Query Optimization

Note

This documentation is under development. More detailed content will be added in future releases.

Overview

Query optimization in Memories-Dev involves improving the efficiency, performance, and resource usage of memory queries. This guide provides techniques and strategies for optimizing queries across different memory tiers and data types.

Query Optimization Strategies

Vector Search Optimization

For vector-based memory retrieval:

# Example of optimized vector query
def optimized_vector_search(query_vector, index, top_k=10, ef_search=100):
    """
    Perform an optimized vector search with HNSW parameters tuned for performance

    Parameters:
    -----------
    query_vector : np.ndarray
        The query vector
    index : VectorIndex
        The vector index to search
    top_k : int
        Number of results to return
    ef_search : int
        Exploration factor - higher values give more accurate but slower search

    Returns:
    --------
    list
        Sorted results with ids and distances
    """
    # Set runtime parameters for this specific query
    search_params = {
        "ef": ef_search,              # Controls accuracy vs. speed tradeoff
        "filter": None,               # No filters for maximum performance
        "batch_mode": top_k > 100,    # Use batch mode for large result sets
        "use_gpu": is_gpu_available() # Use GPU acceleration if available
    }

    # Execute search with optimized parameters
    results = index.search(query_vector, top_k, search_params)

    return results

Query Planning and Execution

Efficient query planning strategies:

Tiered Querying: Start with fast, approximate searches and refine as needed
Query Decomposition: Break complex queries into simpler, more efficient sub-queries
Query Rewriting: Restructure queries for better execution paths
Predicate Pushdown: Apply filters as early as possible in the query pipeline
Parallel Execution: Distribute query workload across available resources

# Example of tiered query execution
def tiered_memory_query(query, context):
    """
    Execute query across memory tiers with progressive refinement
    """
    # First check hot memory (fast, in-memory cache)
    hot_results = hot_memory.query(query, limit=5, threshold=0.8)

    if is_sufficient(hot_results, min_confidence=0.9):
        return hot_results

    # Then check warm memory
    expanded_query = enrich_query(query, hot_results, context)
    warm_results = warm_memory.query(expanded_query, limit=20, threshold=0.7)

    combined_results = merge_results([hot_results, warm_results])
    if is_sufficient(combined_results, min_confidence=0.8):
        return combined_results

    # Finally check cold memory with most context
    full_query = create_comprehensive_query(query, combined_results, context)
    cold_results = cold_memory.query(full_query, limit=50, threshold=0.6)

    # Combine and rank all results
    final_results = merge_and_rank_results([hot_results, warm_results, cold_results])
    return final_results

Indexing Strategies

Optimize index structures for query patterns:

Composite Indexes: Create indexes that cover multiple query dimensions
Partial Indexes: Index only the relevant subset of data
Hierarchical Indexes: Use multi-level indexes for navigating large datasets
Specialized Indexes: Apply domain-specific indexing techniques

Caching and Materialization

Cache frequently accessed query results:

Query Result Caching: Cache results of common queries
Materialized Views: Precompute and store results of complex queries
Dynamic Materialization: Automatically identify and materialize frequent query patterns
Cache Invalidation: Efficiently manage cache freshness

Performance Monitoring and Tuning

Key metrics to monitor:

Query Latency: End-to-end query execution time
Throughput: Number of queries processed per time unit
Resource Utilization: CPU, memory, and I/O usage during query execution
Cache Effectiveness: Cache hit rates for query results
Index Efficiency: Index access patterns and maintenance overhead

Common Issues and Solutions

Slow Vector Searches: Optimize index parameters (M, ef_construction) or use approximate search
Memory Pressure: Implement streaming execution for large result sets
I/O Bottlenecks: Add caching layers or optimize data layout
Poor Relevance: Fine-tune similarity metrics or enhance query context
Cold Starts: Implement query warm-up procedures for critical applications