The Iterate Manual

iterate

Copyright © 1989 Jonathan Amsterdam <jba at ai.mit.edu>
Copyright © 2006 Luís Oliveira <loliveira at common-lisp.net>

The present manual is an adaptation of Jonathan Amsterdam's “The Iterate Manual”, MIT AI Memo No. 1236. Said memo mentioned the following contract information:

This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-85-K-0124.

• Introduction
• Clauses
• Other Features
• Types and Declarations
• Problems with Code Movement
• Differences Between Iterate and Loop
• Rolling Your Own
• Non-portable Extensions to Iterate (Contribs)
• Obtaining Iterate
• Acknowledgements
• Don't Loop Iterate
• Comprehensive Index

1 Introduction

This manual describes iterate, a powerful iteration facility for Common Lisp. iterate provides abstractions for many common iteration patterns and allows for the definition of additional patterns. iterate is a macro that expands into ordinary Lisp at compile-time, so it is more efficient than higher-order functions like map and reduce. While it is similar to loop, iterate offers a more Lisp-like syntax and enhanced extensibility. (For a more complete comparison of iterate with other iteration constructs, see MIT AI Lab Working Paper No. 324, Don't Loop, Iterate. also included in this manual in Don't Loop Iterate.)

An iterate form consists of the symbol iter¹ followed by one or more forms, some of which may be iterate clauses. Here is a simple example of iterate which collects the numbers from 1 to 10 into a list, and returns the list. The return value is shown following the arrow.

  (iter (for i from 1 to 10)
        (collect i))          => (1 2 3 4 5 6 7 8 9 10)

This form contains two clauses: a for clause that steps the variable i over the integers from 1 to 10, and a collect clause that accumulates its argument into a list. With a few exceptions, all iterate clauses have the same format: alternating symbols (called keywords) and expressions (called arguments). The syntax and terminology are those of Common Lisp's keyword lambda lists. One difference is that iterate's keywords do not have to begin with a colon—though they may, except for the first symbol of a clause. So you can also write (for i :from 1 :to 10) if you prefer.

Any Lisp form can appear in the body of an iterate, where it will have its usual meaning. iterate walks the entire body, expanding macros, and recognizing clauses at any level. This example collects all the odd numbers in a list:

  (iter (for el in list)
        (if (and (numberp el) (oddp el))
            (collect el)))

There are clauses for iterating over numbers, lists, arrays and other objects, and for collecting, summing, counting, maximizing and other useful operations. iterate also supports the creation of new variable bindings, stepping over multiple sequences at once, destructuring, and compiler declarations of variable types. The following example illustrates some of these features:

  (iter (for (key . item) in alist)
        (for i from 0)
        (declare (fixnum i))
        (collect (cons i key)))

This loop takes the keys of an alist and returns a new alist associating the keys with their positions in the original list. The compiler declaration for i will appear in the generated code in the appropriate place.

2 Clauses

Most of iterate's clauses will be familiar to loop programmers. (loop is an iteration macro that has been incorporated into Common Lisp. See Guy Steele's Common Lisp, 2nd Edition.) In nearly all cases they behave the same as their loop counterparts, so a loop user can switch to iterate with little pain (and much gain).

All clauses with the standard keyword-argument syntax consist of two parts: a required part, containing keywords that must be present and in the right order; and an optional part, containing keywords that may be omitted and, if present, may occur in any order. In the descriptions below, the parts are separated by the Lisp lambda-list keyword &optional.

• Drivers
• Variable Binding and Setting
• Gathering Clauses
• Control Flow
• Code Placement

2.1 Drivers

An iteration-driving clause conceptually causes the iteration to go forward. Driver clauses in iterate allow iteration over numbers, lists, vectors, hashtables, packages, files and streams. Iteration-driving clauses must appear at the top level of an iterate form; they cannot be nested inside another clause. The driver variable is updated at the point where the driver clause occurs. Before the clause is executed for the first time, the value of the variable is undefined.

Multiple drivers may appear in a single iterate form, in which case all of the driver variables are updated each time through the loop, in the order in which the clauses appear. The first driver to terminate will terminate the entire loop.

In all cases, the value of the driver variable on exit from the loop, including within the epilogue code (see the finally clause), is undefined.

All the parameters of a driver clause are evaluated once, before the loop begins. Hence it is not possible to change the bounds or other properties of an iteration by side-effect from within the loop.

With one exception, driver clauses begin with the word for (or the synonym as) and mention an iteration variable, which is given a binding within the iterate form. The exception is repeat, which just executes a loop a specified number of times:

• Numerical Iteration
• Sequence Iteration
• Generalized Drivers
• Generators
• Previous Values of Driver Variables

2.1.1 Numerical Iteration

2.1.2 Sequence Iteration

There are a number of clauses for iterating over sequences. In all of them, the argument following for may be a list instead of a symbol, in which case destructuring is performed. See Destructuring.

These two clauses use atom to test for the end of a list. Hence, given a list whose final cdr is not nil, they will silently ignore the last cdr. Other choices are endp, which would signal an error, and null, which would probably result in an error somewhere else. If you wish to use an end-test other than atom, set the variable iterate::*list-end-test* to the name of the desired function.

2.1.3 Generalized Drivers

These are primarily useful for writing drivers that can also be used as generators (see Generators).

2.1.4 Generators

In all of the above clauses, the driver variable is updated on each iteration. Sometimes it is desirable to have greater control over updating. For instance, consider the problem of associating numbers, in increasing order and with no gaps, with the non-nil elements of a list. One obvious first pass at writing this is:

  (iter (for el in list)
        (for i upfrom 1)
        (if el (collect (cons el i))))

But on the list (a b nil c) this produces ((a . 1) (b . 2) (c . 4)) instead of the desired ((a . 1) (b . 2) (c . 3)). The problem is that i is incremented each time through the loop, even when el is nil.

The problem could be solved elegantly if we could step i only when we wished to. This can be accomplished for any iterate driver by writing generate (or its synonym generating) instead of for. Doing so produces a generator—a driver whose values are yielded explicitly. To obtain the next value of a generator variable v, write (next v). The value of a next form is the next value of v, as determined by its associated driver clause. next also has the side-effect of updating v to that value. If there is no next value, next will terminate the loop, just as with a normal driver.

Using generators, we can now write our example like this:

  (iter (for el in list)
        (generate i upfrom 1)
        (if el (collect (cons el (next i)))))

Now i is updated only when (next i) is executed, and this occurs only when el is non-nil.

To better understand the relationship between ordinary drivers and generators, observe that we can rewrite an ordinary driver using its generator form immediately followed by next, as this example shows:

  (iter (generating i from 1 to 10)
        (next i)
        ...)

Provided that the loop body contains no (next i) forms, this will behave just as if we had written (for i from 1 to 10).

We can still refer to a driver variable v without using next; in this case, its value is that given to it by the last evaluation of (next v). Before (next v) has been called the first time, the value of v is undefined.

This semantics is more flexible than one in which v begins the loop bound to its first value and calls of next supply subsequent values, because it means the loop will not terminate too soon if the generator's sequence is empty. For instance, consider the following code, which tags non-nil elements of a list using a list of tags, and also counts the null elements. (We assume there are at least as many tags as non-nil elements.)

  (let* ((counter 0)
         (tagged-list (iter (for el in list)
                            (generating tag in tag-list)
                            (if (null el)
                                (incf counter)
                                (collect (cons el (next tag)))))))
    ...)

It may be that there are just as many tags as non-null elements of list. If all the elements of list are null, we still want the counting to proceed, even though tag-list is nil. If tag had to be assigned its first value before the loop begins, we would have had to terminate the loop before the first iteration, since when tag-list is nil, tag has no first value. With the existing semantics, however, (next tag) will never execute, so the iteration will cover all the elements of list.

When the “variable” of a driver clause is actually a destructuring template containing several variables, all the variables are eligible for use with next. As before, (next v) evaluates to v's next value; but the effect is to update all of the template's variables. For instance, the following code will return the list (a 2 c).

  (iter (generating (key . item) in '((a . 1) (b . 2) (c . 3)))
        (collect (next key))
        (collect (next item)))

Only driver clauses with variables can be made into generators. This includes all clauses mentioned so far except for repeat. It does not include for... previous, for... =, for... initially... then or for... first... then (see below).

2.1.5 Previous Values of Driver Variables

Often one would like to access the value of a variable on a previous iteration. iterate provides a special clause for accomplishing this.

2.2 Variable Binding and Setting

Several clauses exist for establishing new variable bindings or for setting variables in the loop. They all support destructuring.

Compatibility Note: loop's for... = works like iterate's, but loop used the syntax for... =... then to mean for... initially... then. It was felt that these two operations were sufficiently different to warrant different keywords.

Also, the for in the above three clauses is misleading, since none is true driver (e.g. none has a corresponding generate form). setting would have been a better choice, but for was used to retain some compatibility with loop.

2.3 Gathering Clauses

Many of iterate's clauses accumulate values into a variable, or set a variable under certain conditions. At the end of the loop, this variable contains the desired result. All these clauses have an optional into keyword, whose argument should be a symbol. If the into keyword is not supplied, the accumulation variable will be internally generated and its value will be returned at the end of the loop; if a variable is specified, that variable is used for the accumulation, and is not returned as a result—it is up to the user to return it explicitly, in the loop's epilogue code (see finally). It is safe to examine the accumulation variable during the loop, but it should not be modified.

These clauses all begin with a verb. When the verb does not conflict with an existing Common Lisp function, then it may be used in either its infinitival or present-participle form (e.g. sum, summing). However, when there is a conflict with Common Lisp, only the present-participle form may be used (e.g. unioning). This is to prevent iterate clauses from clashing with Common Lisp functions.

• Reductions
• Accumulations
• Finders
• Boolean Tests
• Aggregated Boolean Tests

2.3.1 Reductions

Reduction is an extremely common iteration pattern in which the results of successive applications of a binary operation are accumulated. For example, a loop that computes the sum of the elements of a list is performing a reduction with the addition operation. This could be written in Common Lisp as (reduce #'+ list) or with iterate as

  (iter (for el in list)
        (sum el))

2.3.2 Accumulations

All the predefined accumulation clauses add values to a sequence. If the sequence is a list, they all have the property that the partial list is kept in the correct order and available for inspection at any point in the loop.

2.3.3 Finders

A finder is a clause whose value is an expression that meets some condition.

2.3.4 Boolean Tests

2.3.5 Aggregated Boolean Tests

2.4 Control Flow

Several clauses can be used to alter the usual flow of control in a loop.

Note: the clauses of this and subsequent sections don't adhere to iterate's usual syntax, but instead use standard Common Lisp syntax. Hence the format for describing syntax subsequently is like the standard format used in the Common Lisp manual, not like the descriptions of clauses above.

2.5 Code Placement

When fine control is desired over where code appears in a loop generated by iterate, the following special clauses may be useful. They are all subject to code-motion problems (see Problems with Code Movement).

3 Other Features

3.1 Multiple Accumulations

It is permitted to have more than one clause accumulate into the same variable, as in the following:

  (iter (for i from 1 to 10)
        (collect i into nums)
        (collect (sqrt i) into nums)
        (finally (return nums)))

Clauses can only accumulate into the same variable if they are compatible. collect, adjoining, appending, nconcing, unioning and nunioning are compatible with each other; sum, multiply and counting are compatible; always and never are compatible; finding... such-that is compatible with either thereis or always and never when their defaults match; and maximize and minimize clauses are compatible only with other maximize and minimize clauses, respectively.

• Named Blocks
• Destructuring
• On-line Help
• Parallel Binding and Stepping

3.2 Named Blocks

Like Common Lisp blocks, iterate forms can be given names. The name should be a single symbol, and it must be the first form in the iterate. The generated code behaves exactly like a named block; in particular, (return-from name) can be used to exit it:

  (iter fred
        (for i from 1 to 10)
        (iter barney
              (for j from i to 10)
              (if (> (* i j) 17)
                  (return-from fred j))))

An iterate form that is not given a name is implicitly named nil.

Sometimes one would like to write an expression in an inner iterate form, but have it processed by an outer iterate form. This is possible with the in clause.

3.3 Destructuring

In many places within iterate clauses where a variable is expected, a list can be written instead. In these cases, the value to be assigned is destructured according to the pattern described by the list. As a simple example, the clause

  (for (key . item) in alist)

will result in key being set to the car of each element in alist, and item being set to the cdr. The pattern list may be nested to arbitrary depth, and (as the example shows) need not be terminated with nil; the only requirement is that each leaf be a bindable symbol (or nil, in which case no binding is generated for that piece of the structure).

Sometimes, you might like to do the equivalent of a multiple-value-setq in a clause. This “multiple-value destructuring” can be expressed by writing (values pat1 pat2 ...) for a destructuring pattern, as in

  (for (values (a . b) c d) = (three-valued-function ...))

Note that the pati can themselves be destructuring patterns (though not multiple-value destructuring patterns). You can't do multiple-value destructuring in a with clause; instead wrap the whole iterate form in a multiple-value-bind.

Rationale: There are subtle interactions between variable declarations and evaluation order that make the correct implementation of multiple-value destructuring in a with somewhat tricky.

The destructuring feature of iterate is available as a separate mechanism, using the dsetq macro:

3.4 On-line Help

There is a limited facility for on-line help, in the form of the display-iterate-clauses function.

3.5 Parallel Binding and Stepping

The parallel binding and stepping of variables is a feature that iterate does not have. This section attempts to provide a rationale.

We say that two variables are bound in parallel if neither binding shadows the other. This is the usual semantics of let (as opposed to let*). Similarly, we can say that iteration variables are stepped in parallel if neither variable is updated before the other, conceptually speaking; in other words, if the code to update each variable can reference the old values of both variables.

loop allows parallel binding of variables and parallel stepping of driver variables. My view is that if you are depending on the serial/parallel distinction, you are doing something obscure. If you need to bind variables in parallel using with, then you must be using a variable name that shadows a name in the existing lexical environment. Don't do that. The most common use for parallel stepping is to track the values of variables on the previous iteration, but in fact this does not require parallel stepping at all; the following will work:

  (iter (for current in list)
        (for prev previous current)
        ...)

4 Types and Declarations

4.1 Discussion

Sometimes efficiency dictates that the types of variables be declared. This type information needs to be communicated to iterate so it can bind variables to appropriate values. Furthermore, iterate must often generate internal variables invisible to the user; there needs to be a way for these to be declared.

As an example, consider this code, which will return the number of odd elements in number-list:

  (iter (for el in number-list)
        (count (oddp el)))

In processing this form, iterate will create an internal variable, let us call it list17, to hold the successive cdrs of number-list, and will bind the variable to number-list. It will also generate a default binding for el; only inside the body of the loop will el be set to the car of list17. Finally, iterate will generate a variable, call it result, to hold the result of the count, and will bind it to zero.

When dealing with type declarations, iterate observes one simple rule: it will never generate a declaration unless requested to do so. The reason is that such declarations might mask errors in compiled code by avoiding error-checks; the resulting problems would be doubly hard to track down because the declarations would be hidden from the programmer. Of course, a compiler might omit error-checks even in the absence of declarations, though this behavior can usually be avoided, e.g. by saying (declaim (optimize (safety 3))).

So, the above iterate form will generate code with no declarations. But say we wish to declare the types of el and the internal variables list17 and result. How is this done?

Declaring the type of el is easy, since the programmer knows the variable's name:

  (iter (for el in number-list)
        (declare (fixnum el))
        (counting (oddp el)))

iterate can read variable type declarations like this one. Before processing any clauses, it scans the entire top-level form for type declarations and records the types, so that variable bindings can be performed correctly. In this case, el will be bound to zero instead of nil. Also, iterate collects all the top-level declarations and puts them at the begining of the generated code, so it is not necessary to place all declarations at the beginning of an iterate form; instead, they can be written near the variables whose types they declare.

Since iterate is not part of the compiler, it will not know about declarations that occur outside an iterate form; these declarations must be repeated inside the form.

Here is another way we could have declared the type of el:

  (iter (for (the fixnum el) in number-list)
        (counting (oddp el)))

iterate extends the Common Lisp the form to apply to variables as well as value-producing forms; anywhere a variable is allowed—in a with clause, as the iteration variable in a driver clause, as the into argument of an accumulation clause, even inside a destructuring template—you can write (the type symbol) instead.

There is one crucial difference between using a the form and actually declaring the variable: explicit declarations are always placed in the generated code, but type information from a the form is not turned into an actual declaration unless you tell iterate to do so using iterate:declare-variables. See below.

Declaring the types of internal variables is harder than declaring the types of explicitly mentioned variables, since their names are unknown. You do it by declaring iterate:declare-variables somewhere inside the top level of the iterate form. (This will also generate declarations for variables declared using the.) iterate does not provide much selectivity here: it's all or none. And unfortunately, since iterate is not privy to compiler information but instead reads declarations itself, it will not hear if you (declaim (iterate:declare-variables)). Instead, set the variable iterate::*always-declare-variables* to t at compile-time, using eval-when.

To determine the appropriate types for internal variables, iterate uses three sources of information:

• Often, the particular clause dictates a certain type for a variable; iterate will use this information when available. In the current example, the variable list17 will be given the type list, since that is the only type that makes sense; and the variable result will be given the type fixnum, on the assumption that you will not be counting high enough to need bignums. You can override this assumption only by using and explicitly declaring a variable:

         (iter (declare (iterate:declare-variables))
               (for el in number-list)
               (count (oddp el) into my-result)
               (declare (integer my-result))
               (finally (return my-result)))
    

  Other examples of the type assumptions that iterate makes are: type list for into variables of collection clauses; type list for expressions that are to be destructured; type vector for the variable holding the vector in a for... in-vector clause, and similarly for string and the for... in-string clause; and the implementation-dependent type (type-of array-dimension-limit) for the index and limit variables generated by sequence iteration drivers like for... in-vector and for... in-string (but not for... in-sequence, because it may be used to iterate over a list).

• Sometimes, iterate will examine expressions and try to determine their types in a simple-minded way. If the expression is self-evaluating (like a number, for instance), iterate knows that the expression's type is the same as the type of the value it denotes, so it can use that type. If the expression is of the form (the type expr), iterate is smart enough to extract type and use it. However, the current version of iterate does not examine declarations of function result types or do any type inference. It will not determine, for example, that the type of (+ 3 4) is fixnum, or even number.
• In some cases, the type of an internal variable should match the type of some other variable. For instance, iterate generates an internal variable for (f x) in the clause (for i from 1 to (f x)), and in the absence of other information will give it the same type as i. If, however, the expression had been written (the fixnum (f x)), then iterate would have given the internal variable the type fixnum regardless of i's type. The type incompatibility errors that could arise in this situation are not checked for.

Note that if you do declare iterate:declare-variables, then iterate may declare user variables as well as internal ones if they do not already have declarations, though only for variables that it binds. For instance, in this code:

  (iter (declare (iterate:declare-variables))
        (for i from 1 to 10)
        (collect i into var))

the variable var will be declared to be of type list.

4.2 Summary

iterate understands standard Common Lisp variable type declarations that occur within an iterate form and will pass them through to the generated code. If the declaration (iterate:declare-variables) appears at the top level of an iterate form, or if iterate::*always-declare-variables* is non-nil, then iterate will use the type information gleaned from user declarations, self-evaluating expressions and the expressions, combined with reasonable assumptions, to determine variable types and declare them.

5 Problems with Code Movement

Some iterate clauses, or parts of clauses, result in code being moved from the location of the clause to other parts of the loop. Drivers behave this way, as do code-placement clauses like initially and finally. When using these clauses, there is a danger of writing an expression that makes sense in its apparent location but will be invalid or have a different meaning in another location. For example:

  (iter (for i from 1 to 10)
        (let ((x 3))
          (initially (setq x 4))))

While it may appear that the x of (initially (setq x 4)) is the same as the x of (let ((x 3)) ..., in fact they are not: initially moves its code outside the loop body, so x would refer to a global variable. Here is another example of the same problem:

  (iter (for i from 1 to 10)
        (let ((x 3))
          (collect i into x)))

If this code were executed, collect would create a binding for its x at the top level of the iterate form that the let will shadow.

Happily, iterate is smart enough to catch these errors; it walks all problematical code to ensure that free variables are not bound inside the loop body, and checks all variables it binds for the same problem.

However, some errors cannot be caught:

  (iter (with x = 3)
        (for el in list)
        (setq x 1)
        (reducing el by #'+ initial-value x))

reducing moves its initial-value argument to the initialization part of the loop in order to produce more efficient code. Since iterate does not perform data-flow analysis, it cannot determine that x is changed inside the loop; all it can establish is that x is not bound internally. Hence this code will not signal an error and will use 3 as the initial value of the reduction.

The following list summarizes all cases that are subject to these code motion and variable-shadowing problems.

• Any variable for which iterate creates a binding, including those used in with and the into keyword of many clauses.
• The special clauses which place code: initially, after-each, else, finally and finally-protected.
• The variables of a next or do-next form.
• The initially arguments of for... initially... then and for... previous.
• The then argument of for... initially... then.
• The initial-value arguments of reducing and accumulate.
• The on-failure argument of finding... such-that.

6 Differences Between Iterate and Loop

loop contains a great deal of complexity which iterate tries to avoid. Hence many esoteric features of loop don't exist in iterate. Other features have been carried over, but in a cleaned-up form. And of course, many new features have been added; they are not mentioned in this list.

• iterate's syntax is more Lisp-like than loop's, having a higher density of parens.
• The current implementation of iterate, unlike the current version of loop (as documented in Common Lisp, 2nd Ed.), is extensible (see Rolling Your Own).
• loop puts the updates of all driver variables at the top of the loop; iterate leaves them where the driver clauses appear.
• While for the most part iterate clauses that resemble loop clauses behave similarly, there are some differences. For instance, there is no for... =... then in iterate; instead use for... initially... then.
• loop binds the variable it at certain times to allow pseudo-English expressions like when expr return it. In iterate, you must bind expr to a variable yourself. Note that when expr return it is like thereis expr except that the latter is an accumulation clause and therefore competes with other accumulations (remember “Multiple Accumulations” in Other Features).
• loop has a special return clause, illustrated in the previous item. iterate doesn't need one, since an ordinary Lisp return has the same effect.
• loop allows for parallel binding and stepping of iteration variables. iterate does not. (See Parallel Binding and Stepping.)
• loop and iterate handle variable type declarations very differently. loop provides a special syntax for declaring variable types, and does not examine declarations. Moreover, the standard implementation of loop will generate declarations when none are requested. iterate parses standard Common Lisp type declarations, and will never declare a variable itself unless declarations are specifically requested.

7 Rolling Your Own

7.1 Introduction

iterate is extensible—you can write new clauses that embody new iteration patterns. You might want to write a new driver clause for a data structure of your own, or you might want to write a clause that collects or manipulates elements in a way not provided by iterate.

This section describes how to write clauses for iterate. Writing a clause is like writing a macro. In fact, writing a clause is writing a macro: since iterate code-walks its body and macroexpands, you can add new abstractions to iterate with good old defmacro.

Actually, there are two extensions you can make to iterate that are even easier than writing a macro. They are adding a synonym for an existing clause and defining a driver clause for an indexable sequence. These can be done with defsynonym and defclause-sequence, respectively. See Extensibility Aids.

The rest of this section explains how to write macros that expand into iterate clauses. Here's how you could add a simplified version of iterate's multiply clause, if iterate didn't already have one:

  (defmacro multiply (expr)
    `(reducing ,expr by #'* initial-value 1))

If you found yourself summing the square of an expression often, you might want to write a macro for that. A first cut might be

  (defmacro sum-of-squares (expr)
    `(sum (* ,expr ,expr)))

but if you are an experienced macro writer, you will realize that this code will evaluate expr twice, which is probably a bad idea. A better version would use a temporary:

  (defmacro sum-of-squares (expr)
    (let ((temp (gensym)))
      `(let ((,temp ,expr))
         (sum (* ,temp ,temp)))))

Although this may seem complex, it is just the sort of thing you'd have to go through to write any macro, which illustrates the point of this section: if you can write macros, you can extend iterate.

Our macros don't use iterate's keyword-argument syntax. We could just use keywords with defmacro, but we would still not be using iterate's clause indexing mechanism. Unlike Lisp, which uses just the first symbol of a form to determine what function to call, iterate individuates clauses by the list of required keywords. For instance, for... in and for... in-vector are different clauses implemented by distinct Lisp functions.

To buy into this indexing scheme, as well as the keyword-argument syntax, use defmacro-clause:

Here is multiply using defmacro-clause. The keywords are capitalized for readability.

  (defmacro-clause (MULTIPLY expr &optional INTO var)
    `(reducing ,expr by #'* into ,var initial-value 1))

You don't have to worry about the case when var is not supplied; for any clause with an into keyword, saying into nil is equivalent to omitting the into entirely.

As another, more extended example, consider the fairly common iteration pattern that involves finding the sequence element that maximizes (or minimizes) some function. iterate provides this as finding... maximizing, but it's instructive to see how to write it. Here, in pseudocode, is how you might write such a loop for maximizing a function F:

  set variable MAX-VAL to NIL;
  set variable WINNER to NIL;
  for each element EL in the sequence
      if MAX-VAL is NIL or F(EL) > MAX-VAL then
          set MAX-VAL to F(EL);
          set WINNER to EL;
      end if;
  end for;
  return WINNER.

Here is the macro:

  (defmacro-clause (FINDING expr MAXIMIZING func &optional INTO var)
    (let ((max-val (gensym))
          (temp1 (gensym))
          (temp2 (gensym))
          (winner (or var iterate::*result-var*)))
      `(progn
         (with ,max-val = nil)
         (with ,winner = nil)
         (cond
          ((null ,max-val)
           (setq ,winner ,expr)
           (setq ,max-val (funcall ,func ,winner))
          (t
           (let* ((,temp1 ,expr)
                  (,temp2 (funcall ,func ,temp1)))
             (when (> ,temp2 ,max-val)
               (setq ,max-val ,temp2)
               (setq ,winner ,temp1))))))
         (finally (leave ,winner)))))

Note that if no into variable is supplied, we use iterate::*result-var*, which contains the internal variable into which all clauses place their results. If this variable is bound by some clause, then iterate will return its value automatically; otherwise, nil will be returned.

• Writing Drivers
• Extensibility Aids
• Subtleties

7.2 Writing Drivers

In principle, drivers can be implemented just as easily as other iterate clauses. In practice, they are a little harder to get right. As an example, consider writing a driver that iterates over all the elements of a vector, ignoring its fill-pointer. for... in-vector won't work for this, because it observes the fill-pointer. It's necessary to use array-dimension instead of length to obtain the size of the vector. Here is one approach:

  (defmacro-clause (FOR var IN-WHOLE-VECTOR v)
    "All the elements of a vector (disregards fill-pointer)"
    (let ((vect (gensym))
          (index (gensym)))
      `(progn
         (with ,vect = ,v)
         (for ,index from 0 below (array-dimension ,vect 0))
         (for ,var = (aref ,vect ,index)))))

Note that we immediately put v in a variable, in case it is an expression. Again, this is just good Lisp macrology. It also has a subtle effect on the semantics of the driver: v is evaluated only once, at the beginning of the loop, so changes to v in the loop have no effect on the driver. Similarly, the bounds for numerical iteration e.g. the above array-dimension are also evaluated once only. This is how all of iterate's drivers work.

There is an important point concerning the progn in this code. We need the progn, of course, because we are returning several forms, one of which is a driver. But iterate drivers must occur at top-level. Is this code in error? No, because top-level is defined in iterate to include forms inside a progn. This is just the definition of top-level that Common Lisp uses, and for the same reason: to allow macros to return multiple forms at top-level.

While our for... in-whole-vector clause will work, it is not ideal. In particular, it does not support generating. Do do so, we need to use for... next or for... do-next. The job is simplified by the defmacro-driver macro.

With defmacro-driver, our driver looks like this:

  (defmacro-driver (FOR var IN-WHOLE-VECTOR v)
    "All the elements of a vector (disregards fill-pointer)"
     (let ((vect (gensym))
           (end (gensym))
           (index (gensym))
           (kwd (if generate 'generate 'for)))
       `(progn
          (with ,vect = ,v)
          (with ,end = (array-dimension ,vect 0))
          (with ,index = -1)
          (,kwd ,var next (progn (incf ,index)
                                 (if (>= ,index ,end) (terminate))
                                 (aref ,vect ,index))))))

We are still missing one thing: the &sequence keywords. We can get them easily enough, by writing

  (defmacro-driver (FOR var IN-WHOLE-VECTOR v &sequence)
    ...)

We can now refer to parameters from, to, by, etc. which contain either the values for the corresponding keyword, or nil if the keyword was not supplied. Implementing the right code for these keywords is cumbersome but not difficult; it is left as an exercise. But before you begin, see defclause-sequence below for an easier way.

7.3 Extensibility Aids

This section documents assorted features that may be of use in extending iterate.

7.4 Subtleties

There are some subtleties to be aware of when writing iterate clauses. First, the code returned by your macros may be nconc'ed into a list, so you should always returned freshly consed lists, rather than constants. Second, iterate matches clauses by using eq on the first symbol and string= on the subsequent ones, so the package of the first symbol of a clause is relevant. All of the clauses in this manual have their first word in the iterate package. You can use the package system in the usual way to shadow iterate clauses without replacing them.

8 Non-portable Extensions to Iterate (Contribs)

Currently, there is only one non-portable extension to iterate in the distribution: iterate-pg. If you have made an extension that depends on non-portable features, feel free to send them to asf@boinkor.net for inclusion in the iterate distribution.

8.1 An SQL query driver for iterate

The pg package by Eric Marsden (see http://cliki.net/pg) provides an interface to the PostgreSQL database. Using the iterpg extension, it is possible to handle the results of SQL queries using iter.

This usage example should give you an idea of how to use it:

  (pg:with-pg-connection (c "somedb" "someuser")
    (iter (for (impl version date) in-relation "select * from version"
                                   on-connection *dbconn*)
          (collect version)))

To use the extension via ASDF, simply make your system depend on the iterate-pg system instead of the iterate system. To load it manually, use:

  (asdf:oos 'asdf:load-op :iterate-pg)

9 Obtaining Iterate

Implementor's note: This chapter is very, very obsolete.

The information in this chapter is obsolete but included for completeness's sake; Currently, the most up-to-date information on iterate can be found at http://boinkor.net/iterate.html.

iterate currently runs on Lisp Machines, and on HP's, Sun3's and Sparcstations under Lucid. iterate source and binaries are available at the MIT AI Lab in the subdirectories of /src/local/lisplib/. The source file, iterate.lisp, is also available for anonymous FTP in the directory /com/fpt/pub/ on the machine TRIX.AI.MIT.EDU (Internet number 128.52.32.6). If you are unable to obtain iterate in one of these ways, send mail to jba@ai.mit.edu and I will send you the source file.

iterate resides in the iterate package (nickname iter). Just say (use-package :iterate) to make all the necessary symbols available. If a symbol is not exported, it appears in this manual with an “iterate::” prefix.

Send bug reports to bug-iterate@ai.mit.edu. The info-iterate mailing list will have notices of changes and problems; to have yourself added, send mail to info-iterate-request@ai.mit.edu.

10 Acknowledgements

Richard Waters provided invaluable criticism which spurred me to improve iterate greatly. As early users, David Clemens, Oren Etzioni and Jeff Siskind helped ferret out many bugs.

Appendix A Don't Loop, Iterate

Note: This appendix is a Texinfo conversion of Jonathan Amsterdam's Working Paper 324, MIT AI Lab entitled “Don't Loop, Iterate.”

A.1 Introduction

Above all the wonders of Lisp's pantheon stand its metalinguistic tools; by their grace have Lisp's acolytes been liberated from the rigid asceticism of lesser faiths. Thanks to Macro and kin, the jolly, complacent Lisp hacker can gaze through a fragrant cloud of setfs and defstructs at the emaciated unfortunates below, scraping out their meager code in inflexible notation, and sneer superciliously. It's a good feeling.

But all's not joy in Consville. For—I beg your pardon, but—there really is no good way to iterate in Lisp. Now, some are happy to map their way about, whether for real with mapcar and friends, or with the make-believe of Series; others are so satisfied with do it's a wonder they're not C hackers.² Still others have gotten by with loop, but are getting tired of looking up the syntax in the manual over and over again. And in the elegant schemes of some, only tail recursion and lambdas figure. But that still leaves a sizeable majority of folk—well, me, at least—who would simply like to iterate, thank you, but in a way that provides nice abstractions, is extensible, and looks like honest-to-God Lisp.

In what follows I describe a macro package, called iterate, that provides the power and convenient abstractions of loop but in a more syntactically palatable way. iter also has many features that loop lacks, like generators and better support for nested loops. iterate generates inline code, so it's more efficient than using the higher-order function approach. And iterate is also extensible—it's easy to add new clauses to its vocabulary in order to express new patterns of iteration in a convenient way.

A.2 More about iterate

A Common Lisp programmer who wonders what's lacking with present-day iteration features would do well to consider setf. Of course, setf doesn't iterate, but it has some other nice properties. It's easy to use, for one thing. It's extensible—you can define new setf methods very easily, so that setf will work with new forms. setf is also efficient, turning into code that's as good as anyone could write by hand. Arguably, setf provides a nice abstraction: it allows you to view value-returning forms, like (car ...) or (get ...) as locations that can be stored into. Finally and most obviously, setf looks like Lisp; it's got a syntax right out of setq.

iterate attempts to provide all of these properties. Here is a simple use of iterate that returns all the elements of num-list that are greater than three:

  (iterate (for el in num-list)
           (when (> el 3)
             (collect el)))

An iterate form consists of the symbol iterate followed by some Lisp forms. Any legal Lisp form is allowed, as well as certain forms that iterate treats specially, called clauses. for...in and collect are the two clauses in the above example. An iterate clause can appear anywhere a Lisp form can appear; iterate walks its body, looking inside every form, processing iterate clauses when it finds them. It even expands macros, so you can write macros that contain iterate clauses. Almost all clauses use the syntax of function keyword-argument lists: alternating keywords and arguments. iterate keywords don't require a preceding colon, but you can use one if you like.

iterate provides many convenient iteration abstractions, most of them familiar to loop users. Iteration-driving clauses (those beginning with for) can iterate over numbers, lists, arrays, hashtables, packages and files. There are clauses for collecting values into a list, summing and counting, maximizing, finding maximal elements, and various other things. Here are a few examples, for extra flavor.

To sum a list of numbers:

  (iterate (for i in list)
           (sum i))

To find the length of the shortest element in a list:

  (iterate (for el in list)
           (minimize (length el)))

To find the shortest element in a list:

  (iterate (for el in list)
           (finding el minimizing (length el)))

To return t only if every other element of a list is odd:

  (iterate (for els on list by #'cddr)
           (always (oddp (car els))))

To split an association list into two separate lists (this example uses iterate's ability to do destructuring):

  (iterate (for (key . item) in alist)
           (collect key into keys)
           (collect item into items)
           (finally (return (values keys items))))

A.3 Comparisons With Other Iteration Methods

As with any aspect of coding, how to iterate is a matter of taste. I do not wish to dictate taste or even to suggest that iterate is a “better” way to iterate than other methods. I would, however, like to examine the options, and explain why I prefer iterate to its competitors.

A.3.1 do, dotimes and dolist

The do form has long been a Lisp iteration staple. It provides for binding of iteration variables, an end-test, and a body of arbitrary code. It can be a bit cumbersome for simple applications, but the most common special cases—iterating over the integers from zero and over the members of a list—appear more conveniently as dotimes and dolist.

do's major problem is that it provides no abstraction. For example, collection is typically handled by binding a variable to nil, pushing elements onto the variable, and nreverseing the result before returning it. Such a common iteration pattern should be easier to write. (It is, using mapcar—but see below.)

Another problem with do, for me at least, is that it's hard to read. The crucial end-test is buried between the bindings and the body, marked off only by an extra set of parens (and some indentation). It is also unclear, until after a moment of recollection, whether the end-test has the sense of a “while” or an “until.”

Despite its flaws, do is superior to the iteration facilities of every other major programming language except CLU. Perhaps that is the reason many Lisp programmers don't mind using it.

A.3.2 Tail Recursion

Tail-recursive implementations of loops, like those found in Scheme code [SchemeBook], are parsimonious and illuminating. They have the advantage of looking like recursion, hence unifying the notation for two different types of processes. For example, if only tail-recursion is used, a loop that operates on list elements from front to back looks very much like a recursion that operates on them from back to front.

However, using tail-recursion exclusively can lead to cumbersome code and a proliferation of functions, especially when one would like to embed a loop inside a function. Tail-recursion also provides no abstraction for iteration; in Scheme, that is typically done with higher-order functions.

A.3.3 High-order Dunctions

Lisp's age-old mapping functions, recently revamped for Common Lisp [CLM], are another favorite for iteration. They provide a pleasing abstraction, and it's easy to write new higher-order functions to express common iteration patterns. Common Lisp already comes with many such useful functions, for removing, searching, and performing reductions on lists. Another Common Lisp advantage is that these functions work on any sequence—vectors as well as lists.

One problem with higher-order functions is that they are inefficient, requiring multiple calls on their argument function. While the the built-ins, like map and mapcar, can be open-coded, that cannot be so easily done for user-written functions. Also, using higher-order functions often results in the creation of intermediate sequences that could be avoided if the iteration were written out explicitly.

The second problem with higher-order functions is very much a matter of personal taste. While higher-order functions are theoretically elegant, they are often cumbersome to read and write. The unpleasant sharp-quote required by Common Lisp is particularly annoying here, and even in Scheme, I find the presence of a lambda with its argument list visually distracting.

Another problem is that it's difficult to express iteration of sequences of integers without creating such sequences explicitly as lists or arrays. One could resort to tail-recursion or dotimes—but then it becomes very messy to express double iterations where one driver is over integers. Multiple iteration is easy in iterate, as illustrated by the following example, which creates an alist of list elements and their positions:

  (iterate (for el in list)
           (for i from 0)
           (collect (cons el i)))

A.3.4 Streams and Generators

For really heavy-duty iteration jobs, nothing less than a coroutine-like mechanism will do. Such mechanisms hide the state of the iteration behind a convenient abstraction. A generator is a procedure that returns the next element in the iteration each time it is called. A stream (in the terminology of [SchemeBook]) is a data structure which represents the iteration, but which computes the next element only on demand. Generators and streams support a similar style of programming. Here, for example, is how you might enumerate the leaves of a tree (represented as a Lisp list with atoms at the leaves) using streams:

  (defun tree-leaves (tree)
    (if (atom tree)
        (stream-cons tree empty-stream)
        (stream-append (tree-leaves (car tree))
                       (tree-leaves (cdr tree)))))

Although tree-leaves looks like an ordinary recursion, it will only do enough work to find the first leaf before returning. The stream it returns can be accessed with stream-car, which will yield the (already computed) first leaf of the tree, or with stream-cdr, which will initiate computation of the next leaf.

Such a computation would be cumbersome to write using iterate, or any of the other standard iteration constructs; in fact, it is not even technically speaking an iteration, if we confine that term to processes that take constant space and linear time. Streams, then, are definitely more powerful than standard iteration machinery.

Unfortunately, streams are very expensive, since they must somehow save the state of the computation. Generators are typically cheaper, but are less powerful and still require at least a function call. So these powerful tools should be used only when necessary, and that is not very often; most of the time, ordinary iteration suffices.

There is one aspect of generators that iterate can capture, and that is the ability to produce elements on demand. Say we wish to create an alist that pairs the non-null elements of a list with the positive integers. We saw above that it is easy to iterate over a list and a series of numbers simultaneously, but here we would like to do something a little different: we want to iterate over the list of elements, but only draw a number when we need one (namely, when a list element is non-null). The solution employs the iterate generate keyword in place of for and the special clause next:

  (iterate (for el in list)
           (generate i from 1)
           (if el
               (collect (cons el (next i)))))

Using next with any driver variable changes how that driver works. Instead of supplying its values one at a time on each iteration, the driver computes a value only when a next clause is executed. This ability to obtain values on demand greatly increases iterate's power. Here, el is set to the next element of the list on each iteration, as usual; but i is set only when (next i) is executed.

A.3.5 Series

Richard C. Waters has developed a very elegant notation called Series which allows iteration to be expressed as sequence-mapping somewhat in the style of APL, but which compiles to efficient looping code [Series].

My reasons for not using Series are, again, matters of taste. Like many elegant notations, Series can be somewhat cryptic. Understanding what a Series expression does can require some effort until one has mastered the idiom. And if you wish to share your code with others, they will have to learn Series as well. iterate suffers from this problem to some extent, but since the iteration metaphor it proposes is much more familiar to most programmers than that of Series, it is considerably easier to learn and read.

A.3.6 Prog and Go

Oh, don't be silly.

A.3.7 Loop

loop is the iteration construct most similar to iterate in appearance. loop is a macro written originally for MacLisp and in widespread use [Loop]. It has been adopted as part of Common Lisp. loop provides high-level iteration with abstractions for collecting, summing, maximizing and so on. Recall our first iterate example:

  (iterate (for el in num-list)
           (when (> el 3)
             (collect el)))

Expressed with \looP, it would read

  (loop for el in list
        when (> el 3)
          collect el)

The similarity between the two macros should immediately be apparent. Most of iterate's clauses were borrowed from loop. But compared to iterate, loop has a paucity of parens. Though touted as more readable than heavily-parenthesized code, loop's Pascalish syntax creates several problems. First, many dyed-in-the-wool Lisp hackers simply find it ugly. Second, it requires learning the syntax of a whole new sublanguage. Third, the absence of parens makes it hard to parse, both by machine and, more importantly, by human. Fourth, one often has to consult the manual to recall lesser-used aspects of the strange syntax. Fifth, there is no good interface with the rest of Lisp, so loop clauses cannot appear inside Lisp forms and macros cannot expand to pieces of loop. And Sixth, pretty-printers and indenters that don't know about loop will invariably display it wrongly. This is particularly a problem with program-editor indenters. A reasonably clever indenter, like that of Gnu Emacs, can indent nearly any normal Lisp form correctly, and can be easily customized for most new forms. But it can't at present handle loop. The syntax of iterate was designed to keep parens to a minimum, but conform close enough to Lisp so as not to confuse code-display tools. Gnu Emacs indents iterate reasonably with no modifications.

Indenting is a mere annoyance; loop's lack of extensibility is a more serious problem. The original loop was completely extensible, but the Symbolics version only provides for the definition of new iteration-driving clauses, and the Common Lisp specification does not have any extension mechanism. But extensibility is a boon. Consider first the problem of adding the elements of a list together, which can be accomplished with iterate by

  (iterate (for el in list)
           (sum el))

and in loop with

  (loop for el in list
        sum el)

But now, say that you wished to compute the sum of the square roots of the elements. You could of course write, in either loop or iterate,

  (iterate (for el in list)
           (sum (sqrt el)))

But perhaps you find yourself writing such loops often enough to make it worthwhile to create a new abstraction. There is nothing you can do in loop, but in iterate you could simply write a macro:

  (defmacro (sum-of-sqrts expr &optional into-var)
    `(sum (sqrt ,expr) into ,into-var))

sum-of-sqrts is a perfectly ordinary Lisp macro. Since iterate expands all macros and processes the results, (sum-of-sqrts el) will behave exactly as if we'd written (sum (sqrt el)).

There's also a way to define macros that use iterate's clause syntax.

A.4 Implementation

A Common Lisp implementation of iterate has existed for well over a year. It runs under Lucid for HP 300's, Sun 3's and SPARCstations, and on Symbolics Lisp machines.

A.5 Conclusion

Iteration is a matter of taste. I find iterate more palatable than other iteration constructs: it's more readable, more efficient than most, provides nice abstractions, and can be extended.

If you're new to Lisp iteration, start with iterate—look before you loop. If you're already using loop and like the power that it offers, but have had enough of its syntax and inflexibility, then my advice to you is, don't looo—iterate.

A.6 Acknowledgments

Thanks to David Clemens for many helpful suggestions and for the egregious pun near the end. Conversations with Richard Waters prompted me to add many improvements to iterate. Alan Bawden, Sundar Narasimhan, and Jerry Roylance also provided useful comments. David Clemens and Oren Etzioni shared with me the joys of beta-testing.

A.7 Bibliography

• [SchemeBook] Abelson, Harold and Gerald Jay Sussman. Structure and Interpretation of Computer Programs. Cambridge, MA: The MIT Press, 1985.
• [Loop] “The loop Iteration Macro.” In Symbolics Common Lisp—Language Concepts, manual 2A of the Symbolics documentation, pp. 541–567.
• [CLM] Steele, Guy L. Common Lisp: The Language. Bedford, MA: Digital Press, 1984.
• [Series] Waters, Richard C. Optimization of Series Expressions: Part I: User's Manual for the Series Macro Package. MIT AI Lab Memo No. 1082.

Index

• *always-declare-variables*: Types and Declarations
• *list-end-test*: Sequence Iteration
• *result-var*: Extensibility Aids
• accumulate: Accumulations
• adjoining: Accumulations
• after-each: Code Placement
• always: Aggregated Boolean Tests
• appending: Accumulations
• collect: Accumulations
• counting: Reductions
• declare-variables: Types and Declarations
• defclause-sequence: Extensibility Aids
• defmacro-clause: Rolling Your Own
• defmacro-driver: Writing Drivers
• defsynonym: Extensibility Aids
• display-iterate-clauses: On-line Help
• dsetq: Destructuring
• else: Code Placement
• finally: Code Placement
• finally-protected: Code Placement
• finding...maximizing: Finders
• finding...minimizing: Finders
• finding...such-that: Finders
• finish: Control Flow
• first-iteration-p: Boolean Tests
• first-time-p: Boolean Tests
• for: Numerical Iteration
• for...=: Variable Binding and Setting
• for...do-next: Generalized Drivers
• for...first...then: Variable Binding and Setting
• for...in: Sequence Iteration
• for...in-file: Sequence Iteration
• for...in-hashtable: Sequence Iteration
• for...in-package: Sequence Iteration
• for...in-packages: Sequence Iteration
• for...in-sequence: Sequence Iteration
• for...in-stream: Sequence Iteration
• for...in-string: Sequence Iteration
• for...in-vector: Sequence Iteration
• for...index-of-sequence: Sequence Iteration
• for...index-of-string: Sequence Iteration
• for...index-of-vector: Sequence Iteration
• for...initially...then: Variable Binding and Setting
• for...next: Generalized Drivers
• for...on: Sequence Iteration
• for...previous: Previous Values of Driver Variables
• if-first-time: Control Flow
• in: Named Blocks
• initially: Code Placement
• leave: Control Flow
• maximize: Reductions
• minimize: Reductions
• multiply: Reductions
• nconcing: Accumulations
• never: Aggregated Boolean Tests
• next-iteration: Control Flow
• nunioning: Accumulations
• reducing: Reductions
• repeat: Drivers
• sum: Reductions
• terminate: Generalized Drivers
• the: Types and Declarations
• thereis: Aggregated Boolean Tests
• unioning: Accumulations
• until: Control Flow
• while: Control Flow
• with: Variable Binding and Setting