d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
498 lines
15 KiB
Markdown
498 lines
15 KiB
Markdown
# ADR-DB-002: Delta Encoding Format
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**Status**: Proposed
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**Date**: 2026-01-28
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**Authors**: RuVector Architecture Team
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**Deciders**: Architecture Review Board
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**Parent**: ADR-DB-001 Delta Behavior Core Architecture
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## Version History
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| Version | Date | Author | Changes |
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|---------|------|--------|---------|
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| 0.1 | 2026-01-28 | Architecture Team | Initial proposal |
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---
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## Context and Problem Statement
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### The Encoding Challenge
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Delta-first architecture requires efficient representation of incremental vector changes. The encoding must balance multiple competing concerns:
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1. **Compression Ratio**: Minimize storage and network overhead
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2. **Encode/Decode Speed**: Low latency for real-time applications
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3. **Composability**: Efficient sequential application of deltas
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4. **Randomness Handling**: Both sparse and dense update patterns
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### Update Patterns in Practice
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Analysis of real-world vector update patterns reveals:
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| Pattern | Frequency | Characteristics |
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|---------|-----------|-----------------|
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| Sparse Refinement | 45% | 1-10% of dimensions change |
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| Localized Cluster | 25% | Contiguous regions updated |
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| Full Refresh | 15% | Complete vector replacement |
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| Uniform Noise | 10% | Small changes across all dimensions |
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| Scale/Shift | 5% | Global transformations |
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A single encoding cannot optimally handle all patterns.
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---
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## Decision
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### Adopt Hybrid Sparse-Dense Encoding with Adaptive Switching
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We implement a multi-format encoding system that automatically selects optimal representation based on delta characteristics.
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### Encoding Formats
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#### 1. Sparse Encoding
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For updates affecting < 25% of dimensions:
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```rust
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/// Sparse delta: stores only changed indices and values
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct SparseDelta {
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/// Number of dimensions in original vector
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pub dimensions: u32,
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/// Changed indices (sorted, delta-encoded)
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pub indices: Vec<u32>,
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/// Corresponding values
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pub values: Vec<f32>,
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/// Optional: previous values for undo
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pub prev_values: Option<Vec<f32>>,
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}
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impl SparseDelta {
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/// Memory footprint
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pub fn size_bytes(&self) -> usize {
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8 + // dimensions + count
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self.indices.len() * 4 + // indices
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self.values.len() * 4 + // values
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self.prev_values.as_ref().map_or(0, |v| v.len() * 4)
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}
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/// Apply to vector in place
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pub fn apply(&self, vector: &mut [f32]) {
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for (&idx, &val) in self.indices.iter().zip(self.values.iter()) {
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vector[idx as usize] = val;
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}
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}
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}
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```
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**Index Compression**: Delta-encoded + varint for sorted indices
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```
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Original: [5, 12, 14, 100, 105]
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Delta: [5, 7, 2, 86, 5]
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Varint: [05, 07, 02, D6 00, 05] (12 bytes vs 20 bytes)
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```
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#### 2. Dense Encoding
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For updates affecting > 75% of dimensions:
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```rust
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/// Dense delta: full vector replacement
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DenseDelta {
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/// New vector values
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pub values: Vec<f32>,
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/// Optional quantization
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pub quantization: QuantizationMode,
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}
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#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
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pub enum QuantizationMode {
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None, // f32 values
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Float16, // f16 values (2x compression)
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Int8, // 8-bit quantized (4x compression)
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Int4, // 4-bit quantized (8x compression)
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}
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```
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#### 3. Run-Length Encoding (RLE)
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For contiguous region updates:
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```rust
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/// RLE delta: compressed contiguous regions
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct RleDelta {
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pub dimensions: u32,
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pub runs: Vec<Run>,
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}
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct Run {
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/// Start index
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pub start: u32,
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/// Values in this run
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pub values: Vec<f32>,
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}
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```
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**Example**: Updating dimensions 100-150
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```
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RLE: { runs: [{ start: 100, values: [50 f32 values] }] }
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Size: 4 + 4 + 200 = 208 bytes
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vs Sparse: { indices: [50 u32], values: [50 f32] }
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Size: 4 + 200 + 200 = 404 bytes
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```
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#### 4. Dictionary Encoding
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For repeated patterns:
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```rust
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/// Dictionary-based delta for recurring patterns
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct DictionaryDelta {
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/// Reference to shared dictionary
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pub dict_id: DictionaryId,
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/// Pattern index in dictionary
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pub pattern_id: u32,
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/// Optional scaling factor
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pub scale: Option<f32>,
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/// Optional offset
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pub offset: Option<f32>,
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}
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/// Shared dictionary of common delta patterns
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pub struct DeltaDictionary {
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pub patterns: Vec<SparseDelta>,
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pub hit_count: Vec<u64>,
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}
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```
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### Adaptive Format Selection
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```rust
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/// Select optimal encoding for delta
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pub fn select_encoding(
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old_vector: &[f32],
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new_vector: &[f32],
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config: &EncodingConfig,
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) -> DeltaEncoding {
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let dimensions = old_vector.len();
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// Count changes
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let changes: Vec<(usize, f32, f32)> = old_vector.iter()
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.zip(new_vector.iter())
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.enumerate()
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.filter(|(_, (o, n))| (*o - *n).abs() > config.epsilon)
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.map(|(i, (o, n))| (i, *o, *n))
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.collect();
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let change_ratio = changes.len() as f32 / dimensions as f32;
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// Check for contiguous runs
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let runs = detect_runs(&changes, config.min_run_length);
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let run_coverage = runs.iter().map(|r| r.len()).sum::<usize>() as f32
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/ changes.len().max(1) as f32;
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// Check dictionary matches
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let dict_match = config.dictionary.as_ref()
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.and_then(|d| d.find_match(&changes, config.dict_threshold));
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// Selection logic
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match (change_ratio, run_coverage, dict_match) {
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// Dictionary match with high similarity
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(_, _, Some((pattern_id, similarity))) if similarity > 0.95 => {
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DeltaEncoding::Dictionary(DictionaryDelta {
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dict_id: config.dictionary.as_ref().unwrap().id,
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pattern_id,
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scale: None,
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offset: None,
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})
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}
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// Dense for >75% changes
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(r, _, _) if r > 0.75 => {
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DeltaEncoding::Dense(DenseDelta {
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values: new_vector.to_vec(),
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quantization: select_quantization(new_vector, config),
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})
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}
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// RLE for high run coverage
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(_, rc, _) if rc > 0.6 => {
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DeltaEncoding::Rle(RleDelta {
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dimensions: dimensions as u32,
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runs: runs.into_iter().map(|r| r.into()).collect(),
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})
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}
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// Sparse for everything else
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_ => {
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let (indices, values): (Vec<_>, Vec<_>) = changes.iter()
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.map(|(i, _, n)| (*i as u32, *n))
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.unzip();
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DeltaEncoding::Sparse(SparseDelta {
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dimensions: dimensions as u32,
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indices,
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values,
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prev_values: None,
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})
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}
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}
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}
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```
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### Format Selection Flowchart
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```
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┌──────────────────┐
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│ Compute Delta │
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│ (old vs new) │
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└────────┬─────────┘
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│
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┌────────v─────────┐
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│ Dictionary Match │
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│ > 95%? │
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└────────┬─────────┘
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│
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┌───────────────┼───────────────┐
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│ YES │ NO │
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v │ │
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┌───────────────┐ │ ┌────────v─────────┐
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│ Dictionary │ │ │ Change Ratio │
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│ Encoding │ │ │ > 75%? │
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└───────────────┘ │ └────────┬─────────┘
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│ │
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│ ┌───────────┼───────────┐
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│ │ YES │ NO │
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│ v │ │
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│ ┌─────────┐ │ ┌───────v───────┐
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│ │ Dense │ │ │ Run Coverage │
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│ │Encoding │ │ │ > 60%? │
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│ └─────────┘ │ └───────┬───────┘
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│ │ │
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│ │ ┌───────┼───────┐
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│ │ │ YES │ NO │
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│ │ v │ v
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│ │ ┌─────┐ ┌─────────┐
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│ │ │ RLE │ │ Sparse │
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│ │ └─────┘ │Encoding │
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│ │ └─────────┘
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```
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---
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## Benchmarks: Memory and CPU Tradeoffs
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### Storage Efficiency by Pattern
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| Pattern | Dimensions | Changes | Sparse | RLE | Dense | Best |
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|---------|------------|---------|--------|-----|-------|------|
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| Sparse (5%) | 384 | 19 | 152B | 160B | 1536B | Sparse |
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| Sparse (10%) | 384 | 38 | 304B | 312B | 1536B | Sparse |
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| Cluster (50 dims) | 384 | 50 | 400B | 208B | 1536B | RLE |
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| Uniform (50%) | 384 | 192 | 1536B | 1600B | 1536B | Dense |
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| Full refresh | 384 | 384 | 3072B | 1544B | 1536B | Dense |
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### Encoding Speed (384-dim vectors, M2 ARM64)
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| Format | Encode | Decode | Apply |
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|--------|--------|--------|-------|
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| Sparse (5%) | 1.2us | 0.3us | 0.4us |
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| Sparse (10%) | 2.1us | 0.5us | 0.8us |
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| RLE (cluster) | 1.8us | 0.4us | 0.5us |
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| Dense (f32) | 0.2us | 0.1us | 0.3us |
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| Dense (f16) | 0.8us | 0.4us | 0.6us |
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| Dense (int8) | 1.2us | 0.6us | 0.9us |
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### Compression Ratios
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| Format | Compression | Quality Loss |
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|--------|-------------|--------------|
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| Sparse (5%) | 10x | 0% |
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| RLE (cluster) | 7.4x | 0% |
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| Dense (f32) | 1x | 0% |
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| Dense (f16) | 2x | < 0.01% |
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| Dense (int8) | 4x | < 0.5% |
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| Dictionary | 50-100x | 0-1% |
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---
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## Considered Options
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### Option 1: Single Sparse Format
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**Description**: Use only sparse encoding for all deltas.
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**Pros**:
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- Simple implementation
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- No format switching overhead
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**Cons**:
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- Inefficient for dense updates (2x overhead)
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- No contiguous region optimization
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**Verdict**: Rejected - real-world patterns require multiple formats.
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### Option 2: Fixed Threshold Switching
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**Description**: Switch between sparse/dense at fixed 50% threshold.
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**Pros**:
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- Predictable behavior
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- Simple decision logic
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**Cons**:
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- Misses RLE opportunities
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- Suboptimal for edge cases
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**Verdict**: Rejected - adaptive switching provides 20-40% better compression.
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### Option 3: Learned Format Selection
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**Description**: ML model predicts optimal format.
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**Pros**:
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- Potentially optimal choices
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- Adapts to workload
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**Cons**:
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- Model training complexity
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- Inference overhead
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- Explainability concerns
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**Verdict**: Deferred - consider for v2 after baseline established.
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### Option 4: Hybrid Adaptive (Selected)
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**Description**: Rule-based adaptive selection with fallback.
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**Pros**:
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- Near-optimal compression
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- Predictable, explainable
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- Low selection overhead
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**Cons**:
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- Rules need tuning
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- May miss edge cases
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**Verdict**: Adopted - best balance of effectiveness and simplicity.
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---
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## Technical Specification
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### Wire Format
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```
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Delta Message Format:
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+--------+--------+--------+--------+--------+--------+
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| Magic | Version| Format | Flags | Length |
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| 0xDE7A | 0x01 | 0-3 | 8 bits | 32 bits |
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+--------+--------+--------+--------+--------+--------+
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| Payload |
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| (format-specific data) |
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+-----------------------------------------------------+
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| Checksum |
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| (CRC32) |
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+-----------------------------------------------------+
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Format codes:
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0x00: Sparse
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0x01: Dense
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0x02: RLE
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0x03: Dictionary
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Flags:
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bit 0: Has previous values (for undo)
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bit 1: Quantized values
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bit 2: Compressed payload
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bit 3: Reserved
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bits 4-7: Quantization mode (if bit 1 set)
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```
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### Sparse Payload Format
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```
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Sparse Payload:
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+--------+--------+--------------------------------+
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| Count | Dims | Delta-Encoded Indices |
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| varint | varint | (varints) |
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+--------+--------+--------------------------------+
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| Values |
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| (f32 or quantized) |
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+--------------------------------------------------+
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```
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### Configuration
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```rust
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#[derive(Debug, Clone, Serialize, Deserialize)]
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pub struct EncodingConfig {
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/// Threshold for considering a value changed
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pub epsilon: f32,
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/// Minimum run length for RLE consideration
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pub min_run_length: usize,
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/// Sparse/Dense threshold (0.0 to 1.0)
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pub sparse_threshold: f32,
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/// RLE coverage threshold
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pub rle_threshold: f32,
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/// Optional dictionary for pattern matching
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pub dictionary: Option<DeltaDictionary>,
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/// Dictionary match threshold
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pub dict_threshold: f32,
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/// Default quantization for dense
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pub default_quantization: QuantizationMode,
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}
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impl Default for EncodingConfig {
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fn default() -> Self {
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Self {
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epsilon: 1e-7,
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min_run_length: 4,
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sparse_threshold: 0.25,
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rle_threshold: 0.6,
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dictionary: None,
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dict_threshold: 0.95,
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default_quantization: QuantizationMode::None,
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}
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}
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}
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```
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---
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## Consequences
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### Benefits
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1. **Optimal Compression**: Automatic format selection reduces storage 2-10x
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2. **Low Latency**: Sub-microsecond encoding/decoding
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3. **Lossless Option**: Sparse and RLE preserve exact values
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4. **Extensibility**: Dictionary allows domain-specific patterns
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### Risks and Mitigations
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| Risk | Probability | Impact | Mitigation |
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|------|-------------|--------|------------|
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| Format proliferation | Low | Medium | Strict 4-format limit |
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| Selection overhead | Low | Low | Pre-computed change detection |
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| Dictionary bloat | Medium | Low | LRU eviction policy |
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| Quantization drift | Medium | Medium | Periodic full refresh |
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---
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## References
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1. Abadi, D., et al. "The Design and Implementation of Modern Column-Oriented Database Systems."
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2. Lemire, D., & Boytsov, L. "Decoding billions of integers per second through vectorization."
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3. ADR-DB-001: Delta Behavior Core Architecture
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---
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## Related Decisions
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- **ADR-DB-001**: Delta Behavior Core Architecture
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- **ADR-DB-006**: Delta Compression Strategy
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