Alternatives to Amazon SQS logo

Alternatives to Amazon SQS

Amazon MQ, Kafka, Redis, ActiveMQ, and Amazon SNS are the most popular alternatives and competitors to Amazon SQS.
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What is Amazon SQS and what are its top alternatives?

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 SQS is a tool in the Message Queue category of a tech stack.

Top Alternatives to Amazon SQS

  • Amazon MQ

    Amazon MQ

    Amazon MQ is a managed message broker service for Apache ActiveMQ that makes it easy to set up and operate message brokers in the cloud. ...

  • Kafka

    Kafka

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

  • Redis

    Redis

    Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets. ...

  • 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. ...

  • Amazon SNS

    Amazon SNS

    Amazon Simple Notification Service makes it simple and cost-effective to push to mobile devices such as iPhone, iPad, Android, Kindle Fire, and internet connected smart devices, as well as pushing to other distributed services. Besides pushing cloud notifications directly to mobile devices, SNS can also deliver notifications by SMS text message or email, to Simple Queue Service (SQS) queues, or to any HTTP endpoint. ...

  • RabbitMQ

    RabbitMQ

    RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received. ...

  • Amazon Kinesis

    Amazon Kinesis

    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. ...

  • Azure Service Bus

    Azure Service Bus

    It is a cloud messaging system for connecting apps and devices across public and private clouds. You can depend on it when you need highly-reliable cloud messaging service between applications and services, even when one or more is offline. ...

Amazon SQS alternatives & related posts

Amazon MQ logo

Amazon MQ

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Managed Message Broker Service for ActiveMQ
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PROS OF AMAZON MQ
  • 7
    Supports low IQ developers
  • 2
    Supports existing protocols (JMS, NMS, AMQP, STOMP, …)
  • 2
    Easy to migrate existing messaging service
CONS OF AMAZON MQ
  • 3
    Slow AF

related Amazon MQ posts

MITHIRIDI PRASANTH
Software Engineer at LightMetrics · | 4 upvotes · 107.4K views
Shared insights
on
Amazon MQAmazon MQAmazon SQSAmazon SQS

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.

See more
Pedro Arnal Puente
CTO at La Cupula Music SL · | 2 upvotes · 72.4K views

Our command and event buses uses stomp as protocol, over RabbitMQ in development, and Amazon MQ in production.

Currently bus communicates Ruby and PHP based clients.

See more
Kafka logo

Kafka

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Distributed, fault tolerant, high throughput pub-sub messaging system
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PROS OF KAFKA
  • 122
    High-throughput
  • 116
    Distributed
  • 87
    Scalable
  • 81
    High-Performance
  • 65
    Durable
  • 36
    Publish-Subscribe
  • 19
    Simple-to-use
  • 15
    Open source
  • 10
    Written in Scala and java. Runs on JVM
  • 6
    Message broker + Streaming system
  • 4
    Avro schema integration
  • 2
    Suport Multiple clients
  • 2
    Robust
  • 2
    KSQL
  • 2
    Partioned, replayable log
  • 1
    Fun
  • 1
    Extremely good parallelism constructs
  • 1
    Simple publisher / multi-subscriber model
  • 1
    Flexible
CONS OF KAFKA
  • 27
    Non-Java clients are second-class citizens
  • 26
    Needs Zookeeper
  • 7
    Operational difficulties
  • 2
    Terrible Packaging

related Kafka posts

Eric Colson
Chief Algorithms Officer at Stitch Fix · | 21 upvotes · 2.1M views

The algorithms and data infrastructure at Stitch Fix is housed in #AWS. Data acquisition is split between events flowing through Kafka, and periodic snapshots of PostgreSQL DBs. We store data in an Amazon S3 based data warehouse. Apache Spark on Yarn is our tool of choice for data movement and #ETL. Because our storage layer (s3) is decoupled from our processing layer, we are able to scale our compute environment very elastically. We have several semi-permanent, autoscaling Yarn clusters running to serve our data processing needs. While the bulk of our compute infrastructure is dedicated to algorithmic processing, we also implemented Presto for adhoc queries and dashboards.

Beyond data movement and ETL, most #ML centric jobs (e.g. model training and execution) run in a similarly elastic environment as containers running Python and R code on Amazon EC2 Container Service clusters. The execution of batch jobs on top of ECS is managed by Flotilla, a service we built in house and open sourced (see https://github.com/stitchfix/flotilla-os).

At Stitch Fix, algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.

For more info:

#DataScience #DataStack #Data

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John Kodumal

As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

See more
Redis logo

Redis

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An in-memory database that persists on disk
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PROS OF REDIS
  • 875
    Performance
  • 535
    Super fast
  • 511
    Ease of use
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    In-memory cache
  • 321
    Advanced key-value cache
  • 190
    Open source
  • 179
    Easy to deploy
  • 163
    Stable
  • 153
    Free
  • 120
    Fast
  • 40
    High-Performance
  • 39
    High Availability
  • 34
    Data Structures
  • 32
    Very Scalable
  • 23
    Replication
  • 20
    Great community
  • 19
    Pub/Sub
  • 17
    "NoSQL" key-value data store
  • 14
    Hashes
  • 12
    Sets
  • 10
    Sorted Sets
  • 9
    Lists
  • 8
    BSD licensed
  • 8
    NoSQL
  • 7
    Async replication
  • 7
    Integrates super easy with Sidekiq for Rails background
  • 7
    Bitmaps
  • 6
    Open Source
  • 6
    Keys with a limited time-to-live
  • 5
    Strings
  • 5
    Lua scripting
  • 4
    Awesomeness for Free!
  • 4
    Hyperloglogs
  • 3
    outstanding performance
  • 3
    Runs server side LUA
  • 3
    Networked
  • 3
    LRU eviction of keys
  • 3
    Written in ANSI C
  • 3
    Feature Rich
  • 3
    Transactions
  • 2
    Data structure server
  • 2
    Performance & ease of use
  • 1
    Existing Laravel Integration
  • 1
    Automatic failover
  • 1
    Easy to use
  • 1
    Object [key/value] size each 500 MB
  • 1
    Simple
  • 1
    Channels concept
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    Scalable
  • 1
    Temporarily kept on disk
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    Dont save data if no subscribers are found
  • 0
    Jk
CONS OF REDIS
  • 14
    Cannot query objects directly
  • 2
    No secondary indexes for non-numeric data types
  • 1
    No WAL

related Redis posts

Robert Zuber

We use MongoDB as our primary #datastore. Mongo's approach to replica sets enables some fantastic patterns for operations like maintenance, backups, and #ETL.

As we pull #microservices from our #monolith, we are taking the opportunity to build them with their own datastores using PostgreSQL. We also use Redis to cache data we’d never store permanently, and to rate-limit our requests to partners’ APIs (like GitHub).

When we’re dealing with large blobs of immutable data (logs, artifacts, and test results), we store them in Amazon S3. We handle any side-effects of S3’s eventual consistency model within our own code. This ensures that we deal with user requests correctly while writes are in process.

See more

I'm working as one of the engineering leads in RunaHR. As our platform is a Saas, we thought It'd be good to have an API (We chose Ruby and Rails for this) and a SPA (built with React and Redux ) connected. We started the SPA with Create React App since It's pretty easy to start.

We use Jest as the testing framework and react-testing-library to test React components. In Rails we make tests using RSpec.

Our main database is PostgreSQL, but we also use MongoDB to store some type of data. We started to use Redis  for cache and other time sensitive operations.

We have a couple of extra projects: One is an Employee app built with React Native and the other is an internal back office dashboard built with Next.js for the client and Python in the backend side.

Since we have different frontend apps we have found useful to have Bit to document visual components and utils in JavaScript.

See more
ActiveMQ logo

ActiveMQ

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A message broker written in Java together with a full JMS client
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PROS OF ACTIVEMQ
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
  • 5
    Scalable
  • 3
    Support XA (distributed transactions)
  • 3
    Persistence
  • 2
    Distributed Network of brokers
  • 1
    Highly configurable
  • 1
    Docker delievery
  • 0
    RabbitMQ
CONS OF ACTIVEMQ
  • 1
    Support
  • 1
    Low resilience to exceptions and interruptions
  • 1
    Difficult to scale

related ActiveMQ posts

I want to choose Message Queue with the following features - Highly Available, Distributed, Scalable, Monitoring. I have RabbitMQ, ActiveMQ, Kafka and Apache RocketMQ in mind. But I am confused which one to choose.

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Naushad Warsi
software developer at klingelnberg · | 1 upvote · 612.5K views
Shared insights
on
ActiveMQActiveMQRabbitMQRabbitMQ

I use ActiveMQ because RabbitMQ have stopped giving the support for AMQP 1.0 or above version and the earlier version of AMQP doesn't give the functionality to support OAuth.

If OAuth is not required and we can go with AMQP 0.9 then i still recommend rabbitMq.

See more
Amazon SNS logo

Amazon SNS

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Fully managed push messaging service
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PROS OF AMAZON SNS
  • 10
    Low cost
  • 5
    Supports multi subscribers
CONS OF AMAZON SNS
    Be the first to leave a con

    related Amazon SNS posts

    Cyril Duchon-Doris

    We decided to use AWS Lambda for several serverless tasks such as

    • Managing AWS backups
    • Processing emails received on Amazon SES and stored to Amazon S3 and notified via Amazon SNS, so as to push a message on our Redis so our Sidekiq Rails workers can process inbound emails
    • Pushing some relevant Amazon CloudWatch metrics and alarms to Slack
    See more
    Manish Mishra
    Lead Consultant at Knoldus Software LLp · | 6 upvotes · 57K views
    Shared insights
    on
    Amazon PinpointAmazon PinpointAmazon SNSAmazon SNS

    Instead of Amazon SNS, which is currently being used to send outbound push notification and including SMS, we want to build the 2 Way SMS using Amazon Pinpoint. Just want to know about Pinpoint and any outstanding issues if we drop SNS since it does not support 2 Way and use Pinpoint for both incoming and outgoing flow.

    See more
    RabbitMQ logo

    RabbitMQ

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    Open source multiprotocol messaging broker
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    PROS OF RABBITMQ
    • 229
      It's fast and it works with good metrics/monitoring
    • 79
      Ease of configuration
    • 58
      I like the admin interface
    • 50
      Easy to set-up and start with
    • 20
      Durable
    • 18
      Intuitive work through python
    • 18
      Standard protocols
    • 10
      Written primarily in Erlang
    • 8
      Simply superb
    • 6
      Completeness of messaging patterns
    • 3
      Scales to 1 million messages per second
    • 3
      Reliable
    • 2
      Better than most traditional queue based message broker
    • 2
      Distributed
    • 2
      Supports AMQP
    • 1
      Inubit Integration
    • 1
      Supports MQTT
    • 1
      Runs on Open Telecom Platform
    • 1
      High performance
    • 1
      Reliability
    • 1
      Clusterable
    • 1
      Clear documentation with different scripting language
    • 1
      Great ui
    • 1
      Better routing system
    • 1
      Delayed messages
    CONS OF RABBITMQ
    • 9
      Too complicated cluster/HA config and management
    • 6
      Needs Erlang runtime. Need ops good with Erlang runtime
    • 5
      Configuration must be done first, not by your code
    • 4
      Slow

    related RabbitMQ posts

    James Cunningham
    Operations Engineer at Sentry · | 18 upvotes · 1.3M views
    Shared insights
    on
    CeleryCeleryRabbitMQRabbitMQ
    at

    As Sentry runs throughout the day, there are about 50 different offline tasks that we execute—anything from “process this event, pretty please” to “send all of these cool people some emails.” There are some that we execute once a day and some that execute thousands per second.

    Managing this variety requires a reliably high-throughput message-passing technology. We use Celery's RabbitMQ implementation, and we stumbled upon a great feature called Federation that allows us to partition our task queue across any number of RabbitMQ servers and gives us the confidence that, if any single server gets backlogged, others will pitch in and distribute some of the backlogged tasks to their consumers.

    #MessageQueue

    See more
    Yogesh Bhondekar
    Co-Founder at weconnect.chat · | 15 upvotes · 97.4K views

    Hi, I am building an enhanced web-conferencing app that will have a voice/video call, live chats, live notifications, live discussions, screen sharing, etc features. Ref: Zoom.

    I need advise finalizing the tech stack for this app. I am considering below tech stack:

    • Frontend: React
    • Backend: Node.js
    • Database: MongoDB
    • IAAS: #AWS
    • Containers & Orchestration: Docker / Kubernetes
    • DevOps: GitLab, Terraform
    • Brokers: Redis / RabbitMQ

    I need advice at the platform level as to what could be considered to support concurrent video streaming seamlessly.

    Also, please suggest what could be a better tech stack for my app?

    #SAAS #VideoConferencing #WebAndVideoConferencing #zoom #stack

    See more
    Amazon Kinesis logo

    Amazon Kinesis

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    Store and process terabytes of data each hour from hundreds of thousands of sources
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    PROS OF AMAZON KINESIS
    • 7
      Scalable
    • 4
      Cons
    CONS OF AMAZON KINESIS
    • 2
      Cost

    related Amazon Kinesis posts

    John Kodumal

    As we've evolved or added additional infrastructure to our stack, we've biased towards managed services. Most new backing stores are Amazon RDS instances now. We do use self-managed PostgreSQL with TimescaleDB for time-series data—this is made HA with the use of Patroni and Consul.

    We also use managed Amazon ElastiCache instances instead of spinning up Amazon EC2 instances to run Redis workloads, as well as shifting to Amazon Kinesis instead of Kafka.

    See more
    Praveen Mooli
    Engineering Manager at Taylor and Francis · | 14 upvotes · 2M views

    We are in the process of building a modern content platform to deliver our content through various channels. We decided to go with Microservices architecture as we wanted scale. Microservice architecture style is an approach to developing an application as a suite of small independently deployable services built around specific business capabilities. You can gain modularity, extensive parallelism and cost-effective scaling by deploying services across many distributed servers. Microservices modularity facilitates independent updates/deployments, and helps to avoid single point of failure, which can help prevent large-scale outages. We also decided to use Event Driven Architecture pattern which is a popular distributed asynchronous architecture pattern used to produce highly scalable applications. The event-driven architecture is made up of highly decoupled, single-purpose event processing components that asynchronously receive and process events.

    To build our #Backend capabilities we decided to use the following: 1. #Microservices - Java with Spring Boot , Node.js with ExpressJS and Python with Flask 2. #Eventsourcingframework - Amazon Kinesis , Amazon Kinesis Firehose , Amazon SNS , Amazon SQS, AWS Lambda 3. #Data - Amazon RDS , Amazon DynamoDB , Amazon S3 , MongoDB Atlas

    To build #Webapps we decided to use Angular 2 with RxJS

    #Devops - GitHub , Travis CI , Terraform , Docker , Serverless

    See more
    Azure Service Bus logo

    Azure Service Bus

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    Reliable cloud messaging as a service (MaaS)
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    PROS OF AZURE SERVICE BUS
    • 2
      Cloud Native
    • 1
      Easy Integration with .Net
    CONS OF AZURE SERVICE BUS
      Be the first to leave a con

      related Azure Service Bus posts