performance, asynchronous I/O on multi-core systems. It manages its own thread pool and schedules tasks cooperatively, meaning threads don’t block waiting for I/O. This contrasts sharply with Kafka’s traditional thread-per-request model, which can become a bottleneck under heavy load as the OS spends more time switching between threads than doing actual work.
The data format itself plays a role. While both support Avro, Protobuf, and JSON, Redpanda’s internal architecture is optimized for efficient serialization and deserialization. It can achieve higher throughput because it spends less CPU time on data transformation and network protocol handling. Kafka’s reliance on the JVM can introduce garbage collection pauses, which, while often minimized by tuning, can still cause occasional latency spikes that impact sustained throughput.
Redpanda’s adaptive batching is another performance factor. It dynamically adjusts the size of batches it sends to brokers based on current network and disk conditions. This allows it to keep producers and consumers saturated without overwhelming the system. Kafka’s batching is more static, requiring manual tuning of parameters like batch.size and linger.ms to achieve optimal performance for a given workload.
The benchmark results often show Redpanda pulling ahead significantly in scenarios demanding very high throughput or low latency. This is largely due to its fundamental architectural choices: a single-threaded event loop per core model (managed by Seastar) that avoids thread contention and context switching, direct use of io_uring for asynchronous I/O, and optimized internal data structures. Kafka, while mature and highly configurable, often requires extensive tuning of JVM settings, OS parameters, and client-side configurations to approach Redpanda’s out-of-the-box performance in these specific areas.
When Redpanda achieves higher throughput, it’s not just about faster disk or network; it’s about how efficiently it utilizes the CPU and memory, minimizing overhead from OS syscalls, context switches, and garbage collection. The seastar framework’s shared-nothing, message-passing concurrency model allows each core to operate with minimal contention, directly processing network events and I/O completion notifications.
The performance gap often widens with increasing numbers of partitions and topics. Kafka’s ZooKeeper dependency (or its KRaft alternative, which is still evolving) can introduce coordination overhead that scales poorly with cluster size. Redpanda, by contrast, is designed as a single binary with no external dependencies, simplifying cluster management and reducing inter-node communication latency for metadata operations.
One aspect that often surprises people is how Redpanda’s continuous data processing pipeline, from network ingress to disk persistence, is designed to be highly pipelined and asynchronous. This means that a request doesn’t wait for the previous one to fully complete at every single stage. Instead, work is broken down into small, independent tasks that are passed between different stages of the pipeline, allowing multiple requests to be in flight across different stages concurrently. This effectively hides latency and maximizes resource utilization.
The next logical step after understanding these throughput differences is to explore how Redpanda’s operational model, particularly its single-binary deployment and built-in schema registry, compares to Kafka’s multi-component ecosystem.