Prompt: What does this code do?

class BigtableOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_argument(
            '--project_id',
            required=True,
            help='Project ID'
        )
        parser.add_argument(
            '--instance_id',
            default="beam-test",
            help='Cloud Bigtable instance ID'
        )
        parser.add_argument(
            '--table_id',
            default="your-test-table",
            help='Cloud Bigtable table ID'
        )

    options = BigtableOptions()

with beam.Pipeline(options=options) as p:
    output = (p | "Read from Bigtable" >> ReadFromBigtable(
        project_id=options.project_id,
        instance_id=options.instance_id,
        table_id=options.table_id
        )
        | "Extract cells" >> beam.Map(lambda row: row._cells)
        | "Log Data" >> Map(logging.info))

Response: This code uses the Apache Beam BigtableIO connector to read data from a Bigtable table table_id in a Bigtable instance instance_id.

The BigtableOptions class defines the command-line arguments project_id, ‘instance_id’, and table_id, which are used to configure the ReadFromBigtable transform. These arguments are parsed from the command line using pipeline options.

The ReadFromBigtable transform returns a PCollection of PartialRowData objects, each representing a Bigtable row. For more information about this row object, see PartialRowData (row_key).

The Apache Beam pipeline extracts the data from a Bigtable row and logs it to the console.

For more information about how to use the BigtableIO connector with the Python SDK for Apache Beam, see BigtableIO module.