278 lines
6.9 KiB
Markdown
278 lines
6.9 KiB
Markdown
# Memory-Aware Query Optimizer
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Database query optimizer that explicitly considers memory hierarchies and space-time tradeoffs based on Williams' theoretical bounds.
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## Features
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- **Cost Model**: Incorporates L3/RAM/SSD boundaries in cost calculations
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- **Algorithm Selection**: Chooses between hash/sort/nested-loop joins based on true memory costs
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- **Buffer Sizing**: Automatically sizes buffers to √(data_size) for optimal tradeoffs
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- **Spill Planning**: Optimizes when and how to spill to disk
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- **Memory Hierarchy Awareness**: Tracks which level (L1-L3/RAM/Disk) operations will use
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- **AI Explanations**: Clear reasoning for all optimization decisions
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## Installation
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```bash
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# From sqrtspace-tools root directory
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pip install -r requirements-minimal.txt
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```
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## Quick Start
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```python
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from db_optimizer.memory_aware_optimizer import MemoryAwareOptimizer
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import sqlite3
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# Connect to database
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conn = sqlite3.connect('mydb.db')
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# Create optimizer with 10MB memory limit
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optimizer = MemoryAwareOptimizer(conn, memory_limit=10*1024*1024)
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# Optimize a query
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sql = """
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SELECT c.name, SUM(o.total)
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FROM customers c
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JOIN orders o ON c.id = o.customer_id
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GROUP BY c.name
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ORDER BY SUM(o.total) DESC
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"""
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result = optimizer.optimize_query(sql)
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print(result.explanation)
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# "Optimized query plan reduces memory usage by 87.3% with 2.1x estimated speedup.
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# Changed join from nested_loop to hash_join saving 9216KB.
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# Allocated 4 buffers totaling 2048KB for optimal performance."
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```
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## Join Algorithm Selection
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The optimizer intelligently selects join algorithms based on memory constraints:
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### 1. Hash Join
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- **When**: Smaller table fits in memory
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- **Memory**: O(min(n,m))
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- **Time**: O(n+m)
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- **Best for**: Equi-joins with one small table
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### 2. Sort-Merge Join
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- **When**: Both tables fit in memory for sorting
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- **Memory**: O(n+m)
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- **Time**: O(n log n + m log m)
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- **Best for**: Pre-sorted data or when output needs ordering
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### 3. Block Nested Loop
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- **When**: Limited memory, uses √n blocks
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- **Memory**: O(√n)
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- **Time**: O(n*m/√n)
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- **Best for**: Memory-constrained environments
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### 4. Nested Loop
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- **When**: Extreme memory constraints
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- **Memory**: O(1)
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- **Time**: O(n*m)
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- **Last resort**: When memory is critically limited
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## Buffer Management
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The optimizer automatically calculates optimal buffer sizes:
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```python
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# Get buffer recommendations
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result = optimizer.optimize_query(query)
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for buffer_name, size in result.buffer_sizes.items():
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print(f"{buffer_name}: {size / 1024:.1f}KB")
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# Output:
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# scan_buffer: 316.2KB # √n sized for sequential scan
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# join_buffer: 1024.0KB # Optimal for hash table
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# sort_buffer: 447.2KB # √n sized for external sort
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```
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## Spill Strategies
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When memory is exceeded, the optimizer plans spilling:
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```python
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# Check spill strategy
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if result.spill_strategy:
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for operation, strategy in result.spill_strategy.items():
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print(f"{operation}: {strategy}")
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# Output:
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# JOIN_0: grace_hash_join # Partition both inputs
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# SORT_0: multi_pass_external_sort # Multiple merge passes
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# AGGREGATE_0: spill_partial_aggregates # Write intermediate results
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```
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## Query Plan Visualization
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```python
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# View query execution plan
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print(optimizer.explain_plan(result.optimized_plan))
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# Output:
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# AGGREGATE (hash_aggregate)
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# Rows: 100
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# Size: 9.8KB
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# Memory: 14.6KB (L3)
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# Cost: 15234
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# SORT (external_sort)
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# Rows: 1,000
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# Size: 97.7KB
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# Memory: 9.9KB (L3)
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# Cost: 14234
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# JOIN (hash_join)
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# Rows: 1,000
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# Size: 97.7KB
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# Memory: 73.2KB (L3)
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# Cost: 3234
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# SCAN customers (sequential)
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# Rows: 100
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# Size: 9.8KB
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# Memory: 9.8KB (L2)
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# Cost: 98
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# SCAN orders (sequential)
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# Rows: 1,000
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# Size: 48.8KB
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# Memory: 48.8KB (L3)
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# Cost: 488
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```
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## Optimizer Hints
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Apply hints to SQL queries:
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```python
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# Optimize for minimal memory usage
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hinted_sql = optimizer.apply_hints(
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sql,
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target='memory',
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memory_limit='1MB'
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)
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# /* SpaceTime Optimizer: Using block nested loop with √n memory ... */
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# SELECT ...
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# Optimize for speed
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hinted_sql = optimizer.apply_hints(
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sql,
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target='latency'
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)
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# /* SpaceTime Optimizer: Using hash join for minimal latency ... */
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# SELECT ...
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```
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## Real-World Examples
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### 1. Large Table Join with Memory Limit
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```python
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# 1GB tables, 100MB memory limit
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sql = """
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SELECT l.*, r.details
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FROM large_table l
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JOIN reference_table r ON l.ref_id = r.id
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WHERE l.status = 'active'
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"""
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result = optimizer.optimize_query(sql)
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# Chooses: Block nested loop with 10MB blocks
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# Memory: 10MB (fits in L3 cache)
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# Speedup: 10x over naive nested loop
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```
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### 2. Multi-Way Join
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```python
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sql = """
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SELECT *
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FROM a
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JOIN b ON a.id = b.a_id
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JOIN c ON b.id = c.b_id
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JOIN d ON c.id = d.c_id
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"""
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result = optimizer.optimize_query(sql)
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# Optimizes join order based on sizes
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# Uses different algorithms for each join
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# Allocates buffers to minimize spilling
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```
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### 3. Aggregation with Sorting
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```python
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sql = """
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SELECT category, COUNT(*), AVG(price)
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FROM products
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GROUP BY category
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ORDER BY COUNT(*) DESC
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"""
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result = optimizer.optimize_query(sql)
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# Hash aggregation with √n memory
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# External sort for final ordering
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# Explains tradeoffs clearly
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```
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## Performance Characteristics
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### Memory Savings
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- **Typical**: 50-95% reduction vs naive approach
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- **Best case**: 99% reduction (large self-joins)
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- **Worst case**: 10% reduction (already optimal)
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### Speed Impact
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- **Hash to Block Nested**: 2-10x speedup
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- **External Sort**: 20-50% overhead vs in-memory
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- **Overall**: Usually faster despite less memory
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### Memory Hierarchy Benefits
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- **L3 vs RAM**: 8-10x latency improvement
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- **RAM vs SSD**: 100-1000x latency improvement
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- **Optimizer targets**: Keep hot data in faster levels
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## Integration
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### SQLite
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```python
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conn = sqlite3.connect('mydb.db')
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optimizer = MemoryAwareOptimizer(conn)
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```
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### PostgreSQL (via psycopg2)
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```python
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# Use explain analyze to get statistics
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# Apply recommendations via SET commands
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```
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### MySQL (planned)
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```python
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# Similar approach with optimizer hints
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```
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## How It Works
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1. **Statistics Collection**: Gathers table sizes, indexes, cardinalities
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2. **Query Analysis**: Parses SQL to extract operations
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3. **Cost Modeling**: Estimates cost with memory hierarchy awareness
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4. **Algorithm Selection**: Chooses optimal algorithms for each operation
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5. **Buffer Allocation**: Sizes buffers using √n principle
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6. **Spill Planning**: Determines graceful degradation strategy
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## Limitations
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- Simplified cardinality estimation
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- SQLite-focused (PostgreSQL support planned)
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- No runtime adaptation yet
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- Requires accurate statistics
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## Future Enhancements
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- Runtime plan adjustment
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- Learned cost models
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- PostgreSQL native integration
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- Distributed query optimization
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- GPU memory hierarchy support
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## See Also
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- [SpaceTimeCore](../core/spacetime_core.py): Memory hierarchy modeling
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- [SpaceTime Profiler](../profiler/): Find queries needing optimization |