Description

Introduction

kdb+ is a high-performance, column-oriented database widely used for time-series analytics in capital markets, telecom, IoT, and energy sectors. Its powerful query language, q, enables fast data manipulation, real-time processing, and efficient storage management.

This training focuses on mastering in-memory and on-disk database management in kdb+, covering architecture design, performance optimization, partitioning strategies, and best practices for scalable deployments. Participants will gain hands-on experience in designing, maintaining, and optimizing high-throughput kdb+ environments for both real-time and historical data processing.

Prerequisites

1. Basic knowledge of kdb+ and q programming
2. Understanding of database concepts (tables, schemas, indexing)
3. Familiarity with Linux/Unix command line (recommended)
4. Basic understanding of time-series data concepts

Table of Contents

Module 1: kdb+ Architecture Fundamentals

1. Overview of kdb+ architecture
2. In-memory vs on-disk databases
3. Real-time (RDB), Historical (HDB), and Intraday (IDB) concepts
4. Data lifecycle in kdb+

Module 2: In-Memory Database Management

1. Creating and managing in-memory tables
2. Splayed vs unsplayed tables
3. Real-time data ingestion techniques
4. Data updates, inserts, and deletes
5. Memory management and optimization
6. Garbage collection and workspace control

Module 3: On-Disk Database (HDB) Management

1. Creating partitioned databases
2. Date-based partitioning strategies
3. Columnar storage structure
4. Symbol enumeration and sym files
5. Loading and querying historical data efficiently
6. Schema management and metadata handling

Module 4: Data Partitioning & Storage Optimization

1. Partitioning strategies (date, sym, custom)
2. Compression techniques
3. Column ordering for performance
4. Disk I/O optimization
5. Storage best practices

Module 5: Database Performance Tuning

1. Query performance analysis
2. Attribute usage (sorted, parted, grouped)
3. Parallel processing and multithreading
4. Indexing techniques
5. Benchmarking and profiling

Module 6: Data Maintenance & Administration

1. End-of-day (EOD) processing
2. Data rollups and archiving
3. Backup and recovery strategies
4. Data validation and consistency checks
5. Managing schema changes

Module 7: Real-Time + Historical Integration

1. RDB to HDB data flow
2. Tick architecture overview
3. Data consolidation process
4. Handling late or corrected data
5. Replay and recovery mechanisms

Module 8: Deployment & Production Best Practices

1. Production architecture design
2. Scaling strategies
3. Monitoring and logging
4. High availability considerations
5. Security and access control

Module 9: Hands-On Labs

1. Building an in-memory real-time system
2. Creating a partitioned HDB
3. Performance tuning exercises
4. End-to-end mini project implementation