Apache Kafka® vs ActiveMQ: 5 key differences and how to choose

What is Apache Kafka?

Apache Kafka is a distributed event streaming platform for high-throughput, real-time data processing. It enables applications to publish, subscribe to, store, and process streams of records in a fault-tolerant and scalable manner. Kafka is used for log aggregation, real-time analytics, event-driven architectures, and messaging between microservices.

Kafka organizes data into topics, where producers write messages and consumers read them. It handles large-scale data streams efficiently, using a distributed architecture with replication for fault tolerance. Unlike traditional message brokers, Kafka stores messages persistently, allowing multiple consumers to process the same data independently.

Kafka is adopted for use cases such as log processing, stream processing with frameworks like Apache Flink or Apache Spark, and as a backbone for event-driven microservices.

What is ActiveMQ?

ActiveMQ is an open-source message broker that supports asynchronous communication between distributed applications. It implements the Java Message Service (JMS) API and enables reliable messaging for enterprise applications. ActiveMQ ensures message delivery using queuing and publish-subscribe models, making it suitable for decoupling components in a system.

It supports various messaging patterns, including point-to-point, publish-subscribe, and request-response. ActiveMQ also provides features like message persistence, clustering, and fault tolerance, ensuring high availability.

Unlike Kafka, which is optimized for large-scale event streaming, ActiveMQ is for traditional messaging workloads where guaranteed delivery, transaction support, and flexible routing are priorities. It is used in enterprise integration, financial systems, and service-oriented architectures (SOA).

Key features of Apache Kafka

Apache Kafka is built for high-throughput, fault-tolerant, and scalable event streaming. It provides capabilities for handling real-time data efficiently:

• Distributed architecture: Kafka operates as a cluster, distributing data across multiple brokers for scalability and resilience.
• High throughput & low latency: Optimized for processing millions of messages per second with minimal delay.
• Durable storage: Uses a log-based storage system that retains messages for a configurable period, allowing multiple consumers to read data independently.
• Fault tolerance: Supports data replication across multiple nodes to ensure reliability and prevent data loss.
• Pub-sub & consumer groups: Supports both publish-subscribe and consumer group models for flexible data consumption.
• Stream processing: Works with frameworks like Apache Flink and Apache Spark for real-time analytics.
• Scalability: Easily scales horizontally by adding more brokers without downtime.

Related content: Read our guide to Kafka management

Key features of ActiveMQ

ActiveMQ is a message broker to ensure reliable communication between distributed applications. It provides messaging features for enterprise systems:

• JMS compliance: Implements the Java Message Service (JMS) API, ensuring compatibility with Java-based applications.
• Multiple messaging models: Supports point-to-point (queues) and publish-subscribe (topics) messaging patterns.
• Message persistence: Stores messages persistently using databases or file-based storage for reliable delivery.
• Clustering & high availability: Supports clustering and failover mechanisms to maintain service continuity.
• Flexible routing: Offers message filtering, virtual topics, and composite destinations for dynamic message routing.
• Security & access control: Provides authentication, authorization, and SSL/TLS encryption for secure messaging.
• Protocol support: Works with multiple protocols, including MQTT, AMQP, STOMP, and OpenWire, enabling interoperability with various systems.

ActiveMQ vs. Kafka: The key differences

Here’s an overview of the main areas in which these two solutions differ.

1. Architecture

Apache Kafka follows a distributed architecture for large-scale event streaming. It consists of multiple brokers forming a cluster where data is distributed across partitions for parallel processing. Kafka uses a commit log storage mechanism where messages are written sequentially to disk, optimizing performance. Producers send messages to topics, which are split into partitions, and consumers read from these partitions independently. This design ensures high availability, scalability, and fault tolerance through replication.

ActiveMQ follows a traditional broker-based architecture for enterprise messaging. It supports both broker-based and peer-to-peer messaging, where messages are stored in queues (point-to-point) or topics (publish-subscribe) before being delivered to consumers. While ActiveMQ can be clustered to improve availability and scalability, it does not natively distribute data across multiple nodes like Kafka. Instead, it relies on techniques such as networked brokers and master-slave failover configurations.

Learn more in our detailed guide to Kafka architecture

2. Messaging model

Kafka’s messaging model is based on a distributed log, where producers append messages to a topic, and consumers read them sequentially at their own pace. Consumers can belong to consumer groups, where each message is processed by one consumer within the group, enabling parallel consumption. Messages in Kafka persist for a configurable retention period, allowing multiple consumers to read the same data at different times. This makes Kafka suitable for event-driven architectures and stream processing.

ActiveMQ supports both point-to-point (queue-based) and publish-subscribe (topic-based) messaging models. In the queue model, each message is delivered to only one consumer, ensuring one-to-one communication. In the topic model, messages are broadcast to all subscribers, supporting one-to-many communication. ActiveMQ emphasizes guaranteed message delivery, supporting features like message acknowledgments and transactions. ActiveMQ does not retain messages indefinitely; messages are removed once they are consumed, unless durable subscriptions are used.

3. Throughput and performance

Kafka is optimized for high throughput and low latency, making it capable of processing millions of messages per second. Its architecture leverages sequential disk writes, partitioning, and batching to maximize efficiency. Kafka’s consumer model is pull-based, allowing consumers to fetch messages at their own pace without overloading the broker. This makes Kafka suitable for handling massive data streams, such as real-time analytics, log processing, and telemetry data collection.

ActiveMQ is not built for the same level of throughput as Kafka. It is optimized for enterprise messaging use cases where guaranteed delivery and transactional integrity are more important than raw speed. ActiveMQ uses a push-based model, where messages are actively delivered to consumers, which can introduce backpressure if consumers cannot process messages quickly enough. While ActiveMQ performs well for moderate messaging workloads, it is not designed for the extreme scale of Kafka.

4. Fault tolerance and durability

Kafka provides strong fault tolerance through data replication. Each partition in a Kafka topic is replicated across multiple brokers, ensuring data remains available even if a broker fails. Kafka’s distributed storage system ensures that messages persist for a configurable retention period, allowing consumers to recover from failures without message loss. The use of leader-follower replication enables automatic failover in case of node failures, maintaining high availability.

ActiveMQ ensures fault tolerance using clustering, high-availability configurations, and message persistence. It supports master-slave configurations where a backup broker takes over if the primary broker fails. Messages can be persisted using databases or file-based storage, ensuring they are not lost if a broker crashes. However, recovery from failures in ActiveMQ often requires additional configurations and may introduce delays compared to Kafka’s built-in redundancy.

5. Scalability

Kafka is designed for horizontal scalability. New brokers can be added to a cluster dynamically, and partitions can be redistributed across brokers to balance the load. This makes Kafka highly scalable for handling large data volumes. Since Kafka consumers operate independently, more consumers can be added to process data in parallel without affecting performance. This makes Kafka suitable for applications requiring real-time event processing at scale.

ActiveMQ supports scalability through clustering and networked brokers, but it is not as seamless as Kafka’s partitioned model. Scaling ActiveMQ often requires configuring multiple brokers and ensuring proper load balancing. While it can handle increased workloads by adding more nodes, its architecture is more suited for enterprise applications rather than large-scale event streaming. ActiveMQ works well for moderate messaging loads but does not offer effortless scalability for handling high-throughput data streams.

Kafka vs. ActiveMQ: How to choose?

Selecting between Apache Kafka and ActiveMQ depends on the application’s needs, including messaging patterns, scalability, and durability requirements. Here are key considerations to help organizations make the right choice:

• Latency sensitivity: If the application requires ultra-low latency for transactional messaging (e.g., financial transactions), ActiveMQ may be a better fit, as Kafka is optimized for throughput over minimal latency.
• Message ordering guarantees: Kafka preserves message order within partitions, making it suitable for event-driven workflows. ActiveMQ ensures order within queues but may not offer the same guarantees in distributed setups.
• Operational complexity: Kafka requires more infrastructure management, including cluster maintenance, partition rebalancing, and broker monitoring, while ActiveMQcan be simpler to set up and manage.
• Temporary vs. persistent messaging: ActiveMQ is for temporary message delivery, where messages are consumed and removed, whereas Kafka retains data for a configurable period, supporting replays and historical analysis.
• Backpressure handling: Kafka’s pull-based consumption model prevents slow consumers from overwhelming the system, while ActiveMQ’s push-based approach can lead to congestion if consumers cannot keep up.
• Integration with legacy systems: ActiveMQ’s support for JMS and multiple protocols (AMQP, MQTT, STOMP) makes it a better choice for integrating with legacy enterprise applications.
• Message expiry and TTL (time-to-live): ActiveMQ allows message expiration settings, ensuring outdated messages are removed. Kafka, by design, retains messages based on a time or size policy rather than individual expiry rules.
• Security and access control complexity: Kafka’s security features (like ACLs and SASL authentication) require additional configuration, whereas ActiveMQ provides built-in authentication and authorization with simpler setup.

Maximize Kafka’s power with Instaclustr

Apache Kafka is a phenomenal tool for building real-time data pipelines and streaming applications, but effectively managing and scaling Kafka can be a daunting challenge. That’s where Instaclustr for Apache Kafka steps in, making your Kafka experience seamless, efficient, and worry-free.

Instaclustr offers a fully-managed Kafka service, handling everything from deployment to ongoing performance optimization. By taking care of Kafka’s complexity, it lets businesses focus on what truly matters—extracting value from their data. This includes automated monitoring, patches, and updates, saving your team time and reducing operational burdens. The result? A robust streaming platform you can depend on without the headaches of managing it in-house.

Built for reliability and scalability

With Instaclustr for Apache Kafka, you get an environment built for reliability and high availability. Their managed service runs on fault-tolerant infrastructure with built-in replication, ensuring that your data streams are always available, even during unexpected disruptions. And when your business grows, scaling your Kafka cluster becomes effortless, with Instaclustr’s expert team offering guidance to optimize performance.

Open source freedom with enterprise-grade security

Instaclustr is committed to open-source technology, giving you vendor-neutral flexibility and avoiding lock-in. At the same time, security is never compromised. Instaclustr for Kafka is equipped with enterprise-grade security features, including encryption, role-based access controls, and compliance with industry standards.

Switching to Instaclustr for Kafka means more than just outsourcing management; it’s about empowering your team with a reliable, scalable, and efficient streaming solution. Simplify your Kafka operations, and take your data-driven initiatives to the next level with a trusted partner by your side.

For more information:

• How to size Apache Kafka® clusters for Tiered Storage: Part 1–A Kafka performance model for SSDs, network, and I/O
• 12 Kafka Best Practices: Run Kafka Like the Pros
• Apache Kafka cluster: Key components and building your first cluster
• Apache Kafka® versions: Managing upgrades and tips for success