This is an automated email from the ASF dual-hosted git repository.
nevime pushed a commit to branch rust-parquet-arrow-writer
in repository https://gitbox.apache.org/repos/asf/arrow.git
commit 32d328b816e6cb3b89af967e262bf10bd48eca76
Author: Neville Dipale <nevilled...@gmail.com>
AuthorDate: Thu Aug 13 18:47:34 2020 +0200
ARROW-8289: [Rust] Parquet Arrow writer with nested support
**Note**: I started making changes to #6785, and ended up deviating a lot,
so I opted for making a new draft PR in case my approach is not suitable.
___
This is a draft to implement an arrow writer for parquet. It supports the
following (no complete test coverage yet):
* writing primitives except for booleans and binary
* nested structs
* null values (via definition levels)
It does not yet support:
- Boolean arrays (have to be handled differently from numeric values)
- Binary arrays
- Dictionary arrays
- Union arrays (are they even possible?)
I have only added a test by creating a nested schema, which I tested on
pyarrow.
```jupyter
# schema of test_complex.parquet
a: int32 not null
b: int32
c: struct<d: double, e: struct<f: float>> not null
child 0, d: double
child 1, e: struct<f: float>
child 0, f: float
```
This PR potentially addresses:
* https://issues.apache.org/jira/browse/ARROW-8289
* https://issues.apache.org/jira/browse/ARROW-8423
* https://issues.apache.org/jira/browse/ARROW-8424
* https://issues.apache.org/jira/browse/ARROW-8425
And I would like to propose either opening new JIRAs for the above
incomplete items, or renaming the last 3 above.
___
**Help Needed**
I'm implementing the definition and repetition levels on first principle
from an old Parquet blog post from the Twitter engineering blog. It's likely
that I'm not getting some concepts correct, so I would appreciate help with:
* Checking if my logic is correct
* Guidance or suggestions on how to more efficiently extract levels from
arrays
* Adding tests - I suspect we might need a lot of tests, so far we only
test writing 1 batch, so I don't know how paging would work when writing a
large enough file
I also don't know if the various encoding levels (dictionary, RLE, etc.)
and compression levels are applied automagically, or if that'd be something we
need to explicitly enable.
CC @sunchao @sadikovi @andygrove @paddyhoran
Might be of interest to @mcassels @maxburke
Closes #7319 from nevi-me/arrow-parquet-writer
Lead-authored-by: Neville Dipale <nevilled...@gmail.com>
Co-authored-by: Max Burke <m...@urbanlogiq.com>
Co-authored-by: Andy Grove <andygrov...@gmail.com>
Co-authored-by: Max Burke <maxbu...@gmail.com>
Signed-off-by: Neville Dipale <nevilled...@gmail.com>
---
rust/parquet/src/arrow/arrow_writer.rs | 682 +++++++++++++++++++++++++++++++++
rust/parquet/src/arrow/mod.rs | 5 +-
rust/parquet/src/schema/types.rs | 6 +-
3 files changed, 691 insertions(+), 2 deletions(-)
diff --git a/rust/parquet/src/arrow/arrow_writer.rs
b/rust/parquet/src/arrow/arrow_writer.rs
new file mode 100644
index 0000000..0c1c490
--- /dev/null
+++ b/rust/parquet/src/arrow/arrow_writer.rs
@@ -0,0 +1,682 @@
+// 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::rc::Rc;
+
+use arrow::array as arrow_array;
+use arrow::datatypes::{DataType as ArrowDataType, SchemaRef};
+use arrow::record_batch::RecordBatch;
+use arrow_array::Array;
+
+use crate::column::writer::ColumnWriter;
+use crate::errors::{ParquetError, Result};
+use crate::file::properties::WriterProperties;
+use crate::{
+ data_type::*,
+ file::writer::{FileWriter, ParquetWriter, RowGroupWriter,
SerializedFileWriter},
+};
+
+/// Arrow writer
+///
+/// Writes Arrow `RecordBatch`es to a Parquet writer
+pub struct ArrowWriter<W: ParquetWriter> {
+ /// Underlying Parquet writer
+ writer: SerializedFileWriter<W>,
+ /// A copy of the Arrow schema.
+ ///
+ /// The schema is used to verify that each record batch written has the
correct schema
+ arrow_schema: SchemaRef,
+}
+
+impl<W: 'static + ParquetWriter> 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<Rc<WriterProperties>>,
+ ) -> Result<Self> {
+ let schema = crate::arrow::arrow_to_parquet_schema(&arrow_schema)?;
+ let props = match props {
+ Some(props) => props,
+ None => Rc::new(WriterProperties::builder().build()),
+ };
+ let file_writer = SerializedFileWriter::new(
+ writer.try_clone()?,
+ schema.root_schema_ptr(),
+ props,
+ )?;
+
+ Ok(Self {
+ writer: file_writer,
+ arrow_schema,
+ })
+ }
+
+ /// Write a RecordBatch to writer
+ ///
+ /// *NOTE:* The writer currently does not support all Arrow data types
+ 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(),
+ ));
+ }
+ // compute the definition and repetition levels of the batch
+ let mut levels = vec![];
+ batch.columns().iter().for_each(|array| {
+ let mut array_levels =
+ get_levels(array, 0, &vec![1i16; batch.num_rows()][..], None);
+ levels.append(&mut array_levels);
+ });
+ // reverse levels so we can use Vec::pop(&mut self)
+ levels.reverse();
+
+ let mut row_group_writer = self.writer.next_row_group()?;
+
+ // write leaves
+ for column in batch.columns() {
+ write_leaves(&mut row_group_writer, column, &mut levels)?;
+ }
+
+ self.writer.close_row_group(row_group_writer)
+ }
+
+ /// Close and finalise the underlying Parquet writer
+ pub fn close(&mut self) -> Result<()> {
+ self.writer.close()
+ }
+}
+
+/// Convenience method to get the next ColumnWriter from the RowGroupWriter
+#[inline]
+#[allow(clippy::borrowed_box)]
+fn get_col_writer(
+ row_group_writer: &mut Box<dyn RowGroupWriter>,
+) -> Result<ColumnWriter> {
+ let col_writer = row_group_writer
+ .next_column()?
+ .expect("Unable to get column writer");
+ Ok(col_writer)
+}
+
+#[allow(clippy::borrowed_box)]
+fn write_leaves(
+ mut row_group_writer: &mut Box<dyn RowGroupWriter>,
+ array: &arrow_array::ArrayRef,
+ mut levels: &mut Vec<Levels>,
+) -> Result<()> {
+ match array.data_type() {
+ ArrowDataType::Int8
+ | ArrowDataType::Int16
+ | ArrowDataType::Int32
+ | ArrowDataType::Int64
+ | ArrowDataType::UInt8
+ | ArrowDataType::UInt16
+ | ArrowDataType::UInt32
+ | ArrowDataType::UInt64
+ | ArrowDataType::Float16
+ | ArrowDataType::Float32
+ | ArrowDataType::Float64
+ | ArrowDataType::Timestamp(_, _)
+ | ArrowDataType::Date32(_)
+ | ArrowDataType::Date64(_)
+ | ArrowDataType::Time32(_)
+ | ArrowDataType::Time64(_)
+ | ArrowDataType::Duration(_)
+ | ArrowDataType::Interval(_)
+ | ArrowDataType::LargeBinary
+ | ArrowDataType::Binary
+ | ArrowDataType::Utf8
+ | ArrowDataType::LargeUtf8 => {
+ let mut col_writer = get_col_writer(&mut row_group_writer)?;
+ write_leaf(
+ &mut col_writer,
+ array,
+ levels.pop().expect("Levels exhausted"),
+ )?;
+ row_group_writer.close_column(col_writer)?;
+ Ok(())
+ }
+ ArrowDataType::List(_) | ArrowDataType::LargeList(_) => {
+ // write the child list
+ let data = array.data();
+ let child_array =
arrow_array::make_array(data.child_data()[0].clone());
+ write_leaves(&mut row_group_writer, &child_array, &mut levels)?;
+ Ok(())
+ }
+ ArrowDataType::Struct(_) => {
+ let struct_array: &arrow_array::StructArray = array
+ .as_any()
+ .downcast_ref::<arrow_array::StructArray>()
+ .expect("Unable to get struct array");
+ for field in struct_array.columns() {
+ write_leaves(&mut row_group_writer, field, &mut levels)?;
+ }
+ Ok(())
+ }
+ ArrowDataType::FixedSizeList(_, _)
+ | ArrowDataType::Null
+ | ArrowDataType::Boolean
+ | ArrowDataType::FixedSizeBinary(_)
+ | ArrowDataType::Union(_)
+ | ArrowDataType::Dictionary(_, _) => Err(ParquetError::NYI(
+ "Attempting to write an Arrow type that is not yet
implemented".to_string(),
+ )),
+ }
+}
+
+fn write_leaf(
+ writer: &mut ColumnWriter,
+ column: &arrow_array::ArrayRef,
+ levels: Levels,
+) -> Result<i64> {
+ let written = match writer {
+ ColumnWriter::Int32ColumnWriter(ref mut typed) => {
+ let array = arrow::compute::cast(column, &ArrowDataType::Int32)?;
+ let array = array
+ .as_any()
+ .downcast_ref::<arrow_array::Int32Array>()
+ .expect("Unable to get int32 array");
+ typed.write_batch(
+ get_numeric_array_slice::<Int32Type, _>(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ ColumnWriter::BoolColumnWriter(ref mut _typed) => {
+ unreachable!("Currently unreachable because data type not
supported")
+ }
+ ColumnWriter::Int64ColumnWriter(ref mut typed) => {
+ let array = arrow_array::Int64Array::from(column.data());
+ typed.write_batch(
+ get_numeric_array_slice::<Int64Type, _>(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ ColumnWriter::Int96ColumnWriter(ref mut _typed) => {
+ unreachable!("Currently unreachable because data type not
supported")
+ }
+ ColumnWriter::FloatColumnWriter(ref mut typed) => {
+ let array = arrow_array::Float32Array::from(column.data());
+ typed.write_batch(
+ get_numeric_array_slice::<FloatType, _>(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ ColumnWriter::DoubleColumnWriter(ref mut typed) => {
+ let array = arrow_array::Float64Array::from(column.data());
+ typed.write_batch(
+ get_numeric_array_slice::<DoubleType, _>(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ ColumnWriter::ByteArrayColumnWriter(ref mut typed) => match
column.data_type() {
+ ArrowDataType::Binary | ArrowDataType::Utf8 => {
+ let array = arrow_array::BinaryArray::from(column.data());
+ typed.write_batch(
+ get_binary_array(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ ArrowDataType::LargeBinary | ArrowDataType::LargeUtf8 => {
+ let array = arrow_array::LargeBinaryArray::from(column.data());
+ typed.write_batch(
+ get_large_binary_array(&array).as_slice(),
+ Some(levels.definition.as_slice()),
+ levels.repetition.as_deref(),
+ )?
+ }
+ _ => unreachable!("Currently unreachable because data type not
supported"),
+ },
+ ColumnWriter::FixedLenByteArrayColumnWriter(ref mut _typed) => {
+ unreachable!("Currently unreachable because data type not
supported")
+ }
+ };
+ Ok(written as i64)
+}
+
+/// A struct that represents definition and repetition levels.
+/// Repetition levels are only populated if the parent or current leaf is
repeated
+#[derive(Debug)]
+struct Levels {
+ definition: Vec<i16>,
+ repetition: Option<Vec<i16>>,
+}
+
+/// Compute nested levels of the Arrow array, recursing into lists and structs
+fn get_levels(
+ array: &arrow_array::ArrayRef,
+ level: i16,
+ parent_def_levels: &[i16],
+ parent_rep_levels: Option<&[i16]>,
+) -> Vec<Levels> {
+ match array.data_type() {
+ ArrowDataType::Null => unimplemented!(),
+ ArrowDataType::Boolean
+ | ArrowDataType::Int8
+ | ArrowDataType::Int16
+ | ArrowDataType::Int32
+ | ArrowDataType::Int64
+ | ArrowDataType::UInt8
+ | ArrowDataType::UInt16
+ | ArrowDataType::UInt32
+ | ArrowDataType::UInt64
+ | ArrowDataType::Float16
+ | ArrowDataType::Float32
+ | ArrowDataType::Float64
+ | ArrowDataType::Utf8
+ | ArrowDataType::LargeUtf8
+ | ArrowDataType::Timestamp(_, _)
+ | ArrowDataType::Date32(_)
+ | ArrowDataType::Date64(_)
+ | ArrowDataType::Time32(_)
+ | ArrowDataType::Time64(_)
+ | ArrowDataType::Duration(_)
+ | ArrowDataType::Interval(_)
+ | ArrowDataType::Binary
+ | ArrowDataType::LargeBinary => vec![Levels {
+ definition: get_primitive_def_levels(array, parent_def_levels),
+ repetition: None,
+ }],
+ ArrowDataType::FixedSizeBinary(_) => unimplemented!(),
+ ArrowDataType::List(_) | ArrowDataType::LargeList(_) => {
+ let array_data = array.data();
+ let child_data = array_data.child_data().get(0).unwrap();
+ // get offsets, accounting for large offsets if present
+ let offsets: Vec<i64> = {
+ if let ArrowDataType::LargeList(_) = array.data_type() {
+ unsafe { array_data.buffers()[0].typed_data::<i64>()
}.to_vec()
+ } else {
+ let offsets = unsafe {
array_data.buffers()[0].typed_data::<i32>() };
+ offsets.to_vec().into_iter().map(|v| v as i64).collect()
+ }
+ };
+ let child_array = arrow_array::make_array(child_data.clone());
+
+ let mut list_def_levels = Vec::with_capacity(child_array.len());
+ let mut list_rep_levels = Vec::with_capacity(child_array.len());
+ let rep_levels: Vec<i16> = parent_rep_levels
+ .map(|l| l.to_vec())
+ .unwrap_or_else(|| vec![0i16; parent_def_levels.len()]);
+ parent_def_levels
+ .iter()
+ .zip(rep_levels)
+ .zip(offsets.windows(2))
+ .for_each(|((parent_def_level, parent_rep_level), window)| {
+ if *parent_def_level == 0 {
+ // parent is null, list element must also be null
+ list_def_levels.push(0);
+ list_rep_levels.push(0);
+ } else {
+ // parent is not null, check if list is empty or null
+ let start = window[0];
+ let end = window[1];
+ let len = end - start;
+ if len == 0 {
+ list_def_levels.push(*parent_def_level - 1);
+ list_rep_levels.push(parent_rep_level);
+ } else {
+ list_def_levels.push(*parent_def_level);
+ list_rep_levels.push(parent_rep_level);
+ for _ in 1..len {
+ list_def_levels.push(*parent_def_level);
+ list_rep_levels.push(parent_rep_level + 1);
+ }
+ }
+ }
+ });
+
+ // if datatype is a primitive, we can construct levels of the
child array
+ match child_array.data_type() {
+ ArrowDataType::Null => unimplemented!(),
+ ArrowDataType::Boolean => unimplemented!(),
+ ArrowDataType::Int8
+ | ArrowDataType::Int16
+ | ArrowDataType::Int32
+ | ArrowDataType::Int64
+ | ArrowDataType::UInt8
+ | ArrowDataType::UInt16
+ | ArrowDataType::UInt32
+ | ArrowDataType::UInt64
+ | ArrowDataType::Float16
+ | ArrowDataType::Float32
+ | ArrowDataType::Float64
+ | ArrowDataType::Timestamp(_, _)
+ | ArrowDataType::Date32(_)
+ | ArrowDataType::Date64(_)
+ | ArrowDataType::Time32(_)
+ | ArrowDataType::Time64(_)
+ | ArrowDataType::Duration(_)
+ | ArrowDataType::Interval(_) => {
+ let def_levels =
+ get_primitive_def_levels(&child_array,
&list_def_levels[..]);
+ vec![Levels {
+ definition: def_levels,
+ repetition: Some(list_rep_levels),
+ }]
+ }
+ ArrowDataType::Binary
+ | ArrowDataType::Utf8
+ | ArrowDataType::LargeUtf8 => unimplemented!(),
+ ArrowDataType::FixedSizeBinary(_) => unimplemented!(),
+ ArrowDataType::LargeBinary => unimplemented!(),
+ ArrowDataType::List(_) | ArrowDataType::LargeList(_) => {
+ // nested list
+ unimplemented!()
+ }
+ ArrowDataType::FixedSizeList(_, _) => unimplemented!(),
+ ArrowDataType::Struct(_) => get_levels(
+ array,
+ level + 1, // indicates a nesting level of 2 (list +
struct)
+ &list_def_levels[..],
+ Some(&list_rep_levels[..]),
+ ),
+ ArrowDataType::Union(_) => unimplemented!(),
+ ArrowDataType::Dictionary(_, _) => unimplemented!(),
+ }
+ }
+ ArrowDataType::FixedSizeList(_, _) => unimplemented!(),
+ ArrowDataType::Struct(_) => {
+ let struct_array: &arrow_array::StructArray = array
+ .as_any()
+ .downcast_ref::<arrow_array::StructArray>()
+ .expect("Unable to get struct array");
+ let mut struct_def_levels = Vec::with_capacity(struct_array.len());
+ for i in 0..array.len() {
+ struct_def_levels.push(level + struct_array.is_valid(i) as
i16);
+ }
+ // trying to create levels for struct's fields
+ let mut struct_levels = vec![];
+ struct_array.columns().into_iter().for_each(|col| {
+ let mut levels =
+ get_levels(col, level + 1, &struct_def_levels[..],
parent_rep_levels);
+ struct_levels.append(&mut levels);
+ });
+ struct_levels
+ }
+ ArrowDataType::Union(_) => unimplemented!(),
+ ArrowDataType::Dictionary(_, _) => unimplemented!(),
+ }
+}
+
+/// Get the definition levels of the numeric array, with level 0 being null
and 1 being not null
+/// In the case where the array in question is a child of either a list or
struct, the levels
+/// are incremented in accordance with the `level` parameter.
+/// Parent levels are either 0 or 1, and are used to higher (correct
terminology?) leaves as null
+fn get_primitive_def_levels(
+ array: &arrow_array::ArrayRef,
+ parent_def_levels: &[i16],
+) -> Vec<i16> {
+ let mut array_index = 0;
+ let max_def_level = parent_def_levels.iter().max().unwrap();
+ let mut primitive_def_levels = vec![];
+ parent_def_levels.iter().for_each(|def_level| {
+ if def_level < max_def_level {
+ primitive_def_levels.push(*def_level);
+ } else {
+ primitive_def_levels.push(def_level - array.is_null(array_index)
as i16);
+ array_index += 1;
+ }
+ });
+ primitive_def_levels
+}
+
+macro_rules! def_get_binary_array_fn {
+ ($name:ident, $ty:ty) => {
+ fn $name(array: &$ty) -> Vec<ByteArray> {
+ let mut values = Vec::with_capacity(array.len() -
array.null_count());
+ for i in 0..array.len() {
+ if array.is_valid(i) {
+ let bytes = ByteArray::from(array.value(i).to_vec());
+ values.push(bytes);
+ }
+ }
+ values
+ }
+ };
+}
+
+def_get_binary_array_fn!(get_binary_array, arrow_array::BinaryArray);
+def_get_binary_array_fn!(get_large_binary_array,
arrow_array::LargeBinaryArray);
+
+/// Get the underlying numeric array slice, skipping any null values.
+/// If there are no null values, it might be quicker to get the slice directly
instead of
+/// calling this function.
+fn get_numeric_array_slice<T, A>(array: &arrow_array::PrimitiveArray<A>) ->
Vec<T::T>
+where
+ T: DataType,
+ A: arrow::datatypes::ArrowNumericType,
+ T::T: From<A::Native>,
+{
+ let mut values = Vec::with_capacity(array.len() - array.null_count());
+ for i in 0..array.len() {
+ if array.is_valid(i) {
+ values.push(array.value(i).into())
+ }
+ }
+ values
+}
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+
+ use std::io::Seek;
+ use std::sync::Arc;
+
+ use arrow::array::*;
+ use arrow::datatypes::ToByteSlice;
+ use arrow::datatypes::{DataType, Field, Schema};
+ use arrow::record_batch::{RecordBatch, RecordBatchReader};
+
+ use crate::arrow::{ArrowReader, ParquetFileArrowReader};
+ use crate::file::reader::SerializedFileReader;
+ use crate::util::test_common::get_temp_file;
+
+ #[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.clone()),
+ vec![Arc::new(a), Arc::new(b)],
+ )
+ .unwrap();
+
+ let file = get_temp_file("test_arrow_writer.parquet", &[]);
+ let mut writer = ArrowWriter::try_new(file, Arc::new(schema),
None).unwrap();
+ writer.write(&batch).unwrap();
+ writer.close().unwrap();
+ }
+
+ #[test]
+ fn arrow_writer_list() {
+ // define schema
+ let schema = Schema::new(vec![Field::new(
+ "a",
+ DataType::List(Box::new(DataType::Int32)),
+ 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:
+ // [[false], [true, false], null, [true, false, true], [false, true,
false, true]]
+ 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(DataType::Int32)))
+ .len(5)
+ .add_buffer(a_value_offsets)
+ .add_child_data(a_values.data())
+ .build();
+ let a = ListArray::from(a_list_data);
+
+ // build a record batch
+ let batch =
+ RecordBatch::try_new(Arc::new(schema.clone()),
vec![Arc::new(a)]).unwrap();
+
+ let file = get_temp_file("test_arrow_writer_list.parquet", &[]);
+ let mut writer = ArrowWriter::try_new(file, Arc::new(schema),
None).unwrap();
+ writer.write(&batch).unwrap();
+ writer.close().unwrap();
+ }
+
+ #[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.clone()),
+ vec![Arc::new(string_values), Arc::new(binary_values)],
+ )
+ .unwrap();
+
+ let mut file = get_temp_file("test_arrow_writer.parquet", &[]);
+ let mut writer =
+ ArrowWriter::try_new(file.try_clone().unwrap(), Arc::new(schema),
None)
+ .unwrap();
+ writer.write(&batch).unwrap();
+ writer.close().unwrap();
+
+ file.seek(std::io::SeekFrom::Start(0)).unwrap();
+ let file_reader = SerializedFileReader::new(file).unwrap();
+ let mut arrow_reader =
ParquetFileArrowReader::new(Rc::new(file_reader));
+ let mut record_batch_reader =
arrow_reader.get_record_reader(1024).unwrap();
+
+ let batch = record_batch_reader.next_batch().unwrap().unwrap();
+ let string_col = batch
+ .column(0)
+ .as_any()
+ .downcast_ref::<StringArray>()
+ .unwrap();
+ let binary_col = batch
+ .column(1)
+ .as_any()
+ .downcast_ref::<BinaryArray>()
+ .unwrap();
+
+ for i in 0..batch.num_rows() {
+ assert_eq!(string_col.value(i), raw_string_values[i]);
+ assert_eq!(binary_col.value(i), raw_binary_values[i].as_slice());
+ }
+ }
+
+ #[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(DataType::Int16)), false);
+ let struct_field_e = Field::new(
+ "e",
+ DataType::Struct(vec![struct_field_f.clone(),
struct_field_g.clone()]),
+ true,
+ );
+ 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], null, [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)
+ .add_child_data(g_value.data())
+ .build();
+ let g = ListArray::from(g_list_data);
+
+ let e = StructArray::from(vec![
+ (struct_field_f, Arc::new(f) as ArrayRef),
+ (struct_field_g, Arc::new(g) 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.clone()),
+ vec![Arc::new(a), Arc::new(b), Arc::new(c)],
+ )
+ .unwrap();
+
+ let file = get_temp_file("test_arrow_writer_complex.parquet", &[]);
+ let mut writer = ArrowWriter::try_new(file, Arc::new(schema),
None).unwrap();
+ writer.write(&batch).unwrap();
+ writer.close().unwrap();
+ }
+}
diff --git a/rust/parquet/src/arrow/mod.rs b/rust/parquet/src/arrow/mod.rs
index 02f50fd..c8739c2 100644
--- a/rust/parquet/src/arrow/mod.rs
+++ b/rust/parquet/src/arrow/mod.rs
@@ -51,10 +51,13 @@
pub(in crate::arrow) mod array_reader;
pub mod arrow_reader;
+pub mod arrow_writer;
pub(in crate::arrow) mod converter;
pub(in crate::arrow) mod record_reader;
pub mod schema;
pub use self::arrow_reader::ArrowReader;
pub use self::arrow_reader::ParquetFileArrowReader;
-pub use self::schema::{parquet_to_arrow_schema,
parquet_to_arrow_schema_by_columns};
+pub use self::schema::{
+ arrow_to_parquet_schema, parquet_to_arrow_schema,
parquet_to_arrow_schema_by_columns,
+};
diff --git a/rust/parquet/src/schema/types.rs b/rust/parquet/src/schema/types.rs
index 416073a..5799905 100644
--- a/rust/parquet/src/schema/types.rs
+++ b/rust/parquet/src/schema/types.rs
@@ -788,7 +788,7 @@ impl SchemaDescriptor {
result.clone()
}
- fn column_root_of(&self, i: usize) -> &Rc<Type> {
+ fn column_root_of(&self, i: usize) -> &TypePtr {
assert!(
i < self.leaves.len(),
"Index out of bound: {} not in [0, {})",
@@ -810,6 +810,10 @@ impl SchemaDescriptor {
self.schema.as_ref()
}
+ pub fn root_schema_ptr(&self) -> TypePtr {
+ self.schema.clone()
+ }
+
/// Returns schema name.
pub fn name(&self) -> &str {
self.schema.name()
