Ballista should be able to load Partitioned data frame using ListingOptions

[André Claudino]

Is your feature request related to a problem or challenge? Please describe what you are trying to do.
On datafusion, described as one of ballista components, I can load a hive-style partitioned dataset using the ListingOptions struct:

let partition_columns: Vec<(String, DataType)> = vec![("uf".to_owned(), DataType::Utf8), ("municipio".to_owned(), DataType::Utf8)];

let listing_options =
        ListingOptions::new(Arc::new(file_format))
            .with_table_partition_cols(partition_columns);

let config = ListingTableConfig::new(listing_table_url)
        .with_listing_options(listing_options)
        .with_schema(resolved_schema);
        
let table_provider = Arc::new(listing_table);

session_context.register_table(table_name, table_provider)?;

I can use the same table_provider object in register_table for a ballista context, it recognizes file fields and return success, but ignores hive-style partition columns.

ballista_context.register_table(table_name, table_provider.clone())?;

Describe the solution you'd like
Given a hive-style partitioned dataset, local or in an object storage, like in the followin example:

$> tree resources/age_gender_draw/
resources/age_gender_draw/
├── uf=CE
│   └── municipio=Fortaleza
│       └── fortaleza.parquet
├── uf=DF
│   └── municipio=Brasilia
│       └── brasilia.parquet
├── uf=RJ
│   └── municipio=Rio De Janeiro
│       └── rio_de_janeiro.parquet
└── uf=SP
    └── municipio=Sao Paulo
        └── sao_paulo.parquet

Ballista should be able to recognize the listing_options configuration when loading a dataset, registering as table or load as dataframe. Basically, the following code should work.

Given the registering function:

pub async fn register_parquet(table_name: &str, source_path: &str, session_context: &SessionContext, ballista_context: &BallistaContext) -> anyhow::Result<()> {
    let session_state = session_context.state();

    let partition_columns: Vec<(String, DataType)> = vec![("uf".to_owned(), DataType::Utf8), ("municipio".to_owned(), DataType::Utf8)];

    let listing_table_url = ListingTableUrl::parse(source_path)?;
    
    let file_format =
        ParquetFormat::new()
            .with_enable_pruning(Some(true))
            .with_skip_metadata(Some(true));

    let listing_options =
        ListingOptions::new(Arc::new(file_format))
            .with_table_partition_cols(partition_columns);

    let resolved_schema = listing_options
        .infer_schema(&session_state, &listing_table_url)
        .await?;

    let config = ListingTableConfig::new(listing_table_url)
        .with_listing_options(listing_options)
        .with_schema(resolved_schema);

    let listing_table =
        ListingTable::try_new(config)?;
    let table_provider = Arc::new(listing_table);

    ballista_context.register_table(table_name, table_provider.clone())?;
    session_context.register_table(table_name, table_provider)?;
    
    Ok(())
}

and the use as:

let query = "SELECT distinct uf, age_group from age_gender_draw LIMIT 10";
register_parquet(table_name, source_path, &session_context, &ballista_context).await?;

let ballista_processed_data_frame = run_query_in_ballista(&query, &ballista_context).await?;
ballista_processed_data_frame.show().await?;

The following error, that happens when when generating ballista_processed_data_frame should not happen:

Error: Schema error: No field named 'uf'. Valid fields are 'age_gender_draw'.'maid', 'age_gender_draw'.'per_capita_income', 'age_gender_draw'.'gender', 'age_gender_draw'.'age_group'.

Caused by:
    No field named 'uf'. Valid fields are 'age_gender_draw'.'maid', 'age_gender_draw'.'per_capita_income', 'age_gender_draw'.'gender', 'age_gender_draw'.'age_group'.
$> 

The result, should be the same as generating fusion_processed_data_frame:

let fusion_processed_data_frame = run_query_in_fusion(&query, &session_context).await?;
fusion_processed_data_frame.show().await?;

Results in

+----+-----------+
| uf | age_group |
+----+-----------+
| CE | 00-14     |
| DF | 45-59     |
| CE | 30-44     |
| DF | 15-29     |
| DF | 00-14     |
| RJ | 15-29     |
| CE | 45-59     |
| RJ | 45-59     |
| DF | 30-44     |
| DF | 60-pl     |
+----+-----------+

Describe alternatives you've considered
Using data fusion is a possible alternative for small datasets, but in my case, where huge datasets are common, Ballistra should be used to scale processing over distributed nodes.

Additional context
There is nothing more I can share now, sorry.

With some help I am able to execute the task and open a pull request, but would need some help to know where to start.