Apache Beam Cheatsheet: Pipeline
Core Architecture Overview
Learn the fundamentals of creating, running, and managing Apache Beam pipelines.
Initializes the pipeline execution graph context representing the complete data flow.
with beam.Pipeline(options=options) as p:import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
# Configure pipeline options
options = PipelineOptions(runner="DirectRunner")
# Define and execute the pipeline
with beam.Pipeline(options=options) as p:
(p
| "Create Data" >> beam.Create(["A", "B", "C"])
| "Print" >> beam.Map(print))A
B
CO(1) initialization, pipeline building is O(V + E)
Main pipeline initialization block.
PipelineOptions(), Pipeline.run()
Differs from standard programming scripts as it builds a lazy evaluation graph before execution.
Forgetting the 'with' context manager or not calling p.run() if context manager is omitted.
Always name every step. Unique transform names (e.g. 'Create Data' >>) are mandatory for visualization and production debugging.
Parses execution arguments and configures runner environments (Dataflow, Spark, Flink).
options = PipelineOptions(flags=None, **options)from apache_beam.options.pipeline_options import PipelineOptions
# Instantiate options with explicit configuration parameters
options = PipelineOptions(
runner="DirectRunner",
project="my-gcp-project",
temp_location="gs://my-bucket/temp"
)Defining runner settings, GCP project IDs, staging directories, and worker bounds.
Pass standard CLI flags using sys.argv to allow override scripts at execution runtime.