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ARROW-11119: [Rust] Expose functions to parse a single CSV column / StringRecord into an array / recordBatch #9084

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ARROW-11119: [Rust] Expose functions to parse a single CSV column / StringRecord into an array / recordBatch #9084

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2 changes: 1 addition & 1 deletion rust/arrow/src/csv/mod.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -20,8 +20,8 @@
pub mod reader;
pub mod writer;

pub use self::reader::infer_schema_from_files;
pub use self::reader::Reader;
pub use self::reader::ReaderBuilder;
pub use self::reader::{build_array, build_batch, infer_schema_from_files};
pub use self::writer::Writer;
pub use self::writer::WriterBuilder;
157 changes: 92 additions & 65 deletions rust/arrow/src/csv/reader.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -380,8 +380,7 @@ impl<R: Read> Iterator for Reader<R> {
return None;
}

// parse the batches into a RecordBatch
let result = parse(
let result = build_batch(
&self.batch_records[..read_records],
&self.schema.fields(),
&self.projection,
Expand All @@ -394,88 +393,116 @@ impl<R: Read> Iterator for Reader<R> {
}
}

/// parses a slice of [csv_crate::StringRecord] into a [array::record_batch::RecordBatch].
fn parse(
/// Tries to create an [array::Array] from a slice of [csv_crate::StringRecord] by interpreting its
/// values at column `column_index` to be of `data_type`.
/// `line_number` is where the set of rows starts at, and is only used to report the line number in case of errors.
/// # Error
/// This function errors iff:
/// * _any_ entry from `rows` at `column_index` cannot be parsed into the DataType.
/// * The [array::datatypes::DataType] is not supported.
pub fn build_array(
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@Dandandan Dandandan Jan 3, 2021

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The downside is that this creates a dependency on StringRecord in the public API, making it harder to remove it when we want?

rows: &[StringRecord],
data_type: &DataType,
line_number: usize,
column_index: usize,
) -> Result<ArrayRef> {
match data_type {
DataType::Boolean => build_boolean_array(line_number, rows, column_index),
DataType::Int8 => {
build_primitive_array::<Int8Type>(line_number, rows, column_index)
}
DataType::Int16 => {
build_primitive_array::<Int16Type>(line_number, rows, column_index)
}
DataType::Int32 => {
build_primitive_array::<Int32Type>(line_number, rows, column_index)
}
DataType::Int64 => {
build_primitive_array::<Int64Type>(line_number, rows, column_index)
}
DataType::UInt8 => {
build_primitive_array::<UInt8Type>(line_number, rows, column_index)
}
DataType::UInt16 => {
build_primitive_array::<UInt16Type>(line_number, rows, column_index)
}
DataType::UInt32 => {
build_primitive_array::<UInt32Type>(line_number, rows, column_index)
}
DataType::UInt64 => {
build_primitive_array::<UInt64Type>(line_number, rows, column_index)
}
DataType::Float32 => {
build_primitive_array::<Float32Type>(line_number, rows, column_index)
}
DataType::Float64 => {
build_primitive_array::<Float64Type>(line_number, rows, column_index)
}
DataType::Date32(_) => {
build_primitive_array::<Date32Type>(line_number, rows, column_index)
}
DataType::Date64(_) => {
build_primitive_array::<Date64Type>(line_number, rows, column_index)
}
DataType::Timestamp(TimeUnit::Microsecond, _) => {
build_primitive_array::<TimestampMicrosecondType>(
line_number,
rows,
column_index,
)
}
DataType::Timestamp(TimeUnit::Nanosecond, _) => build_primitive_array::<
TimestampNanosecondType,
>(
line_number, rows, column_index
),
DataType::Utf8 => Ok(Arc::new(
rows.iter()
.map(|row| row.get(column_index))
.collect::<StringArray>(),
) as ArrayRef),
other => Err(ArrowError::ParseError(format!(
"Unsupported data type {:?}",
other
))),
}
}

/// Tries to create an [array::record_batch::RecordBatch] from a slice of [csv_crate::StringRecord] by interpreting
/// each of its columns according to `fields`. When `projection` is not None, it is used to select a subset of `fields` to
/// parse.
/// `line_number` is where the set of rows starts at, and is only used to report the line number in case of errors.
/// # Error
/// This function errors iff:
/// * _any_ entry from `rows` cannot be parsed into its corresponding field's `DataType`.
/// * Any of the fields' [array::datatypes::DataType] is not supported.
/// # Panic
/// This function panics if any index in `projection` is larger than `fields.len()`.
pub fn build_batch(
rows: &[StringRecord],
fields: &[Field],
projection: &Option<Vec<usize>>,
line_number: usize,
) -> Result<RecordBatch> {
let projection: Vec<usize> = match projection {
Some(ref v) => v.clone(),
None => fields.iter().enumerate().map(|(i, _)| i).collect(),
None => (0..fields.len()).collect(),
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@Dandandan Dandandan Jan 3, 2021

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👍 I think the v.clone() could maybe even be removed?

};

let arrays: Result<Vec<ArrayRef>> = projection
let columns = projection
.iter()
.map(|i| {
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 => {
build_primitive_array::<Int16Type>(line_number, rows, i)
}
&DataType::Int32 => {
build_primitive_array::<Int32Type>(line_number, rows, i)
}
&DataType::Int64 => {
build_primitive_array::<Int64Type>(line_number, rows, i)
}
&DataType::UInt8 => {
build_primitive_array::<UInt8Type>(line_number, rows, i)
}
&DataType::UInt16 => {
build_primitive_array::<UInt16Type>(line_number, rows, i)
}
&DataType::UInt32 => {
build_primitive_array::<UInt32Type>(line_number, rows, i)
}
&DataType::UInt64 => {
build_primitive_array::<UInt64Type>(line_number, rows, i)
}
&DataType::Float32 => {
build_primitive_array::<Float32Type>(line_number, rows, i)
}
&DataType::Float64 => {
build_primitive_array::<Float64Type>(line_number, rows, i)
}
&DataType::Date32(_) => {
build_primitive_array::<Date32Type>(line_number, rows, i)
}
&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, _) => {
build_primitive_array::<TimestampNanosecondType>(line_number, rows, i)
}
&DataType::Utf8 => Ok(Arc::new(
rows.iter().map(|row| row.get(i)).collect::<StringArray>(),
) as ArrayRef),
other => Err(ArrowError::ParseError(format!(
"Unsupported data type {:?}",
other
))),
}
build_array(rows, fields[i].data_type(), line_number, i)
})
.collect();
.collect::<Result<_>>()?;

let projected_fields: Vec<Field> =
projection.iter().map(|i| fields[*i].clone()).collect();

let projected_schema = Arc::new(Schema::new(projected_fields));

arrays.and_then(|arr| RecordBatch::try_new(projected_schema, arr))
RecordBatch::try_new(projected_schema, columns)
}

/// Specialized parsing implementations
Expand Down