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Support string dictionaries in csv reader (#228) #229

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merged 1 commit into from
Apr 27, 2021
Merged

Support string dictionaries in csv reader (#228) #229

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147 changes: 121 additions & 26 deletions arrow/src/csv/reader.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,9 @@ use std::sync::Arc;

use csv as csv_crate;

use crate::array::{ArrayRef, BooleanArray, PrimitiveArray, StringArray};
use crate::array::{
ArrayRef, BooleanArray, DictionaryArray, PrimitiveArray, StringArray,
};
use crate::datatypes::*;
use crate::error::{ArrowError, Result};
use crate::record_batch::RecordBatch;
Expand Down Expand Up @@ -385,7 +387,7 @@ impl<R: Read> Iterator for Reader<R> {
"Error parsing line {}: {:?}",
self.line_number + i,
e
))))
))));
}
}
}
Expand Down Expand Up @@ -429,56 +431,102 @@ fn parse(
let i = *i;
let field = &fields[i];
match field.data_type() {
&DataType::Boolean => build_boolean_array(line_number, rows, i),
&DataType::Int8 => {
build_primitive_array::<Int8Type>(line_number, rows, i)
}
&DataType::Int16 => {
DataType::Boolean => build_boolean_array(line_number, rows, i),
DataType::Int8 => build_primitive_array::<Int8Type>(line_number, rows, i),
DataType::Int16 => {
build_primitive_array::<Int16Type>(line_number, rows, i)
}
&DataType::Int32 => {
DataType::Int32 => {
build_primitive_array::<Int32Type>(line_number, rows, i)
}
&DataType::Int64 => {
DataType::Int64 => {
build_primitive_array::<Int64Type>(line_number, rows, i)
}
&DataType::UInt8 => {
DataType::UInt8 => {
build_primitive_array::<UInt8Type>(line_number, rows, i)
}
&DataType::UInt16 => {
DataType::UInt16 => {
build_primitive_array::<UInt16Type>(line_number, rows, i)
}
&DataType::UInt32 => {
DataType::UInt32 => {
build_primitive_array::<UInt32Type>(line_number, rows, i)
}
&DataType::UInt64 => {
DataType::UInt64 => {
build_primitive_array::<UInt64Type>(line_number, rows, i)
}
&DataType::Float32 => {
DataType::Float32 => {
build_primitive_array::<Float32Type>(line_number, rows, i)
}
&DataType::Float64 => {
DataType::Float64 => {
build_primitive_array::<Float64Type>(line_number, rows, i)
}
&DataType::Date32 => {
DataType::Date32 => {
build_primitive_array::<Date32Type>(line_number, rows, i)
}
&DataType::Date64 => {
DataType::Date64 => {
build_primitive_array::<Date64Type>(line_number, rows, i)
}
&DataType::Timestamp(TimeUnit::Microsecond, _) => {
build_primitive_array::<TimestampMicrosecondType>(
line_number,
rows,
i,
)
}
&DataType::Timestamp(TimeUnit::Nanosecond, _) => {
DataType::Timestamp(TimeUnit::Microsecond, _) => build_primitive_array::<
TimestampMicrosecondType,
>(
line_number, rows, i
),
DataType::Timestamp(TimeUnit::Nanosecond, _) => {
build_primitive_array::<TimestampNanosecondType>(line_number, rows, i)
}
&DataType::Utf8 => Ok(Arc::new(
DataType::Utf8 => Ok(Arc::new(
rows.iter().map(|row| row.get(i)).collect::<StringArray>(),
) as ArrayRef),
DataType::Dictionary(key_type, value_type)
if value_type.as_ref() == &DataType::Utf8 =>
{
match key_type.as_ref() {
DataType::Int8 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<Int8Type>>(),
) as ArrayRef),
DataType::Int16 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<Int16Type>>(),
) as ArrayRef),
DataType::Int32 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<Int32Type>>(),
) as ArrayRef),
DataType::Int64 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<Int64Type>>(),
) as ArrayRef),
DataType::UInt8 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<UInt8Type>>(),
) as ArrayRef),
DataType::UInt16 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<UInt16Type>>(),
) as ArrayRef),
DataType::UInt32 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<UInt32Type>>(),
) as ArrayRef),
DataType::UInt64 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(i))
.collect::<DictionaryArray<UInt64Type>>(),
) as ArrayRef),
_ => Err(ArrowError::ParseError(format!(
"Unsupported dictionary key type {:?}",
key_type
))),
}
}
other => Err(ArrowError::ParseError(format!(
"Unsupported data type {:?}",
other
Expand Down Expand Up @@ -510,6 +558,7 @@ impl Parser for Float32Type {
lexical_core::parse(string.as_bytes()).ok()
}
}

impl Parser for Float64Type {
fn parse(string: &str) -> Option<f64> {
lexical_core::parse(string.as_bytes()).ok()
Expand Down Expand Up @@ -814,6 +863,7 @@ mod tests {
use tempfile::NamedTempFile;

use crate::array::*;
use crate::compute::cast;
use crate::datatypes::Field;

#[test]
Expand Down Expand Up @@ -1021,6 +1071,51 @@ mod tests {
assert_eq!(2, batch.num_columns());
}

#[test]
fn test_csv_with_dictionary() {
let schema = Schema::new(vec![
Field::new(
"city",
DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8)),
false,
),
Field::new("lat", DataType::Float64, false),
Field::new("lng", DataType::Float64, false),
]);

let file = File::open("test/data/uk_cities.csv").unwrap();

let mut csv = Reader::new(
file,
Arc::new(schema),
false,
None,
1024,
None,
Some(vec![0, 1]),
);
let projected_schema = Arc::new(Schema::new(vec![
Field::new(
"city",
DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8)),
false,
),
Field::new("lat", DataType::Float64, false),
]));
assert_eq!(projected_schema, csv.schema());
let batch = csv.next().unwrap().unwrap();
assert_eq!(projected_schema, batch.schema());
assert_eq!(37, batch.num_rows());
assert_eq!(2, batch.num_columns());

let strings = cast(batch.column(0), &DataType::Utf8).unwrap();
let strings = strings.as_any().downcast_ref::<StringArray>().unwrap();

assert_eq!(strings.value(0), "Elgin, Scotland, the UK");
assert_eq!(strings.value(4), "Eastbourne, East Sussex, UK");
assert_eq!(strings.value(29), "Uckfield, East Sussex, UK");
}

#[test]
fn test_nulls() {
let schema = Schema::new(vec![
Expand Down