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Posted to issues@lucene.apache.org by "rajat315315 (via GitHub)" <gi...@apache.org> on 2026/07/27 16:36:30 UTC
[I] [ENH]: Adaptive Facet Engine: Single-Pass Posting-Based Aggregation for Low-Cardinality Fields (+465% QPS) [lucene]
rajat315315 opened a new issue, #16426:
URL: https://github.com/apache/lucene/issues/16426
### Description
## Background & Problem Statement
Currently, facet counting in Lucene (`lucene-facet`) relies primarily on columnar **DocValues** lookups (`SortedSetDocValues`, `NumericDocValues`) or a **Taxonomy Index**. While DocValues work well across high-cardinality fields, they introduce a performance bottleneck for queries matching large result sets.
When a query matches $100,000$ documents in a segment, traditional faceting performs $100,000$ individual random-access lookups into DocValues (`docValues.ordValue()`) and array increments (`counts[ord]++`). The time complexity is $O(\text{MatchingDocs})$, causing memory cache misses and high latency when match density is large.
---
## Proposed Improvement: Single-Pass Posting-Based Aggregation
For low-cardinality facet fields (e.g. `status`, `category`, `brand`, `availability` with $K \le 50$ unique categories), we can invert the lookup pattern:
1. **Posting List Intersections**: Index category values as indexed terms. Instead of looking up DocValues for every matched document, compute facet counts via fast SIMD bitset intersections between the query posting list $P_Q$ and category posting lists $P_{C_k}$:
$$\text{Count}(C_k) = |P_Q \cap P_{C_k}|$$
2. **Adaptive Zero-Overhead Router**: Before starting the search pass, use $O(1)$ index metadata (`TermsEnum.docFreq()`, `weight.cost()`, `maxDoc()`) to dynamically route execution:
* **Route A (Posting-Based Flow)**: Triggered when category count $K$ is small and estimated hits exceed the threshold $K \times \frac{\text{maxDoc}}{64}$.
* **Route B (Legacy DocValues Flow)**: Triggered when match density is low or field cardinality $K$ is large.
---
## Benchmark Results (JMH)
We implemented JMH micro-benchmarks (`PostingVsDocValuesFacetBenchmark.java` and `BitSetVectorizationBenchmark.java`) on an index of $1,000,000$ documents across varying match densities and category counts ($K$).
### 1. Posting-Based vs. DocValues Aggregation (OpenJDK 25)
| Match Density | Matching Docs | Categories ($K$) | Traditional DocValues | Posting-Based Aggregation | **Throughput Speedup** |
| :---: | :---: | :---: | :---: | :---: | :---: |
| **10%** | **100,000 docs** | **5** | 2,589 ops/sec | **14,643 ops/sec** | 🚀 **+465.4% (5.65x FASTER)** |
| **1%** | **10,000 docs** | **5** | 9,947 ops/sec | **16,378 ops/sec** | 🚀 **+64.6% FASTER** |
| **10%** | **100,000 docs** | **20** | 2,501 ops/sec | **3,747 ops/sec** | 🚀 **+49.8% FASTER** |
| **1%** | **10,000 docs** | **20** | 8,943 ops/sec | 3,762 ops/sec | DocValues better for high $K$, low matches |
### 2. SIMD BitSet Hardware Acceleration
* HotSpot C2 auto-vectorization compiles tight `FixedBitSet.intersectionCount` loops directly into native `POPCNT` / `VPOPCNTDQ` hardware vector instructions, reaching **84,000+ ops/sec** on 1M doc bitsets.
---
## Implementation Details & Notice
I have already completed the initial design, JMH benchmarks, and prototype implementation for this feature on branch `feature/simd-bitset-optimization`.
> ⚠️ **Notice**: I am actively working on submitting the PR for this feature and benchmark suite shortly. Please coordinate here before opening a duplicate PR or starting parallel work on this component!
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Re: [I] [ENH]: Adaptive Facet Engine: Single-Pass Posting-Based Aggregation for Low-Cardinality Fields (+465% QPS) [lucene]
Posted by "rajat315315 (via GitHub)" <gi...@apache.org>.
rajat315315 commented on issue #16426:
URL: https://github.com/apache/lucene/issues/16426#issuecomment-5104929048
cc @stefanvodita @mikemccand @jpountz @rmuir
Hi team! I would love to get your feedback on this proposal for an adaptive posting-based facet engine for low-cardinality fields.
I have prototyped the implementation and benchmarked it across 1M, 10M, 100M, and 1B document indices using JMH (`PostingVsDocValuesFacetBenchmark`). The posting-based set intersection yields a 4x to 6.5x throughput gain (+465% QPS) on dense match sets.
Looking forward to your thoughts and guidance on preparing the PR!
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Re: [I] [ENH]: Adaptive Facet Engine: Single-Pass Posting-Based Aggregation for Low-Cardinality Fields (+465% QPS) [lucene]
Posted by "rajat315315 (via GitHub)" <gi...@apache.org>.
rajat315315 commented on issue #16426:
URL: https://github.com/apache/lucene/issues/16426#issuecomment-5102483301
I executed a full permutation matrix using Lucene's JMH suite (`PostingVsDocValuesFacetBenchmark.java`) across **1 Million**, **10 Million**, **100 Million**, and **1 Billion** documents at $1\%$ and $10\%$ match densities, and category counts $K = 5$ and $K = 10$.
### Throughput Matrix (Ops / Second & Speedup Ratio)
| Index Size ($N$) | Match Density | Matching Docs | Categories ($K$) | Traditional DocValues | Single-Pass Posting-Based | **Throughput Gain / Speedup** |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **1 Billion** | **10%** | **100,000,000** | **5** | 2.508 ops/s | **11.085 ops/s** | 🚀 **+341.9% (4.42x FASTER)** |
| **1 Billion** | **10%** | **100,000,000** | **10** | 2.434 ops/s | **5.561 ops/s** | 🚀 **+128.5% (2.28x FASTER)** |
| **1 Billion** | **1%** | **10,000,000** | **5** | 3.783 ops/s | **11.141 ops/s** | 🚀 **+194.5% (2.94x FASTER)** |
| **1 Billion** | **1%** | **10,000,000** | **10** | 3.778 ops/s | **5.453 ops/s** | 🚀 **+44.3% (1.44x FASTER)** |
| **100 Million** | **10%** | **10,000,000** | **5** | 22.596 ops/s | **111.297 ops/s** | 🚀 **+392.5% (4.93x FASTER)** |
| **100 Million** | **10%** | **10,000,000** | **10** | 23.696 ops/s | **55.912 ops/s** | 🚀 **+135.9% (2.36x FASTER)** |
| **100 Million** | **1%** | **1,000,000** | **5** | 36.262 ops/s | **75.740 ops/s** | 🚀 **+108.8% (2.08x FASTER)** |
| **100 Million** | **1%** | **1,000,000** | **10** | 37.857 ops/s | **54.634 ops/s** | 🚀 **+44.3% (1.44x FASTER)** |
| **10 Million** | **10%** | **1,000,000** | **5** | 250.516 ops/s | **1,241.972 ops/s** | 🚀 **+395.7% (4.96x FASTER)** |
| **10 Million** | **10%** | **1,000,000** | **10** | 251.795 ops/s | **617.005 ops/s** | 🚀 **+145.0% (2.45x FASTER)** |
| **10 Million** | **1%** | **100,000** | **5** | 406.054 ops/s | **1,274.608 ops/s** | 🚀 **+213.9% (3.14x FASTER)** |
| **10 Million** | **1%** | **100,000** | **10** | 391.367 ops/s | **580.738 ops/s** | 🚀 **+48.4% (1.48x FASTER)** |
| **1 Million** | **10%** | **100,000** | **5** | 2,524.309 ops/s | **16,441.163 ops/s** | 🚀 **+551.3% (6.51x FASTER)** |
| **1 Million** | **10%** | **100,000** | **10** | 2,672.635 ops/s | **8,091.425 ops/s** | 🚀 **+202.7% (3.02x FASTER)** |
| **1 Million** | **1%** | **10,000** | **5** | 8,338.912 ops/s | **14,392.758 ops/s** | 🚀 **+72.6% (1.73x FASTER)** |
| **1 Million** | **1%** | **10,000** | **10** | 8,711.087 ops/s | 8,174.646 ops/s | -6.1% (DocValues optimal for low matches, $K=10$) |
Also, attaching the micro-benchmark JMH file if any contributor wishes to analyse further.
[PostingVsDocValuesFacetBenchmark.java](https://github.com/user-attachments/files/30456473/PostingVsDocValuesFacetBenchmark.java)
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