Developer's Catalog

In-Memory Database

SAP HANA

Overview

SAP HANA (High-performance ANalytic Appliance) is an in-memory, column-oriented, multi-model database management system developed by SAP SE. By storing data in memory rather than on disk, SAP HANA achieves processing speeds up to 10,000 times faster than traditional disk-based databases, providing a unified platform that combines real-time analytics and transactional processing in a single system.

Details

SAP HANA employs a revolutionary architecture that unifies OLAP (Online Analytical Processing) and OLTP (Online Transactional Processing) in a single system. The column-oriented in-memory storage enables high-speed processing of massive datasets and includes built-in advanced analytics capabilities such as predictive analytics, spatial data processing, text analytics, streaming analytics, and graph data processing.

As of 2025, SAP HANA Cloud offers cloud-native capabilities supporting diverse data models (relational, document, geospatial, knowledge graph, vector, time series). Enhanced AI and machine learning integration positions it as a next-generation platform for enterprise applications, enabling intelligent applications that leverage generative AI, context awareness, and secure connections to vital business data.

Pros and Cons

Pros

• Ultra-High Performance: Up to 10,000x speed improvement through in-memory technology
• Real-time Analytics: Eliminates batch processing with real-time data processing
• Unified Platform: Single system operation through OLAP/OLTP integration
• Advanced Analytics: Built-in machine learning, predictive analytics, and AI capabilities
• Multi-Model Support: Integrated support for multiple data models
• Flexible Deployment: On-premises, cloud, and hybrid deployment options
• Dynamic Tiering: Automatic data placement optimization based on access patterns

Cons

• High Cost: Expensive licensing fees and memory requirements for implementation
• Specialized Expertise: Requires advanced technical skills for operation and management
• Vendor Lock-in: Risk of dependency on SAP ecosystem
• Memory Dependency: Constraints due to large memory requirements
• Migration Complexity: Complex migration from existing systems

Reference Pages

• SAP HANA Official Site
• SAP HANA Cloud
• SAP HANA Documentation

Code Examples

Database Connection

-- Connection via SAP HANA Studio or SAP HANA Cockpit
-- JDBC URL example
jdbc:sap://hostname:30015/

Table Creation and Optimization

-- Create column store table
CREATE COLUMN TABLE CUSTOMER (
    CUSTOMER_ID INT PRIMARY KEY,
    NAME NVARCHAR(100),
    EMAIL NVARCHAR(100),
    CREATED_DATE DATE
);

-- Create index
CREATE INDEX IDX_CUSTOMER_EMAIL ON CUSTOMER(EMAIL);

-- Partition configuration
CREATE COLUMN TABLE SALES (
    SALE_ID BIGINT,
    CUSTOMER_ID INT,
    AMOUNT DECIMAL(10,2),
    SALE_DATE DATE
) PARTITION BY RANGE (SALE_DATE) (
    PARTITION P2024 VALUES < '2025-01-01',
    PARTITION P2025 VALUES < '2026-01-01'
);

Advanced Analytics Queries

-- Analytics using window functions
SELECT 
    CUSTOMER_ID,
    AMOUNT,
    SALE_DATE,
    AVG(AMOUNT) OVER (PARTITION BY CUSTOMER_ID ORDER BY SALE_DATE 
                      ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) AS moving_avg
FROM SALES
ORDER BY CUSTOMER_ID, SALE_DATE;

-- Machine learning predictions
CREATE MODEL sales_forecast
USING PAL_ARIMA
FROM (
    SELECT SALE_DATE, SUM(AMOUNT) as total_sales
    FROM SALES
    GROUP BY SALE_DATE
    ORDER BY SALE_DATE
)
WITH forecast_length = 30;

JSON and Graph Data Processing

-- JSON data processing
CREATE COLUMN TABLE customer_profile (
    customer_id INT,
    profile NCLOB
);

-- Insert and query JSON data
INSERT INTO customer_profile VALUES (
    1, 
    '{"name": "John Doe", "age": 30, "preferences": ["tech", "sports"]}'
);

SELECT customer_id, JSON_VALUE(profile, '$.name') as customer_name
FROM customer_profile
WHERE JSON_VALUE(profile, '$.age') > 25;

-- Graph data processing
CREATE GRAPH WORKSPACE social_network
    EDGE TABLE connections (
        KEY (from_user, to_user)
        SOURCE KEY (from_user) REFERENCES users (user_id)
        TARGET KEY (to_user) REFERENCES users (user_id)
    )
    VERTEX TABLE users KEY (user_id);

Time Series Data Analysis

-- Time series data aggregation
SELECT 
    SERIES_GENERATE_TIMESTAMP('INTERVAL 1 DAY', '2025-01-01', '2025-12-31') as date_series,
    COALESCE(daily_sales.total, 0) as sales_amount
FROM SERIES_GENERATE_TIMESTAMP('INTERVAL 1 DAY', '2025-01-01', '2025-12-31') as dates
LEFT JOIN (
    SELECT SALE_DATE, SUM(AMOUNT) as total
    FROM SALES
    WHERE SALE_DATE BETWEEN '2025-01-01' AND '2025-12-31'
    GROUP BY SALE_DATE
) daily_sales ON dates.GENERATED_PERIOD_START = daily_sales.SALE_DATE;

Performance Monitoring

-- System monitoring queries
SELECT 
    DATABASE_NAME,
    USED_MEMORY,
    ALLOCATED_MEMORY,
    PEAK_MEMORY
FROM M_DATABASE_MEMORY;

-- Execution plan analysis
EXPLAIN PLAN FOR
SELECT c.NAME, SUM(s.AMOUNT)
FROM CUSTOMER c
JOIN SALES s ON c.CUSTOMER_ID = s.CUSTOMER_ID
WHERE s.SALE_DATE >= '2025-01-01'
GROUP BY c.NAME;

Procedures and Functions

-- Create stored procedure
CREATE PROCEDURE get_top_customers(
    IN top_count INT,
    OUT customer_list TABLE (customer_id INT, total_sales DECIMAL(12,2))
)
AS
BEGIN
    customer_list = SELECT 
        c.CUSTOMER_ID,
        SUM(s.AMOUNT) as total_sales
    FROM CUSTOMER c
    JOIN SALES s ON c.CUSTOMER_ID = s.CUSTOMER_ID
    GROUP BY c.CUSTOMER_ID
    ORDER BY total_sales DESC
    LIMIT :top_count;
END;

-- Execute procedure
CALL get_top_customers(10, ?);