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REVIEW 5 major objections 5 minor 1 cited by

CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data

T0 review · 5 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read CHASE argues that a database purpose-built for hybrid structured-and-vector queries, with plan rewriting, score-reusing operators, and machine-code compilation, outperforms plugin-style vector search by 13% to 7,500x.

desk verdict A coherent native hybrid-query engine with useful optimizations, but the headline speedup rests on approximate-versus-exact comparison and excluded compilation time. read the letter →

arxiv 2501.05006 v1 pith:K46PPFCK submitted 2025-01-09 cs.DB

Rui Ma , Kai Zhang , Zhenying He , Yinan Jing , X. Sean Wang , Zhenqiang Chen This is my paper · ORCID
classification cs.DB
keywords hybridqueriesapproximatenearestneighborvectorsearchquerycompilationlogicalplanrewritingrelationaldatabasestructuredandunstructureddataANNindex
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that today's databases treat vector similarity as an add-on: they bolt an approximate-nearest-neighbor index onto the scan operator and leave the rest of the query plan untouched. CHASE instead makes the vector a first-class citizen across the whole pipeline, rewriting logical plans, adding operators that reuse similarity scores, and compiling the final plan to machine code. The claim is that this end-to-end approach removes redundant distance computations and unlocks ANN acceleration for query shapes that today fall back to brute force. On six hybrid query templates over a million-row real dataset, the reported effect ranges from 13% to 7,500x speedups over existing systems.

What carries the argument

The load-bearing device is the rewritten hybrid query plan itself. For KNN-like queries, a map operator materializes the similarity scores computed by the ANN index scan into a 'sim' column that the sort and limit operators reuse instead of recomputing; for window-KNN queries, the ORDER BY is decoupled from the window and a limit is inserted to form an index-accelerated VKNN-SF sub-query per partition; for range and category queries, a range-search index scan and an updateState operator track per-category completion and stop the scan early. The same rewritten plan is lowered through intermediate dialects to machine code, so the plan itself, not just operator internals, carries the optimization.

What would settle it

Run the six hybrid queries end-to-end on a cold process, including planning and compilation time, with every baseline's recall tuned to match CHASE's; if the 7,500x gap for the KNN-join query collapses or reverses, or if the range-query baselines catch up once recall is equalized, the central performance claim fails.

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Extended reading notes

Core claim

CHASE's central claim is that hybrid query performance is governed not by the ANN index alone but by the interaction between the index scan and the rest of the query plan. For top-k queries, the ANN scan already computes similarity scores that later sort operators recompute; CHASE introduces a map operator that carries those scores into a temporary column so sorting and limiting reuse them. For window-KNN joins, rewriting the plan turns a brute-force join into per-group ANN probes. For range and category-partitioned queries, a range-search index scan plus an updateState operator tracks per-category completion and terminates the traversal early without scanning the full threshold range. These plan-level and operator-level changes are then compiled into machine code, so the speedups come from removing redundant computation at every stage rather than from any single index trick.

Load-bearing premise

The reported speedups assume that measuring only execution time after planning and compilation are complete is the right comparison, and that the baselines are run at comparable recall.

Editorial extensions

If this is right

  • If CHASE is right, vector-extended databases can support range and window-KNN hybrid queries without falling back to brute-force scans, shrinking entity-centric join cost from $O(|A|\cdot|B|)$ to roughly $O(C\cdot|A|)$.
  • Similarity scores become reusable data rather than transient distance computations, so sorting, ranking, and window operations no longer recompute distances.
  • Compiled execution of vector plans cuts branch misses, cache misses, and instruction counts relative to iterator-model engines, so the gains should persist across different hardware.
  • The query classification (vector-KNN with filters, distance-range with filters, windowed vector-KNN with filters) gives database designers a template for deciding which rewriting applies to which SQL shape.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the paper reports execution time after planning, loading, and compilation are complete, so the 7,500x figure is an execution-only bound; charging compile time would shrink the one-shot speedup.
  • Editorial inference: the map and updateState rewriting patterns sit above a generic ANN index interface, so the same plan rewriting could be applied on top of other approximate-nearest-neighbor algorithms, not only the graph index used in the experiments.
  • Editorial inference: a benchmark that equalizes recall across all baselines, including those reported as exact, would be the decisive test of whether the remaining speedups reflect the plan rewriting rather than accuracy differences.
  • Editorial inference: if similarity scores are cached as a virtual column, then repeated or incremental hybrid queries could reuse prior scores, a direction the paper does not explore.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 5 minor

Summary. The paper proposes CHASE, a query engine for hybrid SQL queries over relational attributes and high-dimensional vectors. It identifies six hybrid-query templates, rewrites logical plans using new map and updateState operators, implements optimized index-scan and range-search physical operators, and compiles query plans to machine code via the LingoDB framework. On the LAION-1M dataset, it reports execution-time speedups of 17%–33% over VBASE for Q1–Q2, roughly 64% for Q3, 7,500x for Q4, 33%–46% for Q5, and 3.1x–4.04x for Q6, with recall values around 0.93–1.0. The central claim is that treating vectors as first-class citizens throughout planning, operators, and compilation yields substantial performance gains over existing hybrid-query systems.

Significance. If the reported results were obtained under fair, equal-recall, end-to-end comparisons, the paper would be a meaningful contribution: it gives a concrete taxonomy of hybrid queries, identifies plan-level redundancies in existing engines, and proposes reusable operators and a compilation path for vector workloads. The plan rewrites in Figures 4–6 and the operator algorithms in Section 5 are concrete and useful, and the paper explicitly credits VBASE for the range-search algorithm and LingoDB for the compilation framework. However, the central quantitative claims are not yet established: the evaluation excludes compilation time, the headline Q4 speedup mixes approximate CHASE execution with exact baselines, and the Q1 recall-alignment claim is contradicted by Table 3. The contribution is promising but needs substantial experimental revision before publication.

major comments (5)
  1. [7.1 Metric; 6 Code Generation] Section 7.1 states that execution time excludes planning, data loading, and compilation time, but compilation is one of the three core techniques claimed in Sections 3 and 6. For one-shot queries, CHASE's per-query latency includes compilation, and none of the baselines is charged for it in the reported numbers. The speedup magnitudes, especially the 7,500x Q4 figure, are therefore not end-to-end. Please report end-to-end wall-clock times or, at minimum, measure and report compilation time separately and show how the speedups change when that cost is included.
  2. [7.3.3, Table 7] The Q4 comparison is not apples-to-apples: CHASE uses an approximate ANN scan with recall 0.95, while pgvector, PASE, VBASE, and LingoDB-V are described as exact baselines whose recall is shown as '-' in Tables 6 and 7. The 7,500x speedup therefore includes the effect of replacing exact matching with approximate search, not only the effect of native plan rewriting and code generation. The claimed reduction from 100 million to 94,000 similarity computations is a property of ANN approximation and should be separated from the system-design contribution. Please report an exact CHASE variant or an equal-recall comparison (for example, raising HNSW ef_search until CHASE matches exact recall) before attributing the speedup to native design.
  3. [7.3.1, Table 3] The text in Section 7.3.1 says that to align recall across all systems, the authors manually adjust the ef_search parameter for pgvector and PASE, but Table 3 shows pgvector with recall 0.52 at selectivity 0.03 while CHASE, PASE, and VBASE report recall 0.98–1.0. This contradiction means recall was not equalized in the reported runs, so the Q1 speedup at low selectivity is not a like-for-like comparison. Please re-run with matched recall, disclose the ef_search values used, or report results at several explicit recall levels.
  4. [4.3, 5.3, Figure 9] The correctness of the updateState operator and the R1-to-R2 range reduction rests on the uniform-distribution assumption stated in Section 4.3, but no recall or result-equivalence measurement is reported for the updateState experiments at thresholds 0.7, 0.6, and 0.5: Figure 9 shows only execution time. Since the operator's purpose is to shrink the scanned range without changing query results, the paper must verify on real data that the reduced range returns the same top-K results per category, or report the recall at each threshold when the updateState operator is active.
  5. [PVLDB Artifact Availability] The artifact block contains the placeholder 'URL_TO_YOUR_ARTIFACTS,' so no code, data, or runnable system is actually provided for the empirical claims. In addition, the timing tables report only mean execution times without variance or a per-query breakdown. Given that the measurement protocol has several asymmetries, the absence of artifacts and error statistics makes independent verification impossible. Please provide a working artifact and include standard deviations or full result distributions for the reported execution times.
minor comments (5)
  1. [Abstract; Section 1] The Abstract reports speedups of '13% to an extraordinary 7500 times,' while the Introduction reports '17% to 7,500×'; please make these consistent.
  2. [Figure 1 caption] The caption contains the typo 'query qlans'; it should read 'query plans.'
  3. [5.2, Algorithm 1] The pseudocode in Algorithm 1 has a dangling else after the nested if blocks (lines 3–12); please re-indent so the intended control flow is unambiguous.
  4. [5.3, Algorithm 2] The notation 'filteredK c, queuec' appears to contain subscripting artifacts; please typeset the variables consistently.
  5. [7.3.1, Table 5] Table 5's header reads 'Branches misses' instead of 'Branch misses,' and the branch-miss-rate row would be clearer if it were expressed as a percentage with consistent decimal places.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: CHASE's design derivations are self-contained and use external building blocks; benchmark asymmetries are measurement concerns, not circular reasoning.

full rationale

The paper's central claims are system-design choices and benchmark results. The logical plan rewriting for Q1/Q4/Q5 is justified by the stated query-pattern semantics, and the updateState operator's correctness is argued from an explicitly stated uniform-distribution assumption, not from the experimental outcomes. The physical operators reuse VBASE's range search algorithm and LingoDB's compilation framework as external, cited building blocks, so there is no self-citation chain doing load-bearing work. No equation is fitted to the benchmark data, no predicted quantity is defined as a fitted parameter, and no 'uniqueness theorem' from the authors' own prior work is invoked to force a design choice. The only notable concerns are experimental-fairness issues: Section 7.1 excludes planning, data loading, and compilation time from the reported execution time even though compilation is one of CHASE's three core techniques, and the Q4 comparison in Table 7 reports CHASE at recall 0.95 while exact baselines are marked '-' (recall 1.0). These asymmetries could affect the magnitude of the headline speedups, but they are measurement validity concerns, not circularity: the reported performance numbers are not derived from CHASE's own assumptions by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No fitted equations or physically invented entities appear in the paper. The optimizations rest on domain assumptions about workload coverage, the LingoDB compilation base, and uniform category distribution. The main numeric inputs are experimental configuration parameters (HNSW settings, query range threshold), not parameters fitted to produce the reported speedup.

free parameters (4)
  • HNSW M = 16
    Index graph degree set in Section 7.2 for all HNSW systems; affects recall and search cost.
  • HNSW ef_construction = 200
    Index build effort in Section 7.2; fixed across systems.
  • HNSW ef_search = 48 (manually increased for pgvector/PASE in Q1)
    Search effort in Section 7.2; Section 7.3.1 says ef_search was manually adjusted to align recall, yet Table 3 shows pgvector recall 0.52 at selectivity 0.03.
  • query range threshold R1 = 0.8 (with 0.7, 0.6, and 0.5 for updateState study)
    Defines the benchmark query range in Section 7.1 for Q2/Q3/Q5/Q6 and is varied in Figure 9; it is an author-chosen workload parameter rather than a fitted model parameter.
assumptions (4)
  • domain assumption The three hybrid query classes (VKNN-SF, DR-SF, W-VKNN-SF) cover the broad range of hybrid queries in modern applications.
    Section 2.1 presents a survey-based taxonomy; the general-applicability claim of CHASE rests on this coverage.
  • domain assumption LingoDB's MLIR/LLVM compilation pipeline is correct and beneficial for query execution.
    Section 6 builds CHASE on LingoDB without independently validating its compilation behavior; if LingoDB is not a sound base, CHASE's code-generation claims fail.
  • ad hoc to paper Tuples within a query range are approximately uniformly distributed across categories, allowing range R2 to replace R1 without changing results.
    Section 2.4 explicitly assumes uniform distribution to justify shrinking the search range; Section 5.3's updateState operator depends on this, and no non-uniform validation is provided.
  • domain assumption ANN search returns results with recall comparable to exact search for all systems after tuning.
    Section 7.3 compares systems on execution time while recall differs (Q1 low selectivity for pgvector; exact baselines reported as '-' in Tables 6 and 7), so the same-accuracy premise is not met.

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Pith. "Pith review of CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data." pith.science (2026). https://pith.science/paper/K46PPFCK

@misc{pith2026250105006,
  author       = {Pith},
  title        = {Pith review of: CHASE: A Native Relational Database for Hybrid Queries on Structured and Unstructured Data},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K46PPFCK}},
  note         = {Machine review of arXiv:2501.05006}
}
read the original abstract

Querying both structured and unstructured data has become a new paradigm in data analytics and recommendation. With unstructured data, such as text and videos, are converted to high-dimensional vectors and queried with approximate nearest neighbor search (ANNS). State-of-the-art database systems implement vector search as a plugin in the relational query engine, which tries to utilize the ANN index to enhance performance. After investigating a broad range of hybrid queries, we find that such designs may miss potential optimization opportunities and achieve suboptimal performance for certain queries. In this paper, we propose CHASE, a query engine that is natively designed to support efficient hybrid queries on structured and unstructured data. CHASE performs specific designs and optimizations on multiple stages in query processing. First, semantic analysis is performed to categorize queries and optimize query plans dynamically. Second, new physical operators are implemented to avoid redundant computations, which is the case with existing operators. Third, compilation-based techniques are adopted for efficient machine code generation. Extensive evaluations using real-world datasets demonstrate that CHASE achieves substantial performance improvements, with speedups ranging from 13% to an extraordinary 7500 times compared to existing systems. These results highlight CHASE's potential as a robust solution for executing hybrid queries.

Figures

Figures reproduced from arXiv: 2501.05006 by the authors.

Figure 1
Figure 1. Hybrid query and query qlans performing distance comparisons on all vectors whose attributes satisfy the filters. However, systems adopting this approach, such as PASE, pgvector, and ADBV, often use a conservatively large 𝐾 ′ (𝐾 ′ ≫ 𝐾), where 𝐾 denotes the desired number of results (e.g., top 50 most similar products), to ensure sufficient results meet the constraints, resulting in substantial redundant computations… view at source ↗
Figure 2
Figure 2. Hybrid query examples a significant improvement in search speed while tolerating minor accuracy losses. Modern applications increasingly demand semantic queries that not only rely on vector similarity search but also incorporate struc￾tured data for more precise control. For instance, when recommend￾ing a lightweight backpack, users may also need to filter results by specific attributes, such as price range, user ra… view at source ↗
Figure 3
Figure 3. Architecture for hybrid queries processing [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5: Logical plan comparison for Q4 4.2 Rewriting Entity-Centric VKNN-SF Queries Entity-centric KNN queries require that each tuple in one table serves as a query to identify the top 𝐾 related tuples in another table. Consider two tables: the query table 𝑇𝑞 and the target t…
Figure 6
Figure 6. Figure 6: Logical plan comparison for Q5 4.3 Rewriting Category-Driven VKNN-SF Queries Category-Driven VKNN-SF queries differ from Entity-Centric VKNN-SF queries in both their partitioning method and filtering process. Category-Driven queries first filter 𝑇𝑟 based on a distance …
Figure 7
Figure 7. Figure 7: An example of code generation for Q1 levels of selectivity to enhance query complexity, specifically: 1, 0.9, 0.7, 0.5, 0.3, and 0.03. The selectivity is defined as selectivity = Number of tuples satisfying the predicate Total number of tuples . Notably, when selectivi…
Figure 8
Figure 8. Figure 8: Performance comparison of Q5 and Q6 0.7 0.6 0.5 Query Range 10 2 10 3 Execute Time Unoptimized Optimized (a) Execution time of Q5 0.7 0.6 0.5 Query Range 10 3 10 4 Execute Time Unoptimized Optimized (b) Execution time of Q6 [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: Impact of updateState operator on Q5 and Q6 [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

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Forward citations

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.