Description

Introduction

Modern financial and real-time analytics systems demand ultra-low latency and high-throughput data processing. kdb+, powered by the q language, is designed to efficiently handle massive time-series datasets — but to fully leverage modern multi-core architectures, understanding parallel processing and multithreading is essential.

This training provides a deep dive into how parallelism works in kdb+, including internal multithreading, secondary threads, peach (parallel each), parallel query execution, and distributed workload strategies. Participants will learn how to design high-performance systems that scale across cores and processes while maintaining data integrity and minimal latency.

By the end of this course, attendees will be able to build optimized, multi-core enabled kdb+ solutions suitable for trading systems, real-time analytics, and enterprise-scale data platforms.

Prerequisites

1. Basic understanding of kdb+ architecture
2. Working knowledge of q programming
3. Experience with:
   1. Tables, lists, dictionaries
   2. Select queries and aggregations
4. Familiarity with:
   1. In-memory vs on-disk databases
   2. IPC basics in kdb+
5. Basic understanding of operating system concepts (processes vs threads)
6. kdb+ Fundamentals: Time-Series Database Essentials
7. Advanced q Programming Techniques
8. Inter-Process Communication (IPC) in kdb+

Table of Contents

Module 1: Foundations of Parallelism in kdb+

1. Understanding concurrency vs parallelism
2. Processes vs threads in kdb+
3. kdb+ process model
4. Multi-core architecture basics
5. Threading limitations and design philosophy in kdb+

Module 2: Built-in Multithreading in kdb+

1. Secondary threads in kdb+
2. Configuring -s (number of secondary threads)
3. Thread-safe operations
4. Internal multithreaded primitives
5. Performance characteristics and caveats

Module 3: Parallel Execution with peach

1. Understanding each vs peach
2. When to use peach
3. Practical examples with large datasets
4. Performance benchmarking
5. Memory considerations and pitfalls

Module 4: Multithreaded Queries

1. Parallel select queries
2. Multithreaded aggregations
3. Partition-wise parallelism
4. Attribute usage for performance
5. Real-world query tuning examples

Module 5: Asynchronous & Deferred Execution

1. Async function calls
2. Event-driven execution
3. Non-blocking operations
4. Callback mechanisms
5. Combining async with multithreading

Module 6: Parallel Processing in HDB & RDB

1. Partition-level parallelism
2. Querying large historical databases
3. Real-time + historical hybrid queries
4. Scaling across cores

Module 7: Distributed Parallelism

1. Multi-process architecture
2. Task distribution using IPC
3. Load balancing strategies
4. Map-reduce style processing in kdb+
5. Building scalable distributed analytics systems

Module 8: Performance Tuning & Benchmarking

1. Measuring execution time (\t, \ts)
2. CPU vs memory bottlenecks
3. Avoiding false sharing & contention
4. Optimizing memory usage
5. Practical performance lab

Module 9: Best Practices & Design Patterns

1. Safe parallel design patterns
2. When NOT to use multithreading
3. Debugging concurrent systems
4. Production deployment considerations
5. Case studies from financial systems