¶ Mercury
¶ Programming Languages
Mercury is one of those languages that quietly reshapes the way you think about programming. It doesn’t shout for attention the way newer technologies often do, nor does it cling to nostalgia the way some older languages try to. Instead, it stands in a space of its own—precise, deliberate, deeply logical, and remarkably elegant. If you’ve spent most of your programming life in imperative or object-oriented languages, Mercury might feel almost philosophical at first. It asks you to see computation not as a series of instructions, but as a set of relationships, truths, and guarantees. And once that shift begins, the language reveals an entirely different way of understanding how programs can be built.
This course of one hundred articles is meant to guide you through that transformation with clarity and patience. Mercury deserves that kind of attention. It isn’t a language you skim in a weekend and throw onto your résumé; it’s a language that rewards depth, curiosity, and a willingness to appreciate rigor. But in return, it offers something rare in the modern programming world: confidence. Programs written in Mercury behave exactly as you expect them to. They’re predictable. Verifiable. Robust. And in many cases, they’re faster than what you might imagine possible in a pure logic language. Mercury blends the expressiveness of logic programming with the performance and reliability of strongly typed, statically compiled languages. It’s a combination that feels unusual only until you start using it.
Many people first encounter Mercury through its reputation as the “high-performance Prolog with a real type system,” which is accurate but incomplete. While Mercury’s roots lie in logic programming, the experience of writing Mercury feels more like constructing a mathematical argument in code. You describe the world in terms of predicates and functions, specify the modes in which they operate, and let the compiler ensure that everything is consistent—not just syntactically, but semantically and logically as well. Mercury’s compiler is famously strict, and far from being a burden, that strictness becomes a source of strength. It forces clarity. It catches ambiguities before they can morph into bugs. It encourages you to think intentionally about determinism, purity, input-output behavior, and the structure of your logic.
As you move through this course, you’ll see how these ideas shape the entire language. Early on, we’ll explore what it means to write in a purely declarative style and how Mercury differs from other logic languages you may have tried. If you’re coming from Prolog, you’ll notice immediately that Mercury demands more from you—but it also gives you more in return. Where Prolog leaves many behaviors to runtime, Mercury brings them to the surface at compile time. That shift is not just a technical detail; it changes the way you model problems. Instead of writing code that “tries things and sees what works,” you write code that expresses exactly what is true and how it can be derived.
And yet Mercury is not austere or restrictive. The language is surprisingly expressive once you grow comfortable with its building blocks. As the course progresses, you’ll learn how Mercury handles algebraic data types, higher-order programming, backtracking, and the careful choreography of mode and determinism declarations. Each of these features plays a crucial role in how Mercury achieves both clarity and efficiency. Logic programming is often associated with flexibility at the cost of performance, but Mercury manages to combine both by guiding the programmer toward well-defined, predictable structures.
One of the revelations many learners experience is how much Mercury’s type system contributes to creative problem solving. Instead of feeling boxed in, you start to feel supported by constraints that make your code more articulate and less error-prone. The type system is not merely a safety net; it’s a tool that encourages better design. When you declare your types carefully, the rest of your program begins to slot naturally into place. Error handling becomes more manageable. Relationships become clearer. Functions and predicates develop sharper interfaces. And gradually, your programs feel more like carefully crafted proofs than sequences of operations.
But Mercury isn’t just an academic exercise. It was designed with real applications in mind—large, demanding, performance-critical software that benefits from static guarantees and logical structure. That practical orientation becomes increasingly important as the articles move into more advanced topics. You’ll learn how Mercury compiles down to efficient C or native code, how it handles memory through region-based management, and how its architecture enables optimizations that would be impossible in more permissive logic languages. There’s a certain satisfaction in realizing that code written in a pure logical form can run with the speed of a lower-level system, all without compromising the clarity of your reasoning.
Another dimension of Mercury that we’ll explore is its modularity. As your programs grow, the language offers tools to organize them cleanly: modules that expose only what needs to be shared, type classes that allow flexible abstractions, and interfaces that keep dependencies explicit. Writing large Mercury applications feels surprisingly manageable once you understand these tools. In many languages, scaling a project requires increasingly complex patterns and conventions. In Mercury, scaling often feels more straightforward because the compiler forces every piece to fit together in a coherent structure from the very start.
Beyond syntax and semantics, this course will spend time on the mindset and habits that make Mercury truly enjoyable. Logic programming encourages you to think more declaratively, more compositionally. It nudges you to ask: “What is true?” rather than “What should the machine do?” That shift opens surprising doors. Some problems that feel tangled in imperative languages become elegant when expressed as relations. Conversely, some problems that seem simple in more mainstream languages require deeper thought in Mercury—but that thought often pays dividends in correctness and maintainability. As you work through example programs, larger case studies, and various problem-solving exercises, you’ll start to develop an intuition for when Mercury is at its strongest and how to harness that strength.
Throughout these hundred articles, you’ll also encounter Mercury’s ecosystem. Though not as large as the communities around giant mainstream languages, the Mercury community has produced thoughtful, high-quality tools, libraries, and documentation. You’ll learn how to build projects with Mercury’s compiler, how to test your programs effectively, and how to connect Mercury with the outside world. Interfacing with C, using foreign language bindings, building larger mixed-language systems—these topics matter because real software rarely exists in isolation. Mercury’s foreign function interface is powerful, and learning to use it well opens the door to combining Mercury’s logic clarity with the capabilities of other ecosystems.
One of the recurring themes in this course will be the value of precision. Mercury rewards programmers who think carefully, who appreciate clear definitions, who don’t shy away from understanding the deeper mechanics of a system. If you’re the kind of person who enjoys knowing why a piece of code behaves the way it does, Mercury will feel like a welcoming home. And if you’re not yet that person, Mercury may help you become one. The language’s strictness may feel unfamiliar at first, especially if you’re used to more permissive environments, but that strictness never feels arbitrary. It exists to help you write programs that are correct by construction, not by luck.
Over time, many learners discover that Mercury doesn’t just teach them a new language—it reshapes how they approach programming in general. Even when returning to imperative or functional languages, they find themselves asking clearer questions, thinking more carefully about data and control flow, and designing more robust systems. In that sense, Mercury offers both a practical skill and a conceptual education. It gives you patterns of thought that extend far beyond its syntax.
This introduction is just the beginning of a much longer journey. By the time you’ve completed the entire series, you’ll not only understand Mercury as a language but also appreciate the philosophy behind it. You’ll have written declarative programs, explored multiple determinism strategies, constructed complex data types, optimized logic for performance, and learned how to reason about programs with a level of clarity that carries into every other language you touch.
But perhaps the most important thing you’ll gain is a sense of what programming can feel like when precision isn’t a burden but a source of power. Mercury shows you a world where programs are not mere instructions to machines, but carefully articulated ideas—ideas that can be checked, reasoned about, optimized, and trusted.
Whether you’ve come to Mercury out of curiosity, academic interest, professional ambition, or simply a desire to think differently, this course is designed to help you move with confidence through a language that offers both challenge and reward. It’s a journey through logic, structure, and clarity—one that grows richer with each step.
Welcome to Mercury. Let’s begin.
¶ Mercury
¶ Beginner Topics
1. Introduction to Mercury: What is Mercury and Why Learn It?
2. Installing Mercury: Setting Up the Development Environment
3. Your First Mercury Program: "Hello, World!"
4. Understanding Mercury Syntax: Basic Structure and Layout
5. Mercury Data Types: Integers, Floating Points, and Booleans
6. Working with Variables and Constants in Mercury
7. Basic Arithmetic in Mercury: Performing Calculations
8. Introduction to Mercury's Pure Logic Paradigm
9. Control Flow in Mercury: if, else, and switch
10. Working with Lists in Mercury: Creating, Accessing, and Modifying
11. Functions in Mercury: Defining and Calling Functions
12. Introduction to Mercury's Type System: Types and Polymorphism
13. Understanding Logic Variables in Mercury: Unification and Backtracking
14. Conditional Expressions in Mercury: Using case and if-else
15. Recursion in Mercury: Understanding Recursive Functions
16. Introduction to Mercury's Determinism System
17. Pattern Matching in Mercury: case and switch
18. Working with Strings in Mercury: Manipulating Text
19. Handling Errors in Mercury: Using Exceptions and throw
20. Input and Output in Mercury: Reading and Printing Data
¶ Intermediate Topics
21. Mercury's Module System: Structuring Programs with Modules
22. Using import and export in Mercury
23. Lists and Arrays in Mercury: Advanced List Operations
24. Understanding Mercury’s Ground and Free Variables
25. Working with Tuples in Mercury
26. Complex Data Structures in Mercury: Records and Arrays
27. Higher-Order Functions in Mercury: Functions as Arguments
28. Using Mercury's Built-in Libraries: list, string, io
29. Working with Multi-argument Functions in Mercury
30. Determinism in Mercury: Understanding det, semidet, and nondet
31. Advanced Recursion Techniques in Mercury: Tail Recursion and Accumulators
32. Understanding Mercury’s Lazy Evaluation
33. Introducing Mercury's Foreign Function Interface (FFI)
34. Abstract Data Types in Mercury: Defining New Types
35. Building Custom Data Structures in Mercury: Lists, Trees, and Graphs
36. Introduction to Mercury’s Concurrency Model
37. Managing State in Mercury: Using State Variables and Updates
38. Using Mercury's I/O System for File Handling
39. Using Mercury's Standard Library: Regular Expressions and Pattern Matching
40. Optimizing Mercury Code: Profiling and Performance Tuning
41. Testing Mercury Programs: Unit Testing and Debugging Tools
42. Error Handling in Mercury: Using throw and catch
43. Using Mercury for String Manipulation and Parsing
44. Converting Between Data Types in Mercury
45. Working with Arrays and Lists Efficiently in Mercury
46. Understanding Mercury’s Type Classes and Instances
47. Mercury’s Module System: Advanced Usage and Best Practices
48. Building and Using Abstract Syntax Trees (ASTs) in Mercury
49. Introduction to Mercury’s Constraint Logic Programming
50. Writing and Using Mercury Libraries: Creating Reusable Code
¶ Advanced Topics
51. Advanced Pattern Matching in Mercury: Complex Matching and Guards
52. Advanced Recursion and Optimization in Mercury
53. Understanding Mercury's Coroutines for Concurrent Programming
54. Implementing Algorithms in Mercury: Sorting, Searching, and Graphs
55. Advanced Type System in Mercury: Type Inference and Polymorphism
56. Using Mercury for Symbolic Computation and Mathematics
57. Metaprogramming in Mercury: Writing Code That Writes Code
58. Memory Management and Garbage Collection in Mercury
59. Understanding Mercury’s Foreign Function Interface (FFI) in Detail
60. Parallel Programming in Mercury: Using Concurrency Constructs
61. Advanced Debugging in Mercury: Trace and Debugging Tools
62. Using Mercury for High-Performance Computing (HPC)
63. Mercury's Unification and Backtracking: Deep Dive
64. Advanced Error Handling in Mercury: Using throw, catch, and Exception Handling
65. Functional Logic Programming in Mercury: Higher-Order Logic
66. Implementing Dynamic Programming Solutions in Mercury
67. Building a Domain-Specific Language (DSL) with Mercury
68. Mercury for Real-Time Systems Programming
69. Using Mercury for Natural Language Processing (NLP)
70. Mercury's Constraints System: Advanced Usage
71. Creating and Using Mercury's Meta-Interpreters
72. Understanding Mercury's Polymorphism: Parametric and Ad-hoc
73. Implementing Genetic Algorithms in Mercury
74. Mercury's Foreign Code Interface: Interfacing with C and Other Languages
75. Using Mercury for AI Programming: Search Algorithms and Heuristics
76. Advanced Data Structures in Mercury: Trees, Hashes, and Graphs
77. Mercury for Compiler Construction: Writing Parsers and Lexers
78. Working with Mercury's Compiler Optimizations
79. Functional Reactive Programming in Mercury
80. Using Mercury for Knowledge Representation and Reasoning
81. Building Scalable Systems with Mercury’s Concurrency Model
82. Implementing Monads and Other Functional Constructs in Mercury
83. Mercury’s Meta-Programming: Reflection and Code Transformation
84. Writing Distributed Systems in Mercury: Message Passing and Coordination
85. Mercury for Distributed Databases and Distributed Computing
86. Using Mercury for Simulation and Modeling
87. Integrating Mercury with Databases: Query Languages and Data Persistence
88. Writing Secure Code in Mercury: Best Practices and Security Models
89. Introduction to Mercury’s Garbage Collection System
90. Implementing Machine Learning Algorithms in Mercury
91. Using Mercury for High-Level Simulation in Scientific Computing
92. Building High-Performance Applications in Mercury
93. Using Mercury in Financial Modeling and Risk Management
94. Performance Tuning Mercury Programs: Profiling and Bottlenecks
95. Implementing Cryptographic Algorithms in Mercury
96. Using Mercury for Automated Theorem Proving
97. Building Web Applications in Mercury: HTTP and Web Servers
98. Mercury for Systems Programming: Low-Level Systems Interfacing
99. Mercury for Robotics: Sensor Integration and Control Systems
100. The Future of Mercury: Trends, Libraries, and Evolving Practices