Environment Variables are Dynamically Scoped Keyword Arguments

Posted on by Chris Warburton

Like most of my blog posts, this began as a comment on Hacker News, in response to a post discouraging the use of environment variables.

I disagree with that post’s arguments and conclusions, mainly the way it completely ignores the existence and usefulness of dynamic scope. Since I’ve also been making very good use of dynamic variables in Scala recently, I thought it was worth writing up.


“Scope” describes where to look for the value of a variable. There are three types of scope that are common across most programming languages (inheritance in object-oriented programming can be thought of as a form of scope, but that would be a whole other tangent!)

This doesn’t really come up when we do ‘first-order’ programming with bound variables, like this:

function foo(x, y) {
  let a = x;
  let z = a + y;
  return z * 2;

let a = 5;
print(foo(1, 2));

Here the call to foo binds the variables x and y (to 1 and 2); a and z are bound in the block by the let statements. Hence the first print gives out 6 (since it’s (1 + 2) * 2). In most languages, the second print will give out 5, due to the let a = 5; line (it would give 1 when using global scope).

We need to care about scoping rules when we have free variables, whose values will be fetched from somewhere else; scoping tells us where to get them from.

For example, the a variable is “free” in the function bar, since it is not an argument to bar or defined within bar:

function foo(x) {
  let a = x;
  return function bar(y) {
    let z = a + y;
    return z * 2;

let a = 5;

Global Scope

Global variables are bound to at most one value; all occurrences of a global variable’s name, anywhere in a program, refer to the same thing.

If we treat a as globally scoped in the above example, it gets bound by the line let a = 5;. The call foo(1) also binds the a variable, in the line let a = x;. There are two ways that a language could handle this:

The first approach is pretty reasonable, especially when statically-checked (in which case the above example would not be a valid program). Frustratingly, languages which provide global variables tend to use the second approach instead. Under that scheme, the variable a will be re-bound to the value of x, which is 1.

The bar function is called with the argument 2, which gets bound to the argument name y. When we encounter the line let z = a + y;, these variables resolve to let z = 1 + 2;, hence binding z to 3 and resulting in the value 6 being printed.

Finally, the value of a is printed, which is 1.

Global scope is a “Worse is Better” approach: it’s easy to implement (e.g. using a single hashmap) and easy to understand on a small-scale; yet as a codebase grows it gets harder and harder to predict its behaviour, since we need to keep searching the whole application to see which things may affect which others. We can also encounter surprising breakages, if we choose a variable name which just-so-happens to already be used elsewhere.

Programming with mutable global scope can be made reasonable by following disciplined practices like modularity (e.g. a module should never refer to variables used by another module; variable names should include their module, to prevent clashes; etc.). However, that’s only useful in greenfield work, since we can’t assume that some arbitrary legacy system will obey any particular practices (even if it claims to, without enforcement by the language/tooling it may have crumbled under the weight of maintenance).

Lexical Scope

This is the most common form of scope in most programming languages. Values are looked up by starting where the variable is written (hence “lexical”), and working outwards from there until a binding is found.

In the above example, the variable a in the line let z = a + y; is resolved by starting in the place where this line is written, which is in the bar function:

         function bar(y) {
    return z * 2;

The bar function doesn’t bind an a variable, so we look in the place where the bar function is written, which is in the foo function:

function foo(x) {
  let a = x;
  return ...;

The foo function does bind an a variable, in the line let a = x; (and the x variable is also bound, as the function’s argument). Hence this is the value that will be used in our original line let z = a + y;, which (for the argument x = 1) gives let z = 1 + 2; and prints 6 like in the global version.

However, unlike the global version, the subsequent print(a); line will print 5 this time, since the binding let a = 5; is written in the same place as that print call.

Lexically scoped variables tend to be a good default, since they are modular and encapsulated without requiring discipline from the programmer: they can only be referenced by code which is written in the same unit/module. Clashes are minimised, since the same name can be used in different places to refer to different things.

Lexically scoped variables are controlled by whoever is writing the function, and they usually know what variables in their module/library/etc. should be.

Lexical scope can be tricky to implement, since a function call’s bindings may be needed after it has finished running. For example, when we perform the bar(2) call above, the let z = a + y; line needs to know the binding x = 1 from the foo(1) call that defined bar; yet that call has already finished and returned a value!

We need some way to ‘propagate’ bindings into functions which reference them (known as closures). One way is to keep old stack frames alive in case they’re needed later, resulting in “spaghetti stacks”.

Dynamic Scope

This is similar to lexical scope, but rather than looking for bindings where the code was written, it looks where the code was called. In the above example, we would resolve a in the line let z = a + y; by looking where that line was called from: since it’s called from inside the bar function we look there for an a binding. There isn’t one, so we look where bar was called, which was from the top-level, so we look there and find the binding let a = 5;. Hence we get let z = 5 + 2; and the value 14 is printed. The final line print(a); is called from the top-level, so it also uses the let a = 5; binding, printing the value 5.

Dynamic scope seems to have been invented by accident, when early Lisp implementations wanted to provide higher-order functions like lambda calculus, but hadn’t implemented spaghetti stacks (or equivalent) to allow the re-entrant behaviour required for lexical scope. Hence they looked up variables from the call stack instead, and got this subtley different behaviour.

Dynamic scope turns out to be useful for variables which the caller of the code knows better than the writer of the code. This makes it useful for configuration parameters and dependency injection. In particular it avoids the need to either pass extra arguments along chains of function calls, or write all relevant functions within one big lexical scope; yet still allows overriding as needed (e.g. within tests).

Environment Variables are Dynamically Scoped

This is my main objective disagreement with the linked post: it claims that environment variables act like (mutable) global variables, and uses many of the known problems of (mutable) global variables as reasons to avoid environment variables. Yet these arguments fall apart when we consider environment variables in context: as a form of inter-process communication. When viewed in this way, environment variables are not globals, but are instead much closer to dynamic variables.

Environment Variables are Locally Overridable

We can override environment variables for a particular subprocess without affecting anything else. For example, consider the following script:

echo "BEFORE $FOO"
FOO=bar printFoo
echo "AFTER $FOO"

Let’s assume printFoo is a script which performs echo "FOO is $FOO", and we run our script with an environment containing FOO=foo, we will get:

FOO is bar
FOO is foo

The binding FOO=bar changes the output between the first and second lines, but it could not have mutated the variable FOO, since the original value foo remains intact for the final two lines. This behaviour doesn’t match that of global variables described above (mutable or immutable), since globals only have a single binding (which, in the mutable version, may be irrevocably overwritten).

How Environment Variables Actually Work

The real behaviour of environment variables is that each process gets its own ‘environment’ of variable bindings when started, and those environments begin as copies of the environment that invoked that process; plus any given extras or overrides, like in the FOO=bar example above.

This explains the behaviour of our example script: the line FOO=bar printFoo runs the printFoo process in a new environment, copied from the script’s except for the override FOO=bar. This override is why the second line shows bar.

Once that printFoo process has finished, its environment (containing the override) is discarded. The next printFoo command is also run from the script’s environment, getting its own copy of the original foo value (without any overrides this time). Hence the output goes back to foo.

The final echo uses the script’s environment directly, which contains the original value foo.

This mechanism of copying variable bindings is rather inefficient, compared to the lightweight stack-based approach used by function/procedure calls within a process. However, the resulting behaviour, shown above, is indistinguishable from dynamic variable binding.

A Word on Mutation

Some languages don’t allow environment variables to be mutated, e.g. Scala’s sys.env is an immutable map (although the JVM has other ways to read and write the environment). Many languages do permit mutation, and it acts differently to mutating a dynamic variable (as far as subprocesses are concerned).

Dynamic variables are found by looking further and further up the call stack for a binding, so any mutations made to such a binding will be visible to everything below that binding’s stack frame; even after the code performing the mutation (a function/procedure/etc.) has long since finished and had its own stack frame of bindings discarded.

On the other hand, since environment variables are copied from parent to child at each new scope (process), mutations are only made to that process’s own copy, and hence aren’t visible to any sibling or parent of that process.

In fact, this isolation of mutations makes environment variables even less ‘globalish’ and ‘mutableish’ than ordinary dynamic variables!

As an example, if the printFoo command above were to finish by mutating the FOO variable, e.g. using export FOO=baz, it wouldn’t affect our script at all. Even the call to printFoo which “inherits” the script’s FOO without overriding it, would only be mutating its own copy of FOO, which won’t affect the script’s copy of the variable (and hence won’t alter the final line).

Note that I highly recommend to avoid mutating env vars, for the same reason I avoid mutating any variables, regardless of language; unless there’s a specific reason to do so. Overriding dynamic variables with a new scope is fine; it’s what they’re for!

If we treat environment variables in a sane, immutable way, then they act exactly like dynamic variables.

Environment Variables Within Processes

I can predict an obvious objection to what I’ve said: aren’t environment variables mutable globals within processes (e.g. in a Python script, or whatever)?


Processes are free to do what they like with their given environment variables. Many languages, like Python, choose to treat their given environment variables like mutable globals by default, and we can absolutely criticise them for that choice; but it’s not the fault of environment variables themselves.

Other languages treat environment variables in a saner way. For example, Racket puts the whole environment in a dynamic variable called current-environment-variables, which can be overridden for sub-expressions just like we did for sub-processes above. For example:

(define (print-foo)
  (printf (string-append "FOO is " (getenv "FOO"))))

(parameterize ((current-environment-variables
                (make-environment-variables "FOO" "bar")))

With an initial environment of FOO=foo, this will print:

FOO is foo
FOO is bar
FOO is foo

This keeps the behaviour of environment variables more consistent within and between processes (subprocesses invoked by Racket will inherit whichever current-environment-variables is in scope at the time; this is also true for nice libraries like shell-pipeline).

I hope more languages will adopt this sort of approach, and in the mean time we can use libraries to bypass the crappy defaults of common languages.

Use-Cases for Dynamic Scope

Now that we have a more appropriate mental model of environment variables, we can think about situations where they (and dynamic scoping in general) are appropriate.

Lexical scope is certainly best to use by default; Scheme was a definite improvement over previous Lisps in that regard! However, far from being merely a historical mistake, dynamic scope turns out to be very useful in situations where (as mentioned above) a variable is better specified by the caller than by an application/library itself. In that sense, dynamic variables are like implicit arguments to a function, passed along (also implicitly) to subroutines.


Dynamic variables can be a useful way of referring to the sort of data that is often put into config files; e.g. verbosity level, UI options, paths to required files, etc.

It’s no coincidence that such configuration data is also a good fit for using environment variables.

When it comes to env vars versus commandline arguments, I treat the distinction in a similar way to keyword arguments versus positional arguments for functions (in languages which support both, like Python). Note that this is more than just a passing resemblance: keyword arguments are often the only form which allows default values (commonly used like function-level ‘configuration’)!

I personally try to avoid giving my software config files, since they’re a form of global mutable state, and they also make it harder to isolate and reproduce executions. They also introduce extra headaches like read errors, and require some mechanism to specify the config file location (which itself is a form of config, that could be put in an env var!)

TANGENT: There’s an under-appreciated approach to config files, which manages to avoid some of the problems mentioned above. That is to pipe the config data into an application via process substitution. For example:

myProgram -c <(jq -n '{"verbosity": 1, "port": 8000}')

This lets us specify all of our config up-front, without having to manage extra files on disk (e.g. versioning, permissions, etc.). For this to work, the config file path must be accepted as a commandline argument, the application needs to read it directly (rather than trying to dereference symlinks, etc.) and it should avoid seeking (zsh supports seeking via its =(foo) form of process substitution, but other shells like bash do not).

Note that I agree with that article’s criticism of application-specific formats, like :-separated entries in PATH, but that’s certainly not unique to env vars. Config files and env vars are both strings of bytes, so what works for one will usually work for the other. In particular, I have nothing against JSON inside environment variables (although judgment needs to be made about when to use a single variable containing a JSON object, and when to use multiple variables).

Dependency Injection

Dynamic variables are a great way to implement dependency injection, where we allow clients/handles/connections to external or side-effecting systems to be overriden by callers.


A simple example of dependency injection is logging, where a library might choose when and what to log, but the caller decides how to handle log messages.

There are two ways to achieve this purely using lexical scope. We could write our entire application inside the body of a function, which accepts a logger as argument:

function myLibrary(log) {
  // Entire application is defined here

  // Whenever we want to log, we just do:
  log("My message");

Applications and other libraries can call this function with their desired logger, or multiple times if they need to use several implementations (e.g. silencing certain actions, accumulating output to various buffers during tests, etc.). If we do call this function multiple times, we’ll need to keep track of the different results, calling the correct version when appropriate.

This is clearly impractical, not least because cramming everything into the same function body prevents modularity. A common alternative is to have a single instance of our library, but allow the logger to be passed in as an extra argument wherever it will be used. For example:

def bill(log, customer):
  log("Billing customer " + customer.name)
  # billing logic goes here

def outstanding(db):
  return filter(lambda customer: customer.balance < 0, db.customers)

def sendBills(log, db):
  log("Sending monthly bills")
  list(map(lambda customer: bill(log, customer), outstanding(db)))

This is a reasonable approach, but may become unwieldy as we add more of these cross-cutting concerns. Language features like currying and default arguments can help rein in the external API, but still don’t help with the internal implementation; e.g. we could curry bill and sendBills with a default log, or use a default argument to achieve a similar thing; but internal calls (like sendBills calling bill) would still need to pass everything along explicitly, in case the default has been overridden.

In contrast, dynamic variables can be defined by a library and referenced without much/any ceremony, and overridden as needed by callers. For example, in Racket:

;; Dynamic variable for logging procedure; defaults to printing on stderr
(define logger (make-parameter eprintf))

;; Shorthand for getting logger and applying it to a string
(define (log msg)
  ((logger) msg))

(define (bill customer)
  (log (string-append "Billing customer " (hash-ref customer 'name)))
  ;; billing logic goes here

(define (outstanding db)
  (filter (lambda (customer) (< (hash-ref customer 'balance) 0))
          (hash-ref db 'customers)))

(define (sendBills db)
  (log "Sending monthly bills")
  (map bill (outstanding db)))

This defines a dynamic variable logger (known as a “parameter” in Racket), which defaults to the eprintf procedure for printing on stderr. We can log using this parameter via ((logger) "my string"), but this looks a bit weird so I’ve defined a more conventional log procedure too.

Applications and other libraries can call our functions as-is to use the default logger, or they can override it by wrapping their code in parameterize, e.g.

(parameterize ((logger my-custom-logger))
  (log "About to send bills")
  (sendBills myDb)
  (log "Finished sending bills"))

Note that Racket’s stdio system is actually defined using parameters, so this example is a bit redundant! Instead, we could just write our log messages to the current-error-port, which is already a parameter:

;; No need for any 'logger' parameter

;; Logging just writes strings to current-error-port
(define (log msg)
  (display msg (current-error-port)))

;; bill, outstanding and sendBills remain the same as above

(parameterize ((current-error-port my-custom-port))
  (log "About to send bills")
  (sendBills myDb)
  (log "Finished sending bills"))

I’m not just being a SmugLispWeenie either, since Scala also uses dynamic scope for its stdio, via the Console object. For example:

def log(msg: String): Unit = Console.err.println(msg)

// billing definitions go here

Console.withErr(myStderrStream) {
  log("About to send bills")
  log("Finished sending bills")

Web Services

Recently I’ve been using dynamic variables in Scala for managing connections to Web services like AWS. This involves more complex APIs than the ‘fire and forget’ of logging, as well as static types.

Here’s a simple example of a key/value store referenced by a dynamic variable:

trait Store {
  def get[T](k: Store.Key[T]      ): Try[Option[T]]
  def put[T](k: Store.Key[T], v: T): Try[Unit     ]

object Store {
  // Zero-overhead wrapper around a String. Phantom T represents the type of
  // value stored against this key.
  final case class Key[T](override val toString: String) extends AnyVal

  // Our dynamically bound Store. We wrap it in a 'Try' to represent connection
  // errors, etc. This also lets us default to an "uninitialised" value.
  val store = new scala.util.DynamicVariable[Try[Store]](
    Failure(new AssertionError("No Store in scope"))

  // We can use the current store like 'Store.store.value.flatMap(_.get(myKey))'
  // but that's a bit naff, so we provide some shorthands.

  // Lets us look up keys using `Store(myKey)`
  def apply(k: Key        ): Try[Option[Val]] = store.value.flatMap(_.get(k   ))

  // Lets us insert values using 'Store.put(myKey, myVal)'
  def   put(k: Key, v: Val): Try[Unit       ] = store.value.flatMap(_.put(k, v))

  // We can override the store using `Store.store.withValue(myStore)(myExpr)',
  // but (a) that's a bit long-winded, and (b) there are some common use cases
  // like AWS SSM, Redis, HashMaps, etc. we can provide 'out of the box'

  // Evaluates 'x' in a new scope where Store calls out to AWS SSM
  def withSSM[T](
    x  : => T,
    ssm: Try[AWSSimpleSystemsManagement] = Try(SSMBuilder.defaultClient)
  ) = store.withValue(ssm.map(ssm => new Store {
    def get[A](k: Key[A]      ) = /* Get using SSM client */
    def put[A](k: Key[A], v: A) = /* Store using SSM client */

  // Evaluates 'x' in a new scope where Store uses a HashMap (e.g. for tests)
  def withTempStore[T, V](
    x   : => T,
    init: Map[Key[V], V] = Map.empty
  ) = store.withValue(Success({
    val store = HashMap.empty ++ init
    new Store {
      def get[A](k: Key[A]      ) = Success(store.get(k   )             )
      def put[A](k: Key[A], v: A) = Success(store.put(k, v).pipe(_ => ())

Environment Variables

Interestingly, the environment itself is often a good use-case for a dynamic variable. As shown above, Racket accesses env vars via a dynamic variable called current-environment-variables, and I’ve implemented Scala code similar to the Store above for overriding the environment (without the put, since I prefer to keep all env vars immutable, as mentioned above).

Whilst shells don’t have an explicit variable for “the current environment”, they do mostly treat env vars in the same way as “normal” dynamic variables (AKA “shell variables”); the main difference is that shell variables aren’t inherited by subprocesses. For example:

#!/usr/bin/env bash

myEnv() {
    env | grep FOO || echo "FOO is not in env"

printFoo() {
    echo "BEGIN $*"
    echo "FOO is $FOO"


printFoo A

FOO=bar printFoo B

printFoo C

This script outputs:

FOO is not in env
FOO is foo
FOO is bar
FOO is not in env
FOO is foo

The function calls labelled A and C are using the shell variable FOO, which has a value of foo and doesn’t appear in the environment of subprocesses (like the env command, which prints out its environment). For the call labelled B we override this shell variable, turning it into an environment variable which is visible to the env command (hence the FOO=bar line extracted by grep). They otherwise look pretty much the same; plus, reading a variable which isn’t defined anywhere in our script will “fall back” to checking the environment (options like set -u come in handy for catching failures here!).

SIDE NOTE: I didn’t realise until I was writing this that overriding variables works for function calls in bash as well as “normal” subprocesses!

A Word on Threading

Threading is a Worse Is Better approach to concurrency and parallelism, since it appears rather straightforward at first glance, whilst subtly undermining the semantics of the language, and hence the assumptions made by our mental models.

Threading can interfere with dynamic variables, if they use a “thread-local” implementation. Racket takes care to ensure its dynamic variables (“parameters”) have consistent values, even if code is moved between threads; however, its mutation form (my-parameter my-new-value) will have problems across threads (yet another reason to avoid mutation and threads!).

The story in Scala is worse, since even reading a dynamic variable can break in the presence of multithreading. In particular, the implementation of DynamicVariable only makes new scopes visible to the current thread and its descendents. Using a thread pool to execute concurrent tasks will send some tasks to old threads (to avoid the overhead of spinning up new threads), but those might not have the correct dynamic scopes set up for the task. This can cause values from incorrect scopes to creep in unexpectedly (and non-deterministically!).

This is particularly annoying, since it prevents us using Future to wrap up our Web services (similar to how I used Try in the Store variable above).

To avoid this, I’ve come up with the following alternative to DynamicVariable, which forces new threads to be created whenever a dynamic variable gets a new scope. This isn’t the most efficient thing in the world, but it seems to work nicely for my use-case. In particular:

import java.util.concurrent.Executors
import scala.concurrent.{
import scala.util.DynamicVariable

final class StackedExecutionContext private() extends ExecutionContext {
  private def newPool = ExecutionContext.fromExecutorService(
  private var active = newPool
  private var old    = List[ExecutionContextExecutorService]()

  def freshPool: Unit = this.synchronized {
    old    = (active.tap(_.shutdown)::old).filter(!_.isTerminated)
    active = newPool

  def       execute(runnable: Runnable ): Unit =
    this.synchronized { active.execute      (runnable) }
  def reportFailure(cause   : Throwable): Unit =
    this.synchronized { active.reportFailure(cause   ) }

final object StackedExecutionContext {
  /** Use this to execute `Future` values which involve DynamicallyScoped */
  implicit val ec = new StackedExecutionContext

final class DynamicallyScoped[T] private(d: DynamicVariable[T]) {
  import StackedExecutionContext.ec.freshPool

  /** The value currently bound to this variable */
  def value: T = d.value

  /** Evaluate the given thunk, with the given value bound to this variable.
    * We also spawn a fresh thread pool, to ensure this value is propagated to
    * any new Futures.
  def withValue[A](v: T)(x: => A): A = d
    .withValue(v)(freshPool.pipe(_ => x))
    .tap(_ => freshPool)

final object DynamicallyScoped {
  def apply[T](init: T) = new DynamicallyScoped(new DynamicVariable[T](init))

I was very wary when implementing this, so the test suite needed to be pretty thorough to convince me that it works. I ended up implementing a toy little programming language involving dynamic variables, futures and delays. I then wrote two interpreters: a pure, simple, single-threaded interpreter which I could be confident was correct; and an interpreter which uses DynamicallyScoped, real Futures and a StackedExecutionContext. I used ScalaCheck to generate arbitrary programs in this language, and tested whether both interpreters gave the same result.

That’s actually pretty cool on its own, and probably deserves a standalone blog post!