sql-cli 1.73.1

SQL query tool for CSV/JSON with both interactive TUI and non-interactive CLI modes - perfect for exploration and automation
Documentation
# Step 0 Complete: Preparation for Batch Evaluation

**Date**: 2025-11-03
**Status**: ✅ Complete
**Next Step**: Step 1 - Add batch evaluation data structures

## Summary

Step 0 established a comprehensive safety net before implementing batch evaluation. We now have:

1. **Comprehensive test suite** covering all implemented window functions
2. **Performance benchmarks** for 10k, 50k, and 100k row datasets
3. **Baseline metrics** captured for comparison after batch implementation
4. **Architecture documentation** detailing current per-row evaluation

## Deliverables

### 1. Comprehensive Test Suite
**File**: `tests/sql_examples/test_window_functions_comprehensive.sql`
**Test Data**: `data/sales_test.csv` (18 rows, 3 regions, multiple products)

**Coverage**:
- ✅ LAG: default offset, offset 2, with/without PARTITION BY, NULL handling, edge cases
- ✅ LEAD: default offset, offset 2, with/without PARTITION BY, edge cases
- ✅ ROW_NUMBER: with/without PARTITION BY, different ORDER BY
- ✅ Multiple window functions in one query
- ✅ Same window spec used by multiple functions
- ✅ Different window specs in same query
- ✅ Edge cases: single row partitions, empty results, NULL values

**Total Test Cases**: 28 SQL queries covering various scenarios

**Note**: RANK and DENSE_RANK are NOT yet implemented and were excluded from tests

### 2. Performance Benchmark Script
**File**: `tests/integration/benchmark_window_functions.sh`

**Features**:
- Automatic test data generation (10k, 50k, 100k rows)
- 3 runs per benchmark, reports best and average times
- Captures baseline metrics to `docs/benchmarks/`
- Quick mode (`--quick`) for 10k rows only
- Capture mode (`--capture`) saves results to files

**Benchmarks**:
1. LAG without PARTITION BY
2. LAG with PARTITION BY
3. LEAD without PARTITION BY
4. ROW_NUMBER without PARTITION BY
5. ROW_NUMBER with PARTITION BY
6. Multiple window functions (LAG + LEAD + ROW_NUMBER)

**Usage**:
```bash
# Quick test (10k rows only)
./tests/integration/benchmark_window_functions.sh --quick

# Full suite (10k, 50k, 100k rows)
./tests/integration/benchmark_window_functions.sh --capture
```

### 3. Baseline Performance Metrics

**Location**: `docs/benchmarks/baseline_*_rows.txt`

#### 10,000 Rows Baseline
| Benchmark | Time (ms) | Per Row |
|-----------|-----------|---------|
| LAG without PARTITION BY | 348 | 34.8μs |
| LAG with PARTITION BY | 389 | 38.9μs |
| LEAD without PARTITION BY | 387 | 38.7μs |
| ROW_NUMBER without PARTITION BY | 311 | 31.1μs |
| ROW_NUMBER with PARTITION BY | 367 | 36.7μs |
| Multiple functions (3 funcs) | 917 | 91.7μs |

#### 50,000 Rows Baseline
| Benchmark | Time (ms) | Per Row |
|-----------|-----------|---------|
| LAG without PARTITION BY | 1,863 | 37.3μs |
| LAG with PARTITION BY | 2,667 | 53.3μs |
| LEAD without PARTITION BY | 2,054 | 41.1μs |
| ROW_NUMBER without PARTITION BY | 1,882 | 37.6μs |
| ROW_NUMBER with PARTITION BY | 2,199 | 44.0μs |
| Multiple functions (3 funcs) | 5,289 | 105.8μs |

#### 100,000 Rows Baseline
| Benchmark | Time (ms) | Per Row |
|-----------|-----------|---------|
| LAG without PARTITION BY | 4,007 | 40.1μs |
| LAG with PARTITION BY | 4,953 | 49.5μs |
| LEAD without PARTITION BY | 3,794 | 37.9μs |
| ROW_NUMBER without PARTITION BY | 3,343 | 33.4μs |
| ROW_NUMBER with PARTITION BY | 3,932 | 39.3μs |
| Multiple functions (3 funcs) | 9,992 | 99.9μs |

**Key Observations**:
- Linear scaling: doubling rows approximately doubles execution time
- Per-row cost: ~35-50μs for single window functions
- Multiple functions: roughly additive (3× the per-row cost)
- PARTITION BY adds overhead: ~10-20% slower
- Consistent performance across different row counts

### 4. Architecture Documentation
**File**: `docs/WINDOW_CURRENT_ARCHITECTURE.md`

**Contents**:
- Complete data flow diagram
- Detailed component descriptions
- Code location references with line numbers
- Performance characteristics and bottleneck analysis
- Explanation of why per-row evaluation doesn't scale

**Key Insights Documented**:
- 90% of time spent in per-row overhead (cache lookups, function calls)
- Only 10% in actual window function logic
- Cache hits aren't free at 50,000× scale (4μs each = 200ms total)
- Function call overhead dominates (~800ms for 50k rows)

## Validation

### ✅ All Deliverables Complete
- [x] Comprehensive test suite created
- [x] Test data file created
- [x] Benchmark script implemented and tested
- [x] Baselines captured for 10k, 50k, 100k rows
- [x] Architecture documentation complete
- [x] All tests pass with current implementation

### ✅ Benchmarks Successful
- [x] All benchmarks run without errors
- [x] Results are consistent across runs (±5% variance)
- [x] Performance scales linearly as expected
- [x] Baseline files saved for future comparison

### ✅ Test Coverage
- [x] LAG function: 8 test cases
- [x] LEAD function: 3 test cases
- [x] ROW_NUMBER function: 3 test cases
- [x] Multiple functions: 3 test cases
- [x] Edge cases: 5 test cases
- [x] Performance tests: 3 test cases

## Next Steps (Step 1)

Now that we have a solid foundation, we can safely proceed with Step 1:

**Step 1: Add Batch Evaluation Data Structures (No Behavior Change)**
- Estimated time: 30 minutes
- Risk: None (pure addition, no code changes)
- Goal: Add new structs without changing any existing logic

**Tasks**:
1. Create `src/data/batch_window_evaluator.rs` with stub implementation
2. Add `WindowFunctionSpec` struct to hold metadata
3. Add module declaration to `src/data/mod.rs`
4. Verify `cargo build --release` succeeds
5. Verify `cargo test` passes
6. Verify benchmarks unchanged

**Success Criteria**:
- Code compiles without warnings
- All existing tests pass
- Benchmarks show no regression
- New code not called yet (dead code warnings expected)

## Files Created

### Test Files
- `tests/sql_examples/test_window_functions_comprehensive.sql` - 28 test queries
- `data/sales_test.csv` - Test data (18 rows)
- `data/bench_window_10000.csv` - Benchmark data (generated)
- `data/bench_window_50000.csv` - Benchmark data (generated)
- `data/bench_window_100000.csv` - Benchmark data (generated)

### Scripts
- `tests/integration/benchmark_window_functions.sh` - Performance benchmarks

### Documentation
- `docs/WINDOW_CURRENT_ARCHITECTURE.md` - Detailed architecture documentation
- `docs/WINDOW_BATCH_EVALUATION_PLAN.md` - 9-step implementation plan
- `docs/WINDOW_STEP0_COMPLETE.md` - This document

### Baseline Data
- `docs/benchmarks/baseline_10000_rows.txt` - 10k row baselines
- `docs/benchmarks/baseline_50000_rows.txt` - 50k row baselines
- `docs/benchmarks/baseline_100000_rows.txt` - 100k row baselines

## Lessons Learned

1. **RANK/DENSE_RANK not implemented** - Only LAG, LEAD, and ROW_NUMBER are available
2. **Benchmarks need proper validation** - Initially included unsupported functions
3. **Bash subprocess output needs stderr redirection** - Fixed command substitution issues
4. **Linear scaling confirmed** - Per-row architecture scales predictably but slowly
5. **Multiple functions are additive** - 3 window functions ≈ 3× single function time

## Safety Net Established

Before making ANY changes to the implementation:
- ✅ We can measure performance impact precisely
- ✅ We can verify correctness with 28 test queries
- ✅ We can rollback at any point and compare
- ✅ We understand exactly what the current architecture does
- ✅ We have documented baselines to exceed

**Step 0 Status**: ✅ **COMPLETE** - Ready to proceed with Step 1