Learn K with Real Code Examples
Updated Nov 20, 2025
Architecture
Array-oriented design
Functional and tacit programming
Integration with kdb+ database
Optimized for in-memory and real-time analytics
Vectorized computations over tables and lists
Rendering Model
Operations applied over arrays/lists/tables
Functions can be tacit or explicit
Tables and keyed tables used for structured data
Integration with kdb+ allows in-memory querying
Vectorized computations reduce loops and increase performance
Architectural Patterns
Array-oriented design
Functional and tacit programming
In-memory computation
Real-time data processing
Integration with kdb+ and external libraries
Real World Architectures
High-frequency trading platforms
Real-time financial analytics
Quantitative research systems
Market data aggregation pipelines
Risk modeling and reporting applications
Design Principles
Array-oriented and vectorized
Functional and tacit programming
Optimized for large datasets
Concise symbolic syntax
Integration with kdb+ for storage and analytics
Scalability Guide
Vectorize operations for large datasets
Use in-memory tables for real-time analytics
Optimize keyed tables for lookup speed
Parallelize queries where possible
Profile memory and CPU usage
Migration Guide
Port legacy APL/Q scripts to K
Refactor operations to use vectorized or tacit functions
Integrate with kdb+ database
Optimize queries for large datasets
Validate performance with real-time feeds