learning/prompts/code-explanation/05_io_spanner.md

Prompt:
What does this code do?
```python
class ExampleRow(NamedTuple):
    id: int
    name: str

class SpannerOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_argument(
            '--project_id',
            default='your-project-id',
            help='Google Cloud project ID'
        )
        parser.add_argument(
            '--instance_id',
            default='your-instance-id',
            help='Google Cloud Spanner instance ID'
        )
        parser.add_argument(
            '--database_id',
            default='your-database-id',
            help='Google Cloud Spanner database ID'
        )

options = SpannerOptions()
coders.registry.register_coder(ExampleRow, coders.RowCoder)

with beam.Pipeline(options=options) as p:

    output = (p | "Read from table" >> ReadFromSpanner(
        project_id=options.project_id,
        instance_id=options.instance_id,
        database_id=options.database_id,
        row_type=ExampleRow,
        sql="SELECT * FROM example_row"
        )
        | "Log Data" >> Map(logging.info))

```

Response:
This code uses the Apache Beam [SpannerIO connector](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.spanner.html) to read data from a [Cloud Spanner](https://cloud.google.com/spanner) table `example_row` from the database `your-database-id` in the instance `your-instance-id`. The values `your-database-id`, `your-instance-id`, and `your-project-id` are provided as command-line arguments.

```python
class SpannerOptions(PipelineOptions):
    @classmethod
    def _add_argparse_args(cls, parser):
        parser.add_argument(
            '--project_id',
            default='your-project-id',
            help='Google Cloud project ID'
        )
        parser.add_argument(
            '--instance_id',
            default='your-instance-id',
            help='Google Cloud Spanner instance ID'
        )
        parser.add_argument(
            '--database_id',
            default='your-database-id',
            help='Google Cloud Spanner database ID'
        )

options = SpannerOptions()
```

This code uses [pipeline options](https://beam.apache.org/documentation/patterns/pipeline-options/) to parse command-line arguments. The `SpannerOptions` class defines the command-line arguments `project_id`, `instance_id`, and `database_id`, which are used to configure the `ReadFromSpanner` transform.

```python
class ExampleRow(NamedTuple):
    id: int
    name: str
```

This code defines a `NamedTuple` object `ExampleRow` that represents a Cloud Spanner row. The `NamedTuple` object includes the fields `id` and `name`, serving as attributes for a Cloud Spanner row. The `ReadFromSpanner` transform uses this object as a row type.

```python
 coders.registry.register_coder(ExampleRow, coders.RowCoder)
```

Registering a coder for `NamedTuple` is required to use `NamedTuple` as a row type. For more information about how to register a coder for a custom type, see [Data encoding and type safety](https://beam.apache.org/documentation/programming-guide/#data-encoding-and-type-safety).

```python
output = (p | "Read from table" >> ReadFromSpanner(
    project_id=options.project_id,
    instance_id=options.instance_id,
    database_id=options.database_id,
    row_type=ExampleRow,
    sql="SELECT * FROM example_row"
    )
    | "Log Data" >> Map(logging.info))
```

The `ReadFromSpanner` transform returns a `PCollection` of `NamedTuple` objects, each representing a Cloud Spanner row. For more information about this row object, see [ReadFromSpanner transform](https://beam.apache.org/releases/pydoc/current/apache_beam.io.gcp.spanner.html#apache_beam.io.gcp.spanner.ReadFromSpanner).

The Apache Beam pipeline logs the data from a Cloud Spanner row to the console.