Dagster's asset functionality sits on top of a general orchestration engine that can be used for tasks other than creating and maintaining assets. The core abstractions of this engine are ops and jobs.
Ops are individual units of computation that we wire together to form jobs.
In this section, we'll cover how to define a simple job with a single op, and then execute it.
Let's write our first Dagster op and save it as hello.py.
Typically, you'll define ops by annotating ordinary Python functions with the @op decorator.
Our first op finds the sizes of all the files in our current directory and logs them.
import os
from dagster import job, op, get_dagster_logger
@opdefget_file_sizes():
files =[f for f in os.listdir(".")if os.path.isfile(f)]for f in files:
get_dagster_logger().info(f"Size of {f} is {os.path.getsize(f)}")
In this simple case, our op takes no arguments, and also returns no outputs. Don't worry, we'll soon encounter ops that are more dynamic.
To execute our op, we'll embed it in an equally simple job. A job is a set of ops arranged into a DAG of computation. You'll typically define jobs by annotating ordinary Python functions with the @job decorator.
@jobdeffile_sizes_job():
get_file_sizes()
Here you'll see that we call get_file_sizes(). This call doesn't actually execute the op. Within the bodies of functions decorated with @job, we use function calls to indicate the dependency structure of the op making up the job. Here, we indicate that the execution of get_file_sizes doesn't depend on any other ops by calling it with no arguments.
To visualize your job (which only has one op) in Dagit, just run the following. Make sure you're in the directory in which you've saved the job file:
dagit -f hello.py
You'll see output like
Serving dagit on http://127.0.0.1:3000 in process 70635
You should be able to navigate to http://127.0.0.1:3000 in your web browser and view your job. It isn't very interesting yet, because it only has one op.
Click on the "Launchpad" tab and you'll see the view below.
The large upper left pane is empty here, but, in jobs with parameters, this is where you'll be able to edit job configuration on the fly.
Click the "Launch Run" button on the bottom right to execute this job directly from Dagit. A new window should open, and you'll see a much more structured view of the stream of Dagster events start to appear in the left-hand pane.
If you have pop-up blocking enabled, you may need to tell your browser to allow pop-ups from 127.0.0.1—or, just navigate to the "Runs" tab to see this, and every run of your job.
In this view, you can filter and search through the logs corresponding to your job run.