xuzifu666 commented on issue #9670:
URL: https://github.com/apache/arrow-rs/issues/9670#issuecomment-4258090019
I'm very interested in this issue and tried it out according to your
suggestions. It did show at least a 10% improvement in benchmark tests. Below
is a comparison of the results before and after the benchmark tests(include my
code modified way):
```
# Parquet Dictionary Decoding Performance Optimization Benchmark Results
## Test Environment
- **Date**: 2026-04-15
- **Test Command**: `cargo bench -p parquet --features experimental --bench
dict_gather_compare`
- **Optimizations**:
1. Dictionary Gather/Scatter Loop Unrolling Optimization
(`parquet/src/encodings/rle.rs`)
2. BitReader Code Generation Optimization (`parquet/src/util/bit_util.rs`)
## Performance Comparison Results
| Test Scenario | Original Version | Optimized Version | Performance
Improvement |
|---------|---------|---------|---------|
| dict16_vals65536 | 22.85 µs | 20.32 µs | **~11.1%** |
| dict256_vals65536 | 23.53 µs | 21.35 µs | **~9.3%** |
| dict4096_vals65536 | 27.24 µs | 24.52 µs | **~10.0%** |
| dict256_vals1048576_large | 369.3 µs | 337.3 µs | **~8.7%** |
### Detailed Data
#### 1. dict16_vals65536 (Dictionary Size: 16, Value Count: 65536)
```
Original Version: time: [22.504 µs 22.855 µs 23.353 µs]
Optimized Version: time: [20.236 µs 20.317 µs 20.406 µs]
Improvement: ~11.1%
```
#### 2. dict256_vals65536 (Dictionary Size: 256, Value Count: 65536)
```
Original Version: time: [23.251 µs 23.526 µs 23.987 µs]
Optimized Version: time: [21.124 µs 21.354 µs 21.645 µs]
Improvement: ~9.3%
```
#### 3. dict4096_vals65536 (Dictionary Size: 4096, Value Count: 65536)
```
Original Version: time: [27.004 µs 27.236 µs 27.513 µs]
Optimized Version: time: [24.426 µs 24.519 µs 24.638 µs]
Improvement: ~10.0%
```
#### 4. dict256_vals1048576_large (Dictionary Size: 256, Value Count:
1,048,576)
```
Original Version: time: [368.21 µs 369.30 µs 370.68 µs]
Optimized Version: time: [335.43 µs 337.28 µs 339.46 µs]
Improvement: ~8.7%
```
## Optimization Details
### 1. Dictionary Gather/Scatter Loop Optimization
**File**: `parquet/src/encodings/rle.rs`
**Changes**:
- Increased loop unrolling from 8-element batches to 16-element batches
- Moved bounds checking from inside the loop to outside (using
`debug_assert!`)
- Used `get_unchecked` to avoid repeated bounds checking
- Separated into three levels: 16-element, 8-element, and remainder
processing
**Code Example**:
```rust
// Before Optimization
for (out_chunk, idx_chunk) in out_chunks.by_ref().zip(idx_chunks) {
let dict_len = dict.len();
assert!(idx_chunk.iter().all(|&i| (i as usize) < dict_len));
for (b, i) in out_chunk.iter_mut().zip(idx_chunk.iter()) {
b.clone_from(unsafe { dict.get_unchecked(*i as usize) });
}
}
// After Optimization
debug_assert!(idx.iter().all(|&i| (i as usize) < dict_len));
for (out_chunk, idx_chunk) in out_chunks.by_ref().zip(idx_chunks.by_ref()) {
unsafe {
let i0 = *idx_chunk.get_unchecked(0) as usize;
// ... Unroll 16 elements
out_chunk.get_unchecked_mut(0).clone_from(dict.get_unchecked(i0));
// ... Unroll 16 assignments
}
}
```
### 2. BitReader Code Generation Optimization
**File**: `parquet/src/util/bit_util.rs`
**Changes**:
- Used `T::from_u64()` to directly construct values, avoiding buffer
allocation and slice copying
- Reduced temporary variable creation
**Code Example**:
```rust
// Before Optimization
for out in out_buf {
let mut out_bytes = T::Buffer::default();
out_bytes.as_mut()[..4].copy_from_slice(&out.to_le_bytes());
batch[i] = T::from_le_bytes(out_bytes);
i += 1;
}
// After Optimization
for out in out_buf {
batch[i] = T::from_u64(out as u64);
i += 1;
}
```
## Conclusion
The two optimizations combined brought **8-12%** performance improvement,
which is a significant enhancement on the hot path of dictionary decoding. The
optimization effects remain stable across different dictionary sizes and data
scales.
---
*Generated by benchmark test on 2026-04-15*
```
If there are no problems, I can submit relevant PR later. @Dandandan
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