![]() ![]() One area worth some attention is in making the LocalExecutor a first-class citizen. None of them are deal-breakers for us and as the community grows and as Airbnb dedicates more engineers to supporting what is turning out to be an emerging contender in the DAG scheduling space, I suspect the bug list to get shorter and the feature request list to grow. There is a list of open issues on the Github site, just as you would expect with any project starting out. ![]() Though the Pypi releases are fairly solid.Īgree with Maxime. What are in your opinion as an early adopter and expert in this framework the major shortcomings of Airflow to date?Īirflow is still a young project and moving fast, so it’s compelling to use trunk and hit some bugs in the process. Refer to the section marked “Why Airflow?” on our recent blog Examples of that at Airbnb include our A/B testing framework, an anomaly detection framework, an aggregation framework and others. This makes Airflow the best solution out there for dynamic pipeline generation, which can be used to power concepts as “analytics as a service”, “analysis automation” and computation frameworks, where pipelines are generated dynamically from configuration files or metadata of any form. ![]() How does Airflow compare against Azkaban (LinkedIn), Luigui (Spotify) and Oozie (Yahoo) ? Maxime:Ī key differentiator is the fact that Airflow pipelines are defined as code (as opposed to a markup language in Oozie or Azkaban), and that tasks are instantiated dynamically (as opposed to creating tasks by deriving classes in Luigi). We wanted a simple architecture when starting out, but one that could grow as our needs grew, making the investment in a distributed worker-broker architecture more palatable. This is an annoyance in a private dedicated datacenter and painful in a public cloud - in the latter, you often have to implement some tooling to handle changing IP addresses whenever EC2 instance restart, be it related to a worker or your distributed broker. We did not want the hassle of bringing up a distributed infrastructure involving a distributed broker and a set of remote workers. As an early adopter, we were looking for a workflow scheduler that was easy to install, maintain, and run in the cloud. We run airflow in both QA and Production, which essentially means that the above architecture is replicated in 2 environments. The two airflow components (webserver and scheduler) run on a single machine, whereas the database is running on a shared database instance. More modest installations can use a LocalExecutor and get a fair amount of mileage out of that. a metadata database (mysql or postgres)Īll of this can run on a single box, scale at will. In a scalable production environment, Airflow has the following components InfoQ spoke to Airflow’s creator, Maxime Beauchemin, and Agari’s Data Architect and one of the framework’s early adopters Siddharth Anand, to discuss Airflow including where it can be of use and what’s planned for the future.Ĭould you give us a high level overview of Airflow’s architecture? As well as a command line interface, the tool provides a web-based UI which allows you to visualize your pipelines dependencies, monitor progress, trigger tasks and so on. The platform has mechanisms to interact with Hive, Presto, MySQL, HDFS, Postgres and S3, and hooks are provided to allow the system to be made more extensible. For Airbnb, this includes use-cases across multiple departments such as data warehousing, growth analytics, email targeting, A/B testing and so on. Directed Acyclic Graphs or else DAGs) per a periodic schedule. The platform is written in Python, as are any workflows that run on it.Īirflow is a tool that allows developers of workflows to easily author, maintain, and run workflows (a.k.a. Airflow is being used internally at Airbnb to build, monitor and adjust data pipelines. Airbnb recently open-sourced Airflow, its own data workflow management framework, under the Apache license. ![]()
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