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  1. Stackups
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  3. Background Jobs
  4. Message Queue
  5. Amazon Kinesis vs Amazon SQS

Amazon Kinesis vs Amazon SQS

OverviewDecisionsComparisonAlternatives

Overview

Amazon SQS
Amazon SQS
Stacks2.8K
Followers2.0K
Votes171
Amazon Kinesis
Amazon Kinesis
Stacks794
Followers604
Votes9

Amazon Kinesis vs Amazon SQS: What are the differences?

Introduction

1. Data Volume and Throughput:

Amazon Kinesis is designed for handling massive streams of data with high throughput, allowing real-time processing of large amounts of data. On the other hand, Amazon SQS is best suited for decoupling application components and for smaller message volumes.

2. Data Retention:

In Amazon Kinesis, data retention is based on the time-to-live (TTL) setting, allowing data to be stored for a specified period. Conversely, Amazon SQS retains messages for a maximum of 14 days, after which they are automatically deleted.

3. Data Processing Model:

Amazon Kinesis offers real-time data streaming capabilities for processing continuously flowing data, whereas Amazon SQS follows a traditional message-queuing model, ensuring reliable message delivery between distributed components.

4. Scalability:

Amazon Kinesis provides dynamic scaling capabilities to handle fluctuating workloads efficiently, while Amazon SQS offers horizontal scaling by allowing multiple message queues and concurrent consumers to manage increased message processing.

5. Ordering of Messages:

Amazon Kinesis maintains the order of records within a shard, ensuring chronological processing of data within a partitioned stream. In contrast, Amazon SQS does not guarantee the ordering of messages across multiple queues, prioritizing fast and reliable message delivery.

6. Fan-Out Capability:

Amazon Kinesis supports fan-out architecture, allowing parallel processing of data streams by multiple consumers without impacting the performance. This feature is not available in Amazon SQS, where each message is processed by a single consumer exclusively.

In Summary, Amazon Kinesis and Amazon SQS differ in their handling of data volume, retention policies, data processing models, scalability options, message ordering, and fan-out capabilities.

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Advice on Amazon SQS, Amazon Kinesis

MITHIRIDI
MITHIRIDI

Software Engineer at LightMetrics

May 8, 2020

Needs adviceonAmazon SQSAmazon SQSAmazon MQAmazon 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.

303k views303k
Comments

Detailed Comparison

Amazon SQS
Amazon SQS
Amazon Kinesis
Amazon Kinesis

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.

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

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.
Real-time Processing- Amazon Kinesis enables you to collect and analyze information in real-time, allowing you to answer questions about the current state of your data, from inventory levels to stock trade frequencies, rather than having to wait for an out-of-date report;Easy to use- You can create a new stream, set the throughput requirements, and start streaming data quickly and easily. Amazon Kinesis automatically provisions and manages the storage required to reliably and durably collect your data stream;High throughput. Elastic.- Amazon Kinesis seamlessly scales to match the data throughput rate and volume of your data, from megabytes to terabytes per hour. Amazon Kinesis will scale up or down based on your needs;Integrate with Amazon S3, Amazon Redshift, and Amazon DynamoDB- With Amazon Kinesis, you can reliably collect, process, and transform all of your data in real-time before delivering it to data stores of your choice, where it can be used by existing or new applications. Connectors enable integration with Amazon S3, Amazon Redshift, and Amazon DynamoDB;Build Kinesis Applications- Amazon Kinesis provides developers with client libraries that enable the design and operation of real-time data processing applications. Just add the Amazon Kinesis Client Library to your Java application and it will be notified when new data is available for processing;Low Cost- Amazon Kinesis is cost-efficient for workloads of any scale. You can pay as you go, and you’ll only pay for the resources you use. You can get started by provisioning low throughput streams, and only pay a low hourly rate for the throughput you need
Statistics
Stacks
2.8K
Stacks
794
Followers
2.0K
Followers
604
Votes
171
Votes
9
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
  • 9
    Scalable
Cons
  • 3
    Cost

What are some alternatives to Amazon SQS, Amazon Kinesis?

Kafka

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

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.

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