feat: optimize row based shuffle with DenseRow

[zhangxffff]

What problem does this PR solve?

Row-based shuffle serializes each row with CompactRow, which pads
values to fixed widths, carries a per-row null bitmap, and stores integers at
full width. For the common Spark case of small / narrow / nullable integer
columns this wastes a large fraction of shuffle bytes.

This PR adds DenseRow, a no-waste variable-length row format, and wires it
into the Spark row-based shuffle path behind a pluggable RowFormat option.
The existing CompactRow path is unchanged and remains the default.

Issue Number: close #540

Type of Change

• 🐛 Bug fix (non-breaking change which fixes an issue)
• ✨ New feature (non-breaking change which adds functionality)
• 🚀 Performance improvement (optimization)
• ⚠️ Breaking change (fix or feature that would cause existing functionality to change)
• 🔨 Refactoring (no logic changes)
• 🔧 Build/CI or Infrastructure changes
• 📝 Documentation only

Description

DenseRow codec (bolt/row/dense/) — a column-batched serializer, sibling to
CompactRow / UnsafeRowFast, that processes all rows at once. The on-wire
layout is the "dense" form:

1. variable-length (varint/zigzag) integer values,
2. nulls fused into the structure bytes — no null bitmap,
3. no alignment padding,
4. level-hoisted nesting for ARRAY/MAP/ROW.

Implementation is split into:

• IntVarint.h — varint/zigzag + nullable-int wire codec, with a SIMD
  (xsimd/AVX2) batched size pass and a BMI2 (pdep) varint writer for long
  values; scalar fast path otherwise.
• DenseRowScalar*.cpp — flat fast path (identity / constant / dictionary
  decoded inputs).
• DenseRowGeneral*.cpp — general path for arbitrary nested typed data via a two-pass
  (size, then write) slot-tree walk shared by both passes.
• DenseRow.{h,cpp} — public API mirroring CompactRow (rowSizes() /
  serialize() / deserialize()), grammar documented at the top of the .cpp.

Shuffle integration (bolt/shuffle/sparksql/) — new row::RowFormat enum
({DENSE, COMPACT}) added to ShuffleWriterOptions / ShuffleReaderOptions
(default COMPACT). The columnar↔row converters and the reader/writer dispatch
on it; the wire framing (rowSize | rowData) is unchanged, so only the row body
encoding differs. Writer and reader must be configured with the same format.

Tests — DenseRowTest.cpp (17 cases: scalars, wide/mixed rows, bigint &
hugeint edges, arrays / nested arrays / maps, empty containers, dictionary &
constant encoded inputs, non-contiguous serialize offsets, and golden-byte wire
assertions). The shuffle matrix test is extended to exercise the DENSE format.

Benchmark — DenseRowSerializeBenchmark.cpp compares DenseRow against
UnsafeRow/CompactRow for serialize, deserialize, and serialized size across 20
cases

Benchmark Result

— Intel i7-12700KF, -O3 AVX2+BMI2, VectorFuzzer vectorSize=1000,
pinned core. All times are ns/row; size is bytes/row. Compared against the
two existing Spark row formats (UnsafeRow, CompactRow).

Highlights

• Size: up to 9× smaller than CompactRow on small ints (bigint < 2⁸: 9→1),
  5× on small-int arrays/nested arrays. On par for full-random int64 and wide strings.
• Deserialize: 2.7–2.8× faster than CompactRow on multi-column flat rows
  (10 scalars: 89→32 ns), faster on all flat scalars.
• Trade-off: container serialize is slower than CompactRow
  (CompactRow would do buckcopy for array/map) — smaller payload for higher encode cost.

Serialized size (bytes/row)

case	UnsafeRow	CompactRow	DenseRow	vs Compact
bigint, |v| < 2⁸	16	9	1	9.0×
bigint, |v| < 2³²	16	9	4	2.3×
bigint, random	16	9	9	1.0×
bigint, random + 40% null	16	9	6	1.5×
double	16	9	8	1.1×
string(8)	24	13	9	1.4×
string(100)	120	105	101	1.04×
5 scalars	48	23	23	1.0×
5 scalars, small int	48	23	16	1.4×
10 scalars	88	50	54	0.9×
10 scalars, small int	88	50	31	1.6×
array	116	91	101	0.9×
array⟨bigint < 2⁸⟩	81	56	11	5.1×
array⟨bigint < 2³²⟩	78	53	30	1.8×
nested array	265	135	63	2.1×
nested⟨bigint < 2⁸⟩	368	272	53	5.1×
nested⟨bigint < 2³²⟩	351	259	133	1.9×
map	177	132	136	1.0×
map⟨bigint < 2⁸⟩	127	82	32	2.6×
map⟨bigint < 2³²⟩	123	78	50	1.6×

Note

We use the columnar CompactRow serde interface in this benchmark. In row-based shuffle, the row-based CompactRow serde interface is still used, which means CompactRow would see even greater performance improvement in real row-based shuffle scenarios. The array/map use bulk copy to serde in CompactRow, so it is fast than DenseRow for these type.

case	ser C	ser D	deser C	deser D	round-trip
bigint < 2⁸	1.8	3.6	5.5	2.4	1.2× faster
bigint < 2³²	1.8	4.5	4.9	4.2	0.8×
bigint random	1.8	4.1	5.0	4.1	0.8×
bigint random + null	2.4	4.4	5.4	5.6	0.8×
double	1.7	2.3	5.5	1.8	1.8× faster
string(8)	7.7	6.8	8.3	6.4	1.2× faster
string(100)	9.9	8.1	10.5	8.9	1.2× faster
5 scalars	7.1	8.3	27.4	10.2	1.9× faster
5 scalars, small	6.7	7.6	25.7	7.6	2.1× faster
10 scalars	17.8	29.0	89.2	31.5	1.8× faster
10 scalars, small	17.9	16.6	65.1	15.9	2.6× faster
array	16.4	99.1	57.0	76.5	0.4×
array⟨bigint < 2⁸⟩	12.3	22.2	21.5	40.4	0.5×
array⟨bigint < 2³²⟩	11.9	29.2	22.4	43.6	0.5×
nested array	142.4	142.7	84.0	111.2	0.9×
nested⟨bigint < 2⁸⟩	92.1	186.5	143.3	221.0	0.6×
nested⟨bigint < 2³²⟩	87.4	174.5	103.0	284.2	0.4×
map	25.8	97.1	103.3	91.0	0.7×
map⟨bigint < 2⁸⟩	21.8	28.2	45.8	52.5	0.8×
map⟨bigint < 2³²⟩	21.7	32.7	47.4	57.1	0.8×

Performance Impact

• No Impact: This change does not affect the critical path (e.g., build system, doc, error handling).

• Positive Impact: I have run benchmarks.

  Click to view Benchmark Results

  Paste your google-benchmark or TPC-H results here.
  Before: 10.5s
  After:   8.2s  (+20%)
  

• Negative Impact: Explained below (e.g., trade-off for correctness).

Release Note

Please describe the changes in this PR

Release Note:

Release Note:
- Fixed a crash in `substr` when input is null.
- optimized `group by` performance by 20%.

Checklist (For Author)

• I have added/updated unit tests (ctest).
• I have verified the code with local build (Release/Debug).
• I have run clang-format / linters.
• (Optional) I have run Sanitizers (ASAN/TSAN) locally for complex C++ changes.
• No need to test or manual test.

Breaking Changes

• No

• Yes (Description: ...)

  Click to view Breaking Changes

  Breaking Changes:
  - Description of the breaking change.
  - Possible solutions or workarounds.
  - Any other relevant information.
  

[guhaiyan0221]

check bounds（in） before accessing *in
if (FOLLY_UNLIKELY(in >= end)) { return false}

[guhaiyan0221]

need to check c.cur < c.end?