Apache Kafka vs Valkey & Redis-Based Reliable PubSub
In a distributed system, the messaging infrastructure is perhaps the most important component of the overall application architecture. The messaging system will ultimately define the app's scalability, latency, and operational overhead. Apache Kafka is a common choice due to its high-throughput capabilities and the vast ecosystem that supports it. However, it's important to understand Kafka's complexity and its implications for your application development.
Kafka is a distributed event streaming platform built for massive scale, log-based storage, and complex stream processing. It acts as a distributed commit log, separating storage from coordination and using a partition-based model to achieve the throughput it's known for. Java development teams that already have Valkey or Redis in their tech stacks have a simpler, more familiar option: the Redis/Valkey Java client Redisson PRO and its Reliable PubSub feature.
Java developers often see Reliable PubSub as a more efficient choice, as it eliminates the need for a separate message broker. Because all messaging logic executes atomically within Valkey or Redis, Java teams can streamline their development and maintenance cycles. But is it right for you? Let's compare Reliable PubSub and its feature set against Apache Kafka to find out.
Quick Feature Comparison
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Deployment Model | Self-managed or fully managed via vendor services | Self-managed or fully managed via Valkey/Redis vendor services |
| Storage Model | Distributed commit log | Valkey/Redis data structures |
| Global Distribution | MirrorMaker 2 / Cluster Linking | Achievable via Redis Enterprise active-active replication |
Kafka is engineered for massive scale. It can handle trillions of messages and petabytes of data using a distributed commit log. Reliable PubSub utilizes Valkey/Redis data structures to deliver very low latency via a brokerless deployment model.
Messaging Model
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Pub/Sub Model | ||
| Message Queuing | Via consumer groups | |
| Topic-Subscription-Consumer Model | Topics → consumer groups → consumers | |
| Many-to-One Pattern | ||
| Many-to-Many Pattern | ||
| One-to-Many (Fan-out) | Multiple consumer groups | |
| Multiple Subscriptions per Topic | Consumer groups | |
| Multiple Consumers per Subscription | Within consumer group | |
| Push and Pull Delivery | Pull only (push via Connect) | Both native |
Both platforms support Pub/Sub models, message queuing, and complex routing, including fan-out and many-to-many patterns. However, their delivery mechanisms are quite different. Kafka operates strictly on a pull-based model and requires add-ons for push. Meanwhile, Reliable PubSub natively supports both push and pull delivery methods.
Offset & Replay
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Offset-Based Positioning | Core concept | |
| Seek to Position | ||
| Seek to Timestamp | ||
| Seek to Message ID | ||
| Seek to Earliest | ||
| Seek to Latest | ||
| Per-Consumer-Group Offset | Per-subscription | |
| Message Replay | Within retention |
Both platforms use offset-based positioning, enabling applications to search backward or forward by position, timestamp, or message ID, and to replay data from the earliest or latest offsets. The architectural difference is that Kafka stores these offsets in an internal topic, whereas Reliable PubSub stores offsets directly within the subscription structure.
Message Delivery & Ordering
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| At-Least-Once Delivery | Default | Default |
| At-Most-Once Delivery | Configurable | Via AUTO ack mode |
| Exactly-Once Delivery | Transactional API (Kafka-to-Kafka) |
Via deduplication |
| FIFO Ordering | Per-partition | Native FIFO |
| Message Grouping | Via partition key | GroupId with claim timeout |
| Ordering Throughput | Limited by partition | Limited by Valkey/Redis |
Kafka guarantees strict ordering within a single partition. It can achieve exactly-once delivery using a transactional API specifically for Kafka-to-Kafka workflows. Reliable PubSub provides native FIFO ordering across the board, built on exactly-once semantics.
Consumer Model
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Consumer Groups | Core concept | Subscriptions |
| Partition Assignment | Automatic (rebalancing) | Automatic |
| Rebalancing | Can cause pauses | Seamless |
| Pull Consumer | Only model | Short & long polling |
| Push Consumer | Requires Connect | Native |
Kafka's consumer group model heavily relies on partition assignment and rebalancing. Rebalancing has been known to cause processing pauses, though recent Kafka updates aim to minimize these disruptions.
Reliable PubSub bypasses partition-based rebalancing entirely, providing seamless, automatic assignment for its pull and native push consumers without issue.
Acknowledgment & Offset Management
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Manual Offset Commit | commitSync/commitAsync | |
| Automatic Offset Commit | Configurable interval | AUTO mode |
| Individual Message Ack | Offset-based | |
| Negative Acknowledgment | Seek back | Failed/rejected modes |
| Visibility Timeout | Configurable at multiple levels | |
| Dead Letter Queue | Framework-dependent | Native |
| Max Delivery Attempts | Framework-dependent | Configurable |
| Automatic Redelivery | Via offset reset | After visibility timeout |
Kafka relies on offset-based acknowledgment. So, when a consumer commits a specific offset, it implicitly acknowledges all preceding messages up to that point. This makes it impossible to issue individual, out-of-order acknowledgments with native Kafka.
By contrast, Reliable PubSub supports individual message acknowledgment along with explicit negative acknowledgments for failed and rejected states, with configurable visibility timeouts.
Message Features
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Message Headers | Key-value, string values | Any serializable |
| Message Key | For partitioning | groupId |
| Message Timestamp | Create/log append time | |
| Message TTL | Topic-level retention only | Per-message or topic-level |
| Message Delay | ||
| Message Priority | 0-9 levels | |
| Message Size Limit | Configurable (default 1MB) | Configurable |
| Bulk Publishing | Batching | |
| Message Compression | gzip, snappy, lz4, zstd | Via Redis compression |
Reliable PubSub includes numerous granular message enhancements that Kafka lacks natively. This includes priority levels, message delays, and per-message time-to-live (TTL) settings. While Kafka excels with built-in message compression options and topic-level retention limits, it does not support native delayed delivery or fine-grained message prioritization.
Retention & Storage
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Time-Based Retention | ||
| Size-Based Retention | ||
| Configurable Retention Modes | Limited | 3 modes (DELETE_PROCESSED, RETAIN_ALL, OPTIONAL) |
| Retain After Acknowledgment | Within retention | Via retention modes |
Kafka shines as a long-term storage engine, with integrations to services like AWS S3 for archiving. Reliable PubSub manages retention through size limits and time limits. It offers three configurable retention modes (such as DELETE_PROCESSED) that can automatically clean up messages after acknowledgement.
Scalability & Performance
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Horizontal Scaling | Add brokers/partitions | Via Valkey/Redis Cluster |
| Partition Limit | Performance degrades with many partitions | N/A |
| Throughput | Millions of messages/sec | Limited by Valkey/Redis |
| Latency | 2-10ms typical | <10ms |
Kafka handles immense throughput by adding brokers and horizontally scaling partitions, although performance can degrade if an application creates too many partitions. Reliable PubSub scales horizontally via Redis Cluster, avoiding partition limit degradation while maintaining very low latency.
Durability & Replication
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Persistent Storage | Disk-based | Valkey/Redis AOF/RDB |
| Replication Factor | Configurable per topic | Valkey/Redis replication |
| In-Sync Replicas (ISR) | Via Valkey/Redis Cluster | |
| Synchronous Replication | acks=all | Per-operation modes (ACK, ACK_AOF, AUTO) |
| Geo-Replication | MirrorMaker 2 / Cluster Linking | Via Redis Enterprise active-active |
| Sync Failure Handling | Limited | THROW_EXCEPTION / LOG_WARNING modes |
Both Kafka and Reliable PubSub ensure data persistence and robust replication. Kafka relies on disk-based storage, configurable in-sync replicas, and synchronous replication. However, only Reliable PubSub offers per-operation sync modes, allowing developers to handle sync failures at a granular level.
Operations & Monitoring
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Topic Statistics | JMX metrics | |
| Consumer Group Statistics | Subscription stats | |
| Consumer-Level Statistics | Limited | Per-consumer metrics |
| Consumer Lag | ||
| Event Listeners | Via interceptors | Comprehensive |
| Operation Control | Publish/pull toggles | |
| Background Tasks | Compaction, cleanup | None required |
Kafka equips operators with JMX metrics, consumer lag monitoring, and extensive admin APIs. However, Kafka requires admins to run background tasks for cleanup and to compact logs.
Reliable PubSub operates entirely without background tasks. It enhances operational visibility by providing detailed consumer-level statistics, comprehensive event listeners, and the unique ability to toggle publish/pull operations.
Ecosystem & Integrations
| Apache Kafka | Reliable PubSub | |
|---|---|---|
| Kafka Connect | Hundreds of connectors | |
| Schema Registry | Avro, Protobuf, JSON Schema | Via codec |
| Transactions | Exactly-once Kafka-to-Kafka | Via Valkey/Redis Transactions |
The Kafka ecosystem includes hundreds of connectors via Kafka Connect, robust Schema Registry tools, Change Data Capture (CDC), and specialized stream processing engines like ksqlDB. Meanwhile, Reliable PubSub remains focused on high-performance messaging via Valkey/Redis.
Features Unique to Reliable PubSub
Reliable PubSub has a number of features with no equivalent in Kafka. This includes:
Message Priority: Reliable PubSub features priority levels of 0 to 9, so that VIP orders or critical alerts can process ahead of standard bulk queues.
Delayed Message Delivery: Kafka lacks native scheduling, while Reliable PubSub lets you set timed delays.
Visibility Timeout: Kafka expects applications to track processing states manually. Reliable PubSub temporarily hides pulled messages from other consumers, ensuring automatic redelivery if a worker crashes during processing.
Individual Message Acknowledgment: Kafka's offset commit acknowledges an entire batch sequentially. Reliable PubSub allows for acknowledging specific successful messages while isolating and retrying only the failed ones.
Native Dead Letter Queue (DLQ): Kafka relies on frameworks like Spring Kafka for DLQ routing. Reliable PubSub natively routes persistently failed or explicitly rejected messages to a DLQ, based on configurable delivery limits.
Per-Operation Synchronous Replication: Kafka applies durability rules globally, while Reliable PubSub supports flagging certain messages for a full AOF sync.
Per-Message TTL: Kafka only clears messages based on topic-level retention. Reliable PubSub allows individual messages to self-expire cleanly.
Brokerless Architecture: In the Kafka ecosystem, you must maintain a complex cluster of brokers. Reliable PubSub operates entirely within an existing Valkey or Redis infrastructure.
No Consumer Rebalancing: Kafka consumer additions can trigger partition rebalancing that can halt workflows. Reliable PubSub doesn't assign partitions, resulting in smooth scaling and highly predictable latency.
Operation Control: Kafka lacks a native pause switch. Reliable PubSub allows admins to selectively halt publishing or consuming.
Detailed Consumer Statistics: Kafka's lag metrics are often high-level. Reliable PubSub exposes explicit ack/nack counts and unacknowledged message totals for each consumer, so you can quickly identify stuck or slow workers.
Apache Kafka vs. Reliable PubSub: Which One is For You?
Apache Kafka and Redisson PRO's Reliable PubSub are both excellent solutions, but they serve different needs. Kafka is a high-throughput streaming platform backed by a vast ecosystem for schema management, data connectors, and complex stream processing. However, its partition-based architecture introduces layers of operational complexity and strict sequencing rules.
On the other hand, Reliable PubSub is a nimble, low-latency solution for development teams that value simplicity. Organizations with an existing Valkey or Redis implementation and Java developers can quickly build messaging logic into their distributed applications without deploying a standalone broker. You can try Redisson PRO free or browse the full feature comparison with the Community Edition to see what's included.