// 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::sync::Arc;

use arrow::array as arrow_array;
use arrow::datatypes::{DataType as ArrowDataType, IntervalUnit, SchemaRef};
use arrow::record_batch::RecordBatch;
use arrow_array::Array;

use super::levels::LevelInfo;
use super::schema::{
    add_encoded_arrow_schema_to_metadata, decimal_length_from_precision,
};

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<WriterProperties>,
    ) -> Result<Self> {
        let schema = crate::arrow::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 file_writer = SerializedFileWriter::new(
            writer.try_clone()?,
            schema.root_schema_ptr(),
            Arc::new(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 batch_level = LevelInfo::new_from_batch(batch);
        let mut row_group_writer = self.writer.next_row_group()?;
        for (array, field) in batch.columns().iter().zip(batch.schema().fields()) {
            let mut levels = batch_level.calculate_array_levels(array, field, false);
            // Reverse levels as we pop() them when writing arrays
            levels.reverse();
            write_leaves(&mut row_group_writer, array, &mut levels)?;
        }

        self.writer.close_row_group(row_group_writer)
    }

    /// Close and finalize the underlying Parquet writer
    pub fn close(&mut self) -> Result<parquet_format::FileMetaData> {
        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<LevelInfo>,
) -> Result<()> {
    match array.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::LargeBinary
        | ArrowDataType::Binary
        | ArrowDataType::Utf8
        | ArrowDataType::LargeUtf8
        | ArrowDataType::Decimal(_, _)
        | ArrowDataType::FixedSizeBinary(_) => {
            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::Dictionary(_, value_type) => {
            // cast dictionary to a primitive
            let array = arrow::compute::cast(array, value_type)?;

            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::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", 
                    array.data_type()
                )
            ))
        }
    }
}

fn write_leaf(
    writer: &mut ColumnWriter,
    column: &arrow_array::ArrayRef,
    levels: LevelInfo,
) -> Result<i64> {
    let indices = levels.filter_array_indices();
    let written = match writer {
        ColumnWriter::Int32ColumnWriter(ref mut typed) => {
            let values = 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 = if let ArrowDataType::Date64 = column.data_type() {
                        let array = arrow::compute::cast(column, &ArrowDataType::Date32)?;
                        arrow::compute::cast(&array, &ArrowDataType::Int32)?
                    } else {
                        arrow::compute::cast(column, &ArrowDataType::Int32)?
                    };
                    let array = array
                        .as_any()
                        .downcast_ref::<arrow_array::Int32Array>()
                        .expect("Unable to get int32 array");
                    get_numeric_array_slice::<Int32Type, _>(&array, &indices)
                }
                ArrowDataType::UInt32 => {
                    // 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 = column
                        .as_any()
                        .downcast_ref::<arrow_array::UInt32Array>()
                        .expect("Unable to get u32 array");
                    let array = arrow::compute::unary::<_, _, arrow::datatypes::Int32Type>(
                        array,
                        |x| x as i32,
                    );
                    get_numeric_array_slice::<Int32Type, _>(&array, &indices)
                }
                _ => {
                    let array = arrow::compute::cast(column, &ArrowDataType::Int32)?;
                    let array = array
                        .as_any()
                        .downcast_ref::<arrow_array::Int32Array>()
                        .expect("Unable to get i32 array");
                    get_numeric_array_slice::<Int32Type, _>(&array, &indices)
                }
            };
            typed.write_batch(
                values.as_slice(),
                Some(levels.definition.as_slice()),
                levels.repetition.as_deref(),
            )?
        }
        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(),
                Some(levels.definition.as_slice()),
                levels.repetition.as_deref(),
            )?
        }
        ColumnWriter::Int64ColumnWriter(ref mut typed) => {
            let values = match column.data_type() {
                ArrowDataType::Int64 => {
                    let array = column
                        .as_any()
                        .downcast_ref::<arrow_array::Int64Array>()
                        .expect("Unable to get i64 array");
                    get_numeric_array_slice::<Int64Type, _>(&array, &indices)
                }
                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 array = column
                        .as_any()
                        .downcast_ref::<arrow_array::UInt64Array>()
                        .expect("Unable to get u64 array");
                    let array = arrow::compute::unary::<_, _, arrow::datatypes::Int64Type>(
                        array,
                        |x| x as i64,
                    );
                    get_numeric_array_slice::<Int64Type, _>(&array, &indices)
                }
                _ => {
                    let array = arrow::compute::cast(column, &ArrowDataType::Int64)?;
                    let array = array
                        .as_any()
                        .downcast_ref::<arrow_array::Int64Array>()
                        .expect("Unable to get i64 array");
                    get_numeric_array_slice::<Int64Type, _>(&array, &indices)
                }
            };
            typed.write_batch(
                values.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 = column
                .as_any()
                .downcast_ref::<arrow_array::Float32Array>()
                .expect("Unable to get Float32 array");
            typed.write_batch(
                get_numeric_array_slice::<FloatType, _>(&array, &indices).as_slice(),
                Some(levels.definition.as_slice()),
                levels.repetition.as_deref(),
            )?
        }
        ColumnWriter::DoubleColumnWriter(ref mut typed) => {
            let array = column
                .as_any()
                .downcast_ref::<arrow_array::Float64Array>()
                .expect("Unable to get Float64 array");
            typed.write_batch(
                get_numeric_array_slice::<DoubleType, _>(&array, &indices).as_slice(),
                Some(levels.definition.as_slice()),
                levels.repetition.as_deref(),
            )?
        }
        ColumnWriter::ByteArrayColumnWriter(ref mut typed) => match column.data_type() {
            ArrowDataType::Binary => {
                let array = column
                    .as_any()
                    .downcast_ref::<arrow_array::BinaryArray>()
                    .expect("Unable to get BinaryArray array");
                typed.write_batch(
                    get_binary_array(&array).as_slice(),
                    Some(levels.definition.as_slice()),
                    levels.repetition.as_deref(),
                )?
            }
            ArrowDataType::Utf8 => {
                let array = column
                    .as_any()
                    .downcast_ref::<arrow_array::StringArray>()
                    .expect("Unable to get LargeBinaryArray array");
                typed.write_batch(
                    get_string_array(&array).as_slice(),
                    Some(levels.definition.as_slice()),
                    levels.repetition.as_deref(),
                )?
            }
            ArrowDataType::LargeBinary => {
                let array = column
                    .as_any()
                    .downcast_ref::<arrow_array::LargeBinaryArray>()
                    .expect("Unable to get LargeBinaryArray array");
                typed.write_batch(
                    get_large_binary_array(&array).as_slice(),
                    Some(levels.definition.as_slice()),
                    levels.repetition.as_deref(),
                )?
            }
            ArrowDataType::LargeUtf8 => {
                let array = column
                    .as_any()
                    .downcast_ref::<arrow_array::LargeStringArray>()
                    .expect("Unable to get LargeUtf8 array");
                typed.write_batch(
                    get_large_string_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) => {
            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)
                    }
                },
                ArrowDataType::FixedSizeBinary(_) => {
                    let array = column
                        .as_any()
                        .downcast_ref::<arrow_array::FixedSizeBinaryArray>()
                        .unwrap();
                    get_fsb_array_slice(&array, &indices)
                }
                ArrowDataType::Decimal(_, _) => {
                    let array = column
                        .as_any()
                        .downcast_ref::<arrow_array::DecimalArray>()
                        .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(),
                Some(levels.definition.as_slice()),
                levels.repetition.as_deref(),
            )?
        }
    };
    Ok(written as i64)
}

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: Vec<u8> = array.value(i).into();
                    let bytes = ByteArray::from(bytes);
                    values.push(bytes);
                }
            }
            values
        }
    };
}

def_get_binary_array_fn!(get_binary_array, arrow_array::BinaryArray);
def_get_binary_array_fn!(get_string_array, arrow_array::StringArray);
def_get_binary_array_fn!(get_large_binary_array, arrow_array::LargeBinaryArray);
def_get_binary_array_fn!(get_large_string_array, arrow_array::LargeStringArray);

/// 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>,
    indices: &[usize],
) -> Vec<T::T>
where
    T: DataType,
    A: arrow::datatypes::ArrowNumericType,
    T::T: From<A::Native>,
{
    let mut values = Vec::with_capacity(indices.len());
    for i in indices {
        values.push(array.value(*i).into())
    }
    values
}

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::DecimalArray,
    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).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 std::sync::Arc;
    use std::{fs::File, io::Seek};

    use arrow::datatypes::ToByteSlice;
    use arrow::datatypes::{DataType, Field, Schema, UInt32Type, UInt8Type};
    use arrow::record_batch::RecordBatch;
    use arrow::{array::*, buffer::Buffer};

    use crate::arrow::{ArrowReader, ParquetFileArrowReader};
    use crate::file::{
        reader::{FileReader, SerializedFileReader},
        statistics::Statistics,
        writer::InMemoryWriteableCursor,
    };
    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 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();

        let cursor = InMemoryWriteableCursor::default();

        {
            let mut writer = ArrowWriter::try_new(cursor.clone(), schema, None).unwrap();
            writer.write(&expected_batch).unwrap();
            writer.close().unwrap();
        }

        let buffer = cursor.into_inner().unwrap();

        let cursor = crate::file::serialized_reader::SliceableCursor::new(buffer);
        let reader = SerializedFileReader::new(cursor).unwrap();
        let mut arrow_reader = ParquetFileArrowReader::new(Arc::new(reader));
        let mut record_batch_reader = arrow_reader.get_record_reader(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.clone()), vec![Arc::new(a)]).unwrap();

        let file = get_temp_file("test_arrow_writer_non_null.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(Field::new("item", DataType::Int32, true))),
            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], 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,
            true,
        ))))
        .len(5)
        .add_buffer(a_value_offsets)
        .add_child_data(a_values.data().clone())
        .null_bit_buffer(Buffer::from(vec![0b00011011]))
        .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();

        // I think this setup is incorrect because this should pass
        assert_eq!(batch.column(0).data().null_count(), 1);

        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_binary.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(Arc::new(file_reader));
        let mut record_batch_reader = arrow_reader.get_record_reader(1024).unwrap();

        let batch = record_batch_reader.next().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_decimal() {
        let decimal_field = Field::new("a", DataType::Decimal(5, 2), false);
        let schema = Schema::new(vec![decimal_field]);

        let mut dec_builder = DecimalBuilder::new(4, 5, 2);
        dec_builder.append_value(10_000).unwrap();
        dec_builder.append_value(50_000).unwrap();
        dec_builder.append_value(0).unwrap();
        dec_builder.append_value(-100).unwrap();

        let raw_decimal_i128_values: Vec<i128> = vec![10_000, 50_000, 0, -100];
        let decimal_values = dec_builder.finish();
        let batch = RecordBatch::try_new(
            Arc::new(schema.clone()),
            vec![Arc::new(decimal_values)],
        )
        .unwrap();

        let mut file = get_temp_file("test_arrow_writer_decimal.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(Arc::new(file_reader));
        let mut record_batch_reader = arrow_reader.get_record_reader(1024).unwrap();

        let batch = record_batch_reader.next().unwrap().unwrap();
        let decimal_col = batch
            .column(0)
            .as_any()
            .downcast_ref::<DecimalArray>()
            .unwrap();

        for i in 0..batch.num_rows() {
            assert_eq!(decimal_col.value(i), raw_decimal_i128_values[i]);
        }
    }

    #[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))),
            true,
        );
        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),
            // Note: when the below struct is set to non-nullable, this test fails,
            // but the output data written is correct.
            // Interestingly, pyarrow will read it correctly, but pyspark fails to.
            // This might be a compatibility quirk between arrow and parquet.
            // We have opened https://github.com/apache/arrow-rs/issues/245 to investigate
            Field::new(
                "c",
                DataType::Struct(vec![struct_field_d.clone(), struct_field_e.clone()]),
                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)]);
        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)
            .add_child_data(g_value.data().clone())
            // .null_bit_buffer(Buffer::from(vec![0b00011011])) // TODO: add to test after resolving other issues
            .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),
            vec![Arc::new(a), Arc::new(b), Arc::new(c)],
        )
        .unwrap();

        roundtrip("test_arrow_writer_complex.parquet", batch);
    }

    #[test]
    fn arrow_writer_complex_mixed() {
        // This test was added while investigating https://github.com/apache/arrow-rs/issues/244.
        // Only writing the "offest_field" column works when "some_nested_object" is non-null.
        // This indicates that a non-null struct should not have a null child (with null values).
        // One observation is that spark doesn't consider the parent struct's nullness,
        // and so, we should investigate the impact of always treating structs as null.
        // See https://github.com/apache/arrow-rs/issues/245.

        // define schema
        let offset_field = Field::new("offset", DataType::Int32, true);
        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(),
            ]),
            true,
        )]);

        // 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("test_arrow_writer_complex_mixed.parquet", batch);
    }

    #[test]
    fn arrow_writer_2_level_struct() {
        // tests writing <struct<struct<primitive>>
        let field_c = Field::new("c", DataType::Int32, true);
        let field_b = Field::new("b", DataType::Struct(vec![field_c]), true);
        let field_a = Field::new("a", DataType::Struct(vec![field_b.clone()]), true);
        let schema = Schema::new(vec![field_a.clone()]);

        // create data
        let c = Int32Array::from(vec![Some(1), None, Some(3), None, None, Some(6)]);
        let b_data = ArrayDataBuilder::new(field_b.data_type().clone())
            .len(6)
            .null_bit_buffer(Buffer::from(vec![0b00100111]))
            .add_child_data(c.data().clone())
            .build();
        let b = StructArray::from(b_data);
        let a_data = ArrayDataBuilder::new(field_a.data_type().clone())
            .len(6)
            .null_bit_buffer(Buffer::from(vec![0b00101111]))
            .add_child_data(b.data().clone())
            .build();
        let a = StructArray::from(a_data);

        assert_eq!(a.null_count(), 1);
        assert_eq!(a.column(0).null_count(), 2);

        // build a racord batch
        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();

        roundtrip("test_arrow_writer_2_level_struct.parquet", batch);
    }

    #[test]
    fn arrow_writer_2_level_struct_non_null() {
        // tests writing <struct<struct<primitive>>
        let field_c = Field::new("c", DataType::Int32, false);
        let field_b = Field::new("b", DataType::Struct(vec![field_c]), false);
        let field_a = Field::new("a", DataType::Struct(vec![field_b.clone()]), false);
        let schema = Schema::new(vec![field_a.clone()]);

        // create data
        let c = Int32Array::from(vec![1, 2, 3, 4, 5, 6]);
        let b_data = ArrayDataBuilder::new(field_b.data_type().clone())
            .len(6)
            .add_child_data(c.data().clone())
            .build();
        let b = StructArray::from(b_data);
        let a_data = ArrayDataBuilder::new(field_a.data_type().clone())
            .len(6)
            .add_child_data(b.data().clone())
            .build();
        let a = StructArray::from(a_data);

        assert_eq!(a.null_count(), 0);
        assert_eq!(a.column(0).null_count(), 0);

        // build a racord batch
        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();

        roundtrip("test_arrow_writer_2_level_struct_non_null.parquet", batch);
    }

    #[test]
    fn arrow_writer_2_level_struct_mixed_null() {
        // tests writing <struct<struct<primitive>>
        let field_c = Field::new("c", DataType::Int32, false);
        let field_b = Field::new("b", DataType::Struct(vec![field_c]), true);
        let field_a = Field::new("a", DataType::Struct(vec![field_b.clone()]), false);
        let schema = Schema::new(vec![field_a.clone()]);

        // create data
        // When the null buffer of the struct is created, this test fails.
        // It appears that the nullness of the struct is ignored when the
        // struct is read back.
        // See https://github.com/apache/arrow-rs/issues/245
        let c = Int32Array::from(vec![1, 2, 3, 4, 5, 6]);
        let b_data = ArrayDataBuilder::new(field_b.data_type().clone())
            .len(6)
            // .null_bit_buffer(Buffer::from(vec![0b00100111]))
            .add_child_data(c.data().clone())
            .build();
        let b = StructArray::from(b_data);
        // a intentionally has no null buffer, to test that this is handled correctly
        let a_data = ArrayDataBuilder::new(field_a.data_type().clone())
            .len(6)
            .add_child_data(b.data().clone())
            .build();
        let a = StructArray::from(a_data);

        assert_eq!(a.null_count(), 0);
        assert_eq!(a.column(0).null_count(), 0);

        // build a racord batch
        let batch = RecordBatch::try_new(Arc::new(schema), vec![Arc::new(a)]).unwrap();

        roundtrip("test_arrow_writer_2_level_struct_mixed_null.parquet", batch);
    }

    const SMALL_SIZE: usize = 4;

    fn roundtrip(filename: &str, expected_batch: RecordBatch) -> File {
        let file = get_temp_file(filename, &[]);

        let mut writer = ArrowWriter::try_new(
            file.try_clone().unwrap(),
            expected_batch.schema(),
            None,
        )
        .expect("Unable to write file");
        writer.write(&expected_batch).unwrap();
        writer.close().unwrap();

        let reader = SerializedFileReader::new(file.try_clone().unwrap()).unwrap();
        let mut arrow_reader = ParquetFileArrowReader::new(Arc::new(reader));
        let mut record_batch_reader = arrow_reader.get_record_reader(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();
            let actual_data = actual_batch.column(i).data();

            assert_eq!(expected_data, actual_data);
        }

        file
    }

    fn one_column_roundtrip(filename: &str, values: ArrayRef, nullable: bool) -> File {
        let schema = Schema::new(vec![Field::new(
            "col",
            values.data_type().clone(),
            nullable,
        )]);
        let expected_batch =
            RecordBatch::try_new(Arc::new(schema), vec![values]).unwrap();

        roundtrip(filename, expected_batch)
    }

    fn values_required<A, I>(iter: I, filename: &str)
    where
        A: From<Vec<I::Item>> + Array + 'static,
        I: IntoIterator,
    {
        let raw_values: Vec<_> = iter.into_iter().collect();
        let values = Arc::new(A::from(raw_values));
        one_column_roundtrip(filename, values, false);
    }

    fn values_optional<A, I>(iter: I, filename: &str)
    where
        A: From<Vec<Option<I::Item>>> + Array + 'static,
        I: IntoIterator,
    {
        let optional_raw_values: Vec<_> = iter
            .into_iter()
            .enumerate()
            .map(|(i, v)| if i % 2 == 0 { None } else { Some(v) })
            .collect();
        let optional_values = Arc::new(A::from(optional_raw_values));
        one_column_roundtrip(filename, optional_values, true);
    }

    fn required_and_optional<A, I>(iter: I, filename: &str)
    where
        A: From<Vec<I::Item>> + From<Vec<Option<I::Item>>> + Array + 'static,
        I: IntoIterator + Clone,
    {
        values_required::<A, I>(iter.clone(), filename);
        values_optional::<A, I>(iter, filename);
    }

    #[test]
    fn all_null_primitive_single_column() {
        let values = Arc::new(Int32Array::from(vec![None; SMALL_SIZE]));
        one_column_roundtrip("all_null_primitive_single_column", values, true);
    }
    #[test]
    fn null_single_column() {
        let values = Arc::new(NullArray::new(SMALL_SIZE));
        one_column_roundtrip("null_single_column", values, true);
        // null arrays are always nullable, a test with non-nullable nulls fails
    }

    #[test]
    fn bool_single_column() {
        required_and_optional::<BooleanArray, _>(
            [true, false].iter().cycle().copied().take(SMALL_SIZE),
            "bool_single_column",
        );
    }

    #[test]
    fn i8_single_column() {
        required_and_optional::<Int8Array, _>(0..SMALL_SIZE as i8, "i8_single_column");
    }

    #[test]
    fn i16_single_column() {
        required_and_optional::<Int16Array, _>(0..SMALL_SIZE as i16, "i16_single_column");
    }

    #[test]
    fn i32_single_column() {
        required_and_optional::<Int32Array, _>(0..SMALL_SIZE as i32, "i32_single_column");
    }

    #[test]
    fn i64_single_column() {
        required_and_optional::<Int64Array, _>(0..SMALL_SIZE as i64, "i64_single_column");
    }

    #[test]
    fn u8_single_column() {
        required_and_optional::<UInt8Array, _>(0..SMALL_SIZE as u8, "u8_single_column");
    }

    #[test]
    fn u16_single_column() {
        required_and_optional::<UInt16Array, _>(
            0..SMALL_SIZE as u16,
            "u16_single_column",
        );
    }

    #[test]
    fn u32_single_column() {
        required_and_optional::<UInt32Array, _>(
            0..SMALL_SIZE as u32,
            "u32_single_column",
        );
    }

    #[test]
    fn u64_single_column() {
        required_and_optional::<UInt64Array, _>(
            0..SMALL_SIZE as u64,
            "u64_single_column",
        );
    }

    #[test]
    fn f32_single_column() {
        required_and_optional::<Float32Array, _>(
            (0..SMALL_SIZE).map(|i| i as f32),
            "f32_single_column",
        );
    }

    #[test]
    fn f64_single_column() {
        required_and_optional::<Float64Array, _>(
            (0..SMALL_SIZE).map(|i| i as f64),
            "f64_single_column",
        );
    }

    // The timestamp array types don't implement From<Vec<T>> because they need the timezone
    // argument, and they also doesn't support building from a Vec<Option<T>>, so call
    // one_column_roundtrip manually instead of calling required_and_optional for these tests.

    #[test]
    fn timestamp_second_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE as i64).collect();
        let values = Arc::new(TimestampSecondArray::from_vec(raw_values, None));

        one_column_roundtrip("timestamp_second_single_column", values, false);
    }

    #[test]
    fn timestamp_millisecond_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE as i64).collect();
        let values = Arc::new(TimestampMillisecondArray::from_vec(raw_values, None));

        one_column_roundtrip("timestamp_millisecond_single_column", values, false);
    }

    #[test]
    fn timestamp_microsecond_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE as i64).collect();
        let values = Arc::new(TimestampMicrosecondArray::from_vec(raw_values, None));

        one_column_roundtrip("timestamp_microsecond_single_column", values, false);
    }

    #[test]
    fn timestamp_nanosecond_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE as i64).collect();
        let values = Arc::new(TimestampNanosecondArray::from_vec(raw_values, None));

        one_column_roundtrip("timestamp_nanosecond_single_column", values, false);
    }

    #[test]
    fn date32_single_column() {
        required_and_optional::<Date32Array, _>(
            0..SMALL_SIZE as i32,
            "date32_single_column",
        );
    }

    #[test]
    fn date64_single_column() {
        // Date64 must be a multiple of 86400000, see ARROW-10925
        required_and_optional::<Date64Array, _>(
            (0..(SMALL_SIZE as i64 * 86400000)).step_by(86400000),
            "date64_single_column",
        );
    }

    #[test]
    fn time32_second_single_column() {
        required_and_optional::<Time32SecondArray, _>(
            0..SMALL_SIZE as i32,
            "time32_second_single_column",
        );
    }

    #[test]
    fn time32_millisecond_single_column() {
        required_and_optional::<Time32MillisecondArray, _>(
            0..SMALL_SIZE as i32,
            "time32_millisecond_single_column",
        );
    }

    #[test]
    fn time64_microsecond_single_column() {
        required_and_optional::<Time64MicrosecondArray, _>(
            0..SMALL_SIZE as i64,
            "time64_microsecond_single_column",
        );
    }

    #[test]
    fn time64_nanosecond_single_column() {
        required_and_optional::<Time64NanosecondArray, _>(
            0..SMALL_SIZE as i64,
            "time64_nanosecond_single_column",
        );
    }

    #[test]
    #[should_panic(expected = "Converting Duration to parquet not supported")]
    fn duration_second_single_column() {
        required_and_optional::<DurationSecondArray, _>(
            0..SMALL_SIZE as i64,
            "duration_second_single_column",
        );
    }

    #[test]
    #[should_panic(expected = "Converting Duration to parquet not supported")]
    fn duration_millisecond_single_column() {
        required_and_optional::<DurationMillisecondArray, _>(
            0..SMALL_SIZE as i64,
            "duration_millisecond_single_column",
        );
    }

    #[test]
    #[should_panic(expected = "Converting Duration to parquet not supported")]
    fn duration_microsecond_single_column() {
        required_and_optional::<DurationMicrosecondArray, _>(
            0..SMALL_SIZE as i64,
            "duration_microsecond_single_column",
        );
    }

    #[test]
    #[should_panic(expected = "Converting Duration to parquet not supported")]
    fn duration_nanosecond_single_column() {
        required_and_optional::<DurationNanosecondArray, _>(
            0..SMALL_SIZE as i64,
            "duration_nanosecond_single_column",
        );
    }

    #[test]
    fn interval_year_month_single_column() {
        required_and_optional::<IntervalYearMonthArray, _>(
            0..SMALL_SIZE as i32,
            "interval_year_month_single_column",
        );
    }

    #[test]
    fn interval_day_time_single_column() {
        required_and_optional::<IntervalDayTimeArray, _>(
            0..SMALL_SIZE as i64,
            "interval_day_time_single_column",
        );
    }

    #[test]
    fn binary_single_column() {
        let one_vec: Vec<u8> = (0..SMALL_SIZE as u8).collect();
        let many_vecs: Vec<_> = std::iter::repeat(one_vec).take(SMALL_SIZE).collect();
        let many_vecs_iter = many_vecs.iter().map(|v| v.as_slice());

        // BinaryArrays can't be built from Vec<Option<&str>>, so only call `values_required`
        values_required::<BinaryArray, _>(many_vecs_iter, "binary_single_column");
    }

    #[test]
    fn large_binary_single_column() {
        let one_vec: Vec<u8> = (0..SMALL_SIZE as u8).collect();
        let many_vecs: Vec<_> = std::iter::repeat(one_vec).take(SMALL_SIZE).collect();
        let many_vecs_iter = many_vecs.iter().map(|v| v.as_slice());

        // LargeBinaryArrays can't be built from Vec<Option<&str>>, so only call `values_required`
        values_required::<LargeBinaryArray, _>(
            many_vecs_iter,
            "large_binary_single_column",
        );
    }

    #[test]
    fn fixed_size_binary_single_column() {
        let mut builder = FixedSizeBinaryBuilder::new(16, 4);
        builder.append_value(b"0123").unwrap();
        builder.append_null().unwrap();
        builder.append_value(b"8910").unwrap();
        builder.append_value(b"1112").unwrap();
        let array = Arc::new(builder.finish());

        one_column_roundtrip("fixed_size_binary_single_column", array, true);
    }

    #[test]
    fn string_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE).map(|i| i.to_string()).collect();
        let raw_strs = raw_values.iter().map(|s| s.as_str());

        required_and_optional::<StringArray, _>(raw_strs, "string_single_column");
    }

    #[test]
    fn large_string_single_column() {
        let raw_values: Vec<_> = (0..SMALL_SIZE).map(|i| i.to_string()).collect();
        let raw_strs = raw_values.iter().map(|s| s.as_str());

        required_and_optional::<LargeStringArray, _>(
            raw_strs,
            "large_string_single_column",
        );
    }

    #[test]
    fn list_single_column() {
        let a_values = Int32Array::from(vec![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());
        let a_list_data = ArrayData::builder(DataType::List(Box::new(Field::new(
            "item",
            DataType::Int32,
            true, // TODO: why does this fail when false? Is it related to logical nulls?
        ))))
        .len(5)
        .add_buffer(a_value_offsets)
        .null_bit_buffer(Buffer::from(vec![0b00011011]))
        .add_child_data(a_values.data().clone())
        .build();

        assert_eq!(a_list_data.null_count(), 1);

        let a = ListArray::from(a_list_data);
        let values = Arc::new(a);

        one_column_roundtrip("list_single_column", values, true);
    }

    #[test]
    fn large_list_single_column() {
        let a_values = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
        let a_value_offsets =
            arrow::buffer::Buffer::from(&[0i64, 1, 3, 3, 6, 10].to_byte_slice());
        let a_list_data = ArrayData::builder(DataType::LargeList(Box::new(Field::new(
            "large_item",
            DataType::Int32,
            true,
        ))))
        .len(5)
        .add_buffer(a_value_offsets)
        .add_child_data(a_values.data().clone())
        .null_bit_buffer(Buffer::from(vec![0b00011011]))
        .build();

        // I think this setup is incorrect because this should pass
        assert_eq!(a_list_data.null_count(), 1);

        let a = LargeListArray::from(a_list_data);
        let values = Arc::new(a);

        one_column_roundtrip("large_list_single_column", values, true);
    }

    #[test]
    fn struct_single_column() {
        let a_values = Int32Array::from(vec![1, 2, 3, 4, 5, 6, 7, 8, 9, 10]);
        let struct_field_a = Field::new("f", DataType::Int32, false);
        let s = StructArray::from(vec![(struct_field_a, Arc::new(a_values) as ArrayRef)]);

        let values = Arc::new(s);
        one_column_roundtrip("struct_single_column", values, false);
    }

    #[test]
    fn arrow_writer_string_dictionary() {
        // define schema
        let schema = Arc::new(Schema::new(vec![Field::new_dict(
            "dictionary",
            DataType::Dictionary(Box::new(DataType::Int32), Box::new(DataType::Utf8)),
            true,
            42,
            true,
        )]));

        // create some data
        let d: Int32DictionaryArray = [Some("alpha"), None, Some("beta"), Some("alpha")]
            .iter()
            .copied()
            .collect();

        // build a record batch
        let expected_batch = RecordBatch::try_new(schema, vec![Arc::new(d)]).unwrap();

        roundtrip(
            "test_arrow_writer_string_dictionary.parquet",
            expected_batch,
        );
    }

    #[test]
    fn arrow_writer_primitive_dictionary() {
        // define schema
        let schema = Arc::new(Schema::new(vec![Field::new_dict(
            "dictionary",
            DataType::Dictionary(Box::new(DataType::UInt8), Box::new(DataType::UInt32)),
            true,
            42,
            true,
        )]));

        // create some data
        let key_builder = PrimitiveBuilder::<UInt8Type>::new(3);
        let value_builder = PrimitiveBuilder::<UInt32Type>::new(2);
        let mut builder = PrimitiveDictionaryBuilder::new(key_builder, value_builder);
        builder.append(12345678).unwrap();
        builder.append_null().unwrap();
        builder.append(22345678).unwrap();
        builder.append(12345678).unwrap();
        let d = builder.finish();

        // build a record batch
        let expected_batch = RecordBatch::try_new(schema, vec![Arc::new(d)]).unwrap();

        roundtrip(
            "test_arrow_writer_primitive_dictionary.parquet",
            expected_batch,
        );
    }

    #[test]
    fn arrow_writer_string_dictionary_unsigned_index() {
        // define schema
        let schema = Arc::new(Schema::new(vec![Field::new_dict(
            "dictionary",
            DataType::Dictionary(Box::new(DataType::UInt8), Box::new(DataType::Utf8)),
            true,
            42,
            true,
        )]));

        // create some data
        let d: UInt8DictionaryArray = [Some("alpha"), None, Some("beta"), Some("alpha")]
            .iter()
            .copied()
            .collect();

        // build a record batch
        let expected_batch = RecordBatch::try_new(schema, vec![Arc::new(d)]).unwrap();

        roundtrip(
            "test_arrow_writer_string_dictionary_unsigned_index.parquet",
            expected_batch,
        );
    }

    #[test]
    fn u32_min_max() {
        // check values roundtrip through parquet
        let values = Arc::new(UInt32Array::from_iter_values(vec![
            u32::MIN,
            u32::MIN + 1,
            (i32::MAX as u32) - 1,
            i32::MAX as u32,
            (i32::MAX as u32) + 1,
            u32::MAX - 1,
            u32::MAX,
        ]));
        let file = one_column_roundtrip("u32_min_max_single_column", values, false);

        // check statistics are valid
        let reader = SerializedFileReader::new(file).unwrap();
        let metadata = reader.metadata();
        assert_eq!(metadata.num_row_groups(), 1);
        let row_group = metadata.row_group(0);
        assert_eq!(row_group.num_columns(), 1);
        let column = row_group.column(0);
        let stats = column.statistics().unwrap();
        assert!(stats.has_min_max_set());
        if let Statistics::Int32(stats) = stats {
            assert_eq!(*stats.min() as u32, u32::MIN);
            assert_eq!(*stats.max() as u32, u32::MAX);
        } else {
            panic!("Statistics::Int32 missing")
        }
    }

    #[test]
    fn u64_min_max() {
        // check values roundtrip through parquet
        let values = Arc::new(UInt64Array::from_iter_values(vec![
            u64::MIN,
            u64::MIN + 1,
            (i64::MAX as u64) - 1,
            i64::MAX as u64,
            (i64::MAX as u64) + 1,
            u64::MAX - 1,
            u64::MAX,
        ]));
        let file = one_column_roundtrip("u64_min_max_single_column", values, false);

        // check statistics are valid
        let reader = SerializedFileReader::new(file).unwrap();
        let metadata = reader.metadata();
        assert_eq!(metadata.num_row_groups(), 1);
        let row_group = metadata.row_group(0);
        assert_eq!(row_group.num_columns(), 1);
        let column = row_group.column(0);
        let stats = column.statistics().unwrap();
        assert!(stats.has_min_max_set());
        if let Statistics::Int64(stats) = stats {
            assert_eq!(*stats.min() as u64, u64::MIN);
            assert_eq!(*stats.max() as u64, u64::MAX);
        } else {
            panic!("Statistics::Int64 missing")
        }
    }
}