lance 0.5.8

A columnar data format that is 100x faster than Parquet for random access.
Documentation 
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// Copyright 2023 Lance Developers.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

//! Flat Vector Index.
//!

use arrow::array::as_primitive_array;
use arrow::datatypes::Float32Type;
use arrow_array::{cast::as_struct_array, ArrayRef, RecordBatch, StructArray};
use arrow_ord::sort::sort_to_indices;
use arrow_schema::{DataType, Field as ArrowField};
use arrow_select::{concat::concat_batches, take::take};
use futures::future;
use futures::stream::{repeat_with, Stream, StreamExt, TryStreamExt};

use super::{Query, SCORE_COL};
use crate::arrow::*;
use crate::{Error, Result};

pub async fn flat_search(
    stream: impl Stream<Item = Result<RecordBatch>>,
    query: &Query,
) -> Result<RecordBatch> {
    let batches = stream
        .filter(|batch| {
            let pred = batch.as_ref().map(|b| b.num_rows() > 0).unwrap_or(false);
            future::ready(pred)
        })
        .zip(repeat_with(|| query.metric_type))
        .map(|(batch, mt)| async move {
            let k = query.key.clone();
            let mut batch = batch?;
            if batch.column_by_name(SCORE_COL).is_some() {
                // Ignore the score calculated from inner vector index.
                batch = batch.drop_column(SCORE_COL)?;
            }
            let vectors = batch
                .column_by_name(&query.column)
                .ok_or_else(|| Error::Schema {
                    message: format!("column {} does not exist in dataset", query.column),
                })?
                .clone();
            let flatten_vectors = as_fixed_size_list_array(vectors.as_ref()).values().clone();
            let scores = tokio::task::spawn_blocking(move || {
                mt.batch_func()(
                    k.values(),
                    as_primitive_array::<Float32Type>(flatten_vectors.as_ref()).values(),
                    k.len(),
                )
            })
            .await? as ArrayRef;

            // TODO: use heap
            let indices = sort_to_indices(&scores, None, Some(query.k))?;
            let batch_with_score = batch
                .try_with_column(ArrowField::new(SCORE_COL, DataType::Float32, false), scores)?;
            let struct_arr = StructArray::from(batch_with_score);
            let selected_arr = take(&struct_arr, &indices, None)?;
            Ok::<RecordBatch, Error>(as_struct_array(&selected_arr).into())
        })
        .buffer_unordered(16)
        .try_collect::<Vec<_>>()
        .await?;
    let batch = concat_batches(&batches[0].schema(), &batches)?;
    let scores = batch.column_by_name(SCORE_COL).unwrap();
    let indices = sort_to_indices(scores, None, Some(query.k))?;

    let struct_arr = StructArray::from(batch);
    let selected_arr = take(&struct_arr, &indices, None)?;
    Ok(as_struct_array(&selected_arr).into())
}