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  1. Stackups
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  4. Message Queue
  5. Apache RocketMQ vs MQTT

Apache RocketMQ vs MQTT

OverviewComparisonAlternatives

Overview

MQTT
MQTT
Stacks635
Followers577
Votes7
Apache RocketMQ
Apache RocketMQ
Stacks48
Followers200
Votes8

Apache RocketMQ vs MQTT: What are the differences?

**Introduction:**
Apache RocketMQ and MQTT are both messaging protocols used for communication between distributed systems. Understanding the key differences between them can help in choosing the right solution for your specific use case.

**1. Scalability:** Apache RocketMQ is designed with a focus on high scalability, making it suitable for large-scale distributed systems with high volumes of messages. It supports horizontal scaling effortlessly, allowing for the addition of more servers to handle increased loads. On the other hand, MQTT is more lightweight and may not be as well-suited for extremely high message volumes or large-scale deployments.

**2. Delivery Semantics:** Apache RocketMQ provides strong delivery semantics, ensuring that messages are delivered exactly once in the correct order, making it ideal for applications that require strict consistency guarantees. In contrast, MQTT offers configurable quality of service levels but may not provide the same level of reliability as Apache RocketMQ in terms of message delivery.

**3. Message Filtering:** Apache RocketMQ allows for sophisticated message filtering based on tags, keys, or SQL92 expressions, offering advanced topic-based routing capabilities. MQTT, on the other hand, does not have built-in support for complex message filtering, which may limit its flexibility in certain scenarios.

**4. Ecosystem Support:** Apache RocketMQ has a robust ecosystem with various tools and integrations, making it easier to work with and extend its functionalities. This can be beneficial for developers looking for a rich set of features and capabilities. MQTT, while widely supported in IoT applications, may not have the same level of ecosystem support as Apache RocketMQ in other use cases.

**5. Persistent Storage:** Apache RocketMQ includes built-in support for persistent message storage, ensuring data durability even in the event of failures or system crashes. This feature can be crucial for applications that require reliable message persistence. MQTT, on the other hand, may rely on external persistence mechanisms, which could introduce additional complexity.

**6. Protocol Overhead:** Apache RocketMQ may have higher protocol overhead compared to MQTT, which could impact performance in certain scenarios. Depending on the specific requirements of the application, this difference in protocol overhead may need to be considered when choosing between the two messaging protocols.

In Summary, Apache RocketMQ offers high scalability, strong delivery semantics, sophisticated message filtering, robust ecosystem support, persistent storage, and may have higher protocol overhead compared to MQTT.

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Detailed Comparison

MQTT
MQTT
Apache RocketMQ
Apache RocketMQ

It was designed as an extremely lightweight publish/subscribe messaging transport. It is useful for connections with remote locations where a small code footprint is required and/or network bandwidth is at a premium.

Apache RocketMQ is a distributed messaging and streaming platform with low latency, high performance and reliability, trillion-level capacity and flexible scalability.

Statistics
Stacks
635
Stacks
48
Followers
577
Followers
200
Votes
7
Votes
8
Pros & Cons
Pros
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Very easy to configure and use with open source tools
  • 2
    Lightweight with a relatively small data footprint
Cons
  • 1
    Easy to configure in an unsecure manner
Pros
  • 2
    Million-level message accumulation capacity in a single
  • 2
    Support tracing message and transactional message
  • 1
    Feature-rich administrative dashboard for configuration
  • 1
    Low latency
  • 1
    High throughput messaging
Integrations
No integrations available
Docker
Docker

What are some alternatives to MQTT, Apache RocketMQ?

Kafka

Kafka

Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.

RabbitMQ

RabbitMQ

RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.

Celery

Celery

Celery is an asynchronous task queue/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well.

Amazon SQS

Amazon SQS

Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.

NSQ

NSQ

NSQ is a realtime distributed messaging platform designed to operate at scale, handling billions of messages per day. It promotes distributed and decentralized topologies without single points of failure, enabling fault tolerance and high availability coupled with a reliable message delivery guarantee. See features & guarantees.

ActiveMQ

ActiveMQ

Apache ActiveMQ is fast, supports many Cross Language Clients and Protocols, comes with easy to use Enterprise Integration Patterns and many advanced features while fully supporting JMS 1.1 and J2EE 1.4. Apache ActiveMQ is released under the Apache 2.0 License.

ZeroMQ

ZeroMQ

The 0MQ lightweight messaging kernel is a library which extends the standard socket interfaces with features traditionally provided by specialised messaging middleware products. 0MQ sockets provide an abstraction of asynchronous message queues, multiple messaging patterns, message filtering (subscriptions), seamless access to multiple transport protocols and more.

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Gearman

Gearman

Gearman allows you to do work in parallel, to load balance processing, and to call functions between languages. It can be used in a variety of applications, from high-availability web sites to the transport of database replication events.

Memphis

Memphis

Highly scalable and effortless data streaming platform. Made to enable developers and data teams to collaborate and build real-time and streaming apps fast.

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