datafusion 13.0.0

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// to you under the Apache License, Version 2.0 (the
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//
//   http://www.apache.org/licenses/LICENSE-2.0
//
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// under the License.

//! CSV format abstractions

use std::any::Any;
use std::sync::Arc;

use arrow::datatypes::Schema;
use arrow::{self, datatypes::SchemaRef};
use async_trait::async_trait;
use datafusion_common::DataFusionError;
use futures::TryFutureExt;
use object_store::{ObjectMeta, ObjectStore};

use super::FileFormat;
use crate::datasource::file_format::DEFAULT_SCHEMA_INFER_MAX_RECORD;
use crate::error::Result;
use crate::logical_plan::Expr;
use crate::physical_plan::file_format::{CsvExec, FileScanConfig};
use crate::physical_plan::ExecutionPlan;
use crate::physical_plan::Statistics;

/// The default file extension of csv files
pub const DEFAULT_CSV_EXTENSION: &str = ".csv";
/// Character Separated Value `FileFormat` implementation.
#[derive(Debug)]
pub struct CsvFormat {
    has_header: bool,
    delimiter: u8,
    schema_infer_max_rec: Option<usize>,
}

impl Default for CsvFormat {
    fn default() -> Self {
        Self {
            schema_infer_max_rec: Some(DEFAULT_SCHEMA_INFER_MAX_RECORD),
            has_header: true,
            delimiter: b',',
        }
    }
}

impl CsvFormat {
    /// Set a limit in terms of records to scan to infer the schema
    /// - default to `DEFAULT_SCHEMA_INFER_MAX_RECORD`
    pub fn with_schema_infer_max_rec(mut self, max_rec: Option<usize>) -> Self {
        self.schema_infer_max_rec = max_rec;
        self
    }

    /// Set true to indicate that the first line is a header.
    /// - default to true
    pub fn with_has_header(mut self, has_header: bool) -> Self {
        self.has_header = has_header;
        self
    }

    /// True if the first line is a header.
    pub fn has_header(&self) -> bool {
        self.has_header
    }

    /// The character separating values within a row.
    /// - default to ','
    pub fn with_delimiter(mut self, delimiter: u8) -> Self {
        self.delimiter = delimiter;
        self
    }

    /// The delimiter character.
    pub fn delimiter(&self) -> u8 {
        self.delimiter
    }
}

#[async_trait]
impl FileFormat for CsvFormat {
    fn as_any(&self) -> &dyn Any {
        self
    }

    async fn infer_schema(
        &self,
        store: &Arc<dyn ObjectStore>,
        objects: &[ObjectMeta],
    ) -> Result<SchemaRef> {
        let mut schemas = vec![];

        let mut records_to_read = self.schema_infer_max_rec.unwrap_or(usize::MAX);

        for object in objects {
            let data = store
                .get(&object.location)
                .and_then(|r| r.bytes())
                .await
                .map_err(|e| DataFusionError::External(Box::new(e)))?;

            let (schema, records_read) = arrow::csv::reader::infer_reader_schema(
                &mut data.as_ref(),
                self.delimiter,
                Some(records_to_read),
                self.has_header,
            )?;
            schemas.push(schema.clone());
            if records_read == 0 {
                continue;
            }
            records_to_read -= records_read;
            if records_to_read == 0 {
                break;
            }
        }

        let merged_schema = Schema::try_merge(schemas)?;
        Ok(Arc::new(merged_schema))
    }

    async fn infer_stats(
        &self,
        _store: &Arc<dyn ObjectStore>,
        _table_schema: SchemaRef,
        _object: &ObjectMeta,
    ) -> Result<Statistics> {
        Ok(Statistics::default())
    }

    async fn create_physical_plan(
        &self,
        conf: FileScanConfig,
        _filters: &[Expr],
    ) -> Result<Arc<dyn ExecutionPlan>> {
        let exec = CsvExec::new(conf, self.has_header, self.delimiter);
        Ok(Arc::new(exec))
    }
}

#[cfg(test)]
mod tests {
    use arrow::array::StringArray;

    use super::super::test_util::scan_format;
    use super::*;
    use crate::physical_plan::collect;
    use crate::prelude::{SessionConfig, SessionContext};
    use futures::StreamExt;

    #[tokio::test]
    async fn read_small_batches() -> Result<()> {
        let config = SessionConfig::new().with_batch_size(2);
        let ctx = SessionContext::with_config(config);
        // skip column 9 that overflows the automaticly discovered column type of i64 (u64 would work)
        let projection = Some(vec![0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12]);
        let exec = get_exec("aggregate_test_100.csv", projection, None).await?;
        let task_ctx = ctx.task_ctx();
        let stream = exec.execute(0, task_ctx)?;

        let tt_batches: i32 = stream
            .map(|batch| {
                let batch = batch.unwrap();
                assert_eq!(12, batch.num_columns());
                assert_eq!(2, batch.num_rows());
            })
            .fold(0, |acc, _| async move { acc + 1i32 })
            .await;

        assert_eq!(tt_batches, 50 /* 100/2 */);

        // test metadata
        assert_eq!(exec.statistics().num_rows, None);
        assert_eq!(exec.statistics().total_byte_size, None);

        Ok(())
    }

    #[tokio::test]
    async fn read_limit() -> Result<()> {
        let session_ctx = SessionContext::new();
        let task_ctx = session_ctx.task_ctx();
        let projection = Some(vec![0, 1, 2, 3]);
        let exec = get_exec("aggregate_test_100.csv", projection, Some(1)).await?;
        let batches = collect(exec, task_ctx).await?;
        assert_eq!(1, batches.len());
        assert_eq!(4, batches[0].num_columns());
        assert_eq!(1, batches[0].num_rows());

        Ok(())
    }

    #[tokio::test]
    async fn infer_schema() -> Result<()> {
        let projection = None;
        let exec = get_exec("aggregate_test_100.csv", projection, None).await?;

        let x: Vec<String> = exec
            .schema()
            .fields()
            .iter()
            .map(|f| format!("{}: {:?}", f.name(), f.data_type()))
            .collect();
        assert_eq!(
            vec![
                "c1: Utf8",
                "c2: Int64",
                "c3: Int64",
                "c4: Int64",
                "c5: Int64",
                "c6: Int64",
                "c7: Int64",
                "c8: Int64",
                "c9: Int64",
                "c10: Int64",
                "c11: Float64",
                "c12: Float64",
                "c13: Utf8"
            ],
            x
        );

        Ok(())
    }

    #[tokio::test]
    async fn read_char_column() -> Result<()> {
        let session_ctx = SessionContext::new();
        let task_ctx = session_ctx.task_ctx();
        let projection = Some(vec![0]);
        let exec = get_exec("aggregate_test_100.csv", projection, None).await?;

        let batches = collect(exec, task_ctx).await.expect("Collect batches");

        assert_eq!(1, batches.len());
        assert_eq!(1, batches[0].num_columns());
        assert_eq!(100, batches[0].num_rows());

        let array = batches[0]
            .column(0)
            .as_any()
            .downcast_ref::<StringArray>()
            .unwrap();
        let mut values: Vec<&str> = vec![];
        for i in 0..5 {
            values.push(array.value(i));
        }

        assert_eq!(vec!["c", "d", "b", "a", "b"], values);

        Ok(())
    }

    async fn get_exec(
        file_name: &str,
        projection: Option<Vec<usize>>,
        limit: Option<usize>,
    ) -> Result<Arc<dyn ExecutionPlan>> {
        let root = format!("{}/csv", crate::test_util::arrow_test_data());
        let format = CsvFormat::default();
        scan_format(&format, &root, file_name, projection, limit).await
    }
}