mito2/sst/parquet/

writer.rs

// Copyright 2023 Greptime Team
//
// Licensed 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.

//! Parquet writer.

use std::collections::HashMap;
use std::future::Future;
use std::mem;
use std::pin::Pin;
use std::sync::Arc;
use std::sync::atomic::{AtomicUsize, Ordering};
use std::task::{Context, Poll};
use std::time::Instant;

use bytes::Bytes;
use common_telemetry::debug;
use common_time::Timestamp;
use datatypes::arrow::array::{
    ArrayRef, BinaryArray, TimestampMicrosecondArray, TimestampMillisecondArray,
    TimestampNanosecondArray, TimestampSecondArray, UInt32Array,
};
use datatypes::arrow::compute::{max, min};
use datatypes::arrow::datatypes::{DataType, SchemaRef, TimeUnit};
use datatypes::arrow::record_batch::RecordBatch;
use datatypes::extension::json::is_json2_extension_type;
use object_store::{FuturesAsyncWriter, ObjectStore};
use parquet::arrow::AsyncArrowWriter;
use parquet::basic::{Compression, Encoding, ZstdLevel};
use parquet::file::metadata::KeyValue;
use parquet::file::properties::{WriterProperties, WriterPropertiesBuilder};
use parquet::schema::types::ColumnPath;
use smallvec::smallvec;
use snafu::{OptionExt, ResultExt};
use store_api::metadata::RegionMetadataRef;
use store_api::storage::consts::{OP_TYPE_COLUMN_NAME, SEQUENCE_COLUMN_NAME};
use store_api::storage::{FileId, SequenceNumber};
use tokio::io::AsyncWrite;
use tokio_util::compat::{Compat, FuturesAsyncWriteCompatExt};

use crate::access_layer::{FilePathProvider, Metrics, SstInfoArray, TempFileCleaner};
use crate::config::{IndexBuildMode, IndexConfig};
use crate::error::{
    InvalidMetadataSnafu, InvalidRecordBatchSnafu, OpenDalSnafu, Result, UnexpectedSnafu,
    WriteParquetSnafu,
};
use crate::read::FlatSource;
use crate::sst::file::RegionFileId;
use crate::sst::index::{IndexOutput, Indexer, IndexerBuilder};
use crate::sst::parquet::flat_format::{
    FlatWriteFormat, primary_key_column_index, time_index_column_index,
};
use crate::sst::parquet::format::{PrimaryKeyArray, PrimaryKeyWriteFormat};
use crate::sst::parquet::{PARQUET_METADATA_KEY, SstInfo, WriteOptions};
use crate::sst::{
    DEFAULT_WRITE_BUFFER_SIZE, DEFAULT_WRITE_CONCURRENCY, FlatSchemaOptions, SeriesEstimator,
    maybe_wrap_schema,
};

/// Converts a flat RecordBatch for writing to parquet.
enum FlatBatchConverter {
    /// Write as-is in flat format.
    Flat(FlatWriteFormat),
    /// Convert flat batch to primary-key format by stripping tag columns.
    PrimaryKey {
        format: PrimaryKeyWriteFormat,
        num_fields: usize,
    },
}

impl FlatBatchConverter {
    fn convert_batch(&self, batch: &RecordBatch) -> Result<RecordBatch> {
        match self {
            FlatBatchConverter::Flat(f) => f.convert_batch(batch),
            FlatBatchConverter::PrimaryKey { format, num_fields } => {
                format.convert_flat_batch(batch, *num_fields)
            }
        }
    }
}

/// Result of splitting a batch at the next series boundary.
enum SeriesBoundarySplit {
    /// The whole batch belongs to the current series and stays in the current file.
    Continue(RecordBatch),
    /// The batch crosses a series boundary.
    Split {
        /// Remaining rows of the current series, appended to the current file.
        current_file_tail: Option<RecordBatch>,
        /// Rows of the following series that start the next file.
        next_file_head: RecordBatch,
    },
}

/// Returns the dictionary keys and values of the encoded `__primary_key` column.
fn encoded_primary_keys(batch: &RecordBatch) -> Result<(&UInt32Array, &BinaryArray)> {
    let column = batch.column(primary_key_column_index(batch.num_columns()));
    let primary_keys = column
        .as_any()
        .downcast_ref::<PrimaryKeyArray>()
        .with_context(|| InvalidRecordBatchSnafu {
            reason: format!(
                "expected dictionary primary key column, got {:?}",
                column.data_type()
            ),
        })?;
    let values = primary_keys
        .values()
        .as_any()
        .downcast_ref::<BinaryArray>()
        .with_context(|| InvalidRecordBatchSnafu {
            reason: format!(
                "expected binary primary key values, got {:?}",
                primary_keys.values().data_type()
            ),
        })?;
    Ok((primary_keys.keys(), values))
}

/// Splits `batch` at the first row whose encoded primary key differs from
/// `current_primary_key`, the last primary key written to the current file.
///
/// The writer finishes an oversized file at such a series boundary so that the
/// primary key ranges of output files never overlap, which allows pickers like
/// TWCS to detect overlapping files by their time and primary key ranges. A
/// series is never split in the middle: if no row starts a new series, the whole
/// batch stays in the current file even if it already exceeds the size limit.
fn split_at_next_series(
    batch: RecordBatch,
    current_primary_key: &[u8],
) -> Result<SeriesBoundarySplit> {
    let (keys, values) = encoded_primary_keys(&batch)?;
    let Some(offset) = (0..batch.num_rows())
        .find(|&row| values.value(keys.value(row) as usize) != current_primary_key)
    else {
        return Ok(SeriesBoundarySplit::Continue(batch));
    };

    let next_file_head = batch.slice(offset, batch.num_rows() - offset);
    let current_file_tail = (offset > 0).then(|| batch.slice(0, offset));
    Ok(SeriesBoundarySplit::Split {
        current_file_tail,
        next_file_head,
    })
}

/// Parquet SST writer.
pub struct ParquetWriter<'a, F: WriterFactory, I: IndexerBuilder, P: FilePathProvider> {
    /// Path provider that creates SST and index file paths according to file id.
    path_provider: P,
    writer: Option<AsyncArrowWriter<SizeAwareWriter<F::Writer>>>,
    /// Current active file id.
    current_file: FileId,
    writer_factory: F,
    /// Region metadata of the source and the target SST.
    metadata: RegionMetadataRef,
    /// Global index config.
    index_config: IndexConfig,
    /// Indexer build that can create indexer for multiple files.
    indexer_builder: I,
    /// Current active indexer.
    current_indexer: Option<Indexer>,
    bytes_written: Arc<AtomicUsize>,
    /// Cleaner to remove temp files on failure.
    file_cleaner: Option<TempFileCleaner>,
    /// Write metrics
    metrics: &'a mut Metrics,
}

pub trait WriterFactory {
    type Writer: AsyncWrite + Send + Unpin;
    fn create(&mut self, file_path: &str) -> impl Future<Output = Result<Self::Writer>>;
}

pub struct ObjectStoreWriterFactory {
    object_store: ObjectStore,
}

impl WriterFactory for ObjectStoreWriterFactory {
    type Writer = Compat<FuturesAsyncWriter>;

    async fn create(&mut self, file_path: &str) -> Result<Self::Writer> {
        self.object_store
            .writer_with(file_path)
            .chunk(DEFAULT_WRITE_BUFFER_SIZE.as_bytes() as usize)
            .concurrent(DEFAULT_WRITE_CONCURRENCY)
            .await
            .map(|v| v.into_futures_async_write().compat_write())
            .context(OpenDalSnafu)
    }
}

impl<'a, I, P> ParquetWriter<'a, ObjectStoreWriterFactory, I, P>
where
    P: FilePathProvider,
    I: IndexerBuilder,
{
    pub async fn new_with_object_store(
        object_store: ObjectStore,
        metadata: RegionMetadataRef,
        index_config: IndexConfig,
        indexer_builder: I,
        path_provider: P,
        metrics: &'a mut Metrics,
    ) -> ParquetWriter<'a, ObjectStoreWriterFactory, I, P> {
        ParquetWriter::new(
            ObjectStoreWriterFactory { object_store },
            metadata,
            index_config,
            indexer_builder,
            path_provider,
            metrics,
        )
        .await
    }

    pub(crate) fn with_file_cleaner(mut self, cleaner: TempFileCleaner) -> Self {
        self.file_cleaner = Some(cleaner);
        self
    }
}

impl<'a, F, I, P> ParquetWriter<'a, F, I, P>
where
    F: WriterFactory,
    I: IndexerBuilder,
    P: FilePathProvider,
{
    /// Creates a new parquet SST writer.
    pub async fn new(
        factory: F,
        metadata: RegionMetadataRef,
        index_config: IndexConfig,
        indexer_builder: I,
        path_provider: P,
        metrics: &'a mut Metrics,
    ) -> ParquetWriter<'a, F, I, P> {
        let init_file = FileId::random();
        let indexer = indexer_builder
            .build(RegionFileId::new(metadata.region_id, init_file), 0, None)
            .await;

        ParquetWriter {
            path_provider,
            writer: None,
            current_file: init_file,
            writer_factory: factory,
            metadata,
            index_config,
            indexer_builder,
            current_indexer: Some(indexer),
            bytes_written: Arc::new(AtomicUsize::new(0)),
            file_cleaner: None,
            metrics,
        }
    }

    /// Finishes current SST file and index file.
    async fn finish_current_file(
        &mut self,
        ssts: &mut SstInfoArray,
        stats: &mut SourceStats,
    ) -> Result<()> {
        // maybe_init_writer will re-create a new file.
        if let Some(mut current_writer) = mem::take(&mut self.writer) {
            let mut stats = mem::take(stats);
            // At least one row has been written.
            assert!(stats.num_rows > 0);

            debug!(
                "Finishing current file {}, file size: {}, num rows: {}",
                self.current_file,
                self.bytes_written.load(Ordering::Relaxed),
                stats.num_rows
            );

            // Finish indexer and writer.
            // safety: writer and index can only be both present or not.
            let mut index_output = IndexOutput::default();
            match self.index_config.build_mode {
                IndexBuildMode::Sync => {
                    index_output = self.current_indexer.as_mut().unwrap().finish().await;
                }
                IndexBuildMode::Async => {
                    debug!(
                        "Index for file {} will be built asynchronously later",
                        self.current_file
                    );
                }
            }
            current_writer.flush().await.context(WriteParquetSnafu)?;

            let parquet_metadata = current_writer.close().await.context(WriteParquetSnafu)?;
            let file_size = self.bytes_written.load(Ordering::Relaxed) as u64;

            // Safety: num rows > 0 so we must have min/max.
            let time_range = stats.time_range.unwrap();

            let max_row_group_uncompressed_size: u64 = parquet_metadata
                .row_groups()
                .iter()
                .map(|rg| {
                    rg.columns()
                        .iter()
                        .map(|c| c.uncompressed_size() as u64)
                        .sum::<u64>()
                })
                .max()
                .unwrap_or(0);
            let num_series = stats.series_estimator.finish();
            ssts.push(SstInfo {
                file_id: self.current_file,
                time_range,
                file_size,
                max_row_group_uncompressed_size,
                num_rows: stats.num_rows,
                num_row_groups: parquet_metadata.num_row_groups() as u64,
                file_metadata: Some(Arc::new(parquet_metadata)),
                index_metadata: index_output,
                num_series,
            });
            self.current_file = FileId::random();
            self.bytes_written.store(0, Ordering::Relaxed)
        };

        Ok(())
    }

    /// Iterates FlatSource and writes all RecordBatch in flat format to Parquet file.
    ///
    /// The source must yield batches globally sorted by the encoded primary key.
    /// `opts.max_file_size` is a soft limit for regions with a primary key: the
    /// current file is finished at the next series boundary after exceeding the
    /// limit (see [split_at_next_series]), so a series larger than the limit
    /// stays in one file. Regions without a primary key are split at batch
    /// boundaries.
    ///
    /// Returns the [SstInfo] if the SST is written.
    pub async fn write_all_flat(
        &mut self,
        source: FlatSource,
        override_sequence: Option<SequenceNumber>,
        opts: &WriteOptions,
    ) -> Result<SstInfoArray> {
        let mut options = FlatSchemaOptions::from_encoding(self.metadata.primary_key_encoding);

        if source.schema().fields().iter().any(is_json2_extension_type) {
            options.concretized_json_types = source
                .schema()
                .fields()
                .iter()
                .filter(|&field| is_json2_extension_type(field))
                .map(|field| (field.name().clone(), field.data_type().clone()))
                .collect::<HashMap<_, _>>();
        }

        let converter = FlatBatchConverter::Flat(
            FlatWriteFormat::new(self.metadata.clone(), &options)
                .with_override_sequence(override_sequence),
        );
        let res = self.write_all_flat_inner(source, &converter, opts).await;
        if res.is_err() {
            let file_id = self.current_file;
            if let Some(cleaner) = &self.file_cleaner {
                cleaner.clean_by_file_id(file_id).await;
            }
        }
        res
    }

    /// Iterates FlatSource and writes all RecordBatch in primary-key format to Parquet file.
    ///
    /// The source must yield batches globally sorted by the encoded primary key.
    /// See [Self::write_all_flat] for the `opts.max_file_size` semantics.
    ///
    /// Returns the [SstInfo] if the SST is written.
    pub async fn write_all_flat_as_primary_key(
        &mut self,
        source: FlatSource,
        override_sequence: Option<SequenceNumber>,
        opts: &WriteOptions,
    ) -> Result<SstInfoArray> {
        let num_fields = self.metadata.field_columns().count();
        let converter = FlatBatchConverter::PrimaryKey {
            format: PrimaryKeyWriteFormat::new(self.metadata.clone())
                .with_override_sequence(override_sequence),
            num_fields,
        };
        let res = self.write_all_flat_inner(source, &converter, opts).await;
        if res.is_err() {
            let file_id = self.current_file;
            if let Some(cleaner) = &self.file_cleaner {
                cleaner.clean_by_file_id(file_id).await;
            }
        }
        res
    }

    async fn write_all_flat_inner(
        &mut self,
        mut source: FlatSource,
        converter: &FlatBatchConverter,
        opts: &WriteOptions,
    ) -> Result<SstInfoArray> {
        let mut results = smallvec![];
        let mut stats = SourceStats::default();

        loop {
            let start = Instant::now();
            let batch = match source.next_batch().await {
                Ok(Some(batch)) => batch,
                Ok(None) => break,
                Err(e) => {
                    self.abort_current_indexer().await;
                    return Err(e);
                }
            };
            self.metrics.iter_source += start.elapsed();

            if self.metadata.primary_key.is_empty() {
                self.append_flat_batch(&batch, converter, opts, &mut stats)
                    .await?;
                if self.exceeds_max_file_size(opts) {
                    self.finish_current_file(&mut results, &mut stats).await?;
                }
            } else if self.exceeds_max_file_size(opts)
                && let Some(current_primary_key) = stats.last_primary_key.as_deref()
            {
                let series_split = match split_at_next_series(batch, current_primary_key) {
                    Ok(series_split) => series_split,
                    Err(e) => {
                        self.abort_current_indexer().await;
                        return Err(e);
                    }
                };
                match series_split {
                    SeriesBoundarySplit::Continue(batch) => {
                        self.append_flat_batch(&batch, converter, opts, &mut stats)
                            .await?;
                    }
                    SeriesBoundarySplit::Split {
                        current_file_tail,
                        next_file_head,
                    } => {
                        if let Some(tail) = current_file_tail {
                            self.append_flat_batch(&tail, converter, opts, &mut stats)
                                .await?;
                        }
                        self.finish_current_file(&mut results, &mut stats).await?;
                        self.append_flat_batch(&next_file_head, converter, opts, &mut stats)
                            .await?;
                    }
                }
            } else {
                self.append_flat_batch(&batch, converter, opts, &mut stats)
                    .await?;
            }
        }

        self.finish_current_file(&mut results, &mut stats).await?;

        // object_store.write will make sure all bytes are written or an error is raised.
        Ok(results)
    }

    /// Customizes per-column config according to schema and maybe column cardinality.
    fn customize_column_config(
        builder: WriterPropertiesBuilder,
        region_metadata: &RegionMetadataRef,
    ) -> WriterPropertiesBuilder {
        let ts_col = ColumnPath::new(vec![
            region_metadata
                .time_index_column()
                .column_schema
                .name
                .clone(),
        ]);
        let seq_col = ColumnPath::new(vec![SEQUENCE_COLUMN_NAME.to_string()]);
        let op_type_col = ColumnPath::new(vec![OP_TYPE_COLUMN_NAME.to_string()]);

        builder
            .set_column_encoding(seq_col.clone(), Encoding::DELTA_BINARY_PACKED)
            .set_column_dictionary_enabled(seq_col, false)
            .set_column_encoding(ts_col.clone(), Encoding::DELTA_BINARY_PACKED)
            .set_column_dictionary_enabled(ts_col, false)
            .set_column_compression(op_type_col, Compression::UNCOMPRESSED)
    }

    async fn append_flat_batch(
        &mut self,
        batch: &RecordBatch,
        converter: &FlatBatchConverter,
        opts: &WriteOptions,
        stats: &mut SourceStats,
    ) -> Result<()> {
        let result = async {
            let arrow_batch = converter.convert_batch(batch)?;
            let start = Instant::now();
            self.maybe_init_writer(arrow_batch.schema_ref(), opts)
                .await?
                .write(&arrow_batch)
                .await
                .context(WriteParquetSnafu)?;
            self.metrics.write_batch += start.elapsed();

            stats.update_flat(batch)?;
            if matches!(self.index_config.build_mode, IndexBuildMode::Sync) {
                let start = Instant::now();
                // safety: self.current_indexer must be set when first batch has been written.
                self.current_indexer
                    .as_mut()
                    .unwrap()
                    .update_flat(batch)
                    .await;
                self.metrics.update_index += start.elapsed();
            }
            Ok(())
        }
        .await;

        if result.is_err() {
            self.abort_current_indexer().await;
        }
        result
    }

    fn exceeds_max_file_size(&self, opts: &WriteOptions) -> bool {
        opts.max_file_size
            .is_some_and(|max_size| self.bytes_written.load(Ordering::Relaxed) >= max_size)
    }

    async fn abort_current_indexer(&mut self) {
        if let Some(indexer) = &mut self.current_indexer {
            indexer.abort().await;
        }
    }

    async fn maybe_init_writer(
        &mut self,
        schema: &SchemaRef,
        opts: &WriteOptions,
    ) -> Result<&mut AsyncArrowWriter<SizeAwareWriter<F::Writer>>> {
        if let Some(ref mut w) = self.writer {
            Ok(w)
        } else {
            let json = self.metadata.to_json().context(InvalidMetadataSnafu)?;
            let key_value_meta = KeyValue::new(PARQUET_METADATA_KEY.to_string(), json);

            // TODO(yingwen): Find and set proper column encoding for internal columns: op type and tsid.
            let props_builder = WriterProperties::builder()
                .set_key_value_metadata(Some(vec![key_value_meta]))
                .set_compression(Compression::ZSTD(ZstdLevel::default()))
                .set_encoding(Encoding::PLAIN)
                .set_max_row_group_row_count(Some(opts.row_group_size))
                .set_column_index_truncate_length(None)
                .set_statistics_truncate_length(None);

            let props_builder = Self::customize_column_config(props_builder, &self.metadata);
            let writer_props = props_builder.build();

            let sst_file_path = self.path_provider.build_sst_file_path(RegionFileId::new(
                self.metadata.region_id,
                self.current_file,
            ));
            let writer = SizeAwareWriter::new(
                self.writer_factory.create(&sst_file_path).await?,
                self.bytes_written.clone(),
            );
            let arrow_writer =
                AsyncArrowWriter::try_new(writer, maybe_wrap_schema(schema)?, Some(writer_props))
                    .context(WriteParquetSnafu)?;
            self.writer = Some(arrow_writer);

            let indexer = self
                .indexer_builder
                .build(
                    RegionFileId::new(self.metadata.region_id, self.current_file),
                    0,
                    Some(opts.row_group_size),
                )
                .await;
            self.current_indexer = Some(indexer);

            // safety: self.writer is assigned above
            Ok(self.writer.as_mut().unwrap())
        }
    }
}

#[derive(Default)]
struct SourceStats {
    /// Number of rows fetched.
    num_rows: usize,
    /// Time range of fetched batches.
    time_range: Option<(Timestamp, Timestamp)>,
    /// Last primary key written to the current file.
    last_primary_key: Option<Bytes>,
    /// Series estimator for computing num_series.
    series_estimator: SeriesEstimator,
}

impl SourceStats {
    fn update_flat(&mut self, record_batch: &RecordBatch) -> Result<()> {
        if record_batch.num_rows() == 0 {
            return Ok(());
        }

        self.num_rows += record_batch.num_rows();
        self.series_estimator.update_flat(record_batch);
        let (keys, values) = encoded_primary_keys(record_batch)?;
        let key = keys.value(record_batch.num_rows() - 1);
        self.last_primary_key = Some(Bytes::copy_from_slice(values.value(key as usize)));

        // Get the timestamp column by index
        let time_index_col_idx = time_index_column_index(record_batch.num_columns());
        let timestamp_array = record_batch.column(time_index_col_idx);

        if let Some((min_in_batch, max_in_batch)) = timestamp_range_from_array(timestamp_array)? {
            if let Some(time_range) = &mut self.time_range {
                time_range.0 = time_range.0.min(min_in_batch);
                time_range.1 = time_range.1.max(max_in_batch);
            } else {
                self.time_range = Some((min_in_batch, max_in_batch));
            }
        }

        Ok(())
    }
}

/// Gets min and max timestamp from an timestamp array.
fn timestamp_range_from_array(
    timestamp_array: &ArrayRef,
) -> Result<Option<(Timestamp, Timestamp)>> {
    let (min_ts, max_ts) = match timestamp_array.data_type() {
        DataType::Timestamp(TimeUnit::Second, _) => {
            let array = timestamp_array
                .as_any()
                .downcast_ref::<TimestampSecondArray>()
                .unwrap();
            let min_val = min(array).map(Timestamp::new_second);
            let max_val = max(array).map(Timestamp::new_second);
            (min_val, max_val)
        }
        DataType::Timestamp(TimeUnit::Millisecond, _) => {
            let array = timestamp_array
                .as_any()
                .downcast_ref::<TimestampMillisecondArray>()
                .unwrap();
            let min_val = min(array).map(Timestamp::new_millisecond);
            let max_val = max(array).map(Timestamp::new_millisecond);
            (min_val, max_val)
        }
        DataType::Timestamp(TimeUnit::Microsecond, _) => {
            let array = timestamp_array
                .as_any()
                .downcast_ref::<TimestampMicrosecondArray>()
                .unwrap();
            let min_val = min(array).map(Timestamp::new_microsecond);
            let max_val = max(array).map(Timestamp::new_microsecond);
            (min_val, max_val)
        }
        DataType::Timestamp(TimeUnit::Nanosecond, _) => {
            let array = timestamp_array
                .as_any()
                .downcast_ref::<TimestampNanosecondArray>()
                .unwrap();
            let min_val = min(array).map(Timestamp::new_nanosecond);
            let max_val = max(array).map(Timestamp::new_nanosecond);
            (min_val, max_val)
        }
        _ => {
            return UnexpectedSnafu {
                reason: format!(
                    "Unexpected data type of time index: {:?}",
                    timestamp_array.data_type()
                ),
            }
            .fail();
        }
    };

    // If min timestamp exists, max timestamp should also exist.
    Ok(min_ts.zip(max_ts))
}

/// Workaround for [AsyncArrowWriter] does not provide a method to
/// get total bytes written after close.
struct SizeAwareWriter<W> {
    inner: W,
    size: Arc<AtomicUsize>,
}

impl<W> SizeAwareWriter<W> {
    fn new(inner: W, size: Arc<AtomicUsize>) -> Self {
        Self {
            inner,
            size: size.clone(),
        }
    }
}

impl<W> AsyncWrite for SizeAwareWriter<W>
where
    W: AsyncWrite + Unpin,
{
    fn poll_write(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
        buf: &[u8],
    ) -> Poll<std::result::Result<usize, std::io::Error>> {
        let this = self.as_mut().get_mut();

        match Pin::new(&mut this.inner).poll_write(cx, buf) {
            Poll::Ready(Ok(bytes_written)) => {
                this.size.fetch_add(bytes_written, Ordering::Relaxed);
                Poll::Ready(Ok(bytes_written))
            }
            other => other,
        }
    }

    fn poll_flush(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<std::result::Result<(), std::io::Error>> {
        Pin::new(&mut self.inner).poll_flush(cx)
    }

    fn poll_shutdown(
        mut self: Pin<&mut Self>,
        cx: &mut Context<'_>,
    ) -> Poll<std::result::Result<(), std::io::Error>> {
        Pin::new(&mut self.inner).poll_shutdown(cx)
    }
}