When architecting distributed systems, your mechanism of communication forms the foundation for the entire infrastructure. How messages, events, and data are exchanged and persisted across the network directly shapes the performance, scalability, and reliability of the overall system. When it comes to software that supports this communication, Kafka and NATS are two of the most commonly assessed solutions.
NATS is a lightweight but powerful, high-performance data layer known for its simplicity and efficiency across environments. It's ideal for microservices, IoT messaging, and real-time applications. On the other hand, Apache Kafka is a distributed event streaming platform optimized for high-throughput data streams. It's commonly used for data pipelines, log aggregation, and real-time analytics. Both are open source frameworks popular for enabling high-volume data processing and facilitating real-time or near-real-time communication between disparate systems and components.
While Apache Kafka and NATS share some similarities, their underlying architecture and resultant strengths are different. In this article, you'll explore these differences, looking at each framework's architecture and complexity, performance and ease of scalability, the message delivery guarantees and messaging patterns offered, and finally, their respective use cases and ideal applications.
Architecture and Complexity
Both platforms were conceptualized around the same time period (2010) but with different problems in mind. This is reflected heavily in their architecture and the growth of the platforms since then.
NATS: Lightweight Cloud- and Edge-Focused Architecture
NATS was initially designed to be a lightweight messaging framework that functions efficiently "everywhere," especially in cloud environments and at the edge. It runs on a client-server system where your server could be a process on your local machine, enabling quick, low-latency, and performant communication between your infrastructure's components (clients).
Currently, the NATS framework encompasses two modules: Core NATS and NATS JetStream. Core NATS represents the base functionality within the NATS infrastructure and offers publish-subscribe, request-reply, and queue group messaging for your different communication use cases. NATS JetStream is built into the NATS server, adding a layer of persistence and durability capabilities. This allows messages to be stored and replayed later as needed. While NATS JetStream provides temporal de-coupling of publishers and subscribers and higher qualities of service, Core NATS alone is sufficient and optimal for fast request paths in scalable services with reliable but not guaranteed quality of service.
Kafka: Durable, High-Volume Data Streaming
Apache Kafka was made to handle high-volume data processing and streams "big data" as quickly as possible to the downstream processes, such as data analytics services and other applications. Kafka is meant to serve as the central hub for integrating digitized information from various sources in real time.
Kafka is fundamentally built on a log-based architecture where data is organized into topics. Producers (any systems generating data) append data to these topics, and consumers receive data from them. Kafka brokers are responsible for storing and forwarding messages to consumers. Building on this base, a Kafka cluster can consist of multiple brokers, each managing several topics. Each topic can, in turn, have multiple partitions enabling parallelism of messaging consumption.
The architectural tradeoff for Kafka's durability and scalability is additional complexity often encountered when scaling. To achieve and maintain high availability and prevent data loss, Kafka relies on its replication factor. The operational demands increase with the management of multiple brokers, topics, and partitions that need to work well in parallel and efficiently process data into downstream services. Additionally, configuration policies, such as those for data replication and retention, need to be effectively handled to avoid security and data privacy concerns. For example, data sprawl, which is the uncontrolled growth of data across various topics and partitions, can lead to inefficiencies and potential security risks.
Summary
NATS was built with a focus on simplicity and speed. This means it's a less complex messaging platform than Kafka in terms of configuration, computational demands, and managerial overhead. This is reflected in the use cases the frameworks can be applied to. NATS is ideal for microservices use cases that require low-latency messaging and real-time communication between applications. Its protocol facilitates rapid message delivery with fewer components and less configuration than Kafka.
Performance and Scalability
Both platforms are highly performant, offering horizontal scaling capabilities. Let's look at how performance and scaling are handled in the two messaging platforms.
NATS: High-Performance, Low-Latency Messaging
NATS is optimized to provide low-latency communication in real time at millions of messages per second. By enabling JetStream, NATS can additionally manage large-scale data streams by introducing persistence, thus enhancing the system's durability and preventing data loss.
To ensure high availability and scalability, NATS servers can be configured in a full mesh cluster, where each server connects to all other servers in the cluster. This configuration avoids a single point of failure (SPOF). NATS uses a one-hop message routing mechanism, ensuring messages are delivered efficiently without looping through the cluster. JetStream-enabled servers distribute streams among themselves, allowing clients to connect to any server in the cluster to access the streams. This setup enables clients to be distributed across the cluster, while streams are efficiently managed and balanced across the JetStream-enabled servers within the cluster.
NATS also supports the creation of super-clusters by connecting multiple clusters together, further enhancing scalability. You can extend this architecture with leaf nodes, allowing remote nodes to join the super-cluster with built-in guaranteed store-and-forward mirroring and sourcing between streams.
Kafka: High-Performance, Scalable Streaming
Apache Kafka is a distributed, fault-tolerant, high-performance, and reliable streaming platform. It can potentially process millions of records per second at a rate of over 500 MB per second. It has higher latency than NATS due to its batching and compression of messages, but it can process large volumes of data at near-real-time speeds.
Each component of Kafka is designed to scale horizontally and process workloads in parallel to better handle data traffic. As Kafka can only have a single client application instance and within it just a single thread consuming from a partition, partitioning topics enhances throughput. Additionally, you can configure and load balance consumer groups, adding consumers as workloads increase. This helps ensure efficient and scalable data processing.
Summary
Kafka's approach to processing data leads to better performance in data throughput but higher latency when compared with NATS. NATS is not only lighter in terms of its small binary footprint but also because many NATS built-in features would require additional processes or JVMs in Kafka. While Kafka has moved away from requiring Zookeeper, many deployments still rely on it for managing brokers, adding another layer of complexity compared to NATS. NATS excels at processing messages across your distributed environments, and Kafka’s strength lies in reliably handling high-throughput data streams through its centralized broker model, which leverages robust partitioning and replication mechanisms.
Message Delivery Guarantees
When designing distributed systems, the message delivery guarantees used can have an impact on your system's performance and reliability. You should always consider various approaches to identify the best fit for their problem context.
NATS: Flexible Delivery Guarantees
NATS supports different messaging semantics, offering flexible delivery options tailored to different scenarios. Core NATS offers "at-most-once" delivery, ensuring that messages from a given publisher are delivered intact and in order. However, this guarantee does not extend across different publishers. NATS servers with JetStream enabled provide additional delivery guarantee options, including "at-least-once" and "exactly-once" delivery. NATS also has the ability to acks individual messages within your stream, which includes automated message re-delivery. It offers various acknowledgment types including Acknowledgements, Double Acknowledgements, and Negative Acknowledgements, along with a backoff mechanism for re-delivery.
Kafka: Robust Message Guarantees and Ordering
Apache Kafka also supports these message guarantees at both the producer and consumer end. Message order is also guaranteed within a single topic partition. Maintaining global order can be difficult across multiple partitions, as Kafka's first in, first out (FIFO) ordering is based on an assigned offset dependent on the time of Kafka ingestion, not the time the event was generated.
Summary
Apache Kafka and NATS JetStream both provide strong durability guarantees, each writing messages to disk and supporting replication. While NATS emphasizes high performance, JetStream adds persistence and robust delivery guarantees similar to Kafka’s.
Both Kafka and NATS ensure ordered delivery of messages within a defined context: Kafka within partitions, and NATS with streams. Notably, in NATS JetStream, strict in-order delivery is affected only by acknowledgment settings.
Messages can be individually acknowledged or re-delivered based on configuration, allowing control over re-delivery behavior and ordering. This setup allows for flexibility in parallel processing without compromising ordered delivery, a distinction from Kafka’s partition-based parallelism that limits ordering to partitions.
Messaging Patterns
There are a number of messaging pattern architectures commonly used to exchange data within infrastructure, including pub-sub, fan-out, unidirectional streaming, and bidirectional streaming.
Due to their architectural designs and build choices, both NATS and Kafka can accommodate various messaging patterns, each configurable in distinct ways to suit your specific requirements.
NATS: Subject-Based Messaging
NATS utilizes subject-based messaging. This allows for flexible many-to-many, fan-out communication messaging patterns such as publish-subscribe, request-reply, and queue groups. Each subject in NATS can be structured with subject hierarchy and wildcards, allowing for extensive communication options.
For example, here's a publisher sending a fan-out message to multiple subjects and subscribers at once using subject wildcards:
# Subscriber clientsnats sub flex.testnats sub flex.test.minenats sub flex.proof
# Publisher with wildcard subjectnats pub flex.> "NATS MESSAGE"NATS' subject-based addressing extends into streams, allowing multiple subjects to be stored in a single stream. Client applications can use server-side filters to receive only the messages that match specific subjects.
The connection in NATS is bidirectional, allowing the clients to subscribe and publish to subjects. This enables communication patterns where a continuous flow of messages can be sent between sender and receiver as well as between receiver and sender.
NATS is flexible enough to support more communication patterns than might be available out of the box. For example, you can leverage multiple source or mirror streams within and across clusters and leaf nodes. With NATS Jetstream, you can configure replication between server nodes in the same cluster and implement message persistence and event replays in your workflows.
NATS also features dynamic request permissioning and request subject obfuscation. Dynamic request permissioning allows fine-grained control over who can send requests and receive responses, thus enhancing security. Request subject obfuscation helps protect the messaging infrastructure by hiding the specific subjects used for requests.
Queueing
One strength of NATS JetStream is its ability to serve as a message queue, providing flexible, high-performance queuing capabilities directly within its streaming model. This allows using streams as queues, enabling load-balanced processing, individual message acknowledgment, and configurable redelivery options.
In contrast, while Kafka is developing similar queue functionality, this capability remains in the proposal stage and has not yet been released. Until then, NATS offers a significant advantage for applications requiring queuing within a streaming infrastructure, with queue support already available in JetStream
Kafka: Topic-Based Messaging
Kafka is built solely on the publish-subscribe model and topic-based messaging. It enables load balancing through partitioning and consumer groups, allowing multiple consumers to process data in parallel from different partitions. However, implementing any messaging pattern outside of unidirectional streaming, where a sender emits data continuously to a receiver, can be complex.
For example, fan-out messaging would require the use of multiple consumer groups that each subscribe to the same topic partitions and receive data in the same order. Any form of bidirectional communication requires application code to be manually tweaked to correlate requests and replies across multiple topics. While most patterns are possible with Kafka, the cost often involves increasing complexity and infrastructure.
Summary
NATS offers simple, lightweight primitives that prioritize real-time performance while allowing additional flexibility through composition and configuration. Kafka's focus on durability and throughput supports pub-sub messaging well with strong scalability out of the box. However, implementing alternate messaging patterns incurs additional complexity and generally higher latency compared to NATS.
Use Cases
When designing systems, you can only identify an "ideal" solution if you fully understand the problem being solved. Each technology and approach has unique strengths and weaknesses depending on the use case, and your chosen solution will perform best in use cases that align closely with its architecture.
NATS: Low-Latency, Lightweight Messaging in Diverse Environments
NATS is tailored for scenarios requiring lightweight, low-latency, performant messaging. If you prioritize low latency and ease of use, it's a viable alternative to other messaging frameworks like Kafka, Pulsar, RabbitMQ, and Redis. Common use cases include microservice communication and coordination, IoT and embedded devices, processing and delivery at the edge, and any application prioritizing real-time capabilities.
Kafka: Complex Data Processing and Event-Driven Architectures
Kafka's throughput capabilities shine in event streams and complex big data processing. In scenarios with no need for guaranteed real-time communication, Kafka's latency is fast enough to stream or batch process your mission-critical data across your infrastructure.
Kafka's features are ideal for building event-driven architectures, stream processing, and big data applications, as well as aggregating and analyzing log data from various systems, creating near-real-time data pipelines, and ensuring integration with all of your systems.
Summary
Kafka and NATS each offer unique strengths tailored to different types of applications. Kafka excels in scenarios requiring high throughput, durability, and complex event processing. NATS, on the other hand, is ideal for applications needing low-latency messaging, simplicity, and lightweight communication. Both systems are flexible enough to support many messaging patterns, architectures, and use cases. In NATS, this flexibility is achieved by composing and configuring its core components. Conversely, Kafka's flexibility comes from adding additional pipeline elements and replicated nodes to handle more complex use cases. Choosing between them depends on your project's specific requirements and constraints.
Other Factors to Compare
NATS's lightweight protocols offer a smaller footprint within your infrastructure, enabling you to build extensively on it without overtaxing the integration. It has significantly lower resource requirements for servers, allowing you to use fewer cloud resources and reduce costs across your cloud infrastructure. NATS also offers more security options with multi-tenancy and a flexible security model, including distributed authentication and delegated administration.
Both frameworks are open-source and boast thriving communities of contributors and strong developer ecosystems. This is reflected in their library of integrations and third-party connectors in Kafka and NATS, which significantly reduce integration time when using established enterprise systems and widely adopted services.
Conclusion
In this article, we explored the differences between NATS and Kafka, examining their architecture, ease of use, performance, potential for scalability, and the messaging options and delivery guarantees they support. When deciding between NATS and Kafka, consider their unique advantages and limitations in relation to your project requirements and operational needs.
NATS's lightweight, low-latency messaging is well-suited to microservices systems and fast message delivery. In contrast, Kafka is ideal for data pipelines and near real-time analytics applications due to its high throughput, resilience, and complicated event processing.
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