HeliosDB Tier 2/3 AI/ML Features User Guide

1. Neural Query Planner

Description

A deep learning-based query optimizer that uses a Transformer encoder + Graph Neural Network architecture for plan generation.

Key Features

Deep Learning Query Optimization: Transformer + GNN architecture
Real-time Inference: Sub-5ms latency plan generation
Learned Cost Model: Neural network-based cost estimation
Beam Search Exploration: Guided plan search with neural heuristics
ONNX Export: Production deployment via ONNX

Configuration Options

Option	Default	Description
`inference_batch_size`	64	Batch size for inference
`beam_width`	5	Beam search width
`model_type`	”transformer-gnn”	Model architecture
`learning_rate`	0.001	Training learning rate
`max_plan_depth`	20	Maximum plan tree depth

2. Schema AI (Generative Schema Designer)

Description

AI-powered schema design from natural language descriptions. Converts NL requirements to optimized database schemas with automatic normalization.

Key Features

NL-to-ERD: Natural language to Entity-Relationship Diagrams
Automatic Normalization: 1NF to BCNF normalization
Index Recommendations: AI-suggested indexes
Schema Evolution: Intelligent migration generation
Multi-dialect Support: PostgreSQL, MySQL, SQLite DDL generation

Schema Generation Options

Option	Values	Description
`target_normalization`	1NF, 2NF, 3NF, BCNF	Normalization level
`generate_indexes`	true/false	Auto-generate indexes
`target_dialect`	postgresql, mysql, sqlite	SQL dialect
`include_constraints`	true/false	Generate constraints

3. RL-Based Intelligent Cache

Description

Reinforcement learning-based cache eviction using Deep Q-Network (DQN). Learns optimal caching policies from access patterns.

Key Features

DQN-based Eviction: Deep Q-Network for policy learning
Contextual Features: Uses query patterns, time-of-day, workload type
Adaptive Learning: Continuous improvement from production traffic
Multi-tier Support: Coordinates L1/L2/L3 cache hierarchies
Workload-aware: Different policies for OLTP vs OLAP

Configuration

Option	Default	Description
`capacity_mb`	1024	Cache size in MB
`learning_rate`	0.001	DQN learning rate
`discount_factor`	0.99	Future reward discount
`exploration_rate`	0.1	Epsilon for exploration
`update_frequency`	100	Steps between updates

4. Multi-Armed Bandit Load Balancer

Description

LinUCB contextual bandit algorithm for intelligent request routing. Balances load while optimizing for latency and throughput.

Key Features

Contextual Bandits: LinUCB algorithm with context features
Latency Optimization: Routes to fastest available node
Adaptive Exploration: Balances exploration vs exploitation
Health-aware: Considers node health in routing decisions
Multi-objective: Optimizes latency, throughput, and fairness

5. Anomaly Detection

Description

Multi-algorithm anomaly detection for database metrics, query patterns, and data quality monitoring.

Supported Algorithms

Algorithm	Use Case	Strengths
Isolation Forest	General	Fast, handles high dimensions
Local Outlier Factor	Density-based	Good for clusters
DBSCAN	Clustering	Finds arbitrary shapes
One-Class SVM	Novelty detection	Works with limited data
LSTM Autoencoder	Time-series	Captures temporal patterns
Statistical (Z-score)	Simple metrics	Interpretable, fast

Algorithm Selection Guide

Query latency monitoring → Isolation Forest or LSTM
Connection patterns → DBSCAN
Data quality checks → Statistical (Z-score)
Novel query detection → One-Class SVM
General monitoring → Ensemble (multiple algorithms)

6. Time-Series Forecasting

Description

Comprehensive time-series forecasting for capacity planning, workload prediction, and trend analysis.

Supported Algorithms

Algorithm	Best For	Accuracy
ARIMA	Stationary data	High
Prophet	Seasonal + holidays	High
LSTM	Complex patterns	Very High
Exponential Smoothing	Simple trends	Medium
Ensemble	General	Highest

7. AutoML Tuning

Description

Automatic database configuration tuning using Bayesian Optimization and Genetic Algorithms.

Key Features

Bayesian Optimization: Efficient hyperparameter search
Genetic Algorithms: Evolves optimal configurations
Safe Exploration: Constraints to prevent bad configs
A/B Testing: Validates improvements before rollout
Workload-aware: Different configs for different workloads

8. Auto-Index

Description

ML-based automatic index recommendation and management based on workload analysis.

Key Features

Workload Analysis: Learns from query patterns
Index Recommendations: Suggests optimal indexes
Impact Prediction: Estimates performance improvement
Automatic Creation: Creates indexes during low-traffic periods
Index Consolidation: Removes redundant indexes

9. Probabilistic Data Structures

Description

Memory-efficient probabilistic data structures for approximate queries.

Supported Structures

Structure	Use Case	Space	Error Rate
Bloom Filter	Membership testing	O(n) bits	Configurable FP
Count-Min Sketch	Frequency estimation	O(1)	Configurable
HyperLogLog	Cardinality estimation	~1.5KB	~2%
T-Digest	Percentile estimation	O(compression)	~1%
Cuckoo Filter	Membership + delete	O(n) bits	Configurable
MinHash	Similarity estimation	O(k)	1/√k
SimHash	Near-duplicate detection	O(1)	Configurable

Integration with HeliosDB

Configuration

[ai.neural_planner]
enabled = true
model_path = "models/query_planner.onnx"
inference_timeout_ms = 5
fallback_to_traditional = true

[ai.schema_ai]
enabled = true
default_normalization = "3NF"

[cache.rl]
enabled = true
capacity_mb = 2048
learning_enabled = true

[cluster.mab_balancer]
enabled = true
alpha = 0.5
update_frequency = 100

[monitoring.anomaly_detection]
enabled = true
algorithm = "ensemble"
alert_threshold = 0.8

[monitoring.forecasting]
enabled = true
algorithm = "auto"
horizon_hours = 24

[tuning.automl]
enabled = true
maintenance_window = "02:00-05:00"
require_approval = true

[indexing.auto_index]
enabled = true
min_improvement_threshold = 0.10
max_indexes_per_table = 10

Best Practices

1. Start with Defaults

All packages have sensible defaults. Start with defaults and tune based on your workload.

2. Monitor Before Enabling

Monitor your workload characteristics before enabling AI features:

Query patterns and frequency
Data distribution
Peak vs off-peak traffic

3. Use Gradual Rollout

Enable features gradually:

Start with read-only features (anomaly detection, forecasting)
Enable learning features (neural planner, RL cache)
Enable write features (auto-index, automl tuning)

4. Set Safety Constraints

Always configure safety constraints for features that modify behavior:

Max latency thresholds
Rollback policies
Approval requirements

5. Review Recommendations

AI recommendations should be reviewed before automatic application, especially for:

Index creation
Configuration changes
Schema modifications

Troubleshooting

Common Issues

Neural Planner slow inference

Check model file is loaded (not re-loading per query)
Reduce beam width if latency exceeds 5ms
Use ONNX runtime optimizations

RL Cache low hit rate

Allow more training time (10,000+ accesses)
Check exploration rate isn’t too high
Verify feature extraction includes relevant context

Anomaly Detection false positives

Increase contamination parameter
Use ensemble mode for higher precision
Train on longer historical period

AutoML Tuning not improving

Expand search space ranges
Increase iteration count
Check constraint feasibility

Support

Documentation: https://heliosdb.com/docs/full/
Support: support@heliosdb.com
Community: https://discord.gg/yTykuUrFXc

This guide covers HeliosDB v7.1.2 Tier 2/3 AI/ML features.