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  1. Stackups
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  4. Message Queue
  5. Amazon SQS vs Kafka

Amazon SQS vs Kafka

OverviewDecisionsComparisonAlternatives

Overview

Amazon SQS
Amazon SQS
Stacks2.8K
Followers2.0K
Votes171
Kafka
Kafka
Stacks24.2K
Followers22.3K
Votes607
GitHub Stars31.2K
Forks14.8K

Amazon SQS vs Kafka: What are the differences?

Amazon SQS and Kafka are both messaging platforms that allow for real-time, asynchronous communication between systems. However, there are key differences between the two.

  1. Architecture: Amazon SQS is a fully managed, pull-based system, while Kafka is a distributed, pub-sub system. SQS uses a message broker architecture with a central service managing the message queue, while Kafka uses a distributed log architecture where messages are stored in a distributed commit log.

  2. Scalability: SQS is highly scalable and can handle millions of messages per second, but it has limitations on message size and retention time. On the other hand, Kafka is designed for high-throughput, fault-tolerant, and horizontally scalable streaming, making it suitable for handling large volumes of data and providing longer retention periods.

  3. Message Persistence: SQS guarantees message persistence, storing messages in multiple data centers for durability. Kafka also provides persistence through its replicated log, but it allows users to configure the retention period and disk space used for data storage.

  4. Message Delivery: SQS ensures at-least-once delivery of messages, providing exactly-once delivery semantics using deduplication mechanisms. Kafka guarantees at-least-once message delivery and can achieve exactly-once semantics through idempotent message writes and transactional support.

  5. Consumer Groups: Kafka supports the concept of consumer groups, where multiple consumers can independently read from different partitions of a topic. This allows for load balancing and fault tolerance. SQS, on the other hand, does not have built-in support for consumer groups and requires separate queues for each consumer.

  6. Integration: Amazon SQS is tightly integrated with other AWS services, making it easy to incorporate into an AWS-based architecture. Kafka has a broader integration ecosystem, with connectors available for various data systems and frameworks.

In summary, the key differences between Amazon SQS and Kafka lie in their architecture, scalability, message persistence, delivery guarantees, support for consumer groups, and integration capabilities. It is important to consider these differences when choosing a messaging platform for your specific use case.

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Advice on Amazon SQS, Kafka

viradiya
viradiya

Apr 12, 2020

Needs adviceonAngularJSAngularJSASP.NET CoreASP.NET CoreMSSQLMSSQL

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
Pulkit
Pulkit

Software Engineer

Oct 30, 2020

Needs adviceonDjangoDjangoAmazon SQSAmazon SQSRabbitMQRabbitMQ

Hi! I am creating a scraping system in Django, which involves long running tasks between 1 minute & 1 Day. As I am new to Message Brokers and Task Queues, I need advice on which architecture to use for my system. ( Amazon SQS, RabbitMQ, or Celery). The system should be autoscalable using Kubernetes(K8) based on the number of pending tasks in the queue.

474k views474k
Comments
Kirill
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

Amazon SQS
Amazon SQS
Kafka
Kafka

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.

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

A queue can be created in any region.;The message payload can contain up to 256KB of text in any format. Each 64KB ‘chunk’ of payload is billed as 1 request. For example, a single API call with a 256KB payload will be billed as four requests.;Messages can be sent, received or deleted in batches of up to 10 messages or 256KB. Batches cost the same amount as single messages, meaning SQS can be even more cost effective for customers that use batching.;Long polling reduces extraneous polling to help you minimize cost while receiving new messages as quickly as possible. When your queue is empty, long-poll requests wait up to 20 seconds for the next message to arrive. Long poll requests cost the same amount as regular requests.;Messages can be retained in queues for up to 14 days.;Messages can be sent and read simultaneously.;Developers can get started with Amazon SQS by using only five APIs: CreateQueue, SendMessage, ReceiveMessage, ChangeMessageVisibility, and DeleteMessage. Additional APIs are available to provide advanced functionality.
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
-
GitHub Stars
31.2K
GitHub Forks
-
GitHub Forks
14.8K
Stacks
2.8K
Stacks
24.2K
Followers
2.0K
Followers
22.3K
Votes
171
Votes
607
Pros & Cons
Pros
  • 62
    Easy to use, reliable
  • 40
    Low cost
  • 28
    Simple
  • 14
    Doesn't need to maintain it
  • 8
    It is Serverless
Cons
  • 2
    Proprietary
  • 2
    Difficult to configure
  • 2
    Has a max message size (currently 256K)
  • 1
    Has a maximum 15 minutes of delayed messages only
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

What are some alternatives to Amazon SQS, Kafka?

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.

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.

Apache Pulsar

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.

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