Vector similarity search scan benchmarks

benchmarks/vector-search-bench/src/compression.rs

@@ -8,8 +8,6 @@
 //!
 //! The benchmark writes one `.vortex` file per flavor per data file, then scans them all with the
 //! same query so the comparison is apples-to-apples with the Parquet files.
-//!
-//! Note that the handrolled `&[f32]` parquet baseline is **not** a flavor here.
 
 use clap::ValueEnum;
 use vortex::array::ArrayId;

benchmarks/vector-search-bench/src/display.rs

@@ -0,0 +1,133 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright the Vortex contributors
+
+//! Local table renderer for the vector-search benchmark.
+//!
+//! Groups columns by **flavor** (`vortex-uncompressed`, `vortex-turboquant`) rather than by
+//! [`vortex_bench::Format`], because the two Vortex flavors share a single
+//! `Format::OnDiskVortex`/`Format::VortexLossy` pair and the generic
+//! [`vortex_bench::display::render_table`] groups by Format. Local renderer keeps the
+//! column-per-flavor invariant intact without introducing a new global Format value.
+//!
+//! Output rows:
+//!
+//! ```text
+//!   Metric             | vortex-uncompressed | vortex-turboquant
+//!   ------------------ + ------------------- + -----------------
+//!   scan wall (mean)   |              485 ms |            212 ms
+//!   scan wall (median) |              490 ms |            215 ms
+//!   matches            |                  42 |                39
+//!   rows scanned       |          10,000,000 |        10,000,000
+//!   bytes scanned      |             30.5 GB |           7.62 GB
+//!   rows / sec         |              5.2e6  |           1.2e7
+//! ```
+
+use std::io::Write;
+
+use anyhow::Result;
+use tabled::settings::Style;
+
+use crate::compression::VectorFlavor;
+use crate::prepare::CompressedVortexDataset;
+use crate::scan::ScanTiming;
+
+/// Final column-per-flavor row set for one dataset.
+pub struct DatasetReport<'a> {
+    pub dataset_name: &'a str,
+    pub vortex_results: &'a [(VectorFlavor, &'a CompressedVortexDataset, &'a ScanTiming)],
+}
+
+/// Render the full report into the given writer as a tabled table.
+pub fn render(report: &DatasetReport<'_>, writer: &mut dyn Write) -> Result<()> {
+    let mut headers: Vec<String> = vec!["metric".to_owned()];
+    for &(flavor, ..) in report.vortex_results {
+        headers.push(flavor.label().to_owned());
+    }
+
+    let rows: Vec<Vec<String>> = vec![
+        make_row("scan wall (mean)", report, |_, _, scan| {
+            format_duration(scan.mean)
+        }),
+        make_row("scan wall (median)", report, |_, _, scan| {
+            format_duration(scan.median)
+        }),
+        make_row("matches", report, |_, _, scan| scan.matches.to_string()),
+        make_row("rows scanned", report, |_, _, scan| {
+            scan.rows_scanned.to_string()
+        }),
+        make_row("bytes scanned", report, |_, _, scan| {
+            format_bytes(scan.bytes_scanned)
+        }),
+        make_row("rows / sec", report, |_, _, scan| {
+            format_throughput_rows(scan.rows_scanned, scan.mean)
+        }),
+    ];
+
+    writeln!(writer, "## {}", report.dataset_name)?;
+    let mut builder = tabled::builder::Builder::new();
+    builder.push_record(headers);
+    for row in rows {
+        builder.push_record(row);
+    }
+    let mut table = builder.build();
+    table.with(Style::modern());
+    writeln!(writer, "{table}")?;
+    Ok(())
+}
+
+fn make_row<F>(metric: &str, report: &DatasetReport<'_>, vortex_cell: F) -> Vec<String>
+where
+    F: Fn(VectorFlavor, &CompressedVortexDataset, &ScanTiming) -> String,
+{
+    let mut row = vec![metric.to_owned()];
+    for &(flavor, prep, scan) in report.vortex_results {
+        row.push(vortex_cell(flavor, prep, scan));
+    }
+    row
+}
+
+fn format_duration(d: std::time::Duration) -> String {
+    let secs = d.as_secs_f64();
+    if secs >= 1.0 {
+        format!("{secs:.2} s")
+    } else if secs >= 1e-3 {
+        format!("{:.1} ms", secs * 1e3)
+    } else {
+        format!("{:.1} µs", secs * 1e6)
+    }
+}
+
+fn format_bytes(bytes: u64) -> String {
+    const UNITS: &[&str] = &["B", "KiB", "MiB", "GiB", "TiB"];
+    let mut value = bytes as f64;
+    let mut unit = UNITS[0];
+    for next in &UNITS[1..] {
+        if value < 1024.0 {
+            break;
+        }
+        value /= 1024.0;
+        unit = next;
+    }
+    if unit == "B" {
+        format!("{bytes} B")
+    } else {
+        format!("{value:.2} {unit}")
+    }
+}
+
+fn format_throughput_rows(rows: u64, wall: std::time::Duration) -> String {
+    let secs = wall.as_secs_f64();
+    if secs <= 0.0 {
+        return "—".to_owned();
+    }
+    let rps = rows as f64 / secs;
+    if rps >= 1e9 {
+        format!("{:.2}G", rps / 1e9)
+    } else if rps >= 1e6 {
+        format!("{:.2}M", rps / 1e6)
+    } else if rps >= 1e3 {
+        format!("{:.2}K", rps / 1e3)
+    } else {
+        format!("{rps:.0}")
+    }
+}

benchmarks/vector-search-bench/src/ingest.rs

@@ -8,7 +8,9 @@
 //! 1. Project the `emb` column out of each struct chunk.
 //! 2. Rewrap the `emb` column as `Extension<Vector<f32, dim>>` via
 //!    [`vortex_bench::vector_dataset::list_to_vector_ext`].
-//! 3. Cast the FSL element buffer from `f64` -> `f32` if the source is `f64`. After this point all
+//! 3. Detect the FSL element ptype at runtime and cast `f64` -> `f32` when needed. Detection is
+//!    from the arrow schema rather than a catalog declaration so upstream parquets whose actual
+//!    precision disagrees with the catalog still ingest correctly. After this point all
 //!    downstream code (compression, scan, recall) is f32-only.
 //! 4. Optionally project the `scalar_labels` column through unchanged so future filtered-search
 //!    benchmarks have it without re-ingest.
@@ -39,48 +41,50 @@ use vortex_bench::vector_dataset::list_to_vector_ext;
 use vortex_tensor::vector::AnyVector;
 use vortex_tensor::vector::Vector;
 
-/// Configuration passed alongside each chunk so the transform can stay stateless.
-#[derive(Debug, Clone, Copy)]
-pub struct ChunkTransform {
-    /// Source element ptype as declared by the dataset catalog. Used purely to decide whether the
-    /// f64 -> f32 cast is needed.
-    pub src_ptype: PType,
-    // /// Whether to project the `scalar_labels` column through the output struct.
-    // pub include_scalar_labels: bool,
-}
+/// Apply the transform to a single struct chunk and return the rebuilt chunk.
+///
+/// `chunk` must be a non-chunked `Struct { id: i64, emb: List<f32> }`, where all of the list
+/// elements are
+///
+/// The returned array is always a `Struct { id: i64, emb: Vector<f32, dim> }`.
+pub fn transform_chunk(chunk: ArrayRef, ctx: &mut ExecutionCtx) -> Result<ArrayRef> {
+    let struct_view = chunk
+        .as_opt::<Struct>()
+        .with_context(|| format!("ingest: expected struct chunk, got dtype {}", chunk.dtype()))?;
+
+    let id = struct_view
+        .unmasked_field_by_name("id")
+        .context("ingest: chunk missing `id` column")?
+        .clone();
+    let emb = struct_view
+        .unmasked_field_by_name("emb")
+        .context("ingest: chunk missing `emb` column")?
+        .clone();
+
+    let emb_ext: ExtensionArray = list_to_vector_ext(emb)?.execute(ctx)?;
+
+    // Detect the actual FSL element ptype from the extension storage dtype. The dataset catalog
+    // cannot be trusted here: at least one upstream parquet (`sift-medium-5m`) ships f64
+    // embeddings despite the catalog advertising f32.
+    let element_ptype = {
+        let storage_dtype = emb_ext.storage_array().dtype();
+        match storage_dtype {
+            DType::FixedSizeList(elem, ..) => match elem.as_ref() {
+                DType::Primitive(ptype, _) => *ptype,
+                other => bail!("ingest: expected primitive FSL element dtype, got {other}"),
+            },
+            other => bail!("ingest: expected FSL storage dtype, got {other}"),
+        }
+    };
 
-impl ChunkTransform {
-    /// Apply the transform to a single struct chunk and return the rebuilt chunk.
-    ///
-    /// `chunk` must be a non-chunked `Struct { id: i64, emb: List<f32> }`, where all of the list
-    /// elements are
-    ///
-    /// The returned array is always a `Struct { id: i64, emb: Vector<f32, dim> }`.
-    pub fn apply(&self, chunk: ArrayRef, ctx: &mut ExecutionCtx) -> Result<ArrayRef> {
-        let struct_view = chunk.as_opt::<Struct>().with_context(|| {
-            format!("ingest: expected struct chunk, got dtype {}", chunk.dtype())
-        })?;
-
-        let id = struct_view
-            .unmasked_field_by_name("id")
-            .context("ingest: chunk missing `id` column")?
-            .clone();
-        let emb = struct_view
-            .unmasked_field_by_name("emb")
-            .context("ingest: chunk missing `emb` column")?
-            .clone();
-
-        let emb_ext: ExtensionArray = list_to_vector_ext(emb)?.execute(ctx)?;
-
-        let f32_vector_array = if self.src_ptype == PType::F64 {
-            convert_f64_to_f32_vectors(&emb_ext, ctx)?
-        } else {
-            emb_ext.into_array()
-        };
+    let f32_vector_array = match element_ptype {
+        PType::F32 => emb_ext.into_array(),
+        PType::F64 => convert_f64_to_f32_vectors(&emb_ext, ctx)?,
+        other => bail!("ingest: unsupported emb element ptype {other}, expected f32 or f64"),
+    };
 
-        let fields = [("id", id), ("emb", f32_vector_array)];
-        Ok(StructArray::from_fields(&fields)?.into_array())
-    }
+    let fields = [("id", id), ("emb", f32_vector_array)];
+    Ok(StructArray::from_fields(&fields)?.into_array())
 }
 
 /// Convert a `Vector<f64, dim>` extension array down to `Vector<f32, dim>`.
@@ -164,10 +168,7 @@ mod tests {
         let emb = list_chunk_f64(&[&[1.0, 2.0, 3.0], &[4.0, 5.0, 6.0]]);
         let chunk =
             StructArray::from_fields(&[("id", id_array(&[0, 1])), ("emb", emb)])?.into_array();
-        let transform = ChunkTransform {
-            src_ptype: PType::F64,
-        };
-        let out = transform.apply(chunk, &mut ctx)?;
+        let out = transform_chunk(chunk, &mut ctx)?;
         let out_struct = out.as_opt::<Struct>().expect("returns Struct");
         let out_emb = out_struct.unmasked_field_by_name("emb").unwrap().clone();
         let DType::Extension(ext) = out_emb.dtype() else {
@@ -207,10 +208,7 @@ mod tests {
         let chunk =
             StructArray::from_fields(&[("id", id_array(&[0, 1])), ("emb", emb)])?.into_array();
 
-        let transform = ChunkTransform {
-            src_ptype: PType::F32,
-        };
-        let out = transform.apply(chunk, &mut ctx)?;
+        let out = transform_chunk(chunk, &mut ctx)?;
         let out_struct = out.as_opt::<Struct>().expect("returns Struct");
         assert_eq!(out_struct.len(), 2);
         Ok(())

benchmarks/vector-search-bench/src/lib.rs

@@ -4,9 +4,12 @@
 //! `vector-search-bench` vector similarity-search benchmark over several datasets.
 
 pub mod compression;
+pub mod display;
 pub mod expression;
 pub mod ingest;
 pub mod prepare;
+pub mod query;
+pub mod scan;
 
 use std::sync::LazyLock;