SynapCores Query Optimizer

Target Audience: Solution Architects, Technical Decision Makers, Database Engineers

Executive Summary

The SynapCores Query Optimizer represents a significant advancement in AI-native database performance optimization. Built entirely in Rust for maximum performance and safety, this next-generation optimizer delivers 40-70% query performance improvements on complex analytical workloads while maintaining sub-5ms optimization overhead for transactional queries.

Key Value Propositions

• 40-70% faster complex joins through dynamic programming-based join reordering with bushy tree support
• 15-25% reduced I/O via intelligent projection pushdown and column elimination
• Zero-configuration deployment for immediate performance gains
• ML-ready architecture designed for future machine learning cost model augmentation
• Sub-5ms optimization time ensures minimal overhead even for high-throughput OLTP workloads

Unique Differentiators

Unlike traditional database optimizers that treat AI/ML as an afterthought, SynapCores' optimizer is purpose-built for AI-native workloads:

1. Native Vector Operation Optimization: First-class support for vector similarity operations and semantic search
2. Statistics-Driven ML Workload Awareness: Understands embedding generation, model inference, and multimodal operations
3. Rust Performance: Zero-cost abstractions and memory safety without garbage collection overhead
4. Unified Architecture: Consolidates rule-based and cost-based optimization in a single, coherent framework
5. Production-Hardened Plan Caching: LRU cache with TTL-based invalidation achieves >70% hit rates on OLTP workloads

The AI-Native Database Challenge

Modern applications increasingly combine traditional relational operations with AI/ML workloads:

• Semantic search over product catalogs using vector embeddings
• Real-time fraud detection combining SQL aggregations with ML inference
• Multimodal queries joining structured data with image/text similarity
• Hybrid OLTP/OLAP workloads requiring both transaction and analytical performance

Traditional database optimizers, designed before widespread AI adoption, struggle with these workloads. They lack native understanding of:

• Vector distance metrics and their computational costs
• Embedding generation pipelines and their selectivity characteristics
• ML model inference as query operators
• Semantic similarity vs. keyword matching trade-offs

Optimization Architecture

Query Execution Pipeline

SQL Query Input
       |
       v
+------------------+
|   SQL Parser     |  Custom dialect with AI extensions
+------------------+  Parses: SQL + VECTOR ops + ML functions
       |
       v
+------------------+
|  AST -> Logical  |  Converts parse tree to executable plan
|      Plan        |  Handles: joins, filters, projections
+------------------+
       |
       v
+----------------------------------------------------+
|           UNIFIED QUERY OPTIMIZER                   |
|                                                     |
|  +-----------------------------------------------+  |
|  |          Phase 1: Rule-Based                  |  |
|  |  Fast, always-beneficial transformations      |  |
|  |  - Constant Folding (evaluate at compile)     |  |
|  |  - Predicate Pushdown (filters to scans)      |  |
|  |  - Projection Pushdown (column elimination)   |  |
|  |  - Filter Merging (combine AND predicates)    |  |
|  |  Target: <1ms optimization time               |  |
|  +-----------------------------------------------+  |
|       |                                             |
|       v                                             |
|  +-----------------------------------------------+  |
|  |     Plan Cache Lookup (LRU + TTL)             |  |
|  |  Cache Hit: Return cached plan (<0.1ms)       |  |
|  |  Cache Miss: Continue to Phase 2              |  |
|  +-----------------------------------------------+  |
|       |                                             |
|       v                                             |
|  +-----------------------------------------------+  |
|  |         Phase 2: Cost-Based                   |  |
|  |  Statistics-driven optimizations              |  |
|  |  - Join Reordering (dynamic programming)      |  |
|  |  - Index Selection (cost-based)               |  |
|  |  - Join Algorithm Selection                   |  |
|  |  - Subquery Decorrelation                     |  |
|  |  - Common Subexpression Elimination           |  |
|  |  Target: 3-10ms for complex queries           |  |
|  +-----------------------------------------------+  |
+----------------------------------------------------+
       |
       v
+------------------+
| Optimized Plan   |  Ready for execution
|   Executor       |  Statistics-optimal strategy
+------------------+

Core Optimization Techniques

Dynamic Programming Join Reordering

Problem: Join order dramatically affects query performance. A poorly ordered 5-table join can be 10-100x slower than optimal.

SynapCores Solution: Implements Selinger-style dynamic programming with extensions for bushy trees.

Bushy vs Left-Deep Trees

Left-Deep Trees (Traditional):

      JOIN
     /    \
   JOIN   D
  /    \
JOIN   C
/   \
A    B

Pros: Simple, pipeline-friendly
Cons: Limited optimization space

Bushy Trees (SynapCores Supported):

       JOIN
      /    \
   JOIN    JOIN
   /  \    /  \
  A    B  C    D

Pros: More optimization opportunities
Cons: Slightly higher optimization time

Performance Impact

Histogram-Based Selectivity Estimation

Problem: Accurate cardinality estimation is critical for cost-based optimization. Traditional methods assume uniform distribution, leading to poor estimates.

SynapCores Solution: Multi-bucket histograms with frequency tracking.

Example: Customer age distribution

Bucket Boundaries: [0, 20, 30, 40, 50, 60, 100]
Frequencies:       [5k, 15k, 25k, 20k, 10k, 3k]
Total Count:       78,000 customers

Query: SELECT * FROM customers WHERE age > 45
Estimate: (20k/2 + 10k + 3k) / 78k = 23k / 78k ~ 29.5% selectivity

Default Selectivity Estimates

Projection Pushdown

Problem: Reading unnecessary columns wastes I/O bandwidth and memory.

Transformation Example:

Before Optimization:

SELECT customer_id, total_spend
FROM (
    SELECT * FROM orders WHERE order_date > '2025-01-01'
) subquery
JOIN customers ON customers.id = subquery.customer_id

After Projection Pushdown:

SELECT customer_id, total_spend
FROM (
    SELECT customer_id, total_spend, order_date
    FROM orders WHERE order_date > '2025-01-01'
) subquery
JOIN customers ON customers.id = subquery.customer_id

I/O Reduction:

• Original: Read 50 columns x 1M rows = 50M column-values
• Optimized: Read 3 columns x 1M rows = 3M column-values
• 94% I/O reduction

Plan Caching with LRU Eviction

Problem: Repetitive OLTP queries spend time on optimization when plans rarely change.

SynapCores Solution: In-memory plan cache with intelligent eviction.

Production Metrics (OLTP Workload):

Cache Size: 5,000 plans
Memory Usage: ~5MB (1KB per plan)
TTL: 2 hours

Performance:
- Cache Hit Rate: 73.2%
- Avg Lookup Time: 0.08ms (hit), 4.2ms (miss)
- Hit Rate after warmup (1 hour): 85%+

AI/ML Workload Optimization

Vector Operation Cost Modeling

SynapCores natively understands vector similarity operations:

SELECT product_id, name,
       COSINE_SIMILARITY(embedding, @query_vector) as score
FROM products
WHERE score > 0.8
ORDER BY score DESC
LIMIT 10

Performance Comparison

Scenario: 10M product embeddings, 1536 dimensions

Sequential Scan:

• Time: ~11.5 seconds (unacceptable)

HNSW Vector Index:

• Time: ~3.5ms (acceptable)
• Speedup: 3,291x

Hybrid Query Optimization

Query: Combine traditional SQL filtering with semantic search

SELECT product_id, name, price,
       COSINE_SIMILARITY(embedding, @query) as semantic_score
FROM products
WHERE
    price BETWEEN 50 AND 200
    AND category = 'electronics'
    AND in_stock = true
    AND semantic_score > 0.7
ORDER BY semantic_score DESC
LIMIT 20

Optimization Strategy:

Total: 48.6ms (237x faster than naive approach)

Key Insight: Apply cheap filters before expensive vector operations

Performance Benchmarks

TPC-H Benchmark Results

Setup:

• Hardware: 16-core AMD EPYC, 64GB RAM, NVMe SSD
• Dataset: TPC-H Scale Factor 10 (10GB)

Summary:

• Average improvement: 45.9%
• Optimization overhead: 0.3-7.1ms (0.01-0.2% of query time)
• Complex queries (5+ tables): 60-68% improvement
• Simple queries (1-2 tables): 8-15% improvement

E-Commerce Workload

Setup:

• Dataset: 10M products, 50M reviews, 100M orders

Product Search (OLTP - High Frequency):

SELECT p.id, p.name, p.price,
       COSINE_SIMILARITY(p.embedding, @query) as score
FROM products p
WHERE p.category = 'electronics'
  AND p.price BETWEEN 100 AND 500
  AND score > 0.8
ORDER BY score DESC LIMIT 20

• First run: 85ms (65% improvement)
• Cached: 12ms (95% improvement)

Sales Analytics (OLAP - Low Frequency):

• Legacy: 8,400ms
• Optimized: 2,100ms (75% improvement)

Competitive Advantages

vs Traditional Databases

vs Specialized Vector Databases

Best Practices

Statistics Maintenance

Critical: Optimizer quality depends on statistics freshness.

-- Manual statistics update
ANALYZE TABLE products;

-- Monitor staleness
SELECT table_name, last_analyzed, row_count
FROM system.table_statistics
WHERE last_analyzed < NOW() - INTERVAL '1 day'
ORDER BY row_count DESC;

Recommendations:

• High-write tables: Analyze daily
• Medium-write tables: Analyze weekly
• Read-only tables: Analyze after bulk loads

Query Optimization Tips

Use Explicit Predicates:

-- GOOD: Optimizer can use index
SELECT * FROM orders
WHERE order_date >= '2025-01-01'
  AND order_date < '2025-02-01';

-- BAD: Function prevents index use
SELECT * FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 2025;

Leverage Plan Cache:

-- Use parameterized queries for cache hits
SELECT * FROM products WHERE category = ? AND price > ?;

Conclusion

The SynapCores Query Optimizer delivers:

Proven Performance Gains:

• 40-70% improvement on complex analytical queries
• 15-25% I/O reduction through intelligent projection pushdown
• Sub-5ms optimization overhead maintaining OLTP throughput

AI-Native Advantages:

• First-class vector operation cost modeling
• Native understanding of ML inference pipelines
• Optimized hybrid query execution (SQL + semantic search)
• Multimodal workload awareness

For organizations building AI-native applications, SynapCores' optimizer provides enterprise-grade performance optimization with none of the complexity typically associated with database tuning.

Document Version: 1.0 Last Updated: December 2025 Website: https://synapcores.com