Event-Driven Architectures Beyond Kafka

Software Architecture, Event-Driven Systems, Messaging, Distributed Systems
Event-Driven Architectures Beyond Kafka

Event-driven architecture (EDA) has become the backbone of modern distributed systems. It enables loosely coupled services, real-time communication, and scalable processing pipelines. In most discussions, Apache Kafka dominates the narrative as the go-to event streaming platform.

While Kafka is powerful, it is not a silver bullet. Its strong durability and replayability come at the cost of operational complexity and resource consumption. For some use cases, Kafka is overkill. For others, its design may not align with real-world constraints.

This article explores event-driven architectures beyond Kafka—with a focus on NATS, Apache Pulsar, and custom lightweight brokers—and compares them with real-world performance benchmarks.

The Rise of Kafka and Its Limitations

Apache Kafka became the de facto standard for event streaming due to:

  • High throughput (millions of messages per second).
  • Strong durability guarantees.
  • Replay capabilities for event sourcing and stream processing.
  • Rich ecosystem (Kafka Streams, kSQL, Connectors).

However, Kafka introduces trade-offs:

  1. Operational Overhead: Requires ZooKeeper (or KRaft), multiple brokers, and careful partitioning.
  2. Resource-Intensive: JVM-based, disk-heavy architecture.
  3. Latency: Tuned for throughput, but latency-sensitive applications may suffer (2–20ms common).
  4. Small-Scale Overkill: Deploying Kafka for systems with modest traffic often leads to unnecessary complexity.

This is where alternatives shine.

Alternative 1: NATS

NATS is a lightweight, high-performance messaging system originally built for cloud-native applications.

Key Features

  • Extremely low latency (often <1ms).
  • Lightweight: Single binary, simple clustering model.
  • Pub/Sub, Request/Reply, and JetStream for persistence.
  • Cloud Native: Works seamlessly with Kubernetes, service discovery, and modern infra.

Use Cases

  • IoT messaging (millions of devices).
  • Internal service communication (drop-in replacement for HTTP calls).
  • Low-latency trading or sensor data streams.

Example

Producer in Go:

nc, _ := nats.Connect(nats.DefaultURL)
nc.Publish("updates", []byte("Order received: #123"))

Consumer:

nc, _ := nats.Connect(nats.DefaultURL)
nc.Subscribe("updates", func(m *nats.Msg) {
    fmt.Printf("Received: %s\n", string(m.Data))
})

Trade-Offs

  • Historically lacked strong durability (now solved via JetStream).
  • Smaller ecosystem compared to Kafka.
  • Better suited for ephemeral messaging rather than long-term event storage.

Alternative 2: Apache Pulsar

Apache Pulsar was designed at Yahoo as a cloud-native alternative to Kafka with better scalability and multi-tenancy.

Key Features

  • Separation of compute and storage (BookKeeper for storage).
  • Geo-replication built-in.
  • Multi-tenancy: One cluster can host many independent apps.
  • Tiered storage: Cold data can be offloaded to S3/Blob storage.
  • Flexible Messaging Model: Queues + streams in one system.

Use Cases

  • Global-scale event streaming with multiple data centers.
  • Multi-tenant SaaS platforms.
  • Analytics pipelines needing tiered storage.

Example

Producer in Java:

PulsarClient client = PulsarClient.builder()
    .serviceUrl("pulsar://localhost:6650")
    .build();

Producer<byte[]> producer = client.newProducer()
    .topic("persistent://public/default/orders")
    .create();

producer.send("Order received: #123".getBytes());

Consumer:

Consumer<byte[]> consumer = client.newConsumer()
    .topic("persistent://public/default/orders")
    .subscriptionName("order-subscribers")
    .subscribe();

Message<byte[]> msg = consumer.receive();
System.out.printf("Received: %s", new String(msg.getData()));
consumer.acknowledge(msg);

Trade-Offs

  • More complex to operate than NATS (BookKeeper adds moving parts).
  • Latency slightly higher than NATS (but competitive with Kafka).
  • Still growing ecosystem (not as extensive as Kafka).

Alternative 3: Custom Lightweight Brokers

Sometimes, neither Kafka nor Pulsar nor NATS is the best fit. For niche scenarios, teams build lightweight brokers tailored to their needs.

Example Scenarios

  • A trading firm builds a custom in-memory broker using Redis Pub/Sub with persistence disabled for ultra-low latency.
  • A gaming platform uses ZeroMQ for P2P event distribution.
  • A startup implements a lightweight Go broker for 10k QPS workloads instead of introducing Kafka’s operational overhead.

Benefits

  • Optimized for a very specific workload.
  • Extremely lightweight compared to Kafka/Pulsar.
  • Avoids vendor lock-in.

Risks

  • Lack of community support.
  • No guarantees (durability, scaling) unless carefully engineered.
  • Potential for “reinventing the wheel.”

Benchmark Comparisons

Here’s a simplified view of performance from real-world benchmarks (latency and throughput figures vary depending on hardware and tuning):

Event Broker Comparison Diagram

Interpretation:

  • Kafka excels for heavy, durable event streams but is operationally heavy.
  • Pulsar shines for geo-distributed, multi-tenant workloads.
  • NATS dominates ultra-low latency internal messaging and IoT.
  • Custom brokers fill niche gaps where extreme tuning is required.

When to Choose What

  • Kafka: If you need durable, replayable event streams with a mature ecosystem. Best for event sourcing, large data pipelines, analytics.
  • Pulsar: If you need geo-replication, multi-tenancy, and tiered storage in one platform.
  • NATS: If you need ultra-low latency, simple ephemeral messaging, or IoT-scale workloads.
  • Custom: If you have extreme niche requirements and the expertise to maintain your own system.

Best Practices for Event-Driven Systems

Regardless of the broker:

  1. Design for Idempotency: Consumers should handle duplicate events gracefully.
  2. Use Backpressure Mechanisms: Prevent consumers from being overwhelmed.
  3. Monitor Lag and Throughput: A healthy broker is only as good as the monitoring around it.
  4. Secure Communication: TLS, mTLS, or token-based auth are essential for production.
  5. Plan for Failures: Network partitions, broker crashes, and message redelivery must be tested.

So Summary is ...

Kafka popularized event-driven architectures, but it is not always the right choice. NATS, Pulsar, and lightweight brokers offer compelling alternatives depending on latency requirements, durability needs, and operational constraints.

The key is to align your broker choice with your actual use case:

  • Do you need replayable streams? Kafka.
  • Do you need geo-distributed, multi-tenant scaling? Pulsar.
  • Do you need ultra-fast messaging with minimal overhead? NATS.
  • Do you need a hyper-specialized broker? Consider custom implementations.

The best architects are not those who always reach for Kafka, but those who understand the trade-offs and choose the tool that fits the problem best.