Data-Pipelines mit Airflow aufbauen
Volker Janz
Senior Developer Advocate at Astronomer
@task
def process_file_1():
transform("/data/file_1.csv")
@task
def process_file_2():
transform("/data/file_2.csv")
@task
def process_file_3():
transform("/data/file_3.csv")

@task def fetch_files(): return ["/data/file_1.csv", "/data/file_2.csv", "/data/file_3.csv"]@task(max_active_tis_per_dagrun=2) # maximale Parallelität steuern def process_file(path: str): print(f"processing file: {path}")files = fetch_files() process_file.expand(path=files)


@task def process_file(path: str, output_dir: str): transform(path, output_dir)files = get_files() process_file.partial(output_dir="/out").expand(path=files)

files = get_files() # ["/data/a.csv", "/data/b.csv", "/data/c.csv"] destinations = get_targets() # ["s3://out/a", "s3://out/b", "s3://out/c"]# Kreuzprodukt: 3 x 3 = 9 Instanzen (nicht gewünscht!) process.expand(path=files, dest=destinations)

files = get_files() # ["/data/a.csv", "/data/b.csv", "/data/c.csv"] destinations = get_targets() # ["s3://out/a", "s3://out/b", "s3://out/c"]# Gepairt: 3 Instanzen (a->a, b->b, c->c) process.expand_kwargs(files.zip(destinations))
zip() paart Elemente eins-zu-eins nach Positionexpand_kwargs entpackt jedes Paar in Keyword-Argumente
| Pattern | Use case |
|---|---|
expand() |
Über eine einzelne Liste mappen |
partial() |
Gemeinsame Parameter für alle Instanzen festlegen |
zip() |
Mehrere Listen positionsweise paaren |
Data-Pipelines mit Airflow aufbauen