book Crafting Interpreters programming language compiler

A Map of the Territory

Parts of a Language


  • Scanning or lexing
  • Scanner takes a stream of characters --> chunks them into tokens


  • Syntax gets grammar
    • Ability to compose larger expressions/statements from smaller parts
  • Parser builds an abstract syntax tree from a sequence of tokens
  • Many parsing techniques used today were conceived to parse human language by AI researchers
  • Reports syntax errors to the user

Static analysis

  • Most languages do this in multiple steps
  • Binding or resolution
  • For each identifier: - Find out where the name is defined - Wire the two together
  • Type checking occurs here for statically typed languages
  • Scope --> a region of code where a name can be used to refer to a declaration
  • Results are stored in one of many places:
    • Often as attributes on the AST
    • In a lookup table (symbol table)
      • Keys to the table are identifiers (names of variables, declarations)

Intermediate representations

  • Not tied to the source or destination form
    • An interface between the two languages
  • Allows you to support multiple source targets and target platforms


  • Swapping user's code with more efficient implementations with the same semantics
  • Example: constant folding

Code Generation

  • Produces assembly-like instructions a CPU can run
  • Can produce code for a real CPU or a virtual one
    • When targeting virtual CPUs we produce bytecode

Virtual Machine

  • Runs bytecode
  • Emulates a hypothetical chip supporting a virtual architecture at runtime
  • Slower than running native machine code
    • Every instruction is simulated when it executes
  • More simple and portable than native machine code


Shortcuts and Alternate Routes

Single-pass compilers

  • Skip syntax trees and other intermediate representations
  • Restrict the design of the language

Tree-walk interpreters

  • Begin executing code after parsing into an AST
  • The interpreter traverses the syntax tree one branch and leaf at a time
    • Evaluates each node as it goes
  • Usually slow
    • Not practical for general-purpose languages


  • Compile source code to a different high level language
  • Utilize the target high level language's toolset to compile to lower level code

Compilers and interpreters

  • Compiling
    • Implementation technique
    • Translates source language to some other form (usually lower level language)
  • Compiler
    • Translate source code to some other form but doesn't execute it
    • User has to run the output
  • Interpreter
    • Takes in source code and executes it immediately
    • Runs program from source
  • Something can be both a compiler and an interpreter
    • Eg. CPython
    • Most scripting languages work this way


  1. In an open source implementation of a programming language, find the code that implements the scanner and parser. Are they handwritten or generated?
  2. Just-in-time compilation tends to be the fastest way to implement dynamically typed languages, but not all of them use it. What reasons are there to not JIT?
    • Has a startup delay
      • Must make tradeoffs between compilation time and quality of code generated
    • Can introduce security issues
      • JIT compiler outputs machine code directly into memory then executes it
      • This compiler can be used to execute arbitrary code
  3. Most Lisp implementations that compile to C also contain an interpreter that lets them execute Lisp code on the fly as well. Why?
    • Code can be rewritten at runtime (eg. macros)
      • Staged compilation (requires an interpreter)
        • Compile macros --> link them into compiler --> compile code
    • Late binding