lakehouse/crates/lance-bench/src/main.rs

//! Phase B: Lance pilot benchmark.
//!
//! Standalone binary that compares Lance vector storage against our
//! Parquet-with-binary-blob + in-RAM HNSW approach. See
//! docs/EXECUTION_PLAN.md for the decision rules this fuels.
//!
//! Inputs:
//!   data/vectors/resumes_100k_v2.parquet — existing 100K × 768d embeddings
//!
//! Output:
//!   A JSON report printed to stdout with measurements for:
//!     - Cold load time (parquet → arrow) vs Lance open + scan
//!     - Disk size
//!     - Vector search latency (p50 / p95 / p99)
//!     - Single-row random access
//!     - Append cost (adding 10K rows)
//!
//! Usage:
//!   cargo run --bin lance-bench -- \
//!     --parquet data/vectors/resumes_100k_v2.parquet \
//!     --lance-out /tmp/lance_resumes_100k_v2 \
//!     --json-out /tmp/lance_bench.json

use anyhow::{Context, Result};
use arrow_array::{Array, ArrayRef, BinaryArray, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator};
use arrow_schema::{DataType, Field, Schema};
use serde::Serialize;
use std::sync::Arc;
use std::time::Instant;

#[derive(Debug, Serialize)]
struct BenchReport {
    vectors: usize,
    dimensions: usize,
    parquet_path: String,
    lance_path: String,

    // Parquet baseline
    parquet_disk_bytes: u64,
    parquet_cold_load_secs: f32,

    // Lance numbers
    lance_write_secs: f32,
    lance_disk_bytes: u64,
    lance_cold_open_secs: f32,

    // Index + search
    lance_index_build_secs: Option<f32>,
    lance_index_disk_bytes: Option<u64>,
    lance_search_p50_us: Option<f32>,
    lance_search_p95_us: Option<f32>,
    lance_search_p99_us: Option<f32>,

    // Architectural features Parquet+sidecar can't cheaply do
    lance_random_row_access_us: Option<f32>,   // fetch one row by row_id
    parquet_random_row_access_us: Option<f32>, // for comparison — full scan cost
    lance_append_10k_secs: Option<f32>,        // add 10K new rows
    lance_append_disk_bytes_added: Option<u64>,

    // Head-to-head reference (from our own measurements)
    reference_hnsw_p50_us: f32,
    reference_hnsw_p95_us: f32,
    reference_brute_force_us: f32,
    reference_hnsw_build_secs: f32,
}

#[tokio::main]
async fn main() -> Result<()> {
    // Simple positional args: parquet_in, lance_out.
    let args: Vec<String> = std::env::args().collect();
    let parquet_path = args
        .get(1)
        .cloned()
        .unwrap_or_else(|| "data/vectors/resumes_100k_v2.parquet".to_string());
    let lance_path = args
        .get(2)
        .cloned()
        .unwrap_or_else(|| "/tmp/lance_bench_dataset".to_string());

    eprintln!("=== Phase B Lance pilot ===");
    eprintln!("input  parquet: {}", parquet_path);
    eprintln!("output lance:   {}", lance_path);

    // --- 1. Cold-load the existing Parquet vector index into memory
    eprintln!("\n[1/4] reading Parquet baseline...");
    let t0 = Instant::now();
    let (schema, batches, total_rows) = read_parquet_vectors(&parquet_path)
        .context("read parquet")?;
    let parquet_cold_load_secs = t0.elapsed().as_secs_f32();
    let parquet_disk_bytes = std::fs::metadata(&parquet_path)?.len();

    let dims = detect_vector_dims(&batches)?;
    eprintln!(
        "  loaded {} rows, {} columns, vectors={}d, disk={:.1} MB, cold load={:.2}s",
        total_rows,
        schema.fields().len(),
        dims,
        parquet_disk_bytes as f64 / 1_000_000.0,
        parquet_cold_load_secs,
    );

    // --- 2. Convert from binary-blob-of-f32 to Lance's FixedSizeList<Float32>
    eprintln!("\n[2/4] converting binary-blob vectors to Arrow FixedSizeList...");
    let t0 = Instant::now();
    let (lance_schema, lance_batches) = convert_to_fixed_size_list(&schema, batches, dims)?;
    eprintln!("  conversion took {:.2}s", t0.elapsed().as_secs_f32());

    // --- 3. Write as Lance dataset
    eprintln!("\n[3/4] writing Lance dataset...");
    let t0 = Instant::now();
    // Clean up any prior run
    let _ = std::fs::remove_dir_all(&lance_path);
    write_lance_dataset(&lance_path, lance_schema.clone(), lance_batches).await?;
    let lance_write_secs = t0.elapsed().as_secs_f32();
    let lance_disk_bytes = dir_size_bytes(&lance_path);
    eprintln!(
        "  write took {:.2}s, disk={:.1} MB",
        lance_write_secs,
        lance_disk_bytes as f64 / 1_000_000.0,
    );

    // --- 4. Cold open + scan the Lance dataset
    eprintln!("\n[4/6] cold-opening Lance dataset...");
    let t0 = Instant::now();
    let scanned_rows = cold_open_and_scan_lance(&lance_path).await?;
    let lance_cold_open_secs = t0.elapsed().as_secs_f32();
    eprintln!(
        "  open + full scan: {} rows in {:.2}s",
        scanned_rows, lance_cold_open_secs,
    );

    // --- 5. Build a vector index on the Lance dataset
    eprintln!("\n[5/6] building Lance vector index (IVF_PQ)...");
    let t0 = Instant::now();
    let index_built = build_lance_vector_index(&lance_path, dims).await;
    let (lance_index_build_secs, lance_index_disk_bytes) = match index_built {
        Ok(()) => {
            let secs = t0.elapsed().as_secs_f32();
            let disk = dir_size_bytes(&lance_path) - lance_disk_bytes;
            eprintln!("  built in {:.2}s, index adds {:.1} MB on disk", secs, disk as f64 / 1e6);
            (Some(secs), Some(disk))
        }
        Err(e) => {
            eprintln!("  index build failed: {e:#}");
            (None, None)
        }
    };

    // --- 6. Run search queries, measure latency
    eprintln!("\n[6/6] running vector search benchmarks...");
    let search_stats = if lance_index_build_secs.is_some() {
        run_search_benchmarks(&lance_path, dims).await.ok()
    } else {
        None
    };
    let (lance_search_p50, lance_search_p95, lance_search_p99) = match search_stats {
        Some((p50, p95, p99)) => {
            eprintln!("  p50={:.0}us p95={:.0}us p99={:.0}us", p50, p95, p99);
            (Some(p50), Some(p95), Some(p99))
        }
        None => (None, None, None),
    };

    // --- Random access comparison
    eprintln!("\n[7/8] random row access — Lance vs full-scan Parquet...");
    let lance_random = measure_random_access_lance(&lance_path).await.ok();
    let parquet_random = measure_random_access_parquet(&parquet_path).ok();
    if let Some(us) = lance_random {
        eprintln!("  Lance random-fetch avg: {:.0}us", us);
    }
    if let Some(us) = parquet_random {
        eprintln!("  Parquet full-scan-to-row avg: {:.0}us", us);
    }

    // --- Append cost
    eprintln!("\n[8/8] append 10K new rows to existing dataset...");
    let t0 = Instant::now();
    let pre_append_bytes = dir_size_bytes(&lance_path);
    let append_result = append_10k_rows(&lance_path, dims).await;
    let (lance_append_secs, lance_append_bytes) = match append_result {
        Ok(()) => {
            let secs = t0.elapsed().as_secs_f32();
            let bytes = dir_size_bytes(&lance_path).saturating_sub(pre_append_bytes);
            eprintln!("  append took {:.2}s, added {:.1} MB", secs, bytes as f64 / 1e6);
            (Some(secs), Some(bytes))
        }
        Err(e) => {
            eprintln!("  append failed: {e:#}");
            (None, None)
        }
    };

    // --- Report
    let report = BenchReport {
        vectors: total_rows,
        dimensions: dims,
        parquet_path: parquet_path.clone(),
        lance_path: lance_path.clone(),
        parquet_disk_bytes,
        parquet_cold_load_secs,
        lance_write_secs,
        lance_disk_bytes,
        lance_cold_open_secs,
        lance_index_build_secs,
        lance_index_disk_bytes,
        lance_search_p50_us: lance_search_p50,
        lance_search_p95_us: lance_search_p95,
        lance_search_p99_us: lance_search_p99,
        lance_random_row_access_us: lance_random,
        parquet_random_row_access_us: parquet_random,
        lance_append_10k_secs: lance_append_secs,
        lance_append_disk_bytes_added: lance_append_bytes,
        // From our Phase 15 trial on the SAME index (ec=80 es=30, recall=1.00):
        reference_hnsw_p50_us: 873.0,
        reference_hnsw_p95_us: 1413.0,
        reference_brute_force_us: 43983.0,
        reference_hnsw_build_secs: 230.0,
    };

    let json = serde_json::to_string_pretty(&report)?;
    println!("{}", json);

    eprintln!("\n=== Summary ===");
    eprintln!("  Parquet cold load:  {:.2}s", report.parquet_cold_load_secs);
    eprintln!("  Lance cold open:    {:.2}s  ({})",
        report.lance_cold_open_secs,
        format_ratio(report.parquet_cold_load_secs, report.lance_cold_open_secs));
    eprintln!("  Parquet disk:       {:.1} MB", report.parquet_disk_bytes as f64 / 1e6);
    eprintln!("  Lance disk:         {:.1} MB  ({})",
        report.lance_disk_bytes as f64 / 1e6,
        format_ratio(report.parquet_disk_bytes as f32, report.lance_disk_bytes as f32));
    if let (Some(p50), Some(p95)) = (report.lance_search_p50_us, report.lance_search_p95_us) {
        eprintln!("  Lance search p50:   {:.0}us  vs our HNSW {:.0}us  ({})",
            p50, report.reference_hnsw_p50_us,
            format_ratio(report.reference_hnsw_p50_us, p50));
        eprintln!("  Lance search p95:   {:.0}us  vs our HNSW {:.0}us  ({})",
            p95, report.reference_hnsw_p95_us,
            format_ratio(report.reference_hnsw_p95_us, p95));
        eprintln!("  Speedup vs brute force: {:.1}× (Lance) vs {:.1}× (HNSW)",
            report.reference_brute_force_us / p50,
            report.reference_brute_force_us / report.reference_hnsw_p50_us);
    }
    if let Some(build) = report.lance_index_build_secs {
        eprintln!("  Index build:        {:.1}s (Lance IVF_PQ) vs {:.0}s (our HNSW ec=80)  ({}× faster)",
            build, report.reference_hnsw_build_secs, report.reference_hnsw_build_secs / build);
    }
    if let (Some(lance_us), Some(parquet_us)) = (report.lance_random_row_access_us, report.parquet_random_row_access_us) {
        eprintln!("  Random row access:  {:.0}us (Lance) vs {:.0}us (Parquet scan)  ({})",
            lance_us, parquet_us, format_ratio(parquet_us, lance_us));
    }
    if let Some(append_secs) = report.lance_append_10k_secs {
        eprintln!("  Append 10K rows:    {:.2}s (Lance native)  [Parquet would require full rewrite]",
            append_secs);
    }

    Ok(())
}

fn format_ratio(baseline: f32, candidate: f32) -> String {
    if candidate == 0.0 { return "inf".into(); }
    let ratio = baseline / candidate;
    if ratio >= 1.0 {
        format!("{:.2}× faster/smaller", ratio)
    } else {
        format!("{:.2}× slower/larger", 1.0 / ratio)
    }
}

fn dir_size_bytes(path: &str) -> u64 {
    fn recurse(p: &std::path::Path) -> u64 {
        let Ok(meta) = std::fs::metadata(p) else { return 0; };
        if meta.is_file() { return meta.len(); }
        let Ok(entries) = std::fs::read_dir(p) else { return 0; };
        entries
            .filter_map(|e| e.ok())
            .map(|e| recurse(&e.path()))
            .sum()
    }
    recurse(std::path::Path::new(path))
}

/// Read the existing vector Parquet (binary-blob format: source, doc_id,
/// chunk_idx, chunk_text, vector as Binary bytes).
fn read_parquet_vectors(path: &str) -> Result<(Arc<Schema>, Vec<RecordBatch>, usize)> {
    use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder;
    use std::fs::File;

    let file = File::open(path).with_context(|| format!("open {path}"))?;
    let builder = ParquetRecordBatchReaderBuilder::try_new(file)?;
    let schema = builder.schema().clone();
    let reader = builder.build()?;
    let batches: Vec<RecordBatch> = reader.collect::<Result<Vec<_>, _>>()?;
    let rows: usize = batches.iter().map(|b| b.num_rows()).sum();
    Ok((schema, batches, rows))
}

fn detect_vector_dims(batches: &[RecordBatch]) -> Result<usize> {
    for batch in batches {
        let vector_col_idx = batch
            .schema()
            .index_of("vector")
            .context("no 'vector' column in parquet")?;
        let col = batch.column(vector_col_idx);
        if let Some(binary) = col.as_any().downcast_ref::<BinaryArray>() {
            for i in 0..binary.len() {
                if !binary.is_null(i) {
                    let bytes = binary.value(i);
                    return Ok(bytes.len() / 4); // f32 = 4 bytes
                }
            }
        }
    }
    anyhow::bail!("could not determine vector dimensions")
}

/// Convert our binary-blob vector representation into Arrow's native
/// FixedSizeList<Float32> — that's what Lance expects for vector columns.
fn convert_to_fixed_size_list(
    schema: &Arc<Schema>,
    batches: Vec<RecordBatch>,
    dims: usize,
) -> Result<(Arc<Schema>, Vec<RecordBatch>)> {
    // New schema keeps everything identical but replaces the vector column
    // with a FixedSizeList<Float32, dims>.
    let new_fields: Vec<Arc<Field>> = schema
        .fields()
        .iter()
        .map(|f| {
            if f.name() == "vector" {
                Arc::new(Field::new(
                    "vector",
                    DataType::FixedSizeList(
                        Arc::new(Field::new("item", DataType::Float32, true)),
                        dims as i32,
                    ),
                    false,
                ))
            } else {
                f.clone()
            }
        })
        .collect();
    let new_schema = Arc::new(Schema::new(new_fields));

    let mut new_batches = Vec::with_capacity(batches.len());
    for batch in batches {
        let vector_idx = batch.schema().index_of("vector")?;
        let mut new_arrays: Vec<ArrayRef> = Vec::with_capacity(batch.num_columns());
        for (i, col) in batch.columns().iter().enumerate() {
            if i == vector_idx {
                let binary = col
                    .as_any()
                    .downcast_ref::<BinaryArray>()
                    .context("vector column must be Binary")?;
                let fsl = binary_to_fixed_size_list(binary, dims)?;
                new_arrays.push(Arc::new(fsl));
            } else {
                new_arrays.push(col.clone());
            }
        }
        new_batches.push(RecordBatch::try_new(new_schema.clone(), new_arrays)?);
    }

    Ok((new_schema, new_batches))
}

fn binary_to_fixed_size_list(binary: &BinaryArray, dims: usize) -> Result<FixedSizeListArray> {
    let n = binary.len();
    let mut all_floats: Vec<f32> = Vec::with_capacity(n * dims);
    for i in 0..n {
        if binary.is_null(i) {
            all_floats.extend(std::iter::repeat(0.0).take(dims));
            continue;
        }
        let bytes = binary.value(i);
        if bytes.len() != dims * 4 {
            anyhow::bail!(
                "row {} has {} bytes, expected {} ({} × f32)",
                i, bytes.len(), dims * 4, dims,
            );
        }
        for chunk in bytes.chunks_exact(4) {
            all_floats.push(f32::from_le_bytes([chunk[0], chunk[1], chunk[2], chunk[3]]));
        }
    }
    let values = Float32Array::from(all_floats);
    let field = Arc::new(Field::new("item", DataType::Float32, true));
    FixedSizeListArray::try_new(field, dims as i32, Arc::new(values), None)
        .context("build FixedSizeListArray")
}

/// Write batches into a Lance dataset at the given path.
async fn write_lance_dataset(
    path: &str,
    schema: Arc<Schema>,
    batches: Vec<RecordBatch>,
) -> Result<()> {
    use lance::dataset::{Dataset, WriteParams};

    let reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
    Dataset::write(reader, path, Some(WriteParams::default()))
        .await
        .context("Dataset::write")?;
    Ok(())
}

/// Open a Lance dataset cold (from disk) and scan it fully — measuring the
/// equivalent of our "load embeddings from Parquet" cost.
async fn cold_open_and_scan_lance(path: &str) -> Result<usize> {
    use futures::StreamExt;
    use lance::dataset::Dataset;

    let dataset = Dataset::open(path).await.context("Dataset::open")?;
    let scanner = dataset.scan();
    let mut stream = scanner.try_into_stream().await?;
    let mut total = 0usize;
    while let Some(batch) = stream.next().await {
        let batch = batch?;
        total += batch.num_rows();
    }
    Ok(total)
}

/// Build an IVF_PQ vector index on the `vector` column. IVF_PQ (Inverted File
/// with Product Quantization) is Lance's native ANN index — comparable to
/// HNSW in intent, but on-disk and compatible with Lance's random-access
/// model.
async fn build_lance_vector_index(path: &str, _dims: usize) -> Result<()> {
    use lance::dataset::Dataset;
    use lance::index::vector::VectorIndexParams;
    use lance_index::{DatasetIndexExt, IndexType};
    use lance_linalg::distance::MetricType;

    let mut dataset = Dataset::open(path).await?;

    // IVF_PQ with ~sqrt(N) partitions is a reasonable default for 100K.
    // num_sub_vectors must divide dims evenly: 768/48 = 16 dims per subvector.
    // num_bits = 8 gives 256 codes per subvector (good recall/size trade).
    // max_iterations = 50 is plenty for this scale.
    let params = VectorIndexParams::ivf_pq(
        316,               // num_partitions (~sqrt(100000))
        8,                 // num_bits
        48,                // num_sub_vectors
        MetricType::Cosine,
        50,                // max_iterations
    );

    dataset
        .create_index(
            &["vector"],
            IndexType::Vector,
            Some("vec_idx".into()),
            &params,
            true,
        )
        .await
        .context("create_index")?;

    Ok(())
}

/// Run N vector searches against the Lance dataset and return (p50, p95, p99) latencies in us.
/// Uses a handful of random rows as queries — same pattern as our harness::synthetic_from_chunks.
async fn run_search_benchmarks(path: &str, _dims: usize) -> Result<(f32, f32, f32)> {
    use futures::StreamExt;
    use lance::dataset::Dataset;

    let dataset = Dataset::open(path).await?;

    // Pick 20 representative query vectors from the data itself.
    // (Synthetic — same pattern as our existing harness.)
    let query_vectors = sample_query_vectors(&dataset, 20).await?;

    let mut latencies_us: Vec<f32> = Vec::with_capacity(query_vectors.len());
    for (i, qv) in query_vectors.iter().enumerate() {
        let qarr = Arc::new(Float32Array::from(qv.clone())) as ArrayRef;

        let t0 = Instant::now();
        let mut scanner = dataset.scan();
        scanner
            .nearest("vector", qarr.as_any().downcast_ref::<Float32Array>().unwrap(), 10)
            .context("scanner.nearest")?;
        let mut stream = scanner.try_into_stream().await?;
        let mut hits = 0;
        while let Some(batch) = stream.next().await {
            let batch = batch?;
            hits += batch.num_rows();
        }
        let us = t0.elapsed().as_micros() as f32;
        latencies_us.push(us);
        if i == 0 {
            eprintln!("  first query: {} hits in {:.0}us (includes any lazy init)", hits, us);
        }
    }

    latencies_us.sort_by(|a, b| a.partial_cmp(b).unwrap());
    let p = |pct: f32| -> f32 {
        let idx = ((latencies_us.len() as f32 - 1.0) * pct).round() as usize;
        latencies_us[idx.min(latencies_us.len() - 1)]
    };
    Ok((p(0.50), p(0.95), p(0.99)))
}

/// Random row access via Lance's `take` — fetch 20 random rows by index, measure avg latency.
async fn measure_random_access_lance(path: &str) -> Result<f32> {
    use lance::dataset::Dataset;
    let dataset = Dataset::open(path).await?;
    let n = dataset.count_rows(None).await?;
    let indices: Vec<u64> = (0..20).map(|i| ((i as u64) * (n as u64 / 23)) % (n as u64)).collect();

    // Full-schema projection — Lance's Schema implements Into<ProjectionRequest>.
    let schema = dataset.schema().clone();
    let mut total_us: u128 = 0;
    for idx in &indices {
        let t0 = Instant::now();
        let _batch = dataset.take(&[*idx], schema.clone()).await?;
        total_us += t0.elapsed().as_micros();
    }
    Ok(total_us as f32 / indices.len() as f32)
}

/// Random row access for Parquet — full scan + filter. There's no random-access
/// primitive in vanilla Parquet, so this is the cost of finding one specific row.
/// This is the cost our current design pays for "get doc X's full text for RAG."
fn measure_random_access_parquet(path: &str) -> Result<f32> {
    use parquet::arrow::arrow_reader::ParquetRecordBatchReaderBuilder;
    use std::fs::File;

    // We simulate 5 lookups — full scan each time. 20 would be painful.
    let iters = 5;
    let mut total_us: u128 = 0;
    for _ in 0..iters {
        let t0 = Instant::now();
        let file = File::open(path)?;
        let builder = ParquetRecordBatchReaderBuilder::try_new(file)?;
        let reader = builder.build()?;
        // Iterate until we've conceptually found a row — we stop early if
        // we wanted row 50000, but we have to at least read its batch.
        let mut seen = 0usize;
        for b in reader {
            let b = b?;
            seen += b.num_rows();
            if seen > 50000 { break; }
        }
        total_us += t0.elapsed().as_micros();
    }
    Ok(total_us as f32 / iters as f32)
}

/// Append 10K new rows to the existing Lance dataset.
/// Measures the "ingest delta" cost without full rewrite.
async fn append_10k_rows(path: &str, dims: usize) -> Result<()> {
    use lance::dataset::{Dataset, WriteMode, WriteParams};

    let dataset = Dataset::open(path).await?;
    let schema = dataset.schema();
    let arrow_schema: Arc<Schema> = Arc::new(schema.into());

    // Build a 10K row batch with random-ish data matching the existing schema.
    let n = 10_000;
    let arrays: Vec<ArrayRef> = arrow_schema
        .fields()
        .iter()
        .map(|f| -> Result<ArrayRef> {
            match f.data_type() {
                DataType::Utf8 => {
                    let vals: Vec<String> = (0..n).map(|i| format!("appended-{}", i)).collect();
                    Ok(Arc::new(arrow_array::StringArray::from(vals)))
                }
                DataType::Int32 => {
                    let vals: Vec<i32> = (0..n as i32).collect();
                    Ok(Arc::new(arrow_array::Int32Array::from(vals)))
                }
                DataType::FixedSizeList(_, _) => {
                    let floats: Vec<f32> = (0..n * dims).map(|i| (i as f32).sin()).collect();
                    let values = Float32Array::from(floats);
                    let field = Arc::new(Field::new("item", DataType::Float32, true));
                    let fsl = FixedSizeListArray::try_new(field, dims as i32, Arc::new(values), None)?;
                    Ok(Arc::new(fsl))
                }
                other => anyhow::bail!("unsupported append column type: {:?}", other),
            }
        })
        .collect::<Result<Vec<_>>>()?;

    let batch = RecordBatch::try_new(arrow_schema.clone(), arrays)?;
    let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), arrow_schema);
    let params = WriteParams { mode: WriteMode::Append, ..Default::default() };
    Dataset::write(reader, path, Some(params)).await?;
    Ok(())
}

/// Grab a few existing vectors from the dataset to use as self-similar queries.
async fn sample_query_vectors(
    dataset: &lance::dataset::Dataset,
    count: usize,
) -> Result<Vec<Vec<f32>>> {
    use futures::StreamExt;

    // Just take the first `count` rows; good enough for latency measurement.
    let scanner = dataset.scan();
    let mut scanner = scanner;
    scanner.limit(Some(count as i64), None)?;
    scanner.project(&["vector"])?;
    let mut stream = scanner.try_into_stream().await?;

    let mut out = Vec::with_capacity(count);
    while let Some(batch) = stream.next().await {
        let batch = batch?;
        let vector_col = batch
            .column(0)
            .as_any()
            .downcast_ref::<FixedSizeListArray>()
            .context("vector column must be FixedSizeList")?;

        for row in 0..vector_col.len() {
            if out.len() >= count { break; }
            let values = vector_col.value(row);
            let f32_arr = values
                .as_any()
                .downcast_ref::<Float32Array>()
                .context("inner array must be Float32")?;
            let mut v = Vec::with_capacity(f32_arr.len());
            for i in 0..f32_arr.len() {
                v.push(f32_arr.value(i));
            }
            out.push(v);
        }
        if out.len() >= count { break; }
    }
    Ok(out)
}