StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Background Jobs
  4. Message Queue
  5. Kafka vs apache qpid

Kafka vs apache qpid

OverviewComparisonAlternatives

Overview

Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K
apache qpid
apache qpid
Stacks4
Followers9
Votes0

Kafka vs apache qpid: What are the differences?

Introduction

This Markdown code provides key differences between Kafka and Apache Qpid.

  1. Message Architecture: Kafka is designed as a distributed pub-sub messaging system, where producers publish messages to topics and consumers subscribe to these topics to receive messages. On the other hand, Apache Qpid is a messaging system based on the Advanced Message Queuing Protocol (AMQP). It follows a messaging-queue model where messages are sent to specific queues and consumers pull messages from these queues.

  2. Broker and Brokerless: Kafka follows a broker-based architecture, where brokers handle the storing and forwarding of messages. It requires a central broker to distribute messages across multiple consumers. In contrast, Apache Qpid can be used in both broker-based and brokerless architectures. It provides an option to eliminate the need for a central broker and allows direct peer-to-peer communication between producers and consumers.

  3. Message Persistence: Kafka persists messages to disk, ensuring durability and fault-tolerance. It allows messages to be stored for a configurable amount of time or a specific size. Apache Qpid also offers message persistence, but it provides more flexibility in terms of storage options. It supports various pluggable message stores, including databases, file systems, or in-memory storage.

  4. Scalability and Performance: Kafka is known for its high scalability and performance. It can handle a large number of producers and consumers, processing millions of messages per second. Apache Qpid also provides good scalability and performance, but it may not scale as efficiently as Kafka in extreme high-velocity scenarios due to additional protocol overhead introduced by AMQP.

  5. Message Ordering: Kafka guarantees message ordering at the partition level. Messages published to a single partition are consumed in the same order they were produced. Apache Qpid, being a queue-based system, also maintains ordering within a single queue, but it does not provide ordering guarantees across multiple queues.

  6. Message Semantics: Kafka supports both at-least-once and at-most-once message delivery semantics. It ensures that messages are not lost in case of failures but may result in duplicate deliveries. Apache Qpid also supports at-least-once delivery semantics, but it provides more fine-grained control over message acknowledgments and transactions, allowing applications to achieve exactly-once semantics.

In summary, Kafka and Apache Qpid differ in their message architecture, deployment options, persistence capabilities, scalability, message ordering guarantees, and delivery semantics.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Kafka
Kafka
apache qpid
apache qpid

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

Apache Qpid is an open-source (Apache 2.0 licensed) messaging system which implements the Advanced Message Queuing Protocol (AMQP). It provides transaction management, queuing, distribution, security, management, clustering and federation

Written at LinkedIn in Scala;Used by LinkedIn to offload processing of all page and other views;Defaults to using persistence, uses OS disk cache for hot data (has higher throughput then any of the above having persistence enabled);Supports both on-line as off-line processing
transaction management, queuing, distribution, security, management, clustering, federation
Statistics
GitHub Stars
31.2K
GitHub Stars
-
GitHub Forks
14.8K
GitHub Forks
-
Stacks
24.2K
Stacks
4
Followers
22.3K
Followers
9
Votes
607
Votes
0
Pros & Cons
Pros
  • 126
    High-throughput
  • 119
    Distributed
  • 92
    Scalable
  • 86
    High-Performance
  • 66
    Durable
Cons
  • 32
    Non-Java clients are second-class citizens
  • 29
    Needs Zookeeper
  • 9
    Operational difficulties
  • 5
    Terrible Packaging
No community feedback yet

What are some alternatives to Kafka, apache qpid?

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.

IronMQ

IronMQ

An easy-to-use highly available message queuing service. Built for distributed cloud applications with critical messaging needs. Provides on-demand message queuing with advanced features and cloud-optimized performance.

Related Comparisons

Bootstrap
Materialize

Bootstrap vs Materialize

Laravel
Django

Django vs Laravel vs Node.js

Bootstrap
Foundation

Bootstrap vs Foundation vs Material UI

Node.js
Spring Boot

Node.js vs Spring-Boot

Liquibase
Flyway

Flyway vs Liquibase