mod.rs

// 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 reader which reads parquet data into arrow [`RecordBatch`]

use std::collections::VecDeque;
use std::sync::Arc;

use arrow::array::Array;
use arrow::compute::prep_null_mask_filter;
use arrow::datatypes::{DataType as ArrowType, Schema, SchemaRef};
use arrow::error::Result as ArrowResult;
use arrow::record_batch::{RecordBatch, RecordBatchReader};
use arrow::{array::StructArray, error::ArrowError};

use crate::arrow::array_reader::{
    build_array_reader, ArrayReader, FileReaderRowGroupCollection, RowGroupCollection,
};
use crate::arrow::schema::parquet_to_arrow_schema;
use crate::arrow::schema::parquet_to_arrow_schema_by_columns;
use crate::arrow::ProjectionMask;
use crate::errors::{ParquetError, Result};
use crate::file::metadata::{KeyValue, ParquetMetaData};
use crate::file::reader::{ChunkReader, FileReader, SerializedFileReader};
use crate::file::serialized_reader::ReadOptionsBuilder;
use crate::schema::types::SchemaDescriptor;

mod filter;
mod selection;

pub use filter::{ArrowPredicate, ArrowPredicateFn, RowFilter};
pub use selection::{RowSelection, RowSelector};

/// A generic builder for constructing sync or async arrow parquet readers. This is not intended
/// to be used directly, instead you should use the specialization for the type of reader
/// you wish to use
///
/// * For a synchronous API - [`ParquetRecordBatchReaderBuilder`]
/// * For an asynchronous API - [`ParquetRecordBatchStreamBuilder`]
///
/// [`ParquetRecordBatchStreamBuilder`]: [crate::arrow::async_reader::ParquetRecordBatchStreamBuilder]
pub struct ArrowReaderBuilder<T> {
    pub(crate) input: T,

    pub(crate) metadata: Arc<ParquetMetaData>,

    pub(crate) schema: SchemaRef,

    pub(crate) batch_size: usize,

    pub(crate) row_groups: Option<Vec<usize>>,

    pub(crate) projection: ProjectionMask,

    pub(crate) filter: Option<RowFilter>,

    pub(crate) selection: Option<RowSelection>,
}

impl<T> ArrowReaderBuilder<T> {
    pub(crate) fn new_builder(
        input: T,
        metadata: Arc<ParquetMetaData>,
        options: ArrowReaderOptions,
    ) -> Result<Self> {
        let kv_metadata = match options.skip_arrow_metadata {
            true => None,
            false => metadata.file_metadata().key_value_metadata(),
        };

        let schema = Arc::new(parquet_to_arrow_schema(
            metadata.file_metadata().schema_descr(),
            kv_metadata,
        )?);

        Ok(Self {
            input,
            metadata,
            schema,
            batch_size: 1024,
            row_groups: None,
            projection: ProjectionMask::all(),
            filter: None,
            selection: None,
        })
    }

    /// Returns a reference to the [`ParquetMetaData`] for this parquet file
    pub fn metadata(&self) -> &Arc<ParquetMetaData> {
        &self.metadata
    }

    /// Returns the parquet [`SchemaDescriptor`] for this parquet file
    pub fn parquet_schema(&self) -> &SchemaDescriptor {
        self.metadata.file_metadata().schema_descr()
    }

    /// Returns the arrow [`SchemaRef`] for this parquet file
    pub fn schema(&self) -> &SchemaRef {
        &self.schema
    }

    /// Set the size of [`RecordBatch`] to produce. Defaults to 1024
    pub fn with_batch_size(self, batch_size: usize) -> Self {
        Self { batch_size, ..self }
    }

    /// Only read data from the provided row group indexes
    pub fn with_row_groups(self, row_groups: Vec<usize>) -> Self {
        Self {
            row_groups: Some(row_groups),
            ..self
        }
    }

    /// Only read data from the provided column indexes
    pub fn with_projection(self, mask: ProjectionMask) -> Self {
        Self {
            projection: mask,
            ..self
        }
    }

    /// Provide a [`RowSelection`] to filter out rows, and avoid fetching their
    /// data into memory.
    ///
    /// Row group filtering is applied prior to this, and therefore rows from skipped
    /// row groups should not be included in the [`RowSelection`]
    ///
    /// An example use case of this would be applying a selection determined by
    /// evaluating predicates against the [`Index`]
    ///
    /// [`Index`]: [parquet::file::page_index::index::Index]
    pub fn with_row_selection(self, selection: RowSelection) -> Self {
        Self {
            selection: Some(selection),
            ..self
        }
    }

    /// Provide a [`RowFilter`] to skip decoding rows
    ///
    /// Row filters are applied after row group selection and row selection
    pub fn with_row_filter(self, filter: RowFilter) -> Self {
        Self {
            filter: Some(filter),
            ..self
        }
    }
}

/// Arrow reader api.
/// With this api, user can get arrow schema from parquet file, and read parquet data
/// into arrow arrays.
#[deprecated(note = "Use ParquetRecordBatchReaderBuilder instead")]
pub trait ArrowReader {
    type RecordReader: RecordBatchReader;

    /// Read parquet schema and convert it into arrow schema.
    fn get_schema(&mut self) -> Result<Schema>;

    /// Read parquet schema and convert it into arrow schema.
    /// This schema only includes columns identified by `mask`.
    fn get_schema_by_columns(&mut self, mask: ProjectionMask) -> Result<Schema>;

    /// Returns record batch reader from whole parquet file.
    ///
    /// # Arguments
    ///
    /// `batch_size`: The size of each record batch returned from this reader. Only the
    /// last batch may contain records less than this size, otherwise record batches
    /// returned from this reader should contains exactly `batch_size` elements.
    fn get_record_reader(&mut self, batch_size: usize) -> Result<Self::RecordReader>;

    /// Returns record batch reader whose record batch contains columns identified by
    /// `mask`.
    ///
    /// # Arguments
    ///
    /// `mask`: The columns that should be included in record batches.
    /// `batch_size`: Please refer to `get_record_reader`.
    fn get_record_reader_by_columns(
        &mut self,
        mask: ProjectionMask,
        batch_size: usize,
    ) -> Result<Self::RecordReader>;
}

/// Options that control how metadata is read for a parquet file
///
/// See [`ArrowReaderBuilder`] for how to configure how the column data
/// is then read from the file, including projection and filter pushdown
#[derive(Debug, Clone, Default)]
pub struct ArrowReaderOptions {
    skip_arrow_metadata: bool,
    pub(crate) page_index: bool,
}

impl ArrowReaderOptions {
    /// Create a new [`ArrowReaderOptions`] with the default settings
    pub fn new() -> Self {
        Self::default()
    }

    /// Parquet files generated by some writers may contain embedded arrow
    /// schema and metadata. This may not be correct or compatible with your system.
    ///
    /// For example:[ARROW-16184](https://issues.apache.org/jira/browse/ARROW-16184)
    ///
    /// Set `skip_arrow_metadata` to true, to skip decoding this
    pub fn with_skip_arrow_metadata(self, skip_arrow_metadata: bool) -> Self {
        Self {
            skip_arrow_metadata,
            ..self
        }
    }

    /// Set this true to enable decoding of the [PageIndex] if present. This can be used
    /// to push down predicates to the parquet scan, potentially eliminating unnecessary IO
    ///
    /// [PageIndex]: [https://github.com/apache/parquet-format/blob/master/PageIndex.md]
    pub fn with_page_index(self, page_index: bool) -> Self {
        Self { page_index, ..self }
    }
}

/// An `ArrowReader` that can be used to synchronously read parquet data as [`RecordBatch`]
///
/// See [`crate::arrow::async_reader`] for an asynchronous interface
#[deprecated(note = "Use ParquetRecordBatchReaderBuilder instead")]
pub struct ParquetFileArrowReader {
    file_reader: Arc<dyn FileReader>,

    #[allow(deprecated)]
    options: ArrowReaderOptions,
}

#[allow(deprecated)]
impl ArrowReader for ParquetFileArrowReader {
    type RecordReader = ParquetRecordBatchReader;

    fn get_schema(&mut self) -> Result<Schema> {
        let file_metadata = self.file_reader.metadata().file_metadata();
        parquet_to_arrow_schema(file_metadata.schema_descr(), self.get_kv_metadata())
    }

    fn get_schema_by_columns(&mut self, mask: ProjectionMask) -> Result<Schema> {
        let file_metadata = self.file_reader.metadata().file_metadata();
        parquet_to_arrow_schema_by_columns(
            file_metadata.schema_descr(),
            mask,
            self.get_kv_metadata(),
        )
    }

    fn get_record_reader(
        &mut self,
        batch_size: usize,
    ) -> Result<ParquetRecordBatchReader> {
        self.get_record_reader_by_columns(ProjectionMask::all(), batch_size)
    }

    fn get_record_reader_by_columns(
        &mut self,
        mask: ProjectionMask,
        batch_size: usize,
    ) -> Result<ParquetRecordBatchReader> {
        let array_reader =
            build_array_reader(Arc::new(self.get_schema()?), mask, &self.file_reader)?;

        // Try to avoid allocate large buffer
        let batch_size = self.file_reader.num_rows().min(batch_size);
        Ok(ParquetRecordBatchReader::new(
            batch_size,
            array_reader,
            None,
        ))
    }
}

#[allow(deprecated)]
impl ParquetFileArrowReader {
    /// Create a new [`ParquetFileArrowReader`] with the provided [`ChunkReader`]
    ///
    /// ```no_run
    /// # use std::fs::File;
    /// # use bytes::Bytes;
    /// # use parquet::arrow::ParquetFileArrowReader;
    ///
    /// let file = File::open("file.parquet").unwrap();
    /// let reader = ParquetFileArrowReader::try_new(file).unwrap();
    ///
    /// let bytes = Bytes::from(vec![]);
    /// let reader = ParquetFileArrowReader::try_new(bytes).unwrap();
    /// ```
    pub fn try_new<R: ChunkReader + 'static>(chunk_reader: R) -> Result<Self> {
        Self::try_new_with_options(chunk_reader, Default::default())
    }

    /// Create a new [`ParquetFileArrowReader`] with the provided [`ChunkReader`]
    /// and [`ArrowReaderOptions`]
    pub fn try_new_with_options<R: ChunkReader + 'static>(
        chunk_reader: R,
        options: ArrowReaderOptions,
    ) -> Result<Self> {
        let file_reader = Arc::new(SerializedFileReader::new(chunk_reader)?);
        Ok(Self::new_with_options(file_reader, options))
    }

    /// Create a new [`ParquetFileArrowReader`] with the provided [`Arc<dyn FileReader>`]
    pub fn new(file_reader: Arc<dyn FileReader>) -> Self {
        Self::new_with_options(file_reader, Default::default())
    }

    /// Create a new [`ParquetFileArrowReader`] with the provided [`Arc<dyn FileReader>`]
    /// and [`ArrowReaderOptions`]
    pub fn new_with_options(
        file_reader: Arc<dyn FileReader>,
        options: ArrowReaderOptions,
    ) -> Self {
        Self {
            file_reader,
            options,
        }
    }

    /// Expose the reader metadata
    #[deprecated = "use metadata() instead"]
    pub fn get_metadata(&mut self) -> ParquetMetaData {
        self.file_reader.metadata().clone()
    }

    /// Returns the parquet metadata
    pub fn metadata(&self) -> &ParquetMetaData {
        self.file_reader.metadata()
    }

    /// Returns the parquet schema
    pub fn parquet_schema(&self) -> &SchemaDescriptor {
        self.file_reader.metadata().file_metadata().schema_descr()
    }

    /// Returns the key value metadata, returns `None` if [`ArrowReaderOptions::skip_arrow_metadata`]
    fn get_kv_metadata(&self) -> Option<&Vec<KeyValue>> {
        if self.options.skip_arrow_metadata {
            return None;
        }

        self.file_reader
            .metadata()
            .file_metadata()
            .key_value_metadata()
    }
}

#[doc(hidden)]
/// A newtype used within [`ReaderOptionsBuilder`] to distinguish sync readers from async
pub struct SyncReader<T: ChunkReader>(SerializedFileReader<T>);

/// A synchronous builder used to construct [`ParquetRecordBatchReader`] for a file
///
/// For an async API see [`crate::arrow::async_reader::ParquetRecordBatchStreamBuilder`]
pub type ParquetRecordBatchReaderBuilder<T> = ArrowReaderBuilder<SyncReader<T>>;

impl<T: ChunkReader + 'static> ArrowReaderBuilder<SyncReader<T>> {
    /// Create a new [`ParquetRecordBatchReaderBuilder`]
    pub fn try_new(reader: T) -> Result<Self> {
        Self::try_new_with_options(reader, Default::default())
    }

    /// Create a new [`ParquetRecordBatchReaderBuilder`] with [`ArrowReaderOptions`]
    pub fn try_new_with_options(reader: T, options: ArrowReaderOptions) -> Result<Self> {
        let reader = match options.page_index {
            true => {
                let read_options = ReadOptionsBuilder::new().with_page_index().build();
                SerializedFileReader::new_with_options(reader, read_options)?
            }
            false => SerializedFileReader::new(reader)?,
        };

        let metadata = Arc::clone(reader.metadata_ref());
        Self::new_builder(SyncReader(reader), metadata, options)
    }

    /// Build a [`ParquetRecordBatchReader`]
    ///
    /// Note: this will eagerly evaluate any `RowFilter` before returning
    pub fn build(self) -> Result<ParquetRecordBatchReader> {
        let reader =
            FileReaderRowGroupCollection::new(Arc::new(self.input.0), self.row_groups);

        let mut filter = self.filter;
        let mut selection = self.selection;

        // Try to avoid allocate large buffer
        let batch_size = self
            .batch_size
            .min(self.metadata.file_metadata().num_rows() as usize);
        if let Some(filter) = filter.as_mut() {
            for predicate in filter.predicates.iter_mut() {
                if !selects_any(selection.as_ref()) {
                    break;
                }

                let projection = predicate.projection().clone();
                let array_reader =
                    build_array_reader(Arc::clone(&self.schema), projection, &reader)?;

                selection = Some(evaluate_predicate(
                    batch_size,
                    array_reader,
                    selection,
                    predicate.as_mut(),
                )?);
            }
        }

        let array_reader = build_array_reader(self.schema, self.projection, &reader)?;

        // If selection is empty, truncate
        if !selects_any(selection.as_ref()) {
            selection = Some(RowSelection::from(vec![]));
        }

        Ok(ParquetRecordBatchReader::new(
            batch_size,
            array_reader,
            selection,
        ))
    }
}

/// An `Iterator<Item = ArrowResult<RecordBatch>>` that yields [`RecordBatch`]
/// read from a parquet data source
pub struct ParquetRecordBatchReader {
    batch_size: usize,
    array_reader: Box<dyn ArrayReader>,
    schema: SchemaRef,
    selection: Option<VecDeque<RowSelector>>,
}

impl Iterator for ParquetRecordBatchReader {
    type Item = ArrowResult<RecordBatch>;

    fn next(&mut self) -> Option<Self::Item> {
        let mut read_records = 0;
        match self.selection.as_mut() {
            Some(selection) => {
                while read_records < self.batch_size && !selection.is_empty() {
                    let front = selection.pop_front().unwrap();
                    if front.skip {
                        let skipped =
                            match self.array_reader.skip_records(front.row_count) {
                                Ok(skipped) => skipped,
                                Err(e) => return Some(Err(e.into())),
                            };

                        if skipped != front.row_count {
                            return Some(Err(general_err!(
                                "failed to skip rows, expected {}, got {}",
                                front.row_count,
                                skipped
                            )
                            .into()));
                        }
                        continue;
                    }

                    // try to read record
                    let need_read = self.batch_size - read_records;
                    let to_read = match front.row_count.checked_sub(need_read) {
                        Some(remaining) if remaining != 0 => {
                            // if page row count less than batch_size we must set batch size to page row count.
                            // add check avoid dead loop
                            selection.push_front(RowSelector::select(remaining));
                            need_read
                        }
                        _ => front.row_count,
                    };
                    match self.array_reader.read_records(to_read) {
                        Ok(0) => break,
                        Ok(rec) => read_records += rec,
                        Err(error) => return Some(Err(error.into())),
                    }
                }
            }
            None => {
                if let Err(error) = self.array_reader.read_records(self.batch_size) {
                    return Some(Err(error.into()));
                }
            }
        };

        match self.array_reader.consume_batch() {
            Err(error) => Some(Err(error.into())),
            Ok(array) => {
                let struct_array =
                    array.as_any().downcast_ref::<StructArray>().ok_or_else(|| {
                        ArrowError::ParquetError(
                            "Struct array reader should return struct array".to_string(),
                        )
                    });

                match struct_array {
                    Err(err) => Some(Err(err)),
                    Ok(e) => (e.len() > 0).then(|| Ok(RecordBatch::from(e))),
                }
            }
        }
    }
}

impl RecordBatchReader for ParquetRecordBatchReader {
    fn schema(&self) -> SchemaRef {
        self.schema.clone()
    }
}

impl ParquetRecordBatchReader {
    /// Create a new [`ParquetRecordBatchReader`] from the provided chunk reader
    ///
    /// See [`ParquetRecordBatchReaderBuilder`] for more options
    pub fn try_new<T: ChunkReader + 'static>(
        reader: T,
        batch_size: usize,
    ) -> Result<Self> {
        ParquetRecordBatchReaderBuilder::try_new(reader)?
            .with_batch_size(batch_size)
            .build()
    }

    /// Create a new [`ParquetRecordBatchReader`] that will read at most `batch_size` rows at
    /// a time from [`ArrayReader`] based on the configured `selection`. If `selection` is `None`
    /// all rows will be returned
    pub(crate) fn new(
        batch_size: usize,
        array_reader: Box<dyn ArrayReader>,
        selection: Option<RowSelection>,
    ) -> Self {
        let schema = match array_reader.get_data_type() {
            ArrowType::Struct(ref fields) => Schema::new(fields.clone()),
            _ => unreachable!("Struct array reader's data type is not struct!"),
        };

        Self {
            batch_size,
            array_reader,
            schema: Arc::new(schema),
            selection: selection.map(Into::into),
        }
    }
}

/// Returns `true` if `selection` is `None` or selects some rows
pub(crate) fn selects_any(selection: Option<&RowSelection>) -> bool {
    selection.map(|x| x.selects_any()).unwrap_or(true)
}

/// Evaluates an [`ArrowPredicate`] returning the [`RowSelection`]
///
/// If this [`ParquetRecordBatchReader`] has a [`RowSelection`], the
/// returned [`RowSelection`] will be the conjunction of this and
/// the rows selected by `predicate`
pub(crate) fn evaluate_predicate(
    batch_size: usize,
    array_reader: Box<dyn ArrayReader>,
    input_selection: Option<RowSelection>,
    predicate: &mut dyn ArrowPredicate,
) -> Result<RowSelection> {
    let reader =
        ParquetRecordBatchReader::new(batch_size, array_reader, input_selection.clone());
    let mut filters = vec![];
    for maybe_batch in reader {
        let filter = predicate.evaluate(maybe_batch?)?;
        match filter.null_count() {
            0 => filters.push(filter),
            _ => filters.push(prep_null_mask_filter(&filter)),
        };
    }

    let raw = RowSelection::from_filters(&filters);
    Ok(match input_selection {
        Some(selection) => selection.and_then(&raw),
        None => raw,
    })
}

#[cfg(test)]
mod tests {
    use bytes::Bytes;
    use std::cmp::min;
    use std::collections::VecDeque;
    use std::fmt::Formatter;
    use std::fs::File;
    use std::io::Seek;
    use std::path::PathBuf;
    use std::sync::Arc;

    use rand::{thread_rng, Rng, RngCore};
    use tempfile::tempfile;

    use arrow::array::*;
    use arrow::buffer::Buffer;
    use arrow::datatypes::{DataType as ArrowDataType, Field, Schema};
    use arrow::error::Result as ArrowResult;
    use arrow::record_batch::{RecordBatch, RecordBatchReader};

    use crate::arrow::arrow_reader::{
        ArrowPredicateFn, ArrowReaderOptions, ParquetRecordBatchReader,
        ParquetRecordBatchReaderBuilder, RowFilter, RowSelection, RowSelector,
    };
    use crate::arrow::buffer::converter::{
        Converter, FixedSizeArrayConverter, IntervalDayTimeArrayConverter,
    };
    use crate::arrow::schema::add_encoded_arrow_schema_to_metadata;
    use crate::arrow::{ArrowWriter, ProjectionMask};
    use crate::basic::{ConvertedType, Encoding, Repetition, Type as PhysicalType};
    use crate::data_type::{
        BoolType, ByteArray, ByteArrayType, DataType, FixedLenByteArray,
        FixedLenByteArrayType, Int32Type, Int64Type,
    };
    use crate::errors::Result;
    use crate::file::properties::{EnabledStatistics, WriterProperties, WriterVersion};
    use crate::file::writer::SerializedFileWriter;
    use crate::schema::parser::parse_message_type;
    use crate::schema::types::{Type, TypePtr};
    use crate::util::test_common::rand_gen::RandGen;

    #[test]
    fn test_arrow_reader_all_columns() {
        let file = get_test_file("parquet/generated_simple_numerics/blogs.parquet");

        let builder = ParquetRecordBatchReaderBuilder::try_new(file).unwrap();
        let original_schema = Arc::clone(builder.schema());
        let reader = builder.build().unwrap();

        // Verify that the schema was correctly parsed
        assert_eq!(original_schema.fields(), reader.schema().fields());
    }

    #[test]
    fn test_arrow_reader_single_column() {
        let file = get_test_file("parquet/generated_simple_numerics/blogs.parquet");

        let builder = ParquetRecordBatchReaderBuilder::try_new(file).unwrap();
        let original_schema = Arc::clone(builder.schema());

        let mask = ProjectionMask::leaves(builder.parquet_schema(), [2]);
        let reader = builder.with_projection(mask).build().unwrap();

        // Verify that the schema was correctly parsed
        assert_eq!(1, reader.schema().fields().len());
        assert_eq!(original_schema.fields()[1], reader.schema().fields()[0]);
    }

    #[test]
    fn test_null_column_reader_test() {
        let mut file = tempfile::tempfile().unwrap();

        let schema = "
            message message {
                OPTIONAL INT32 int32;
            }
        ";
        let schema = Arc::new(parse_message_type(schema).unwrap());

        let def_levels = vec![vec![0, 0, 0], vec![0, 0, 0, 0]];
        generate_single_column_file_with_data::<Int32Type>(
            &[vec![], vec![]],
            Some(&def_levels),
            file.try_clone().unwrap(), // Cannot use &mut File (#1163)
            schema,
            Some(Field::new("int32", ArrowDataType::Null, true)),
            &Default::default(),
        )
        .unwrap();

        file.rewind().unwrap();

        let record_reader = ParquetRecordBatchReader::try_new(file, 2).unwrap();
        let batches = record_reader.collect::<ArrowResult<Vec<_>>>().unwrap();

        assert_eq!(batches.len(), 4);
        for batch in &batches[0..3] {
            assert_eq!(batch.num_rows(), 2);
            assert_eq!(batch.num_columns(), 1);
            assert_eq!(batch.column(0).null_count(), 2);
        }

        assert_eq!(batches[3].num_rows(), 1);
        assert_eq!(batches[3].num_columns(), 1);
        assert_eq!(batches[3].column(0).null_count(), 1);
    }

    #[test]
    fn test_primitive_single_column_reader_test() {
        run_single_column_reader_tests::<BoolType, _, BoolType>(
            2,
            ConvertedType::NONE,
            None,
            |vals| Arc::new(BooleanArray::from_iter(vals.iter().cloned())),
            &[Encoding::PLAIN, Encoding::RLE, Encoding::RLE_DICTIONARY],
        );
        run_single_column_reader_tests::<Int32Type, _, Int32Type>(
            2,
            ConvertedType::NONE,
            None,
            |vals| Arc::new(Int32Array::from_iter(vals.iter().cloned())),
            &[
                Encoding::PLAIN,
                Encoding::RLE_DICTIONARY,
                Encoding::DELTA_BINARY_PACKED,
            ],
        );
        run_single_column_reader_tests::<Int64Type, _, Int64Type>(
            2,
            ConvertedType::NONE,
            None,
            |vals| Arc::new(Int64Array::from_iter(vals.iter().cloned())),
            &[
                Encoding::PLAIN,
                Encoding::RLE_DICTIONARY,
                Encoding::DELTA_BINARY_PACKED,
            ],
        );
    }

    struct RandFixedLenGen {}

    impl RandGen<FixedLenByteArrayType> for RandFixedLenGen {
        fn gen(len: i32) -> FixedLenByteArray {
            let mut v = vec![0u8; len as usize];
            rand::thread_rng().fill_bytes(&mut v);
            ByteArray::from(v).into()
        }
    }

    #[test]
    fn test_fixed_length_binary_column_reader() {
        let converter = FixedSizeArrayConverter::new(20);
        run_single_column_reader_tests::<FixedLenByteArrayType, _, RandFixedLenGen>(
            20,
            ConvertedType::NONE,
            None,
            |vals| Arc::new(converter.convert(vals.to_vec()).unwrap()),
            &[Encoding::PLAIN, Encoding::RLE_DICTIONARY],
        );
    }

    #[test]
    fn test_interval_day_time_column_reader() {
        let converter = IntervalDayTimeArrayConverter {};
        run_single_column_reader_tests::<FixedLenByteArrayType, _, RandFixedLenGen>(
            12,
            ConvertedType::INTERVAL,
            None,
            |vals| Arc::new(converter.convert(vals.to_vec()).unwrap()),
            &[Encoding::PLAIN, Encoding::RLE_DICTIONARY],
        );
    }

    struct RandUtf8Gen {}

    impl RandGen<ByteArrayType> for RandUtf8Gen {
        fn gen(len: i32) -> ByteArray {
            Int32Type::gen(len).to_string().as_str().into()
        }
    }

    #[test]
    fn test_utf8_single_column_reader_test() {
        fn string_converter<O: OffsetSizeTrait>(vals: &[Option<ByteArray>]) -> ArrayRef {
            Arc::new(GenericStringArray::<O>::from_iter(vals.iter().map(|x| {
                x.as_ref().map(|b| std::str::from_utf8(b.data()).unwrap())
            })))
        }

        let encodings = &[
            Encoding::PLAIN,
            Encoding::RLE_DICTIONARY,
            Encoding::DELTA_LENGTH_BYTE_ARRAY,
            Encoding::DELTA_BYTE_ARRAY,
        ];

        run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
            2,
            ConvertedType::NONE,
            None,
            |vals| {
                Arc::new(BinaryArray::from_iter(
                    vals.iter().map(|x| x.as_ref().map(|x| x.data())),
                ))
            },
            encodings,
        );

        run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
            2,
            ConvertedType::UTF8,
            None,
            string_converter::<i32>,
            encodings,
        );

        run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
            2,
            ConvertedType::UTF8,
            Some(ArrowDataType::Utf8),
            string_converter::<i32>,
            encodings,
        );

        run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
            2,
            ConvertedType::UTF8,
            Some(ArrowDataType::LargeUtf8),
            string_converter::<i64>,
            encodings,
        );

        let small_key_types = [ArrowDataType::Int8, ArrowDataType::UInt8];
        for key in &small_key_types {
            for encoding in encodings {
                let mut opts = TestOptions::new(2, 20, 15).with_null_percent(50);
                opts.encoding = *encoding;

                let data_type = ArrowDataType::Dictionary(
                    Box::new(key.clone()),
                    Box::new(ArrowDataType::Utf8),
                );

                // Cannot run full test suite as keys overflow, run small test instead
                single_column_reader_test::<ByteArrayType, _, RandUtf8Gen>(
                    opts,
                    2,
                    ConvertedType::UTF8,
                    Some(data_type.clone()),
                    move |vals| {
                        let vals = string_converter::<i32>(vals);
                        arrow::compute::cast(&vals, &data_type).unwrap()
                    },
                );
            }
        }

        let key_types = [
            ArrowDataType::Int16,
            ArrowDataType::UInt16,
            ArrowDataType::Int32,
            ArrowDataType::UInt32,
            ArrowDataType::Int64,
            ArrowDataType::UInt64,
        ];

        for key in &key_types {
            let data_type = ArrowDataType::Dictionary(
                Box::new(key.clone()),
                Box::new(ArrowDataType::Utf8),
            );

            run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
                2,
                ConvertedType::UTF8,
                Some(data_type.clone()),
                move |vals| {
                    let vals = string_converter::<i32>(vals);
                    arrow::compute::cast(&vals, &data_type).unwrap()
                },
                encodings,
            );

            // https://github.com/apache/arrow-rs/issues/1179
            // let data_type = ArrowDataType::Dictionary(
            //     Box::new(key.clone()),
            //     Box::new(ArrowDataType::LargeUtf8),
            // );
            //
            // run_single_column_reader_tests::<ByteArrayType, _, RandUtf8Gen>(
            //     2,
            //     ConvertedType::UTF8,
            //     Some(data_type.clone()),
            //     move |vals| {
            //         let vals = string_converter::<i64>(vals);
            //         arrow::compute::cast(&vals, &data_type).unwrap()
            //     },
            //     encodings,
            // );
        }
    }

    #[test]
    fn test_decimal_nullable_struct() {
        let decimals = Decimal128Array::from_iter_values([1, 2, 3, 4, 5, 6, 7, 8]);

        let data = ArrayDataBuilder::new(ArrowDataType::Struct(vec![Field::new(
            "decimals",
            decimals.data_type().clone(),
            false,
        )]))
        .len(8)
        .null_bit_buffer(Some(Buffer::from(&[0b11101111])))
        .child_data(vec![decimals.into_data()])
        .build()
        .unwrap();

        let written = RecordBatch::try_from_iter([(
            "struct",
            Arc::new(StructArray::from(data)) as ArrayRef,
        )])
        .unwrap();

        let mut buffer = Vec::with_capacity(1024);
        let mut writer =
            ArrowWriter::try_new(&mut buffer, written.schema(), None).unwrap();
        writer.write(&written).unwrap();
        writer.close().unwrap();

        let read = ParquetRecordBatchReader::try_new(Bytes::from(buffer), 3)
            .unwrap()
            .collect::<ArrowResult<Vec<_>>>()
            .unwrap();

        assert_eq!(&written.slice(0, 3), &read[0]);
        assert_eq!(&written.slice(3, 3), &read[1]);
        assert_eq!(&written.slice(6, 2), &read[2]);
    }

    #[test]
    fn test_int32_nullable_struct() {
        let int32 = Int32Array::from_iter_values([1, 2, 3, 4, 5, 6, 7, 8]);
        let data = ArrayDataBuilder::new(ArrowDataType::Struct(vec![Field::new(
            "int32",
            int32.data_type().clone(),
            false,
        )]))
        .len(8)
        .null_bit_buffer(Some(Buffer::from(&[0b11101111])))
        .child_data(vec![int32.into_data()])
        .build()
        .unwrap();

        let written = RecordBatch::try_from_iter([(
            "struct",
            Arc::new(StructArray::from(data)) as ArrayRef,
        )])
        .unwrap();

        let mut buffer = Vec::with_capacity(1024);
        let mut writer =
            ArrowWriter::try_new(&mut buffer, written.schema(), None).unwrap();
        writer.write(&written).unwrap();
        writer.close().unwrap();

        let read = ParquetRecordBatchReader::try_new(Bytes::from(buffer), 3)
            .unwrap()
            .collect::<ArrowResult<Vec<_>>>()
            .unwrap();

        assert_eq!(&written.slice(0, 3), &read[0]);
        assert_eq!(&written.slice(3, 3), &read[1]);
        assert_eq!(&written.slice(6, 2), &read[2]);
    }

    #[test]
    #[ignore] // https://github.com/apache/arrow-rs/issues/2253
    fn test_decimal_list() {
        let decimals = Decimal128Array::from_iter_values([1, 2, 3, 4, 5, 6, 7, 8]);

        // [[], [1], [2, 3], null, [4], null, [6, 7, 8]]
        let data = ArrayDataBuilder::new(ArrowDataType::List(Box::new(Field::new(
            "item",
            decimals.data_type().clone(),
            false,
        ))))
        .len(7)
        .add_buffer(Buffer::from_iter([0_i32, 0, 1, 3, 3, 4, 5, 8]))
        .null_bit_buffer(Some(Buffer::from(&[0b01010111])))
        .child_data(vec![decimals.into_data()])
        .build()
        .unwrap();

        let written = RecordBatch::try_from_iter([(
            "list",
            Arc::new(ListArray::from(data)) as ArrayRef,
        )])
        .unwrap();

        let mut buffer = Vec::with_capacity(1024);
        let mut writer =
            ArrowWriter::try_new(&mut buffer, written.schema(), None).unwrap();
        writer.write(&written).unwrap();
        writer.close().unwrap();

        let read = ParquetRecordBatchReader::try_new(Bytes::from(buffer), 3)
            .unwrap()
            .collect::<ArrowResult<Vec<_>>>()
            .unwrap();

        assert_eq!(&written.slice(0, 3), &read[0]);
        assert_eq!(&written.slice(3, 3), &read[1]);
        assert_eq!(&written.slice(6, 1), &read[2]);
    }

    #[test]
    fn test_read_decimal_file() {
        use arrow::array::Decimal128Array;
        let testdata = arrow::util::test_util::parquet_test_data();
        let file_variants = vec![
            ("byte_array", 4),
            ("fixed_length", 25),
            ("int32", 4),
            ("int64", 10),
        ];
        for (prefix, target_precision) in file_variants {
            let path = format!("{}/{}_decimal.parquet", testdata, prefix);
            let file = File::open(&path).unwrap();
            let mut record_reader = ParquetRecordBatchReader::try_new(file, 32).unwrap();

            let batch = record_reader.next().unwrap().unwrap();
            assert_eq!(batch.num_rows(), 24);
            let col = batch
                .column(0)
                .as_any()
                .downcast_ref::<Decimal128Array>()
                .unwrap();

            let expected = 1..25;

            assert_eq!(col.precision(), target_precision);
            assert_eq!(col.scale(), 2);

            for (i, v) in expected.enumerate() {
                assert_eq!(col.value(i).as_i128(), v * 100_i128);
            }
        }
    }

    /// Parameters for single_column_reader_test
    #[derive(Clone)]
    struct TestOptions {
        /// Number of row group to write to parquet (row group size =
        /// num_row_groups / num_rows)
        num_row_groups: usize,
        /// Total number of rows per row group
        num_rows: usize,
        /// Size of batches to read back
        record_batch_size: usize,
        /// Percentage of nulls in column or None if required
        null_percent: Option<usize>,
        /// Set write batch size
        ///
        /// This is the number of rows that are written at once to a page and
        /// therefore acts as a bound on the page granularity of a row group
        write_batch_size: usize,
        /// Maximum size of page in bytes
        max_data_page_size: usize,
        /// Maximum size of dictionary page in bytes
        max_dict_page_size: usize,
        /// Writer version
        writer_version: WriterVersion,
        /// Enabled statistics
        enabled_statistics: EnabledStatistics,
        /// Encoding
        encoding: Encoding,
        /// row selections and total selected row count
        row_selections: Option<(RowSelection, usize)>,
        /// row filter
        row_filter: Option<Vec<bool>>,
    }

    /// Manually implement this to avoid printing entire contents of row_selections and row_filter
    impl std::fmt::Debug for TestOptions {
        fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
            f.debug_struct("TestOptions")
                .field("num_row_groups", &self.num_row_groups)
                .field("num_rows", &self.num_rows)
                .field("record_batch_size", &self.record_batch_size)
                .field("null_percent", &self.null_percent)
                .field("write_batch_size", &self.write_batch_size)
                .field("max_data_page_size", &self.max_data_page_size)
                .field("max_dict_page_size", &self.max_dict_page_size)
                .field("writer_version", &self.writer_version)
                .field("enabled_statistics", &self.enabled_statistics)
                .field("encoding", &self.encoding)
                .field("row_selections", &self.row_selections.is_some())
                .field("row_filter", &self.row_filter.is_some())
                .finish()
        }
    }

    impl Default for TestOptions {
        fn default() -> Self {
            Self {
                num_row_groups: 2,
                num_rows: 100,
                record_batch_size: 15,
                null_percent: None,
                write_batch_size: 64,
                max_data_page_size: 1024 * 1024,
                max_dict_page_size: 1024 * 1024,
                writer_version: WriterVersion::PARQUET_1_0,
                enabled_statistics: EnabledStatistics::Page,
                encoding: Encoding::PLAIN,
                row_selections: None,
                row_filter: None,
            }
        }
    }

    impl TestOptions {
        fn new(num_row_groups: usize, num_rows: usize, record_batch_size: usize) -> Self {
            Self {
                num_row_groups,
                num_rows,
                record_batch_size,
                ..Default::default()
            }
        }

        fn with_null_percent(self, null_percent: usize) -> Self {
            Self {
                null_percent: Some(null_percent),
                ..self
            }
        }

        fn with_max_data_page_size(self, max_data_page_size: usize) -> Self {
            Self {
                max_data_page_size,
                ..self
            }
        }

        fn with_max_dict_page_size(self, max_dict_page_size: usize) -> Self {
            Self {
                max_dict_page_size,
                ..self
            }
        }

        fn with_enabled_statistics(self, enabled_statistics: EnabledStatistics) -> Self {
            Self {
                enabled_statistics,
                ..self
            }
        }

        fn with_row_selections(self) -> Self {
            assert!(self.row_filter.is_none(), "Must set row selection first");

            let mut rng = thread_rng();
            let step = rng.gen_range(self.record_batch_size..self.num_rows);
            let row_selections = create_test_selection(
                step,
                self.num_row_groups * self.num_rows,
                rng.gen::<bool>(),
            );
            Self {
                row_selections: Some(row_selections),
                ..self
            }
        }

        fn with_row_filter(self) -> Self {
            let row_count = match &self.row_selections {
                Some((_, count)) => *count,
                None => self.num_row_groups * self.num_rows,
            };

            let mut rng = thread_rng();
            Self {
                row_filter: Some((0..row_count).map(|_| rng.gen_bool(0.9)).collect()),
                ..self
            }
        }

        fn writer_props(&self) -> WriterProperties {
            let builder = WriterProperties::builder()
                .set_data_pagesize_limit(self.max_data_page_size)
                .set_write_batch_size(self.write_batch_size)
                .set_writer_version(self.writer_version)
                .set_statistics_enabled(self.enabled_statistics);

            let builder = match self.encoding {
                Encoding::RLE_DICTIONARY | Encoding::PLAIN_DICTIONARY => builder
                    .set_dictionary_enabled(true)
                    .set_dictionary_pagesize_limit(self.max_dict_page_size),
                _ => builder
                    .set_dictionary_enabled(false)
                    .set_encoding(self.encoding),
            };

            builder.build()
        }
    }

    /// Create a parquet file and then read it using
    /// `ParquetFileArrowReader` using a standard set of parameters
    /// `opts`.
    ///
    /// `rand_max` represents the maximum size of value to pass to to
    /// value generator
    fn run_single_column_reader_tests<T, F, G>(
        rand_max: i32,
        converted_type: ConvertedType,
        arrow_type: Option<ArrowDataType>,
        converter: F,
        encodings: &[Encoding],
    ) where
        T: DataType,
        G: RandGen<T>,
        F: Fn(&[Option<T::T>]) -> ArrayRef,
    {
        let all_options = vec![
            // choose record_batch_batch (15) so batches cross row
            // group boundaries (50 rows in 2 row groups) cases.
            TestOptions::new(2, 100, 15),
            // choose record_batch_batch (5) so batches sometime fall
            // on row group boundaries and (25 rows in 3 row groups
            // --> row groups of 10, 10, and 5). Tests buffer
            // refilling edge cases.
            TestOptions::new(3, 25, 5),
            // Choose record_batch_size (25) so all batches fall
            // exactly on row group boundary (25). Tests buffer
            // refilling edge cases.
            TestOptions::new(4, 100, 25),
            // Set maximum page size so row groups have multiple pages
            TestOptions::new(3, 256, 73).with_max_data_page_size(128),
            // Set small dictionary page size to test dictionary fallback
            TestOptions::new(3, 256, 57).with_max_dict_page_size(128),
            // Test optional but with no nulls
            TestOptions::new(2, 256, 127).with_null_percent(0),
            // Test optional with nulls
            TestOptions::new(2, 256, 93).with_null_percent(25),
            // Test with no page-level statistics
            TestOptions::new(2, 256, 91)
                .with_null_percent(25)
                .with_enabled_statistics(EnabledStatistics::Chunk),
            // Test with no statistics
            TestOptions::new(2, 256, 91)
                .with_null_percent(25)
                .with_enabled_statistics(EnabledStatistics::None),
            // Test skip

            // choose record_batch_batch (15) so batches cross row
            // group boundaries (50 rows in 2 row groups) cases.
            TestOptions::new(2, 100, 15).with_row_selections(),
            // choose record_batch_batch (5) so batches sometime fall
            // on row group boundaries and (25 rows in 3 row groups
            // --> row groups of 10, 10, and 5). Tests buffer
            // refilling edge cases.
            TestOptions::new(3, 25, 5).with_row_selections(),
            // Choose record_batch_size (25) so all batches fall
            // exactly on row group boundary (25). Tests buffer
            // refilling edge cases.
            TestOptions::new(4, 100, 25).with_row_selections(),
            // Set maximum page size so row groups have multiple pages
            TestOptions::new(3, 256, 73)
                .with_max_data_page_size(128)
                .with_row_selections(),
            // Set small dictionary page size to test dictionary fallback
            TestOptions::new(3, 256, 57)
                .with_max_dict_page_size(128)
                .with_row_selections(),
            // Test optional but with no nulls
            TestOptions::new(2, 256, 127)
                .with_null_percent(0)
                .with_row_selections(),
            // Test optional with nulls
            TestOptions::new(2, 256, 93)
                .with_null_percent(25)
                .with_row_selections(),
            // Test filter

            // Test with row filter
            TestOptions::new(4, 100, 25).with_row_filter(),
            // Test with row selection and row filter
            TestOptions::new(4, 100, 25)
                .with_row_selections()
                .with_row_filter(),
            // Test with nulls and row filter
            TestOptions::new(2, 256, 93)
                .with_null_percent(25)
                .with_row_filter(),
            // Test with nulls and row filter
            TestOptions::new(2, 256, 93)
                .with_null_percent(25)
                .with_row_selections()
                .with_row_filter(),
        ];

        all_options.into_iter().for_each(|opts| {
            for writer_version in [WriterVersion::PARQUET_1_0, WriterVersion::PARQUET_2_0]
            {
                for encoding in encodings {
                    let opts = TestOptions {
                        writer_version,
                        encoding: *encoding,
                        ..opts.clone()
                    };

                    single_column_reader_test::<T, _, G>(
                        opts,
                        rand_max,
                        converted_type,
                        arrow_type.clone(),
                        &converter,
                    )
                }
            }
        });
    }

    /// Create a parquet file and then read it using
    /// `ParquetFileArrowReader` using the parameters described in
    /// `opts`.
    fn single_column_reader_test<T, F, G>(
        opts: TestOptions,
        rand_max: i32,
        converted_type: ConvertedType,
        arrow_type: Option<ArrowDataType>,
        converter: F,
    ) where
        T: DataType,
        G: RandGen<T>,
        F: Fn(&[Option<T::T>]) -> ArrayRef,
    {
        // Print out options to facilitate debugging failures on CI
        println!(
            "Running type {:?} single_column_reader_test ConvertedType::{}/ArrowType::{:?} with Options: {:?}",
            T::get_physical_type(), converted_type, arrow_type, opts
        );

        //according to null_percent generate def_levels
        let (repetition, def_levels) = match opts.null_percent.as_ref() {
            Some(null_percent) => {
                let mut rng = thread_rng();

                let def_levels: Vec<Vec<i16>> = (0..opts.num_row_groups)
                    .map(|_| {
                        std::iter::from_fn(|| {
                            Some((rng.next_u32() as usize % 100 >= *null_percent) as i16)
                        })
                        .take(opts.num_rows)
                        .collect()
                    })
                    .collect();
                (Repetition::OPTIONAL, Some(def_levels))
            }
            None => (Repetition::REQUIRED, None),
        };

        //generate random table data
        let values: Vec<Vec<T::T>> = (0..opts.num_row_groups)
            .map(|idx| {
                let null_count = match def_levels.as_ref() {
                    Some(d) => d[idx].iter().filter(|x| **x == 0).count(),
                    None => 0,
                };
                G::gen_vec(rand_max, opts.num_rows - null_count)
            })
            .collect();

        let len = match T::get_physical_type() {
            crate::basic::Type::FIXED_LEN_BYTE_ARRAY => rand_max,
            crate::basic::Type::INT96 => 12,
            _ => -1,
        };

        let mut fields = vec![Arc::new(
            Type::primitive_type_builder("leaf", T::get_physical_type())
                .with_repetition(repetition)
                .with_converted_type(converted_type)
                .with_length(len)
                .build()
                .unwrap(),
        )];

        let schema = Arc::new(
            Type::group_type_builder("test_schema")
                .with_fields(&mut fields)
                .build()
                .unwrap(),
        );

        let arrow_field = arrow_type.map(|t| Field::new("leaf", t, false));

        let mut file = tempfile::tempfile().unwrap();

        generate_single_column_file_with_data::<T>(
            &values,
            def_levels.as_ref(),
            file.try_clone().unwrap(), // Cannot use &mut File (#1163)
            schema,
            arrow_field,
            &opts,
        )
        .unwrap();

        file.rewind().unwrap();

        // TODO: Should be able to always enable page index (#2434)
        let options = ArrowReaderOptions::new()
            .with_page_index(opts.enabled_statistics == EnabledStatistics::Page);

        let mut builder =
            ParquetRecordBatchReaderBuilder::try_new_with_options(file, options).unwrap();

        let expected_data = match opts.row_selections {
            Some((selections, row_count)) => {
                let mut without_skip_data = gen_expected_data::<T>(&def_levels, &values);

                let mut skip_data: Vec<Option<T::T>> = vec![];
                let dequeue: VecDeque<RowSelector> = selections.clone().into();
                for select in dequeue {
                    if select.skip {
                        without_skip_data.drain(0..select.row_count);
                    } else {
                        skip_data.extend(without_skip_data.drain(0..select.row_count));
                    }
                }
                builder = builder.with_row_selection(selections);

                assert_eq!(skip_data.len(), row_count);
                skip_data
            }
            None => {
                //get flatten table data
                let expected_data = gen_expected_data::<T>(&def_levels, &values);
                assert_eq!(expected_data.len(), opts.num_rows * opts.num_row_groups);
                expected_data
            }
        };

        let expected_data = match opts.row_filter {
            Some(filter) => {
                let expected_data = expected_data
                    .into_iter()
                    .zip(filter.iter())
                    .filter_map(|(d, f)| f.then(|| d))
                    .collect();

                let mut filter_offset = 0;
                let filter = RowFilter::new(vec![Box::new(ArrowPredicateFn::new(
                    ProjectionMask::all(),
                    move |b| {
                        let array = BooleanArray::from_iter(
                            filter
                                .iter()
                                .skip(filter_offset)
                                .take(b.num_rows())
                                .map(|x| Some(*x)),
                        );
                        filter_offset += b.num_rows();
                        Ok(array)
                    },
                ))]);

                builder = builder.with_row_filter(filter);
                expected_data
            }
            None => expected_data,
        };

        let mut record_reader = builder
            .with_batch_size(opts.record_batch_size)
            .build()
            .unwrap();

        let mut total_read = 0;
        loop {
            let maybe_batch = record_reader.next();
            if total_read < expected_data.len() {
                let end = min(total_read + opts.record_batch_size, expected_data.len());
                let batch = maybe_batch.unwrap().unwrap();
                assert_eq!(end - total_read, batch.num_rows());

                let a = converter(&expected_data[total_read..end]);
                let b = Arc::clone(batch.column(0));

                assert_eq!(a.data_type(), b.data_type());
                assert_eq!(a.data(), b.data(), "{:#?} vs {:#?}", a.data(), b.data());

                total_read = end;
            } else {
                assert!(maybe_batch.is_none());
                break;
            }
        }
    }

    fn gen_expected_data<T: DataType>(
        def_levels: &Option<Vec<Vec<i16>>>,
        values: &[Vec<T::T>],
    ) -> Vec<Option<T::T>> {
        let data: Vec<Option<T::T>> = match def_levels {
            Some(levels) => {
                let mut values_iter = values.iter().flatten();
                levels
                    .iter()
                    .flatten()
                    .map(|d| match d {
                        1 => Some(values_iter.next().cloned().unwrap()),
                        0 => None,
                        _ => unreachable!(),
                    })
                    .collect()
            }
            None => values.iter().flatten().map(|b| Some(b.clone())).collect(),
        };
        data
    }

    fn generate_single_column_file_with_data<T: DataType>(
        values: &[Vec<T::T>],
        def_levels: Option<&Vec<Vec<i16>>>,
        file: File,
        schema: TypePtr,
        field: Option<Field>,
        opts: &TestOptions,
    ) -> Result<parquet_format::FileMetaData> {
        let mut writer_props = opts.writer_props();
        if let Some(field) = field {
            let arrow_schema = Schema::new(vec![field]);
            add_encoded_arrow_schema_to_metadata(&arrow_schema, &mut writer_props);
        }

        let mut writer = SerializedFileWriter::new(file, schema, Arc::new(writer_props))?;

        for (idx, v) in values.iter().enumerate() {
            let def_levels = def_levels.map(|d| d[idx].as_slice());
            let mut row_group_writer = writer.next_row_group()?;
            {
                let mut column_writer = row_group_writer
                    .next_column()?
                    .expect("Column writer is none!");

                column_writer
                    .typed::<T>()
                    .write_batch(v, def_levels, None)?;

                column_writer.close()?;
            }
            row_group_writer.close()?;
        }

        writer.close()
    }

    fn get_test_file(file_name: &str) -> File {
        let mut path = PathBuf::new();
        path.push(arrow::util::test_util::arrow_test_data());
        path.push(file_name);

        File::open(path.as_path()).expect("File not found!")
    }

    #[test]
    fn test_read_structs() {
        // This particular test file has columns of struct types where there is
        // a column that has the same name as one of the struct fields
        // (see: ARROW-11452)
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/nested_structs.rust.parquet", testdata);
        let file = File::open(&path).unwrap();
        let record_batch_reader = ParquetRecordBatchReader::try_new(file, 60).unwrap();

        for batch in record_batch_reader {
            batch.unwrap();
        }

        let file = File::open(&path).unwrap();
        let builder = ParquetRecordBatchReaderBuilder::try_new(file).unwrap();

        let mask = ProjectionMask::leaves(builder.parquet_schema(), [3, 8, 10]);
        let projected_reader = builder
            .with_projection(mask)
            .with_batch_size(60)
            .build()
            .unwrap();

        let expected_schema = Schema::new(vec![
            Field::new(
                "roll_num",
                ArrowDataType::Struct(vec![Field::new(
                    "count",
                    ArrowDataType::UInt64,
                    false,
                )]),
                false,
            ),
            Field::new(
                "PC_CUR",
                ArrowDataType::Struct(vec![
                    Field::new("mean", ArrowDataType::Int64, false),
                    Field::new("sum", ArrowDataType::Int64, false),
                ]),
                false,
            ),
        ]);

        // Tests for #1652 and #1654
        assert_eq!(&expected_schema, projected_reader.schema().as_ref());

        for batch in projected_reader {
            let batch = batch.unwrap();
            assert_eq!(batch.schema().as_ref(), &expected_schema);
        }
    }

    #[test]
    fn test_read_maps() {
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/nested_maps.snappy.parquet", testdata);
        let file = File::open(&path).unwrap();
        let record_batch_reader = ParquetRecordBatchReader::try_new(file, 60).unwrap();

        for batch in record_batch_reader {
            batch.unwrap();
        }
    }

    #[test]
    fn test_nested_nullability() {
        let message_type = "message nested {
          OPTIONAL Group group {
            REQUIRED INT32 leaf;
          }
        }";

        let file = tempfile::tempfile().unwrap();
        let schema = Arc::new(parse_message_type(message_type).unwrap());

        {
            // Write using low-level parquet API (#1167)
            let writer_props = Arc::new(WriterProperties::builder().build());
            let mut writer = SerializedFileWriter::new(
                file.try_clone().unwrap(),
                schema,
                writer_props,
            )
            .unwrap();

            {
                let mut row_group_writer = writer.next_row_group().unwrap();
                let mut column_writer = row_group_writer.next_column().unwrap().unwrap();

                column_writer
                    .typed::<Int32Type>()
                    .write_batch(&[34, 76], Some(&[0, 1, 0, 1]), None)
                    .unwrap();

                column_writer.close().unwrap();
                row_group_writer.close().unwrap();
            }

            writer.close().unwrap();
        }

        let builder = ParquetRecordBatchReaderBuilder::try_new(file).unwrap();
        let mask = ProjectionMask::leaves(builder.parquet_schema(), [0]);

        let reader = builder.with_projection(mask).build().unwrap();

        let expected_schema = Schema::new(vec![Field::new(
            "group",
            ArrowDataType::Struct(vec![Field::new("leaf", ArrowDataType::Int32, false)]),
            true,
        )]);

        let batch = reader.into_iter().next().unwrap().unwrap();
        assert_eq!(batch.schema().as_ref(), &expected_schema);
        assert_eq!(batch.num_rows(), 4);
        assert_eq!(batch.column(0).data().null_count(), 2);
    }

    #[test]
    fn test_invalid_utf8() {
        // a parquet file with 1 column with invalid utf8
        let data = vec![
            80, 65, 82, 49, 21, 6, 21, 22, 21, 22, 92, 21, 2, 21, 0, 21, 2, 21, 0, 21, 4,
            21, 0, 18, 28, 54, 0, 40, 5, 104, 101, 255, 108, 111, 24, 5, 104, 101, 255,
            108, 111, 0, 0, 0, 3, 1, 5, 0, 0, 0, 104, 101, 255, 108, 111, 38, 110, 28,
            21, 12, 25, 37, 6, 0, 25, 24, 2, 99, 49, 21, 0, 22, 2, 22, 102, 22, 102, 38,
            8, 60, 54, 0, 40, 5, 104, 101, 255, 108, 111, 24, 5, 104, 101, 255, 108, 111,
            0, 0, 0, 21, 4, 25, 44, 72, 4, 114, 111, 111, 116, 21, 2, 0, 21, 12, 37, 2,
            24, 2, 99, 49, 37, 0, 76, 28, 0, 0, 0, 22, 2, 25, 28, 25, 28, 38, 110, 28,
            21, 12, 25, 37, 6, 0, 25, 24, 2, 99, 49, 21, 0, 22, 2, 22, 102, 22, 102, 38,
            8, 60, 54, 0, 40, 5, 104, 101, 255, 108, 111, 24, 5, 104, 101, 255, 108, 111,
            0, 0, 0, 22, 102, 22, 2, 0, 40, 44, 65, 114, 114, 111, 119, 50, 32, 45, 32,
            78, 97, 116, 105, 118, 101, 32, 82, 117, 115, 116, 32, 105, 109, 112, 108,
            101, 109, 101, 110, 116, 97, 116, 105, 111, 110, 32, 111, 102, 32, 65, 114,
            114, 111, 119, 0, 130, 0, 0, 0, 80, 65, 82, 49,
        ];

        let file = Bytes::from(data);
        let mut record_batch_reader =
            ParquetRecordBatchReader::try_new(file, 10).unwrap();

        let error = record_batch_reader.next().unwrap().unwrap_err();

        assert!(
            error.to_string().contains("invalid utf-8 sequence"),
            "{}",
            error
        );
    }

    #[test]
    fn test_dictionary_preservation() {
        let mut fields = vec![Arc::new(
            Type::primitive_type_builder("leaf", PhysicalType::BYTE_ARRAY)
                .with_repetition(Repetition::OPTIONAL)
                .with_converted_type(ConvertedType::UTF8)
                .build()
                .unwrap(),
        )];

        let schema = Arc::new(
            Type::group_type_builder("test_schema")
                .with_fields(&mut fields)
                .build()
                .unwrap(),
        );

        let dict_type = ArrowDataType::Dictionary(
            Box::new(ArrowDataType::Int32),
            Box::new(ArrowDataType::Utf8),
        );

        let arrow_field = Field::new("leaf", dict_type, true);

        let mut file = tempfile::tempfile().unwrap();

        let values = vec![
            vec![
                ByteArray::from("hello"),
                ByteArray::from("a"),
                ByteArray::from("b"),
                ByteArray::from("d"),
            ],
            vec![
                ByteArray::from("c"),
                ByteArray::from("a"),
                ByteArray::from("b"),
            ],
        ];

        let def_levels = vec![
            vec![1, 0, 0, 1, 0, 0, 1, 1],
            vec![0, 0, 1, 1, 0, 0, 1, 0, 0],
        ];

        let opts = TestOptions {
            encoding: Encoding::RLE_DICTIONARY,
            ..Default::default()
        };

        generate_single_column_file_with_data::<ByteArrayType>(
            &values,
            Some(&def_levels),
            file.try_clone().unwrap(), // Cannot use &mut File (#1163)
            schema,
            Some(arrow_field),
            &opts,
        )
        .unwrap();

        file.rewind().unwrap();

        let record_reader = ParquetRecordBatchReader::try_new(file, 3).unwrap();

        let batches = record_reader
            .collect::<ArrowResult<Vec<RecordBatch>>>()
            .unwrap();

        assert_eq!(batches.len(), 6);
        assert!(batches.iter().all(|x| x.num_columns() == 1));

        let row_counts = batches
            .iter()
            .map(|x| (x.num_rows(), x.column(0).null_count()))
            .collect::<Vec<_>>();

        assert_eq!(
            row_counts,
            vec![(3, 2), (3, 2), (3, 1), (3, 1), (3, 2), (2, 2)]
        );

        let get_dict =
            |batch: &RecordBatch| batch.column(0).data().child_data()[0].clone();

        // First and second batch in same row group -> same dictionary
        assert_eq!(get_dict(&batches[0]), get_dict(&batches[1]));
        // Third batch spans row group -> computed dictionary
        assert_ne!(get_dict(&batches[1]), get_dict(&batches[2]));
        assert_ne!(get_dict(&batches[2]), get_dict(&batches[3]));
        // Fourth, fifth and sixth from same row group -> same dictionary
        assert_eq!(get_dict(&batches[3]), get_dict(&batches[4]));
        assert_eq!(get_dict(&batches[4]), get_dict(&batches[5]));
    }

    #[test]
    fn test_read_null_list() {
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/null_list.parquet", testdata);
        let file = File::open(&path).unwrap();
        let mut record_batch_reader =
            ParquetRecordBatchReader::try_new(file, 60).unwrap();

        let batch = record_batch_reader.next().unwrap().unwrap();
        assert_eq!(batch.num_rows(), 1);
        assert_eq!(batch.num_columns(), 1);
        assert_eq!(batch.column(0).len(), 1);

        let list = batch
            .column(0)
            .as_any()
            .downcast_ref::<ListArray>()
            .unwrap();
        assert_eq!(list.len(), 1);
        assert!(list.is_valid(0));

        let val = list.value(0);
        assert_eq!(val.len(), 0);
    }

    #[test]
    fn test_null_schema_inference() {
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/null_list.parquet", testdata);
        let file = File::open(&path).unwrap();

        let arrow_field = Field::new(
            "emptylist",
            ArrowDataType::List(Box::new(Field::new("item", ArrowDataType::Null, true))),
            true,
        );

        let options = ArrowReaderOptions::new().with_skip_arrow_metadata(true);
        let builder =
            ParquetRecordBatchReaderBuilder::try_new_with_options(file, options).unwrap();
        let schema = builder.schema();
        assert_eq!(schema.fields().len(), 1);
        assert_eq!(schema.field(0), &arrow_field);
    }

    #[test]
    fn test_skip_metadata() {
        let col = Arc::new(TimestampNanosecondArray::from_iter_values(vec![0, 1, 2]));
        let field = Field::new("col", col.data_type().clone(), true);

        let schema_without_metadata = Arc::new(Schema::new(vec![field.clone()]));

        let metadata = [("key".to_string(), "value".to_string())]
            .into_iter()
            .collect();

        let schema_with_metadata =
            Arc::new(Schema::new(vec![field.with_metadata(Some(metadata))]));

        assert_ne!(schema_with_metadata, schema_without_metadata);

        let batch =
            RecordBatch::try_new(schema_with_metadata.clone(), vec![col as ArrayRef])
                .unwrap();

        let file = |version: WriterVersion| {
            let props = WriterProperties::builder()
                .set_writer_version(version)
                .build();

            let file = tempfile().unwrap();
            let mut writer = ArrowWriter::try_new(
                file.try_clone().unwrap(),
                batch.schema(),
                Some(props),
            )
            .unwrap();
            writer.write(&batch).unwrap();
            writer.close().unwrap();
            file
        };

        let skip_options = ArrowReaderOptions::new().with_skip_arrow_metadata(true);

        let v1_reader = file(WriterVersion::PARQUET_1_0);
        let v2_reader = file(WriterVersion::PARQUET_2_0);

        let arrow_reader =
            ParquetRecordBatchReader::try_new(v1_reader.try_clone().unwrap(), 1024)
                .unwrap();
        assert_eq!(arrow_reader.schema(), schema_with_metadata);

        let reader = ParquetRecordBatchReaderBuilder::try_new_with_options(
            v1_reader,
            skip_options.clone(),
        )
        .unwrap()
        .build()
        .unwrap();
        assert_eq!(reader.schema(), schema_without_metadata);

        let arrow_reader =
            ParquetRecordBatchReader::try_new(v2_reader.try_clone().unwrap(), 1024)
                .unwrap();
        assert_eq!(arrow_reader.schema(), schema_with_metadata);

        let reader = ParquetRecordBatchReaderBuilder::try_new_with_options(
            v2_reader,
            skip_options,
        )
        .unwrap()
        .build()
        .unwrap();
        assert_eq!(reader.schema(), schema_without_metadata);
    }

    #[test]
    fn test_empty_projection() {
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/alltypes_plain.parquet", testdata);
        let file = File::open(&path).unwrap();

        let builder = ParquetRecordBatchReaderBuilder::try_new(file).unwrap();
        let file_metadata = builder.metadata().file_metadata();
        let expected_rows = file_metadata.num_rows() as usize;

        let mask = ProjectionMask::leaves(builder.parquet_schema(), []);
        let batch_reader = builder
            .with_projection(mask)
            .with_batch_size(2)
            .build()
            .unwrap();

        let mut total_rows = 0;
        for maybe_batch in batch_reader {
            let batch = maybe_batch.unwrap();
            total_rows += batch.num_rows();
            assert_eq!(batch.num_columns(), 0);
            assert!(batch.num_rows() <= 2);
        }

        assert_eq!(total_rows, expected_rows);
    }

    fn test_row_group_batch(row_group_size: usize, batch_size: usize) {
        let schema = Arc::new(Schema::new(vec![Field::new(
            "list",
            ArrowDataType::List(Box::new(Field::new("item", ArrowDataType::Int32, true))),
            true,
        )]));

        let mut buf = Vec::with_capacity(1024);

        let mut writer = ArrowWriter::try_new(
            &mut buf,
            schema.clone(),
            Some(
                WriterProperties::builder()
                    .set_max_row_group_size(row_group_size)
                    .build(),
            ),
        )
        .unwrap();
        for _ in 0..2 {
            let mut list_builder = ListBuilder::new(Int32Builder::new(batch_size));
            for _ in 0..(batch_size) {
                list_builder.append(true);
            }
            let batch = RecordBatch::try_new(
                schema.clone(),
                vec![Arc::new(list_builder.finish())],
            )
            .unwrap();
            writer.write(&batch).unwrap();
        }
        writer.close().unwrap();

        let mut record_reader =
            ParquetRecordBatchReader::try_new(Bytes::from(buf), batch_size).unwrap();
        assert_eq!(
            batch_size,
            record_reader.next().unwrap().unwrap().num_rows()
        );
        assert_eq!(
            batch_size,
            record_reader.next().unwrap().unwrap().num_rows()
        );
    }

    #[test]
    fn test_row_group_exact_multiple() {
        use crate::arrow::record_reader::MIN_BATCH_SIZE;
        test_row_group_batch(8, 8);
        test_row_group_batch(10, 8);
        test_row_group_batch(8, 10);
        test_row_group_batch(MIN_BATCH_SIZE, MIN_BATCH_SIZE);
        test_row_group_batch(MIN_BATCH_SIZE + 1, MIN_BATCH_SIZE);
        test_row_group_batch(MIN_BATCH_SIZE, MIN_BATCH_SIZE + 1);
        test_row_group_batch(MIN_BATCH_SIZE, MIN_BATCH_SIZE - 1);
        test_row_group_batch(MIN_BATCH_SIZE - 1, MIN_BATCH_SIZE);
    }

    /// Given a RecordBatch containing all the column data, return the expected batches given
    /// a `batch_size` and `selection`
    fn get_expected_batches(
        column: &RecordBatch,
        selection: &RowSelection,
        batch_size: usize,
    ) -> Vec<RecordBatch> {
        let mut expected_batches = vec![];

        let mut selection: VecDeque<_> = selection.clone().into();
        let mut row_offset = 0;
        let mut last_start = None;
        while row_offset < column.num_rows() && !selection.is_empty() {
            let mut batch_remaining = batch_size.min(column.num_rows() - row_offset);
            while batch_remaining > 0 && !selection.is_empty() {
                let (to_read, skip) = match selection.front_mut() {
                    Some(selection) if selection.row_count > batch_remaining => {
                        selection.row_count -= batch_remaining;
                        (batch_remaining, selection.skip)
                    }
                    Some(_) => {
                        let select = selection.pop_front().unwrap();
                        (select.row_count, select.skip)
                    }
                    None => break,
                };

                batch_remaining -= to_read;

                match skip {
                    true => {
                        if let Some(last_start) = last_start.take() {
                            expected_batches
                                .push(column.slice(last_start, row_offset - last_start))
                        }
                        row_offset += to_read
                    }
                    false => {
                        last_start.get_or_insert(row_offset);
                        row_offset += to_read
                    }
                }
            }
        }

        if let Some(last_start) = last_start.take() {
            expected_batches.push(column.slice(last_start, row_offset - last_start))
        }

        // Sanity check, all batches except the final should be the batch size
        for batch in &expected_batches[..expected_batches.len() - 1] {
            assert_eq!(batch.num_rows(), batch_size);
        }

        expected_batches
    }

    fn create_test_selection(
        step_len: usize,
        total_len: usize,
        skip_first: bool,
    ) -> (RowSelection, usize) {
        let mut remaining = total_len;
        let mut skip = skip_first;
        let mut vec = vec![];
        let mut selected_count = 0;
        while remaining != 0 {
            let step = if remaining > step_len {
                step_len
            } else {
                remaining
            };
            vec.push(RowSelector {
                row_count: step,
                skip,
            });
            remaining -= step;
            if !skip {
                selected_count += step;
            }
            skip = !skip;
        }
        (vec.into(), selected_count)
    }

    #[test]
    fn test_scan_row_with_selection() {
        let testdata = arrow::util::test_util::parquet_test_data();
        let path = format!("{}/alltypes_tiny_pages_plain.parquet", testdata);
        let test_file = File::open(&path).unwrap();

        let mut serial_reader =
            ParquetRecordBatchReader::try_new(File::open(path).unwrap(), 7300).unwrap();
        let data = serial_reader.next().unwrap().unwrap();

        let do_test = |batch_size: usize, selection_len: usize| {
            for skip_first in [false, true] {
                let selections =
                    create_test_selection(batch_size, data.num_rows(), skip_first).0;

                let expected = get_expected_batches(&data, &selections, batch_size);
                let skip_reader = create_skip_reader(&test_file, batch_size, selections);
                assert_eq!(
                    skip_reader.collect::<ArrowResult<Vec<_>>>().unwrap(),
                    expected,
                    "batch_size: {}, selection_len: {}, skip_first: {}",
                    batch_size,
                    selection_len,
                    skip_first
                );
            }
        };

        // total row count 7300
        // 1. test selection len more than one page row count
        do_test(1000, 1000);

        // 2. test selection len less than one page row count
        do_test(20, 20);

        // 3. test selection_len less than batch_size
        do_test(20, 5);

        // 4. test selection_len more than batch_size
        // If batch_size < selection_len
        do_test(20, 5);

        fn create_skip_reader(
            test_file: &File,
            batch_size: usize,
            selections: RowSelection,
        ) -> ParquetRecordBatchReader {
            let options = ArrowReaderOptions::new().with_page_index(true);
            let file = test_file.try_clone().unwrap();
            ParquetRecordBatchReaderBuilder::try_new_with_options(file, options)
                .unwrap()
                .with_batch_size(batch_size)
                .with_row_selection(selections)
                .build()
                .unwrap()
        }
    }

    #[test]
    fn test_batch_size_overallocate() {
        let testdata = arrow::util::test_util::parquet_test_data();
        // `alltypes_plain.parquet` only have 8 rows
        let path = format!("{}/alltypes_plain.parquet", testdata);
        let test_file = File::open(&path).unwrap();

        let builder = ParquetRecordBatchReaderBuilder::try_new(test_file).unwrap();
        let num_rows = builder.metadata.file_metadata().num_rows();
        let reader = builder
            .with_batch_size(1024)
            .with_projection(ProjectionMask::all())
            .build()
            .unwrap();
        assert_ne!(1024, num_rows);
        assert_eq!(reader.batch_size, num_rows as usize);
    }
}