Overview

Amazon SQS

Stacks2.8K

Followers2.0K

Votes171

Confluent

Stacks337

Followers239

Votes14

Amazon SQS vs Confluent: What are the differences?

Introduction

In this article, we will explore the key differences between Amazon SQS (Simple Queue Service) and Confluent. Both Amazon SQS and Confluent are messaging systems that provide reliable communication between distributed components in modern software architectures. However, there are distinct differences that set them apart. Let's delve into these differences below.

Focus: Amazon SQS primarily focuses on providing a fully managed message queuing service, while Confluent is a streaming platform built around Apache Kafka. While SQS focuses on message queuing and delivery, Confluent provides a broader set of capabilities for building real-time streaming applications.
Data Model: In Amazon SQS, messages are stored as discrete entities and the system guarantees at-least-once delivery. On the other hand, Confluent uses Kafka, a distributed streaming platform, where messages are stored in distributed commit logs called topics. Kafka provides strong durability guarantees as it persists messages to disk, ensuring high availability and fault tolerance.
Scalability: Amazon SQS is fully managed and can automatically scale to handle large amounts of message traffic. It allows you to create an unlimited number of queues and supports a high number of concurrent readers and writers. Confluent also offers scalability, but it requires manual configuration and tuning of Kafka brokers, topics, and partitions to achieve desired throughput and latency.
Latency: While Amazon SQS guarantees high message durability, it may introduce some additional latency due to its fully managed nature. In contrast, Confluent Kafka offers exceptionally low end-to-end latency, often in the millisecond range, making it well-suited for real-time event streaming and high-throughput environments.
Ecosystem: Amazon SQS is tightly integrated with other AWS services, making it easy to build serverless architectures and leverage other AWS capabilities like AWS Lambda for message processing. Confluent, being built around Kafka, benefits from a rich ecosystem that includes connectors, stream processing frameworks like Apache Flink and Apache Samza, and various tooling for data integration, monitoring, and management.
Community and Support: Being a key offering of Amazon Web Services, Amazon SQS has a large user community and benefits from the support and documentation provided by AWS. Confluent also has a growing community around Apache Kafka, with extensive documentation, online resources, and a vibrant ecosystem that actively contributes to its development and maintenance.

In summary, the key differences between Amazon SQS and Confluent lie in their focus, data model, scalability, latency, ecosystem, and community and support. While Amazon SQS is a managed message queuing service, Confluent is a broader streaming platform built around Kafka, offering high scalability, low latency, and a rich ecosystem for real-time event streaming applications.

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Advice on Amazon SQS, Confluent

MITHIRIDI

Software Engineer at LightMetrics

May 8, 2020

Needs adviceon

Amazon SQS

Amazon MQ

I want to schedule a message. Amazon SQS provides a delay of 15 minutes, but I want it in some hours.

Example: Let's say a Message1 is consumed by a consumer A but somehow it failed inside the consumer. I would want to put it in a queue and retry after 4hrs. Can I do this in Amazon MQ? I have seen in some Amazon MQ videos saying scheduling messages can be done. But, I'm not sure how.

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Detailed Comparison

Amazon SQS	Confluent
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.	It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream
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.	Reliable; High-performance stream data platform; Manage and organize data from different sources.
Statistics
Stacks 2.8K	Stacks 337
Followers 2.0K	Followers 239
Votes 171	Votes 14
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 Has a max message size (currently 256K) 2 Difficult to configure 2 Proprietary 1 Has a maximum 15 minutes of delayed messages only	Pros 4 Free for casual use 3 Dashboard for kafka insight 3 No hypercloud lock-in 2 Easily scalable 2 Zero devops Cons 1 Proprietary
Integrations
No integrations available	Microsoft SharePoint Java Python Salesforce Sales Cloud Kafka Streams

What are some alternatives to Amazon SQS, Confluent?

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 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.

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

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.