airflow - airflow create sub process based on number of files

Question

A newbie question in airflow, I am having a list of 100+ servers in a text file. Currently, a python script is used to login to each server, read a file, and write the output. It's taking a long time to get the output. If this job is converted to Airflow DAG, is it possible to split the servers into multiple groups and a new task can be initiated by using any operators? Or this can be achieved by only modifying the Python script(like using async) and execute using the Python operator. Seeking advice/best practice. I tried searching for examples but was not able to find one. Thanks!

标签： airflow

Answer 1

Airflow is not really a "map-reduce" type of framework (which you seem to be trying to implement). The tasks of Airflow are not (at least currently) designed to split the work between them. This is very atypical for Airflow to have N tasks that do the same thing on a subset of data each. Airflow is more for orchestrating the logic, so each task in Airflow conceptually does a different thing and there are rarely cases where N parallel task do the same thing (but on a different subset of data). More often than not Airflow "tasks" do not "do" the job themselves, they are rather telling others what to do and wait until this gets done.

Typically Airflow can be used to orchestrate such services which excel in doing this kind of processing - you could have a Hadoop job which processes such "parallel" map-reduce kind of jobs using other tools. You could also - as you mentioned - perform an async, multi-threading or even multi-processing python operator, but at some scale, I think typically other, dedicated tools should be much easier to use and better to get the most value of (with efficient utilization of parallelism for example).