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  1. Stackups
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  4. Message Queue
  5. ActiveMQ vs Airflow

ActiveMQ vs Airflow

OverviewDecisionsComparisonAlternatives

Overview

ActiveMQ
ActiveMQ
Stacks880
Followers1.3K
Votes77
GitHub Stars2.4K
Forks1.5K
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

ActiveMQ vs Airflow: What are the differences?

Introduction

ActiveMQ and Airflow are both widely used technologies in the field of data processing and workflow management. While they serve similar purposes, there are key differences between the two that set them apart in terms of their features and functionalities.

  1. Messaging system vs. Workflow management: The fundamental difference between ActiveMQ and Airflow lies in their primary purpose. ActiveMQ is a messaging system that facilitates the exchange of messages between different applications and services, while Airflow is a workflow management platform that allows users to define, schedule, and monitor workflows.

  2. Language support and integration: ActiveMQ primarily focuses on supporting the Java Messaging Service (JMS), making it more suitable for Java-based applications. On the other hand, Airflow supports a wider range of programming languages and offers integrations with various databases, cloud services, and other tools commonly used in data engineering and processing.

  3. Message-oriented communication vs. Directed Acyclic Graph (DAG) execution: ActiveMQ provides message-oriented communication based on publish and subscribe patterns or point-to-point messaging. It serves as a reliable and scalable messaging backbone. In contrast, Airflow focuses on executing DAGs, which represent complex workflows with dependencies between tasks. It allows for the definition and execution of tasks in a programmatically defined workflow.

  4. Scalability and fault tolerance: ActiveMQ is designed to be highly scalable and fault-tolerant, ensuring reliable message delivery even in the face of failures or high message volumes. It can be used in distributed architectures to handle large-scale messaging needs. Airflow, on the other hand, focuses on orchestrating workflows and has mechanisms for task retries, but it may require additional configuration and infrastructure to achieve fault tolerance and scalability.

  5. Message persistence and storage: ActiveMQ provides built-in message persistence, allowing messages to be stored on disk and retrieved even in the event of system failures. It offers different storage options based on the requirements of the application. In Airflow, while the metadata of workflows and task states can be stored in a database, the storage of intermediate data or results from tasks depends on the specific tasks and technologies involved.

  6. Community and ecosystem: Both ActiveMQ and Airflow have vibrant communities and a wide range of user-contributed extensions and integrations. ActiveMQ has a history of being used in enterprise messaging systems, with extensive community support and a mature ecosystem. Airflow, while relatively newer, has gained popularity in the data engineering and processing space and has a growing ecosystem with integrations to various tools commonly used in the data domain.

In summary, ActiveMQ and Airflow differ in their primary purpose, language support, communication patterns, execution models, scalability and fault tolerance features, message persistence and storage options, as well as the maturity of their communities and ecosystems. These differences make them suitable for different use cases and environments in the realm of data processing and workflow management.

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Advice on ActiveMQ, Airflow

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

294k views294k
Comments

Detailed Comparison

ActiveMQ
ActiveMQ
Airflow
Airflow

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.

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.

Protect your data & Balance your Load; Easy enterprise integration patterns; Flexible deployment
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
GitHub Stars
2.4K
GitHub Stars
-
GitHub Forks
1.5K
GitHub Forks
-
Stacks
880
Stacks
1.7K
Followers
1.3K
Followers
2.8K
Votes
77
Votes
128
Pros & Cons
Pros
  • 18
    Easy to use
  • 14
    Open source
  • 13
    Efficient
  • 10
    JMS compliant
  • 6
    High Availability
Cons
  • 1
    ONLY Vertically Scalable
  • 1
    Difficult to scale
  • 1
    Low resilience to exceptions and interruptions
  • 1
    Support
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 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 ActiveMQ, Airflow?

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 SQS

Amazon SQS

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.

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.

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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