Google Cloud Pub/Sub vs Kafka

Overview

Kafka

Stacks24.2K

Followers22.3K

Votes607

GitHub Stars31.2K

Forks14.8K

Google Cloud Pub/Sub

Stacks550

Followers428

Votes13

Google Cloud Pub/Sub vs Kafka: What are the differences?

Introduction

In this article, we will explore the key differences between Google Cloud Pub/Sub and Kafka, two popular messaging systems used for building scalable, distributed applications.

Scalability: Google Cloud Pub/Sub is a fully-managed messaging service provided by Google Cloud Platform. It automatically handles the scaling of resources based on the demand, making it a highly scalable solution. On the other hand, Kafka requires manual configuration and management of resources for scalability, making it more suitable for experienced users who require fine-grained control over resource allocation.
Durability: Google Cloud Pub/Sub ensures message durability by replicating messages across multiple data centers. It guarantees the successful delivery of messages even in the event of failures. Kafka also provides durability by persisting messages to disk, allowing for message replay in case of failures. However, the level of durability and replication in Kafka can be customized based on the configuration.
Persistence: Kafka uses distributed commit logs to store messages on disk. This provides long-term persistence and allows for historical analysis of data. On the other hand, Google Cloud Pub/Sub does not persist messages on disk by default. It focuses on the real-time streaming of messages rather than long-term storage.
Message Order: Kafka maintains the order of messages within a partition, ensuring that messages are processed in the order they were received. This makes it suitable for applications with strict ordering requirements. On the other hand, Google Cloud Pub/Sub supports unordered message delivery by design. It focuses on delivering messages with low latency and high throughput, while sacrificing strict ordering guarantees.
Ease of Use: Google Cloud Pub/Sub provides a managed service that abstracts away the operational complexities of managing a messaging system. It offers a simple API for message publishing and subscription management. Kafka, on the other hand, requires more manual configuration and management, making it more suitable for users with advanced knowledge of distributed systems.
Ecosystem: Kafka has a rich and mature ecosystem with a wide range of tools and libraries built around it. This includes connectors for integrating with other data systems, frameworks for stream processing, and support for various programming languages. Google Cloud Pub/Sub, being a managed service, has a more limited ecosystem. Although it has support for popular programming languages, it may lack some of the specialized integrations and tools available in the Kafka ecosystem.

In summary, Google Cloud Pub/Sub and Kafka differ in terms of scalability, durability, persistence, message ordering, ease of use, and ecosystem. Google Cloud Pub/Sub is a fully-managed messaging service that focuses on simplicity and scalability, while Kafka provides more control and flexibility at the cost of increased complexity.

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Advice on Kafka, Google Cloud Pub/Sub

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

Ishfaq

Feb 28, 2020

Needs advice

Our backend application is sending some external messages to a third party application at the end of each backend (CRUD) API call (from UI) and these external messages take too much extra time (message building, processing, then sent to the third party and log success/failure), UI application has no concern to these extra third party messages.

So currently we are sending these third party messages by creating a new child thread at end of each REST API call so UI application doesn't wait for these extra third party API calls.

I want to integrate Apache Kafka for these extra third party API calls, so I can also retry on failover third party API calls in a queue(currently third party messages are sending from multiple threads at the same time which uses too much processing and resources) and logging, etc.

Question 1: Is this a use case of a message broker?

Question 2: If it is then Kafka vs RabitMQ which is the better?

804k views804k

Comments

Roman

Senior Back-End Developer, Software Architect

Feb 12, 2019

Reviewon

Kafka

I use Kafka because it has almost infinite scaleability in terms of processing events (could be scaled to process hundreds of thousands of events), great monitoring (all sorts of metrics are exposed via JMX).

Downsides of using Kafka are:

you have to deal with Zookeeper
you have to implement advanced routing yourself (compared to RabbitMQ it has no advanced routing)

10.9k views10.9k

Comments

Detailed Comparison

Kafka	Google Cloud Pub/Sub
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.	Cloud Pub/Sub is a fully-managed real-time messaging service that allows you to send and receive messages between independent applications. You can leverage Cloud Pub/Sub’s flexibility to decouple systems and components hosted on Google Cloud Platform or elsewhere on the Internet.
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	-
Statistics
GitHub Stars 31.2K	GitHub Stars -
GitHub Forks 14.8K	GitHub Forks -
Stacks 24.2K	Stacks 550
Followers 22.3K	Followers 428
Votes 607	Votes 13
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 9 Easy to set-up and start with 2 Efficient and practical for complex systems 2 A great choice for microservice architecture Cons 2 Need integration with stackdriver for monitoring
Integrations
No integrations available	Google Cloud Functions Cloud Functions for Firebase

What are some alternatives to Kafka, Google Cloud Pub/Sub?

Firebase

Firebase is a cloud service designed to power real-time, collaborative applications. Simply add the Firebase library to your application to gain access to a shared data structure; any changes you make to that data are automatically synchronized with the Firebase cloud and with other clients within milliseconds.

Socket.IO

It enables real-time bidirectional event-based communication. It works on every platform, browser or device, focusing equally on reliability and speed.

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.

PubNub

PubNub makes it easy for you to add real-time capabilities to your apps, without worrying about the infrastructure. Build apps that allow your users to engage in real-time across mobile, browser, desktop and server.

Pusher

Pusher is the category leader in delightful APIs for app developers building communication and collaboration features.

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 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.

SignalR

SignalR allows bi-directional communication between server and client. Servers can now push content to connected clients instantly as it becomes available. SignalR supports Web Sockets, and falls back to other compatible techniques for older browsers. SignalR includes APIs for connection management (for instance, connect and disconnect events), grouping connections, and authorization.

Ably

Ably offers WebSockets, stream resume, history, presence, and managed third-party integrations to make it simple to build, extend, and deliver digital realtime experiences at scale.

Related Comparisons

Google Cloud Pub/Sub vs Kafka: What are the differences?

Introduction

In this article, we will explore the key differences between Google Cloud Pub/Sub and Kafka, two popular messaging systems used for building scalable, distributed applications.

Scalability: Google Cloud Pub/Sub is a fully-managed messaging service provided by Google Cloud Platform. It automatically handles the scaling of resources based on the demand, making it a highly scalable solution. On the other hand, Kafka requires manual configuration and management of resources for scalability, making it more suitable for experienced users who require fine-grained control over resource allocation.
Durability: Google Cloud Pub/Sub ensures message durability by replicating messages across multiple data centers. It guarantees the successful delivery of messages even in the event of failures. Kafka also provides durability by persisting messages to disk, allowing for message replay in case of failures. However, the level of durability and replication in Kafka can be customized based on the configuration.
Persistence: Kafka uses distributed commit logs to store messages on disk. This provides long-term persistence and allows for historical analysis of data. On the other hand, Google Cloud Pub/Sub does not persist messages on disk by default. It focuses on the real-time streaming of messages rather than long-term storage.
Message Order: Kafka maintains the order of messages within a partition, ensuring that messages are processed in the order they were received. This makes it suitable for applications with strict ordering requirements. On the other hand, Google Cloud Pub/Sub supports unordered message delivery by design. It focuses on delivering messages with low latency and high throughput, while sacrificing strict ordering guarantees.
Ease of Use: Google Cloud Pub/Sub provides a managed service that abstracts away the operational complexities of managing a messaging system. It offers a simple API for message publishing and subscription management. Kafka, on the other hand, requires more manual configuration and management, making it more suitable for users with advanced knowledge of distributed systems.
Ecosystem: Kafka has a rich and mature ecosystem with a wide range of tools and libraries built around it. This includes connectors for integrating with other data systems, frameworks for stream processing, and support for various programming languages. Google Cloud Pub/Sub, being a managed service, has a more limited ecosystem. Although it has support for popular programming languages, it may lack some of the specialized integrations and tools available in the Kafka ecosystem.

Google Cloud Pub/Sub vs Kafka

Overview

Google Cloud Pub/Sub vs Kafka: What are the differences?