Airflow vs Amazon SQS

Overview

Amazon SQS

Stacks2.8K

Followers2.0K

Votes171

Airflow

Stacks1.7K

Followers2.8K

Votes128

Airflow vs Amazon SQS: What are the differences?

Introduction

In this analysis, we will explore the key differences between Airflow and Amazon SQS. Both Airflow and Amazon SQS are workflow management systems that are commonly used in the industry. While they have some similarities, there are distinct differences that set them apart.

Scalability: One major difference between Airflow and Amazon SQS is their scalability. Airflow is designed to handle large-scale workflows with ease, allowing for the execution of complex workflows across multiple machines. On the other hand, Amazon SQS provides a highly scalable message queuing service, primarily used for decoupling the sending and receiving of messages in a distributed system architecture.
Functionality: Airflow offers a rich set of functionalities and features that allow users to design and manage workflows effectively. It provides a readily available user interface for scheduling, monitoring, and managing workflows, along with a wide range of pre-built operators and connectors. In contrast, Amazon SQS focuses solely on message queuing functionality, providing a simple and reliable messaging service with no additional workflow management features.
Deployment: Airflow can be self-hosted on-premises or deployed on cloud infrastructure, providing flexibility in terms of deployment options. It can be installed and managed on any infrastructure, allowing for customization and control over the environment. On the other hand, Amazon SQS is a fully managed service provided by Amazon Web Services (AWS), meaning that it is hosted and maintained by AWS, relieving users of the burden of deployment and management.
Integration: Airflow provides seamless integration with various databases, message brokers, and third-party services, offering a wide range of connectors and operators out of the box. This makes it easy to incorporate Airflow into existing technologies and systems. In contrast, Amazon SQS integrates seamlessly with other AWS services, allowing for efficient and reliable communication within the AWS ecosystem.
Message Reliability: Another difference between Airflow and Amazon SQS lies in their message reliability mechanisms. Airflow ensures that tasks within a workflow are executed exactly once, guaranteeing end-to-end reliability. On the other hand, Amazon SQS provides a highly reliable messaging service by replicating messages across multiple servers within a region, ensuring that messages are not lost.
Cost Structure: Airflow is an open-source platform and can be used free of charge. However, the cost of running Airflow includes infrastructure costs for hosting the platform. On the other hand, Amazon SQS follows a pay-as-you-go pricing model, where users are billed based on the number of requests and data transfer in and out of the service. The cost structure of Amazon SQS is directly tied to the usage of the service.

In Summary, Airflow and Amazon SQS differ in scalability, functionality, deployment options, integration capabilities, message reliability mechanisms, and cost structure.

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Advice on Amazon SQS, Airflow

Pulkit

Software Engineer

Oct 30, 2020

Needs adviceon

Django

Amazon SQS

RabbitMQ

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

Meili

Software engineer at Digital Science

Sep 24, 2020

Needs adviceon

ZeroMQ

RabbitMQ

Amazon SQS

Hi, we are in a ZMQ set up in a push/pull pattern, and we currently start to have more traffic and cases that the service is unavailable or stuck. We want to:

Not loose messages in services outages
Safely restart service without losing messages (@{ZeroMQ}|tool:1064| seems to need to close the socket in the receiver before restart manually)

Do you have experience with this setup with ZeroMQ? Would you suggest RabbitMQ or Amazon SQS (we are in AWS setup) instead? Something else?

Thank you for your time

500k views500k

Comments

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.

303k views303k

Comments

Detailed Comparison

Amazon SQS	Airflow
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.	Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
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.	Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
Stacks 2.8K	Stacks 1.7K
Followers 2.0K	Followers 2.8K
Votes 171	Votes 128
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 Difficult to configure 2 Proprietary 2 Has a max message size (currently 256K) 1 Has a maximum 15 minutes of delayed messages only	Pros 53 Features 14 Task Dependency Management 12 Beautiful UI 12 Cluster of workers 10 Extensibility Cons 2 Running it on kubernetes cluster relatively complex 2 Observability is not great when the DAGs exceed 250 2 Open source - provides minimum or no support 1 Logical separation of DAGs is not straight forward

What are some alternatives to Amazon SQS, Airflow?

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.

Related Comparisons

Airflow vs Amazon SQS: What are the differences?

Introduction

Scalability: One major difference between Airflow and Amazon SQS is their scalability. Airflow is designed to handle large-scale workflows with ease, allowing for the execution of complex workflows across multiple machines. On the other hand, Amazon SQS provides a highly scalable message queuing service, primarily used for decoupling the sending and receiving of messages in a distributed system architecture.
Functionality: Airflow offers a rich set of functionalities and features that allow users to design and manage workflows effectively. It provides a readily available user interface for scheduling, monitoring, and managing workflows, along with a wide range of pre-built operators and connectors. In contrast, Amazon SQS focuses solely on message queuing functionality, providing a simple and reliable messaging service with no additional workflow management features.
Deployment: Airflow can be self-hosted on-premises or deployed on cloud infrastructure, providing flexibility in terms of deployment options. It can be installed and managed on any infrastructure, allowing for customization and control over the environment. On the other hand, Amazon SQS is a fully managed service provided by Amazon Web Services (AWS), meaning that it is hosted and maintained by AWS, relieving users of the burden of deployment and management.
Integration: Airflow provides seamless integration with various databases, message brokers, and third-party services, offering a wide range of connectors and operators out of the box. This makes it easy to incorporate Airflow into existing technologies and systems. In contrast, Amazon SQS integrates seamlessly with other AWS services, allowing for efficient and reliable communication within the AWS ecosystem.
Message Reliability: Another difference between Airflow and Amazon SQS lies in their message reliability mechanisms. Airflow ensures that tasks within a workflow are executed exactly once, guaranteeing end-to-end reliability. On the other hand, Amazon SQS provides a highly reliable messaging service by replicating messages across multiple servers within a region, ensuring that messages are not lost.
Cost Structure: Airflow is an open-source platform and can be used free of charge. However, the cost of running Airflow includes infrastructure costs for hosting the platform. On the other hand, Amazon SQS follows a pay-as-you-go pricing model, where users are billed based on the number of requests and data transfer in and out of the service. The cost structure of Amazon SQS is directly tied to the usage of the service.

In Summary, Airflow and Amazon SQS differ in scalability, functionality, deployment options, integration capabilities, message reliability mechanisms, and cost structure.

Airflow vs Amazon SQS

Overview