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Airflow vs Celery: What are the differences?

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows, while Celery is a distributed task queue system for executing tasks asynchronously in a distributed manner. Let's explore the key differences between them:

  1. Architecture: Airflow is a workflow scheduler system while Celery is a distributed task queue. Airflow provides a way to schedule and manage data pipelines, where tasks can be orchestrated based on dependencies and time. On the other hand, Celery focuses on executing distributed tasks asynchronously, allowing for parallel processing and scaling of workloads.

  2. Task Execution: In Airflow, tasks are executed in a sequential manner, following the dependencies defined in the workflow. Each task represents a single unit of work. In contrast, Celery allows for parallel execution of tasks, distributing the workload across multiple worker nodes, which enables efficient utilization of system resources and faster task execution.

  3. Monitoring and Logging: Airflow provides a built-in web-based user interface for monitoring and managing workflows. It offers visual representations of task states, task history, and workflow status. Whereas Celery lacks a dedicated web-based interface for monitoring and logging. However, Celery integrates well with other monitoring and logging tools, allowing users to leverage existing frameworks for tracking task progress and logging outputs.

  4. Workflow Definition: Airflow workflows are defined using Python code, allowing for a high degree of flexibility and customization. Workflows in Airflow are defined as Directed Acyclic Graphs (DAGs) with tasks represented as Python functions. On the contrary, Celery tasks are defined as independent units of work, typically written as Python functions. The task dependencies can be defined programmatically or through task queues.

  5. Scheduling: Airflow provides a rich scheduling functionality, allowing users to define complex dependencies and time-based triggers for task execution. It supports scheduling of tasks based on fixed schedules, cron expressions, or external triggers. Celery, on the other hand, does not provide built-in scheduling capabilities. It relies on external systems or custom code to define the execution timing and order of tasks.

  6. Community and Ecosystem: Airflow has a larger and more mature community compared to Celery, which results in a wider range of integrations, plugins, and community-driven contributions. Airflow has built-in support for various data-processing frameworks, databases, and cloud platforms, making it well-suited for data engineering and analytics tasks. Celery also has an active community but is more focused on distributed task processing, with a smaller ecosystem of extensions and integrations.

In summary, Airflow is designed for orchestrating workflows and managing dependencies, while Celery is specialized in distributing and executing tasks across a network.

Advice on Airflow and Celery
Needs advice
on
CeleryCelery
and
RabbitMQRabbitMQ

I am just a beginner at these two technologies.

Problem statement: I am getting lakh of users from the sequel server for whom I need to create caches in MongoDB by making different REST API requests.

Here these users can be treated as messages. Each REST API request is a task.

I am confused about whether I should go for RabbitMQ alone or Celery.

If I have to go with RabbitMQ, I prefer to use python with Pika module. But the challenge with Pika is, it is not thread-safe. So I am not finding a way to execute a lakh of API requests in parallel using multiple threads using Pika.

If I have to go with Celery, I don't know how I can achieve better scalability in executing these API requests in parallel.

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Replies (1)
Recommends
on
rqrqRedisRedis

For large amounts of small tasks and caches I have had good luck with Redis and RQ. I have not personally used celery but I am fairly sure it would scale well, and I have not used RabbitMQ for anything besides communication between services. If you prefer python my suggestions should feel comfortable.

Sorry I do not have a more information

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Needs advice
on
AirflowAirflowLuigiLuigi
and
Apache SparkApache Spark

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.

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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 267.8K views
Recommends
on
CassandraCassandra

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of Celery
  • 51
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
  • 99
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
  • 8
    Various backend broker
  • 6
    Easy to use
  • 5
    Great community
  • 5
    Workflow
  • 4
    Free
  • 1
    Dynamic

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Cons of Airflow
Cons of Celery
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Open source - provides minimum or no support
  • 1
    Logical separation of DAGs is not straight forward
  • 4
    Sometimes loses tasks
  • 1
    Depends on broker

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What is Airflow?

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.

What is 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.

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What companies use Airflow?
What companies use Celery?
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It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
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.
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AWS Step Functions
AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
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