Cyclic Workflow in Data Comparison Process

Question

I am searching for a solution to automatize an iterative data comparison process until all data packages are consistent. My general guess is to use something like Apache Airflow, but the iterative nature seem to be a cyclic graph. Apache airflow only allows DAGs (directed acyclic graph). Since I have not even a lot of knowledge in Airflow, I am a bit lost and would appreciate some expert knowledge here.

Current status: I am in a position were I regularly need to compare data packages for consistency and communicate errors to and between the two different parties manually.

On the one hand there is a design data set and on the other hand there are measured data sets. Both datasets involve many manual steps from different parties. So if an inconsistency occurs, I contact one or the other party and the error is removed manually. There are also regular changes to both data sets that can introduce new errors to already checked datasets. I guess this process was not automatized yet, because the datasets are not directly comparable, but some transformations need to be done in between. I automatized this transformation process the last weeks so all that need to be done now from my side is to run the script and to communicate the errors.

What I would need now is a tool that orchestrates my script against the correct datasets and contacts the according persons as long as errors exists. In case something changes or was added the script needs to be run again.

My first guess was that I would need to create a workflow in apache airflow, but this iterative process seems to me as a cyclic graph, which is not allowed in Airflow. Do you have any suggestions or is this a common occurrence, were also solutions with Airflow exists?

user_1762101 · Answer

I think one way to solve your problem could be to have a DAG workflow for the main task of comparing the datasets and sending notifications. Then run a periodical task in Cron, Quartz, etc, that triggers that DAG workflow. You are correct in Airflow not liking cyclic workflows.

I worked on Cylc, a cyclic graph workflow tool. Cyclic workflows (or workflows with loops) are very common in areas such as Numerical Weather Prediction NWP (reason why Cylc was created), and also in other fields such as optimization.

In NWP workflows, some steps may be waiting for datasets, and the workflow may stall and send notifications if the data is not as it was expected (e.g. some satellite imaging data is missing, and also the tides model output file is missing).

Also, in production, NWP models run multiple times a day. Either because you have new observation data, or new input data, or maybe because you want to run ensemble models, etc. So you end up with multiple runs of the workflow in parallel, where the workflow manager is responsible to manage dependencies, optimize the use of resources, send notifications, and more.

Cyclic workflows are complicated, that's probably why most implementations opt to support only DAGs.

If you'd like to try Cylc, the team has been trying to make it more generic so that it's not specific to NWP only. It has a new GUI, and the input format and documentation were improved with ease of use in mind.

There are other tools that support cyclic workflows too, such as StackStorm, Prefect, and I am currently checking if Autosubmit supports it too. Take a look at these tools if you'd like.

If you are in life sciences, or are interested in reproducible workflows, the CWL standard also has some ongoing discussion about adding support to loops, which could allow you to achieve something akin to what you described I reckon.

Cyclic Workflow in Data Comparison Process

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