Using Apache Beam GCP DataflowRunner to write to BigQuery (Python)

Attempting to write a pipeline in Apache Beam (Python) that will read an input file from a GCP storage bucket, apply transformations then write to BigQuery.

Here is the excerpt for the Apache Beam pipeline:

import logging
import apache_beam as beam
import argparse
import csv
import json
from apache_beam.io.gcp.internal.clients import bigquery

def build_argument_parser():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--input',
        dest='input',
        required=True,
        help='Input file to process.')
    parser.add_argument(
        '--project',
        dest='project',
        required=True,
        help='Project ID.'
    )
    parser.add_argument(
        '--datasetId',
        dest='datasetId',
        required=True,
        help='BigQuery dataset ID.'
    )
    parser.add_argument(
        '--tableId',
        dest='tableId',
        required=True,
        help='BigQuery table ID.'
    )
    return parser


def create_pipeline_options(pipeline_arguments):
    pipeline_options = beam.options.pipeline_options.PipelineOptions(pipeline_arguments)
    pipeline_options.view_as(beam.options.pipeline_options.SetupOptions).save_main_session = True
    return pipeline_options


def run(argv=None):
    parser = build_argument_parser()
    known_args, pipeline_args = parser.parse_known_args(argv)

    table_spec = beam.io.gcp.internal.clients.bigquery.TableReference(
        projectId=known_args.project,
        datasetId=known_args.datasetId,
        tableId=known_args.tableId
    )

    table_schema = {
        'fields': [
            ...
        ]
    }

    with beam.Pipeline(options=create_pipeline_options(pipeline_args)) as p:
        input_data = p | "Read input csv" >> beam.io.ReadFromText(known_args.input, skip_header_lines=1)

        input_data_as_json = input_data | "Transforming to json" >> (
            beam.ParDo(
                TransformCsvToJson(build_field_mapping_names())
            )
        ).with_outputs('error', 'success')

        input_data_as_json.success | beam.io.WriteToBigQuery(
            table_spec,
            schema=table_schema,
            write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND,
            create_disposition=beam.io.BigQueryDisposition.CREATE_NEVER,
            additional_bq_parameters={'timePartitioning': {
                'type': 'MONTH'
            }}
        )


if __name__ == '__main__':
    run()

Here is the script to execute the pipeline:

    #!/usr/bin/env bash

python ../pipelines/pipeline.py \
    --input "gs://storage/input_file.csv" \
    --runner DataFlowRunner \
    --project "project_name" \
    --datasetId "dataset_name" \
    --tableId "table_name" \
    --region europe-west2 \
    --staging_location "gs://storage/staging" \
    --temp_location "gs://storage/temp"

Here is the error I get:

    /bin/zsh path/utils/execute_pipeline.sh
Traceback (most recent call last):
  File "../pipelines/pipeline.py", line 138, in <module>
    run()
  File "../pipelines/pipeline.py", line 117, in run
    with beam.Pipeline(options=create_pipeline_options(pipeline_args)) as p:
  File "/environment_path/lib/python3.8/site-packages/apache_beam/pipeline.py", line 202, in __init__
    raise ValueError(
ValueError: Pipeline has validations errors: 
Missing required option: project.

1. When I use DirectRunner, I don't get this error.
2. When I use DataflowRunner, the pipeline works when it doesn't have to write to BigQuery.
3. When I use DataflowRunner and hard code the project, datasetId and tableId it works fine.

UPDATE

Found the culprit, though not sure why this is happening...

Modifying the following works

def run(argv=None):
    parser = build_argument_parser()
    known_args, pipeline_args = parser.parse_known_args(argv)

    #this bit below is new
    pipeline_args.extend([
        '--project=' + known_args.projectId
    ])

When I printed the known_args and pipeline_args, "project" wasn't showing up. Though I'm not sure why it's not picking up the value from the execute script.