mito2/memtable/bulk/

part.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.

//! Bulk part encoder/decoder.

use std::collections::{HashMap, HashSet};
use std::sync::Arc;
use std::time::{Duration, Instant};

use api::helper::{ColumnDataTypeWrapper, to_grpc_value};
use api::v1::bulk_wal_entry::Body;
use api::v1::{ArrowIpc, BulkWalEntry, Mutation, OpType};
use bytes::Bytes;
use common_grpc::flight::{FlightDecoder, FlightEncoder, FlightMessage};
use common_recordbatch::DfRecordBatch as RecordBatch;
use common_time::Timestamp;
use datafusion_common::Column;
use datafusion_common::pruning::PruningStatistics;
use datafusion_expr::utils::expr_to_columns;
use datatypes::arrow;
use datatypes::arrow::array::{
    Array, ArrayRef, BinaryArray, BooleanArray, StringDictionaryBuilder, UInt8Array, UInt64Array,
};
use datatypes::arrow::compute::{SortColumn, SortOptions};
use datatypes::arrow::datatypes::{
    DataType as ArrowDataType, Field, Schema, SchemaRef, UInt32Type,
};
use datatypes::data_type::DataType;
use datatypes::extension::json::is_json_extension_type;
use datatypes::prelude::{MutableVector, Vector};
use datatypes::schema::ext::ArrowSchemaExt;
use datatypes::types::JsonType;
use datatypes::value::ValueRef;
use datatypes::vectors::Helper;
use datatypes::vectors::json::array::JsonArray;
use mito_codec::key_values::{KeyValue, KeyValues};
use mito_codec::row_converter::{PrimaryKeyCodec, SortField, build_primary_key_codec_with_fields};
use parquet::arrow::ArrowWriter;
use parquet::basic::{Compression, ZstdLevel};
use parquet::file::metadata::ParquetMetaData;
use parquet::file::properties::WriterProperties;
use smallvec::SmallVec;
use snafu::{OptionExt, ResultExt};
use store_api::codec::PrimaryKeyEncoding;
use store_api::metadata::{RegionMetadata, RegionMetadataRef};
use store_api::storage::consts::PRIMARY_KEY_COLUMN_NAME;
use store_api::storage::{ColumnId, FileId, SequenceNumber, SequenceRange};

use crate::error::{
    self, ColumnNotFoundSnafu, ComputeArrowSnafu, ConvertValueSnafu, CreateDefaultSnafu,
    DataTypeMismatchSnafu, EncodeMemtableSnafu, EncodeSnafu, InvalidMetadataSnafu,
    InvalidRequestSnafu, NewRecordBatchSnafu, Result,
};
use crate::memtable::bulk::context::{BulkIterContext, BulkIterContextRef};
use crate::memtable::bulk::part_reader::EncodedBulkPartIter;
use crate::memtable::time_series::{ValueBuilder, Values};
use crate::memtable::{BoxedRecordBatchIterator, MemScanMetrics, MemtableStats};
use crate::sst::SeriesEstimator;
use crate::sst::index::IndexOutput;
use crate::sst::parquet::flat_format::primary_key_column_index;
use crate::sst::parquet::format::{PrimaryKeyArray, PrimaryKeyArrayBuilder};
use crate::sst::parquet::{PARQUET_METADATA_KEY, SstInfo};

const INIT_DICT_VALUE_CAPACITY: usize = 8;

/// A raw bulk part in the memtable.
#[derive(Clone)]
pub struct BulkPart {
    pub batch: RecordBatch,
    pub max_timestamp: i64,
    pub min_timestamp: i64,
    pub sequence: u64,
    pub timestamp_index: usize,
    pub raw_data: Option<ArrowIpc>,
}

impl TryFrom<BulkWalEntry> for BulkPart {
    type Error = error::Error;

    fn try_from(value: BulkWalEntry) -> std::result::Result<Self, Self::Error> {
        match value.body.expect("Entry payload should be present") {
            Body::ArrowIpc(ipc) => {
                let mut decoder = FlightDecoder::try_from_schema_bytes(&ipc.schema)
                    .context(error::ConvertBulkWalEntrySnafu)?;
                let batch = decoder
                    .try_decode_record_batch(&ipc.data_header, &ipc.payload)
                    .context(error::ConvertBulkWalEntrySnafu)?;
                Ok(Self {
                    batch,
                    max_timestamp: value.max_ts,
                    min_timestamp: value.min_ts,
                    sequence: value.sequence,
                    timestamp_index: value.timestamp_index as usize,
                    raw_data: Some(ipc),
                })
            }
        }
    }
}

impl TryFrom<&BulkPart> for BulkWalEntry {
    type Error = error::Error;

    fn try_from(value: &BulkPart) -> Result<Self> {
        if let Some(ipc) = &value.raw_data {
            Ok(BulkWalEntry {
                sequence: value.sequence,
                max_ts: value.max_timestamp,
                min_ts: value.min_timestamp,
                timestamp_index: value.timestamp_index as u32,
                body: Some(Body::ArrowIpc(ipc.clone())),
            })
        } else {
            let mut encoder = FlightEncoder::default();
            let schema_bytes = encoder
                .encode_schema(value.batch.schema().as_ref())
                .data_header;
            let [rb_data] = encoder
                .encode(FlightMessage::RecordBatch(value.batch.clone()))
                .try_into()
                .map_err(|_| {
                    error::UnsupportedOperationSnafu {
                        err_msg: "create BulkWalEntry from RecordBatch with dictionary arrays",
                    }
                    .build()
                })?;
            Ok(BulkWalEntry {
                sequence: value.sequence,
                max_ts: value.max_timestamp,
                min_ts: value.min_timestamp,
                timestamp_index: value.timestamp_index as u32,
                body: Some(Body::ArrowIpc(ArrowIpc {
                    schema: schema_bytes,
                    data_header: rb_data.data_header,
                    payload: rb_data.data_body,
                })),
            })
        }
    }
}

impl BulkPart {
    pub(crate) fn estimated_size(&self) -> usize {
        record_batch_estimated_size(&self.batch)
    }

    /// Returns the estimated series count in this BulkPart.
    /// This is calculated from the dictionary values count of the PrimaryKeyArray.
    pub fn estimated_series_count(&self) -> usize {
        let pk_column_idx = primary_key_column_index(self.batch.num_columns());
        let pk_column = self.batch.column(pk_column_idx);
        if let Some(dict_array) = pk_column.as_any().downcast_ref::<PrimaryKeyArray>() {
            dict_array.values().len()
        } else {
            0
        }
    }

    /// Creates MemtableStats from this BulkPart.
    pub fn to_memtable_stats(&self, region_metadata: &RegionMetadataRef) -> MemtableStats {
        let ts_type = region_metadata.time_index_type();
        let min_ts = ts_type.create_timestamp(self.min_timestamp);
        let max_ts = ts_type.create_timestamp(self.max_timestamp);

        MemtableStats {
            estimated_bytes: self.estimated_size(),
            time_range: Some((min_ts, max_ts)),
            num_rows: self.num_rows(),
            num_ranges: 1,
            max_sequence: self.sequence,
            series_count: self.estimated_series_count(),
        }
    }

    /// Fills missing columns in the BulkPart batch with default values.
    ///
    /// This function checks if the batch schema matches the region metadata schema,
    /// and if there are missing columns, it fills them with default values (or null
    /// for nullable columns).
    ///
    /// # Arguments
    ///
    /// * `region_metadata` - The region metadata containing the expected schema
    pub fn fill_missing_columns(&mut self, region_metadata: &RegionMetadata) -> Result<()> {
        // Builds a map of existing columns in the batch
        let batch_schema = self.batch.schema();
        let batch_columns: HashSet<_> = batch_schema
            .fields()
            .iter()
            .map(|f| f.name().as_str())
            .collect();

        // Finds columns that need to be filled
        let mut columns_to_fill = Vec::new();
        for column_meta in &region_metadata.column_metadatas {
            // TODO(yingwen): Returns error if it is impure default after we support filling
            // bulk insert request in the frontend
            if !batch_columns.contains(column_meta.column_schema.name.as_str()) {
                columns_to_fill.push(column_meta);
            }
        }

        if columns_to_fill.is_empty() {
            return Ok(());
        }

        let num_rows = self.batch.num_rows();

        let mut new_columns = Vec::new();
        let mut new_fields = Vec::new();

        // First, adds all existing columns
        new_fields.extend(batch_schema.fields().iter().cloned());
        new_columns.extend_from_slice(self.batch.columns());

        let region_id = region_metadata.region_id;
        // Then adds the missing columns with default values
        for column_meta in columns_to_fill {
            let default_vector = column_meta
                .column_schema
                .create_default_vector(num_rows)
                .context(CreateDefaultSnafu {
                    region_id,
                    column: &column_meta.column_schema.name,
                })?
                .with_context(|| InvalidRequestSnafu {
                    region_id,
                    reason: format!(
                        "column {} does not have default value",
                        column_meta.column_schema.name
                    ),
                })?;
            let arrow_array = default_vector.to_arrow_array();
            column_meta.column_schema.data_type.as_arrow_type();

            new_fields.push(Arc::new(Field::new(
                column_meta.column_schema.name.clone(),
                column_meta.column_schema.data_type.as_arrow_type(),
                column_meta.column_schema.is_nullable(),
            )));
            new_columns.push(arrow_array);
        }

        // Create a new schema and batch with the filled columns
        let new_schema = Arc::new(Schema::new(new_fields));
        let new_batch =
            RecordBatch::try_new(new_schema, new_columns).context(NewRecordBatchSnafu)?;

        // Update the batch
        self.batch = new_batch;

        Ok(())
    }

    /// Converts [BulkPart] to [Mutation] for fallback `write_bulk` implementation.
    pub(crate) fn to_mutation(&self, region_metadata: &RegionMetadataRef) -> Result<Mutation> {
        let vectors = region_metadata
            .schema
            .column_schemas()
            .iter()
            .map(|col| match self.batch.column_by_name(&col.name) {
                None => Ok(None),
                Some(col) => Helper::try_into_vector(col).map(Some),
            })
            .collect::<datatypes::error::Result<Vec<_>>>()
            .context(error::ComputeVectorSnafu)?;

        let rows = (0..self.num_rows())
            .map(|row_idx| {
                let values = (0..self.batch.num_columns())
                    .map(|col_idx| {
                        if let Some(v) = &vectors[col_idx] {
                            to_grpc_value(v.get(row_idx))
                        } else {
                            api::v1::Value { value_data: None }
                        }
                    })
                    .collect::<Vec<_>>();
                api::v1::Row { values }
            })
            .collect::<Vec<_>>();

        let schema = region_metadata
            .column_metadatas
            .iter()
            .map(|c| {
                let data_type_wrapper =
                    ColumnDataTypeWrapper::try_from(c.column_schema.data_type.clone())?;
                Ok(api::v1::ColumnSchema {
                    column_name: c.column_schema.name.clone(),
                    datatype: data_type_wrapper.datatype() as i32,
                    semantic_type: c.semantic_type as i32,
                    ..Default::default()
                })
            })
            .collect::<api::error::Result<Vec<_>>>()
            .context(error::ConvertColumnDataTypeSnafu {
                reason: "failed to convert region metadata to column schema",
            })?;

        let rows = api::v1::Rows { schema, rows };

        Ok(Mutation {
            op_type: OpType::Put as i32,
            sequence: self.sequence,
            rows: Some(rows),
            write_hint: None,
        })
    }

    pub fn timestamps(&self) -> &ArrayRef {
        self.batch.column(self.timestamp_index)
    }

    pub fn num_rows(&self) -> usize {
        self.batch.num_rows()
    }
}

/// A collection of small unordered bulk parts.
/// Used to batch small parts together before merging them into a sorted part.
pub struct UnorderedPart {
    /// Small bulk parts that haven't been sorted yet.
    parts: Vec<BulkPart>,
    /// Total number of rows across all parts.
    total_rows: usize,
    /// Minimum timestamp across all parts.
    min_timestamp: i64,
    /// Maximum timestamp across all parts.
    max_timestamp: i64,
    /// Maximum sequence number across all parts.
    max_sequence: u64,
    /// Row count threshold for accepting parts (default: 1024).
    threshold: usize,
    /// Row count threshold for compacting (default: 4096).
    compact_threshold: usize,
}

impl Default for UnorderedPart {
    fn default() -> Self {
        Self::new()
    }
}

impl UnorderedPart {
    /// Creates a new empty UnorderedPart.
    pub fn new() -> Self {
        Self {
            parts: Vec::new(),
            total_rows: 0,
            min_timestamp: i64::MAX,
            max_timestamp: i64::MIN,
            max_sequence: 0,
            threshold: 1024,
            compact_threshold: 4096,
        }
    }

    /// Sets the threshold for accepting parts into unordered_part.
    pub fn set_threshold(&mut self, threshold: usize) {
        self.threshold = threshold;
    }

    /// Sets the threshold for compacting unordered_part.
    pub fn set_compact_threshold(&mut self, compact_threshold: usize) {
        self.compact_threshold = compact_threshold;
    }

    /// Returns the threshold for accepting parts.
    pub fn threshold(&self) -> usize {
        self.threshold
    }

    /// Returns the compact threshold.
    pub fn compact_threshold(&self) -> usize {
        self.compact_threshold
    }

    /// Returns true if this part should accept the given row count.
    pub fn should_accept(&self, num_rows: usize) -> bool {
        num_rows < self.threshold
    }

    /// Returns true if this part should be compacted.
    pub fn should_compact(&self) -> bool {
        self.total_rows >= self.compact_threshold
    }

    /// Adds a BulkPart to this unordered collection.
    pub fn push(&mut self, part: BulkPart) {
        self.total_rows += part.num_rows();
        self.min_timestamp = self.min_timestamp.min(part.min_timestamp);
        self.max_timestamp = self.max_timestamp.max(part.max_timestamp);
        self.max_sequence = self.max_sequence.max(part.sequence);
        self.parts.push(part);
    }

    /// Returns the total number of rows across all parts.
    pub fn num_rows(&self) -> usize {
        self.total_rows
    }

    /// Returns true if there are no parts.
    pub fn is_empty(&self) -> bool {
        self.parts.is_empty()
    }

    /// Returns the number of parts in this collection.
    pub fn num_parts(&self) -> usize {
        self.parts.len()
    }

    /// Concatenates and sorts all parts into a single RecordBatch.
    /// Returns None if the collection is empty.
    pub fn concat_and_sort(&self) -> Result<Option<RecordBatch>> {
        if self.parts.is_empty() {
            return Ok(None);
        }

        if self.parts.len() == 1 {
            // If there's only one part, return its batch directly
            return Ok(Some(self.parts[0].batch.clone()));
        }

        // Get the schema from the first part
        let schema = self.parts[0].batch.schema();
        let concatenated = if schema.has_json_extension_field() {
            let (schema, batches) = align_parts(&self.parts)?;
            arrow::compute::concat_batches(&schema, &batches).context(ComputeArrowSnafu)?
        } else {
            arrow::compute::concat_batches(&schema, self.parts.iter().map(|x| &x.batch))
                .context(ComputeArrowSnafu)?
        };

        // Sort the concatenated batch
        let sorted_batch = sort_primary_key_record_batch(&concatenated)?;

        Ok(Some(sorted_batch))
    }

    /// Converts all parts into a single sorted BulkPart.
    /// Returns None if the collection is empty.
    pub fn to_bulk_part(&self) -> Result<Option<BulkPart>> {
        let Some(sorted_batch) = self.concat_and_sort()? else {
            return Ok(None);
        };

        let timestamp_index = self.parts[0].timestamp_index;

        Ok(Some(BulkPart {
            batch: sorted_batch,
            max_timestamp: self.max_timestamp,
            min_timestamp: self.min_timestamp,
            sequence: self.max_sequence,
            timestamp_index,
            raw_data: None,
        }))
    }

    /// Clears all parts from this collection.
    pub fn clear(&mut self) {
        self.parts.clear();
        self.total_rows = 0;
        self.min_timestamp = i64::MAX;
        self.max_timestamp = i64::MIN;
        self.max_sequence = 0;
    }
}

/// Align the JSON columns in [BulkPart]s, to unified Arrow arrays. So that we can compute (concat,
/// sort, etc.) on them.
fn align_parts(parts: &[BulkPart]) -> Result<(SchemaRef, Vec<RecordBatch>)> {
    debug_assert!(
        !parts.is_empty()
            && parts
                .windows(2)
                .all(|w| w[0].batch.schema_ref().fields().len()
                    == w[1].batch.schema_ref().fields().len())
    );

    let first = &parts[0];
    let base_schema = first.batch.schema_ref();
    let rest = &parts[1..];

    let mut merged_types = HashMap::new();
    let mut aligned_fields = Vec::with_capacity(base_schema.fields().len());
    for (i, field) in base_schema.fields().iter().enumerate() {
        if is_json_extension_type(field) {
            let mut merged = JsonType::from(field.data_type());
            rest.iter()
                .try_fold(&mut merged, |acc, x| {
                    acc.merge(&JsonType::from(x.batch.schema_ref().field(i).data_type()))?;
                    Ok(acc)
                })
                .context(DataTypeMismatchSnafu)?;
            merged_types.insert(i, merged.as_arrow_type());

            aligned_fields.push(Arc::new(
                Field::new(
                    field.name().clone(),
                    merged.as_arrow_type(),
                    field.is_nullable(),
                )
                .with_metadata(field.metadata().clone()),
            ));
        } else {
            aligned_fields.push(field.clone())
        };
    }
    let aligned_schema = Arc::new(Schema::new_with_metadata(
        aligned_fields,
        base_schema.metadata().clone(),
    ));

    let mut aligned_batches = Vec::with_capacity(parts.len());
    for part in parts {
        let mut columns = Vec::with_capacity(part.batch.num_columns());
        for (i, column) in part.batch.columns().iter().enumerate() {
            if let Some(expect) = merged_types.get(&i) {
                columns.push(
                    JsonArray::from(column)
                        .try_align(expect)
                        .context(ConvertValueSnafu)?,
                );
            } else {
                columns.push(column.clone());
            }
        }
        aligned_batches.push(
            RecordBatch::try_new(aligned_schema.clone(), columns).context(NewRecordBatchSnafu)?,
        );
    }

    Ok((aligned_schema, aligned_batches))
}

/// More accurate estimation of the size of a record batch.
pub fn record_batch_estimated_size(batch: &RecordBatch) -> usize {
    batch
        .columns()
        .iter()
        // If can not get slice memory size, assume 0 here.
        .map(|c| c.to_data().get_slice_memory_size().unwrap_or(0))
        .sum()
}

/// Primary key column builder for handling strings specially.
enum PrimaryKeyColumnBuilder {
    /// String dictionary builder for string types.
    StringDict(StringDictionaryBuilder<UInt32Type>),
    /// Generic mutable vector for other types.
    Vector(Box<dyn MutableVector>),
}

impl PrimaryKeyColumnBuilder {
    /// Appends a value to the builder.
    fn push_value_ref(&mut self, value: ValueRef) -> Result<()> {
        match self {
            PrimaryKeyColumnBuilder::StringDict(builder) => {
                if let Some(s) = value.try_into_string().context(DataTypeMismatchSnafu)? {
                    // We know the value is a string.
                    builder.append_value(s);
                } else {
                    builder.append_null();
                }
            }
            PrimaryKeyColumnBuilder::Vector(builder) => {
                builder.push_value_ref(&value);
            }
        }
        Ok(())
    }

    /// Converts the builder to an ArrayRef.
    fn into_arrow_array(self) -> ArrayRef {
        match self {
            PrimaryKeyColumnBuilder::StringDict(mut builder) => Arc::new(builder.finish()),
            PrimaryKeyColumnBuilder::Vector(mut builder) => builder.to_vector().to_arrow_array(),
        }
    }
}

/// Converter that converts structs into [BulkPart].
pub struct BulkPartConverter {
    /// Schema of the converted batch.
    schema: SchemaRef,
    /// Primary key codec for encoding keys
    primary_key_codec: Arc<dyn PrimaryKeyCodec>,
    /// Buffer for encoding primary key.
    key_buf: Vec<u8>,
    /// Primary key array builder.
    key_array_builder: PrimaryKeyArrayBuilder,
    /// Builders for non-primary key columns.
    value_builder: ValueBuilder,
    /// Builders for individual primary key columns.
    /// The order of builders is the same as the order of primary key columns in the region metadata.
    primary_key_column_builders: Vec<PrimaryKeyColumnBuilder>,

    /// Max timestamp value.
    max_ts: i64,
    /// Min timestamp value.
    min_ts: i64,
    /// Max sequence number.
    max_sequence: SequenceNumber,
}

impl BulkPartConverter {
    /// Creates a new converter.
    ///
    /// If `store_primary_key_columns` is true and the encoding is not sparse encoding, it
    /// stores primary key columns in arrays additionally.
    pub fn new(
        region_metadata: &RegionMetadataRef,
        schema: SchemaRef,
        capacity: usize,
        primary_key_codec: Arc<dyn PrimaryKeyCodec>,
        store_primary_key_columns: bool,
    ) -> Self {
        debug_assert_eq!(
            region_metadata.primary_key_encoding,
            primary_key_codec.encoding()
        );

        let primary_key_column_builders = if store_primary_key_columns
            && region_metadata.primary_key_encoding != PrimaryKeyEncoding::Sparse
        {
            new_primary_key_column_builders(region_metadata, capacity)
        } else {
            Vec::new()
        };

        Self {
            schema,
            primary_key_codec,
            key_buf: Vec::new(),
            key_array_builder: PrimaryKeyArrayBuilder::new(),
            value_builder: ValueBuilder::new(region_metadata, capacity),
            primary_key_column_builders,
            min_ts: i64::MAX,
            max_ts: i64::MIN,
            max_sequence: SequenceNumber::MIN,
        }
    }

    /// Appends a [KeyValues] into the converter.
    pub fn append_key_values(&mut self, key_values: &KeyValues) -> Result<()> {
        for kv in key_values.iter() {
            self.append_key_value(&kv)?;
        }

        Ok(())
    }

    /// Appends a [KeyValue] to builders.
    ///
    /// If the primary key uses sparse encoding, callers must encoded the primary key in the [KeyValue].
    fn append_key_value(&mut self, kv: &KeyValue) -> Result<()> {
        // Handles primary key based on encoding type
        if self.primary_key_codec.encoding() == PrimaryKeyEncoding::Sparse {
            // For sparse encoding, the primary key is already encoded in the KeyValue
            // Gets the first (and only) primary key value which contains the encoded key
            let mut primary_keys = kv.primary_keys();
            if let Some(encoded) = primary_keys
                .next()
                .context(ColumnNotFoundSnafu {
                    column: PRIMARY_KEY_COLUMN_NAME,
                })?
                .try_into_binary()
                .context(DataTypeMismatchSnafu)?
            {
                self.key_array_builder
                    .append(encoded)
                    .context(ComputeArrowSnafu)?;
            } else {
                self.key_array_builder
                    .append("")
                    .context(ComputeArrowSnafu)?;
            }
        } else {
            // For dense encoding, we need to encode the primary key columns
            self.key_buf.clear();
            self.primary_key_codec
                .encode_key_value(kv, &mut self.key_buf)
                .context(EncodeSnafu)?;
            self.key_array_builder
                .append(&self.key_buf)
                .context(ComputeArrowSnafu)?;
        };

        // If storing primary key columns, append values to individual builders
        if !self.primary_key_column_builders.is_empty() {
            for (builder, pk_value) in self
                .primary_key_column_builders
                .iter_mut()
                .zip(kv.primary_keys())
            {
                builder.push_value_ref(pk_value)?;
            }
        }

        // Pushes other columns.
        self.value_builder.push(
            kv.timestamp(),
            kv.sequence(),
            kv.op_type() as u8,
            kv.fields(),
        );

        // Updates statistics
        // Safety: timestamp of kv must be both present and a valid timestamp value.
        let ts = kv
            .timestamp()
            .try_into_timestamp()
            .unwrap()
            .unwrap()
            .value();
        self.min_ts = self.min_ts.min(ts);
        self.max_ts = self.max_ts.max(ts);
        self.max_sequence = self.max_sequence.max(kv.sequence());

        Ok(())
    }

    /// Converts buffered content into a [BulkPart].
    ///
    /// It sorts the record batch by (primary key, timestamp, sequence desc).
    pub fn convert(mut self) -> Result<BulkPart> {
        let values = Values::from(self.value_builder);
        let mut columns =
            Vec::with_capacity(4 + values.fields.len() + self.primary_key_column_builders.len());

        // Build primary key column arrays if enabled.
        for builder in self.primary_key_column_builders {
            columns.push(builder.into_arrow_array());
        }
        // Then fields columns.
        columns.extend(values.fields.iter().map(|field| field.to_arrow_array()));
        // Time index.
        let timestamp_index = columns.len();
        columns.push(values.timestamp.to_arrow_array());
        // Primary key.
        let pk_array = self.key_array_builder.finish();
        columns.push(Arc::new(pk_array));
        // Sequence and op type.
        columns.push(values.sequence.to_arrow_array());
        columns.push(values.op_type.to_arrow_array());

        // The actual datatype of JSON array is data oriented, not to be derived from the Region
        // metadata, which is static. So here we have to align the schema.
        let schema = align_schema_with_json_array(self.schema, &columns);
        let batch = RecordBatch::try_new(schema, columns).context(NewRecordBatchSnafu)?;
        // Sorts the record batch.
        let batch = sort_primary_key_record_batch(&batch)?;

        Ok(BulkPart {
            batch,
            max_timestamp: self.max_ts,
            min_timestamp: self.min_ts,
            sequence: self.max_sequence,
            timestamp_index,
            raw_data: None,
        })
    }
}

fn align_schema_with_json_array(schema: SchemaRef, columns: &[ArrayRef]) -> SchemaRef {
    if schema.fields().iter().all(|f| !is_json_extension_type(f)) {
        return schema;
    }

    let mut fields = Vec::with_capacity(schema.fields().len());
    for (field, array) in schema.fields().iter().zip(columns) {
        if !is_json_extension_type(field) {
            fields.push(field.clone());
            continue;
        }

        let mut field = field.as_ref().clone();
        field.set_data_type(array.data_type().clone());
        fields.push(Arc::new(field));
    }

    Arc::new(Schema::new_with_metadata(fields, schema.metadata().clone()))
}

fn new_primary_key_column_builders(
    metadata: &RegionMetadata,
    capacity: usize,
) -> Vec<PrimaryKeyColumnBuilder> {
    metadata
        .primary_key_columns()
        .map(|col| {
            if col.column_schema.data_type.is_string() {
                PrimaryKeyColumnBuilder::StringDict(StringDictionaryBuilder::with_capacity(
                    capacity,
                    INIT_DICT_VALUE_CAPACITY,
                    capacity,
                ))
            } else {
                PrimaryKeyColumnBuilder::Vector(
                    col.column_schema.data_type.create_mutable_vector(capacity),
                )
            }
        })
        .collect()
}

/// Sorts the record batch with primary key format.
pub fn sort_primary_key_record_batch(batch: &RecordBatch) -> Result<RecordBatch> {
    let total_columns = batch.num_columns();
    let sort_columns = vec![
        // Primary key column (ascending)
        SortColumn {
            values: batch.column(total_columns - 3).clone(),
            options: Some(SortOptions {
                descending: false,
                nulls_first: true,
            }),
        },
        // Time index column (ascending)
        SortColumn {
            values: batch.column(total_columns - 4).clone(),
            options: Some(SortOptions {
                descending: false,
                nulls_first: true,
            }),
        },
        // Sequence column (descending)
        SortColumn {
            values: batch.column(total_columns - 2).clone(),
            options: Some(SortOptions {
                descending: true,
                nulls_first: true,
            }),
        },
    ];

    let indices = datatypes::arrow::compute::lexsort_to_indices(&sort_columns, None)
        .context(ComputeArrowSnafu)?;

    datatypes::arrow::compute::take_record_batch(batch, &indices).context(ComputeArrowSnafu)
}

/// Converts a `BulkPart` that is unordered and without encoded primary keys into a `BulkPart`
/// with the same format as produced by [BulkPartConverter].
///
/// This function takes a `BulkPart` where:
/// - For dense encoding: Primary key columns may be stored as individual columns
/// - For sparse encoding: The `__primary_key` column should already be present with encoded keys
/// - The batch may not be sorted
///
/// And produces a `BulkPart` where:
/// - Primary key columns are optionally stored (depending on `store_primary_key_columns` and encoding)
/// - An encoded `__primary_key` dictionary column is present
/// - The batch is sorted by (primary_key, timestamp, sequence desc)
///
/// # Arguments
///
/// * `part` - The input `BulkPart` to convert
/// * `region_metadata` - Region metadata containing schema information
/// * `primary_key_codec` - Codec for encoding primary keys
/// * `schema` - Target schema for the output batch
/// * `store_primary_key_columns` - If true and encoding is not sparse, stores individual primary key columns
///
/// # Returns
///
/// Returns `None` if the input part has no rows, otherwise returns a new `BulkPart` with
/// encoded primary keys and sorted data.
pub fn convert_bulk_part(
    part: BulkPart,
    region_metadata: &RegionMetadataRef,
    primary_key_codec: Arc<dyn PrimaryKeyCodec>,
    schema: SchemaRef,
    store_primary_key_columns: bool,
) -> Result<Option<BulkPart>> {
    if part.num_rows() == 0 {
        return Ok(None);
    }

    let num_rows = part.num_rows();
    let is_sparse = region_metadata.primary_key_encoding == PrimaryKeyEncoding::Sparse;

    // Builds a column name-to-index map for efficient lookups
    let input_schema = part.batch.schema();
    let column_indices: HashMap<&str, usize> = input_schema
        .fields()
        .iter()
        .enumerate()
        .map(|(idx, field)| (field.name().as_str(), idx))
        .collect();

    // Determines the structure of the input batch by looking up columns by name
    let mut output_columns = Vec::new();

    // Extracts primary key columns if we need to encode them (dense encoding)
    let pk_array = if is_sparse {
        // For sparse encoding, the input should already have the __primary_key column
        // We need to find it in the input batch
        None
    } else {
        // For dense encoding, extract and encode primary key columns by name
        let pk_vectors: Result<Vec<_>> = region_metadata
            .primary_key_columns()
            .map(|col_meta| {
                let col_idx = column_indices
                    .get(col_meta.column_schema.name.as_str())
                    .context(ColumnNotFoundSnafu {
                        column: &col_meta.column_schema.name,
                    })?;
                let col = part.batch.column(*col_idx);
                Helper::try_into_vector(col).context(error::ComputeVectorSnafu)
            })
            .collect();
        let pk_vectors = pk_vectors?;

        let mut key_array_builder = PrimaryKeyArrayBuilder::new();
        let mut encode_buf = Vec::new();

        for row_idx in 0..num_rows {
            encode_buf.clear();

            // Collects primary key values with column IDs for this row
            let pk_values_with_ids: Vec<_> = region_metadata
                .primary_key
                .iter()
                .zip(pk_vectors.iter())
                .map(|(col_id, vector)| (*col_id, vector.get_ref(row_idx)))
                .collect();

            // Encodes the primary key
            primary_key_codec
                .encode_value_refs(&pk_values_with_ids, &mut encode_buf)
                .context(EncodeSnafu)?;

            key_array_builder
                .append(&encode_buf)
                .context(ComputeArrowSnafu)?;
        }

        Some(key_array_builder.finish())
    };

    // Adds primary key columns if storing them (only for dense encoding)
    if store_primary_key_columns && !is_sparse {
        for col_meta in region_metadata.primary_key_columns() {
            let col_idx = column_indices
                .get(col_meta.column_schema.name.as_str())
                .context(ColumnNotFoundSnafu {
                    column: &col_meta.column_schema.name,
                })?;
            let col = part.batch.column(*col_idx);

            // Converts to dictionary if needed for string types
            let col = if col_meta.column_schema.data_type.is_string() {
                let target_type = ArrowDataType::Dictionary(
                    Box::new(ArrowDataType::UInt32),
                    Box::new(ArrowDataType::Utf8),
                );
                arrow::compute::cast(col, &target_type).context(ComputeArrowSnafu)?
            } else {
                col.clone()
            };
            output_columns.push(col);
        }
    }

    // Adds field columns
    for col_meta in region_metadata.field_columns() {
        let col_idx = column_indices
            .get(col_meta.column_schema.name.as_str())
            .context(ColumnNotFoundSnafu {
                column: &col_meta.column_schema.name,
            })?;
        output_columns.push(part.batch.column(*col_idx).clone());
    }

    // Adds timestamp column
    let new_timestamp_index = output_columns.len();
    let ts_col_idx = column_indices
        .get(
            region_metadata
                .time_index_column()
                .column_schema
                .name
                .as_str(),
        )
        .context(ColumnNotFoundSnafu {
            column: &region_metadata.time_index_column().column_schema.name,
        })?;
    output_columns.push(part.batch.column(*ts_col_idx).clone());

    // Adds encoded primary key dictionary column
    let pk_dictionary = if let Some(pk_dict_array) = pk_array {
        Arc::new(pk_dict_array) as ArrayRef
    } else {
        let pk_col_idx =
            column_indices
                .get(PRIMARY_KEY_COLUMN_NAME)
                .context(ColumnNotFoundSnafu {
                    column: PRIMARY_KEY_COLUMN_NAME,
                })?;
        let col = part.batch.column(*pk_col_idx);

        // Casts to dictionary type if needed
        let target_type = ArrowDataType::Dictionary(
            Box::new(ArrowDataType::UInt32),
            Box::new(ArrowDataType::Binary),
        );
        arrow::compute::cast(col, &target_type).context(ComputeArrowSnafu)?
    };
    output_columns.push(pk_dictionary);

    let sequence_array = UInt64Array::from(vec![part.sequence; num_rows]);
    output_columns.push(Arc::new(sequence_array) as ArrayRef);

    let op_type_array = UInt8Array::from(vec![OpType::Put as u8; num_rows]);
    output_columns.push(Arc::new(op_type_array) as ArrayRef);

    let batch = RecordBatch::try_new(schema, output_columns).context(NewRecordBatchSnafu)?;

    // Sorts the batch by (primary_key, timestamp, sequence desc)
    let sorted_batch = sort_primary_key_record_batch(&batch)?;

    Ok(Some(BulkPart {
        batch: sorted_batch,
        max_timestamp: part.max_timestamp,
        min_timestamp: part.min_timestamp,
        sequence: part.sequence,
        timestamp_index: new_timestamp_index,
        raw_data: None,
    }))
}

#[derive(Debug, Clone)]
pub struct EncodedBulkPart {
    data: Bytes,
    metadata: BulkPartMeta,
}

impl EncodedBulkPart {
    pub fn new(data: Bytes, metadata: BulkPartMeta) -> Self {
        Self { data, metadata }
    }

    pub fn metadata(&self) -> &BulkPartMeta {
        &self.metadata
    }

    /// Returns the size of the encoded data in bytes
    pub(crate) fn size_bytes(&self) -> usize {
        self.data.len()
    }

    /// Returns the encoded data.
    pub fn data(&self) -> &Bytes {
        &self.data
    }

    /// Creates MemtableStats from this EncodedBulkPart.
    pub fn to_memtable_stats(&self) -> MemtableStats {
        let meta = &self.metadata;
        let ts_type = meta.region_metadata.time_index_type();
        let min_ts = ts_type.create_timestamp(meta.min_timestamp);
        let max_ts = ts_type.create_timestamp(meta.max_timestamp);

        MemtableStats {
            estimated_bytes: self.size_bytes(),
            time_range: Some((min_ts, max_ts)),
            num_rows: meta.num_rows,
            num_ranges: 1,
            max_sequence: meta.max_sequence,
            series_count: meta.num_series as usize,
        }
    }

    /// Converts this `EncodedBulkPart` to `SstInfo`.
    ///
    /// # Arguments
    /// * `file_id` - The SST file ID to assign to this part
    ///
    /// # Returns
    /// Returns a `SstInfo` instance with information derived from this bulk part's metadata
    pub(crate) fn to_sst_info(&self, file_id: FileId) -> SstInfo {
        let unit = self.metadata.region_metadata.time_index_type().unit();
        let max_row_group_uncompressed_size: u64 = self
            .metadata
            .parquet_metadata
            .row_groups()
            .iter()
            .map(|rg| {
                rg.columns()
                    .iter()
                    .map(|c| c.uncompressed_size() as u64)
                    .sum::<u64>()
            })
            .max()
            .unwrap_or(0);
        SstInfo {
            file_id,
            time_range: (
                Timestamp::new(self.metadata.min_timestamp, unit),
                Timestamp::new(self.metadata.max_timestamp, unit),
            ),
            file_size: self.data.len() as u64,
            max_row_group_uncompressed_size,
            num_rows: self.metadata.num_rows,
            num_row_groups: self.metadata.parquet_metadata.num_row_groups() as u64,
            file_metadata: Some(self.metadata.parquet_metadata.clone()),
            index_metadata: IndexOutput::default(),
            num_series: self.metadata.num_series,
        }
    }

    pub(crate) fn read(
        &self,
        context: BulkIterContextRef,
        sequence: Option<SequenceRange>,
        mem_scan_metrics: Option<MemScanMetrics>,
    ) -> Result<Option<BoxedRecordBatchIterator>> {
        // Compute skip_fields for row group pruning from the configured pre-filter mode.
        let skip_fields_for_pruning = context.pre_filter_mode().skip_fields();

        // use predicate to find row groups to read.
        let row_groups_to_read =
            context.row_groups_to_read(&self.metadata.parquet_metadata, skip_fields_for_pruning);

        if row_groups_to_read.is_empty() {
            // All row groups are filtered.
            return Ok(None);
        }

        let iter = EncodedBulkPartIter::try_new(
            self,
            context,
            row_groups_to_read,
            sequence,
            mem_scan_metrics,
        )?;
        Ok(Some(Box::new(iter) as BoxedRecordBatchIterator))
    }
}

// TODO(yingwen): max_sequence
#[derive(Debug, Clone)]
pub struct BulkPartMeta {
    /// Total rows in part.
    pub num_rows: usize,
    /// Max timestamp in part.
    pub max_timestamp: i64,
    /// Min timestamp in part.
    pub min_timestamp: i64,
    /// Part file metadata.
    pub parquet_metadata: Arc<ParquetMetaData>,
    /// Part region schema.
    pub region_metadata: RegionMetadataRef,
    /// Number of series.
    pub num_series: u64,
    /// Maximum sequence number in part.
    pub max_sequence: u64,
}

/// Metrics for encoding a part.
#[derive(Default, Debug)]
pub struct BulkPartEncodeMetrics {
    /// Cost of iterating over the data.
    pub iter_cost: Duration,
    /// Cost of writing the data.
    pub write_cost: Duration,
    /// Size of data before encoding.
    pub raw_size: usize,
    /// Size of data after encoding.
    pub encoded_size: usize,
    /// Number of rows in part.
    pub num_rows: usize,
}

pub struct BulkPartEncoder {
    metadata: RegionMetadataRef,
    writer_props: Option<WriterProperties>,
}

impl BulkPartEncoder {
    pub fn new(metadata: RegionMetadataRef, row_group_size: usize) -> Result<BulkPartEncoder> {
        // TODO(yingwen): Skip arrow schema if needed.
        let json = metadata.to_json().context(InvalidMetadataSnafu)?;
        let key_value_meta =
            parquet::file::metadata::KeyValue::new(PARQUET_METADATA_KEY.to_string(), json);

        // TODO(yingwen): Do we need compression?
        let writer_props = Some(
            WriterProperties::builder()
                .set_key_value_metadata(Some(vec![key_value_meta]))
                .set_write_batch_size(row_group_size)
                .set_max_row_group_row_count(Some(row_group_size))
                .set_compression(Compression::ZSTD(ZstdLevel::default()))
                .set_column_index_truncate_length(None)
                .set_statistics_truncate_length(None)
                .build(),
        );

        Ok(Self {
            metadata,
            writer_props,
        })
    }
}

impl BulkPartEncoder {
    /// Encodes [BoxedRecordBatchIterator] into [EncodedBulkPart] with min/max timestamps.
    pub fn encode_record_batch_iter(
        &self,
        iter: BoxedRecordBatchIterator,
        arrow_schema: SchemaRef,
        min_timestamp: i64,
        max_timestamp: i64,
        max_sequence: u64,
        metrics: &mut BulkPartEncodeMetrics,
    ) -> Result<Option<EncodedBulkPart>> {
        let mut buf = Vec::with_capacity(4096);
        let mut writer = ArrowWriter::try_new(&mut buf, arrow_schema, self.writer_props.clone())
            .context(EncodeMemtableSnafu)?;
        let mut total_rows = 0;
        let mut series_estimator = SeriesEstimator::default();

        // Process each batch from the iterator
        let mut iter_start = Instant::now();
        for batch_result in iter {
            metrics.iter_cost += iter_start.elapsed();
            let batch = batch_result?;
            if batch.num_rows() == 0 {
                continue;
            }

            series_estimator.update_flat(&batch);
            metrics.raw_size += record_batch_estimated_size(&batch);
            let write_start = Instant::now();
            writer.write(&batch).context(EncodeMemtableSnafu)?;
            metrics.write_cost += write_start.elapsed();
            total_rows += batch.num_rows();
            iter_start = Instant::now();
        }
        metrics.iter_cost += iter_start.elapsed();

        if total_rows == 0 {
            return Ok(None);
        }

        let close_start = Instant::now();
        let file_metadata = writer.close().context(EncodeMemtableSnafu)?;
        metrics.write_cost += close_start.elapsed();
        metrics.encoded_size += buf.len();
        metrics.num_rows += total_rows;

        let buf = Bytes::from(buf);
        let parquet_metadata = Arc::new(file_metadata);
        let num_series = series_estimator.finish();

        Ok(Some(EncodedBulkPart {
            data: buf,
            metadata: BulkPartMeta {
                num_rows: total_rows,
                max_timestamp,
                min_timestamp,
                parquet_metadata,
                region_metadata: self.metadata.clone(),
                num_series,
                max_sequence,
            },
        }))
    }

    /// Encodes bulk part to a [EncodedBulkPart], returns the encoded data.
    pub fn encode_part(&self, part: &BulkPart) -> Result<Option<EncodedBulkPart>> {
        if part.batch.num_rows() == 0 {
            return Ok(None);
        }

        let mut buf = Vec::with_capacity(4096);
        let arrow_schema = part.batch.schema();

        let file_metadata = {
            let mut writer =
                ArrowWriter::try_new(&mut buf, arrow_schema, self.writer_props.clone())
                    .context(EncodeMemtableSnafu)?;
            writer.write(&part.batch).context(EncodeMemtableSnafu)?;
            writer.finish().context(EncodeMemtableSnafu)?
        };

        let buf = Bytes::from(buf);
        let parquet_metadata = Arc::new(file_metadata);

        Ok(Some(EncodedBulkPart {
            data: buf,
            metadata: BulkPartMeta {
                num_rows: part.batch.num_rows(),
                max_timestamp: part.max_timestamp,
                min_timestamp: part.min_timestamp,
                parquet_metadata,
                region_metadata: self.metadata.clone(),
                num_series: part.estimated_series_count() as u64,
                max_sequence: part.sequence,
            },
        }))
    }
}

/// Per-batch min/max statistics for the first tag column in a `MultiBulkPart`.
///
/// Since batches are sorted by primary key, we can extract the min/max of the first tag
/// from the first/last row's encoded primary key in each batch. These statistics enable
/// batch-level pruning using predicates, analogous to row-group pruning in parquet.
#[derive(Debug, Clone)]
struct BatchStats {
    /// Number of batches.
    num_batches: usize,
    /// Column id of the first tag.
    first_tag_id: ColumnId,
    /// Min values of the first tag, one element per batch.
    min_values: ArrayRef,
    /// Max values of the first tag, one element per batch.
    max_values: ArrayRef,
}

impl BatchStats {
    /// Computes batch statistics from a slice of record batches.
    ///
    /// Returns `None` if there is no primary key (no first tag to collect stats for)
    /// or if extracting statistics fails.
    fn compute(batches: &[RecordBatch], metadata: &RegionMetadata) -> Option<Self> {
        // `primary_key.first()` is correct for both dense and sparse encodings.
        // For dense, values follow the order of `metadata.primary_key`.
        // For sparse, `decode_leftmost` decodes the first value which also
        // corresponds to `primary_key.first()`. See `SparsePrimaryKeyCodec` for format details.
        let first_tag_id = *metadata.primary_key.first()?;
        let first_tag_column = metadata.column_by_id(first_tag_id)?;
        let data_type = &first_tag_column.column_schema.data_type;

        let converter = build_primary_key_codec_with_fields(
            metadata.primary_key_encoding,
            [(first_tag_id, SortField::new(data_type.clone()))].into_iter(),
        );
        let pk_index = primary_key_column_index(batches.first()?.num_columns());

        let mut min_builder = data_type.create_mutable_vector(batches.len());
        let mut max_builder = data_type.create_mutable_vector(batches.len());

        for batch in batches {
            match Self::extract_first_tag_bounds(batch, pk_index, &*converter) {
                Some((min_val, max_val)) => {
                    min_builder.push_value_ref(&min_val.as_value_ref());
                    max_builder.push_value_ref(&max_val.as_value_ref());
                }
                None => {
                    min_builder.push_null();
                    max_builder.push_null();
                }
            }
        }

        Some(Self {
            num_batches: batches.len(),
            first_tag_id,
            min_values: min_builder.to_vector().to_arrow_array(),
            max_values: max_builder.to_vector().to_arrow_array(),
        })
    }

    /// Extracts the first tag value from the first and last rows of a batch.
    fn extract_first_tag_bounds(
        batch: &RecordBatch,
        pk_index: usize,
        converter: &dyn PrimaryKeyCodec,
    ) -> Option<(datatypes::value::Value, datatypes::value::Value)> {
        if batch.num_rows() == 0 {
            return None;
        }

        let pk_dict = batch
            .column(pk_index)
            .as_any()
            .downcast_ref::<PrimaryKeyArray>()?;
        let pk_values = pk_dict.values().as_any().downcast_ref::<BinaryArray>()?;

        let keys = pk_dict.keys();
        let min_key = keys.value(0);
        let max_key = keys.value(batch.num_rows() - 1);
        let min_bytes = pk_values.value(min_key as usize);
        let max_bytes = pk_values.value(max_key as usize);

        Some((
            converter.decode_leftmost(min_bytes).ok()??,
            converter.decode_leftmost(max_bytes).ok()??,
        ))
    }
}

/// Adapter implementing `PruningStatistics` for `BatchStats`.
///
/// Used with `Predicate::prune_with_stats()` to skip batches whose first-tag
/// min/max range does not match the query predicate.
struct BatchPruningStats<'a> {
    stats: &'a BatchStats,
    metadata: &'a RegionMetadataRef,
}

impl PruningStatistics for BatchPruningStats<'_> {
    fn min_values(&self, column: &Column) -> Option<ArrayRef> {
        let col = self.metadata.column_by_name(&column.name)?;
        if col.column_id == self.stats.first_tag_id {
            Some(self.stats.min_values.clone())
        } else {
            None
        }
    }

    fn max_values(&self, column: &Column) -> Option<ArrayRef> {
        let col = self.metadata.column_by_name(&column.name)?;
        if col.column_id == self.stats.first_tag_id {
            Some(self.stats.max_values.clone())
        } else {
            None
        }
    }

    fn num_containers(&self) -> usize {
        self.stats.num_batches
    }

    fn null_counts(&self, _column: &Column) -> Option<ArrayRef> {
        None
    }

    fn row_counts(&self, _column: &Column) -> Option<ArrayRef> {
        None
    }

    fn contained(
        &self,
        _column: &Column,
        _values: &std::collections::HashSet<datafusion_common::ScalarValue>,
    ) -> Option<BooleanArray> {
        None
    }
}

/// Returns true if the predicate references the given column name.
fn predicate_references_column(predicate: &table::predicate::Predicate, column_name: &str) -> bool {
    let mut columns = HashSet::new();
    for expr in predicate.exprs() {
        let _ = expr_to_columns(expr, &mut columns);
    }
    columns.iter().any(|col| col.name == column_name)
}

/// Returns true if the batch should be pruned (skipped) based on the first-tag min/max
/// statistics and the predicate in the context. Returns false if no pruning is possible
/// (no primary key, no predicate, or the batch matches the predicate).
pub(crate) fn should_prune_bulk_part(
    batch: &RecordBatch,
    context: &BulkIterContext,
    metadata: &RegionMetadata,
) -> bool {
    let predicate = match &context.predicate {
        Some(p) => p,
        None => return false,
    };
    // Check if the predicate references the first tag column to avoid computing
    // expensive batch statistics when they won't help with pruning.
    let first_tag_id = match metadata.primary_key.first() {
        Some(id) => *id,
        None => return false,
    };
    // Safety: `first_tag_id` comes from `metadata.primary_key` so the column always exists.
    let first_tag_name = &metadata
        .column_by_id(first_tag_id)
        .unwrap()
        .column_schema
        .name;
    if !predicate_references_column(predicate, first_tag_name) {
        return false;
    }
    let stats = match BatchStats::compute(std::slice::from_ref(batch), metadata) {
        Some(s) => s,
        None => return false,
    };
    let region_meta = context.read_format().metadata();
    let pruning_stats = BatchPruningStats {
        stats: &stats,
        metadata: region_meta,
    };
    let mask = predicate.prune_with_stats(&pruning_stats, region_meta.schema.arrow_schema());
    !mask.first().copied().unwrap_or(true)
}

/// A collection of ordered RecordBatches representing a bulk part without parquet encoding.
///
/// Similar to `EncodedBulkPart` but stores raw RecordBatches instead of encoded parquet data.
/// The RecordBatches must be ordered by (primary key, timestamp, sequence desc).
/// Uses SmallVec to optimize for the common case of few batches while avoiding heap allocation.
#[derive(Debug, Clone)]
pub struct MultiBulkPart {
    /// Ordered record batches. SmallVec optimized for up to 4 batches inline.
    batches: SmallVec<[RecordBatch; 4]>,
    /// Total rows across all batches.
    total_rows: usize,
    /// Max timestamp in part.
    max_timestamp: i64,
    /// Min timestamp in part.
    min_timestamp: i64,
    /// Max sequence number in part.
    max_sequence: SequenceNumber,
    /// Number of series.
    series_count: usize,
    /// Pre-computed per-batch statistics for the first tag column.
    /// `None` if there is no primary key.
    batch_stats: Option<BatchStats>,
}

impl MultiBulkPart {
    /// Creates a new MultiBulkPart from a single BulkPart.
    pub fn from_bulk_part(part: BulkPart, metadata: &RegionMetadata) -> Self {
        let num_rows = part.num_rows();
        let series_count = part.estimated_series_count();
        let batch_stats = BatchStats::compute(std::slice::from_ref(&part.batch), metadata);
        let mut batches = SmallVec::new();
        batches.push(part.batch);

        Self {
            batches,
            total_rows: num_rows,
            max_timestamp: part.max_timestamp,
            min_timestamp: part.min_timestamp,
            max_sequence: part.sequence,
            series_count,
            batch_stats,
        }
    }

    /// Creates a new MultiBulkPart from multiple ordered RecordBatches.
    ///
    /// # Arguments
    /// * `batches` - Ordered record batches
    /// * `min_timestamp` - Minimum timestamp across all batches
    /// * `max_timestamp` - Maximum timestamp across all batches
    /// * `max_sequence` - Maximum sequence number across all batches
    /// * `series_count` - Number of series in the batches
    /// * `metadata` - Region metadata for computing batch statistics
    ///
    /// # Panics
    /// Panics if batches is empty.
    pub fn new(
        batches: Vec<RecordBatch>,
        min_timestamp: i64,
        max_timestamp: i64,
        max_sequence: SequenceNumber,
        series_count: usize,
        metadata: &RegionMetadata,
    ) -> Self {
        assert!(!batches.is_empty(), "batches must not be empty");

        let total_rows = batches.iter().map(|b| b.num_rows()).sum();
        let batch_stats = BatchStats::compute(&batches, metadata);

        Self {
            batches: SmallVec::from_vec(batches),
            total_rows,
            max_timestamp,
            min_timestamp,
            max_sequence,
            series_count,
            batch_stats,
        }
    }

    /// Returns the total number of rows across all batches.
    pub fn num_rows(&self) -> usize {
        self.total_rows
    }

    /// Returns the minimum timestamp.
    pub fn min_timestamp(&self) -> i64 {
        self.min_timestamp
    }

    /// Returns the maximum timestamp.
    pub fn max_timestamp(&self) -> i64 {
        self.max_timestamp
    }

    /// Returns the maximum sequence number.
    pub fn max_sequence(&self) -> SequenceNumber {
        self.max_sequence
    }

    /// Returns the number of series.
    pub fn series_count(&self) -> usize {
        self.series_count
    }

    /// Returns the number of record batches in this part.
    pub fn num_batches(&self) -> usize {
        self.batches.len()
    }

    /// Returns the estimated memory size of all batches.
    pub(crate) fn estimated_size(&self) -> usize {
        self.batches.iter().map(record_batch_estimated_size).sum()
    }

    /// Reads data from this part with the given context and filters.
    ///
    /// If batch-level statistics are available and a predicate is set, prunes
    /// batches whose first-tag min/max range doesn't match the predicate before
    /// creating the iterator.
    pub(crate) fn read(
        &self,
        context: BulkIterContextRef,
        sequence: Option<SequenceRange>,
        mem_scan_metrics: Option<MemScanMetrics>,
    ) -> Result<Option<BoxedRecordBatchIterator>> {
        if self.batches.is_empty() {
            return Ok(None);
        }

        let batches_to_read = self.prune_batches(&context);

        if batches_to_read.is_empty() {
            return Ok(None);
        }

        let iter = crate::memtable::bulk::part_reader::BulkPartBatchIter::new(
            batches_to_read,
            context,
            sequence,
            self.series_count,
            mem_scan_metrics,
        );
        Ok(Some(Box::new(iter) as BoxedRecordBatchIterator))
    }

    /// Prunes batches using the first-tag min/max statistics and the predicate.
    /// Returns all batches if no stats or no predicate is available.
    fn prune_batches(&self, context: &BulkIterContextRef) -> Vec<RecordBatch> {
        if let Some(stats) = &self.batch_stats
            && let Some(predicate) = &context.predicate
        {
            let region_meta = context.read_format().metadata();
            let pruning_stats = BatchPruningStats {
                stats,
                metadata: region_meta,
            };
            let mask =
                predicate.prune_with_stats(&pruning_stats, region_meta.schema.arrow_schema());
            self.batches
                .iter()
                .zip(mask.iter())
                .filter_map(
                    |(batch, &selected)| {
                        if selected { Some(batch.clone()) } else { None }
                    },
                )
                .collect()
        } else {
            self.batches.iter().cloned().collect()
        }
    }

    /// Converts this `MultiBulkPart` to `MemtableStats`.
    pub fn to_memtable_stats(&self, region_metadata: &RegionMetadataRef) -> MemtableStats {
        let ts_type = region_metadata.time_index_type();
        let min_ts = ts_type.create_timestamp(self.min_timestamp);
        let max_ts = ts_type.create_timestamp(self.max_timestamp);

        MemtableStats {
            estimated_bytes: self.estimated_size(),
            time_range: Some((min_ts, max_ts)),
            num_rows: self.num_rows(),
            num_ranges: 1,
            max_sequence: self.max_sequence,
            series_count: self.series_count,
        }
    }
}

#[cfg(test)]
mod tests {
    use api::v1::{Row, SemanticType, WriteHint};
    use datafusion_common::ScalarValue;
    use datatypes::arrow::array::{
        BinaryArray, DictionaryArray, Float64Array, TimestampMillisecondArray,
    };
    use datatypes::arrow::datatypes::UInt32Type;
    use datatypes::prelude::{ConcreteDataType, Value};
    use datatypes::schema::ColumnSchema;
    use mito_codec::row_converter::build_primary_key_codec;
    use store_api::metadata::{ColumnMetadata, RegionMetadataBuilder};
    use store_api::storage::RegionId;
    use store_api::storage::consts::ReservedColumnId;
    use table::predicate::Predicate;

    use super::*;
    use crate::memtable::bulk::context::BulkIterContext;
    use crate::sst::{FlatSchemaOptions, to_flat_sst_arrow_schema};
    use crate::test_util::memtable_util::{build_key_values_with_ts_seq_values, metadata_for_test};

    struct MutationInput<'a> {
        k0: &'a str,
        k1: u32,
        timestamps: &'a [i64],
        v1: &'a [Option<f64>],
        sequence: u64,
    }

    fn encode(input: &[MutationInput]) -> EncodedBulkPart {
        let metadata = metadata_for_test();
        let kvs = input
            .iter()
            .map(|m| {
                build_key_values_with_ts_seq_values(
                    &metadata,
                    m.k0.to_string(),
                    m.k1,
                    m.timestamps.iter().copied(),
                    m.v1.iter().copied(),
                    m.sequence,
                )
            })
            .collect::<Vec<_>>();
        let schema = to_flat_sst_arrow_schema(&metadata, &FlatSchemaOptions::default());
        let primary_key_codec = build_primary_key_codec(&metadata);
        let mut converter = BulkPartConverter::new(&metadata, schema, 64, primary_key_codec, true);
        for kv in kvs {
            converter.append_key_values(&kv).unwrap();
        }
        let part = converter.convert().unwrap();
        let encoder = BulkPartEncoder::new(metadata, 1024).unwrap();
        encoder.encode_part(&part).unwrap().unwrap()
    }

    #[test]
    fn test_write_and_read_part_projection() {
        let part = encode(&[
            MutationInput {
                k0: "a",
                k1: 0,
                timestamps: &[1],
                v1: &[Some(0.1)],
                sequence: 0,
            },
            MutationInput {
                k0: "b",
                k1: 0,
                timestamps: &[1],
                v1: &[Some(0.0)],
                sequence: 0,
            },
            MutationInput {
                k0: "a",
                k1: 0,
                timestamps: &[2],
                v1: &[Some(0.2)],
                sequence: 1,
            },
        ]);

        let projection = &[4u32];
        let reader = part
            .read(
                Arc::new(
                    BulkIterContext::new(
                        part.metadata.region_metadata.clone(),
                        Some(projection.as_slice()),
                        None,
                        false,
                    )
                    .unwrap(),
                ),
                None,
                None,
            )
            .unwrap()
            .expect("expect at least one row group");

        let mut total_rows_read = 0;
        let mut field: Vec<f64> = vec![];
        for res in reader {
            let batch = res.unwrap();
            assert_eq!(5, batch.num_columns());
            field.extend_from_slice(
                batch
                    .column(0)
                    .as_any()
                    .downcast_ref::<Float64Array>()
                    .unwrap()
                    .values(),
            );
            total_rows_read += batch.num_rows();
        }
        assert_eq!(3, total_rows_read);
        assert_eq!(vec![0.1, 0.2, 0.0], field);
    }

    fn prepare(key_values: Vec<(&str, u32, (i64, i64), u64)>) -> EncodedBulkPart {
        let metadata = metadata_for_test();
        let kvs = key_values
            .into_iter()
            .map(|(k0, k1, (start, end), sequence)| {
                let ts = start..end;
                let v1 = (start..end).map(|_| None);
                build_key_values_with_ts_seq_values(&metadata, k0.to_string(), k1, ts, v1, sequence)
            })
            .collect::<Vec<_>>();
        let schema = to_flat_sst_arrow_schema(&metadata, &FlatSchemaOptions::default());
        let primary_key_codec = build_primary_key_codec(&metadata);
        let mut converter = BulkPartConverter::new(&metadata, schema, 64, primary_key_codec, true);
        for kv in kvs {
            converter.append_key_values(&kv).unwrap();
        }
        let part = converter.convert().unwrap();
        let encoder = BulkPartEncoder::new(metadata, 1024).unwrap();
        encoder.encode_part(&part).unwrap().unwrap()
    }

    fn check_prune_row_group(
        part: &EncodedBulkPart,
        predicate: Option<Predicate>,
        expected_rows: usize,
    ) {
        let context = Arc::new(
            BulkIterContext::new(
                part.metadata.region_metadata.clone(),
                None,
                predicate,
                false,
            )
            .unwrap(),
        );
        let reader = part
            .read(context, None, None)
            .unwrap()
            .expect("expect at least one row group");
        let mut total_rows_read = 0;
        for res in reader {
            let batch = res.unwrap();
            total_rows_read += batch.num_rows();
        }
        // Should only read row group 1.
        assert_eq!(expected_rows, total_rows_read);
    }

    #[test]
    fn test_prune_row_groups() {
        let part = prepare(vec![
            ("a", 0, (0, 40), 1),
            ("a", 1, (0, 60), 1),
            ("b", 0, (0, 100), 2),
            ("b", 1, (100, 180), 3),
            ("b", 1, (180, 210), 4),
        ]);

        let context = Arc::new(
            BulkIterContext::new(
                part.metadata.region_metadata.clone(),
                None,
                Some(Predicate::new(vec![datafusion_expr::col("ts").eq(
                    datafusion_expr::lit(ScalarValue::TimestampMillisecond(Some(300), None)),
                )])),
                false,
            )
            .unwrap(),
        );
        assert!(part.read(context, None, None).unwrap().is_none());

        check_prune_row_group(&part, None, 310);

        check_prune_row_group(
            &part,
            Some(Predicate::new(vec![
                datafusion_expr::col("k0").eq(datafusion_expr::lit("a")),
                datafusion_expr::col("k1").eq(datafusion_expr::lit(0u32)),
            ])),
            40,
        );

        check_prune_row_group(
            &part,
            Some(Predicate::new(vec![
                datafusion_expr::col("k0").eq(datafusion_expr::lit("a")),
                datafusion_expr::col("k1").eq(datafusion_expr::lit(1u32)),
            ])),
            60,
        );

        check_prune_row_group(
            &part,
            Some(Predicate::new(vec![
                datafusion_expr::col("k0").eq(datafusion_expr::lit("a")),
            ])),
            100,
        );

        check_prune_row_group(
            &part,
            Some(Predicate::new(vec![
                datafusion_expr::col("k0").eq(datafusion_expr::lit("b")),
                datafusion_expr::col("k1").eq(datafusion_expr::lit(0u32)),
            ])),
            100,
        );

        // Predicates over field column can do precise filtering.
        check_prune_row_group(
            &part,
            Some(Predicate::new(vec![
                datafusion_expr::col("v0").eq(datafusion_expr::lit(150i64)),
            ])),
            1,
        );
    }

    #[test]
    fn test_bulk_part_converter_append_and_convert() {
        let metadata = metadata_for_test();
        let capacity = 100;
        let primary_key_codec = build_primary_key_codec(&metadata);
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let mut converter =
            BulkPartConverter::new(&metadata, schema, capacity, primary_key_codec, true);

        let key_values1 = build_key_values_with_ts_seq_values(
            &metadata,
            "key1".to_string(),
            1u32,
            vec![1000, 2000].into_iter(),
            vec![Some(1.0), Some(2.0)].into_iter(),
            1,
        );

        let key_values2 = build_key_values_with_ts_seq_values(
            &metadata,
            "key2".to_string(),
            2u32,
            vec![1500].into_iter(),
            vec![Some(3.0)].into_iter(),
            2,
        );

        converter.append_key_values(&key_values1).unwrap();
        converter.append_key_values(&key_values2).unwrap();

        let bulk_part = converter.convert().unwrap();

        assert_eq!(bulk_part.num_rows(), 3);
        assert_eq!(bulk_part.min_timestamp, 1000);
        assert_eq!(bulk_part.max_timestamp, 2000);
        assert_eq!(bulk_part.sequence, 2);
        assert_eq!(bulk_part.timestamp_index, bulk_part.batch.num_columns() - 4);

        // Validate primary key columns are stored
        // Schema should include primary key columns k0 and k1 at the beginning
        let schema = bulk_part.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec![
                "k0",
                "k1",
                "v0",
                "v1",
                "ts",
                "__primary_key",
                "__sequence",
                "__op_type"
            ]
        );
    }

    #[test]
    fn test_bulk_part_converter_sorting() {
        let metadata = metadata_for_test();
        let capacity = 100;
        let primary_key_codec = build_primary_key_codec(&metadata);
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let mut converter =
            BulkPartConverter::new(&metadata, schema, capacity, primary_key_codec, true);

        let key_values1 = build_key_values_with_ts_seq_values(
            &metadata,
            "z_key".to_string(),
            3u32,
            vec![3000].into_iter(),
            vec![Some(3.0)].into_iter(),
            3,
        );

        let key_values2 = build_key_values_with_ts_seq_values(
            &metadata,
            "a_key".to_string(),
            1u32,
            vec![1000].into_iter(),
            vec![Some(1.0)].into_iter(),
            1,
        );

        let key_values3 = build_key_values_with_ts_seq_values(
            &metadata,
            "m_key".to_string(),
            2u32,
            vec![2000].into_iter(),
            vec![Some(2.0)].into_iter(),
            2,
        );

        converter.append_key_values(&key_values1).unwrap();
        converter.append_key_values(&key_values2).unwrap();
        converter.append_key_values(&key_values3).unwrap();

        let bulk_part = converter.convert().unwrap();

        assert_eq!(bulk_part.num_rows(), 3);

        let ts_column = bulk_part.batch.column(bulk_part.timestamp_index);
        let seq_column = bulk_part.batch.column(bulk_part.batch.num_columns() - 2);

        let ts_array = ts_column
            .as_any()
            .downcast_ref::<TimestampMillisecondArray>()
            .unwrap();
        let seq_array = seq_column.as_any().downcast_ref::<UInt64Array>().unwrap();

        assert_eq!(ts_array.values(), &[1000, 2000, 3000]);
        assert_eq!(seq_array.values(), &[1, 2, 3]);

        // Validate primary key columns are stored
        let schema = bulk_part.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec![
                "k0",
                "k1",
                "v0",
                "v1",
                "ts",
                "__primary_key",
                "__sequence",
                "__op_type"
            ]
        );
    }

    #[test]
    fn test_bulk_part_converter_empty() {
        let metadata = metadata_for_test();
        let capacity = 10;
        let primary_key_codec = build_primary_key_codec(&metadata);
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let converter =
            BulkPartConverter::new(&metadata, schema, capacity, primary_key_codec, true);

        let bulk_part = converter.convert().unwrap();

        assert_eq!(bulk_part.num_rows(), 0);
        assert_eq!(bulk_part.min_timestamp, i64::MAX);
        assert_eq!(bulk_part.max_timestamp, i64::MIN);
        assert_eq!(bulk_part.sequence, SequenceNumber::MIN);

        // Validate primary key columns are present in schema even for empty batch
        let schema = bulk_part.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec![
                "k0",
                "k1",
                "v0",
                "v1",
                "ts",
                "__primary_key",
                "__sequence",
                "__op_type"
            ]
        );
    }

    #[test]
    fn test_bulk_part_converter_without_primary_key_columns() {
        let metadata = metadata_for_test();
        let primary_key_codec = build_primary_key_codec(&metadata);
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions {
                raw_pk_columns: false,
                string_pk_use_dict: true,
                ..Default::default()
            },
        );

        let capacity = 100;
        let mut converter =
            BulkPartConverter::new(&metadata, schema, capacity, primary_key_codec, false);

        let key_values1 = build_key_values_with_ts_seq_values(
            &metadata,
            "key1".to_string(),
            1u32,
            vec![1000, 2000].into_iter(),
            vec![Some(1.0), Some(2.0)].into_iter(),
            1,
        );

        let key_values2 = build_key_values_with_ts_seq_values(
            &metadata,
            "key2".to_string(),
            2u32,
            vec![1500].into_iter(),
            vec![Some(3.0)].into_iter(),
            2,
        );

        converter.append_key_values(&key_values1).unwrap();
        converter.append_key_values(&key_values2).unwrap();

        let bulk_part = converter.convert().unwrap();

        assert_eq!(bulk_part.num_rows(), 3);
        assert_eq!(bulk_part.min_timestamp, 1000);
        assert_eq!(bulk_part.max_timestamp, 2000);
        assert_eq!(bulk_part.sequence, 2);
        assert_eq!(bulk_part.timestamp_index, bulk_part.batch.num_columns() - 4);

        // Validate primary key columns are NOT stored individually
        let schema = bulk_part.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec!["v0", "v1", "ts", "__primary_key", "__sequence", "__op_type"]
        );
    }

    #[allow(clippy::too_many_arguments)]
    fn build_key_values_with_sparse_encoding(
        metadata: &RegionMetadataRef,
        primary_key_codec: &Arc<dyn PrimaryKeyCodec>,
        table_id: u32,
        tsid: u64,
        k0: String,
        k1: String,
        timestamps: impl Iterator<Item = i64>,
        values: impl Iterator<Item = Option<f64>>,
        sequence: SequenceNumber,
    ) -> KeyValues {
        // Encode the primary key (__table_id, __tsid, k0, k1) into binary format using the sparse codec
        let pk_values = vec![
            (ReservedColumnId::table_id(), Value::UInt32(table_id)),
            (ReservedColumnId::tsid(), Value::UInt64(tsid)),
            (0, Value::String(k0.clone().into())),
            (1, Value::String(k1.clone().into())),
        ];
        let mut encoded_key = Vec::new();
        primary_key_codec
            .encode_values(&pk_values, &mut encoded_key)
            .unwrap();
        assert!(!encoded_key.is_empty());

        // Create schema for sparse encoding: __primary_key, ts, v0, v1
        let column_schema = vec![
            api::v1::ColumnSchema {
                column_name: PRIMARY_KEY_COLUMN_NAME.to_string(),
                datatype: api::helper::ColumnDataTypeWrapper::try_from(
                    ConcreteDataType::binary_datatype(),
                )
                .unwrap()
                .datatype() as i32,
                semantic_type: api::v1::SemanticType::Tag as i32,
                ..Default::default()
            },
            api::v1::ColumnSchema {
                column_name: "ts".to_string(),
                datatype: api::helper::ColumnDataTypeWrapper::try_from(
                    ConcreteDataType::timestamp_millisecond_datatype(),
                )
                .unwrap()
                .datatype() as i32,
                semantic_type: api::v1::SemanticType::Timestamp as i32,
                ..Default::default()
            },
            api::v1::ColumnSchema {
                column_name: "v0".to_string(),
                datatype: api::helper::ColumnDataTypeWrapper::try_from(
                    ConcreteDataType::int64_datatype(),
                )
                .unwrap()
                .datatype() as i32,
                semantic_type: api::v1::SemanticType::Field as i32,
                ..Default::default()
            },
            api::v1::ColumnSchema {
                column_name: "v1".to_string(),
                datatype: api::helper::ColumnDataTypeWrapper::try_from(
                    ConcreteDataType::float64_datatype(),
                )
                .unwrap()
                .datatype() as i32,
                semantic_type: api::v1::SemanticType::Field as i32,
                ..Default::default()
            },
        ];

        let rows = timestamps
            .zip(values)
            .map(|(ts, v)| Row {
                values: vec![
                    api::v1::Value {
                        value_data: Some(api::v1::value::ValueData::BinaryValue(
                            encoded_key.clone(),
                        )),
                    },
                    api::v1::Value {
                        value_data: Some(api::v1::value::ValueData::TimestampMillisecondValue(ts)),
                    },
                    api::v1::Value {
                        value_data: Some(api::v1::value::ValueData::I64Value(ts)),
                    },
                    api::v1::Value {
                        value_data: v.map(api::v1::value::ValueData::F64Value),
                    },
                ],
            })
            .collect();

        let mutation = api::v1::Mutation {
            op_type: 1,
            sequence,
            rows: Some(api::v1::Rows {
                schema: column_schema,
                rows,
            }),
            write_hint: Some(WriteHint {
                primary_key_encoding: api::v1::PrimaryKeyEncoding::Sparse.into(),
            }),
        };
        KeyValues::new(metadata.as_ref(), mutation).unwrap()
    }

    #[test]
    fn test_bulk_part_converter_sparse_primary_key_encoding() {
        use api::v1::SemanticType;
        use datatypes::schema::ColumnSchema;
        use store_api::metadata::{ColumnMetadata, RegionMetadataBuilder};
        use store_api::storage::RegionId;

        let mut builder = RegionMetadataBuilder::new(RegionId::new(123, 456));
        builder
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("k0", ConcreteDataType::string_datatype(), false),
                semantic_type: SemanticType::Tag,
                column_id: 0,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("k1", ConcreteDataType::string_datatype(), false),
                semantic_type: SemanticType::Tag,
                column_id: 1,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new(
                    "ts",
                    ConcreteDataType::timestamp_millisecond_datatype(),
                    false,
                ),
                semantic_type: SemanticType::Timestamp,
                column_id: 2,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("v0", ConcreteDataType::int64_datatype(), true),
                semantic_type: SemanticType::Field,
                column_id: 3,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("v1", ConcreteDataType::float64_datatype(), true),
                semantic_type: SemanticType::Field,
                column_id: 4,
            })
            .primary_key(vec![0, 1])
            .primary_key_encoding(PrimaryKeyEncoding::Sparse);
        let metadata = Arc::new(builder.build().unwrap());

        let primary_key_codec = build_primary_key_codec(&metadata);
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        assert_eq!(metadata.primary_key_encoding, PrimaryKeyEncoding::Sparse);
        assert_eq!(primary_key_codec.encoding(), PrimaryKeyEncoding::Sparse);

        let capacity = 100;
        let mut converter =
            BulkPartConverter::new(&metadata, schema, capacity, primary_key_codec.clone(), true);

        let key_values1 = build_key_values_with_sparse_encoding(
            &metadata,
            &primary_key_codec,
            2048u32, // table_id
            100u64,  // tsid
            "key11".to_string(),
            "key21".to_string(),
            vec![1000, 2000].into_iter(),
            vec![Some(1.0), Some(2.0)].into_iter(),
            1,
        );

        let key_values2 = build_key_values_with_sparse_encoding(
            &metadata,
            &primary_key_codec,
            4096u32, // table_id
            200u64,  // tsid
            "key12".to_string(),
            "key22".to_string(),
            vec![1500].into_iter(),
            vec![Some(3.0)].into_iter(),
            2,
        );

        converter.append_key_values(&key_values1).unwrap();
        converter.append_key_values(&key_values2).unwrap();

        let bulk_part = converter.convert().unwrap();

        assert_eq!(bulk_part.num_rows(), 3);
        assert_eq!(bulk_part.min_timestamp, 1000);
        assert_eq!(bulk_part.max_timestamp, 2000);
        assert_eq!(bulk_part.sequence, 2);
        assert_eq!(bulk_part.timestamp_index, bulk_part.batch.num_columns() - 4);

        // For sparse encoding, primary key columns should NOT be stored individually
        // even when store_primary_key_columns is true, because sparse encoding
        // stores the encoded primary key in the __primary_key column
        let schema = bulk_part.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec!["v0", "v1", "ts", "__primary_key", "__sequence", "__op_type"]
        );

        // Verify the __primary_key column contains encoded sparse keys
        let primary_key_column = bulk_part.batch.column_by_name("__primary_key").unwrap();
        let dict_array = primary_key_column
            .as_any()
            .downcast_ref::<DictionaryArray<UInt32Type>>()
            .unwrap();

        // Should have non-zero entries indicating encoded primary keys
        assert!(!dict_array.is_empty());
        assert_eq!(dict_array.len(), 3); // 3 rows total

        // Verify values are properly encoded binary data (not empty)
        let values = dict_array
            .values()
            .as_any()
            .downcast_ref::<BinaryArray>()
            .unwrap();
        for i in 0..values.len() {
            assert!(
                !values.value(i).is_empty(),
                "Encoded primary key should not be empty"
            );
        }
    }

    #[test]
    fn test_convert_bulk_part_empty() {
        let metadata = metadata_for_test();
        let schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );
        let primary_key_codec = build_primary_key_codec(&metadata);

        // Create empty batch
        let empty_batch = RecordBatch::new_empty(schema.clone());
        let empty_part = BulkPart {
            batch: empty_batch,
            max_timestamp: 0,
            min_timestamp: 0,
            sequence: 0,
            timestamp_index: 0,
            raw_data: None,
        };

        let result =
            convert_bulk_part(empty_part, &metadata, primary_key_codec, schema, true).unwrap();
        assert!(result.is_none());
    }

    #[test]
    fn test_convert_bulk_part_dense_with_pk_columns() {
        let metadata = metadata_for_test();
        let primary_key_codec = build_primary_key_codec(&metadata);

        let k0_array = Arc::new(arrow::array::StringArray::from(vec![
            "key1", "key2", "key1",
        ]));
        let k1_array = Arc::new(arrow::array::UInt32Array::from(vec![1, 2, 1]));
        let v0_array = Arc::new(arrow::array::Int64Array::from(vec![100, 200, 300]));
        let v1_array = Arc::new(arrow::array::Float64Array::from(vec![1.0, 2.0, 3.0]));
        let ts_array = Arc::new(TimestampMillisecondArray::from(vec![1000, 2000, 1500]));

        let input_schema = Arc::new(Schema::new(vec![
            Field::new("k0", ArrowDataType::Utf8, false),
            Field::new("k1", ArrowDataType::UInt32, false),
            Field::new("v0", ArrowDataType::Int64, true),
            Field::new("v1", ArrowDataType::Float64, true),
            Field::new(
                "ts",
                ArrowDataType::Timestamp(arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        let input_batch = RecordBatch::try_new(
            input_schema,
            vec![k0_array, k1_array, v0_array, v1_array, ts_array],
        )
        .unwrap();

        let part = BulkPart {
            batch: input_batch,
            max_timestamp: 2000,
            min_timestamp: 1000,
            sequence: 5,
            timestamp_index: 4,
            raw_data: None,
        };

        let output_schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let result = convert_bulk_part(
            part,
            &metadata,
            primary_key_codec,
            output_schema,
            true, // store primary key columns
        )
        .unwrap();

        let converted = result.unwrap();

        assert_eq!(converted.num_rows(), 3);
        assert_eq!(converted.max_timestamp, 2000);
        assert_eq!(converted.min_timestamp, 1000);
        assert_eq!(converted.sequence, 5);

        let schema = converted.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec![
                "k0",
                "k1",
                "v0",
                "v1",
                "ts",
                "__primary_key",
                "__sequence",
                "__op_type"
            ]
        );

        let k0_col = converted.batch.column_by_name("k0").unwrap();
        assert!(matches!(
            k0_col.data_type(),
            ArrowDataType::Dictionary(_, _)
        ));

        let pk_col = converted.batch.column_by_name("__primary_key").unwrap();
        let dict_array = pk_col
            .as_any()
            .downcast_ref::<DictionaryArray<UInt32Type>>()
            .unwrap();
        let keys = dict_array.keys();

        assert_eq!(keys.len(), 3);
    }

    #[test]
    fn test_convert_bulk_part_dense_without_pk_columns() {
        let metadata = metadata_for_test();
        let primary_key_codec = build_primary_key_codec(&metadata);

        // Create input batch with primary key columns (k0, k1)
        let k0_array = Arc::new(arrow::array::StringArray::from(vec!["key1", "key2"]));
        let k1_array = Arc::new(arrow::array::UInt32Array::from(vec![1, 2]));
        let v0_array = Arc::new(arrow::array::Int64Array::from(vec![100, 200]));
        let v1_array = Arc::new(arrow::array::Float64Array::from(vec![1.0, 2.0]));
        let ts_array = Arc::new(TimestampMillisecondArray::from(vec![1000, 2000]));

        let input_schema = Arc::new(Schema::new(vec![
            Field::new("k0", ArrowDataType::Utf8, false),
            Field::new("k1", ArrowDataType::UInt32, false),
            Field::new("v0", ArrowDataType::Int64, true),
            Field::new("v1", ArrowDataType::Float64, true),
            Field::new(
                "ts",
                ArrowDataType::Timestamp(arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        let input_batch = RecordBatch::try_new(
            input_schema,
            vec![k0_array, k1_array, v0_array, v1_array, ts_array],
        )
        .unwrap();

        let part = BulkPart {
            batch: input_batch,
            max_timestamp: 2000,
            min_timestamp: 1000,
            sequence: 3,
            timestamp_index: 4,
            raw_data: None,
        };

        let output_schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions {
                raw_pk_columns: false,
                string_pk_use_dict: true,
                ..Default::default()
            },
        );

        let result = convert_bulk_part(
            part,
            &metadata,
            primary_key_codec,
            output_schema,
            false, // don't store primary key columns
        )
        .unwrap();

        let converted = result.unwrap();

        assert_eq!(converted.num_rows(), 2);
        assert_eq!(converted.max_timestamp, 2000);
        assert_eq!(converted.min_timestamp, 1000);
        assert_eq!(converted.sequence, 3);

        // Verify schema does NOT include individual primary key columns
        let schema = converted.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec!["v0", "v1", "ts", "__primary_key", "__sequence", "__op_type"]
        );

        // Verify __primary_key column is present and is a dictionary
        let pk_col = converted.batch.column_by_name("__primary_key").unwrap();
        assert!(matches!(
            pk_col.data_type(),
            ArrowDataType::Dictionary(_, _)
        ));
    }

    #[test]
    fn test_convert_bulk_part_sparse_encoding() {
        let mut builder = RegionMetadataBuilder::new(RegionId::new(123, 456));
        builder
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("k0", ConcreteDataType::string_datatype(), false),
                semantic_type: SemanticType::Tag,
                column_id: 0,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("k1", ConcreteDataType::string_datatype(), false),
                semantic_type: SemanticType::Tag,
                column_id: 1,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new(
                    "ts",
                    ConcreteDataType::timestamp_millisecond_datatype(),
                    false,
                ),
                semantic_type: SemanticType::Timestamp,
                column_id: 2,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("v0", ConcreteDataType::int64_datatype(), true),
                semantic_type: SemanticType::Field,
                column_id: 3,
            })
            .push_column_metadata(ColumnMetadata {
                column_schema: ColumnSchema::new("v1", ConcreteDataType::float64_datatype(), true),
                semantic_type: SemanticType::Field,
                column_id: 4,
            })
            .primary_key(vec![0, 1])
            .primary_key_encoding(PrimaryKeyEncoding::Sparse);
        let metadata = Arc::new(builder.build().unwrap());

        let primary_key_codec = build_primary_key_codec(&metadata);

        // Create input batch with __primary_key column (sparse encoding)
        let pk_array = Arc::new(arrow::array::BinaryArray::from(vec![
            b"encoded_key_1".as_slice(),
            b"encoded_key_2".as_slice(),
        ]));
        let v0_array = Arc::new(arrow::array::Int64Array::from(vec![100, 200]));
        let v1_array = Arc::new(arrow::array::Float64Array::from(vec![1.0, 2.0]));
        let ts_array = Arc::new(TimestampMillisecondArray::from(vec![1000, 2000]));

        let input_schema = Arc::new(Schema::new(vec![
            Field::new("__primary_key", ArrowDataType::Binary, false),
            Field::new("v0", ArrowDataType::Int64, true),
            Field::new("v1", ArrowDataType::Float64, true),
            Field::new(
                "ts",
                ArrowDataType::Timestamp(arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        let input_batch =
            RecordBatch::try_new(input_schema, vec![pk_array, v0_array, v1_array, ts_array])
                .unwrap();

        let part = BulkPart {
            batch: input_batch,
            max_timestamp: 2000,
            min_timestamp: 1000,
            sequence: 7,
            timestamp_index: 3,
            raw_data: None,
        };

        let output_schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let result = convert_bulk_part(
            part,
            &metadata,
            primary_key_codec,
            output_schema,
            true, // store_primary_key_columns (ignored for sparse)
        )
        .unwrap();

        let converted = result.unwrap();

        assert_eq!(converted.num_rows(), 2);
        assert_eq!(converted.max_timestamp, 2000);
        assert_eq!(converted.min_timestamp, 1000);
        assert_eq!(converted.sequence, 7);

        // Verify schema does NOT include individual primary key columns (sparse encoding)
        let schema = converted.batch.schema();
        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
        assert_eq!(
            field_names,
            vec!["v0", "v1", "ts", "__primary_key", "__sequence", "__op_type"]
        );

        // Verify __primary_key is dictionary encoded
        let pk_col = converted.batch.column_by_name("__primary_key").unwrap();
        assert!(matches!(
            pk_col.data_type(),
            ArrowDataType::Dictionary(_, _)
        ));
    }

    #[test]
    fn test_convert_bulk_part_sorting_with_multiple_series() {
        let metadata = metadata_for_test();
        let primary_key_codec = build_primary_key_codec(&metadata);

        // Create unsorted batch with multiple series and timestamps
        let k0_array = Arc::new(arrow::array::StringArray::from(vec![
            "series_b", "series_a", "series_b", "series_a",
        ]));
        let k1_array = Arc::new(arrow::array::UInt32Array::from(vec![2, 1, 2, 1]));
        let v0_array = Arc::new(arrow::array::Int64Array::from(vec![200, 100, 400, 300]));
        let v1_array = Arc::new(arrow::array::Float64Array::from(vec![2.0, 1.0, 4.0, 3.0]));
        let ts_array = Arc::new(TimestampMillisecondArray::from(vec![
            2000, 1000, 4000, 3000,
        ]));

        let input_schema = Arc::new(Schema::new(vec![
            Field::new("k0", ArrowDataType::Utf8, false),
            Field::new("k1", ArrowDataType::UInt32, false),
            Field::new("v0", ArrowDataType::Int64, true),
            Field::new("v1", ArrowDataType::Float64, true),
            Field::new(
                "ts",
                ArrowDataType::Timestamp(arrow::datatypes::TimeUnit::Millisecond, None),
                false,
            ),
        ]));

        let input_batch = RecordBatch::try_new(
            input_schema,
            vec![k0_array, k1_array, v0_array, v1_array, ts_array],
        )
        .unwrap();

        let part = BulkPart {
            batch: input_batch,
            max_timestamp: 4000,
            min_timestamp: 1000,
            sequence: 10,
            timestamp_index: 4,
            raw_data: None,
        };

        let output_schema = to_flat_sst_arrow_schema(
            &metadata,
            &FlatSchemaOptions::from_encoding(metadata.primary_key_encoding),
        );

        let result =
            convert_bulk_part(part, &metadata, primary_key_codec, output_schema, true).unwrap();

        let converted = result.unwrap();

        assert_eq!(converted.num_rows(), 4);

        // Verify data is sorted by (primary_key, timestamp, sequence desc)
        let ts_col = converted.batch.column(converted.timestamp_index);
        let ts_array = ts_col
            .as_any()
            .downcast_ref::<TimestampMillisecondArray>()
            .unwrap();

        // After sorting by (pk, ts), we should have:
        // series_a,1: ts=1000, 3000
        // series_b,2: ts=2000, 4000
        let timestamps: Vec<i64> = ts_array.values().to_vec();
        assert_eq!(timestamps, vec![1000, 3000, 2000, 4000]);
    }

    /// Helper to create a converted BulkPart (with __primary_key column) from MutationInputs.
    fn build_converted_bulk_part(inputs: &[MutationInput]) -> BulkPart {
        let metadata = metadata_for_test();
        let kvs = inputs
            .iter()
            .map(|m| {
                build_key_values_with_ts_seq_values(
                    &metadata,
                    m.k0.to_string(),
                    m.k1,
                    m.timestamps.iter().copied(),
                    m.v1.iter().copied(),
                    m.sequence,
                )
            })
            .collect::<Vec<_>>();
        let schema = to_flat_sst_arrow_schema(&metadata, &FlatSchemaOptions::default());
        let primary_key_codec = build_primary_key_codec(&metadata);
        let mut converter = BulkPartConverter::new(&metadata, schema, 64, primary_key_codec, true);
        for kv in kvs {
            converter.append_key_values(&kv).unwrap();
        }
        converter.convert().unwrap()
    }

    /// Helper to create a MultiBulkPart where each group becomes a separate batch.
    fn build_multi_bulk_part(groups: &[&[MutationInput]]) -> (MultiBulkPart, RegionMetadataRef) {
        let metadata = metadata_for_test();
        let mut all_batches = Vec::new();
        let mut min_ts = i64::MAX;
        let mut max_ts = i64::MIN;
        let mut max_seq = 0u64;

        for inputs in groups {
            let part = build_converted_bulk_part(inputs);
            min_ts = min_ts.min(part.min_timestamp);
            max_ts = max_ts.max(part.max_timestamp);
            max_seq = max_seq.max(part.sequence);
            all_batches.push(part.batch);
        }

        let multi = MultiBulkPart::new(
            all_batches,
            min_ts,
            max_ts,
            max_seq,
            groups.len(),
            &metadata,
        );
        (multi, metadata)
    }

    #[test]
    fn test_multi_bulk_part_prune_batches() {
        // Three batches with distinct k0 ranges: ["a"], ["m"], ["z"].
        let (multi, metadata) = build_multi_bulk_part(&[
            &[MutationInput {
                k0: "a",
                k1: 0,
                timestamps: &[1, 2],
                v1: &[Some(1.0), Some(2.0)],
                sequence: 0,
            }],
            &[MutationInput {
                k0: "m",
                k1: 0,
                timestamps: &[3, 4],
                v1: &[Some(3.0), Some(4.0)],
                sequence: 1,
            }],
            &[MutationInput {
                k0: "z",
                k1: 0,
                timestamps: &[5, 6],
                v1: &[Some(5.0), Some(6.0)],
                sequence: 2,
            }],
        ]);
        assert_eq!(multi.num_rows(), 6);
        assert_eq!(multi.num_batches(), 3);

        // k0 = "m" => only middle batch (2 rows).
        let context = Arc::new(
            BulkIterContext::new(
                metadata.clone(),
                None,
                Some(Predicate::new(vec![
                    datafusion_expr::col("k0").eq(datafusion_expr::lit("m")),
                ])),
                false,
            )
            .unwrap(),
        );
        let reader = multi
            .read(context, None, None)
            .unwrap()
            .expect("should have results");
        let total_rows: usize = reader.map(|r| r.unwrap().num_rows()).sum();
        assert_eq!(total_rows, 2);

        // k0 = "nonexistent" => all pruned, returns None.
        let context = Arc::new(
            BulkIterContext::new(
                metadata.clone(),
                None,
                Some(Predicate::new(vec![
                    datafusion_expr::col("k0").eq(datafusion_expr::lit("nonexistent")),
                ])),
                false,
            )
            .unwrap(),
        );
        assert!(multi.read(context, None, None).unwrap().is_none());

        // No predicate => all 6 rows.
        let context = Arc::new(BulkIterContext::new(metadata.clone(), None, None, false).unwrap());
        let reader = multi
            .read(context, None, None)
            .unwrap()
            .expect("should have results");
        let total_rows: usize = reader.map(|r| r.unwrap().num_rows()).sum();
        assert_eq!(total_rows, 6);
    }
}