Best Practices

This guide outlines best practices for using the memories-dev framework effectively and efficiently.

System Design

Memory Tier Selection

Choose the appropriate memory tier based on your data access patterns:

# Hot memory for frequently accessed data
hot_store = HotMemory(
    max_size_gb=32,
    eviction_policy="lru"
)

# Warm memory for moderately accessed data
warm_store = WarmMemory(
    max_size_gb=128,
    compression_level="medium"
)

# Cold memory for infrequently accessed data
cold_store = ColdMemory(
    storage_type="object_store",
    lifecycle_policy="90_days"
)

Data Organization

Structure your data for optimal retrieval:

# Use hierarchical organization
memory_store.configure_hierarchy(
    temporal_resolution="1d",
    spatial_resolution="100m",
    semantic_clustering=True
)

# Set up efficient indexing
memory_store.configure_indices(
    spatial_index="rtree",
    temporal_index="b_tree",
    semantic_index="hnsw"
)

Performance Optimization

Resource Management

Monitor and manage system resources:

from memories.utils.monitoring import ResourceMonitor

# Set up monitoring
monitor = ResourceMonitor(
    check_interval_seconds=60,
    alert_threshold=0.85  # Alert at 85% usage
)

# Configure automatic scaling
monitor.configure_autoscaling(
    min_instances=2,
    max_instances=10,
    scale_up_threshold=0.75,
    scale_down_threshold=0.25
)

Batch Processing

Optimize batch operations:

# Use batch processing for better performance
with memory_store.batch_context(
    batch_size=1000,
    parallel_workers=4
):
    for item in large_dataset:
        memory_store.process_item(item)

Security

Data Protection

Implement robust security measures:

# Enable encryption at rest
memory_store.enable_encryption(
    algorithm="aes-256-gcm",
    key_rotation_days=30
)

# Configure access control
memory_store.configure_access_control(
    authentication="oauth2",
    authorization="rbac",
    audit_logging=True
)

Error Handling

Implement proper error handling:

from memories.utils.error_handling import retry_with_backoff

@retry_with_backoff(
    max_retries=3,
    initial_delay=1,
    max_delay=10
)
async def process_data(data):
    try:
        result = await memory_store.process(data)
        return result
    except MemoryException as e:
        logger.error(f"Processing failed: {e}")
        raise
    except Exception as e:
        logger.critical(f"Unexpected error: {e}")
        raise

Monitoring and Logging

Set up comprehensive monitoring:

from memories.utils.monitoring import setup_monitoring

# Configure monitoring
setup_monitoring(
    metrics=[
        "memory_usage",
        "query_latency",
        "error_rate",
        "throughput"
    ],
    alerting={
        "error_rate": {
            "threshold": 0.01,
            "window": "5m"
        },
        "latency_p95": {
            "threshold": 500,  # ms
            "window": "1m"
        }
    }
)

Testing

Implement thorough testing:

from memories.testing import MemoryTestCase

class TestMemoryOperations(MemoryTestCase):
    async def test_data_integrity(self):
        # Test data write and read
        data = generate_test_data()
        await self.memory_store.write(data)

        retrieved = await self.memory_store.read(
            data.id,
            consistency="strong"
        )
        self.assertEqual(data, retrieved)

    async def test_performance(self):
        # Test performance under load
        with self.assertQueryTime(max_ms=100):
            await self.memory_store.query(
                complex_query,
                timeout=5
            )

Deployment

Follow deployment best practices:

Environment Configuration - Use environment variables for configuration - Implement proper secrets management - Use configuration validation
Monitoring Setup - Set up comprehensive logging - Configure metric collection - Implement alerting
Backup Strategy - Regular automated backups - Backup validation - Disaster recovery testing
Scaling Strategy - Implement horizontal scaling - Use load balancing - Configure auto-scaling

Documentation

Maintain comprehensive documentation:

Code Documentation - Use docstrings for all public APIs - Include usage examples - Document error conditions
System Documentation - Architecture diagrams - Deployment guides - Troubleshooting guides
Operational Documentation - Runbooks for common issues - Monitoring dashboards - Alert response procedures