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mod.rs
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// 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.
//! Contains writer which writes arrow data into parquet data.
use std::collections::VecDeque;
use std::io::Write;
use std::sync::Arc;
use arrow::array as arrow_array;
use arrow::array::ArrayRef;
use arrow::datatypes::{DataType as ArrowDataType, IntervalUnit, SchemaRef};
use arrow::record_batch::RecordBatch;
use arrow_array::Array;
use super::schema::{
add_encoded_arrow_schema_to_metadata, arrow_to_parquet_schema,
decimal_length_from_precision,
};
use crate::arrow::arrow_writer::byte_array::ByteArrayWriter;
use crate::column::writer::{ColumnWriter, ColumnWriterImpl};
use crate::errors::{ParquetError, Result};
use crate::file::metadata::RowGroupMetaDataPtr;
use crate::file::properties::WriterProperties;
use crate::file::writer::SerializedRowGroupWriter;
use crate::{data_type::*, file::writer::SerializedFileWriter};
use levels::{calculate_array_levels, LevelInfo};
mod byte_array;
mod levels;
/// Arrow writer
///
/// Writes Arrow `RecordBatch`es to a Parquet writer, buffering up `RecordBatch` in order
/// to produce row groups with `max_row_group_size` rows. Any remaining rows will be
/// flushed on close, leading the final row group in the output file to potentially
/// contain fewer than `max_row_group_size` rows
///
/// ```
/// # use std::sync::Arc;
/// # use bytes::Bytes;
/// # use arrow::array::{ArrayRef, Int64Array};
/// # use arrow::record_batch::RecordBatch;
/// # use parquet::arrow::{ArrowReader, ArrowWriter, ParquetFileArrowReader};
/// let col = Arc::new(Int64Array::from_iter_values([1, 2, 3])) as ArrayRef;
/// let to_write = RecordBatch::try_from_iter([("col", col)]).unwrap();
///
/// let mut buffer = Vec::new();
/// let mut writer = ArrowWriter::try_new(&mut buffer, to_write.schema(), None).unwrap();
/// writer.write(&to_write).unwrap();
/// writer.close().unwrap();
///
/// let mut reader = ParquetFileArrowReader::try_new(Bytes::from(buffer)).unwrap();
/// let mut reader = reader.get_record_reader(1024).unwrap();
/// let read = reader.next().unwrap().unwrap();
///
/// assert_eq!(to_write, read);
/// ```
pub struct ArrowWriter<W: Write> {
/// Underlying Parquet writer
writer: SerializedFileWriter<W>,
/// For each column, maintain an ordered queue of arrays to write
buffer: Vec<VecDeque<ArrayRef>>,
/// The total number of rows currently buffered
buffered_rows: usize,
/// A copy of the Arrow schema.
///
/// The schema is used to verify that each record batch written has the correct schema
arrow_schema: SchemaRef,
/// The length of arrays to write to each row group
max_row_group_size: usize,
}
impl<W: Write> ArrowWriter<W> {
/// Try to create a new Arrow writer
///
/// The writer will fail if:
/// * a `SerializedFileWriter` cannot be created from the ParquetWriter
/// * the Arrow schema contains unsupported datatypes such as Unions
pub fn try_new(
writer: W,
arrow_schema: SchemaRef,
props: Option<WriterProperties>,
) -> Result<Self> {
let schema = arrow_to_parquet_schema(&arrow_schema)?;
// add serialized arrow schema
let mut props = props.unwrap_or_else(|| WriterProperties::builder().build());
add_encoded_arrow_schema_to_metadata(&arrow_schema, &mut props);
let max_row_group_size = props.max_row_group_size();
let file_writer =
SerializedFileWriter::new(writer, schema.root_schema_ptr(), Arc::new(props))?;
Ok(Self {
writer: file_writer,
buffer: vec![Default::default(); arrow_schema.fields().len()],
buffered_rows: 0,
arrow_schema,
max_row_group_size,
})
}
/// Returns metadata for any flushed row groups
pub fn flushed_row_groups(&self) -> &[RowGroupMetaDataPtr] {
self.writer.flushed_row_groups()
}
/// Enqueues the provided `RecordBatch` to be written
///
/// If following this there are more than `max_row_group_size` rows buffered,
/// this will flush out one or more row groups with `max_row_group_size` rows,
/// and drop any fully written `RecordBatch`
pub fn write(&mut self, batch: &RecordBatch) -> Result<()> {
// validate batch schema against writer's supplied schema
if self.arrow_schema != batch.schema() {
return Err(ParquetError::ArrowError(
"Record batch schema does not match writer schema".to_string(),
));
}
for (buffer, column) in self.buffer.iter_mut().zip(batch.columns()) {
buffer.push_back(column.clone())
}
self.buffered_rows += batch.num_rows();
self.flush_completed()?;
Ok(())
}
/// Flushes buffered data until there are less than `max_row_group_size` rows buffered
fn flush_completed(&mut self) -> Result<()> {
while self.buffered_rows >= self.max_row_group_size {
self.flush_rows(self.max_row_group_size)?;
}
Ok(())
}
/// Flushes all buffered rows into a new row group
pub fn flush(&mut self) -> Result<()> {
self.flush_rows(self.buffered_rows)
}
/// Flushes `num_rows` from the buffer into a new row group
fn flush_rows(&mut self, num_rows: usize) -> Result<()> {
if num_rows == 0 {
return Ok(());
}
assert!(
num_rows <= self.buffered_rows,
"cannot flush {} rows only have {}",
num_rows,
self.buffered_rows
);
assert!(
num_rows <= self.max_row_group_size,
"cannot flush {} rows would exceed max row group size of {}",
num_rows,
self.max_row_group_size
);
let mut row_group_writer = self.writer.next_row_group()?;
for (col_buffer, field) in self.buffer.iter_mut().zip(self.arrow_schema.fields())
{
// Collect the number of arrays to append
let mut remaining = num_rows;
let mut arrays = Vec::with_capacity(col_buffer.len());
while remaining != 0 {
match col_buffer.pop_front() {
Some(next) if next.len() > remaining => {
col_buffer
.push_front(next.slice(remaining, next.len() - remaining));
arrays.push(next.slice(0, remaining));
remaining = 0;
}
Some(next) => {
remaining -= next.len();
arrays.push(next);
}
_ => break,
}
}
let mut levels = arrays
.iter()
.map(|array| {
let mut levels = calculate_array_levels(array, field)?;
// Reverse levels as we pop() them when writing arrays
levels.reverse();
Ok(levels)
})
.collect::<Result<Vec<_>>>()?;
write_leaves(&mut row_group_writer, &arrays, &mut levels)?;
}
row_group_writer.close()?;
self.buffered_rows -= num_rows;
Ok(())
}
/// Flushes any outstanding data and returns the underlying writer.
pub fn into_inner(mut self) -> Result<W> {
self.flush()?;
self.writer.into_inner()
}
/// Close and finalize the underlying Parquet writer
pub fn close(mut self) -> Result<crate::format::FileMetaData> {
self.flush()?;
self.writer.close()
}
}
fn write_leaves<W: Write>(
row_group_writer: &mut SerializedRowGroupWriter<'_, W>,
arrays: &[ArrayRef],
levels: &mut [Vec<LevelInfo>],
) -> Result<()> {
assert_eq!(arrays.len(), levels.len());
assert!(!arrays.is_empty());
let data_type = arrays.first().unwrap().data_type().clone();
assert!(arrays.iter().all(|a| a.data_type() == &data_type));
match &data_type {
ArrowDataType::Null
| ArrowDataType::Boolean
| ArrowDataType::Int8
| ArrowDataType::Int16
| ArrowDataType::Int32
| ArrowDataType::Int64
| ArrowDataType::UInt8
| ArrowDataType::UInt16
| ArrowDataType::UInt32
| ArrowDataType::UInt64
| ArrowDataType::Float32
| ArrowDataType::Float64
| ArrowDataType::Timestamp(_, _)
| ArrowDataType::Date32
| ArrowDataType::Date64
| ArrowDataType::Time32(_)
| ArrowDataType::Time64(_)
| ArrowDataType::Duration(_)
| ArrowDataType::Interval(_)
| ArrowDataType::Decimal128(_, _)
| ArrowDataType::Decimal256(_, _)
| ArrowDataType::FixedSizeBinary(_) => {
let mut col_writer = row_group_writer.next_column()?.unwrap();
for (array, levels) in arrays.iter().zip(levels.iter_mut()) {
write_leaf(col_writer.untyped(), array, levels.pop().expect("Levels exhausted"))?;
}
col_writer.close()
}
ArrowDataType::LargeBinary
| ArrowDataType::Binary
| ArrowDataType::Utf8
| ArrowDataType::LargeUtf8 => {
let mut col_writer = row_group_writer.next_column_with_factory(ByteArrayWriter::new)?.unwrap();
for (array, levels) in arrays.iter().zip(levels.iter_mut()) {
col_writer.write(array, levels.pop().expect("Levels exhausted"))?;
}
col_writer.close()
}
ArrowDataType::List(_) | ArrowDataType::LargeList(_) => {
let arrays: Vec<_> = arrays.iter().map(|array|{
// write the child list
let data = array.data();
arrow_array::make_array(data.child_data()[0].clone())
}).collect();
write_leaves(row_group_writer, &arrays, levels)?;
Ok(())
}
ArrowDataType::Struct(fields) => {
// Groups child arrays by field
let mut field_arrays = vec![Vec::with_capacity(arrays.len()); fields.len()];
for array in arrays {
let struct_array: &arrow_array::StructArray = array
.as_any()
.downcast_ref::<arrow_array::StructArray>()
.expect("Unable to get struct array");
assert_eq!(struct_array.columns().len(), fields.len());
for (child_array, field) in field_arrays.iter_mut().zip(struct_array.columns()) {
child_array.push(field.clone())
}
}
for field in field_arrays {
write_leaves(row_group_writer, &field, levels)?;
}
Ok(())
}
ArrowDataType::Map(_, _) => {
let mut keys = Vec::with_capacity(arrays.len());
let mut values = Vec::with_capacity(arrays.len());
for array in arrays {
let map_array: &arrow_array::MapArray = array
.as_any()
.downcast_ref::<arrow_array::MapArray>()
.expect("Unable to get map array");
keys.push(map_array.keys());
values.push(map_array.values());
}
write_leaves(row_group_writer, &keys, levels)?;
write_leaves(row_group_writer, &values, levels)?;
Ok(())
}
ArrowDataType::Dictionary(_, value_type) => match value_type.as_ref() {
ArrowDataType::Utf8 | ArrowDataType::LargeUtf8 | ArrowDataType::Binary | ArrowDataType::LargeBinary => {
let mut col_writer = row_group_writer.next_column_with_factory(ByteArrayWriter::new)?.unwrap();
for (array, levels) in arrays.iter().zip(levels.iter_mut()) {
col_writer.write(array, levels.pop().expect("Levels exhausted"))?;
}
col_writer.close()
}
_ => {
let mut col_writer = row_group_writer.next_column()?.unwrap();
for (array, levels) in arrays.iter().zip(levels.iter_mut()) {
write_leaf(col_writer.untyped(), array, levels.pop().expect("Levels exhausted"))?;
}
col_writer.close()
}
}
ArrowDataType::Float16 => Err(ParquetError::ArrowError(
"Float16 arrays not supported".to_string(),
)),
ArrowDataType::FixedSizeList(_, _) | ArrowDataType::Union(_, _, _) => {
Err(ParquetError::NYI(
format!(
"Attempting to write an Arrow type {:?} to parquet that is not yet implemented",
data_type
)
))
}
}
}
fn write_leaf(
writer: &mut ColumnWriter<'_>,
column: &ArrayRef,
levels: LevelInfo,
) -> Result<i64> {
let indices = levels.non_null_indices();
let written = match writer {
ColumnWriter::Int32ColumnWriter(ref mut typed) => {
match column.data_type() {
ArrowDataType::Date64 => {
// If the column is a Date64, we cast it to a Date32, and then interpret that as Int32
let array = arrow::compute::cast(column, &ArrowDataType::Date32)?;
let array = arrow::compute::cast(&array, &ArrowDataType::Int32)?;
let array = array
.as_any()
.downcast_ref::<arrow_array::Int32Array>()
.expect("Unable to get int32 array");
write_primitive(typed, array.values(), levels)?
}
ArrowDataType::UInt32 => {
let data = column.data();
let offset = data.offset();
// follow C++ implementation and use overflow/reinterpret cast from u32 to i32 which will map
// `(i32::MAX as u32)..u32::MAX` to `i32::MIN..0`
let array: &[i32] = data.buffers()[0].typed_data();
write_primitive(typed, &array[offset..offset + data.len()], levels)?
}
_ => {
let array = arrow::compute::cast(column, &ArrowDataType::Int32)?;
let array = array
.as_any()
.downcast_ref::<arrow_array::Int32Array>()
.expect("Unable to get i32 array");
write_primitive(typed, array.values(), levels)?
}
}
}
ColumnWriter::BoolColumnWriter(ref mut typed) => {
let array = column
.as_any()
.downcast_ref::<arrow_array::BooleanArray>()
.expect("Unable to get boolean array");
typed.write_batch(
get_bool_array_slice(array, indices).as_slice(),
levels.def_levels(),
levels.rep_levels(),
)?
}
ColumnWriter::Int64ColumnWriter(ref mut typed) => {
match column.data_type() {
ArrowDataType::Int64 => {
let array = column
.as_any()
.downcast_ref::<arrow_array::Int64Array>()
.expect("Unable to get i64 array");
write_primitive(typed, array.values(), levels)?
}
ArrowDataType::UInt64 => {
// follow C++ implementation and use overflow/reinterpret cast from u64 to i64 which will map
// `(i64::MAX as u64)..u64::MAX` to `i64::MIN..0`
let data = column.data();
let offset = data.offset();
let array: &[i64] = data.buffers()[0].typed_data();
write_primitive(typed, &array[offset..offset + data.len()], levels)?
}
_ => {
let array = arrow::compute::cast(column, &ArrowDataType::Int64)?;
let array = array
.as_any()
.downcast_ref::<arrow_array::Int64Array>()
.expect("Unable to get i64 array");
write_primitive(typed, array.values(), levels)?
}
}
}
ColumnWriter::Int96ColumnWriter(ref mut _typed) => {
unreachable!("Currently unreachable because data type not supported")
}
ColumnWriter::FloatColumnWriter(ref mut typed) => {
let array = column
.as_any()
.downcast_ref::<arrow_array::Float32Array>()
.expect("Unable to get Float32 array");
write_primitive(typed, array.values(), levels)?
}
ColumnWriter::DoubleColumnWriter(ref mut typed) => {
let array = column
.as_any()
.downcast_ref::<arrow_array::Float64Array>()
.expect("Unable to get Float64 array");
write_primitive(typed, array.values(), levels)?
}
ColumnWriter::ByteArrayColumnWriter(_) => {
unreachable!("should use ByteArrayWriter")
}
ColumnWriter::FixedLenByteArrayColumnWriter(ref mut typed) => {
let bytes = match column.data_type() {
ArrowDataType::Interval(interval_unit) => match interval_unit {
IntervalUnit::YearMonth => {
let array = column
.as_any()
.downcast_ref::<arrow_array::IntervalYearMonthArray>()
.unwrap();
get_interval_ym_array_slice(array, indices)
}
IntervalUnit::DayTime => {
let array = column
.as_any()
.downcast_ref::<arrow_array::IntervalDayTimeArray>()
.unwrap();
get_interval_dt_array_slice(array, indices)
}
_ => {
return Err(ParquetError::NYI(
format!(
"Attempting to write an Arrow interval type {:?} to parquet that is not yet implemented",
interval_unit
)
));
}
},
ArrowDataType::FixedSizeBinary(_) => {
let array = column
.as_any()
.downcast_ref::<arrow_array::FixedSizeBinaryArray>()
.unwrap();
get_fsb_array_slice(array, indices)
}
ArrowDataType::Decimal128(_, _) => {
let array = column
.as_any()
.downcast_ref::<arrow_array::Decimal128Array>()
.unwrap();
get_decimal_array_slice(array, indices)
}
_ => {
return Err(ParquetError::NYI(
"Attempting to write an Arrow type that is not yet implemented"
.to_string(),
));
}
};
typed.write_batch(
bytes.as_slice(),
levels.def_levels(),
levels.rep_levels(),
)?
}
};
Ok(written as i64)
}
fn write_primitive<'a, T: DataType>(
writer: &mut ColumnWriterImpl<'a, T>,
values: &[T::T],
levels: LevelInfo,
) -> Result<usize> {
writer.write_batch_internal(
values,
Some(levels.non_null_indices()),
levels.def_levels(),
levels.rep_levels(),
None,
None,
None,
)
}
fn get_bool_array_slice(
array: &arrow_array::BooleanArray,
indices: &[usize],
) -> Vec<bool> {
let mut values = Vec::with_capacity(indices.len());
for i in indices {
values.push(array.value(*i))
}
values
}
/// Returns 12-byte values representing 3 values of months, days and milliseconds (4-bytes each).
/// An Arrow YearMonth interval only stores months, thus only the first 4 bytes are populated.
fn get_interval_ym_array_slice(
array: &arrow_array::IntervalYearMonthArray,
indices: &[usize],
) -> Vec<FixedLenByteArray> {
let mut values = Vec::with_capacity(indices.len());
for i in indices {
let mut value = array.value(*i).to_le_bytes().to_vec();
let mut suffix = vec![0; 8];
value.append(&mut suffix);
values.push(FixedLenByteArray::from(ByteArray::from(value)))
}
values
}
/// Returns 12-byte values representing 3 values of months, days and milliseconds (4-bytes each).
/// An Arrow DayTime interval only stores days and millis, thus the first 4 bytes are not populated.
fn get_interval_dt_array_slice(
array: &arrow_array::IntervalDayTimeArray,
indices: &[usize],
) -> Vec<FixedLenByteArray> {
let mut values = Vec::with_capacity(indices.len());
for i in indices {
let mut prefix = vec![0; 4];
let mut value = array.value(*i).to_le_bytes().to_vec();
prefix.append(&mut value);
debug_assert_eq!(prefix.len(), 12);
values.push(FixedLenByteArray::from(ByteArray::from(prefix)));
}
values
}
fn get_decimal_array_slice(
array: &arrow_array::Decimal128Array,
indices: &[usize],
) -> Vec<FixedLenByteArray> {
let mut values = Vec::with_capacity(indices.len());
let size = decimal_length_from_precision(array.precision());
for i in indices {
let as_be_bytes = array.value(*i).as_i128().to_be_bytes();
let resized_value = as_be_bytes[(16 - size)..].to_vec();
values.push(FixedLenByteArray::from(ByteArray::from(resized_value)));
}
values
}
fn get_fsb_array_slice(
array: &arrow_array::FixedSizeBinaryArray,
indices: &[usize],
) -> Vec<FixedLenByteArray> {
let mut values = Vec::with_capacity(indices.len());
for i in indices {
let value = array.value(*i).to_vec();
values.push(FixedLenByteArray::from(ByteArray::from(value)))
}
values
}
#[cfg(test)]
mod tests {
use super::*;
use bytes::Bytes;
use std::fs::File;
use std::sync::Arc;
use crate::arrow::arrow_reader::{
ParquetRecordBatchReader, ParquetRecordBatchReaderBuilder,
};
use arrow::datatypes::ToByteSlice;
use arrow::datatypes::{DataType, Field, Schema, UInt32Type, UInt8Type};
use arrow::error::Result as ArrowResult;
use arrow::record_batch::RecordBatch;
use arrow::util::pretty::pretty_format_batches;
use arrow::{array::*, buffer::Buffer};
use crate::basic::Encoding;
use crate::file::metadata::ParquetMetaData;
use crate::file::properties::WriterVersion;
use crate::file::{
reader::{FileReader, SerializedFileReader},
statistics::Statistics,
};
#[test]
fn arrow_writer() {
// define schema
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, true),
]);
// create some data
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![Some(1), None, None, Some(4), Some(5)]);
// build a record batch
let batch =
RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a), Arc::new(b)])
.unwrap();
roundtrip(batch, Some(SMALL_SIZE / 2));
}
fn get_bytes_after_close(schema: SchemaRef, expected_batch: &RecordBatch) -> Vec<u8> {
let mut buffer = vec![];
let mut writer = ArrowWriter::try_new(&mut buffer, schema, None).unwrap();
writer.write(expected_batch).unwrap();
writer.close().unwrap();
buffer
}
fn get_bytes_by_into_inner(
schema: SchemaRef,
expected_batch: &RecordBatch,
) -> Vec<u8> {
let mut writer = ArrowWriter::try_new(Vec::new(), schema, None).unwrap();
writer.write(expected_batch).unwrap();
writer.into_inner().unwrap()
}
#[test]
fn roundtrip_bytes() {
// define schema
let schema = Arc::new(Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, true),
]));
// create some data
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![Some(1), None, None, Some(4), Some(5)]);
// build a record batch
let expected_batch =
RecordBatch::try_new(schema.clone(), vec![Arc::new(a), Arc::new(b)]).unwrap();
for buffer in vec![
get_bytes_after_close(schema.clone(), &expected_batch),
get_bytes_by_into_inner(schema, &expected_batch),
] {
let cursor = Bytes::from(buffer);
let mut record_batch_reader =
ParquetRecordBatchReader::try_new(cursor, 1024).unwrap();
let actual_batch = record_batch_reader
.next()
.expect("No batch found")
.expect("Unable to get batch");
assert_eq!(expected_batch.schema(), actual_batch.schema());
assert_eq!(expected_batch.num_columns(), actual_batch.num_columns());
assert_eq!(expected_batch.num_rows(), actual_batch.num_rows());
for i in 0..expected_batch.num_columns() {
let expected_data = expected_batch.column(i).data().clone();
let actual_data = actual_batch.column(i).data().clone();
assert_eq!(expected_data, actual_data);
}
}
}
#[test]
fn arrow_writer_non_null() {
// define schema
let schema = Schema::new(vec![Field::new("a", DataType::Int32, false)]);
// create some data
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
// build a record batch
let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();
roundtrip(batch, Some(SMALL_SIZE / 2));
}
#[test]
fn arrow_writer_list() {
// define schema
let schema = Schema::new(vec![Field::new(
"a",
DataType::List(Box::new(Field::new("item", DataType::Int32, false))),
true,
)]);
// create some data
let a_values = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
// Construct a buffer for value offsets, for the nested array:
// [[1], [2, 3], null, [4, 5, 6], [7, 8, 9, 10]]
let a_value_offsets =
arrow::buffer::Buffer::from(&[0, 1, 3, 3, 6, 10].to_byte_slice());
// Construct a list array from the above two
let a_list_data = ArrayData::builder(DataType::List(Box::new(Field::new(
"item",
DataType::Int32,
false,
))))
.len(5)
.add_buffer(a_value_offsets)
.add_child_data(a_values.into_data())
.null_bit_buffer(Some(Buffer::from(vec![0b00011011])))
.build()
.unwrap();
let a = ListArray::from(a_list_data);
// build a record batch
let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();
assert_eq!(batch.column(0).data().null_count(), 1);
// This test fails if the max row group size is less than the batch's length
// see https://github.com/apache/arrow-rs/issues/518
roundtrip(batch, None);
}
#[test]
fn arrow_writer_list_non_null() {
// define schema
let schema = Schema::new(vec![Field::new(
"a",
DataType::List(Box::new(Field::new("item", DataType::Int32, false))),
false,
)]);
// create some data
let a_values = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
// Construct a buffer for value offsets, for the nested array:
// [[1], [2, 3], [], [4, 5, 6], [7, 8, 9, 10]]
let a_value_offsets =
arrow::buffer::Buffer::from(&[0, 1, 3, 3, 6, 10].to_byte_slice());
// Construct a list array from the above two
let a_list_data = ArrayData::builder(DataType::List(Box::new(Field::new(
"item",
DataType::Int32,
false,
))))
.len(5)
.add_buffer(a_value_offsets)
.add_child_data(a_values.into_data())
.build()
.unwrap();
let a = ListArray::from(a_list_data);
// build a record batch
let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();
// This test fails if the max row group size is less than the batch's length
// see https://github.com/apache/arrow-rs/issues/518
assert_eq!(batch.column(0).data().null_count(), 0);
roundtrip(batch, None);
}
#[test]
fn arrow_writer_binary() {
let string_field = Field::new("a", DataType::Utf8, false);
let binary_field = Field::new("b", DataType::Binary, false);
let schema = Schema::new(vec![string_field, binary_field]);
let raw_string_values = vec!["foo", "bar", "baz", "quux"];
let raw_binary_values = vec![
b"foo".to_vec(),
b"bar".to_vec(),
b"baz".to_vec(),
b"quux".to_vec(),
];
let raw_binary_value_refs = raw_binary_values
.iter()
.map(|x| x.as_slice())
.collect::<Vec<_>>();
let string_values = StringArray::from(raw_string_values.clone());
let binary_values = BinaryArray::from(raw_binary_value_refs);
let batch = RecordBatch::try_new(
Arc::new(schema),
vec![Arc::new(string_values), Arc::new(binary_values)],
)
.unwrap();
roundtrip(batch, Some(SMALL_SIZE / 2));
}
#[test]
fn arrow_writer_decimal() {
let decimal_field = Field::new("a", DataType::Decimal128(5, 2), false);
let schema = Schema::new(vec![decimal_field]);
let decimal_values = vec![10_000, 50_000, 0, -100]
.into_iter()
.map(Some)
.collect::<Decimal128Array>()
.with_precision_and_scale(5, 2)
.unwrap();
let batch =
RecordBatch::try_new(Arc::new(schema), vec![Arc::new(decimal_values)])
.unwrap();
roundtrip(batch, Some(SMALL_SIZE / 2));
}
#[test]
fn arrow_writer_complex() {
// define schema
let struct_field_d = Field::new("d", DataType::Float64, true);
let struct_field_f = Field::new("f", DataType::Float32, true);
let struct_field_g = Field::new(
"g",
DataType::List(Box::new(Field::new("item", DataType::Int16, true))),
false,
);
let struct_field_h = Field::new(
"h",
DataType::List(Box::new(Field::new("item", DataType::Int16, false))),
true,
);
let struct_field_e = Field::new(
"e",
DataType::Struct(vec![
struct_field_f.clone(),
struct_field_g.clone(),
struct_field_h.clone(),
]),
false,
);
let schema = Schema::new(vec![
Field::new("a", DataType::Int32, false),
Field::new("b", DataType::Int32, true),
Field::new(
"c",
DataType::Struct(vec![struct_field_d.clone(), struct_field_e.clone()]),
false,
),
]);
// create some data
let a = Int32Array::from(vec![1, 2, 3, 4, 5]);
let b = Int32Array::from(vec![Some(1), None, None, Some(4), Some(5)]);
let d = Float64Array::from(vec![None, None, None, Some(1.0), None]);
let f = Float32Array::from(vec![Some(0.0), None, Some(333.3), None, Some(5.25)]);
let g_value = Int16Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
// Construct a buffer for value offsets, for the nested array:
// [[1], [2, 3], [], [4, 5, 6], [7, 8, 9, 10]]
let g_value_offsets =
arrow::buffer::Buffer::from(&[0, 1, 3, 3, 6, 10].to_byte_slice());
// Construct a list array from the above two
let g_list_data = ArrayData::builder(struct_field_g.data_type().clone())
.len(5)
.add_buffer(g_value_offsets.clone())
.add_child_data(g_value.data().clone())
.build()
.unwrap();
let g = ListArray::from(g_list_data);
// The difference between g and h is that h has a null bitmap
let h_list_data = ArrayData::builder(struct_field_h.data_type().clone())
.len(5)
.add_buffer(g_value_offsets)
.add_child_data(g_value.data().clone())
.null_bit_buffer(Some(Buffer::from(vec![0b00011011])))
.build()
.unwrap();
let h = ListArray::from(h_list_data);
let e = StructArray::from(vec![
(struct_field_f, Arc::new(f) as ArrayRef),
(struct_field_g, Arc::new(g) as ArrayRef),
(struct_field_h, Arc::new(h) as ArrayRef),
]);
let c = StructArray::from(vec![
(struct_field_d, Arc::new(d) as ArrayRef),
(struct_field_e, Arc::new(e) as ArrayRef),
]);
// build a record batch
let batch = RecordBatch::try_new(
Arc::new(schema),
vec![Arc::new(a), Arc::new(b), Arc::new(c)],
)
.unwrap();
roundtrip(batch.clone(), Some(SMALL_SIZE / 2));
roundtrip(batch, Some(SMALL_SIZE / 3));
}
#[test]
fn arrow_writer_complex_mixed() {
// This test was added while investigating https://github.com/apache/arrow-rs/issues/244.
// It was subsequently fixed while investigating https://github.com/apache/arrow-rs/issues/245.
// define schema
let offset_field = Field::new("offset", DataType::Int32, false);
let partition_field = Field::new("partition", DataType::Int64, true);
let topic_field = Field::new("topic", DataType::Utf8, true);
let schema = Schema::new(vec![Field::new(
"some_nested_object",
DataType::Struct(vec![
offset_field.clone(),
partition_field.clone(),
topic_field.clone(),
]),
false,
)]);
// create some data
let offset = Int32Array::from(vec![1, 2, 3, 4, 5]);
let partition = Int64Array::from(vec![Some(1), None, None, Some(4), Some(5)]);
let topic = StringArray::from(vec![Some("A"), None, Some("A"), Some(""), None]);
let some_nested_object = StructArray::from(vec![
(offset_field, Arc::new(offset) as ArrayRef),
(partition_field, Arc::new(partition) as ArrayRef),
(topic_field, Arc::new(topic) as ArrayRef),
]);
// build a record batch
let batch =
RecordBatch::try_new(Arc::new(schema), vec![Arc::new(some_nested_object)])
.unwrap();
roundtrip(batch, Some(SMALL_SIZE / 2));
}
#[test]
fn arrow_writer_map() {
// Note: we are using the JSON Arrow reader for brevity
let json_content = r#"
{"stocks":{"long": "$AAA", "short": "$BBB"}}
{"stocks":{"long": null, "long": "$CCC", "short": null}}
{"stocks":{"hedged": "$YYY", "long": null, "short": "$D"}}
"#;
let entries_struct_type = DataType::Struct(vec![
Field::new("key", DataType::Utf8, false),
Field::new("value", DataType::Utf8, true),
]);
let stocks_field = Field::new(
"stocks",
DataType::Map(
Box::new(Field::new("entries", entries_struct_type, false)),
false,
),
true,
);