Description

Introduction

The Advanced q Programming Techniques training is designed for professionals who already possess foundational knowledge of q language and want to master high-performance data manipulation, functional programming paradigms, and advanced analytics in kdb+.

This course focuses on writing optimized, scalable, and production-ready q code. Participants will explore advanced querying strategies, memory-efficient operations, performance tuning, parallel processing, and real-world time-series data engineering use cases.

By the end of this training, learners will be able to design high-performance data pipelines, implement advanced analytical computations, and optimize large-scale time-series workloads.

Prerequisites

1. Basic to intermediate knowledge of q language syntax
2. Understanding of data types, lists, dictionaries, and tables in q
3. Experience with select, update, delete, and joins
4. Familiarity with in-memory and on-disk tables
5. Basic understanding of time-series data concepts
6. Exposure to kdb+ architecture is recommended

Table of Contents

Module 1: Deep Dive into q Internals

1. q execution model and evaluation order
2. Memory management and data representation
3. Understanding columnar storage
4. Symbol internals and performance considerations

Module 2: Advanced Functional Programming in q

1. Higher-order functions
2. Adverbs and derived functions in depth
3. Function projection and partial application
4. Recursion and tail-call optimization
5. Dynamic function generation

Module 3: Advanced Data Manipulation Techniques

1. Complex joins and as-of joins optimization
2. Window joins and temporal queries
3. Nested data structures
4. Working with keyed tables efficiently
5. Advanced aggregation patterns

Module 4: Performance Optimization & Tuning

1. Profiling q code
2. Vectorization best practices
3. Minimizing memory overhead
4. Avoiding common performance pitfalls
5. Efficient use of attributes (sorted, grouped, parted)
6. Query optimization strategies

Module 5: Working with Large-Scale & HDB Architectures

1. Partitioned databases (HDB)
2. Splayed tables
3. Incremental loading techniques
4. Compression strategies
5. Data lifecycle management

Module 6: Parallel & Distributed Processing

1. Multithreaded primitives
2. Parallel processing techniques
3. Load balancing strategies
4. Inter-process communication (IPC)
5. Scaling q applications

Module 7: Advanced Time-Series Analytics

1. Event-based analytics
2. Tick data processing
3. Real-time streaming patterns
4. Complex event processing logic
5. Rolling and moving analytics optimization

Module 8: Production-Ready q Development

1. Error handling and debugging techniques
2. Logging frameworks
3. Code modularization and namespaces
4. Testing strategies in q
5. Deployment best practices

Module 9: Real-World Case Studies

1. High-frequency trading analytics
2. Risk calculations and portfolio aggregation
3. Market surveillance use cases
4. IoT and telemetry time-series analysis