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Airflow vs Digdag: What are the differences?
Key Differences between Airflow and Digdag
Airflow and Digdag are both workflow management platforms but they have key differences that set them apart from each other.
Task Dependency: Airflow uses a directed acyclic graph (DAG) to represent dependencies between tasks, which allows for complex workflows with multiple task dependencies. Digdag, on the other hand, uses a simpler dependency model with one-to-one dependencies between tasks. This makes Airflow more suited for complex workflows with intricate dependencies.
Execution Model: Airflow follows a pull-based execution model, where tasks are executed by workers that periodically poll the Airflow scheduler for new tasks. Digdag, on the other hand, uses a push-based execution model where tasks are executed immediately after their dependencies are satisfied. This makes Digdag more suited for real-time processing and low-latency workflows.
Scripting Language: Airflow uses Python as its scripting language, allowing for a wide range of customizations and integrations with existing Python libraries and frameworks. Digdag, on the other hand, uses a proprietary YAML-based language for defining workflows, which limits the customizability and integrations compared to Airflow.
Workflow Visualization: Airflow provides a web-based user interface that allows users to visualize the workflow dependencies and monitor the execution status of tasks. Digdag, on the other hand, lacks a dedicated web-based UI for workflow visualization and monitoring, making it less user-friendly for visualizing complex workflows.
Community Ecosystem: Airflow has a larger and more active community compared to Digdag, with a vast number of plugins, integrations, and community-contributed resources available. Digdag's community ecosystem is relatively smaller, limiting the availability of plugins and integrations compared to Airflow.
Maturity and Scalability: Airflow is a more mature and widely adopted workflow management system with a proven track record of large-scale deployments. Digdag, on the other hand, is a relatively newer and less mature platform with limited scalability features compared to Airflow.
In summary, Airflow and Digdag have key differences in their task dependency models, execution models, scripting languages, workflow visualization capabilities, community ecosystems, and maturity and scalability levels.
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.
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
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Digdag
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Digdag
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