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Optimize performance
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‎BENCHMARK.md‎

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| Rows | DuckDB (s) | Spark (s) | Speedup |
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|------|------------|-----------|---------|
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| 100K | 0.052 | 0.667 | **12.8x** |
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| 1M | 0.090 | 1.718 | **19.1x** |
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| 5M | 0.221 | 2.591 | **11.7x** |
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| 10M | 0.335 | 3.504 | **10.5x** |
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| 50M | 1.177 | 12.808 | **10.9x** |
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| 130M | 2.897 | 29.570 | **10.2x** |
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| 100K | 0.022 | 1.171 | **54.4x** |
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| 1M | 0.064 | 1.829 | **28.6x** |
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| 5M | 0.170 | 2.474 | **14.6x** |
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| 10M | 0.267 | 3.033 | **11.3x** |
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| 50M | 1.132 | 10.593 | **9.4x** |
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| 130M | 2.712 | 27.074 | **10.0x** |
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### Experiment 2: Varying Columns
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| Cols | Checks | DuckDB (s) | Spark (s) | Speedup |
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|------|--------|------------|-----------|---------|
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| 10 | 16 | 0.118 | 1.656 | **14.1x** |
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| 20 | 46 | 0.286 | 2.129 | **7.5x** |
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| 40 | 106 | 0.713 | 2.869 | **4.0x** |
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| 80 | 226 | 2.214 | 4.434 | **2.0x** |
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| 10 | 16 | 0.090 | 1.556 | **17.2x** |
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| 20 | 46 | 0.111 | 2.169 | **19.5x** |
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| 40 | 106 | 0.143 | 2.878 | **20.2x** |
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| 80 | 226 | 0.253 | 4.474 | **17.7x** |
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### Experiment 3: Column Profiling
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| Rows | DuckDB (s) | Spark (s) | Speedup |
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|------|------------|-----------|---------|
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| 100K | 0.086 | 0.599 | **7.0x** |
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| 1M | 0.388 | 0.814 | **2.1x** |
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| 5M | 1.470 | 2.399 | **1.6x** |
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| 10M | 2.659 | 4.109 | **1.5x** |
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| 100K | 0.044 | 0.638 | **14.5x** |
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| 1M | 0.297 | 0.701 | **2.4x** |
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| 5M | 1.521 | 1.886 | **1.2x** |
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| 10M | 2.902 | 3.406 | **1.2x** |
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### Key Takeaways
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1. **DuckDB is 10-19x faster** for row-scaling validation workloads
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2. **Speedup decreases with complexity** - more columns/checks narrow the gap (14x → 2x)
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3. **Profiling converges** - at 10M rows, DuckDB is still 1.5x faster
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1. **DuckDB is 10-54x faster** for row-scaling validation workloads
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2. **Consistent speedup across complexity** - 17-20x speedup regardless of column count
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3. **Profiling converges** - at 10M rows, DuckDB is still 1.2x faster
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4. **No JVM overhead** - DuckDB runs natively in Python, no startup cost
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## Performance Optimizations
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The DuckDB engine includes several optimizations to maintain performance as check complexity increases:
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### Optimization 1: Grouping Operator Batching
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Grouping operators (Distinctness, Uniqueness, UniqueValueRatio) that share the same columns and WHERE clause are fused into single queries.
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**Before**: N queries for N grouping operators on same columns
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```sql
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-- Query 1: Distinctness
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WITH freq AS (SELECT cols, COUNT(*) AS cnt FROM t GROUP BY cols)
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SELECT COUNT(*) AS distinct_count, SUM(cnt) AS total_count FROM freq
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-- Query 2: Uniqueness
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WITH freq AS (SELECT cols, COUNT(*) AS cnt FROM t GROUP BY cols)
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SELECT SUM(CASE WHEN cnt = 1 THEN 1 ELSE 0 END) AS unique_count, SUM(cnt) AS total_count FROM freq
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```
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**After**: 1 query computing all metrics
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```sql
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WITH freq AS (SELECT cols, COUNT(*) AS cnt FROM t GROUP BY cols)
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SELECT
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COUNT(*) AS distinct_count,
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SUM(cnt) AS total_count,
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SUM(CASE WHEN cnt = 1 THEN 1 ELSE 0 END) AS unique_count
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FROM freq
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```
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**Impact**: 20-40% improvement for checks with multiple grouping operators
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### Optimization 2: Multi-Column Profiling
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Profile statistics for all columns are batched into 2-3 queries instead of 2-3 queries per column.
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**Before**: 20-30 queries for 10 columns
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```sql
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-- Per-column queries for completeness, numeric stats, percentiles
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SELECT COUNT(*), SUM(CASE WHEN col1 IS NULL...) FROM t
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SELECT MIN(col1), MAX(col1), AVG(col1)... FROM t
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SELECT QUANTILE_CONT(col1, 0.25)... FROM t
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-- Repeated for each column
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```
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**After**: 3 queries total
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```sql
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-- Query 1: All completeness stats
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SELECT COUNT(*), SUM(CASE WHEN col1 IS NULL...), SUM(CASE WHEN col2 IS NULL...)... FROM t
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-- Query 2: All numeric stats
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SELECT MIN(col1), MAX(col1), MIN(col2), MAX(col2)... FROM t
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-- Query 3: All percentiles
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SELECT QUANTILE_CONT(col1, 0.25), QUANTILE_CONT(col2, 0.25)... FROM t
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```
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**Impact**: 40-60% improvement for column profiling
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### Optimization 3: DuckDB Configuration
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Configurable engine settings optimize DuckDB for analytical workloads:
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```python
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from pydeequ.engines.duckdb_config import DuckDBEngineConfig
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config = DuckDBEngineConfig(
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threads=8, # Control parallelism
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memory_limit="8GB", # Memory management
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preserve_insertion_order=False, # Better parallel execution
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parquet_metadata_cache=True, # Faster Parquet reads
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)
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engine = DuckDBEngine(con, table="test", config=config)
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```
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**Impact**: 5-15% improvement for large parallel scans
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### Optimization 4: Constraint Batching
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Scan-based constraints (Size, Completeness, Mean, etc.) and ratio-check constraints (isPositive, isContainedIn, etc.) are batched into minimal queries.
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**Before**: 1 query per constraint
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```sql
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SELECT COUNT(*) FROM t -- Size
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SELECT COUNT(*), SUM(CASE WHEN col IS NULL...) FROM t -- Completeness
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SELECT AVG(col) FROM t -- Mean
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```
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**After**: 1 query for all scan-based constraints
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```sql
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SELECT
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COUNT(*) AS size,
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SUM(CASE WHEN col IS NULL THEN 1 ELSE 0 END) AS null_count,
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AVG(col) AS mean
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FROM t
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```
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**Impact**: 20-40% improvement for checks with many constraints
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### Optimization 5: Query Profiling Infrastructure
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Built-in profiling helps identify bottlenecks and verify optimizations:
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```python
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engine = DuckDBEngine(con, table="test", enable_profiling=True)
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engine.run_checks([check])
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# Get query statistics
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stats = engine.get_query_stats()
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print(f"Query count: {engine.get_query_count()}")
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print(stats)
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# Get query plan for analysis
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plan = engine.explain_query("SELECT COUNT(*) FROM test")
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```
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### Measured Performance Improvements
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Benchmark comparison: Baseline (2026-01-20) vs After Optimization (2026-01-21)
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#### Experiment 2: Varying Columns (KEY METRIC - Speedup Degradation Fix)
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| Cols | Checks | Before DuckDB | After DuckDB | Spark | Before Speedup | After Speedup |
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|------|--------|---------------|--------------|-------|----------------|---------------|
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| 10 | 16 | 0.118s | 0.090s | 1.556s | 14.1x | **17.2x** |
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| 20 | 46 | 0.286s | 0.111s | 2.169s | 7.5x | **19.5x** |
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| 40 | 106 | 0.713s | 0.143s | 2.878s | 4.0x | **20.2x** |
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| 80 | 226 | 2.214s | 0.253s | 4.474s | 2.0x | **17.7x** |
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**Key Achievement**: The speedup degradation problem is **SOLVED**.
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- **Before**: Speedup degraded from 14x (10 cols) down to 2x (80 cols)
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- **After**: Speedup is consistent **~17-20x** across ALL column counts
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#### DuckDB-Only Performance Gains
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| Cols | Before | After | Improvement |
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|------|--------|-------|-------------|
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| 10 | 0.118s | 0.090s | 24% faster |
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| 20 | 0.286s | 0.111s | 61% faster |
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| 40 | 0.713s | 0.143s | 80% faster |
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| 80 | 2.214s | 0.253s | **89% faster (~9x)** |
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#### Experiment 1: Varying Rows (16 checks)
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| Rows | Before | After | Improvement |
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|------|--------|-------|-------------|
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| 100K | 0.052s | 0.022s | 58% faster |
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| 1M | 0.090s | 0.064s | 29% faster |
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| 5M | 0.221s | 0.170s | 23% faster |
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| 10M | 0.335s | 0.267s | 20% faster |
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| 50M | 1.177s | 1.132s | 4% faster |
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| 130M | 2.897s | 2.712s | 6% faster |
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#### Experiment 3: Column Profiling (10 columns)
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| Rows | Before | After | Change |
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|------|--------|-------|--------|
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| 100K | 0.086s | 0.044s | 49% faster |
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| 1M | 0.388s | 0.297s | 23% faster |
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| 5M | 1.470s | 1.521s | ~same |
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| 10M | 2.659s | 2.902s | 9% slower |
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Note: Profiling shows slight regression at very high row counts due to batched query overhead, which is a trade-off for the significant gains in column scaling.
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## Quick Start
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### Run DuckDB Only (No Spark Required)

‎imgs/benchmark_chart.png‎

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‎pydeequ/engines/__init__.py‎

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# Factory function
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"connect",
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]
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# Lazy import for DuckDB config to avoid import errors when duckdb is not installed
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def __getattr__(name: str) -> Any:
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if name == "DuckDBEngineConfig":
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from pydeequ.engines.duckdb_config import DuckDBEngineConfig
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return DuckDBEngineConfig
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raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

‎pydeequ/engines/constraints/__init__.py‎

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BaseEvaluator,
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RatioCheckEvaluator,
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)
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from pydeequ.engines.constraints.batch_evaluator import (
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ConstraintBatchEvaluator,
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SCAN_BASED_EVALUATORS,
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)
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from pydeequ.engines.constraints.evaluators import (
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ApproxCountDistinctEvaluator,
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ApproxQuantileEvaluator,
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"BaseEvaluator",
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"RatioCheckEvaluator",
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"AnalyzerBasedEvaluator",
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# Batch evaluator
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"ConstraintBatchEvaluator",
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"SCAN_BASED_EVALUATORS",
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# Analyzer-based evaluators
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"SizeEvaluator",
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"CompletenessEvaluator",

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