rust/datafusion/examples/csv_sql.rs

// Licensed to the Apache Software Foundation (ASF) under one
// or more contributor license agreements.  See the NOTICE file
// distributed with this work for additional information
// regarding copyright ownership.  The ASF licenses this file
// to you under the Apache License, Version 2.0 (the
// "License"); you may not use this file except in compliance
// with the License.  You may obtain a copy of the License at
//
//   http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing,
// software distributed under the License is distributed on an
// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied.  See the License for the
// specific language governing permissions and limitations
// under the License.

use std::sync::Arc;

extern crate arrow;
extern crate datafusion;

use arrow::array::{BinaryArray, Float64Array};
use arrow::datatypes::{DataType, Field, Schema};

use datafusion::execution::context::ExecutionContext;

/// This example demonstrates executing a simple query against an Arrow data source (CSV) and
/// fetching results
fn main() {
    // create local 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),
    ]));

    let testdata =
        ::std::env::var("ARROW_TEST_DATA").expect("ARROW_TEST_DATA not defined");

    // register csv file with the execution context
    ctx.register_csv(
        "aggregate_test_100",
        &format!("{}/csv/aggregate_test_100.csv", testdata),
        &schema,
        true,
    );

    // simple projection and selection
    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, 1024 * 1024).unwrap();

    // display the relation
    let mut results = relation.borrow_mut();

    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),);
        }
    }
}