lance 4.0.0

// SPDX-License-Identifier: Apache-2.0
// SPDX-FileCopyrightText: Copyright The Lance Authors

//! IVF - Inverted File index.

use super::{builder::IvfIndexBuilder, utils::PartitionLoadLock};
use super::{
    pq::{PQIndex, build_pq_model},
    utils::{filter_finite_training_data, maybe_sample_training_data},
};
use crate::dataset::index::dataset_format_version;
use crate::index::DatasetIndexInternalExt;
use crate::index::vector::utils::{get_vector_dim, get_vector_type};
use crate::{
    dataset::Dataset,
    index::{INDEX_FILE_NAME, pb, prefilter::PreFilter, vector::ivf::io::write_pq_partitions},
};
use crate::{dataset::builder::DatasetBuilder, index::vector::IndexFileVersion};
use arrow::datatypes::UInt8Type;
use arrow_arith::numeric::sub;
use arrow_array::Float32Array;
use arrow_array::{
    Array, FixedSizeListArray, PrimitiveArray, RecordBatch, UInt32Array,
    cast::AsArray,
    types::{ArrowPrimitiveType, Float16Type, Float32Type, Float64Type},
};
use arrow_schema::{DataType, Schema};
use async_trait::async_trait;
use datafusion::execution::SendableRecordBatchStream;
use deepsize::DeepSizeOf;
use futures::TryFutureExt;
use futures::{
    Stream, TryStreamExt,
    stream::{self, StreamExt},
};
use io::write_hnsw_quantization_index_partitions;
use lance_arrow::*;
use lance_core::{
    Error, ROW_ID_FIELD, Result,
    cache::{LanceCache, UnsizedCacheKey, WeakLanceCache},
    traits::DatasetTakeRows,
    utils::tracing::{IO_TYPE_LOAD_VECTOR_PART, TRACE_IO_EVENTS},
};
use lance_file::{
    format::MAGIC,
    previous::writer::{
        FileWriter as PreviousFileWriter, FileWriterOptions as PreviousFileWriterOptions,
    },
    reader::{FileReader as V2Reader, FileReaderOptions as V2ReaderOptions},
    writer::{FileWriter as V2Writer, FileWriterOptions as V2WriterOptions},
};
use lance_index::metrics::MetricsCollector;
use lance_index::metrics::NoOpMetricsCollector;
use lance_index::vector::DISTANCE_TYPE_KEY;
use lance_index::vector::bq::builder::RabitQuantizer;
use lance_index::vector::flat::index::{FlatBinQuantizer, FlatIndex, FlatQuantizer};
use lance_index::vector::hnsw::HnswMetadata;
use lance_index::vector::hnsw::builder::HNSW_METADATA_KEY;
use lance_index::vector::ivf::storage::IVF_METADATA_KEY;
use lance_index::vector::ivf::storage::IvfModel;
use lance_index::vector::kmeans::KMeansParams;
use lance_index::vector::pq::storage::transpose;
use lance_index::vector::quantizer::QuantizationType;
use lance_index::vector::v3::shuffler::create_ivf_shuffler;
use lance_index::vector::v3::subindex::{IvfSubIndex, SubIndexType};
use lance_index::{
    INDEX_AUXILIARY_FILE_NAME, INDEX_METADATA_SCHEMA_KEY, Index, IndexMetadata, IndexSegment,
    IndexSegmentPlan, IndexType,
    optimize::OptimizeOptions,
    vector::{
        Query, VectorIndex,
        hnsw::{HNSW, HNSWIndex, builder::HnswBuildParams},
        ivf::{
            IvfBuildParams, builder::load_precomputed_partitions, shuffler::shuffle_dataset,
            storage::IVF_PARTITION_KEY,
        },
        pq::{PQBuildParams, ProductQuantizer},
        quantizer::{Quantization, QuantizationMetadata, Quantizer},
        sq::ScalarQuantizer,
    },
};
use lance_io::scheduler::{ScanScheduler, SchedulerConfig};
use lance_io::utils::CachedFileSize;
use lance_io::{
    encodings::plain::PlainEncoder,
    local::to_local_path,
    object_store::ObjectStore,
    stream::RecordBatchStream,
    traits::{Reader, WriteExt, Writer},
};
use lance_linalg::distance::{DistanceType, Dot, L2, MetricType};
use lance_linalg::{distance::Normalize, kernels::normalize_fsl_owned};
use lance_table::format::IndexMetadata as TableIndexMetadata;
use log::{info, warn};
use object_store::path::Path;
use prost::Message;
use roaring::RoaringBitmap;
use serde::Serialize;
use serde_json::json;
use std::collections::HashSet;
use std::{any::Any, collections::HashMap, sync::Arc};
use tokio::sync::mpsc;
use tracing::instrument;
use uuid::Uuid;

pub mod builder;
pub mod io;
pub mod v2;

// Cache wrapper for vector index trait objects
// Cache key for IVF partitions in the legacy IVF index
#[derive(Debug, Clone)]
pub struct LegacyIVFPartitionKey {
    pub partition_id: usize,
}

impl LegacyIVFPartitionKey {
    pub fn new(partition_id: usize) -> Self {
        Self { partition_id }
    }
}

impl UnsizedCacheKey for LegacyIVFPartitionKey {
    type ValueType = dyn VectorIndex;

    fn key(&self) -> std::borrow::Cow<'_, str> {
        format!("ivf-{}", self.partition_id).into()
    }
}

/// IVF Index.
/// WARNING: Internal API with no stability guarantees.
pub struct IVFIndex {
    uuid: String,

    /// Ivf model
    pub ivf: IvfModel,

    reader: Arc<dyn Reader>,

    /// Index in each partition.
    sub_index: Arc<dyn VectorIndex>,

    partition_locks: PartitionLoadLock,

    pub metric_type: MetricType,

    index_cache: WeakLanceCache,
}

impl DeepSizeOf for IVFIndex {
    fn deep_size_of_children(&self, context: &mut deepsize::Context) -> usize {
        self.uuid.deep_size_of_children(context)
            + self.reader.deep_size_of_children(context)
            + self.sub_index.deep_size_of_children(context)
    }
}

impl IVFIndex {
    /// Create a new IVF index.
    pub(crate) fn try_new(
        uuid: &str,
        ivf: IvfModel,
        reader: Arc<dyn Reader>,
        sub_index: Arc<dyn VectorIndex>,
        metric_type: MetricType,
        index_cache: LanceCache,
    ) -> Result<Self> {
        if !sub_index.is_loadable() {
            return Err(Error::index(format!(
                "IVF sub index must be loadable, got: {:?}",
                sub_index
            )));
        }

        let num_partitions = ivf.num_partitions();
        Ok(Self {
            uuid: uuid.to_owned(),
            ivf,
            reader,
            sub_index,
            metric_type,
            partition_locks: PartitionLoadLock::new(num_partitions),
            index_cache: WeakLanceCache::from(&index_cache),
        })
    }

    /// Load one partition of the IVF sub-index.
    ///
    /// Internal API with no stability guarantees.
    ///
    /// Parameters
    /// ----------
    ///  - partition_id: partition ID.
    #[instrument(level = "debug", skip(self, metrics))]
    pub async fn load_partition(
        &self,
        partition_id: usize,
        write_cache: bool,
        metrics: &dyn MetricsCollector,
    ) -> Result<Arc<dyn VectorIndex>> {
        let cache_key = LegacyIVFPartitionKey::new(partition_id);
        let part_index = if let Some(part_idx) =
            self.index_cache.get_unsized_with_key(&cache_key).await
        {
            part_idx
        } else {
            metrics.record_part_load();
            tracing::info!(target: TRACE_IO_EVENTS, r#type=IO_TYPE_LOAD_VECTOR_PART, index_type="ivf", part_id=cache_key.key().as_ref());

            let mtx = self.partition_locks.get_partition_mutex(partition_id);
            let _guard = mtx.lock().await;
            // check the cache again, as the partition may have been loaded by another
            // thread that held the lock on loading the partition
            if let Some(part_idx) = self.index_cache.get_unsized_with_key(&cache_key).await {
                part_idx
            } else {
                if partition_id >= self.ivf.num_partitions() {
                    return Err(Error::index(format!(
                        "partition id {} is out of range of {} partitions",
                        partition_id,
                        self.ivf.num_partitions()
                    )));
                }

                let range = self.ivf.row_range(partition_id);
                let idx = self
                    .sub_index
                    .load_partition(
                        self.reader.clone(),
                        range.start,
                        range.end - range.start,
                        partition_id,
                    )
                    .await?;
                let idx: Arc<dyn VectorIndex> = idx.into();
                if write_cache {
                    self.index_cache
                        .insert_unsized_with_key(&cache_key, idx.clone())
                        .await;
                }
                idx
            }
        };
        Ok(part_index)
    }

    /// preprocess the query vector given the partition id.
    ///
    /// Internal API with no stability guarantees.
    pub fn preprocess_query(&self, partition_id: usize, query: &Query) -> Result<Query> {
        if self.sub_index.use_residual() {
            let partition_centroids = self.ivf.centroids.as_ref().unwrap().value(partition_id);
            let residual_key = sub(&query.key, &partition_centroids)?;
            let mut part_query = query.clone();
            part_query.key = residual_key;
            Ok(part_query)
        } else {
            Ok(query.clone())
        }
    }
}

impl std::fmt::Debug for IVFIndex {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        write!(f, "Ivf({}) -> {:?}", self.metric_type, self.sub_index)
    }
}

// TODO: move to `lance-index` crate.
///
/// Returns (new_uuid, num_indices_merged)
pub(crate) async fn optimize_vector_indices(
    dataset: Dataset,
    unindexed: Option<impl RecordBatchStream + Unpin + 'static>,
    vector_column: &str,
    existing_indices: &[Arc<dyn Index>],
    options: &OptimizeOptions,
) -> Result<(Uuid, usize)> {
    // Sanity check the indices
    if existing_indices.is_empty() {
        return Err(Error::index(
            "optimizing vector index: no existing index found".to_string(),
        ));
    }

    // try cast to v1 IVFIndex,
    // fallback to v2 IVFIndex if it's not v1 IVFIndex
    if !existing_indices[0].as_any().is::<IVFIndex>() {
        return optimize_vector_indices_v2(
            &dataset,
            unindexed,
            vector_column,
            existing_indices,
            options,
        )
        .await;
    }

    let new_uuid = Uuid::new_v4();
    let object_store = dataset.object_store();
    let index_file = dataset
        .indices_dir()
        .child(new_uuid.to_string())
        .child(INDEX_FILE_NAME);
    let writer = object_store.create(&index_file).await?;

    let first_idx = existing_indices[0]
        .as_any()
        .downcast_ref::<IVFIndex>()
        .ok_or(Error::index(
            "optimizing vector index: the first index isn't IVF".to_string(),
        ))?;

    let merged = if let Some(pq_index) = first_idx.sub_index.as_any().downcast_ref::<PQIndex>() {
        optimize_ivf_pq_indices(
            first_idx,
            pq_index,
            vector_column,
            unindexed,
            existing_indices,
            options,
            writer,
            dataset.version().version,
        )
        .await?
    } else if let Some(hnsw_sq) = first_idx
        .sub_index
        .as_any()
        .downcast_ref::<HNSWIndex<ScalarQuantizer>>()
    {
        let aux_file = dataset
            .indices_dir()
            .child(new_uuid.to_string())
            .child(INDEX_AUXILIARY_FILE_NAME);
        let aux_writer = object_store.create(&aux_file).await?;
        optimize_ivf_hnsw_indices(
            Arc::new(dataset),
            first_idx,
            hnsw_sq,
            vector_column,
            unindexed,
            existing_indices,
            options,
            writer,
            aux_writer,
        )
        .await?
    } else {
        return Err(Error::index(
            "optimizing vector index: the sub index isn't PQ or HNSW".to_string(),
        ));
    };

    // never change the index version,
    // because we won't update the legacy vector index format
    Ok((new_uuid, merged))
}

pub(crate) async fn optimize_vector_indices_v2(
    dataset: &Dataset,
    unindexed: Option<impl RecordBatchStream + Unpin + 'static>,
    vector_column: &str,
    existing_indices: &[Arc<dyn Index>],
    options: &OptimizeOptions,
) -> Result<(Uuid, usize)> {
    // Sanity check the indices
    if existing_indices.is_empty() {
        return Err(Error::index(
            "optimizing vector index: no existing index found".to_string(),
        ));
    }
    let existing_indices = existing_indices
        .iter()
        .cloned()
        .map(|idx| idx.as_vector_index())
        .collect::<Result<Vec<_>>>()?;

    let new_uuid = Uuid::new_v4();
    let index_dir = dataset.indices_dir().child(new_uuid.to_string());
    let ivf_model = existing_indices[0].ivf_model();
    let quantizer = existing_indices[0].quantizer();
    let distance_type = existing_indices[0].metric_type();
    let num_partitions = ivf_model.num_partitions();
    let index_type = existing_indices[0].sub_index_type();
    let frag_reuse_index = dataset.open_frag_reuse_index(&NoOpMetricsCollector).await?;

    let format_version = dataset_format_version(dataset);

    let temp_dir = lance_core::utils::tempfile::TempStdDir::default();
    let temp_dir_path = Path::from_filesystem_path(&temp_dir)?;
    let shuffler = create_ivf_shuffler(temp_dir_path, num_partitions, format_version, None);

    let (_, element_type) = get_vector_type(dataset.schema(), vector_column)?;
    let merged_num = match index_type {
        // IVF_FLAT
        (SubIndexType::Flat, QuantizationType::Flat) => {
            if element_type == DataType::UInt8 {
                IvfIndexBuilder::<FlatIndex, FlatBinQuantizer>::new_incremental(
                    dataset.clone(),
                    vector_column.to_owned(),
                    index_dir,
                    distance_type,
                    shuffler,
                    (),
                    frag_reuse_index,
                    options.clone(),
                )?
                .with_ivf(ivf_model.clone())
                .with_quantizer(quantizer.try_into()?)
                .with_existing_indices(existing_indices.clone())
                .shuffle_data(unindexed)
                .await?
                .build()
                .await?
            } else {
                IvfIndexBuilder::<FlatIndex, FlatQuantizer>::new_incremental(
                    dataset.clone(),
                    vector_column.to_owned(),
                    index_dir,
                    distance_type,
                    shuffler,
                    (),
                    frag_reuse_index,
                    options.clone(),
                )?
                .with_ivf(ivf_model.clone())
                .with_quantizer(quantizer.try_into()?)
                .with_existing_indices(existing_indices.clone())
                .shuffle_data(unindexed)
                .await?
                .build()
                .await?
            }
        }
        // IVF_PQ
        (SubIndexType::Flat, QuantizationType::Product) => {
            IvfIndexBuilder::<FlatIndex, ProductQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                (),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        // IVF_SQ
        (SubIndexType::Flat, QuantizationType::Scalar) => {
            IvfIndexBuilder::<FlatIndex, ScalarQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                (),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        (SubIndexType::Flat, QuantizationType::Rabit) => {
            IvfIndexBuilder::<FlatIndex, RabitQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                (),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        // IVF_HNSW_FLAT
        (SubIndexType::Hnsw, QuantizationType::Flat) => {
            IvfIndexBuilder::<HNSW, FlatQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                HnswBuildParams::default(),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        // IVF_HNSW_SQ
        (SubIndexType::Hnsw, QuantizationType::Scalar) => {
            IvfIndexBuilder::<HNSW, ScalarQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                HnswBuildParams::default(),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        // IVF_HNSW_PQ
        (SubIndexType::Hnsw, QuantizationType::Product) => {
            IvfIndexBuilder::<HNSW, ProductQuantizer>::new_incremental(
                dataset.clone(),
                vector_column.to_owned(),
                index_dir,
                distance_type,
                shuffler,
                HnswBuildParams::default(),
                frag_reuse_index,
                options.clone(),
            )?
            .with_ivf(ivf_model.clone())
            .with_quantizer(quantizer.try_into()?)
            .with_existing_indices(existing_indices.clone())
            .shuffle_data(unindexed)
            .await?
            .build()
            .await?
        }
        (sub_index_type, quantization_type) => {
            unimplemented!(
                "unsupported index type: {}, {}",
                sub_index_type,
                quantization_type
            )
        }
    };

    Ok((new_uuid, merged_num))
}

#[allow(clippy::too_many_arguments)]
async fn optimize_ivf_pq_indices(
    first_idx: &IVFIndex,
    pq_index: &PQIndex,
    vector_column: &str,
    unindexed: Option<impl RecordBatchStream + Unpin + 'static>,
    existing_indices: &[Arc<dyn Index>],
    options: &OptimizeOptions,
    mut writer: Box<dyn Writer>,
    dataset_version: u64,
) -> Result<usize> {
    let metric_type = first_idx.metric_type;
    let dim = first_idx.ivf.dimension();

    // TODO: merge `lance::vector::ivf::IVF` and `lance-index::vector::ivf::Ivf`` implementations.
    let ivf = lance_index::vector::ivf::IvfTransformer::with_pq(
        first_idx.ivf.centroids.clone().unwrap(),
        metric_type,
        vector_column,
        pq_index.pq.clone(),
        None,
    );

    // Shuffled un-indexed data with partition.
    let shuffled = match unindexed {
        Some(unindexed) => Some(
            shuffle_dataset(
                unindexed,
                ivf.into(),
                None,
                first_idx.ivf.num_partitions() as u32,
                10000,
                2,
                None,
            )
            .await?,
        ),
        None => None,
    };

    let mut ivf_mut = IvfModel::new(first_idx.ivf.centroids.clone().unwrap(), first_idx.ivf.loss);

    let start_pos = existing_indices
        .len()
        .saturating_sub(options.num_indices_to_merge.unwrap_or(1));

    let indices_to_merge = existing_indices[start_pos..]
        .iter()
        .map(|idx| {
            idx.as_any().downcast_ref::<IVFIndex>().ok_or(Error::index(
                "optimizing vector index: it is not a IVF index".to_string(),
            ))
        })
        .collect::<Result<Vec<_>>>()?;
    write_pq_partitions(
        writer.as_mut(),
        &mut ivf_mut,
        shuffled,
        Some(&indices_to_merge),
    )
    .await?;
    let metadata = IvfPQIndexMetadata {
        name: format!("_{}_idx", vector_column),
        column: vector_column.to_string(),
        dimension: dim as u32,
        dataset_version,
        metric_type,
        ivf: ivf_mut,
        pq: pq_index.pq.clone(),
        transforms: vec![],
    };

    let metadata = pb::Index::try_from(&metadata)?;
    let pos = writer.write_protobuf(&metadata).await?;
    // TODO: for now the IVF_PQ index file format hasn't been updated, so keep the old version,
    // change it to latest version value after refactoring the IVF_PQ
    writer.write_magics(pos, 0, 1, MAGIC).await?;
    Writer::shutdown(writer.as_mut()).await?;

    Ok(existing_indices.len() - start_pos)
}

#[allow(clippy::too_many_arguments)]
async fn optimize_ivf_hnsw_indices<Q: Quantization>(
    dataset: Arc<dyn DatasetTakeRows>,
    first_idx: &IVFIndex,
    hnsw_index: &HNSWIndex<Q>,
    vector_column: &str,
    unindexed: Option<impl RecordBatchStream + Unpin + 'static>,
    existing_indices: &[Arc<dyn Index>],
    options: &OptimizeOptions,
    writer: Box<dyn Writer>,
    aux_writer: Box<dyn Writer>,
) -> Result<usize> {
    let distance_type = first_idx.metric_type;
    let quantizer = hnsw_index.quantizer().clone();
    let ivf = lance_index::vector::ivf::new_ivf_transformer_with_quantizer(
        first_idx.ivf.centroids.clone().unwrap(),
        distance_type,
        vector_column,
        quantizer.clone(),
        None,
    )?;

    // Shuffled un-indexed data with partition.
    let unindexed_data = match unindexed {
        Some(unindexed) => Some(
            shuffle_dataset(
                unindexed,
                Arc::new(ivf),
                None,
                first_idx.ivf.num_partitions() as u32,
                10000,
                2,
                None,
            )
            .await?,
        ),
        None => None,
    };

    let mut ivf_mut = IvfModel::new(first_idx.ivf.centroids.clone().unwrap(), first_idx.ivf.loss);

    let num_to_merge = options.num_indices_to_merge.unwrap_or(1);
    let start_pos = if num_to_merge > existing_indices.len() {
        0
    } else {
        existing_indices.len() - num_to_merge
    };

    let indices_to_merge = existing_indices[start_pos..]
        .iter()
        .map(|idx| {
            idx.as_any().downcast_ref::<IVFIndex>().ok_or(Error::index(
                "optimizing vector index: it is not a IVF index".to_string(),
            ))
        })
        .collect::<Result<Vec<_>>>()?;

    // Prepare the HNSW writer
    let schema = lance_core::datatypes::Schema::try_from(HNSW::schema().as_ref())?;
    let mut writer = PreviousFileWriter::with_object_writer(
        writer,
        schema,
        &PreviousFileWriterOptions::default(),
    )?;
    writer.add_metadata(
        INDEX_METADATA_SCHEMA_KEY,
        json!(IndexMetadata {
            index_type: format!("IVF_HNSW_{}", quantizer.quantization_type()),
            distance_type: distance_type.to_string(),
        })
        .to_string()
        .as_str(),
    );

    // Prepare the quantization storage writer
    let schema = Schema::new(vec![
        ROW_ID_FIELD.clone(),
        arrow_schema::Field::new(
            quantizer.column(),
            DataType::FixedSizeList(
                Arc::new(arrow_schema::Field::new("item", DataType::UInt8, true)),
                quantizer.code_dim() as i32,
            ),
            false,
        ),
    ]);
    let schema = lance_core::datatypes::Schema::try_from(&schema)?;
    let mut aux_writer = PreviousFileWriter::with_object_writer(
        aux_writer,
        schema,
        &PreviousFileWriterOptions::default(),
    )?;
    aux_writer.add_metadata(
        INDEX_METADATA_SCHEMA_KEY,
        json!(IndexMetadata {
            index_type: quantizer.quantization_type().to_string(),
            distance_type: distance_type.to_string(),
        })
        .to_string()
        .as_str(),
    );

    // Write the metadata of quantizer
    let quantization_metadata = match &quantizer {
        Quantizer::Product(pq) => {
            let codebook_tensor = pb::Tensor::try_from(&pq.codebook)?;
            let codebook_pos = aux_writer.tell().await?;
            aux_writer
                .object_writer
                .write_protobuf(&codebook_tensor)
                .await?;

            Some(QuantizationMetadata {
                codebook_position: Some(codebook_pos),
                ..Default::default()
            })
        }
        _ => None,
    };

    aux_writer.add_metadata(
        quantizer.metadata_key(),
        quantizer
            .metadata(quantization_metadata)?
            .to_string()
            .as_str(),
    );

    let hnsw_params = &hnsw_index.metadata().params;
    let (hnsw_metadata, aux_ivf) = write_hnsw_quantization_index_partitions(
        dataset,
        vector_column,
        distance_type,
        hnsw_params,
        &mut writer,
        Some(&mut aux_writer),
        &mut ivf_mut,
        quantizer,
        unindexed_data,
        Some(&indices_to_merge),
    )
    .await?;

    // Add the metadata of HNSW partitions
    let hnsw_metadata_json = json!(hnsw_metadata);
    writer.add_metadata(IVF_PARTITION_KEY, &hnsw_metadata_json.to_string());

    ivf_mut.write(&mut writer).await?;
    writer.finish().await?;

    // Write the aux file
    aux_ivf.write(&mut aux_writer).await?;
    aux_writer.finish().await?;

    Ok(existing_indices.len() - start_pos)
}

#[derive(Serialize)]
pub struct IvfIndexPartitionStatistics {
    size: u32,
}

#[derive(Serialize)]
pub struct IvfIndexStatistics {
    index_type: String,
    uuid: String,
    uri: String,
    metric_type: String,
    num_partitions: usize,
    sub_index: serde_json::Value,
    partitions: Vec<IvfIndexPartitionStatistics>,
    centroids: Vec<Vec<f32>>,
    loss: Option<f64>,
    index_file_version: IndexFileVersion,
}

fn centroids_to_vectors(centroids: &FixedSizeListArray) -> Result<Vec<Vec<f32>>> {
    centroids
        .iter()
        .map(|v| {
            if let Some(row) = v {
                match row.data_type() {
                    DataType::Float16 => Ok(row
                        .as_primitive::<Float16Type>()
                        .values()
                        .iter()
                        .map(|v| v.to_f32())
                        .collect::<Vec<_>>()),
                    DataType::Float32 => Ok(row.as_primitive::<Float32Type>().values().to_vec()),
                    DataType::Float64 => Ok(row
                        .as_primitive::<Float64Type>()
                        .values()
                        .iter()
                        .map(|v| *v as f32)
                        .collect::<Vec<_>>()),
                    DataType::UInt8 => Ok(row
                        .as_primitive::<UInt8Type>()
                        .values()
                        .iter()
                        .map(|v| *v as f32)
                        .collect::<Vec<_>>()),
                    _ => Err(Error::index(format!(
                        "IVF centroids must be FixedSizeList of floating number, got: {}",
                        row.data_type()
                    ))),
                }
            } else {
                Err(Error::index("Invalid centroid".to_string()))
            }
        })
        .collect()
}

#[async_trait]
impl Index for IVFIndex {
    fn as_any(&self) -> &dyn Any {
        self
    }

    fn as_index(self: Arc<Self>) -> Arc<dyn Index> {
        self
    }

    fn as_vector_index(self: Arc<Self>) -> Result<Arc<dyn VectorIndex>> {
        Ok(self)
    }

    fn index_type(&self) -> IndexType {
        if self.sub_index.as_any().downcast_ref::<PQIndex>().is_some() {
            IndexType::IvfPq
        } else if self
            .sub_index
            .as_any()
            .downcast_ref::<HNSWIndex<ScalarQuantizer>>()
            .is_some()
        {
            IndexType::IvfHnswSq
        } else if self
            .sub_index
            .as_any()
            .downcast_ref::<HNSWIndex<ProductQuantizer>>()
            .is_some()
        {
            IndexType::IvfHnswPq
        } else {
            IndexType::Vector
        }
    }

    async fn prewarm(&self) -> Result<()> {
        futures::stream::iter(0..self.ivf.num_partitions())
            .map(Ok)
            .try_for_each_concurrent(Some(self.reader.io_parallelism()), |part_id| {
                self.load_partition(part_id, true, &NoOpMetricsCollector)
                    .map_ok(|_| ())
            })
            .await
    }

    fn statistics(&self) -> Result<serde_json::Value> {
        let partitions_statistics = (0..self.ivf.num_partitions())
            .map(|part_id| IvfIndexPartitionStatistics {
                size: self.ivf.partition_size(part_id) as u32,
            })
            .collect::<Vec<_>>();

        let centroid_vecs = centroids_to_vectors(self.ivf.centroids.as_ref().unwrap())?;

        Ok(serde_json::to_value(IvfIndexStatistics {
            index_type: self.index_type().to_string(),
            uuid: self.uuid.clone(),
            uri: to_local_path(self.reader.path()),
            metric_type: self.metric_type.to_string(),
            num_partitions: self.ivf.num_partitions(),
            sub_index: self.sub_index.statistics()?,
            partitions: partitions_statistics,
            centroids: centroid_vecs,
            loss: self.ivf.loss(),
            index_file_version: IndexFileVersion::Legacy,
        })?)
    }

    async fn calculate_included_frags(&self) -> Result<RoaringBitmap> {
        let mut frag_ids = RoaringBitmap::default();
        let part_ids = 0..self.ivf.num_partitions();
        for part_id in part_ids {
            let part = self
                .load_partition(part_id, false, &NoOpMetricsCollector)
                .await?;
            frag_ids |= part.calculate_included_frags().await?;
        }
        Ok(frag_ids)
    }
}

#[async_trait]
impl VectorIndex for IVFIndex {
    #[instrument(level = "debug", skip_all, name = "IVFIndex::search")]
    async fn search(
        &self,
        _query: &Query,
        _pre_filter: Arc<dyn PreFilter>,
        _metrics: &dyn MetricsCollector,
    ) -> Result<RecordBatch> {
        unimplemented!(
            "IVFIndex not currently used as sub-index and top-level indices do partition-aware search"
        )
    }

    /// find the IVF partitions ids given the query vector.
    ///
    /// Internal API with no stability guarantees.
    ///
    /// Assumes the query vector is normalized if the metric type is cosine.
    fn find_partitions(&self, query: &Query) -> Result<(UInt32Array, Float32Array)> {
        let mt = if self.metric_type == MetricType::Cosine {
            MetricType::L2
        } else {
            self.metric_type
        };

        let max_nprobes = query.maximum_nprobes.unwrap_or(self.ivf.num_partitions());

        self.ivf.find_partitions(&query.key, max_nprobes, mt)
    }

    async fn search_in_partition(
        &self,
        partition_id: usize,
        query: &Query,
        pre_filter: Arc<dyn PreFilter>,
        metrics: &dyn MetricsCollector,
    ) -> Result<RecordBatch> {
        let part_index = self.load_partition(partition_id, true, metrics).await?;

        let query = self.preprocess_query(partition_id, query)?;
        let batch = part_index.search(&query, pre_filter, metrics).await?;
        Ok(batch)
    }

    fn total_partitions(&self) -> usize {
        self.ivf.num_partitions()
    }

    fn is_loadable(&self) -> bool {
        false
    }

    fn use_residual(&self) -> bool {
        false
    }

    async fn load(
        &self,
        _reader: Arc<dyn Reader>,
        _offset: usize,
        _length: usize,
    ) -> Result<Box<dyn VectorIndex>> {
        Err(Error::index("Flat index does not support load".to_string()))
    }

    async fn partition_reader(
        &self,
        partition_id: usize,
        with_vector: bool,
        metrics: &dyn MetricsCollector,
    ) -> Result<SendableRecordBatchStream> {
        let partition = self.load_partition(partition_id, false, metrics).await?;
        partition.to_batch_stream(with_vector).await
    }

    async fn to_batch_stream(&self, _with_vector: bool) -> Result<SendableRecordBatchStream> {
        unimplemented!("this method is for only sub index")
    }

    fn num_rows(&self) -> u64 {
        self.ivf.num_rows()
    }

    fn row_ids(&self) -> Box<dyn Iterator<Item = &u64>> {
        todo!("this method is for only IVF_HNSW_* index");
    }

    async fn remap(&mut self, _mapping: &HashMap<u64, Option<u64>>) -> Result<()> {
        // This will be needed if we want to clean up IVF to allow more than just
        // one layer (e.g. IVF -> IVF -> PQ).  We need to pass on the call to
        // remap to the lower layers.

        // Currently, remapping for IVF is implemented in remap_index_file which
        // mirrors some of the other IVF routines like build_ivf_pq_index
        Err(Error::index(
            "Remapping IVF in this way not supported".to_string(),
        ))
    }

    fn ivf_model(&self) -> &IvfModel {
        &self.ivf
    }

    fn quantizer(&self) -> Quantizer {
        unimplemented!("only for v2 IVFIndex")
    }

    fn partition_size(&self, part_id: usize) -> usize {
        self.ivf.partition_size(part_id)
    }

    /// the index type of this vector index.
    fn sub_index_type(&self) -> (SubIndexType, QuantizationType) {
        unimplemented!("only for v2 IVFIndex")
    }

    fn metric_type(&self) -> MetricType {
        self.metric_type
    }
}

/// Ivf PQ index metadata.
///
/// It contains the on-disk data for a IVF PQ index.
#[derive(Debug)]
pub struct IvfPQIndexMetadata {
    /// Index name
    name: String,

    /// The column to build the index for.
    column: String,

    /// Vector dimension.
    dimension: u32,

    /// The version of dataset where this index was built.
    dataset_version: u64,

    /// Metric to compute distance
    pub(crate) metric_type: MetricType,

    /// IVF model
    pub(crate) ivf: IvfModel,

    /// Product Quantizer
    pub(crate) pq: ProductQuantizer,

    /// Transforms to be applied before search.
    transforms: Vec<pb::Transform>,
}

impl IvfPQIndexMetadata {
    /// Create a new IvfPQIndexMetadata object
    pub fn new(
        name: String,
        column: String,
        dataset_version: u64,
        metric_type: MetricType,
        ivf: IvfModel,
        pq: ProductQuantizer,
        transforms: Vec<pb::Transform>,
    ) -> Self {
        let dimension = ivf.dimension() as u32;
        Self {
            name,
            column,
            dimension,
            dataset_version,
            metric_type,
            ivf,
            pq,
            transforms,
        }
    }
}

/// Convert a IvfPQIndex to protobuf payload
impl TryFrom<&IvfPQIndexMetadata> for pb::Index {
    type Error = Error;

    fn try_from(idx: &IvfPQIndexMetadata) -> Result<Self> {
        let mut stages: Vec<pb::VectorIndexStage> = idx
            .transforms
            .iter()
            .map(|tf| {
                Ok(pb::VectorIndexStage {
                    stage: Some(pb::vector_index_stage::Stage::Transform(tf.clone())),
                })
            })
            .collect::<Result<Vec<_>>>()?;

        stages.extend_from_slice(&[
            pb::VectorIndexStage {
                stage: Some(pb::vector_index_stage::Stage::Ivf(pb::Ivf::try_from(
                    &idx.ivf,
                )?)),
            },
            pb::VectorIndexStage {
                stage: Some(pb::vector_index_stage::Stage::Pq(pb::Pq::try_from(
                    &idx.pq,
                )?)),
            },
        ]);

        Ok(Self {
            name: idx.name.clone(),
            columns: vec![idx.column.clone()],
            dataset_version: idx.dataset_version,
            index_type: pb::IndexType::Vector.into(),
            implementation: Some(pb::index::Implementation::VectorIndex(pb::VectorIndex {
                spec_version: 1,
                dimension: idx.dimension,
                stages,
                metric_type: match idx.metric_type {
                    MetricType::L2 => pb::VectorMetricType::L2.into(),
                    MetricType::Cosine => pb::VectorMetricType::Cosine.into(),
                    MetricType::Dot => pb::VectorMetricType::Dot.into(),
                    MetricType::Hamming => pb::VectorMetricType::Hamming.into(),
                },
            })),
        })
    }
}

fn sanity_check_ivf_params(ivf: &IvfBuildParams) -> Result<()> {
    if ivf.precomputed_partitions_file.is_some() && ivf.centroids.is_none() {
        return Err(Error::index(
            "precomputed_partitions_file requires centroids to be set".to_string(),
        ));
    }

    if ivf.precomputed_shuffle_buffers.is_some() && ivf.centroids.is_none() {
        return Err(Error::index(
            "precomputed_shuffle_buffers requires centroids to be set".to_string(),
        ));
    }

    if ivf.precomputed_shuffle_buffers.is_some() && ivf.precomputed_partitions_file.is_some() {
        return Err(Error::index(
            "precomputed_shuffle_buffers and precomputed_partitions_file are mutually exclusive"
                .to_string(),
        ));
    }

    Ok(())
}

fn sanity_check_params(ivf: &IvfBuildParams, pq: &PQBuildParams) -> Result<()> {
    sanity_check_ivf_params(ivf)?;
    if ivf.precomputed_shuffle_buffers.is_some() && pq.codebook.is_none() {
        return Err(Error::index(
            "precomputed_shuffle_buffers requires codebooks to be set".to_string(),
        ));
    }

    Ok(())
}

/// Build IVF model from the dataset.
///
/// Parameters
/// ----------
/// - *dataset*: Dataset instance
/// - *column*: vector column.
/// - *dim*: vector dimension.
/// - *metric_type*: distance metric type.
/// - *params*: IVF build parameters.
///
/// Returns
/// -------
/// - IVF model.
///
/// Visibility: pub(super) for testing
#[instrument(level = "debug", skip_all, name = "build_ivf_model")]
pub async fn build_ivf_model(
    dataset: &Dataset,
    column: &str,
    dim: usize,
    metric_type: MetricType,
    params: &IvfBuildParams,
    progress: std::sync::Arc<dyn lance_index::progress::IndexBuildProgress>,
) -> Result<IvfModel> {
    let num_partitions = params.num_partitions.unwrap();
    let centroids = params.centroids.clone();
    if let (Some(centroids), false) = (centroids.as_deref(), params.retrain) {
        info!("Pre-computed IVF centroids is provided, skip IVF training");
        if centroids.values().len() != num_partitions * dim {
            return Err(Error::index(format!(
                "IVF centroids length mismatch: {} != {}",
                centroids.len(),
                num_partitions * dim,
            )));
        }
        return Ok(IvfModel::new(centroids.clone(), None));
    }
    let sample_size_hint = num_partitions * params.sample_rate;

    let start = std::time::Instant::now();
    info!(
        "Loading training data for IVF. Sample size: {}",
        sample_size_hint
    );
    let training_data = maybe_sample_training_data(dataset, column, sample_size_hint).await?;
    info!(
        "Finished loading training data in {:02} seconds",
        start.elapsed().as_secs_f32()
    );
    if params.sample_rate >= 1024 && training_data.value_type() == DataType::Float16 {
        warn!(
            "Large sample_rate ({} >= 1024) for float16 vectors is possible to result in all zeros cluster centroid",
            params.sample_rate
        );
    }

    // If metric type is cosine, normalize the training data, and after this point,
    // treat the metric type as L2.
    let (training_data, mt) = if metric_type == MetricType::Cosine {
        let training_data = normalize_fsl_owned(training_data)?;
        (training_data, MetricType::L2)
    } else {
        (training_data, metric_type)
    };

    // we filtered out nulls when sampling, but we still need to filter out NaNs and INFs here
    let training_data = filter_finite_training_data(training_data)?;

    info!("Start to train IVF model");
    let start = std::time::Instant::now();
    let ivf = train_ivf_model(centroids, &training_data, mt, params, progress).await?;
    info!(
        "Trained IVF model in {:02} seconds",
        start.elapsed().as_secs_f32()
    );
    Ok(ivf)
}

async fn build_ivf_model_and_pq(
    dataset: &Dataset,
    column: &str,
    metric_type: MetricType,
    ivf_params: &IvfBuildParams,
    pq_params: &PQBuildParams,
    progress: std::sync::Arc<dyn lance_index::progress::IndexBuildProgress>,
) -> Result<(IvfModel, ProductQuantizer)> {
    sanity_check_params(ivf_params, pq_params)?;

    // `num_partitions` should be set before building the IVF model,
    // we use 32 as the default to avoid panicking, 32 is the default value
    // before we make `num_partitions` optional.
    let num_partitions = ivf_params.num_partitions.unwrap_or(32);
    info!(
        "Building vector index: IVF{},PQ{}, metric={}",
        num_partitions, pq_params.num_sub_vectors, metric_type,
    );

    // sanity check
    get_vector_type(dataset.schema(), column)?;
    let dim = get_vector_dim(dataset.schema(), column)?;

    let ivf_model =
        build_ivf_model(dataset, column, dim, metric_type, ivf_params, progress).await?;

    let ivf_residual = if matches!(metric_type, MetricType::Cosine | MetricType::L2) {
        Some(&ivf_model)
    } else {
        None
    };

    let pq = build_pq_model(dataset, column, dim, metric_type, pq_params, ivf_residual).await?;

    Ok((ivf_model, pq))
}

async fn scan_index_field_stream(
    dataset: &Dataset,
    column: &str,
) -> Result<impl RecordBatchStream + Unpin + 'static> {
    let mut scanner = dataset.scan();
    scanner.project(&[column])?;
    scanner.with_row_id();
    scanner.try_into_stream().await
}

pub async fn load_precomputed_partitions_if_available(
    ivf_params: &IvfBuildParams,
) -> Result<Option<HashMap<u64, u32>>> {
    match &ivf_params.precomputed_partitions_file {
        Some(file) => {
            info!("Loading precomputed partitions from file: {}", file);
            let mut builder = DatasetBuilder::from_uri(file);
            if let Some(storage_options) = &ivf_params.storage_options {
                builder = builder.with_storage_options(storage_options.clone());
            }
            let ds = builder.load().await?;
            let stream = ds.scan().try_into_stream().await?;
            Ok(Some(
                load_precomputed_partitions(stream, ds.count_rows(None).await?).await?,
            ))
        }
        None => Ok(None),
    }
}

#[allow(clippy::too_many_arguments)]
pub async fn build_ivf_pq_index(
    dataset: &Dataset,
    column: &str,
    index_name: &str,
    uuid: &str,
    metric_type: MetricType,
    ivf_params: &IvfBuildParams,
    pq_params: &PQBuildParams,
    progress: std::sync::Arc<dyn lance_index::progress::IndexBuildProgress>,
) -> Result<()> {
    let (ivf_model, pq) = build_ivf_model_and_pq(
        dataset,
        column,
        metric_type,
        ivf_params,
        pq_params,
        progress,
    )
    .await?;
    let stream = scan_index_field_stream(dataset, column).await?;
    let precomputed_partitions = load_precomputed_partitions_if_available(ivf_params).await?;

    write_ivf_pq_file(
        dataset.object_store(),
        dataset.indices_dir(),
        column,
        index_name,
        uuid,
        dataset.version().version,
        ivf_model,
        pq,
        metric_type,
        stream,
        precomputed_partitions,
        ivf_params.shuffle_partition_batches,
        ivf_params.shuffle_partition_concurrency,
        ivf_params.precomputed_shuffle_buffers.clone(),
    )
    .await
}

#[allow(clippy::too_many_arguments)]
pub async fn build_ivf_hnsw_pq_index(
    dataset: &Dataset,
    column: &str,
    index_name: &str,
    uuid: &str,
    metric_type: MetricType,
    ivf_params: &IvfBuildParams,
    hnsw_params: &HnswBuildParams,
    pq_params: &PQBuildParams,
) -> Result<()> {
    let (ivf_model, pq) = build_ivf_model_and_pq(
        dataset,
        column,
        metric_type,
        ivf_params,
        pq_params,
        lance_index::progress::noop_progress(),
    )
    .await?;
    let stream = scan_index_field_stream(dataset, column).await?;
    let precomputed_partitions = load_precomputed_partitions_if_available(ivf_params).await?;

    write_ivf_hnsw_file(
        dataset,
        column,
        index_name,
        uuid,
        ivf_model,
        Quantizer::Product(pq),
        metric_type,
        hnsw_params,
        stream,
        precomputed_partitions,
        ivf_params.shuffle_partition_batches,
        ivf_params.shuffle_partition_concurrency,
        ivf_params.precomputed_shuffle_buffers.clone(),
    )
    .await
}

struct RemapPageTask {
    offset: usize,
    length: u32,
    page: Option<Box<dyn VectorIndex>>,
}

impl RemapPageTask {
    fn new(offset: usize, length: u32) -> Self {
        Self {
            offset,
            length,
            page: None,
        }
    }
}

impl RemapPageTask {
    async fn load_and_remap(
        mut self,
        reader: Arc<dyn Reader>,
        index: &IVFIndex,
        mapping: &HashMap<u64, Option<u64>>,
    ) -> Result<Self> {
        let mut page = index
            .sub_index
            .load(reader, self.offset, self.length as usize)
            .await?;
        page.remap(mapping).await?;
        self.page = Some(page);
        Ok(self)
    }

    async fn write(self, writer: &mut dyn Writer, ivf: &mut IvfModel) -> Result<()> {
        let page = self.page.as_ref().expect("Load was not called");
        let page: &PQIndex = page
            .as_any()
            .downcast_ref()
            .expect("Generic index writing not supported yet");
        ivf.offsets.push(writer.tell().await?);
        ivf.lengths
            .push(page.row_ids.as_ref().unwrap().len() as u32);
        let original_pq = transpose(
            page.code.as_ref().unwrap(),
            page.pq.code_dim(),
            page.row_ids.as_ref().unwrap().len(),
        );
        PlainEncoder::write(writer, &[&original_pq]).await?;
        PlainEncoder::write(writer, &[page.row_ids.as_ref().unwrap().as_ref()]).await?;
        Ok(())
    }
}

fn generate_remap_tasks(offsets: &[usize], lengths: &[u32]) -> Result<Vec<RemapPageTask>> {
    let mut tasks: Vec<RemapPageTask> = Vec::with_capacity(offsets.len() * 2 + 1);

    for (offset, length) in offsets.iter().zip(lengths.iter()) {
        tasks.push(RemapPageTask::new(*offset, *length));
    }

    Ok(tasks)
}

#[allow(clippy::too_many_arguments)]
pub(crate) async fn remap_index_file_v3(
    dataset: &Dataset,
    new_uuid: &str,
    index: Arc<dyn VectorIndex>,
    mapping: &HashMap<u64, Option<u64>>,
    column: String,
) -> Result<()> {
    let dataset = dataset.clone();
    let index_dir = dataset.indices_dir().child(new_uuid);
    let (_, element_type) = get_vector_type(dataset.schema(), &column)?;
    match index.sub_index_type() {
        (SubIndexType::Flat, QuantizationType::Flat) => match element_type {
            DataType::Float16 | DataType::Float32 | DataType::Float64 => {
                IvfIndexBuilder::<FlatIndex, FlatQuantizer>::new_remapper(
                    dataset, column, index_dir, index,
                )?
                .remap(mapping)
                .await
            }
            DataType::UInt8 => {
                IvfIndexBuilder::<FlatIndex, FlatBinQuantizer>::new_remapper(
                    dataset, column, index_dir, index,
                )?
                .remap(mapping)
                .await
            }
            _ => Err(Error::index(format!(
                "the field type {} is not supported for FLAT index",
                element_type
            ))),
        },
        (SubIndexType::Flat, QuantizationType::Product) => {
            IvfIndexBuilder::<FlatIndex, ProductQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }
        (SubIndexType::Flat, QuantizationType::Scalar) => {
            IvfIndexBuilder::<FlatIndex, ScalarQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }
        (SubIndexType::Flat, QuantizationType::Rabit) => {
            IvfIndexBuilder::<FlatIndex, RabitQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }
        (SubIndexType::Hnsw, QuantizationType::Flat) => {
            IvfIndexBuilder::<HNSW, FlatQuantizer>::new_remapper(dataset, column, index_dir, index)?
                .remap(mapping)
                .await
        }
        (SubIndexType::Hnsw, QuantizationType::Product) => {
            IvfIndexBuilder::<HNSW, ProductQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }

        (SubIndexType::Hnsw, QuantizationType::Scalar) => {
            IvfIndexBuilder::<HNSW, ScalarQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }
        (SubIndexType::Hnsw, QuantizationType::Rabit) => {
            IvfIndexBuilder::<HNSW, RabitQuantizer>::new_remapper(
                dataset, column, index_dir, index,
            )?
            .remap(mapping)
            .await
        }
    }
}

#[allow(clippy::too_many_arguments)]
pub(crate) async fn remap_index_file(
    dataset: &Dataset,
    old_uuid: &str,
    new_uuid: &str,
    old_version: u64,
    index: &IVFIndex,
    mapping: &HashMap<u64, Option<u64>>,
    name: String,
    column: String,
    transforms: Vec<pb::Transform>,
) -> Result<()> {
    let object_store = dataset.object_store();
    let old_path = dataset.indices_dir().child(old_uuid).child(INDEX_FILE_NAME);
    let new_path = dataset.indices_dir().child(new_uuid).child(INDEX_FILE_NAME);

    let reader: Arc<dyn Reader> = object_store.open(&old_path).await?.into();
    let mut writer = object_store.create(&new_path).await?;

    let tasks = generate_remap_tasks(&index.ivf.offsets, &index.ivf.lengths)?;

    let mut task_stream = stream::iter(tasks.into_iter())
        .map(|task| task.load_and_remap(reader.clone(), index, mapping))
        .buffered(object_store.io_parallelism());

    let mut ivf = IvfModel {
        centroids: index.ivf.centroids.clone(),
        offsets: Vec::with_capacity(index.ivf.offsets.len()),
        lengths: Vec::with_capacity(index.ivf.lengths.len()),
        loss: index.ivf.loss,
    };
    while let Some(write_task) = task_stream.try_next().await? {
        write_task.write(writer.as_mut(), &mut ivf).await?;
    }

    let pq_sub_index = index
        .sub_index
        .as_any()
        .downcast_ref::<PQIndex>()
        .ok_or_else(|| Error::not_supported_source("Remapping a non-pq sub-index".into()))?;

    let metadata = IvfPQIndexMetadata {
        name,
        column,
        dimension: index.ivf.dimension() as u32,
        dataset_version: old_version,
        ivf,
        metric_type: index.metric_type,
        pq: pq_sub_index.pq.clone(),
        transforms,
    };

    let metadata = pb::Index::try_from(&metadata)?;
    let pos = writer.write_protobuf(&metadata).await?;
    // TODO: for now the IVF_PQ index file format hasn't been updated, so keep the old version,
    // change it to latest version value after refactoring the IVF_PQ
    writer.write_magics(pos, 0, 1, MAGIC).await?;
    Writer::shutdown(writer.as_mut()).await?;

    Ok(())
}

/// Write the index to the index file.
///
#[allow(clippy::too_many_arguments)]
async fn write_ivf_pq_file(
    object_store: &ObjectStore,
    index_dir: Path,
    column: &str,
    index_name: &str,
    uuid: &str,
    dataset_version: u64,
    mut ivf: IvfModel,
    pq: ProductQuantizer,
    metric_type: MetricType,
    stream: impl RecordBatchStream + Unpin + 'static,
    precomputed_partitions: Option<HashMap<u64, u32>>,
    shuffle_partition_batches: usize,
    shuffle_partition_concurrency: usize,
    precomputed_shuffle_buffers: Option<(Path, Vec<String>)>,
) -> Result<()> {
    let path = index_dir.child(uuid).child(INDEX_FILE_NAME);
    let mut writer = object_store.create(&path).await?;

    let start = std::time::Instant::now();
    let num_partitions = ivf.num_partitions() as u32;
    builder::build_partitions(
        writer.as_mut(),
        stream,
        column,
        &mut ivf,
        pq.clone(),
        metric_type,
        0..num_partitions,
        precomputed_partitions,
        shuffle_partition_batches,
        shuffle_partition_concurrency,
        precomputed_shuffle_buffers,
    )
    .await?;
    info!("Built IVF partitions: {}s", start.elapsed().as_secs_f32());

    let metadata = IvfPQIndexMetadata {
        name: index_name.to_string(),
        column: column.to_string(),
        dimension: pq.dimension as u32,
        dataset_version,
        metric_type,
        ivf,
        pq,
        transforms: vec![],
    };

    let metadata = pb::Index::try_from(&metadata)?;
    let pos = writer.write_protobuf(&metadata).await?;
    // TODO: for now the IVF_PQ index file format hasn't been updated, so keep the old version,
    // change it to latest version value after refactoring the IVF_PQ
    writer.write_magics(pos, 0, 1, MAGIC).await?;
    Writer::shutdown(writer.as_mut()).await?;

    Ok(())
}

pub async fn write_ivf_pq_file_from_existing_index(
    dataset: &Dataset,
    column: &str,
    index_name: &str,
    index_id: Uuid,
    mut ivf: IvfModel,
    pq: ProductQuantizer,
    streams: Vec<impl Stream<Item = Result<RecordBatch>>>,
) -> Result<()> {
    let obj_store = dataset.object_store();
    let path = dataset
        .indices_dir()
        .child(index_id.to_string())
        .child("index.idx");
    let mut writer = obj_store.create(&path).await?;
    write_pq_partitions(writer.as_mut(), &mut ivf, Some(streams), None).await?;

    let metadata = IvfPQIndexMetadata::new(
        index_name.to_string(),
        column.to_string(),
        dataset.version().version,
        pq.distance_type,
        ivf,
        pq,
        vec![],
    );

    let metadata = pb::Index::try_from(&metadata)?;
    let pos = writer.write_protobuf(&metadata).await?;
    writer.write_magics(pos, 0, 1, MAGIC).await?;
    Writer::shutdown(writer.as_mut()).await?;

    Ok(())
}

#[allow(clippy::too_many_arguments)]
async fn write_ivf_hnsw_file(
    dataset: &Dataset,
    column: &str,
    _index_name: &str,
    uuid: &str,
    mut ivf: IvfModel,
    quantizer: Quantizer,
    distance_type: DistanceType,
    hnsw_params: &HnswBuildParams,
    stream: impl RecordBatchStream + Unpin + 'static,
    precomputed_partitions: Option<HashMap<u64, u32>>,
    shuffle_partition_batches: usize,
    shuffle_partition_concurrency: usize,
    precomputed_shuffle_buffers: Option<(Path, Vec<String>)>,
) -> Result<()> {
    let object_store = dataset.object_store();
    let path = dataset.indices_dir().child(uuid).child(INDEX_FILE_NAME);
    let writer = object_store.create(&path).await?;

    let schema = lance_core::datatypes::Schema::try_from(HNSW::schema().as_ref())?;
    let mut writer = PreviousFileWriter::with_object_writer(
        writer,
        schema,
        &PreviousFileWriterOptions::default(),
    )?;
    writer.add_metadata(
        INDEX_METADATA_SCHEMA_KEY,
        json!(IndexMetadata {
            index_type: format!("IVF_HNSW_{}", quantizer.quantization_type()),
            distance_type: distance_type.to_string(),
        })
        .to_string()
        .as_str(),
    );

    let aux_path = dataset
        .indices_dir()
        .child(uuid)
        .child(INDEX_AUXILIARY_FILE_NAME);
    let aux_writer = object_store.create(&aux_path).await?;
    let schema = Schema::new(vec![
        ROW_ID_FIELD.clone(),
        arrow_schema::Field::new(
            quantizer.column(),
            DataType::FixedSizeList(
                Arc::new(arrow_schema::Field::new("item", DataType::UInt8, true)),
                quantizer.code_dim() as i32,
            ),
            false,
        ),
    ]);
    let schema = lance_core::datatypes::Schema::try_from(&schema)?;
    let mut aux_writer = PreviousFileWriter::with_object_writer(
        aux_writer,
        schema,
        &PreviousFileWriterOptions::default(),
    )?;
    aux_writer.add_metadata(
        INDEX_METADATA_SCHEMA_KEY,
        json!(IndexMetadata {
            index_type: quantizer.quantization_type().to_string(),
            distance_type: distance_type.to_string(),
        })
        .to_string()
        .as_str(),
    );

    // For PQ, we need to store the codebook
    let quantization_metadata = match &quantizer {
        Quantizer::Product(pq) => {
            let codebook_tensor = pb::Tensor::try_from(&pq.codebook)?;
            let codebook_pos = aux_writer.tell().await?;
            aux_writer
                .object_writer
                .write_protobuf(&codebook_tensor)
                .await?;

            Some(QuantizationMetadata {
                codebook_position: Some(codebook_pos),
                ..Default::default()
            })
        }
        _ => None,
    };

    aux_writer.add_metadata(
        quantizer.metadata_key(),
        quantizer
            .metadata(quantization_metadata)?
            .to_string()
            .as_str(),
    );

    let start = std::time::Instant::now();
    let num_partitions = ivf.num_partitions() as u32;

    let (hnsw_metadata, aux_ivf) = builder::build_hnsw_partitions(
        Arc::new(dataset.clone()),
        &mut writer,
        Some(&mut aux_writer),
        stream,
        column,
        &mut ivf,
        quantizer,
        distance_type,
        hnsw_params,
        0..num_partitions,
        precomputed_partitions,
        shuffle_partition_batches,
        shuffle_partition_concurrency,
        precomputed_shuffle_buffers,
    )
    .await?;
    info!("Built IVF partitions: {}s", start.elapsed().as_secs_f32());

    // Add the metadata of HNSW partitions
    let hnsw_metadata_json = json!(hnsw_metadata);
    writer.add_metadata(IVF_PARTITION_KEY, &hnsw_metadata_json.to_string());

    ivf.write(&mut writer).await?;
    writer.finish().await?;

    // Write the aux file
    aux_ivf.write(&mut aux_writer).await?;
    aux_writer.finish().await?;
    Ok(())
}

/// Distributed vector segment build uses three storage-level concepts:
///
/// - A **segment** is a worker output written by `execute_uncommitted()`. It
///   already lives at its final storage path under `indices/<segment_uuid>/`,
///   but it is not yet published in the manifest.
/// - A **physical segment** is an `IndexSegment` that can be committed into the
///   manifest with `commit_existing_index_segments(...)`.
/// - A **logical index** is the user-visible index identified by name; it may
///   contain one or more physical segments.
///
/// The segment-build path is therefore:
///
/// 1. workers build segments
/// 2. the caller groups those segments into one or more physical segments
/// 3. each grouped segment is built from its selected inputs
/// 4. the resulting physical segments are committed as one logical index
///
/// Each plan says:
/// - which source segments should be consumed together
/// - what the physical segment metadata should look like
///
/// The planner returns a `Vec<IndexSegmentPlan>` so callers can decide whether
/// to execute the work serially or fan it out externally.
///
/// This function does not touch storage. It only:
/// - validates that the caller-supplied segment contract is self-consistent
/// - enforces that source fragment coverage is disjoint
/// - groups source segments into physical segments according to
///   `target_segment_bytes`
///
/// The grouping rule is intentionally simple:
/// - `target_segment_bytes = None`: keep the existing segment boundary, so each
///   input segment becomes one physical segment
/// - `target_segment_bytes = Some(limit)`: greedily pack consecutive source
///   segments until the next source would exceed `limit`
pub(crate) async fn plan_segments(
    segments: &[TableIndexMetadata],
    requested_index_type: Option<IndexType>,
    target_segment_bytes: Option<u64>,
) -> Result<Vec<IndexSegmentPlan>> {
    if let Some(index_type) = requested_index_type
        && !matches!(
            index_type,
            IndexType::IvfFlat
                | IndexType::IvfPq
                | IndexType::IvfSq
                | IndexType::IvfRq
                | IndexType::IvfHnswFlat
                | IndexType::IvfHnswPq
                | IndexType::IvfHnswSq
                | IndexType::Vector
        )
    {
        return Err(Error::invalid_input(format!(
            "Unsupported distributed vector segment build type: {}",
            index_type
        )));
    }

    if let Some(0) = target_segment_bytes {
        return Err(Error::invalid_input(
            "target_segment_bytes must be greater than zero".to_string(),
        ));
    }

    if segments.is_empty() {
        return Err(Error::index("No segment metadata was provided".to_string()));
    }

    let mut sorted_segments = segments.to_vec();
    sorted_segments.sort_by_key(|index| index.uuid);
    let mut expected_segment_ids = HashSet::with_capacity(sorted_segments.len());
    for segment in &sorted_segments {
        if !expected_segment_ids.insert(segment.uuid) {
            return Err(Error::index(format!(
                "Distributed vector segment '{}' was provided more than once",
                segment.uuid
            )));
        }
    }

    let mut covered_fragments = RoaringBitmap::new();
    for segment in &sorted_segments {
        let fragment_bitmap = segment.fragment_bitmap.as_ref().ok_or_else(|| {
            Error::index(format!(
                "Segment '{}' is missing fragment coverage",
                segment.uuid
            ))
        })?;
        if covered_fragments.intersection_len(fragment_bitmap) > 0 {
            return Err(Error::index(
                "Distributed vector shards have overlapping fragment coverage".to_string(),
            ));
        }
        covered_fragments |= fragment_bitmap.clone();
    }

    if target_segment_bytes.is_none() {
        return sorted_segments
            .into_iter()
            .map(|segment| build_segment_plan(vec![segment], requested_index_type))
            .collect();
    }

    let target_segment_bytes = target_segment_bytes.unwrap();
    let mut plans = Vec::new();
    let mut current_group = Vec::new();
    let mut current_bytes = 0_u64;

    for segment in sorted_segments {
        let source_bytes = estimate_source_index_bytes(&segment);
        if !current_group.is_empty()
            && current_bytes.saturating_add(source_bytes) > target_segment_bytes
        {
            plans.push(build_segment_plan(
                std::mem::take(&mut current_group),
                requested_index_type,
            )?);
            current_bytes = 0;
        }
        current_bytes = current_bytes.saturating_add(source_bytes);
        current_group.push(segment);
    }

    if !current_group.is_empty() {
        plans.push(build_segment_plan(current_group, requested_index_type)?);
    }

    Ok(plans)
}

/// Build one planned segment into its output directory.
///
/// Single-source plans are already materialized and return immediately. For
/// multi-source plans, this function writes a new merged physical segment under
/// `indices/<segment_uuid>/`.
pub(crate) async fn build_segment(
    object_store: &ObjectStore,
    indices_dir: &Path,
    segment_plan: &IndexSegmentPlan,
) -> Result<IndexSegment> {
    let built_segment = segment_plan.segment().clone();
    let segments = segment_plan.segments();
    debug_assert!(
        !segments.is_empty(),
        "segment plans must have at least one source segment"
    );

    if segments.len() == 1 && segments[0].uuid == built_segment.uuid() {
        return Ok(built_segment);
    }

    let final_dir = indices_dir.child(built_segment.uuid().to_string());
    merge_segments_to_dir(object_store, indices_dir, &final_dir, segment_plan).await?;

    Ok(built_segment)
}

/// Merge the selected input segments into `final_dir`.
///
/// Callers must only invoke this helper for multi-source plans. It reads the
/// selected input segments directly from `indices/<segment_uuid>/` and writes
/// the merged auxiliary/index files into `final_dir`.
async fn merge_segments_to_dir(
    object_store: &ObjectStore,
    indices_dir: &Path,
    final_dir: &Path,
    segment_plan: &IndexSegmentPlan,
) -> Result<()> {
    reset_final_segment_dir(object_store, final_dir).await?;

    let segments = segment_plan.segments();
    debug_assert!(
        segments.len() > 1,
        "merge helper should only be used for multi-source plans"
    );

    let aux_paths = segments
        .iter()
        .map(|segment| {
            indices_dir
                .child(segment.uuid.to_string())
                .child(INDEX_AUXILIARY_FILE_NAME)
        })
        .collect::<Vec<_>>();
    let source_index_paths = segments
        .iter()
        .map(|segment| {
            indices_dir
                .child(segment.uuid.to_string())
                .child(INDEX_FILE_NAME)
        })
        .collect::<Vec<_>>();

    lance_index::vector::distributed::index_merger::merge_partial_vector_auxiliary_files(
        object_store,
        &aux_paths,
        final_dir,
    )
    .await?;
    write_root_vector_index_from_auxiliary(
        object_store,
        final_dir,
        segment_plan.requested_index_type(),
        &source_index_paths,
    )
    .await?;

    Ok(())
}

/// Collapse one group of source segments into a single physical-segment plan.
fn build_segment_plan(
    group: Vec<TableIndexMetadata>,
    requested_index_type: Option<IndexType>,
) -> Result<IndexSegmentPlan> {
    debug_assert!(!group.is_empty());
    let first = &group[0];
    let mut fragment_bitmap = RoaringBitmap::new();
    let mut estimated_bytes = 0_u64;
    let mut segments = Vec::with_capacity(group.len());

    for segment in &group {
        let source_fragment_bitmap = segment.fragment_bitmap.as_ref().ok_or_else(|| {
            Error::index(format!(
                "Segment '{}' is missing fragment coverage",
                segment.uuid
            ))
        })?;
        fragment_bitmap |= source_fragment_bitmap.clone();
        estimated_bytes = estimated_bytes.saturating_add(estimate_source_index_bytes(segment));
        segments.push(segment.clone());
    }

    let segment_uuid = if group.len() == 1 {
        first.uuid
    } else {
        Uuid::new_v4()
    };
    let index_version = match requested_index_type {
        Some(index_type) => index_type.version(),
        None => infer_source_index_version(&group)?,
    };
    let segment = IndexSegment::new(
        segment_uuid,
        fragment_bitmap,
        Arc::new(crate::index::vector_index_details()),
        index_version,
    );

    Ok(IndexSegmentPlan::new(
        segment,
        segments,
        estimated_bytes,
        requested_index_type,
    ))
}

fn infer_source_index_version(group: &[TableIndexMetadata]) -> Result<i32> {
    debug_assert!(!group.is_empty());
    let first = group[0].index_version;
    if group.iter().any(|segment| segment.index_version != first) {
        return Err(Error::index(
            "Distributed vector segments must all have the same index version".to_string(),
        ));
    }
    Ok(first)
}

fn estimate_source_index_bytes(index_metadata: &TableIndexMetadata) -> u64 {
    index_metadata
        .files
        .as_ref()
        .map(|files| files.iter().map(|file| file.size_bytes).sum())
        .unwrap_or(0)
}

/// Best-effort reset of one target directory before rewriting it.
async fn reset_final_segment_dir(object_store: &ObjectStore, final_dir: &Path) -> Result<()> {
    match object_store.remove_dir_all(final_dir.clone()).await {
        Ok(()) => {}
        Err(Error::NotFound { .. }) => {}
        Err(err) => return Err(err),
    }
    Ok(())
}

async fn write_root_vector_index_from_auxiliary(
    object_store: &ObjectStore,
    index_dir: &Path,
    requested_index_type: Option<IndexType>,
    centroid_source_index_paths: &[Path],
) -> Result<()> {
    let aux_path = index_dir.child(INDEX_AUXILIARY_FILE_NAME);
    let scheduler = ScanScheduler::new(
        Arc::new(object_store.clone()),
        SchedulerConfig::max_bandwidth(object_store),
    );
    let fh = scheduler
        .open_file(&aux_path, &CachedFileSize::unknown())
        .await?;
    let aux_reader = V2Reader::try_open(
        fh,
        None,
        Arc::default(),
        &LanceCache::no_cache(),
        V2ReaderOptions::default(),
    )
    .await?;

    let meta = aux_reader.metadata();
    // Inherit file format version from the unified auxiliary (which inherited it from shards)
    let format_version = meta.version();
    let ivf_buf_idx: u32 = meta
        .file_schema
        .metadata
        .get(IVF_METADATA_KEY)
        .ok_or_else(|| Error::index("IVF meta missing in unified auxiliary".to_string()))?
        .parse()
        .map_err(|_| Error::index("IVF index parse error".to_string()))?;

    let raw_ivf_bytes = aux_reader.read_global_buffer(ivf_buf_idx).await?;
    let mut pb_ivf: lance_index::pb::Ivf = Message::decode(raw_ivf_bytes.clone())?;

    // If the unified IVF metadata does not contain centroids, try to source them
    // from one of the shard index files that fed this merge.
    if pb_ivf.centroids_tensor.is_none() {
        for partial_index_path in centroid_source_index_paths {
            if !object_store.exists(partial_index_path).await? {
                continue;
            }
            let fh = scheduler
                .open_file(partial_index_path, &CachedFileSize::unknown())
                .await?;
            let partial_reader = V2Reader::try_open(
                fh,
                None,
                Arc::default(),
                &LanceCache::no_cache(),
                V2ReaderOptions::default(),
            )
            .await?;
            let partial_meta = partial_reader.metadata();
            if let Some(ivf_idx_str) = partial_meta.file_schema.metadata.get(IVF_METADATA_KEY)
                && let Ok(ivf_idx) = ivf_idx_str.parse::<u32>()
            {
                let partial_ivf_bytes = partial_reader.read_global_buffer(ivf_idx).await?;
                let partial_pb_ivf: lance_index::pb::Ivf = Message::decode(partial_ivf_bytes)?;
                if partial_pb_ivf.centroids_tensor.is_some() {
                    pb_ivf.centroids_tensor = partial_pb_ivf.centroids_tensor;
                    break;
                }
            }
        }
    }

    let ivf_model: IvfModel = IvfModel::try_from(pb_ivf.clone())?;
    let nlist = ivf_model.num_partitions();
    let ivf_bytes = pb_ivf.encode_to_vec().into();

    // Determine index metadata JSON from auxiliary or requested index type.
    let index_meta_json =
        if let Some(idx_json) = meta.file_schema.metadata.get(INDEX_METADATA_SCHEMA_KEY) {
            idx_json.clone()
        } else {
            let dt = meta
                .file_schema
                .metadata
                .get(DISTANCE_TYPE_KEY)
                .cloned()
                .unwrap_or_else(|| "l2".to_string());
            let index_type = requested_index_type.ok_or_else(|| {
                Error::index(
                    "Index type must be provided when auxiliary metadata is missing index metadata"
                        .to_string(),
                )
            })?;
            serde_json::to_string(&IndexMetadata {
                index_type: index_type.to_string(),
                distance_type: dt,
            })?
        };

    // Write root index.idx via V2 writer so downstream opens through v2 path.
    let index_path = index_dir.child(INDEX_FILE_NAME);
    let obj_writer = object_store.create(&index_path).await?;

    // Schema for HNSW sub-index: include neighbors/dist fields; empty batch is fine.
    let arrow_schema = HNSW::schema();
    let schema = lance_core::datatypes::Schema::try_from(arrow_schema.as_ref())?;
    let mut v2_writer = V2Writer::try_new(
        obj_writer,
        schema,
        V2WriterOptions {
            format_version: Some(format_version),
            ..Default::default()
        },
    )?;

    // Attach precise index metadata (type + distance).
    v2_writer.add_schema_metadata(INDEX_METADATA_SCHEMA_KEY, &index_meta_json);

    // Add IVF protobuf as a global buffer and reference via IVF_METADATA_KEY.
    let pos = v2_writer.add_global_buffer(ivf_bytes).await?;
    v2_writer.add_schema_metadata(IVF_METADATA_KEY, pos.to_string());

    // For HNSW variants, attach per-partition metadata list; for FLAT-based
    // variants, attach minimal placeholder metadata.
    let idx_meta: IndexMetadata = serde_json::from_str(&index_meta_json)?;
    let is_hnsw = idx_meta.index_type.starts_with("IVF_HNSW");
    let is_flat_based = matches!(
        idx_meta.index_type.as_str(),
        "IVF_FLAT" | "IVF_PQ" | "IVF_SQ"
    );

    if is_hnsw {
        let default_meta = HnswMetadata::default();
        let meta_vec: Vec<String> = (0..nlist)
            .map(|_| serde_json::to_string(&default_meta).unwrap())
            .collect();
        let meta_vec_json = serde_json::to_string(&meta_vec)?;
        v2_writer.add_schema_metadata(HNSW_METADATA_KEY, meta_vec_json);
    } else if is_flat_based {
        let meta_vec: Vec<String> = (0..nlist).map(|_| "{}".to_string()).collect();
        let meta_vec_json = serde_json::to_string(&meta_vec)?;
        v2_writer.add_schema_metadata("lance:flat", meta_vec_json);
    }

    let empty_batch = RecordBatch::new_empty(arrow_schema);
    v2_writer.write_batch(&empty_batch).await?;
    v2_writer.finish().await?;
    Ok(())
}

async fn do_train_ivf_model<T: ArrowPrimitiveType>(
    centroids: Option<Arc<FixedSizeListArray>>,
    data: &PrimitiveArray<T>,
    dimension: usize,
    metric_type: MetricType,
    params: &IvfBuildParams,
    progress: std::sync::Arc<dyn lance_index::progress::IndexBuildProgress>,
) -> Result<IvfModel>
where
    <T as ArrowPrimitiveType>::Native: Dot + L2 + Normalize,
    PrimitiveArray<T>: From<Vec<T::Native>>,
{
    const REDOS: usize = 1;
    let (progress_tx, mut progress_rx) = mpsc::unbounded_channel::<u64>();
    let progress_worker = {
        let progress = progress.clone();
        tokio::spawn(async move {
            while let Some(iter) = progress_rx.recv().await {
                if let Err(e) = progress.stage_progress("train_ivf", iter).await {
                    warn!("Progress callback error during train_ivf: {e}");
                }
            }
        })
    };

    let on_progress: Arc<dyn Fn(u32, u32) + Send + Sync> = {
        let progress_tx = progress_tx.clone();
        let cumulative_iters = std::sync::atomic::AtomicU64::new(0);
        Arc::new(move |_iter: u32, _max_iters: u32| {
            // Track cumulative iterations across all kmeans runs in this stage
            // (flat and hierarchical both invoke the callback per-iteration).
            let total = cumulative_iters.fetch_add(1, std::sync::atomic::Ordering::Relaxed) + 1;
            // Non-blocking send from sync kmeans loop into async progress worker.
            let _ = progress_tx.send(total);
        })
    };
    let kmeans_params = KMeansParams::new(centroids, params.max_iters as u32, REDOS, metric_type)
        .with_balance_factor(1.0)
        .with_on_progress(on_progress);
    let kmeans = lance_index::vector::kmeans::train_kmeans::<T>(
        data,
        kmeans_params,
        dimension,
        params.num_partitions.unwrap_or(32),
        params.sample_rate,
    );
    drop(progress_tx);
    if let Err(e) = progress_worker.await {
        warn!("Progress worker join error during train_ivf: {e}");
    }
    let kmeans = kmeans?;
    Ok(IvfModel::new(
        FixedSizeListArray::try_new_from_values(kmeans.centroids, dimension as i32)?,
        Some(kmeans.loss),
    ))
}

/// Train IVF partitions using kmeans.
async fn train_ivf_model(
    centroids: Option<Arc<FixedSizeListArray>>,
    data: &FixedSizeListArray,
    distance_type: DistanceType,
    params: &IvfBuildParams,
    progress: std::sync::Arc<dyn lance_index::progress::IndexBuildProgress>,
) -> Result<IvfModel> {
    assert!(
        distance_type != DistanceType::Cosine,
        "Cosine metric should be done by normalized L2 distance",
    );
    let values = data.values();
    let dim = data.value_length() as usize;
    match (values.data_type(), distance_type) {
        (DataType::Float16, _) => {
            do_train_ivf_model::<Float16Type>(
                centroids,
                values.as_primitive::<Float16Type>(),
                dim,
                distance_type,
                params,
                progress.clone(),
            )
            .await
        }
        (DataType::Float32, _) => {
            do_train_ivf_model::<Float32Type>(
                centroids,
                values.as_primitive::<Float32Type>(),
                dim,
                distance_type,
                params,
                progress.clone(),
            )
            .await
        }
        (DataType::Float64, _) => {
            do_train_ivf_model::<Float64Type>(
                centroids,
                values.as_primitive::<Float64Type>(),
                dim,
                distance_type,
                params,
                progress.clone(),
            )
            .await
        }
        (DataType::Int8, DistanceType::L2)
        | (DataType::Int8, DistanceType::Dot)
        | (DataType::Int8, DistanceType::Cosine) => {
            do_train_ivf_model::<Float32Type>(
                centroids,
                data.convert_to_floating_point()?
                    .values()
                    .as_primitive::<Float32Type>(),
                dim,
                distance_type,
                params,
                progress.clone(),
            )
            .await
        }
        (DataType::UInt8, DistanceType::Hamming) => {
            do_train_ivf_model::<UInt8Type>(
                centroids,
                values.as_primitive::<UInt8Type>(),
                dim,
                distance_type,
                params,
                progress.clone(),
            )
            .await
        }
        _ => Err(Error::index(format!(
            "Unsupported data type {} with distance type {}",
            values.data_type(),
            distance_type
        ))),
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    use std::collections::HashSet;
    use std::iter::repeat_n;
    use std::ops::Range;

    use arrow_array::types::UInt64Type;
    use arrow_array::{
        FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator, RecordBatchReader,
        UInt64Array, make_array,
    };
    use arrow_buffer::{BooleanBuffer, NullBuffer};
    use arrow_schema::{DataType, Field, Schema};
    use itertools::Itertools;
    use lance_core::ROW_ID;
    use lance_core::utils::address::RowAddress;
    use lance_core::utils::tempfile::TempStrDir;
    use lance_datagen::{ArrayGeneratorExt, Dimension, RowCount, array, gen_batch};
    use lance_index::VECTOR_INDEX_VERSION;
    use lance_index::metrics::NoOpMetricsCollector;
    use lance_index::vector::sq::builder::SQBuildParams;
    use lance_linalg::distance::l2_distance_batch;
    use lance_testing::datagen::{
        generate_random_array, generate_random_array_with_range, generate_random_array_with_seed,
        generate_scaled_random_array, sample_without_replacement,
    };
    use rand::{rng, seq::SliceRandom};
    use rstest::rstest;

    use crate::dataset::{InsertBuilder, WriteMode, WriteParams};
    use crate::index::prefilter::DatasetPreFilter;
    use crate::index::vector::IndexFileVersion;
    use crate::index::vector_index_details;
    use crate::index::{DatasetIndexExt, DatasetIndexInternalExt, vector::VectorIndexParams};
    use crate::utils::test::copy_test_data_to_tmp;

    const DIM: usize = 32;

    /// This goal of this function is to generate data that behaves in a very deterministic way so that
    /// we can evaluate the correctness of an IVF_PQ implementation.  Currently it is restricted to the
    /// L2 distance metric.
    ///
    /// First, we generate a set of centroids.  These are generated randomly but we ensure that is
    /// sufficient distance between each of the centroids.
    ///
    /// Then, we generate 256 vectors per centroid.  Each vector is generated by making a line by
    /// adding / subtracting [1,1,1...,1] (with the centroid in the middle)
    ///
    /// The trained result should have our generated centroids (without these centroids actually being
    /// a part of the input data) and the PQ codes for every data point should be identical and, given
    /// any three data points a, b, and c that are in the same centroid then the distance between a and
    /// b should be different than the distance between a and c.
    struct WellKnownIvfPqData {
        dim: u32,
        num_centroids: u32,
        centroids: Option<Float32Array>,
        vectors: Option<Float32Array>,
    }

    impl WellKnownIvfPqData {
        // Right now we are assuming 8-bit codes
        const VALS_PER_CODE: u32 = 256;
        const COLUMN: &'static str = "vector";

        fn new(dim: u32, num_centroids: u32) -> Self {
            Self {
                dim,
                num_centroids,
                centroids: None,
                vectors: None,
            }
        }

        fn generate_centroids(dim: u32, num_centroids: u32) -> Float32Array {
            const MAX_ATTEMPTS: u32 = 10;
            let distance_needed = (dim as f32).sqrt() * Self::VALS_PER_CODE as f32 * 2_f32;
            let mut attempts_remaining = MAX_ATTEMPTS;
            let num_values = dim * num_centroids;
            while attempts_remaining > 0 {
                // Use some biggish numbers to ensure we get the distance we want but make them positive
                // and not too big for easier debugging.
                let centroids: Float32Array =
                    generate_scaled_random_array(num_values as usize, 0_f32, 1000_f32);
                let mut broken = false;
                for (index, centroid) in centroids.values().chunks_exact(dim as usize).enumerate() {
                    let offset = (index + 1) * dim as usize;
                    let length = centroids.len() - offset;
                    if length == 0 {
                        // This will be true for the last item since we ignore comparison with self
                        continue;
                    }
                    let distances = l2_distance_batch(
                        centroid,
                        &centroids.values()[offset..offset + length],
                        dim as usize,
                    );
                    let min_distance = distances.min_by(|a, b| a.total_cmp(b)).unwrap();
                    // In theory we could just replace this one vector but, out of laziness, we just retry all of them
                    if min_distance < distance_needed {
                        broken = true;
                        break;
                    }
                }
                if !broken {
                    return centroids;
                }
                attempts_remaining -= 1;
            }
            panic!(
                "Unable to generate centroids with sufficient distance after {} attempts",
                MAX_ATTEMPTS
            );
        }

        fn get_centroids(&mut self) -> &Float32Array {
            let dim = self.dim;
            let num_centroids = self.num_centroids;
            self.centroids
                .get_or_insert_with(|| Self::generate_centroids(dim, num_centroids))
        }

        fn get_centroids_as_list_arr(&mut self) -> Arc<FixedSizeListArray> {
            Arc::new(
                FixedSizeListArray::try_new_from_values(
                    self.get_centroids().clone(),
                    self.dim as i32,
                )
                .unwrap(),
            )
        }

        fn generate_vectors(
            dim: u32,
            num_centroids: u32,
            centroids: &Float32Array,
        ) -> Float32Array {
            let dim = dim as usize;
            let mut vectors: Vec<f32> =
                vec![0_f32; Self::VALS_PER_CODE as usize * dim * num_centroids as usize];
            for (centroid, dst_batch) in centroids
                .values()
                .chunks_exact(dim)
                .zip(vectors.chunks_exact_mut(dim * Self::VALS_PER_CODE as usize))
            {
                for (offset, dst) in (-128..0).chain(1..129).zip(dst_batch.chunks_exact_mut(dim)) {
                    for (cent_val, dst_val) in centroid.iter().zip(dst) {
                        *dst_val = *cent_val + offset as f32;
                    }
                }
            }
            Float32Array::from(vectors)
        }

        fn get_vectors(&mut self) -> &Float32Array {
            let dim = self.dim;
            let num_centroids = self.num_centroids;
            let centroids = self.get_centroids().clone();
            self.vectors
                .get_or_insert_with(|| Self::generate_vectors(dim, num_centroids, &centroids))
        }

        fn get_vector(&mut self, idx: u32) -> Float32Array {
            let dim = self.dim as usize;
            let vectors = self.get_vectors();
            let start = idx as usize * dim;
            vectors.slice(start, dim)
        }

        fn generate_batches(&mut self) -> impl RecordBatchReader + Send + 'static {
            let dim = self.dim as usize;
            let vectors_array = self.get_vectors();

            let schema = Arc::new(Schema::new(vec![Field::new(
                Self::COLUMN,
                DataType::FixedSizeList(
                    Arc::new(Field::new("item", DataType::Float32, true)),
                    dim as i32,
                ),
                true,
            )]));
            let array = Arc::new(
                FixedSizeListArray::try_new_from_values(vectors_array.clone(), dim as i32).unwrap(),
            );
            let batch = RecordBatch::try_new(schema.clone(), vec![array]).unwrap();
            RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema)
        }

        async fn generate_dataset(&mut self, test_uri: &str) -> Result<Dataset> {
            let batches = self.generate_batches();
            Dataset::write(batches, test_uri, None).await
        }

        async fn check_index<F: Fn(u64) -> Option<u64>>(
            &mut self,
            index: &IVFIndex,
            prefilter: Arc<dyn PreFilter>,
            ids_to_test: &[u64],
            row_id_map: F,
        ) {
            const ROWS_TO_TEST: u32 = 500;
            let num_vectors = ids_to_test.len() as u32 / self.dim;
            let num_tests = ROWS_TO_TEST.min(num_vectors);
            let row_ids_to_test = sample_without_replacement(ids_to_test, num_tests);
            for row_id in row_ids_to_test {
                let row = self.get_vector(row_id as u32);
                let query = Query {
                    column: Self::COLUMN.to_string(),
                    key: Arc::new(row),
                    k: 5,
                    lower_bound: None,
                    upper_bound: None,
                    minimum_nprobes: 1,
                    maximum_nprobes: None,
                    ef: None,
                    refine_factor: None,
                    metric_type: Some(MetricType::L2),
                    use_index: true,
                    dist_q_c: 0.0,
                };
                let (partitions, _) = index.find_partitions(&query).unwrap();
                let nearest_partition_id = partitions.value(0) as usize;
                let search_result = index
                    .search_in_partition(
                        nearest_partition_id,
                        &query,
                        prefilter.clone(),
                        &NoOpMetricsCollector,
                    )
                    .await
                    .unwrap();

                let found_ids = search_result.column(1);
                let found_ids = found_ids.as_any().downcast_ref::<UInt64Array>().unwrap();
                let expected_id = row_id_map(row_id);

                match expected_id {
                    // Original id was deleted, results can be anything, just make sure they don't
                    // include the original id
                    None => assert!(!found_ids.iter().any(|f_id| f_id.unwrap() == row_id)),
                    // Original id remains or was remapped, make sure expected id in results
                    Some(expected_id) => {
                        assert!(found_ids.iter().any(|f_id| f_id.unwrap() == expected_id))
                    }
                };
                // The invalid row id should never show up in results
                assert!(
                    !found_ids
                        .iter()
                        .any(|f_id| f_id.unwrap() == RowAddress::TOMBSTONE_ROW)
                );
            }
        }
    }

    async fn generate_test_dataset(
        test_uri: &str,
        range: Range<f32>,
    ) -> (Dataset, Arc<FixedSizeListArray>) {
        let vectors = generate_random_array_with_range::<Float32Type>(1000 * DIM, range);
        let metadata: HashMap<String, String> = vec![("test".to_string(), "ivf_pq".to_string())]
            .into_iter()
            .collect();

        let schema = Arc::new(
            Schema::new(vec![Field::new(
                "vector",
                DataType::FixedSizeList(
                    Arc::new(Field::new("item", DataType::Float32, true)),
                    DIM as i32,
                ),
                true,
            )])
            .with_metadata(metadata),
        );
        let array = Arc::new(FixedSizeListArray::try_new_from_values(vectors, DIM as i32).unwrap());
        let batch = RecordBatch::try_new(schema.clone(), vec![array.clone()]).unwrap();

        let batches = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema.clone());
        let dataset = Dataset::write(batches, test_uri, None).await.unwrap();
        (dataset, array)
    }

    #[tokio::test]
    async fn test_create_ivf_pq_with_centroids() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (mut dataset, vector_array) = generate_test_dataset(test_uri, 0.0..1.0).await;

        let centroids = generate_random_array(2 * DIM);
        let ivf_centroids = FixedSizeListArray::try_new_from_values(centroids, DIM as i32).unwrap();
        let ivf_params = IvfBuildParams::try_with_centroids(2, Arc::new(ivf_centroids)).unwrap();

        let codebook = Arc::new(generate_random_array(256 * DIM));
        let pq_params = PQBuildParams::with_codebook(4, 8, codebook);

        let params = VectorIndexParams::with_ivf_pq_params(MetricType::L2, ivf_params, pq_params);

        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let sample_query = vector_array.value(10);
        let query = sample_query.as_primitive::<Float32Type>();
        let results = dataset
            .scan()
            .nearest("vector", query, 5)
            .unwrap()
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(1, results.len());
        assert_eq!(5, results[0].num_rows());
    }

    fn partition_ids(mut ids: Vec<u64>, num_parts: u32) -> Vec<Vec<u64>> {
        if num_parts > ids.len() as u32 {
            panic!("Not enough ids to break into {num_parts} parts");
        }
        let mut rng = rng();
        ids.shuffle(&mut rng);

        let values_per_part = ids.len() / num_parts as usize;
        let parts_with_one_extra = ids.len() % num_parts as usize;

        let mut parts = Vec::with_capacity(num_parts as usize);
        let mut offset = 0;
        for part_size in (0..num_parts).map(|part_idx| {
            if part_idx < parts_with_one_extra as u32 {
                values_per_part + 1
            } else {
                values_per_part
            }
        }) {
            parts.push(Vec::from_iter(
                ids[offset..(offset + part_size)].iter().copied(),
            ));
            offset += part_size;
        }

        parts
    }

    const BIG_OFFSET: u64 = 10000;

    fn build_mapping(
        row_ids_to_modify: &[u64],
        row_ids_to_remove: &[u64],
        max_id: u64,
    ) -> HashMap<u64, Option<u64>> {
        // Some big number we can add to row ids so they are remapped but don't intersect with anything
        if max_id > BIG_OFFSET {
            panic!("This logic will only work if the max row id is less than BIG_OFFSET");
        }
        row_ids_to_modify
            .iter()
            .copied()
            .map(|val| (val, Some(val + BIG_OFFSET)))
            .chain(row_ids_to_remove.iter().copied().map(|val| (val, None)))
            .collect()
    }

    #[tokio::test]
    async fn remap_ivf_pq_index() {
        // Use small numbers to keep runtime down
        const DIM: u32 = 8;
        const CENTROIDS: u32 = 2;
        const NUM_SUBVECTORS: u32 = 4;
        const NUM_BITS: u32 = 8;
        const INDEX_NAME: &str = "my_index";

        // In this test we create a sample dataset with reliable data, train an IVF PQ index
        // remap the rows, and then verify that we can still search the index and will get
        // back the remapped row ids.

        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let mut test_data = WellKnownIvfPqData::new(DIM, CENTROIDS);

        let dataset = Arc::new(test_data.generate_dataset(test_uri).await.unwrap());
        let ivf_params = IvfBuildParams::try_with_centroids(
            CENTROIDS as usize,
            test_data.get_centroids_as_list_arr(),
        )
        .unwrap();
        let pq_params = PQBuildParams::new(NUM_SUBVECTORS as usize, NUM_BITS as usize);

        let uuid = Uuid::new_v4();
        let uuid_str = uuid.to_string();

        build_ivf_pq_index(
            &dataset,
            WellKnownIvfPqData::COLUMN,
            INDEX_NAME,
            &uuid_str,
            MetricType::L2,
            &ivf_params,
            &pq_params,
            lance_index::progress::noop_progress(),
        )
        .await
        .unwrap();

        // After building the index file, we need to register the index metadata
        // so that the dataset can find it when we try to open it
        let field = dataset.schema().field(WellKnownIvfPqData::COLUMN).unwrap();
        let index_meta = lance_table::format::IndexMetadata {
            uuid,
            dataset_version: dataset.version().version,
            fields: vec![field.id],
            name: INDEX_NAME.to_string(),
            fragment_bitmap: Some(dataset.fragment_bitmap.as_ref().clone()),
            index_details: Some(Arc::new(vector_index_details())),
            index_version: VECTOR_INDEX_VERSION as i32,
            created_at: Some(chrono::Utc::now()),
            base_id: None,
            files: None,
        };

        // We need to commit this index to the dataset so that it can be found
        use crate::dataset::transaction::{Operation, Transaction};
        let transaction = Transaction::new(
            dataset.version().version,
            Operation::CreateIndex {
                new_indices: vec![index_meta.clone()],
                removed_indices: vec![],
            },
            None,
        );

        // Apply the transaction to register the index
        // Since dataset is Arc<Dataset>, we need to create a mutable reference
        let mut dataset_mut = (*dataset).clone();
        dataset_mut
            .apply_commit(transaction, &Default::default(), &Default::default())
            .await
            .unwrap();

        let index = dataset_mut
            .open_vector_index(WellKnownIvfPqData::COLUMN, &uuid_str, &NoOpMetricsCollector)
            .await
            .unwrap();

        let ivf_index = index.as_any().downcast_ref::<IVFIndex>().unwrap();

        let index_meta = lance_table::format::IndexMetadata {
            uuid,
            dataset_version: 0,
            fields: Vec::new(),
            name: INDEX_NAME.to_string(),
            fragment_bitmap: None,
            index_details: Some(Arc::new(vector_index_details())),
            index_version: VECTOR_INDEX_VERSION as i32,
            created_at: None, // Test index, not setting timestamp
            base_id: None,
            files: None,
        };

        let prefilter = Arc::new(DatasetPreFilter::new(dataset.clone(), &[index_meta], None));

        let is_not_remapped = Some;
        let is_remapped = |row_id| Some(row_id + BIG_OFFSET);
        let is_removed = |_| None;
        let max_id = test_data.get_vectors().len() as u64 / test_data.dim as u64;
        let row_ids = Vec::from_iter(0..max_id);

        // Sanity check to make sure the index we built is behaving correctly.  Any
        // input row, when used as a query, should be found in the results list with
        // the same id
        test_data
            .check_index(ivf_index, prefilter.clone(), &row_ids, is_not_remapped)
            .await;

        // When remapping we change the id of 1/3 of the rows, we remove another 1/3,
        // and we keep 1/3 as they are
        let partitioned_row_ids = partition_ids(row_ids, 3);
        let row_ids_to_modify = &partitioned_row_ids[0];
        let row_ids_to_remove = &partitioned_row_ids[1];
        let row_ids_to_remain = &partitioned_row_ids[2];

        let mapping = build_mapping(row_ids_to_modify, row_ids_to_remove, max_id);

        let new_uuid = Uuid::new_v4();
        let new_uuid_str = new_uuid.to_string();

        remap_index_file(
            &dataset_mut,
            &uuid_str,
            &new_uuid_str,
            dataset_mut.version().version,
            ivf_index,
            &mapping,
            INDEX_NAME.to_string(),
            WellKnownIvfPqData::COLUMN.to_string(),
            vec![],
        )
        .await
        .unwrap();

        // After remapping the index file, we need to register the new index metadata
        // so that the dataset can find it when we try to open it
        let field = dataset_mut
            .schema()
            .field(WellKnownIvfPqData::COLUMN)
            .unwrap();
        let new_index_meta = lance_table::format::IndexMetadata {
            uuid: new_uuid,
            dataset_version: dataset_mut.version().version,
            fields: vec![field.id],
            name: format!("{}_remapped", INDEX_NAME),
            fragment_bitmap: Some(dataset_mut.fragment_bitmap.as_ref().clone()),
            index_details: Some(Arc::new(vector_index_details())),
            index_version: VECTOR_INDEX_VERSION as i32,
            created_at: Some(chrono::Utc::now()),
            base_id: None,
            files: None,
        };

        // We need to commit this new index to the dataset so it can be found
        let transaction = Transaction::new(
            dataset_mut.version().version,
            Operation::CreateIndex {
                new_indices: vec![new_index_meta],
                removed_indices: vec![],
            },
            None,
        );

        // Apply the transaction to register the new index
        dataset_mut
            .apply_commit(transaction, &Default::default(), &Default::default())
            .await
            .unwrap();

        let remapped = dataset_mut
            .open_vector_index(
                WellKnownIvfPqData::COLUMN,
                &new_uuid.to_string(),
                &NoOpMetricsCollector,
            )
            .await
            .unwrap();
        let ivf_remapped = remapped.as_any().downcast_ref::<IVFIndex>().unwrap();

        // If the ids were remapped then make sure the new row id is in the results
        test_data
            .check_index(
                ivf_remapped,
                prefilter.clone(),
                row_ids_to_modify,
                is_remapped,
            )
            .await;
        // If the ids were removed then make sure the old row id isn't in the results
        test_data
            .check_index(
                ivf_remapped,
                prefilter.clone(),
                row_ids_to_remove,
                is_removed,
            )
            .await;
        // If the ids were not remapped then make sure they still return the old id
        test_data
            .check_index(
                ivf_remapped,
                prefilter.clone(),
                row_ids_to_remain,
                is_not_remapped,
            )
            .await;
    }

    struct TestPqParams {
        num_sub_vectors: usize,
        num_bits: usize,
    }

    impl TestPqParams {
        fn small() -> Self {
            Self {
                num_sub_vectors: 2,
                num_bits: 8,
            }
        }
    }

    // Clippy doesn't like that all start with Ivf but we might have some in the future
    // that _don't_ start with Ivf so I feel it is meaningful to keep the prefix
    #[allow(clippy::enum_variant_names)]
    enum TestIndexType {
        IvfPq { pq: TestPqParams },
        IvfHnswPq { pq: TestPqParams, num_edges: usize },
        IvfHnswSq { num_edges: usize },
        IvfFlat,
    }

    struct CreateIndexCase {
        metric_type: MetricType,
        num_partitions: usize,
        dimension: usize,
        index_type: TestIndexType,
    }

    // We test L2 and Dot, because L2 PQ uses residuals while Dot doesn't,
    // so they have slightly different code paths.
    #[tokio::test]
    #[rstest]
    #[case::ivf_pq_l2(CreateIndexCase {
        metric_type: MetricType::L2,
        num_partitions: 2,
        dimension: 16,
        index_type: TestIndexType::IvfPq { pq: TestPqParams::small() },
    })]
    #[case::ivf_pq_dot(CreateIndexCase {
        metric_type: MetricType::Dot,
        num_partitions: 2,
        dimension: 2000,
        index_type: TestIndexType::IvfPq { pq: TestPqParams::small() },
    })]
    #[case::ivf_flat(CreateIndexCase { num_partitions: 1, metric_type: MetricType::Dot, dimension: 16, index_type: TestIndexType::IvfFlat })]
    #[case::ivf_hnsw_pq(CreateIndexCase {
        num_partitions: 2,
        metric_type: MetricType::Dot,
        dimension: 16,
        index_type: TestIndexType::IvfHnswPq { pq: TestPqParams::small(), num_edges: 100 },
    })]
    #[case::ivf_hnsw_sq(CreateIndexCase {
        metric_type: MetricType::Dot,
        num_partitions: 2,
        dimension: 16,
        index_type: TestIndexType::IvfHnswSq { num_edges: 100 },
    })]
    async fn test_create_index_nulls(
        #[case] test_case: CreateIndexCase,
        #[values(IndexFileVersion::Legacy, IndexFileVersion::V3)] index_version: IndexFileVersion,
    ) {
        let mut index_params = match test_case.index_type {
            TestIndexType::IvfPq { pq } => VectorIndexParams::with_ivf_pq_params(
                test_case.metric_type,
                IvfBuildParams::new(test_case.num_partitions),
                PQBuildParams::new(pq.num_sub_vectors, pq.num_bits),
            ),
            TestIndexType::IvfHnswPq { pq, num_edges } => {
                VectorIndexParams::with_ivf_hnsw_pq_params(
                    test_case.metric_type,
                    IvfBuildParams::new(test_case.num_partitions),
                    HnswBuildParams::default().num_edges(num_edges),
                    PQBuildParams::new(pq.num_sub_vectors, pq.num_bits),
                )
            }
            TestIndexType::IvfFlat => {
                VectorIndexParams::ivf_flat(test_case.num_partitions, test_case.metric_type)
            }
            TestIndexType::IvfHnswSq { num_edges } => VectorIndexParams::with_ivf_hnsw_sq_params(
                test_case.metric_type,
                IvfBuildParams::new(test_case.num_partitions),
                HnswBuildParams::default().num_edges(num_edges),
                SQBuildParams::default(),
            ),
        };
        index_params.version(index_version);

        let nrows = 2_000;
        let data = gen_batch()
            .col(
                "vec",
                array::rand_vec::<Float32Type>(Dimension::from(test_case.dimension as u32)),
            )
            .into_batch_rows(RowCount::from(nrows))
            .unwrap();

        // Make every other row null
        let null_buffer = (0..nrows).map(|i| i % 2 == 0).collect::<BooleanBuffer>();
        let null_buffer = NullBuffer::new(null_buffer);
        let vectors = data["vec"]
            .clone()
            .to_data()
            .into_builder()
            .nulls(Some(null_buffer))
            .build()
            .unwrap();
        let vectors = make_array(vectors);
        let num_non_null = vectors.len() - vectors.logical_null_count();
        let data = RecordBatch::try_new(data.schema(), vec![vectors]).unwrap();

        let mut dataset = InsertBuilder::new("memory://")
            .execute(vec![data])
            .await
            .unwrap();

        // Create index
        dataset
            .create_index(&["vec"], IndexType::Vector, None, &index_params, false)
            .await
            .unwrap();

        let query = vec![0.0; test_case.dimension]
            .into_iter()
            .collect::<Float32Array>();
        let results = dataset
            .scan()
            .nearest("vec", &query, 2_000)
            .unwrap()
            .ef(100_000)
            .minimum_nprobes(2)
            .try_into_batch()
            .await
            .unwrap();
        assert_eq!(results.num_rows(), num_non_null);
        assert_eq!(results["vec"].logical_null_count(), 0);
    }

    #[tokio::test]
    async fn test_index_lifecycle_nulls() {
        // Generate random data with nulls
        let nrows = 2_000;
        let dims = 32;
        let data = gen_batch()
            .col(
                "vec",
                array::rand_vec::<Float32Type>(Dimension::from(dims as u32)).with_random_nulls(0.5),
            )
            .into_batch_rows(RowCount::from(nrows))
            .unwrap();
        let num_non_null = data["vec"].len() - data["vec"].logical_null_count();

        let mut dataset = InsertBuilder::new("memory://")
            .execute(vec![data])
            .await
            .unwrap();

        // Create index
        let index_params = VectorIndexParams::with_ivf_pq_params(
            MetricType::L2,
            IvfBuildParams::new(2),
            PQBuildParams::new(2, 8),
        );
        dataset
            .create_index(&["vec"], IndexType::Vector, None, &index_params, false)
            .await
            .unwrap();

        // Check that the index is working
        async fn check_index(dataset: &Dataset, num_non_null: usize, dims: usize) {
            let query = vec![0.0; dims].into_iter().collect::<Float32Array>();
            let results = dataset
                .scan()
                .nearest("vec", &query, 2_000)
                .unwrap()
                .minimum_nprobes(2)
                .try_into_batch()
                .await
                .unwrap();
            assert_eq!(results.num_rows(), num_non_null);
        }
        check_index(&dataset, num_non_null, dims).await;

        // Append more data
        let data = gen_batch()
            .col(
                "vec",
                array::rand_vec::<Float32Type>(Dimension::from(dims as u32)).with_random_nulls(0.5),
            )
            .into_batch_rows(RowCount::from(500))
            .unwrap();
        let num_non_null = data["vec"].len() - data["vec"].logical_null_count() + num_non_null;
        let mut dataset = InsertBuilder::new(Arc::new(dataset))
            .with_params(&WriteParams {
                mode: WriteMode::Append,
                ..Default::default()
            })
            .execute(vec![data])
            .await
            .unwrap();
        check_index(&dataset, num_non_null, dims).await;

        // Optimize the index
        dataset.optimize_indices(&Default::default()).await.unwrap();
        check_index(&dataset, num_non_null, dims).await;
    }

    #[tokio::test]
    async fn test_create_ivf_pq_cosine() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (mut dataset, vector_array) = generate_test_dataset(test_uri, 0.0..1.0).await;

        let centroids = generate_random_array(2 * DIM);
        let ivf_centroids = FixedSizeListArray::try_new_from_values(centroids, DIM as i32).unwrap();
        let ivf_params = IvfBuildParams::try_with_centroids(2, Arc::new(ivf_centroids)).unwrap();

        let pq_params = PQBuildParams::new(4, 8);

        let params =
            VectorIndexParams::with_ivf_pq_params(MetricType::Cosine, ivf_params, pq_params);

        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let sample_query = vector_array.value(10);
        let query = sample_query.as_primitive::<Float32Type>();
        let results = dataset
            .scan()
            .nearest("vector", query, 5)
            .unwrap()
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(1, results.len());
        assert_eq!(5, results[0].num_rows());
        for batch in results.iter() {
            let dist = &batch["_distance"];
            dist.as_primitive::<Float32Type>()
                .values()
                .iter()
                .for_each(|v| {
                    assert!(
                        (0.0..2.0).contains(v),
                        "Expect cosine value in range [0.0, 2.0], got: {}",
                        v
                    )
                });
        }
    }

    #[tokio::test]
    async fn test_build_ivf_model_l2() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (dataset, _) = generate_test_dataset(test_uri, 1000.0..1100.0).await;

        let ivf_params = IvfBuildParams::new(2);
        let ivf_model = build_ivf_model(
            &dataset,
            "vector",
            DIM,
            MetricType::L2,
            &ivf_params,
            lance_index::progress::noop_progress(),
        )
        .await
        .unwrap();
        assert_eq!(2, ivf_model.centroids.as_ref().unwrap().len());
        assert_eq!(32, ivf_model.centroids.as_ref().unwrap().value_length());
        assert_eq!(2, ivf_model.num_partitions());

        // All centroids values should be in the range [1000, 1100]
        ivf_model
            .centroids
            .unwrap()
            .values()
            .as_primitive::<Float32Type>()
            .values()
            .iter()
            .for_each(|v| {
                assert!((1000.0..1100.0).contains(v));
            });
    }

    #[tokio::test]
    async fn test_build_ivf_model_cosine() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (dataset, _) = generate_test_dataset(test_uri, 1000.0..1100.0).await;

        let ivf_params = IvfBuildParams::new(2);
        let ivf_model = build_ivf_model(
            &dataset,
            "vector",
            DIM,
            MetricType::Cosine,
            &ivf_params,
            lance_index::progress::noop_progress(),
        )
        .await
        .unwrap();
        assert_eq!(2, ivf_model.centroids.as_ref().unwrap().len());
        assert_eq!(32, ivf_model.centroids.as_ref().unwrap().value_length());
        assert_eq!(2, ivf_model.num_partitions());

        // All centroids values should be in the range [1000, 1100]
        ivf_model
            .centroids
            .unwrap()
            .values()
            .as_primitive::<Float32Type>()
            .values()
            .iter()
            .for_each(|v| {
                assert!(
                    (-1.0..1.0).contains(v),
                    "Expect cosine value in range [-1.0, 1.0], got: {}",
                    v
                );
            });
    }

    #[tokio::test]
    async fn test_create_ivf_pq_dot() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (mut dataset, vector_array) = generate_test_dataset(test_uri, 0.0..1.0).await;

        let centroids = generate_random_array(2 * DIM);
        let ivf_centroids = FixedSizeListArray::try_new_from_values(centroids, DIM as i32).unwrap();
        let ivf_params = IvfBuildParams::try_with_centroids(2, Arc::new(ivf_centroids)).unwrap();

        let codebook = Arc::new(generate_random_array(256 * DIM));
        let pq_params = PQBuildParams::with_codebook(4, 8, codebook);

        let params = VectorIndexParams::with_ivf_pq_params(MetricType::Dot, ivf_params, pq_params);

        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let sample_query = vector_array.value(10);
        let query = sample_query.as_primitive::<Float32Type>();
        let results = dataset
            .scan()
            .nearest("vector", query, 5)
            .unwrap()
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(1, results.len());
        assert_eq!(5, results[0].num_rows());

        for batch in results.iter() {
            let dist = &batch["_distance"];
            dist.as_primitive::<Float32Type>()
                .values()
                .iter()
                .for_each(|v| {
                    assert!(
                        (-2.0 * DIM as f32..0.0).contains(v),
                        "Expect dot product value in range [-2.0 * DIM, 0.0], got: {}",
                        v
                    )
                });
        }
    }

    #[tokio::test]
    async fn test_create_ivf_pq_f16() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        const DIM: usize = 32;
        let schema = Arc::new(Schema::new(vec![Field::new(
            "vector",
            DataType::FixedSizeList(
                Arc::new(Field::new("item", DataType::Float16, true)),
                DIM as i32,
            ),
            true,
        )]));

        let arr = generate_random_array_with_seed::<Float16Type>(1000 * DIM, [22; 32]);
        let fsl = FixedSizeListArray::try_new_from_values(arr, DIM as i32).unwrap();
        let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(fsl)]).unwrap();
        let batches = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema.clone());
        let mut dataset = Dataset::write(batches, test_uri, None).await.unwrap();

        let params = VectorIndexParams::with_ivf_pq_params(
            MetricType::L2,
            IvfBuildParams::new(2),
            PQBuildParams::new(4, 8),
        );
        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let results = dataset
            .scan()
            .nearest(
                "vector",
                &Float32Array::from_iter_values(repeat_n(0.5, DIM)),
                5,
            )
            .unwrap()
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0].num_rows(), 5);
        let batch = &results[0];
        assert_eq!(
            batch.schema(),
            Arc::new(Schema::new(vec![
                Field::new(
                    "vector",
                    DataType::FixedSizeList(
                        Arc::new(Field::new("item", DataType::Float16, true)),
                        DIM as i32,
                    ),
                    true,
                ),
                Field::new("_distance", DataType::Float32, true)
            ]))
        );
    }

    #[tokio::test]
    async fn test_create_ivf_pq_f16_with_codebook() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        const DIM: usize = 32;
        let schema = Arc::new(Schema::new(vec![Field::new(
            "vector",
            DataType::FixedSizeList(
                Arc::new(Field::new("item", DataType::Float16, true)),
                DIM as i32,
            ),
            true,
        )]));

        let arr = generate_random_array_with_seed::<Float16Type>(1000 * DIM, [22; 32]);
        let fsl = FixedSizeListArray::try_new_from_values(arr, DIM as i32).unwrap();
        let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(fsl)]).unwrap();
        let batches = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema.clone());
        let mut dataset = Dataset::write(batches, test_uri, None).await.unwrap();

        let codebook = Arc::new(generate_random_array_with_seed::<Float16Type>(
            256 * DIM,
            [22; 32],
        ));
        let params = VectorIndexParams::with_ivf_pq_params(
            MetricType::L2,
            IvfBuildParams::new(2),
            PQBuildParams::with_codebook(4, 8, codebook),
        );
        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let results = dataset
            .scan()
            .nearest(
                "vector",
                &Float32Array::from_iter_values(repeat_n(0.5, DIM)),
                5,
            )
            .unwrap()
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(results.len(), 1);
        assert_eq!(results[0].num_rows(), 5);
        let batch = &results[0];
        assert_eq!(
            batch.schema(),
            Arc::new(Schema::new(vec![
                Field::new(
                    "vector",
                    DataType::FixedSizeList(
                        Arc::new(Field::new("item", DataType::Float16, true)),
                        DIM as i32,
                    ),
                    true,
                ),
                Field::new("_distance", DataType::Float32, true)
            ]))
        );
    }

    #[tokio::test]
    async fn test_create_ivf_pq_with_invalid_num_sub_vectors() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        const DIM: usize = 32;
        let schema = Arc::new(Schema::new(vec![Field::new(
            "vector",
            DataType::FixedSizeList(
                Arc::new(Field::new("item", DataType::Float32, true)),
                DIM as i32,
            ),
            true,
        )]));

        let arr = generate_random_array_with_seed::<Float32Type>(1000 * DIM, [22; 32]);
        let fsl = FixedSizeListArray::try_new_from_values(arr, DIM as i32).unwrap();
        let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(fsl)]).unwrap();
        let batches = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema.clone());
        let mut dataset = Dataset::write(batches, test_uri, None).await.unwrap();

        let params = VectorIndexParams::with_ivf_pq_params(
            MetricType::L2,
            IvfBuildParams::new(256),
            PQBuildParams::new(6, 8),
        );
        let res = dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await;
        match &res {
            Err(Error::InvalidInput { source, .. }) => {
                assert!(
                    source
                        .to_string()
                        .contains("num_sub_vectors must divide vector dimension"),
                    "{:?}",
                    res
                );
            }
            _ => panic!("Expected InvalidInput error: {:?}", res),
        }
    }

    fn ground_truth(
        fsl: &FixedSizeListArray,
        query: &[f32],
        k: usize,
        distance_type: DistanceType,
    ) -> Vec<(f32, u32)> {
        let dim = fsl.value_length() as usize;
        let mut dists = fsl
            .values()
            .as_primitive::<Float32Type>()
            .values()
            .chunks(dim)
            .enumerate()
            .map(|(i, vec)| {
                let dist = distance_type.func()(query, vec);
                (dist, i as u32)
            })
            .collect_vec();
        dists.sort_by(|a, b| a.0.partial_cmp(&b.0).unwrap());
        dists.truncate(k);
        dists
    }

    #[tokio::test]
    async fn test_create_ivf_hnsw_pq() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let nlist = 4;
        let (mut dataset, vector_array) = generate_test_dataset(test_uri, 0.0..1.0).await;

        let ivf_params = IvfBuildParams::new(nlist);
        let pq_params = PQBuildParams::default();
        let hnsw_params = HnswBuildParams::default();
        let params = VectorIndexParams::with_ivf_hnsw_pq_params(
            MetricType::L2,
            ivf_params,
            hnsw_params,
            pq_params,
        );

        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let query = vector_array.value(0);
        let query = query.as_primitive::<Float32Type>();
        let k = 100;
        let results = dataset
            .scan()
            .with_row_id()
            .nearest("vector", query, k)
            .unwrap()
            .minimum_nprobes(nlist)
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(1, results.len());
        assert_eq!(k, results[0].num_rows());

        let row_ids = results[0]
            .column_by_name(ROW_ID)
            .unwrap()
            .as_any()
            .downcast_ref::<UInt64Array>()
            .unwrap()
            .iter()
            .map(|v| v.unwrap() as u32)
            .collect::<Vec<_>>();
        let dists = results[0]
            .column_by_name("_distance")
            .unwrap()
            .as_any()
            .downcast_ref::<Float32Array>()
            .unwrap()
            .values()
            .to_vec();

        let results = dists.into_iter().zip(row_ids.into_iter()).collect_vec();
        let gt = ground_truth(&vector_array, query.values(), k, DistanceType::L2);

        let results_set = results.iter().map(|r| r.1).collect::<HashSet<_>>();
        let gt_set = gt.iter().map(|r| r.1).collect::<HashSet<_>>();

        let recall = results_set.intersection(&gt_set).count() as f32 / k as f32;
        assert!(
            recall >= 0.9,
            "recall: {}\n results: {:?}\n\ngt: {:?}",
            recall,
            results,
            gt,
        );
    }

    #[tokio::test]
    async fn test_create_ivf_hnsw_with_empty_partition() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        // the generate_test_dataset function generates a dataset with 1000 vectors,
        // so 1001 partitions will have at least one empty partition
        let nlist = 1001;
        let (mut dataset, vector_array) = generate_test_dataset(test_uri, 0.0..1.0).await;

        let centroids = generate_random_array(nlist * DIM);
        let ivf_centroids = FixedSizeListArray::try_new_from_values(centroids, DIM as i32).unwrap();
        let ivf_params =
            IvfBuildParams::try_with_centroids(nlist, Arc::new(ivf_centroids)).unwrap();

        let distance_type = DistanceType::L2;
        let sq_params = SQBuildParams::default();
        let hnsw_params = HnswBuildParams::default();
        let params = VectorIndexParams::with_ivf_hnsw_sq_params(
            distance_type,
            ivf_params,
            hnsw_params,
            sq_params,
        );

        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();

        let query = vector_array.value(0);
        let query = query.as_primitive::<Float32Type>();
        let k = 100;
        let results = dataset
            .scan()
            .with_row_id()
            .nearest("vector", query, k)
            .unwrap()
            .minimum_nprobes(nlist)
            .try_into_stream()
            .await
            .unwrap()
            .try_collect::<Vec<_>>()
            .await
            .unwrap();
        assert_eq!(1, results.len());
        assert_eq!(k, results[0].num_rows());

        let row_ids = results[0]
            .column_by_name(ROW_ID)
            .unwrap()
            .as_any()
            .downcast_ref::<UInt64Array>()
            .unwrap()
            .iter()
            .map(|v| v.unwrap() as u32)
            .collect::<Vec<_>>();
        let dists = results[0]
            .column_by_name("_distance")
            .unwrap()
            .as_any()
            .downcast_ref::<Float32Array>()
            .unwrap()
            .values()
            .to_vec();

        let results = dists.into_iter().zip(row_ids.into_iter()).collect_vec();
        let gt = ground_truth(&vector_array, query.values(), k, distance_type);

        let results_set = results.iter().map(|r| r.1).collect::<HashSet<_>>();
        let gt_set = gt.iter().map(|r| r.1).collect::<HashSet<_>>();

        let recall = results_set.intersection(&gt_set).count() as f32 / k as f32;
        assert!(
            recall >= 0.9,
            "recall: {}\n results: {:?}\n\ngt: {:?}",
            recall,
            results,
            gt,
        );
    }

    #[tokio::test]
    async fn test_check_cosine_normalization() {
        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();
        const DIM: usize = 32;

        let schema = Arc::new(Schema::new(vec![Field::new(
            "vector",
            DataType::FixedSizeList(
                Arc::new(Field::new("item", DataType::Float32, true)),
                DIM as i32,
            ),
            true,
        )]));

        let arr = generate_random_array_with_range::<Float32Type>(1000 * DIM, 1000.0..1001.0);
        let fsl = FixedSizeListArray::try_new_from_values(arr.clone(), DIM as i32).unwrap();
        let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(fsl)]).unwrap();
        let batches = RecordBatchIterator::new(vec![batch].into_iter().map(Ok), schema.clone());
        let mut dataset = Dataset::write(batches, test_uri, None).await.unwrap();

        let params = VectorIndexParams::ivf_pq(2, 8, 4, MetricType::Cosine, 50);
        dataset
            .create_index(&["vector"], IndexType::Vector, None, &params, false)
            .await
            .unwrap();
        let indices = dataset.load_indices().await.unwrap();
        let idx = dataset
            .open_generic_index(
                "vector",
                indices[0].uuid.to_string().as_str(),
                &NoOpMetricsCollector,
            )
            .await
            .unwrap();
        let ivf_idx = idx.as_any().downcast_ref::<v2::IvfPq>().unwrap();

        assert!(
            ivf_idx
                .ivf_model()
                .centroids
                .as_ref()
                .unwrap()
                .values()
                .as_primitive::<Float32Type>()
                .values()
                .iter()
                .all(|v| (0.0..=1.0).contains(v))
        );

        // PQ code is on residual space
        let pq_store = ivf_idx.load_partition_storage(0).await.unwrap();
        pq_store
            .codebook()
            .values()
            .as_primitive::<Float32Type>()
            .values()
            .iter()
            .for_each(|v| assert!((-1.0..=1.0).contains(v), "Got {}", v));

        let dataset = Dataset::open(test_uri).await.unwrap();

        let mut correct_times = 0;
        for query_id in 0..10 {
            let query = &arr.slice(query_id * DIM, DIM);
            let results = dataset
                .scan()
                .with_row_id()
                .nearest("vector", query, 1)
                .unwrap()
                .try_into_batch()
                .await
                .unwrap();
            assert_eq!(results.num_rows(), 1);
            let row_id = results
                .column_by_name("_rowid")
                .unwrap()
                .as_primitive::<UInt64Type>()
                .value(0);
            if row_id == (query_id as u64) {
                correct_times += 1;
            }
        }

        assert!(correct_times >= 9, "correct: {}", correct_times);
    }

    #[tokio::test(flavor = "multi_thread")]
    async fn test_build_ivf_model_progress_callback() {
        use lance_index::progress::IndexBuildProgress;
        use tokio::sync::Mutex;

        #[derive(Debug)]
        struct RecordingProgress {
            calls: Arc<Mutex<Vec<(String, u64)>>>,
        }

        #[async_trait::async_trait]
        impl IndexBuildProgress for RecordingProgress {
            async fn stage_start(&self, _: &str, _: Option<u64>, _: &str) -> Result<()> {
                Ok(())
            }
            async fn stage_progress(&self, stage: &str, completed: u64) -> Result<()> {
                self.calls.lock().await.push((stage.to_string(), completed));
                Ok(())
            }
            async fn stage_complete(&self, _: &str) -> Result<()> {
                Ok(())
            }
        }

        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let (dataset, _) = generate_test_dataset(test_uri, 1000.0..1100.0).await;

        let ivf_params = IvfBuildParams::new(2);
        let calls: Arc<Mutex<Vec<(String, u64)>>> = Arc::new(Mutex::new(Vec::new()));
        let progress: Arc<dyn IndexBuildProgress> = Arc::new(RecordingProgress {
            calls: calls.clone(),
        });

        let ivf_model = build_ivf_model(
            &dataset,
            "vector",
            DIM,
            MetricType::L2,
            &ivf_params,
            progress,
        )
        .await
        .unwrap();
        assert_eq!(2, ivf_model.num_partitions());

        // Let spawned progress tasks complete.
        tokio::task::yield_now().await;

        let recorded = calls.lock().await;
        assert!(
            !recorded.is_empty(),
            "Expected progress callbacks to be called"
        );
        // All calls should be for train_ivf stage
        for (stage, _) in recorded.iter() {
            assert_eq!(stage, "train_ivf");
        }
        // Completed values should be monotonically increasing
        for window in recorded.windows(2) {
            assert!(
                window[1].1 >= window[0].1,
                "Expected monotonically increasing progress: {} >= {}",
                window[1].1,
                window[0].1,
            );
        }
    }

    #[tokio::test]
    async fn test_prewarm_ivf_legacy() {
        use lance_io::assert_io_eq;

        let test_dir = TempStrDir::default();
        let test_uri = test_dir.as_str();

        let dim = DIM as i32;
        let schema = Arc::new(Schema::new(vec![Field::new(
            "vector",
            DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), dim),
            false,
        )]));
        let vectors = generate_random_array(512 * DIM);
        let fsl = FixedSizeListArray::try_new_from_values(vectors, dim).unwrap();
        let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(fsl)]).unwrap();
        let batches = RecordBatchIterator::new(vec![Ok(batch)], schema);
        let mut dataset = Dataset::write(batches, test_uri, None).await.unwrap();

        let nlist = 4;
        let params = VectorIndexParams::with_ivf_pq_params(
            DistanceType::L2,
            IvfBuildParams::new(nlist),
            PQBuildParams::default(),
        )
        .version(IndexFileVersion::Legacy)
        .clone();
        dataset
            .create_index(
                &["vector"],
                IndexType::Vector,
                Some("my_idx".to_owned()),
                &params,
                true,
            )
            .await
            .unwrap();

        // Reset IO stats after index creation
        dataset.object_store().io_stats_incremental();

        // Prewarm should perform IO to load all partitions into cache
        dataset.prewarm_index("my_idx").await.unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert!(
            stats.read_iops > 0,
            "prewarm should have read from disk, but read_iops was 0"
        );

        // Can query index without IO
        let q = Float32Array::from_iter_values(repeat_n(0.0, DIM));
        dataset
            .scan()
            .nearest("vector", &q, 10)
            .unwrap()
            .project(&["_rowid"])
            .unwrap()
            .try_into_batch()
            .await
            .unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert_io_eq!(
            stats,
            read_iops,
            0,
            "query should not perform IO after prewarm"
        );

        // Second prewarm should not need IO (already cached)
        dataset.prewarm_index("my_idx").await.unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert_io_eq!(stats, read_iops, 0, "second prewarm should not perform IO");
    }

    #[tokio::test]
    async fn test_prewarm_ivf_legacy_multiple_deltas() {
        use lance_io::assert_io_eq;

        let test_dir = copy_test_data_to_tmp("v0.21.0/bad_index_fragment_bitmap").unwrap();
        let test_uri = test_dir.path_str();
        let test_uri = &test_uri;

        // Trigger migration to repair legacy corrupt fragment bitmaps.
        let mut dataset = Dataset::open(test_uri).await.unwrap();
        dataset.index_statistics("vector_idx").await.unwrap();
        dataset.checkout_latest().await.unwrap();

        // Reopen dataset to avoid carrying index state in-memory from migration.
        let dataset = Dataset::open(test_uri).await.unwrap();
        let indices = dataset.load_indices_by_name("vector_idx").await.unwrap();
        assert_eq!(indices.len(), 2, "expected two index deltas for vector_idx");
        let unique_uuids: HashSet<_> = indices.iter().map(|meta| meta.uuid).collect();
        assert_eq!(unique_uuids.len(), 2, "expected two unique index UUIDs");

        let sample_batch = dataset
            .scan()
            .limit(Some(1), None)
            .unwrap()
            .project(&["vector"])
            .unwrap()
            .try_into_batch()
            .await
            .unwrap();
        let q = sample_batch["vector"]
            .as_any()
            .downcast_ref::<FixedSizeListArray>()
            .unwrap()
            .value(0)
            .as_any()
            .downcast_ref::<Float32Array>()
            .unwrap()
            .clone();

        // Reset IO stats after migration and sampling.
        dataset.object_store().io_stats_incremental();

        // Prewarm should perform IO to load all index deltas into cache.
        dataset.prewarm_index("vector_idx").await.unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert!(
            stats.read_iops > 0,
            "prewarm should have read from disk, but read_iops was 0"
        );

        // Query should not perform index IO after prewarm of all deltas.
        dataset
            .scan()
            .nearest("vector", &q, 10)
            .unwrap()
            .project(&["_rowid"])
            .unwrap()
            .try_into_batch()
            .await
            .unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert_io_eq!(
            stats,
            read_iops,
            0,
            "query should not perform IO after prewarm"
        );

        // Second prewarm should not need IO (already cached).
        dataset.prewarm_index("vector_idx").await.unwrap();
        let stats = dataset.object_store().io_stats_incremental();
        assert_io_eq!(stats, read_iops, 0, "second prewarm should not perform IO");
    }
}