In this data-driven era, message queues have become the “nervous system” of modern application architecture. From microservices communication to real-time data stream processing, from IoT devices to AI systems, message queues are ubiquitous. However, with the exponential growth of business complexity, traditional message queues are facing unprecedented challenges. It is time to rethink this field with a fresh perspective.
🔥 The “New Era Dilemma” of Message Queues
In daily architecture design and system operations, have you encountered these issues?
1. The Pain of Protocol Fragmentation:
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🤔 Scenario 1: IoT projects require MQTT
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🤔 Scenario 2: Big data processing requires Kafka
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🤔 Scenario 3: Microservices communication requires RabbitMQ
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🤔 Scenario 4: Financial transactions require RocketMQ
The result? A company may need to maintain four different messaging systems, each with its own:
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Deployment method
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Monitoring system
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Operation and maintenance process
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Learning curve
This not only increases technical complexity but also acts as an “invisible killer” of team efficiency.
2. Limitations of Integrated Storage and Computing Architecture
Traditional MQs adopt an integrated storage and computing architecture, which exposes serious adaptation issues in cloud-native environments:
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Difficulties in elastic scaling: Taking Kafka as an example, scaling requires Partition Rebalance, a process that can last for hours, affecting business performance and even leading to message accumulation.
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Inability to support Serverless: Storage and computation are tightly coupled, with each Broker node needing to maintain local storage, making true on-demand computation and second-level cold starts impossible.
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Low resource utilization: Compute-intensive and storage-intensive loads cannot be scheduled independently, often resulting in situations where CPU is idle but disk is overloaded, or disk is idle but CPU is insufficient.
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High operational complexity: When a node fails, both computation and storage recovery need to be handled simultaneously, with data migration and load balancing affecting each other, leading to long recovery times.
3. The Dilemma of Single Storage Engine
Traditional message queues typically only support one storage engine, making it difficult to flexibly adapt to the differentiated needs of different business scenarios:
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High-performance scenarios: Real-time transactions, IoT data collection, etc., require extremely low latency reads and writes, but the data volume is relatively small, suitable for memory or SSD storage.
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Big data scenarios: Log collection, data analysis, etc., have low performance requirements but huge data volumes, requiring low-cost object storage.
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Mixed scenarios: In the same business system, there are both hot data requiring high-performance access and cold data needing long-term low-cost storage.
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Cost optimization needs: Unable to automatically select the most suitable storage medium based on data access patterns, leading to high overall costs.
4. Bottlenecks in Latency Performance
Traditional message queues generally exhibit unstable performance in high-concurrency scenarios:
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Severe latency jitter: Under high load, message processing latency is extremely unstable, suddenly jumping from milliseconds to seconds.
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Unpredictable processing time: The same message takes vastly different times to process at different points in time, making it difficult to meet real-time requirements.
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Significant performance degradation: As the number of connections and message volume increases, system performance shows a cliff-like decline.
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GC pauses impact: Garbage collection in Java systems leads to periodic service interruptions, affecting user experience.
5. New Challenges in the AI Era
With the explosive development of AI technology, message queues face unprecedented new challenges:
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Exponential growth in data volume: AI training data and multimodal data (text/image/video/audio) have increased data volume from TB to PB, making traditional MQ storage architecture difficult to bear.
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Complex and diverse AI workflows: From data collection to model training to inference services, each stage has completely different requirements for latency, throughput, and persistence from the message queue.
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Cost reduction and efficiency improvement demands for AI infrastructure: GPU computing power is expensive, and training data storage costs are high, requiring elastic scheduling and intelligent tiered storage to reduce costs and improve efficiency.
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Multi-tenant AI platform requirements: Different AI teams need resource isolation, fine-grained permission control, and cost accounting management.
💡 RobustMQ: A Solution Born for the Future
Against this backdrop, RobustMQ has emerged.
It is not just another message queue, but a rethink and redesign aimed at the AI era and cloud-native needs.
The core design goals of RobustMQ: AI-Ready, Cloud-Native, Protocol-Unified, Storage-Flexible.
1. 🦀 Rust: The Perfect Combination of Performance and Safety
Choosing Rust as the development language is not just a pursuit of technical fashion, but a well-considered technical choice.
Why is Rust the ideal choice for message queues?
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Memory safety: Eliminates security risks such as dangling pointers and buffer overflows.
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Zero-cost abstraction: High-level language features do not sacrifice runtime performance.
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No GC pauses: Friendly to latency-sensitive scenarios.
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Concurrency primitives: Native async/await support for large-scale concurrency.
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Mature ecosystem: High-quality libraries such as Tokio, Serde, RocksDB.
2. 🌐 Multi-Protocol Unification: One Cluster, Supporting All Scenarios
One of the core innovations of RobustMQ is the multi-protocol unified architecture:
┌─────────────────────────────────────────────┐│ RobustMQ Cluster │├─────────────┬─────────────┬─────────────────┤│ MQTT │ Kafka │ AMQP ││ Port: 1883 │ Port: 9092 │ Port: 5672 ││ ├─ IoT │ ├─ Big Data │ ├─ Enterprise Integration ││ ├─ Mobile App│ ├─ Stream Processing │ ├─ Microservices ││ └─ Real-time Communication │ └─ Log Collection │ └─ Transaction Messages │└─────────────┴─────────────┴─────────────────┘What does this mean?
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Operational costs reduced by 80%: From four systems to one system.
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Significantly reduced learning costs: One API, one monitoring system, one deployment process.
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Improved resource utilization: A unified resource pool avoids resource silos.
3. ☁️ Designed for Serverless and Cost
From the very beginning, RobustMQ’s architecture design has targeted two core goals: Serverless and cost optimization.
Core advantages:
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Serverless ready: Stateless computing layer, supporting second-level cold starts and on-demand scaling.
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Plugin-based storage: Supports multiple storage engines, from memory to object storage, flexibly adapting to different scenario needs.
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Elastic scaling: No data migration required, scaling time reduced from hours to seconds.
🎯 Technical Architecture: Modern Design of Storage and Computing Separation
RobustMQ adopts a storage-computing separation architecture, consisting of three core components:
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Broker Server: Stateless protocol processing layer, supporting multiple protocols and millions of connections.
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Meta Service: Raft-based scheduling layer, responsible for cluster management and service discovery.
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Journal Server: Plugin-based storage layer, supporting multiple storage engines and intelligent tiering.
🌟 Core Features: Redefining Message Queues
Core feature highlights
1. 🔌 Multi-protocol unification: A single cluster supports MQTT, Kafka, AMQP, etc., eliminating technology stack fragmentation.
2. 🚀 Extreme performance: Based on Rust’s zero-cost abstraction, a single machine can handle millions of connections with microsecond-level latency.
3. 💾 Plugin-based storage: Intelligent tiered storage, hot data accessed in memory, cold data stored as objects, reducing costs by 90%.
4. ☁️ Serverless: Capable of rapid stateless elastic scaling, supporting second-level cold starts and on-demand computation.
5. 🔐 Enterprise-level security: Multiple authentication methods, fine-grained permission control, end-to-end encryption.
6. 📊 Full-link observability: Built-in monitoring alerts, performance analysis, and link tracing.
🛠️ Development Experience: Making Complexity Simple
Simple and easy-to-use development experience
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Multiple deployment methods: Source compilation, pre-compiled binaries, Docker, Kubernetes, meeting different environment needs.
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Visual management interface: The web console provides complete functions for cluster monitoring, user management, configuration management, etc.
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Powerful CLI tools: Support for user management, permission control, real-time monitoring, message testing, and more.
📈 Current Development Status: A Reflection of Community Power
Community development status
GitHub data: 1000+ Stars, 100+ Forks, 50+ Contributors, 2100+ Commits, an active global developer community.
Technical maturity: Complete support for MQTT protocol in production, Kafka protocol under development, AMQP and RocketMQ protocols planned.
Deployment support: Supports various deployment modes including standalone, cluster, Docker, Kubernetes.
🗺️ 2025 Roadmap: Moving Towards Production Level
2025 development roadmap:
Q4 goal: Achieve production readiness for MQTT, release the first version 0.2.0.
2026 plans:
Core tasks: Improve Kafka capabilities, enhance protocol compatibility and performance.
Long-term goal:
To become an Apache top-level project, alongside Kafka and Pulsar.
🎯 Our Vision: Open Source First
Open source is not only our development model but also our core value.
🌍 Open Source Drives Innovation
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Fully open source code: All core code is fully open source under the Apache 2.0 license, with no commercial restrictions.
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Community co-construction and sharing: Building together with global developers, allowing technological innovation to benefit everyone.
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Transparent development: Project decisions, technical routes, and code reviews are all open and transparent.
🚀 Pursuit of Technical Excellence
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Extreme performance: A high-performance message queue built with Rust, pursuing optimization at every microsecond.
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Innovative architecture: Storage-computing separation, multi-protocol unification, using innovation to solve traditional MQ pain points.
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Engineering aesthetics: Elegant code design, making technology an art.
🏆 The Road to Apache
Our goal is to become an Apache top-level project, which represents:
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A technical benchmark alongside projects like Kafka and Pulsar
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Global open source community recognition and trust
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International influence of Chinese open source projects
Open source first, technology above all, this is the original intention of RobustMQ and our commitment to the technical community.
🌟 Why Choose RobustMQ?
RobustMQ has advantages that traditional MQs cannot compare to: Rust-driven extreme performance, multi-protocol unified platform, storage-computing separated Serverless architecture, plugin-based intelligent storage, enterprise-level security governance, and full-link observability.
🚀 Experience Now: Get Started with RobustMQ in 5 Minutes
Quick start steps:
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Installation: One-click installation script or download pre-compiled package
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Start: robust-server start to launch the cluster
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Test: Create users, publish and subscribe to messages
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Management: Access the web console for visual management
Detailed tutorial: Visit https://robustmq.com/ for the complete guide.
🤝 Join the RobustMQ Community
The success of RobustMQ relies on the power of the community. We sincerely invite you to be part of this exciting project!
🎯 How to Participate?
We welcome various forms of participation: code contributions, documentation improvements, testing feedback, community promotion. Whether you are a developer, architect, or technology enthusiast, you can find ways to participate in RobustMQ.
📞 How to Contact Us?
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🐙 GitHub:https://github.com/robustmq/robustmq
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🌐 Official Website:https://robustmq.com/
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💬 WeChat Group: Scan the QR code to join the Chinese community
🌈 Conclusion: Let’s Create the Future of Message Queues Together
Message queues are the “circulatory system” of modern application architecture, and in the AI era, they are the “nervous network” connecting data, algorithms, and applications.
RobustMQ, through innovations such as rewriting in Rust, storage-computing separation, multi-protocol unification, and intelligent storage, aims to become the new benchmark for message queues in the AI era.
🚀 RobustMQ — The Next Generation Cloud-Native Message Queue, the Future is Here!
If you are interested in RobustMQ, feel free to follow our official account for the latest updates, or directly participate in the project development. Let’s change the world with code together!