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Airflow

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Airflow vs Celery: What are the differences?

Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. 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; Celery: Distributed task queue. 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.

Airflow and Celery are primarily classified as "Workflow Manager" and "Message Queue" tools respectively.

Airflow and Celery are both open source tools. Airflow with 12.9K GitHub stars and 4.71K forks on GitHub appears to be more popular than Celery with 12.9K GitHub stars and 3.33K GitHub forks.

Udemy, Sentry, and Postmates are some of the popular companies that use Celery, whereas Airflow is used by Airbnb, Slack, and 9GAG. Celery has a broader approval, being mentioned in 272 company stacks & 77 developers stacks; compared to Airflow, which is listed in 72 company stacks and 33 developer stacks.

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
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 · 189.4K views
Recommends
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
  • 50
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
  • 5
    Complex workflows
  • 5
    Python
  • 5
    Open source
  • 3
    Good api
  • 3
    Custom operators
  • 2
    Dashboard
  • 2
    Apache project
  • 98
    Task queue
  • 63
    Python integration
  • 40
    Django integration
  • 30
    Scheduled Task
  • 19
    Publish/subsribe
  • 8
    Various backend broker
  • 6
    Easy to use
  • 5
    Workflow
  • 5
    Great community
  • 4
    Free
  • 1
    Dynamic

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Cons of Airflow
Cons of Celery
  • 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
  • 1
    Observability is not great when the DAGs exceed 250
  • 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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Jobs that mention Airflow and Celery as a desired skillset
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What are some alternatives to Airflow and Celery?
Luigi
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.
Jenkins
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
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.
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
See all alternatives