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RaptorDB is ServiceNow’s next-generation database engine, designed to power the Now Platform with high-speed, scalable data processing. It’s a hybrid transactional and analytical processing (HTAP) system built on a customized PostgreSQL foundation, optimized for real-time analytics and massive workflow throughput. This means it can process real-time transactions and complex analytics on the same database — without requiring separate OLTP and OLAP systems. Built on a unique architecture, RaptorDB leverages in-memory processing to deliver rapid query responses. This allows developers to access and manipulate data seamlessly without the bottlenecks typically associated with disk-based storage systems. One of its standout features is the ability to support various data types, accommodating everything from text and images to complex objects. This versatility makes it suitable for diverse industries that require dynamic information handling. With a focus on high availability and performance, RaptorDB empowers organizations to harness their data’s full potential while minimizing latency issues often experienced in conventional setups. 

The Evolution: From MariaDB to RaptorDB 

ServiceNow’s transition from MariaDB to RaptorDB marks a significant shift in its backend infrastructure. While MariaDB served well for traditional workloads, the increasing demand for real-time analytics and scalability necessitated a more robust solution. 

RaptorDB addresses these needs by providing: 

• Enhanced Performance: Processing up to 93,257 transactions per minute, a substantial increase from MariaDB’s 35,121.  

• Improved Scalability: Designed to handle growing data volumes without compromising speed. 

• Advanced Analytics: Facilitates complex queries and real-time data analysis within the same platform. 

Why RaptorDB Matters in 2025 

As enterprises increasingly demand real-time insights and scalable operations, traditional databases often fall short. RaptorDB addresses these challenges by: 

• Accelerating Performance: Up to 53% faster transaction times and 27x quicker analytics compared to MariaDB.  

• Enhancing Scalability: Supports 3x more transactional throughput, accommodating growing user bases and workflows.  

• Simplifying Architecture: Combines transactional and analytical processing, eliminating the need for separate systems and reducing complexity.  

How RaptorDB Works – A Deep Dive 

RaptorDB’s architecture is optimized for speed and concurrency. It uses: 

• Columnar storage for fast analytical queries 

•  Row-based storage for transactional operations 

•  In-memory processing for ultra-low latency 

•  Smart indexing to reduce query time 

This hybrid model allows it to process transactions and run analytics simultaneously, without performance degradation.  

1.HTAP Architecture: One Engine for Transactions and Analytics Most legacy databases force enterprises to maintain: 

• An OLTP (Online Transaction Processing) database for day-to-day operations (e.g., service requests, workflows). 
• An OLAP (Online Analytical Processing) system for reports and analytics (e.g., Power BI, Tableau). 

RaptorDB replaces both with a single HTAP system. 

How is it implemented? Transactional data (OLTP) is stored in row format for fast reads/writes. 

Analytical queries (OLAP) are processed through columnar indexes that are auto created, compressed, and optimized for performance. 

One source of truth: No ETL jobs or data duplication is needed. 

This hybrid model significantly reduces latency, avoids data silos, and eliminates the need for secondary BI tools or data lakes for reporting. 

2.Columnar Indexing & Auto-Compression To support fast analytics: 

RaptorDB creates column-store indexes automatically. 

These indexes are compressed and memory-optimized, enabling rapid scans across millions of records. 

Benefits 

• Reduces data size (helping with cost and speed). 
• Allows sub-second query performance. 
• Supports ad-hoc analytics, dashboards, and KPIs within ServiceNow natively. 

3.Massively Parallel Processing (MPP) RaptorDB executes queries using a parallel execution model, distributing workloads across multiple CPU cores. 

Why it matters?  

• Improves performance for large queries. 
• Reduces time to insight from hours/minutes to seconds. 
• Ensures the system stays responsive even under high load. 

4.Smart Query Optimization RaptorDB includes a customized PostgreSQL query planner that  

• Detects query intent.  
• Routes workloads to either row-based (transactional) or column-based (analytical) execution paths. 
• Prunes irrelevant data early in the query pipeline, reducing compute time. 

 It’s like having an AI-powered traffic cop directing the best route for each query in real time. 

5.Write-Ahead Logging (WAL) and Data Consistency RaptorDB uses write-ahead logging, a standard technique in PostgreSQL, to ensure: 

• ACID compliance (Atomicity, Consistency, Isolation, Durability). 
• Instant recovery from failure or crashes. 
• Consistency across distributed nodes (important for multi-region deployments). 

6.Elastic Scalability & Performance Tuning RaptorDB is vertically and horizontally scalable, which means: 

• You can add more CPU and memory as your data and workloads grow. 
• It supports multi-node clusters for enterprise-level resilience and scalability. 
• Admins can tune performance using: 
• Workload management rules. 
• Priority settings for real-time queries vs. background jobs. 
• Integration with ServiceNow performance monitoring tools. 

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