DataFusion：Apache Arrow 的原生 Rust 查询引擎

// create execution context
let mut ctx = ExecutionContext::new();

// define schema for data source (csv file)
let schema = Arc::new(Schema::new(vec![
    Field::new("c1", DataType::Utf8, false),
    Field::new("c2", DataType::UInt32, false),
    Field::new("c3", DataType::Int8, false),
    Field::new("c4", DataType::Int16, false),
    Field::new("c5", DataType::Int32, false),
    Field::new("c6", DataType::Int64, false),
    Field::new("c7", DataType::UInt8, false),
    Field::new("c8", DataType::UInt16, false),
    Field::new("c9", DataType::UInt32, false),
    Field::new("c10", DataType::UInt64, false),
    Field::new("c11", DataType::Float32, false),
    Field::new("c12", DataType::Float64, false),
    Field::new("c13", DataType::Utf8, false),
]));

// register csv file with the execution context
let csv_datasource =
    CsvDataSource::new("test/data/aggregate_test_100.csv", schema.clone(), 1024);
ctx.register_datasource("aggregate_test_100", Rc::new(RefCell::new(csv_datasource)));

let sql = "SELECT c1, MIN(c12), MAX(c12) FROM aggregate_test_100 WHERE c11 > 0.1 AND c11 < 0.9 GROUP BY c1";

// execute the query
let relation = ctx.sql(&sql).unwrap();
let mut results = relation.borrow_mut();

// iterate over the results
while let Some(batch) = results.next().unwrap() {
    println!(
        "RecordBatch has {} rows and {} columns",
        batch.num_rows(),
        batch.num_columns()
    );

    let c1 = batch
        .column(0)
        .as_any()
        .downcast_ref::<BinaryArray>()
        .unwrap();

    let min = batch
        .column(1)
        .as_any()
        .downcast_ref::<Float64Array>()
        .unwrap();

    let max = batch
        .column(2)
        .as_any()
        .downcast_ref::<Float64Array>()
        .unwrap();

    for i in 0..batch.num_rows() {
        let c1_value: String = String::from_utf8(c1.value(i).to_vec()).unwrap();
        println!("{}, Min: {}, Max: {}", c1_value, min.value(i), max.value(i),);
    }
}