MySQL HeatWave: The Fully Managed Multi-Cloud Database with Integrated AI

MySQL HeatWave represents a revolutionary approach to database management, combining transaction processing, real-time analytics, machine learning, and generative AI in a single, fully managed service. Developed and supported by the MySQL team at Oracle, HeatWave transforms how organizations build modern data applications across multiple cloud platforms.

What is MySQL HeatWave?

Unified Database Platform

MySQL HeatWave is a fully managed database service that runs MySQL, providing a single platform that eliminates the complexity of managing separate systems for different workloads.

Integrated Capabilities:
MySQL HeatWave is the only MySQL database service to combine Online Transaction Processing (OLTP), Online Analytical Processing (OLAP), Machine Learning (ML), and Generative AI workloads within a single MySQL Database—without requiring ETL duplication or separate analytics databases.

Development and Support:
Developed and supported by the MySQL team at Oracle, ensuring deep MySQL expertise, community alignment, and continuous innovation.

Multi-Cloud Availability

Supported Cloud Platforms:

• Oracle Cloud Infrastructure (OCI): Native deployment with full feature set
• Amazon Web Services (AWS): Complete HeatWave capabilities on AWS
• Microsoft Azure: Integrated with Azure cloud services

Multi-Cloud Benefits:

• Deploy where your applications and data reside
• Avoid cloud vendor lock-in
• Consistent experience across cloud providers
• Flexible hybrid and multi-cloud architectures

Key Advantages Over On-Premises MySQL

Rapid Provisioning and Deployment

Infrastructure Automation:
Rapid provisioning of database systems and all underlying infrastructure eliminates weeks of manual setup and configuration.

Deployment Speed:

• Database instances available in minutes
• Automatic compute, storage, and network configuration
• Pre-configured high availability and security
• Instant scaling without downtime

Automated Management Operations

Comprehensive Automation:
Automated tasks including patching, upgrading, and backups free database administrators from routine maintenance.

Managed Operations:

• Automatic security patches and updates
• Zero-downtime upgrades to new versions
• Continuous automated backups
• Disaster recovery configuration

Advanced Data Protection

Security Features:
Comprehensive data protection including encryption, firewall, and data masking ensures enterprise-grade security.

Protection Layers:

• Encryption: TLS/SSL for data in transit, encryption at rest
• Firewall: Network-level access controls
• Data Masking: Sensitive data protection for non-production environments
• Audit Logging: Comprehensive activity tracking

Always Current Features

Latest Enterprise Features:
Always up-to-date with the latest enterprise features and security enhancements without manual intervention.

Continuous Innovation:

• Quarterly feature releases
• Security updates applied automatically
• Performance improvements continuously deployed
• New AI and ML capabilities added regularly

Shared Responsibility Model

Customer Responsibilities

Application-Level Management:

1. Deployment Initiation:
Customers initiate the deployment of MySQL database systems through the cloud console or APIs.

2. User Account Management:
Create and manage database user accounts, roles, and privileges according to application requirements.

3. Database Object Creation:
Design and create database objects including schemas, tables, indexes, views, and stored procedures.

4. Data Management:
Load initial data, manage ongoing data operations, and maintain data quality and integrity.

Oracle Responsibilities

Infrastructure and Platform Management:

1. Infrastructure Provisioning:
Oracle handles provisioning of MySQL database systems and all underlying resources including compute, storage, and networking.

2. Patching and Updates:
Oracle patches the database software and operating system automatically with zero downtime.

3. Automated Backups:
Oracle performs automatic backups according to configured retention policies.

4. Disaster Recovery:
Oracle restores database systems from backups when needed and maintains high availability infrastructure.

Extreme Performance

100x Faster Than Standard MySQL

Query Acceleration:
MySQL HeatWave is extremely performant, delivering 100 times faster performance than standard MySQL database systems without HeatWave for analytical queries.

Performance Architecture:

• In-memory columnar processing
• Massively parallel execution
• Intelligent query optimization
• Hardware acceleration

Benchmark Results:
Independent benchmarks demonstrate HeatWave's superior performance compared to cloud data warehouse alternatives including Snowflake, Databricks, and Google BigQuery.

Application Compatibility

Zero Changes Required:
All existing MySQL applications work without any changes, ensuring seamless migration and adoption.

Compatibility Features:

• Standard MySQL wire protocol
• MySQL SQL dialect support
• Existing driver compatibility
• Familiar tools and interfaces

HeatWave Components and Capabilities

HeatWave MySQL: Transaction Processing

Enterprise-Grade OLTP:
HeatWave MySQL enables OLTP workloads to leverage Enterprise Edition features of MySQL Database and delivers unique capabilities such as Auto Shape Prediction, Auto Thread Pooling, Autopilot Indexing, and in-database JavaScript.

OLTP Enhancements:

• Auto Shape Prediction: Intelligent right-sizing recommendations
• Auto Thread Pooling: Optimized connection management
• Autopilot Indexing: Automatic index recommendations
• Native JavaScript: Write stored procedures in JavaScript
• Bulk Ingest: Load data up to 5X faster

New in MySQL HeatWave 9.4:

• Hypergraph Optimizer: True cost-based join optimization for complex queries
• OCI Ops Insights Integration: ML-based performance analytics and capacity planning
• Enhanced Concurrency: Process multiple queries simultaneously across sessions

HeatWave Lakehouse: Object Storage Analytics

Query Data in Place:
HeatWave Lakehouse enables querying structured, semi-structured, and unstructured data directly in object storage without data movement.

Supported Storage:

• Amazon S3
• Oracle Object Storage
• Azure Blob Storage
• Amazon Redshift
• Amazon Aurora

Supported File Formats:

• CSV: Comma-separated values
• JSON: JavaScript Object Notation
• XML: Extensible Markup Language
• Avro: Row-based binary format
• Parquet: Columnar storage format
• ORC: Optimized Row Columnar

Lakehouse Features:

• Query object storage at database speed
• Write results back to object storage
• Automatic change propagation from object storage
• MapReduce application support
• Guided Load for optimized data loading

HeatWave GenAI: Integrated Generative AI

In-Database Large Language Models:
MySQL HeatWave GenAI provides integrated, automated, and secure generative AI with in-database large language models (LLMs), an automated vector store, scale-out vector processing, and natural language conversation capabilities.

GenAI Features:

In-Database LLMs:
Use large language models running directly inside the database without external API calls or data movement.

Supported In-Database Models (as of MySQL 9.4):

• llama3.2-3b-instruct-v1: Default LLM, multilingual support (8 languages)
• llama3.2-1b-instruct-v1: Lightweight model, multilingual support
• llama3.1-8b-instruct-v1: Advanced model, multilingual support
• mistral-7b-instruct-v3: Efficient model, multilingual support

External LLM Support:

• OCI Generative AI Service: Access Cohere and Meta models on OCI
• OCI Dedicated Clusters: Enhanced flexibility and capabilities
• Amazon Bedrock: Access foundation models on AWS
• Vision Language Models (VLMs): Process vision-language tasks

HeatWave Vector Store:
MySQL HeatWave Vector Store houses proprietary documents in various formats, acting as the knowledge base for Retrieval-Augmented Generation (RAG) to deliver more accurate and contextually relevant answers.

Vector Store Features:

• Automated Embedding Generation: No manual vector creation required
• 27 Language Support: Multilingual document processing
• OCR Support: Optical character recognition for images and PDFs
• Auto Parallel Load: Efficient document ingestion
• Automatic Updates: Vector store synchronization with source changes

Performance Advantages:
For similarity search, MySQL HeatWave GenAI is 15X faster than Databricks, 18X faster than Google BigQuery, and 30X faster than Snowflake, while being less expensive.

Natural Language Conversations:
Have contextual conversations in natural language with HeatWave Chat, a chatbot that enables multiple follow-up questions about specific topics within a single session.

NL2ML - Natural Language to Machine Learning:
MySQL HeatWave now supports NL2ML, providing an intuitive natural-language interface to machine learning, making ML accessible without coding expertise.

NL2SQL - Natural Language to SQL:
Generate SQL queries from natural-language statements using the ML_NL_SQL routine, making database interactions easier for non-technical users.

Document Summarization:
Advanced document summarization capabilities for unstructured documents stored in Object Storage, enabling quick insights from large document collections.

HeatWave AutoML: Automated Machine Learning

Simplified Machine Learning:
HeatWave AutoML enables advanced machine learning capabilities directly within MySQL without data movement, separate ML services, or AI expertise.

AutoML Capabilities:

• Automated Model Building: Algorithm selection, feature engineering, hyperparameter tuning
• Model Training: In-database training on MySQL data
• Model Deployment: Seamless deployment for predictions
• Model Explanation: Understand model decisions and feature importance
• Hyperparameter Recording: Enhanced transparency and reproducibility

New AutoML Features:

• Enhanced Data Preparation: NL2ML-powered data preparation workflows
• TRAIN_TEST_SPLIT: Automated dataset splitting for training and testing
• Improved Concurrency: Process multiple ML queries simultaneously
• Enhanced Performance: Faster predictions with ML_PREDICT_TABLE and ML_EXPLAIN_TABLE

Use Cases:

• Predictive maintenance
• Fraud detection
• Personalized recommendations
• Customer churn prediction
• Demand forecasting

HeatWave Autopilot: Intelligent Automation

Automated Performance at Scale:
HeatWave Autopilot automates processes to improve performance at scale, handling complex optimization tasks automatically.

Autopilot Capabilities:

• Auto Provisioning: Intelligent resource sizing
• Auto Parallel Load: Optimized data loading
• Auto Query Plan Improvement: Automatic query optimization
• Auto Scheduling: Intelligent workload scheduling
• Auto Encoding: Automatic data compression
• Auto Data Placement: Optimal data distribution
• Auto Change Propagation: Sync changes from object storage
• Auto Indexing: Automatic index recommendations and management

Getting Started with MySQL HeatWave

Provisioning a HeatWave Instance

1. Access Cloud Console:
Navigate to MySQL HeatWave service in OCI, AWS, or Azure console

2. Create DB System:

• Select cloud region
• Choose compute shape (HeatWave.Free, HeatWave.32GB, HeatWave.512GB)
• Configure storage
• Set up networking (VCN, subnet)
• Configure backups

3. Enable HeatWave Cluster:

• Specify number of HeatWave nodes
• Select appropriate shape for workload
• Enable AutoML and GenAI features

4. Configure Security:

• Set up users and privileges
• Configure network access controls
• Enable encryption
• Set up audit logging

Loading Data

From MySQL Database:

-- Load table into HeatWave
ALTER TABLE sales SECONDARY_LOAD;

-- Load all tables in schema
CALL sys.heatwave_load(JSON_OBJECT('schema', 'sales_db'));

From Object Storage (Lakehouse):

-- Create external table
CREATE TABLE sales_external
ENGINE=RAPID
COMMENT='lakehouse:parquet'
AS SELECT * FROM performance_schema.file_locations
WHERE path = 's3://bucket/sales/*.parquet';

-- Load into HeatWave
ALTER TABLE sales_external SECONDARY_LOAD;

Using HeatWave GenAI

Generate Content:

-- Generate text content
SELECT ML_GENERATE(
    'Write a product description for a smartphone',
    JSON_OBJECT('task', 'generation')
);

Semantic Search with Vector Store:

-- Load documents into vector store
CALL sys.VECTOR_STORE_LOAD(
    's3://bucket/documents/',
    JSON_OBJECT('table_name', 'my_documents')
);

-- Perform RAG query
SELECT ML_RAG(
    'What are our return policies?',
    'my_documents',
    JSON_OBJECT('top_k', 5)
);

Natural Language Conversation:

-- Chat with your data
CALL sys.HEATWAVE_CHAT(
    'Show me top selling products last quarter',
    JSON_OBJECT('store_name', 'sales_vectors')
);

Using HeatWave AutoML

Train Model:

-- Train predictive model
CALL sys.ML_TRAIN(
    'customer_data',
    'churn_prediction',
    'churn_flag',
    JSON_OBJECT('task', 'classification')
);

Make Predictions:

-- Score new data
CALL sys.ML_PREDICT_TABLE(
    'customer_data',
    'churn_prediction',
    'predictions',
    NULL
);

Real-World Success Stories

SmarterD: Fast-Tracked Development

SmarterD fast-tracked its roadmap by 12 months and went from development to production in only one month using Oracle MySQL HeatWave GenAI.

NTT Solmare: Revenue Growth

NTT Solmare improved marketing campaigns and uncovered new revenue opportunities using HeatWave analytics and machine learning capabilities.

Fortune 100 Applications

Fortune 100 companies use HeatWave AutoML for predictive maintenance, fraud detection, and personalized recommendations without machine learning expertise while keeping data secure within the database.

Best Practices

Performance Optimization

• Enable HeatWave cluster for analytical workloads
• Use AUTO SECONDARY_LOAD for automatic data loading
• Leverage Autopilot recommendations
• Monitor query performance with Performance Schema
• Use bulk ingest for large data loads

Cost Management

• Right-size compute shapes using Auto Shape Prediction
• Use HeatWave only when needed (start/stop capability)
• Leverage object storage for cold data
• Monitor usage with OCI Ops Insights integration
• Take advantage of HeatWave.Free tier for development

Security

• Enable encryption at rest and in transit
• Configure network access controls appropriately
• Use principle of least privilege for user accounts
• Enable audit logging for compliance
• Regularly review security configurations

GenAI Applications

• Start with in-database LLMs for prototyping
• Use RAG with Vector Store for domain-specific accuracy
• Leverage Lakehouse Navigator for cost-effective searches
• Monitor LLM usage and response quality
• Implement guardrails for production deployments

Conclusion

MySQL HeatWave represents a paradigm shift in database technology, delivering a unified platform for transaction processing, analytics, machine learning, and generative AI across multiple clouds. Developed and supported by the MySQL team at Oracle, HeatWave provides 100x faster analytics performance than standard MySQL while maintaining complete application compatibility.

Key Value Propositions:

Unified Platform:

• Single service for OLTP, OLAP, ML, and GenAI
• No ETL between separate systems
• Simplified architecture and operations
• Reduced total cost of ownership

Multi-Cloud Flexibility:

• Deploy on OCI, AWS, or Azure
• Consistent experience across clouds
• Avoid vendor lock-in
• Support hybrid architectures

Integrated AI:

• In-database LLMs without data movement
• Automated vector store for RAG
• AutoML without AI expertise
• Natural language interfaces (NL2ML, NL2SQL)

Extreme Performance:

• 100x faster than MySQL without HeatWave
• Outperforms cloud data warehouses
• 15-30X faster vector search than competitors
• Real-time analytics on transactional data

Fully Managed:

• Automated provisioning, patching, backups
• Zero-downtime upgrades
• Intelligent automation with Autopilot
• Enterprise-grade security and compliance

Whether building transaction-heavy applications, performing real-time analytics, developing machine learning models, or creating generative AI applications, MySQL HeatWave provides the comprehensive, integrated, and automated platform necessary for modern data-driven applications—all while maintaining the simplicity, familiarity, and compatibility of MySQL.