Document Date: January 15, 2025
Based on: Dual Database Performance Analysis
Priority: High - Critical for Production Deployment
Based on comprehensive load testing results, the VCF Analysis Agent requires targeted optimizations to achieve production-scale performance targets. While the dual-database architecture demonstrates excellent foundational performance, memory management emerges as the critical bottleneck requiring immediate attention.
- Current Issue: Memory usage peaks at 1,275MB (25% over 1,024MB limit)
- Impact: System failure under sustained load, 0% success rate in stress tests
- Root Cause: Unoptimized embedding generation and lack of memory management
Implementation: Add LRU cache for embedding vectors
from functools import lru_cache
import gc
class EmbeddingCache:
def __init__(self, max_size=1000):
self.cache = {}
self.max_size = max_size
self.access_order = []
def get_embedding(self, variant_key):
if variant_key in self.cache:
self._update_access(variant_key)
return self.cache[variant_key]
# Generate new embedding
embedding = self._generate_embedding(variant_key)
self._store_embedding(variant_key, embedding)
return embedding
def _store_embedding(self, key, embedding):
if len(self.cache) >= self.max_size:
self._evict_oldest()
self.cache[key] = embedding
self.access_order.append(key)Expected Impact: 60-70% reduction in memory usage for repeated variants
Implementation: Chunked processing with garbage collection
import gc
import psutil
def process_variants_memory_efficient(variants, chunk_size=10, memory_limit_mb=800):
"""Process variants in memory-efficient chunks"""
total_processed = 0
for chunk in chunks(variants, chunk_size):
# Check memory before processing
current_memory = psutil.Process().memory_info().rss / 1024 / 1024
if current_memory > memory_limit_mb:
gc.collect() # Force garbage collection
# Process chunk
process_chunk(chunk)
total_processed += len(chunk)
# Periodic cleanup
if total_processed % 50 == 0:
gc.collect()
return total_processedExpected Impact: Maintain memory usage below 1GB threshold
Current: 768-dimensional vectors (Ollama default) Target: 384-dimensional vectors (50% reduction)
def optimize_embedding_dimensions(embedding_vector, target_dim=384):
"""Reduce embedding dimensions using PCA or truncation"""
if len(embedding_vector) > target_dim:
# Option 1: Simple truncation
return embedding_vector[:target_dim]
# Option 2: PCA reduction (more sophisticated)
# return pca_reduce(embedding_vector, target_dim)Expected Impact: 50% reduction in vector storage memory
- Current Issue: Synchronous embedding generation, no batch processing
- Impact: Processing bottleneck, inefficient resource utilization
- Root Cause: Placeholder random vectors, no Ollama optimization
import asyncio
import aiohttp
class AsyncEmbeddingGenerator:
def __init__(self, ollama_endpoint="http://localhost:11434"):
self.endpoint = ollama_endpoint
self.session = None
async def generate_embeddings_batch(self, texts, batch_size=10):
"""Generate embeddings for multiple texts concurrently"""
if not self.session:
self.session = aiohttp.ClientSession()
tasks = []
for batch in chunks(texts, batch_size):
task = self._generate_batch(batch)
tasks.append(task)
results = await asyncio.gather(*tasks)
return flatten(results)
async def _generate_batch(self, texts):
"""Generate embeddings for a batch of texts"""
payload = {
"model": "nomic-embed-text",
"prompt": texts
}
async with self.session.post(f"{self.endpoint}/api/embeddings",
json=payload) as response:
data = await response.json()
return data.get("embeddings", [])Expected Impact: 3-5x improvement in embedding generation throughput
Current: Full Ollama model loading per request Target: Persistent model with connection pooling
class OptimizedOllamaClient:
def __init__(self, model_name="nomic-embed-text", pool_size=3):
self.model_name = model_name
self.connection_pool = []
self.pool_size = pool_size
self._initialize_pool()
def _initialize_pool(self):
"""Pre-load model connections"""
for _ in range(self.pool_size):
connection = self._create_connection()
self.connection_pool.append(connection)
async def get_embedding(self, text):
"""Get embedding using pooled connection"""
connection = await self._get_connection()
try:
embedding = await connection.generate_embedding(text)
return embedding
finally:
await self._return_connection(connection)Expected Impact: 2-3x reduction in embedding generation latency
- Current Issue: Limited to 3 concurrent users before memory issues
- Impact: Poor scalability, production deployment limitations
- Root Cause: Synchronous dual-database operations
import asyncio
class AsyncDualDatabaseManager:
def __init__(self, lancedb_client, kuzu_client):
self.lancedb = lancedb_client
self.kuzu = kuzu_client
async def add_variant_async(self, variant_data):
"""Add variant to both databases concurrently"""
# Prepare data for both databases
lance_data = self._prepare_lance_data(variant_data)
kuzu_data = self._prepare_kuzu_data(variant_data)
# Execute operations concurrently
lance_task = asyncio.create_task(
self._add_to_lancedb(lance_data)
)
kuzu_task = asyncio.create_task(
self._add_to_kuzu(kuzu_data)
)
# Wait for both operations to complete
lance_result, kuzu_result = await asyncio.gather(
lance_task, kuzu_task, return_exceptions=True
)
return {
"lancedb_result": lance_result,
"kuzu_result": kuzu_result,
"success": not isinstance(lance_result, Exception) and
not isinstance(kuzu_result, Exception)
}Expected Impact: 2-3x improvement in concurrent user capacity
from sqlalchemy.pool import QueuePool
import kuzu
class DatabaseConnectionManager:
def __init__(self, max_connections=10):
self.kuzu_pool = self._create_kuzu_pool(max_connections)
self.lance_pool = self._create_lance_pool(max_connections)
def _create_kuzu_pool(self, max_connections):
"""Create connection pool for KuzuDB"""
return QueuePool(
creator=lambda: kuzu.Connection(kuzu.Database("./kuzu_db")),
max_overflow=5,
pool_size=max_connections,
pool_timeout=30
)
async def execute_kuzu_query(self, query, params=None):
"""Execute query using pooled connection"""
connection = self.kuzu_pool.connect()
try:
result = connection.execute(query, params or {})
return result
finally:
connection.close()Expected Impact: Stable performance under 10+ concurrent users
def create_relationships_batch(connection, relationships, batch_size=100):
"""Create relationships in optimized batches"""
for batch in chunks(relationships, batch_size):
# Build batch query
values = []
for rel in batch:
values.append(f"('{rel.from_id}', '{rel.to_id}')")
query = f"""
UNWIND {values} AS rel
MATCH (s:Sample {{id: rel[0]}}), (v:Variant {{id: rel[1]}})
CREATE (s)-[:HasVariant]->(v)
"""
connection.execute(query)def optimized_batch_insert(connection, data):
"""Use transactions for batch operations"""
with connection.begin_transaction():
try:
for item in data:
connection.execute(item.query, item.params)
connection.commit()
except Exception as e:
connection.rollback()
raise edef optimize_lance_table(table):
"""Optimize LanceDB table configuration"""
table.create_index(
column="embedding",
index_type="IVF_PQ",
num_partitions=256,
num_sub_vectors=96
)def optimized_lance_insert(table, data, batch_size=1000):
"""Optimized batch insertion for LanceDB"""
for batch in chunks(data, batch_size):
# Convert to Arrow format for efficiency
arrow_batch = pa.Table.from_pylist(batch)
table.add(arrow_batch)- Implement embedding caching system
- Add memory-efficient batch processing
- Deploy garbage collection triggers
- Optimize vector dimensions
Expected Outcome: Memory usage below 1GB threshold
- Implement asynchronous embedding generation
- Add Ollama connection pooling
- Optimize embedding model loading
- Add batch embedding requests
Expected Outcome: 3-5x improvement in embedding throughput
- Implement asynchronous dual-database operations
- Add database connection pooling
- Optimize transaction handling
- Add concurrent user monitoring
Expected Outcome: Support for 10+ concurrent users
- Implement batch relationship creation
- Optimize LanceDB indexing
- Add transaction optimization
- Performance monitoring integration
Expected Outcome: 20-30% improvement in database operations
| Metric | Current | Target | Expected |
|---|---|---|---|
| Throughput | 0 variants/sec* | 1,000+ variants/sec | 1,500+ variants/sec |
| Memory Usage | 1,275MB peak | <1,024MB | <800MB |
| Concurrent Users | 3 (with issues) | 10+ | 15+ |
| Search Response | 40.97ms | <100ms | <50ms |
| Embedding Generation | Synchronous | Async batch | 5x faster |
*Under memory pressure
def performance_monitoring():
"""Continuous performance monitoring"""
metrics = {
"memory_usage_mb": get_memory_usage(),
"active_connections": get_active_connections(),
"throughput_per_sec": calculate_throughput(),
"error_rate_percent": calculate_error_rate(),
"response_time_p95": get_p95_response_time()
}
# Alert if thresholds exceeded
if metrics["memory_usage_mb"] > 900:
send_alert("Memory usage approaching limit")
return metrics-
Memory Optimization: Risk of cache misses affecting performance
- Mitigation: Implement adaptive cache sizing
-
Async Implementation: Risk of race conditions in dual-database operations
- Mitigation: Add transaction coordination and rollback mechanisms
-
Connection Pooling: Risk of connection leaks
- Mitigation: Implement connection monitoring and automatic cleanup
- Maintain current synchronous implementation as fallback
- Implement feature flags for gradual rollout
- Add performance regression detection
These optimization recommendations address the critical performance bottlenecks identified in load testing. Implementation of Phase 1 (memory optimizations) is essential for production deployment, while subsequent phases will achieve full-scale performance targets.
Immediate Action Required: Begin Phase 1 implementation to enable production deployment with memory constraints resolved.
Next Review: January 22, 2025
Implementation Status: Ready for Development
Priority: CRITICAL - Required for Production Deployment