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use std::fs::File;
use arrow::csv;
use arrow::datatypes::{Field, Schema};
use arrow::record_batch::RecordBatch;
use std::string::String;
use std::sync::Arc;
use crate::datasource::{ScanResult, TableProvider};
use crate::error::Result;
use crate::execution::physical_plan::csv::CsvExec;
use crate::execution::physical_plan::{BatchIterator, ExecutionPlan};
pub struct CsvFile {
filename: String,
schema: Arc<Schema>,
has_header: bool,
}
impl CsvFile {
#[allow(missing_docs)]
pub fn new(filename: &str, schema: &Schema, has_header: bool) -> Self {
Self {
filename: String::from(filename),
schema: Arc::new(schema.clone()),
has_header,
}
}
}
impl TableProvider for CsvFile {
fn schema(&self) -> &Arc<Schema> {
&self.schema
}
fn scan(
&self,
projection: &Option<Vec<usize>>,
batch_size: usize,
) -> Result<Vec<ScanResult>> {
let exec = CsvExec::try_new(
&self.filename,
self.schema.clone(),
self.has_header,
projection.clone(),
batch_size,
)?;
let partitions = exec.partitions()?;
let iterators = partitions
.iter()
.map(|p| p.execute())
.collect::<Result<Vec<_>>>()?;
Ok(iterators)
}
}
pub struct CsvBatchIterator {
schema: Arc<Schema>,
reader: csv::Reader<File>,
}
impl CsvBatchIterator {
#[allow(missing_docs)]
pub fn try_new(
filename: &str,
schema: Arc<Schema>,
has_header: bool,
projection: &Option<Vec<usize>>,
batch_size: usize,
) -> Result<Self> {
let file = File::open(filename)?;
let reader = csv::Reader::new(
file,
schema.clone(),
has_header,
batch_size,
projection.clone(),
);
let projected_schema = match projection {
Some(p) => {
let projected_fields: Vec<Field> =
p.iter().map(|i| schema.fields()[*i].clone()).collect();
Arc::new(Schema::new(projected_fields))
}
None => schema,
};
Ok(Self {
schema: projected_schema,
reader,
})
}
}
impl BatchIterator for CsvBatchIterator {
fn schema(&self) -> Arc<Schema> {
self.schema.clone()
}
fn next(&mut self) -> Result<Option<RecordBatch>> {
Ok(self.reader.next()?)
}
}