Description

Introduction

This training provides a deep dive into the core building blocks of the q language, the powerful vector programming language of kdb+. Understanding data types and functional programming concepts in q is essential for writing efficient, high-performance, and scalable analytics—especially in time-series and real-time environments.

Participants will learn how q handles atomic and complex data types, lists, dictionaries, tables, and keyed tables, followed by mastering q’s functional paradigm including lambdas, projections, adverbs, and higher-order functions. The course emphasizes practical exercises and real-world use cases relevant to financial data analytics, large-scale data processing, and streaming environments.

By the end of this training, learners will confidently manipulate data structures and write elegant, concise, and high-performance functional code in q.

Prerequisites

1. Basic understanding of programming concepts
2. Familiarity with databases or data structures
3. Prior exposure to q fundamentals (recommended but not mandatory)
4. Basic knowledge of command-line usage
5. Understanding of vectorized programming concepts (helpful)

Table of Contents

Module 1: Overview of q & kdb+ Architecture

1. Introduction to q language
2. q console and development environment
3. Relationship between q and k
4. Overview of kdb+ data model

Module 2: q Data Types – Fundamentals

1. Atomic data types
   1. Boolean
   2. Byte
   3. Short
   4. Int
   5. Long
   6. Real & Float
   7. Char & Symbol
   8. Date, Time, Timestamp types
2. Type codes and type introspection
3. Null values and infinities
4. Type casting and conversions

Module 3: Lists and Vector Programming

1. Homogeneous vs heterogeneous lists
2. List creation and manipulation
3. Indexing and slicing
4. Enumeration
5. Implicit iteration
6. Vector operations and performance advantages

Module 4: Dictionaries & Tables

1. Creating dictionaries
2. Dictionary operations
3. Tables as flipped dictionaries
4. Keyed tables
5. Column operations
6. Metadata inspection

Module 5: Functions in q

1. Defining functions
2. Lambda expressions
3. Local and global scope
4. Function arguments and return values
5. Implicit return behavior
6. Conditional logic inside functions

Module 6: Functional Programming Concepts

1. Functions as first-class objects
2. Projections and partial application
3. Higher-order functions
4. Composition
5. Each, Over, Scan
6. Adverbs (each, over, scan, prior, peach)
7. Iterators and functional patterns

Module 7: Advanced Functional Techniques

1. Dynamic function generation
2. Functional form of queries
3. Using parse trees
4. Functional updates on tables
5. Error handling in functional style

Module 8: Performance & Best Practices

1. Vectorization vs loops
2. Memory efficiency considerations
3. Avoiding common pitfalls
4. Writing idiomatic q code
5. Debugging functional code