# Phase 7: Query Optimization
**Status:** 🚀 In Progress
**Timeline:** 1-2 weeks
**Target:** Advanced compression, caching, indexing
## Overview
Query optimization enables:
- ✅ Adaptive compression selection
- ✅ Cost-based query planning
- ✅ Metadata caching
- ✅ Column/partition pruning
- ✅ Predicate pushdown
- ✅ Index-based lookups
## Optimizations
### 1. Adaptive Compression
Select best codec per column:
- **RLE** → Low cardinality (states, flags)
- **FOR** → Numeric (integers, timestamps)
- **Dictionary** → Text/strings with repeats
- **LZSS** → High entropy data
### 2. Cost-Based Planning
Estimate query cost:
- Rows to read
- Bytes to decompress
- CPU cycles needed
- Choose fastest execution plan
### 3. Metadata Caching
Cache expensive operations:
- Schema inference
- Column statistics
- Partition metadata
- TTL-based invalidation
### 4. Column Pruning
Read only needed columns:
- Query `SELECT name, age` → skip salary
- Reduces I/O by 2-3x
### 5. Predicate Pushdown
Apply filters at read layer:
- `WHERE age > 30` → filter during read
- Reduces rows processed
### 6. Partition Pruning
Skip irrelevant partitions:
- `WHERE date >= '2024-01-01'` → skip old partitions
- Reduces data scanned
### 7. Index-Based Lookups
Fast point queries:
- Hash index for equality
- B-tree for range queries
- Bitmap index for boolean
## Implementation
### Statistics Collection
```rust
struct ColumnStats {
cardinality: u64, // Unique values
null_count: u64, // NULL rows
min_value: String, // Min (nullable)
max_value: String, // Max (nullable)
compression_ratio: f32, // Actual ratio
}
```
### Compression Selection
```rust
fn select_compression(column: &Column) -> CompressionCodec {
match (column.data_type, column.cardinality) {
(String, low) => Dictionary, // < 1000 unique
(String, high) => Snappy, // Many unique
(Integer, _) => FOR, // Good for numbers
(Float, _) => LZSS, // General purpose
(Boolean, _) => RLE, // 2 values max
_ => Snappy, // Fallback
}
}
```
### Query Planning
```rust
fn plan_query(query: &str, stats: &[ColumnStats]) -> QueryPlan {
// 1. Parse query
let parsed = parse_sql(query);
// 2. Extract required columns
let needed_cols = extract_columns(&parsed);
// 3. Extract predicates
let predicates = extract_predicates(&parsed);
// 4. Estimate costs
let cost = estimate_cost(&needed_cols, &predicates, stats);
// 5. Choose plan
QueryPlan { columns: needed_cols, predicates, cost }
}
```
## Performance Targets
| Column pruning | 2-3x | Read 2 of 10 columns |
| Predicate pushdown | 2-5x | Filter 1M → 100K rows |
| Partition pruning | 5-10x | Skip 9 of 10 partitions |
| Compression selection | 1.5-2x | Right codec per column |
| Metadata caching | 10x | Cached vs fresh stat |
## Implementation Phases
### Phase 7A: Compression Optimization (Week 1)
- [ ] Adaptive codec selection
- [ ] Per-column compression
- [ ] Compression benchmarking
### Phase 7B: Statistics & Caching (Week 1)
- [ ] Statistics collection
- [ ] Metadata cache layer
- [ ] Cache invalidation
### Phase 7C: Index Management (Week 2)
- [ ] Hash index
- [ ] B-tree index
- [ ] Bitmap index
## Roadmap
- [ ] Statistics collection
- [ ] Compression selection logic
- [ ] Cost estimator
- [ ] Metadata cache
- [ ] Column pruning
- [ ] Predicate pushdown
- [ ] Partition pruning
- [ ] Index structures
- [ ] Query planner
- [ ] Unit tests
- [ ] Benchmarks
- [ ] Documentation
## Testing
```bash
# Unit tests
cargo test --release
# Benchmarks
cargo bench
# Compression ratios
./bench_compression
# Query plans
./bench_query_planning
```
## Expected Results
After Phase 7, queries should be:
- **10-100x faster** with proper optimization
- **50-70% compression** with adaptive codecs
- **Sub-millisecond** metadata lookups (cached)
- **1-10ms** for typical analytical queries
## Contributors
Assigned for Phase 7 development.
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**Next:** Begin with compression selection and statistics collection.