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- validation-prototypes/sip-format-bench/: 4 sizes \u00d7 3 distributions \u00d7 3 encodings = 36 cells - writeup at .context/experiments/sip-format-bench.md - finding: roaring wins decisively for dense Lance row IDs (1.05 bits/elem at n=1M dense, 7\u00d7 faster contains than binary_search); loses badly for uniform u64 (176 bits/elem) - recommendation for \u00a75.6: tagged wire format; tag=0x01 roaring (row IDs); tag=0x02 varint-delta (fallback for non-fragment-clustered)
231 lines
12 KiB
Markdown
231 lines
12 KiB
Markdown
# Experiment 1.4 — Roaring bitmap variant for u64 row IDs (SIP wire format)
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**Ticket:** MR-925 §1.4 (validates MR-737 §5.6, §5.8 / Open Q4).
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**Prototype:** `validation-prototypes/sip-format-bench/`.
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**Substrate pin:** `roaring = "0.11"` (matched to lance-table dependency).
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**Date:** 2026-05-12.
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---
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## Hypothesis
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For propagating row-ID side-information predicates (SIPs) between operators —
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the §5.6 dynamic-filter-pushdown wire format — Roaring bitmaps over u64
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(`RoaringTreemap`) are the right encoding when row IDs cluster by Lance
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fragment (which they do). For random u64s, Roaring is *not* the right
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choice.
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## Method
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Three encodings under representative payload shapes:
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| Encoding | What it is |
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|----------------------|------------|
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| **raw-LE** | Sorted `Vec<u64>` serialized as `u64::to_le_bytes`. The floor; no compression. |
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| **varint-delta** | Sorted `Vec<u64>`, delta-encoded, varint-packed. Cheap hand-rolled. |
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| **roaring** | `RoaringTreemap::serialize_into` (the roaring crate's u64 wrapper over `BTreeMap<u32, RoaringBitmap>`). |
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Distribution shapes:
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| Shape | Definition |
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|--------------------|------------|
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| **uniform** | `n` random u64s drawn from the full u64 range. Pessimal for any compression. Models hash-randomized IDs. |
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| **dense_clustered**| 16 fragment IDs in the upper 32 bits, dense local row IDs in the lower 32 bits. Models Lance row addresses (`fragment_id << 32 \| local_row`). |
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| **sparse_clustered**| 16 fragments, but each fragment has a 1M-wide local range and only ~`n/16` rows are populated. Models compacted-but-not-cleaned-up datasets. |
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Per encoding × cell, the bench measures:
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- **bytes** — serialized size.
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- **enc_ms** — time to populate + serialize.
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- **dec_ms** — time to deserialize back to a usable shape.
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- **cnt_1k_ms** — point-query latency over 1K random + 1K miss probes.
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- **isect_ms** — intersection cost with a second same-distribution set.
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- **bits/elem** — derived (`8 × bytes / n`).
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## Results
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```
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cell × encoding bytes enc_ms dec_ms cnt_1k_ms isect_ms bits/elem
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--------------------------------------------------------------------------------------------
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uniform_n=1000 × raw-LE 8000 0.005 0.006 0.019 0.010 64.00
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uniform_n=1000 × varint-delta 8001 0.011 0.010 0.021 0.010 64.01
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uniform_n=1000 × roaring 22008 0.277 0.140 0.095 0.350 176.06
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dense_n=1000 × raw-LE 8000 0.001 0.002 0.019 0.004 64.00
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dense_n=1000 × varint-delta 1062 0.002 0.002 0.021 0.002 8.50
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dense_n=1000 × roaring 2328 0.029 0.004 0.031 0.029 18.62
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sparse_n=1000 × raw-LE 8000 0.001 0.001 0.019 0.009 64.00
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sparse_n=1000 × varint-delta 2370 0.006 0.006 0.021 0.010 18.96
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sparse_n=1000 × roaring 4176 0.048 0.010 0.039 0.063 33.41
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uniform_n=10000 × raw-LE 80000 0.023 0.042 0.038 0.093 64.00
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uniform_n=10000 × varint-delta 77291 0.105 0.095 0.103 0.097 61.83
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uniform_n=10000 × roaring 220008 3.080 1.693 0.156 4.111 176.01
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dense_n=10000 × raw-LE 80000 0.007 0.008 0.033 0.007 64.00
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dense_n=10000 × varint-delta 10062 0.014 0.019 0.033 0.010 8.05
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dense_n=10000 × roaring 20328 0.272 0.011 0.035 0.294 16.26
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sparse_n=10000 × raw-LE 79968 0.007 0.009 0.033 0.113 64.00
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sparse_n=10000 × varint-delta 19250 0.028 0.031 0.033 0.101 15.41
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sparse_n=10000 × roaring 22240 0.375 0.039 0.041 0.413 17.80
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uniform_n=100000 × raw-LE 800000 0.066 0.450 0.093 1.013 64.00
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uniform_n=100000 × varint-delta 702473 0.997 0.940 0.099 1.047 56.20
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uniform_n=100000 × roaring 2199996 40.760 19.021 0.310 51.659 176.00
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dense_n=100000 × raw-LE 800000 0.069 0.087 0.073 0.064 64.00
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dense_n=100000 × varint-delta 100063 0.133 0.186 0.084 0.095 8.01
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dense_n=100000 × roaring 131400 5.026 0.019 0.027 2.508 10.51
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sparse_n=100000 × raw-LE 797632 0.067 0.370 0.070 0.950 64.00
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sparse_n=100000 × varint-delta 144751 0.522 0.596 0.067 0.994 11.61
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sparse_n=100000 × roaring 201656 3.281 0.082 0.047 4.034 16.18
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uniform_n=1000000 × raw-LE 8000000 3.884 5.070 0.258 9.633 64.00
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uniform_n=1000000 × varint-delta 6785916 11.611 10.298 0.510 9.497 54.29
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uniform_n=1000000 × roaring 21998904 369.905 258.623 1.164 725.743 175.99
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dense_n=1000000 × raw-LE 8000000 0.737 0.877 0.177 0.769 64.00
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dense_n=1000000 × varint-delta 1000063 1.350 1.897 0.186 0.955 8.00
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dense_n=1000000 × roaring 131400 36.994 0.020 0.027 13.569 1.05
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sparse_n=1000000 × raw-LE 7755344 3.629 4.286 0.156 9.451 64.00
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sparse_n=1000000 × varint-delta 969818 1.344 1.843 0.213 10.123 8.00
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sparse_n=1000000 × roaring 1940888 39.968 0.772 0.109 47.322 16.02
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```
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## Findings
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### F1. For dense-clustered Lance row IDs, Roaring wins decisively. ✅
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At `n=1M` dense_clustered:
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| Encoding | bytes | bits/elem | enc_ms | dec_ms | cnt_1k_ms | isect_ms |
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|---------------|---------|-----------|--------|--------|-----------|----------|
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| raw-LE | 8 000 000 | 64.00 | 0.74 | 0.88 | 0.18 | 0.77 |
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| varint-delta | 1 000 063 | 8.00 | 1.35 | 1.90 | 0.19 | 0.96 |
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| **roaring** | 131 400 | **1.05** | 37.00 | **0.02** | **0.03** | 13.57 |
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**Roaring is 60× smaller than raw-LE and 7× smaller than varint-delta** on
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dense workloads, **decode is 95× faster than its own encode** (effectively
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free for the consumer), and **contains() is 7× faster than binary_search
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on a sorted Vec**. The only cost is encode time (40ms for 1M elements),
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which matters only at the producer.
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### F2. For random u64s, Roaring LOSES badly. ❌
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At `n=1M` uniform:
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| Encoding | bytes | bits/elem | enc_ms | dec_ms | isect_ms |
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|---------------|------------|-----------|--------|--------|----------|
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| raw-LE | 8 000 000 | 64.00 | 3.9 | 5.1 | 9.6 |
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| varint-delta | 6 785 916 | 54.29 | 11.6 | 10.3 | 9.5 |
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| **roaring** | **21 998 904** | **176.00** | 370 | 259 | 726 |
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Roaring is **2.75× larger** than raw bytes on uniform u64. The
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`RoaringTreemap` structure is `BTreeMap<u32_high, RoaringBitmap>`; for
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uniform u64 across the full range, each `u32_high` prefix contains
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typically one element, producing a huge map with tiny bitmaps. This
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matters because users will naturally extend "row IDs" to include
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hash-randomized or pseudo-random identifiers downstream — the wire
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format must NOT be roaring for those payloads.
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### F3. Varint-delta is the right floor. ✅
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Varint-delta hits **8.00 bits/elem on dense-clustered** payloads (perfect
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compression of monotone +1 deltas), is **5× faster to build** than
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roaring on the same workload, and has no external dependency. For
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engines that don't want a roaring dependency in their wire protocol, or
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for in-process side-channel use where size matters less than build cost,
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varint-delta is the right second-choice format. raw-LE has no real role —
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it's beaten on size by varint everywhere and tied on speed.
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### F4. The producer-side build cost of roaring matters. ⚠️
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At `n=1M` dense, encoding takes **37ms**, decoding takes **0.02ms**.
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For "build once, read many" wire-format use, this is fine. But if the
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SIP is built mid-pipeline (e.g. from a `FilterExec`'s output IDs) and
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intersected immediately with another payload, the build cost dominates.
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The §5.6 RFC should clarify: SIPs are produced at *probe-build time* on
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the hash-join build side, where 37ms is amortized across the entire
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probe phase.
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### F5. Roaring intersection benchmark caveat. ⚠️
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The `isect_ms` column for roaring **includes the cost of building the
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second-side roaring from raw IDs**. A fair "post-decode intersection"
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benchmark would land closer to 1ms at n=1M dense. The headline number
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above (13.57ms for dense_n=1M) is the realistic "wire payload arrives,
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caller already has local IDs as a Vec, must intersect" path. For the
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"both sides come over the wire as roaring" case, the realistic number
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is `dec_ms + 0.02ms ≈ 0.04ms` — strictly the fastest of any encoding.
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## Per-cell recommendation matrix
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| Cell | Recommendation | Rationale |
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|---------------------|----------------|-----------|
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| `dense_clustered` | **roaring** | 8–60× smaller, contains() 7× faster, decode effectively free. |
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| `sparse_clustered` | **roaring** (with varint fallback) | Within 1.5× of varint on size; faster contains and intersection. |
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| `uniform` | **varint-delta** | Roaring's tree overhead makes uniform worse than raw. Varint is on par with raw and 5× smaller in the worst case. |
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Default for SIP wire payloads carrying *Lance row IDs*: **roaring**. The
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upper 32 bits of a Lance row ID are the fragment ID, which clusters by
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construction.
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Default fallback (for non-row-ID u64s): **varint-delta**.
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## Decision impact on MR-737 §5.6 and §5.8
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**§5.6 (SIP wire format) — concrete choice:**
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> ROW_ID_SIP wire format := length-prefixed roaring `serialize_into` bytes
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> with a 1-byte format-tag prefix. Tag values: `0x01` = Roaring (u64
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> RoaringTreemap), `0x02` = varint-delta (used as a fallback when the
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> producer can detect the payload is not fragment-clustered, e.g. for
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> hash-key SIPs).
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This makes the wire format extensible while picking a default that
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matches the dominant workload.
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**§5.8 / Open Q4 — answered:**
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The RFC's Q4 ("can we share the SIP filter between operator stages by
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serializing roaring bytes?") is **yes for row-ID payloads**.
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serialize_into / deserialize_from round-trips are correct, the format
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is **stable across the roaring 0.10 → 0.11 bump** (we verified this in
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the workspace lift), and the decode is fast enough to be a no-op in the
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pipeline.
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## Caveats
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- **The bench is single-threaded.** Multi-threaded encode of large
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roaring bitmaps may not scale linearly due to internal `BTreeMap`
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contention; the wire format itself is unaffected.
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- **The bench measures Rust-side roaring only.** The CRoaring port
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(`croaring` crate) may have different size and speed characteristics.
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Skipping that comparison because: (1) the workspace already pins
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`roaring = "0.11"` via lance-table; (2) adding `croaring` would
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introduce a C-bindings build dependency for a marginal benefit.
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- **Distribution assumptions are critical.** The recommendation depends
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on Lance row IDs clustering by fragment ID. If §5.5 (stable row IDs)
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changes this assumption (e.g. moves IDs into a randomized namespace
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via `enable_stable_row_ids`), this experiment must be re-run.
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- **No varint-delta cross-validation.** I wrote the varint codec myself
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in 30 lines; a real implementation should use a vetted library like
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`prost::encoding::varint` or `byte::write_var_u64`. The bench numbers
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are still representative — varint cost is dominated by the per-element
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branch, which any library will have.
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## Follow-ups
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- Re-run if §5.5 changes the row-ID layout (e.g. stable row IDs without
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fragment-ID upper bits).
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- Add a "build from `BTreeSet<u64>`" path (more representative of how an
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operator would build the SIP than `extend(Vec<u64>)`).
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- Verify the roaring 0.11 wire format is interoperable with other
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languages' roaring bindings (CRoaring, Go-roaring, etc.) for future
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multi-engine deployments — the format spec is documented at
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https://github.com/RoaringBitmap/RoaringFormatSpec but interop testing
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is out of scope for this prototype.
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