No matter where you work or what you do, data will always be a part of your process. With every organization generating data like never before, it is essential to orchestrate tasks and automate data workflows in order to make sure they are properly executed without any delay. Apache Airflow is one of the most popular Automation and Workflow Management tools that come with the broadest range of features. Argo, on the other hand, is a container native Workflow Engine for orchestrating jobs on Kubernetes. This article presents a detailed comparison of Argo vs Airflow.
Automation plays a key role in improving production rates and work efficiency in various industries. Recently, there has been an explosion of new Automation and Management tools in the market, and thus, it becomes difficult for the users to choose the best ones for their use cases from a pool of new-age technologies. This piece on Argo vs Airflow will help you understand the ins and outs of both platforms and will ultimately let you zero in on one.
Table of Contents
What is Airflow?
Image Source: www.en.wikipedia.org
Apache Airflow is a well-known open-source Automation and Workflow Management platform for Authoring, Scheduling, and Monitoring workflows. Starting in October 2014 at Airbnb, Airflow joined the Apache Incubator program in 2016 and it has been gaining popularity ever since.
Airflow allows organizations to write workflows as Directed Acyclic Graphs (DAGs) in a standard Python programming language, ensuring anyone with minimal knowledge of the language can deploy one. Each DAG contains nodes and connectors, and nodes connect to other nodes via connectors to generate a dependency tree. Airflow helps organizations to schedule their tasks by specifying the plan and frequency of flows. Airflow also provides an interactive interface along with a bunch of different tools to monitor workflows in real-time.
Apache Airflow has gained a lot of popularity among organizations dealing with significant amounts of Data Collection, Processing, and Analysis. Each IT expert has a different job or workflow to perform, right from collecting data from other sources to processing it, uploading, and creating reports. There are many tasks that experts need to perform manually on a daily basis. Airflow triggers automatic workflow and reduces the time and effort required for collecting data from various sources, processing it, uploading it, and finally creating reports.
Key Features of Airflow
- Open-Source: Airflow is an open-source platform and is available free of cost for everyone to use. It comes with a large community of active users that makes it easier for Developers to access resources.
- Dynamic Integration: Airflow uses Python programming language for writing workflows as DAGs. This allows Airflow to be integrated with several operators, hooks, and connectors to generate dynamic pipelines. It can also easily integrate with other platforms like Amazon AWS, Microsoft Azure, Google Cloud, etc.
- Customizability: Airflow supports customization, and it allows users to design their own custom Operators, Executors, and Hooks. You can also extend the libraries as per your needs so that it fits the desired level of abstraction.
- Rich User Interface: Airflow’s rich User Interface (UI) helps in monitoring and managing complex workflows. It uses Jinja templates to create pipelines and it further makes it easy to keep track of the ongoing tasks.
- Scalability: Airflow is highly scalable and is designed to support multiple dependent workflows simultaneously.
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What is Argo?
Image Source: www.argoproj.github.io
Argo is an open-source Workflow Engine for orchestrating tasks on Kubernetes. Introduced by Applatex, Argo allows you to create and run advanced workflows entirely on your Kubernetes cluster. Argo Workflows is built on top of Kubernetes, and each task is run as a separate Kubernetes pod. Many reputable organizations in the industry use Argo Workflows for ML (Machine Learning), ETL (Extract, Transform, Load), Data Processing, and CI/CD Pipelines.
Argo is basically a Kubernetes extension and hence, it is installed using Kubernetes. Argo Workflows allows organizations to define their tasks as DAGs using YAML. Argo comes with a native Workflow Archive for auditing, Cron Workflows for scheduled workflows, and a fully-featured REST API. Argo comes with a list of killer features that set it apart from similar products, let’s take a look at them.
Key Features of Argo
- Open-Source: Argo is also fully open-source and is an incubating project at the Cloud Native Computing Foundation (CNCF). It is available free of cost for everyone to use.
- Native Integrations: Argo comes with native artifact support to download, transport, and upload your files during runtime. It supports any S3 compatible Artifact Repository such as AWS, GCS, Alibaba Cloud OSS, HTTP, Git, Raw, and Minio.
- Scalability: Argo Workflows has robust retry mechanisms for high reliability and is highly scalable. It is capable of managing thousands of pods and workflows in parallel.
- Customizability: Argo is highly customizable and it supports templating and composability to create and reuse workflows.
- Powerful User Interface: Argo comes with a fully-featured User Interface (UI) that is easy to use. Argo Workflows v3.0 UI also supports Argo Events and is more robust and reliable. It has embeddable widgets and a new workflow log viewer.
Argo vs Airflow: Key Differences
Now that you have a basic understanding of both platforms, let’s dive straight into a head-to-head comparison of Argo vs Airflow. Airflow and Argo both allow you to define your workflows as DAGs, but there are a few differences in how both the platforms operate which can be critical in choosing the right one for your requirements.
Workflow Language
Image Source: www.python.org
The first key differentiator in Argo Workflow vs Airflow is the programming language used to define DAGs. As discussed in the previous sections, Airflow allows organizations to define their workflows as DAGs in standard Python programming language. Airflow runs each task within the Python ecosystem. Having a basic fundamental understanding of Python is sufficient to write code and simplify complex pipelines and workflows. Its Python-based API is one of the main reasons for its immense popularity and adaptability.
Image Source: www.developers.redhat.com
Argo also allows organizations to define their workflows as DAGs, but unlike Airflow, the definitions are written in YAML instead of Python. Argo runs each task as a Kubernetes pod. However, workflows are usually complex, and complex processes are best expressed with code rather than a configuration language like YAML.
Task Scheduling
Airflow excels at running tasks on a schedule, and it has a fault-tolerant scheduler that is capable of recognizing when a schedule has been missed. Unfortunately, the scheduler can’t run in a highly available or busy setup as it is a single point of failure for the system. However, the Airflow scheduler can take up to 5-minutes to rescan a DAG file for updates, and to execute the state loop to schedule new tasks. Hence, it doesn’t support low latency scheduling.
Argo is also quite good at running scheduled tasks, but it has the ability to reschedule only 1 missed task if the controller faces an outage during a scheduled interval. It will reschedule a missed task up to the StartingDeadlineSeconds interval setting. However, no tasks will be rescheduled if the outage lasts longer than StartingDeadlineSeconds. However, the Argo scheduler receives events from Kubernetes and is capable of immediately responding to new workflows and state changes without a state loop making it an ideal choice for low latency scheduling.
Scalability
Airflow supports horizontal scalability and is capable of running multiple schedulers concurrently. Coming to tasks, Airflow relies on a dedicated pool of workers to execute tasks. So, the maximum task parallelism is equal to the number of active workers.
Argo runs each task as a separate Kubernetes pod, and hence it is capable of managing thousands of pods and workflows in parallel. Unlike Airflow, the parallelism of a workflow isn’t limited by a fixed number of workers in Argo. Hence, it is best suited for jobs with sequence and parallel steps dependencies.
Third-Party Integrations
Airflow uses Python programming language for writing workflows as DAGs. This allows Airflow to be connected to almost any third-party system. Airflow also has its own community-supported library of operators for Databases, Cloud Services, Compute Clusters, etc.
Argo being an open-source container, doesn’t come with pre-packaged operators to connect to third-party systems. However, it supports any S3 compatible Artifact Repository such as AWS, GCS, Alibaba Cloud OSS, HTTP, etc to download, transport, and upload your files during runtime.
Supported Workflows
Airflow DAGs are static and once defined, they don’t have the ability to add or modify steps during runtime. Airflow runs DAGs only with a schedule, and hence external systems can’t trigger a workflow run. This means 2 DAG runs can’t be started at the same time. On top of that, Airflow assumes all DAGs are self-contained and hence it doesn’t have a first-class mechanism to pass parameters to DAG runs.
DAG definitions can be created dynamically for each run of the workflow in Argo. It can map tasks over dynamically generated lists of results to process items in parallel. Argo Workflows v3.0 also supports Argo Events which is an Agro-ecosystem project dedicated to event-driven workflow automation. Agro’s parameter passing syntax allows you to pass input and output parameters at the task level, and input parameters at the workflow level.
Interacting with Kubernetes Resources
Airflow has a Kubernetes operator that can be used to run pods as part of a workflow. However, it doesn’t have any support for creating other resources.
Argo is built on top of Kubernetes, and each task is run as a separate Kubernetes pod. Argo has an exceptional support system for performing CRUD operations on Kubernetes objects like pods and deployments.
This brings us to the end of Argo vs Airflow, let’s just take a look at all the important points discussed till now.
Argo vs Airflow: Summary
| Feature | Argo | Airflow |
| Workflow Definition Language | YAML | Python |
| Fault-Tolerant Scheduling | No | No |
| Low Latency Scheduler | Yes | Yes |
| Highly Parallel | Yes | No |
| Third-Party Integrations | No | Yes |
| Dynamic Workflows | Yes | No |
| Event-Driven Workflows | Yes | No |
| Parameterized Workflows | Yes | No |
| Kubernetes Interaction | Yes | No |
Argo vs AirflowConclusion
Based on this Argo vs Airflow comparison, you must have noticed that both the tools have different focus points and different strengths. Hence, there is no silver bullet for deciding which tool is the best. The choice depends largely on your use case, requirements, and running environment.
Argo and Airflow both allow you to define your tasks as DAGs, but Airflow is more versatile, whereas Argo offers limited flexibility in terms of interacting with third-party services. If you’re already using Kubernetes for most of your infrastructure, it is recommended to use Argo for your tasks. If your Developers are more comfortable in writing DAG definitions in Python than YAML, you can consider using Airflow.
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Raj is a skilled data analyst with a strong passion for data analysis and architecture, having a flair for writing technical content as well. With extensive experience in handling marketing data, Raj has adeptly navigated abstract business problems to derive actionable insights that drive significant results.