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  1. Stackups
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  4. Message Queue
  5. MQTT vs Protobuf

MQTT vs Protobuf

OverviewComparisonAlternatives

Overview

MQTT
MQTT
Stacks635
Followers577
Votes7
Protobuf
Protobuf
Stacks3.8K
Followers393
Votes0
GitHub Stars69.5K
Forks15.9K

MQTT vs Protobuf: What are the differences?

Introduction

In this article, we will discuss the key differences between MQTT and Protobuf. Both MQTT (Message Queuing Telemetry Transport) and Protobuf (Protocol Buffers) are commonly used technologies for messaging and data serialization, respectively.

  1. Message Exchange Model: MQTT is a publish-subscribe messaging protocol, where publishers send messages to a central message broker, and subscribers receive messages from the broker based on their subscribed topics. On the other hand, Protobuf is a data serialization format that allows structuring and transmitting data in an efficient and compact manner.

  2. Data Representation: MQTT focuses on the exchange of lightweight messages, which are typically textual or binary payloads. On the other hand, Protobuf is more concerned with the structure and definition of data objects using a schema, allowing for strong typing and data validation.

  3. Transport Protocol: MQTT is a transport protocol itself, built on top of TCP/IP, making it suitable for low-power devices and unreliable network connections. Protobuf, on the other hand, is a data serialization format that can be used over various transport protocols such as TCP/IP, HTTP, or even local interprocess communication.

  4. Flexibility and Extensibility: MQTT provides a simple and lightweight protocol for messaging, allowing for easy implementation and interoperability between different devices and platforms. Protobuf, on the other hand, provides a more flexible and extensible data serialization format, allowing for the addition or modification of fields in a backward-compatible manner.

  5. Efficiency and Speed: MQTT is designed for efficient and lightweight message exchange, making it suitable for resource-constrained devices and networks. Protobuf, on the other hand, focuses on efficient data serialization, providing a compact binary format and efficient encoding/decoding mechanisms, leading to faster processing and reduced data size.

  6. Language Support: MQTT has broad language support, with client libraries available for various programming languages like Java, Python, C++, etc. Protobuf, on the other hand, also has language bindings for multiple programming languages, making it easy to integrate into different projects.

In summary, MQTT is a lightweight publish-subscribe messaging protocol focused on efficient message exchange, while Protobuf is a data serialization format that enables structured and efficient transmission of data. MQTT is more suitable for messaging scenarios, while Protobuf is ideal for applications that require data serialization and efficient encoding.

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Detailed Comparison

MQTT
MQTT
Protobuf
Protobuf

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.

Protocol buffers are Google's language-neutral, platform-neutral, extensible mechanism for serializing structured data – think XML, but smaller, faster, and simpler.

Statistics
GitHub Stars
-
GitHub Stars
69.5K
GitHub Forks
-
GitHub Forks
15.9K
Stacks
635
Stacks
3.8K
Followers
577
Followers
393
Votes
7
Votes
0
Pros & Cons
Pros
  • 3
    Varying levels of Quality of Service to fit a range of
  • 2
    Lightweight with a relatively small data footprint
  • 2
    Very easy to configure and use with open source tools
Cons
  • 1
    Easy to configure in an unsecure manner
No community feedback yet

What are some alternatives to MQTT, Protobuf?

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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