![]() Less flexibility with actions and dependency, for example: Dependency check for partitions should be in MM, dd, YY format, if you have integer partitions in M or d, it’ll not work.Doesn’t require learning a programming language. ![]() The properties file contains configuration parameters like start date, end date and metastore configuration information for the job.Īt GoDaddy, we use Hue UI for monitoring Oozie jobs.The bundle file is used to launch multiple coordinators.The coordinator file is used for dependency checks to execute the workflow.Some of the common actions we use in our team are the Hive action to run hive scripts, ssh action, shell action, pig action and fs action for creating, moving, and removing files/folders The workflow file contains the actions needed to complete the job.A workflow file is required whereas others are optional. When we develop Oozie jobs, we write bundle, coordinator, workflow, properties file. It’s an open source project written in Java. Pig, Hive, Sqoop, Distcp, Java functions). OozieĪpache Oozie is a workflow scheduler which uses Directed Acyclic Graphs (DAG) to schedule Map Reduce Jobs (e.g. With cron, you have to write code for the above functionality, whereas Oozie and Airflow provide it. Add Service Level Agreement (SLA) to jobs.Cause the job to timeout when a dependency is not available.Add dependency checks for example triggering a job if a file exists, or triggering one job after the completion of another.Automatically rerun jobs after failure.These are some of the scenarios for which built-in code is available in the tools but not in cron: These tools (Oozie/Airflow) have many built-in functionalities compared to Cron. Why use scheduling tools (Oozie/Airflow) over Cron? To help you get started with pipeline scheduling tools I’ve included some sample plugin code to show how simple it is to modify or add functionality in Airflow. In this article, I’ll give an overview of the pros and cons of using Oozie and Airflow to manage your data pipeline jobs. In the past we’ve found each tool to be useful for managing data pipelines but are migrating all of our jobs to Airflow because of the reasons discussed below. On the Data Platform team at GoDaddy we use both Oozie and Airflow for scheduling jobs. You may want to subclass it though if there are common invocation patterns (e.g.Data pipeline job scheduling in GoDaddy: Developer’s point of view on Oozie vs Airflow If you are just getting started, it's fine to just use BashOperator to call your rust jobs. We have lots of things that run on the worker, but we also have containers and trigger EMR jobs. However if you write your tasks so they are idempotent and they can just be retried on failure, then it doesn't matter so much if a task fails due to a intermittant OOM error. If you want guaranteed memory available, you need to limit to 1 job per worker which can be inefficient. ![]() The main constraint is memory as Airflow doesn't (afaik) give you any way to control job allocation based on memory usage. Honestly you can go very far just running stuff on airflow workers, despite all the shouting about doing it the "right" way. We already have a nicely setup rust environment, and that’s how I would handle our pipeline if it was just gonna be rust scripts and a bunch of cron jobs.īut since airflow has so many easy integrations, we decided just to let airflow (and thusly python DAGs) handle all of the data pulling and normalization.Ģ) having airflow handle everything from within airflow? Ideally, I’d have all of my data pulling/normalizing code in rust. If that’s too vague, here’s a specific example: So I’m wondering: do y’all use airflow primarily as a scheduler for jobs that are owned by other services, or do you also rely on it to run business logic? I saw another post that mentioned how airflow is mostly a “job scheduler”, which made me second-guess keeping all of our code in airflow DAGs. We’re trying out airflow for this, and we’ve been putting all of the actual code into DAGs in airflow. ![]() Nothing fancy, just reads data from a few API’s, normalizes them and sticks em in a table in our DB.
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