Kafka vs NSQ

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

NSQ

Stacks142

Followers356

Votes148

Kafka vs NSQ: What are the differences?

Message Delivery guarantee: One key difference between Kafka and NSQ is the message delivery guarantee. Kafka guarantees at least once delivery of messages, meaning that every message will be delivered at least once. On the other hand, NSQ provides at most once delivery guarantee, which means that messages may be dropped or delivered multiple times in case of failures.
Message Replay: Another significant difference is the ability to replay messages. Kafka retains messages for a configurable period of time and allows consumers to rewind to any point in the log and re-consume messages. This ability to replay messages is not available in NSQ, as it only keeps the most recent messages and does not support rewinding or re-consuming messages.
Scalability: Kafka is designed for high scalability and can handle large numbers of messages and consumers. It distributes messages across multiple brokers and supports horizontal scalability. NSQ, on the other hand, is also scalable but is more suitable for smaller-scale applications where high throughput is not a requirement.
Message Durability: Kafka ensures durability of messages by persisting them to disk, allowing for message replay even in case of broker failures. NSQ, on the other hand, does not persist messages to disk by default and relies on in-memory storage, making it more suitable for use cases where durability is not a critical factor.
Partitioning: Kafka uses partitioning to distribute the load across multiple brokers and achieve high throughput. It allows for fine-grained control over message distribution and consumer parallelism. NSQ also supports partitioning but the partitioning mechanism is simpler compared to Kafka.
Ecosystem and Community Support: Kafka has a mature ecosystem and a thriving community with extensive documentation, libraries, and connectors available. NSQ, although a capable messaging system, has a smaller ecosystem and community support compared to Kafka.

In Summary, Kafka provides stronger message delivery guarantees, supports message replay and durability, and has better scalability, partitioning, and ecosystem support compared to NSQ.

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)

Manual

Advice on Kafka, NSQ

Tarun

Senior Software Developer at Okta

Dec 4, 2021

Review

We have faced the same question some time ago. Before I begin, DO NOT use Redis as a message broker. It is fast and easy to set up in the beginning but it does not scale. It is not made to be reliable in scale and that is mentioned in the official docs. This analysis of our problems with Redis may help you.

We have used Kafka and RabbitMQ both in scale. We concluded that RabbitMQ is a really good general purpose message broker (for our case) and Kafka is really fast but limited in features. That’s the trade off that we understood from using it. In-fact I blogged about the trade offs between Kafka and RabbitMQ to document it. I hope it helps you in choosing the best pub-sub layer for your use case.

153k views153k

Comments

viradiya

Apr 12, 2020

Needs adviceon

AngularJS

ASP.NET Core

MSSQL

We are going to develop a microservices-based application. It consists of AngularJS, ASP.NET Core, and MSSQL.

We have 3 types of microservices. Emailservice, Filemanagementservice, Filevalidationservice

I am a beginner in microservices. But I have read about RabbitMQ, but come to know that there are Redis and Kafka also in the market. So, I want to know which is best.

933k views933k

Comments

Kirill

GO/C developer at Duckling Sales

Feb 16, 2021

Decided

Maybe not an obvious comparison with Kafka, since Kafka is pretty different from rabbitmq. But for small service, Rabbit as a pubsub platform is super easy to use and pretty powerful. Kafka as an alternative was the original choice, but its really a kind of overkill for a small-medium service. Especially if you are not planning to use k8s, since pure docker deployment can be a pain because of networking setup. Google PubSub was another alternative, its actually pretty cheap, but I never tested it since Rabbit was matching really good for mailing/notification services.

266k views266k

Comments

Detailed Comparison

Kafka	NSQ
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	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.
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	support distributed topologies with no SPOF;horizontally scalable (no brokers, seamlessly add more nodes to the cluster);low-latency push based message delivery (performance);combination load-balanced and multicast style message routing;excel at both streaming (high-throughput) and job oriented (low-throughput) workloads;primarily in-memory (beyond a high-water mark messages are transparently kept on disk);runtime discovery service for consumers to find producers (nsqlookupd);transport layer security (TLS);data format agnostic;few dependencies (easy to deploy) and a sane, bounded, default configuration;simple TCP protocol supporting client libraries in any language;HTTP interface for stats, admin actions, and producers (no client library needed to publish);integrates with statsd for realtime instrumentation;robust cluster administration interface (nsqadmin)
Statistics
GitHub Stars 31.2K	GitHub Stars -
GitHub Forks 14.8K	GitHub Forks -
Stacks 24.2K	Stacks 142
Followers 22.3K	Followers 356
Votes 607	Votes 148
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	Pros 29 It's in golang 20 Lightweight 20 Distributed 18 Easy setup 17 High throughput Cons 1 HA 1 Long term persistence 1 Get NSQ behavior out of Kafka but not inverse

What are some alternatives to Kafka, NSQ?

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 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

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.

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

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

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 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

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

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.

Apache Pulsar

Apache Pulsar is a distributed messaging solution developed and released to open source at Yahoo. Pulsar supports both pub-sub messaging and queuing in a platform designed for performance, scalability, and ease of development and operation.

Related Comparisons

Kafka vs NSQ: What are the differences?

Message Delivery guarantee: One key difference between Kafka and NSQ is the message delivery guarantee. Kafka guarantees at least once delivery of messages, meaning that every message will be delivered at least once. On the other hand, NSQ provides at most once delivery guarantee, which means that messages may be dropped or delivered multiple times in case of failures.
Message Replay: Another significant difference is the ability to replay messages. Kafka retains messages for a configurable period of time and allows consumers to rewind to any point in the log and re-consume messages. This ability to replay messages is not available in NSQ, as it only keeps the most recent messages and does not support rewinding or re-consuming messages.
Scalability: Kafka is designed for high scalability and can handle large numbers of messages and consumers. It distributes messages across multiple brokers and supports horizontal scalability. NSQ, on the other hand, is also scalable but is more suitable for smaller-scale applications where high throughput is not a requirement.
Message Durability: Kafka ensures durability of messages by persisting them to disk, allowing for message replay even in case of broker failures. NSQ, on the other hand, does not persist messages to disk by default and relies on in-memory storage, making it more suitable for use cases where durability is not a critical factor.
Partitioning: Kafka uses partitioning to distribute the load across multiple brokers and achieve high throughput. It allows for fine-grained control over message distribution and consumer parallelism. NSQ also supports partitioning but the partitioning mechanism is simpler compared to Kafka.
Ecosystem and Community Support: Kafka has a mature ecosystem and a thriving community with extensive documentation, libraries, and connectors available. NSQ, although a capable messaging system, has a smaller ecosystem and community support compared to Kafka.

In Summary, Kafka provides stronger message delivery guarantees, supports message replay and durability, and has better scalability, partitioning, and ecosystem support compared to NSQ.

Kafka vs NSQ

Overview