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  1. Stackups
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  3. Background Jobs
  4. Message Queue
  5. Amazon SQS vs Azure Cosmos DB

Amazon SQS vs Azure Cosmos DB

OverviewDecisionsComparisonAlternatives

Overview

Amazon SQS
Amazon SQS
Stacks2.8K
Followers2.0K
Votes171
Azure Cosmos DB
Azure Cosmos DB
Stacks594
Followers1.1K
Votes130

Amazon SQS vs Azure Cosmos DB: What are the differences?

What is Amazon SQS? Fully managed message queuing service. 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.

What is Azure Cosmos DB? A fully-managed, globally distributed NoSQL database service. Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

Amazon SQS can be classified as a tool in the "Message Queue" category, while Azure Cosmos DB is grouped under "NoSQL Database as a Service".

Some of the features offered by Amazon SQS are:

  • 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.

On the other hand, Azure Cosmos DB provides the following key features:

  • Fully managed with 99.99% Availability SLA
  • Elastically and highly scalable (both throughput and storage)
  • Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes

"Easy to use, reliable" is the top reason why over 45 developers like Amazon SQS, while over 13 developers mention "Best-of-breed NoSQL features" as the leading cause for choosing Azure Cosmos DB.

Medium, Lyft, and Coursera are some of the popular companies that use Amazon SQS, whereas Azure Cosmos DB is used by Microsoft, Property With Potential, and Rumble. Amazon SQS has a broader approval, being mentioned in 384 company stacks & 103 developers stacks; compared to Azure Cosmos DB, which is listed in 24 company stacks and 24 developer stacks.

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Advice on Amazon SQS, Azure Cosmos DB

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
Azure Cosmos DB
Azure Cosmos DB

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.

Azure DocumentDB is a fully managed NoSQL database service built for fast and predictable performance, high availability, elastic scaling, global distribution, and ease of development.

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.
Fully managed with 99.99% Availability SLA;Elastically and highly scalable (both throughput and storage);Predictable low latency: <10ms @ P99 reads and <15ms @ P99 fully-indexed writes;Globally distributed with multi-region replication;Rich SQL queries over schema-agnostic automatic indexing;JavaScript language integrated multi-record ACID transactions with snapshot isolation;Well-defined tunable consistency models: Strong, Bounded Staleness, Session, and Eventual
Statistics
Stacks
2.8K
Stacks
594
Followers
2.0K
Followers
1.1K
Votes
171
Votes
130
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
  • 28
    Best-of-breed NoSQL features
  • 22
    High scalability
  • 15
    Globally distributed
  • 14
    Automatic indexing over flexible json data model
  • 10
    Tunable consistency
Cons
  • 18
    Pricing
  • 4
    Poor No SQL query support
Integrations
No integrations available
Azure Machine Learning
Azure Machine Learning
MongoDB
MongoDB
Hadoop
Hadoop
Java
Java
Azure Functions
Azure Functions
Azure Container Service
Azure Container Service
Azure Storage
Azure Storage
Azure Websites
Azure Websites
Apache Spark
Apache Spark
Python
Python

What are some alternatives to Amazon SQS, Azure Cosmos DB?

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.

Amazon DynamoDB

Amazon DynamoDB

With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.

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.

Cloud Firestore

Cloud Firestore

Cloud Firestore is a NoSQL document database that lets you easily store, sync, and query data for your mobile and web apps - at global scale.

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.

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