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Airflow vs Apache Oozie: What are the differences?
Introduction
Airflow and Apache Oozie are both widely used workflow management systems, with the aim of scheduling and orchestrating complex processes. While they share some similarities, there are key differences that set them apart from each other. In this section, we will explore and highlight the six main differences between Airflow and Apache Oozie.
Architecture: Airflow follows a distributed architecture model and is built on a scalable message queuing system, providing high availability and fault tolerance. On the other hand, Oozie uses a centralized architecture with a single server managing the workflow execution, which may limit scalability for larger deployments.
Workflow Design: Airflow uses Python-based scripting to define workflows, which offers greater flexibility and customizability. Oozie, on the other hand, relies on XML-based configuration files, which although provides a certain level of portability, can be more verbose and less intuitive for developers.
User Interface: Airflow has a web-based user interface that allows users to easily monitor and manage workflows, providing real-time insights into job statuses, monitoring graphs, and logs. Oozie, on the other hand, lacks a user interface and relies primarily on command-line tools or external plugins for monitoring and managing workflows, which can make it less user-friendly for non-technical users.
Ease of Deployment: Airflow can be easily deployed using containerization platforms like Docker, with pre-built images available, simplifying the setup and deployment process. Oozie, on the other hand, requires setting up and configuring various components of the Hadoop ecosystem, making it a more complex and time-consuming deployment process.
Integration with Ecosystem: Airflow has a wide range of integrations with popular data processing frameworks and services, allowing seamless integration into existing data pipelines. Oozie, on the other hand, is tightly integrated with the Hadoop ecosystem, making it a better choice for organizations heavily relying on Hadoop technologies.
Community Support and Development: Airflow has gained significant popularity in recent years, with a large and active open-source community contributing to its development and maintenance. This translates into frequent updates, bug fixes, and new features being regularly released. Oozie, on the other hand, has seen a decline in community support, with fewer updates and new features being introduced, making it less likely to keep up with evolving technologies and requirements.
Summary
In summary, Airflow and Apache Oozie differ in their architecture, workflow design, user interface, ease of deployment, integration with the ecosystem, and community support. These differences make each system better suited for different scenarios and requirements.
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
- Features51
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Apache Oozie
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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 Apache Oozie
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What is Airflow?
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