April 21, 2010

Lessons I should have learned, Episode 4: Coroutines in C

Filed under: Lessons I should have learned,Programming — Tags: , , — Nathan @ 2:19 pm

A while ago, I read this article on coroutines in C. One of the main downfalls of this technique is that since it uses the static keyword, it makes it much harder to write recursive coroutines, and nigh-impossible to have multiple coroutines using the same function.

Examples

Let’s first look at an example of how it can work. One way of efficiently computing Fibonacci numbers in a language like Haskell is to construct an infinite list of the Fibonacci values.

fibs a b = a : fibs b (a+b)

Since Haskell is lazily evaluated, such an infinite list is fine until you attempt to compute the whole thing. So, as long as you just say something like take 10 $ fibs 1 1 it will only compute how many you need.

One important use of Coroutines is as iterators. Consider the Haskell example from above. If we assume that we have such an infinite list of values, we may want to iterate over the list, inspecting the values as we go. The following C code achieves this using some helper macros we’ll describe below.

int fibs(int a, int b, CoroutineState *s) {
  initializeCoroutineState(s, FibState, mystate);

  mystate->a = a;
  mystate->b = b;
  yield(s,mystate->a);
  recur (s,fibs(mystate->b, mystate->a + mystate->b, s->next));

  finalizeCoroutine(s);
  return 0;
}

Now, if we wanted to print out the first 10 of these values:

s = createCoroutineState();
for(i = 0; i < 10; i++) {
  printf("%d ", fibs(1,1,s));
}

The yield method is similar to Python's yield key word. It returns a value, but also records where the yield was called so that the next time the function is executed it can begin again from that point.

Another example of how coroutines can be used as iterators is as iterators over a binary tree. Consider the following tree:

       4
  2         6
1   3     5    7 

A standard way to traverse this tree is a post-order traversal. Consider the following code which traverses this tree and prints it out:

void print(Tree *t) {
  if(t->left)
    print(t->left);
  if(t->right)
    print(t->right);
  printf("%d ", t->value);
}

Building a post-order iterator is slightly more complex. It involves creating a stack and pushing wrapped tree-nodes onto the stack. When one is popped, it is marked as 'visited' and then pushed onto the stack, followed by its right child then left child. However, we can use coroutines to achieve the same thing while staying true to the original traversal code:

int treePostOrderIterator(Tree *t, CoroutineState *s) {
  initializeCoroutineState(s,TreeIteratorState, mystate);
  mystate->tree = t;

  if(mystate->tree->left)
    recur (s, treePostOrderIterator(mystate->tree->left, s->next));

  if(mystate->tree->right)
    recur (s, treePostOrderIterator(mystate->tree->right, s->next));

  yield (s,mystate->tree->value);

  finalizeCoroutine(s);
  return 0;
}

How it works

When I was learning to program, I learned that switch statements were just conditional structures that were equivalent to a series of if statements. While you can almost always get away with thinking about them this way, they aren't actually. Instead, think of switch statements as parameterized gotos. One of the less objectionable uses of goto is as a means of breaking out of nested loops. So, obviously goto can jump out of scopes. In fact, it can be used to jump into and out of any scope inside of a single function. The same is true of the cases of a switch statement. This is what makes Duff's device possible.

In order to facilitate recursion and having multiple coroutines running at once, we need to pass a mutable state parameter. This is an object of type struct CoroutineState.

typedef struct CoroutineState CoroutineState;
struct CoroutineState {
  void *state;            /* Pointer to a struct representing the internal state
                             of the coroutine.  We need this since we have to
                             reinstate the scope whenever the function is
                             re-intered.                                      */
  long current;           /* The line number of where the coroutine should
                             return.  Used in 'yield' and 'recur'             */
  unsigned char done;     /* A marker to show that a recurrence is completed  */
  CoroutineState *next;   /* The next in the stack of states                  */
  CoroutineState *parent; /* NULL if actually the parent, pointer otherwise   */
  CoroutineState *tail;   /* Only the parent is guaranteed to know the tail   */
};

Basically this struct stores everything needed to reinstate the coroutine when it is next called. This includes the line number it should begin executing on, a pointer to the user's saved values, and a stack containing the recursive calls.

Every coroutine begins with an invocation of the initializeCoroutineState macro. This is a macro that takes three parameters. The first is a CoroutineState object, the second is the type defining the user's saved values, and the last is the variable name to use to refer to this state.

#define initializeCoroutineState(coroutineState, UserStateType, userStateVar) \
  UserStateType *userStateVar = (UserStateType*) coroutineState->state;       \
  switch(coroutineState->current) {                                           \
  case 0:                                                                     \
  if(coroutineState->state == 0) {                                            \
    coroutineState->state = (void*) malloc(sizeof (UserStateType));           \
    bzero(coroutineState->state, sizeof(UserStateType));                      \
  }                                                                           \
  userStateVar = (UserStateType*) coroutineState->state;                      \

Note that there is a switch statement embedded here. This switch statement jumps to the appropriate line in the execution.

Every coroutine ends with finalizeCoroutine(s) which indicates that after this point the execution of the function should proceed normally. It also says that when this function returns that the coroutine is complete and should be removed from the stack. This is indicated internally with s->done =1;.

#define finalizeCoroutine(s)                                                  \
  }                                                                           \
  s->done = 1;                                                                \
  free(s->next);                                                              \
  s->next = 0;

Now, to the meat of the matter. yield needs to return the value the user wants to yield, but also record where this occurred so that the switch statement from above will jump to here. It also needs to reinstate any variables that the user had in scope. Unfortunately there's no good way of doing that in C, so we have to do it manually. Thus the need for the helper struct. Before the return in the following macro, we're just manipulating the stack so that we can record where we are. Notice coroutineState->current = __LINE__;. __LINE__ is a macro that evaluates to the current line number in the program. So, here we're recording the line number and then after the return, we have case __LINE__:. This will cause the coroutine to begin executing after the return the next time it's called.

#define yield(coroutineState, value)                                          \
  do{                                                                         \
    CoroutineState *parent = coroutineState ? coroutineState                  \
        : coroutineState->parent;                                             \
    coroutineState->current = __LINE__;                                       \
    parent->tail = coroutineState;                                            \
    free(coroutineState->next);                                               \
    coroutineState->next = 0;                                                 \
    return value;                                                             \
    case __LINE__: 1;                                                         \
  } while(0)                                                             

The last macro is perhaps the most interesting. It's the one that allows things like the infinite list iterator and the binary tree iterator. Basically, there are times when you want to have a recursive call and have the coroutine state stored along with having a return value bubble up.

The while loop is responsible for executing until the coroutine called below it is completed.

#define recur(coroutineState,func)                                            \
  do{                                                                         \
    CoroutineState *parent = coroutineState ? coroutineState                  \
        : coroutineState->parent;                                             \
    coroutineState->next = createCoroutineState();                            \
    coroutineState->next->parent = parent;                                    \
    parent->tail = coroutineState->next;                                      \
    while(!coroutineState->next->done) {                                      \
      CoroutineState *parent = coroutineState ? coroutineState                \
          : coroutineState->parent;                                           \
      typeof(func) tmp =  func;                                               \
      coroutineState->current = __LINE__;                                     \
      if(!parent->tail->done)                                                 \
        return tmp;                                                           \
      case __LINE__: 1;                                                       \
    }                                                                         \
  } while(0)                                                                  \

Using it

In order to get this to work, you have to do one extra thing. You need to create a struct which you use to store your state rather than use local-variables in your functions. This is necessary since we'll be re-entering the functions later and that state would be lost. So, just put all the variables you would want to use into the struct. Then use this struct in the initializeCoroutineState and then whenever you use a local variable.

If you would normally return a computed value (not the result of a recursive call) then just say yield (value); If you are doing a recursive call and intend for the coroutine to proceed down the recurrence, just wrap it in a recur(s, yourRecursiveCall()).

Get it

I've posted the implementation as a library on GitHub: http://github.com/nathanwiegand/coroutines.

April 11, 2010

The ugliest code I ever wrote: Parser generator written in XSLT

Filed under: Programming — Tags: , , , , — Nathan @ 4:41 pm

I took a compiler design course in the Spring semester of 2004. I cannot recommend enough that you should take a course like this at some point in your academic career. I hear more and more about how departments are removing this as a requirement, or making the course easier. I think this is a real shame, but that’s a debate for another day. This post is about my semester project for Compilers.

The assignment was to write a compiler for a subset of Pascal that generated bytecode for a VM that we were provided. Our compiler was to be written in C/C++, and we weren’t to use Lex/Yacc. So, my teammate Charles and I set upon breaking down the tasks. He wrote the lexer, and I wrote the parser.

First, a quick note. It was 2004. Things were weird in 2004. We were in the midst of several wars. Facebook was founded. Cassini reached Saturn. And, XML was big. At least in my group of friends it was BIG. So, when it came time to build the the parse tree, the obvious choice was to just use LibXML and use an XML tree. This let me trivially dump the entire tree as an XML doc. I then wrote an XSLT (even remember it?) to transform the XML to Graphviz Dot so I could visualize the parse tree. This was my version of testing.

After quite a few iterations, I had plenty of time until the deadline so I set out working on the obvious: a parser generator. We weren’t allowed to use Yacc, but no one said anything about writing our own.

Luckily the language we were parsing was quite simple so my parser was just recursive-descent. Thus, it was fairly easy to generalize it. So, what language does one write a parser generator in? XSLT of course. The choice was motivated by #1 someone told me I couldn’t do it and #2 there were serious wins to representing the grammar itself in XML.

So, how did it work? It took a grammar represented in XML. Then an XSLT transformed that grammar into a C parser for that language which then produced the parse tree as an XML document. But, a compiler doesn’t end at parsing. The type-checking step was also done in XSLT, but over a decorated AST instead. Unfortunately we were running low on time (down to mere hours) in the end so we ended up writing the code generator in C instead of XSLT.

Here’s some gems from the parser generator:

This bit of code is near the top of the file. Notice what it does.



    
    true
    
    
    

It calls another template, much like a function that can’t return anything since it can only emit text/elements, with the parameter “prototype = true”. So, looking at the beginning of functions:


    
    
    
int
parse
(xmlNode* par)
    ;
    
{
    int didSomething=0;
    
    ...

So, functions is responsible for both emitting the code for each parse function and also for making the prototypes.

Sadly, it’s been 6 years so I don’t remember enough about the code to do anything with it these days. And like all school projects, documentation was considered a sign of weakness or some such. So, the only artifacts are the source files themselves.

Without further ado, several of the files from our compiler:
parser-generator.xsl
grammar.xml
type-checker.xml

March 16, 2010

Profiling C with Haskell

Filed under: Programming — Tags: , , , , — Nathan @ 11:33 pm

I had the great pleasure of attending the ICFP in 2009. I’m pretty new to the functional programming game, so it was really cool to meet the movers and shakers. I got to see Bryan O’Sullivan give this presentation on Criterion — a “robust, reliable performance measurement and analysis” package for analyzing Haskell programs. You can read Bryan’s description here.

When I first saw the Criterion presentation, I immediately thought about porting this to C. Unfortunately, I’ve been writing a dissertation and I haven’t gotten around to it yet. However, I did decide to do something that some might consider unholy. I’ve used Criterion to profile my C functions.

How to profile C with Criterion

For this example, I’m going to profile a Fibonacci heap that I’ve been working on.

We’re going to use Haskell’s Foreign Function Interface (FFI). So, let’s start with a simple header file for our test function:

/* FILE: test-fibheap.h */
#ifndef __TESTFIBHEAP__
#define __TESTFIBHEAP__
int test(int, int);
#endif

and now our test function:

/* FILE: test-fibheap.c */
#include "test-fibheap.h"
#include "fibheap.h"
#include < math.h>

int test(int n, int seed) {

  FibHeap *heap = FibHeapMakeHeap();

  int *arr = (int*) malloc(sizeof(int) * n);
  int i; 

  srand(seed);

  for(i = 0; i < n; i++) {
    arr[i] = i;
  }
  /* Insert the numbers [0, n) into the heap in a random order */
  for(i = 0; i < n-1; i++) {
    int r = rand() % (n - i) + i;
    int t = arr[i];
    arr[i] = arr[r];
    arr[r] = t;
    FibHeapInsert(heap, (double) i, 0);
  }
  /* remove all of them one at a time.  Same as sorting the array*/
  for(i = 0; i < n; i++) {
    FibHeapExtractMin(heap);
  }
  return 0;
}

This function takes two parameters, n which is how many elements to insert into the heap, and seed which is the seed to the random number generator. I don't want to use the current time as the seed because I want to be able to compare the efficiency on exactly the same structure.

Now, how to use the FFI:

{-# INCLUDE "test-fibheap.h" #-}
{-# LANGUAGE ForeignFunctionInterface #-}
module Main where
import Foreign.C -- get the C types

foreign import ccall unsafe "test" testFib ::
                  CInt -> CInt -> CInt

Of note are the first and last lines. The first line is just like #include. Then, the last line binds our C function int test(int,int) to the name testFib.

So, bringing it all together with Criterion:

{-# INCLUDE "test-fibheap.h" #-}
{-# LANGUAGE ForeignFunctionInterface #-}
module Main where
import Foreign.C -- get the C types
import Criterion.Main

-- "unsafe" means it's slightly faster but can't callback to haskell
foreign import ccall unsafe "test" testFib ::
               CInt -> CInt -> CInt

main = defaultMain [
          bgroup "fib" [bench title $ whnf (testFib n) seed]
          ]
  where
    n = 1000000
    seed = 7
    title = "testFibHeap " ++ (show n) ++ " " ++ (show seed)

Compile this with ghc -O --make profile.hs fibheap.o test-fibheap.o.
Then, run it ./profile -t png:500x175 -k png:500x175

I've run this twice. First with my fibheap code compiled without optimizations and then again with -O3

Results

Unoptimized

./profile -t png:500x175 -k png:500x175
warming up
estimating clock resolution...
mean is 9.843398 us (80001 iterations)
found 1259 outliers among 79999 samples (1.6%)
  1157 (1.4%) high severe
estimating cost of a clock call...
mean is 522.7480 ns (95 iterations)
found 3 outliers among 95 samples (3.2%)
  1 (1.1%) high mild
  2 (2.1%) high severe

benchmarking fib/testFibHeap 1000000 7
collecting 100 samples, 1 iterations each, in estimated 189.5377 s
bootstrapping with 100000 resamples
mean: 1.916353 s, lb 1.909884 s, ub 1.932393 s, ci 0.950
std dev: 48.79063 ms, lb 23.67417 ms, ub 99.28092 ms, ci 0.950
found 9 outliers among 100 samples (9.0%)
  4 (4.0%) high mild
  5 (5.0%) high severe
variance introduced by outliers: 0.998%
variance is unaffected by outliers



Optimized with -O3

./profile -t png:500x175 -k png:500x175
warming up
estimating clock resolution...
mean is 9.928848 us (80001 iterations)
found 1264 outliers among 79999 samples (1.6%)
  1150 (1.4%) high severe
estimating cost of a clock call...
mean is 522.4059 ns (95 iterations)
found 3 outliers among 95 samples (3.2%)
  1 (1.1%) high mild
  2 (2.1%) high severe

benchmarking fib/testFibHeapOptimized 1000000 7
collecting 100 samples, 1 iterations each, in estimated 67.12110 s
bootstrapping with 100000 resamples
mean: 670.1145 ms, lb 669.3443 ms, ub 671.1168 ms, ci 0.950
std dev: 4.445731 ms, lb 3.547659 ms, ub 5.887752 ms, ci 0.950
found 7 outliers among 100 samples (7.0%)
  4 (4.0%) high mild
  3 (3.0%) high severe
variance introduced by outliers: 0.990%
variance is unaffected by outliers



Post Mortem

Running the optimized test above yields:

real    0m0.722s
user    0m0.690s
sys     0m0.030s

which is comparable to the Criterion results. There's certainly an overhead involved because of the Haskell run-time, but for profiling relatively long running functions I don't think this will be that much of an issue.

I think it's worth adding to the arsenal, don't you?

February 22, 2010

Lessons I should have learned, Episode 3: Hot swapping binaries

About a year ago I was having a discussion with my friend Crutcher when he suggested that one could hot-swap versions of a running program. This post describes my implementation of just such a thing.

Why would you hot-swap? One of the major benefits of hotswapping is that the new version of the program will have access to all of the old version’s file-descriptors. This means that any files, sockets, or pipes that the previous version currently had open can still be open. For example, if one was careful, you could hotswap an application that was in the middle of serving a very large file to a user without him being aware that anything happened.

Before we begin discussing how this should work, let’s look at some of the problems. Sure we get file descriptors, but what about all of our state? Well, this is a problem. We cannot easily take our state with us. Since we’re updating versions here, you want to be particularly sure you only take state that you need. To do this, I would recommend using a serialization library for C. A bit of Googling showed me this one, TPL, though I haven’t tried it yet. For our example here, we’ll just manually move the only pieces of state we care about: a counter and a file descriptor to a file we’re currently writing to.

The basic idea of what’s going to happen is that we will create a pair of pipes and then fork(). The child process will hold the pipe that does the writing and the parent the one that does the reading. Now, the parent will exec. This is a bit odd. Normally when you fork, then exec, it’s the child process which does the exec. However, here we really want the new version of the program to have access to all of the old file descriptors. Luckily, execl preserves these. As an added benefit, the program gets the exact same process ID.

So, let’s look at the important bits of the hot-swap (reader and writer are the file descriptors for the pipe):

unsigned int outputFD = fileno(outputFile);

if(fork()) {
  /* I am the parent. */
  char readBuf[20] = {0};

  close(writer);
  sprintf(readBuf, "%d", reader);

  execl("./newbinary", "--hotswapping", readBuf, (char*)0);
  exit(0);
} else {
  /* I am the child.*/
  FILE *outputStream = fdopen(writer, "w");
  close(reader);

  fprintf(outputStream, "%d\n", i);
  fprintf(outputStream, "%u\n", (unsigned int) outputFD);
  fclose(outputStream);
  exit(0);
}

First, let’s look at what the parent process does. It simply closes its “writer” since it will never need to write to the pipe then it execs “newbinary” which is the new version of the program. It does so with a flag “–hotswapping”. This flag indicates another parameter will follow which is the file descriptor for the “read” end of the pipe we created. We do this so that the new binary can then get the state serialized across the pipe from the old binary.

Now, onto the child process. Line 32 creates a file handler from the file descriptor which is the “write” end of the pipe. Why? Because I’m lazy and I would prefer to work with fprintf() to write(). Now that we have this file handler, we can fprintf() directly to it and serialize the state we want. In this contrived case the only state I care about is my counter variable and the file descriptor of my output file. Line 19 gives me the descriptor from the handler using int fileno(FILE *).

So, to recap, we fork() then the parent exec’s to the new version of the binary and the child writes any relevant state to a pipe which the new binary is listening to.

Now, let’s look at what has to exist in the new binary. The new binary must recognize the argument “–hotswapping” which passes along the file descriptor of the “read” pipe. The following, in newversion.c does just this:

for(i = 0; i < argc; i++) {
  if(!strcmp(argv[i], "--hotswapping")) {
    int reader = atoi(argv[++i]);
    inputStream = fdopen(reader, "r");
  }
}

Notice that Line 17 does something interesting. It converts the file descriptor back to a file handler using fdopen(int, char*). Thus we can use this pipe just like we were reading from a file. So, now we can use fscanf to read from the pipe instead of having to worry about read() and buffers. This is done starting at line 21:

fscanf(inputStream, "%d", &i);
fscanf(inputStream, "%u", &outputFD);
fclose(inputStream);
outputFile = fdopen(outputFD, "w");

Once again, at line 24, we turn the file descriptor we read from the pipe back into a file handler. Now, we can resume writing to it, just as we did before. It will continue to append to the end of the file.

The files which implement this are available as gists here:

The output when run for 11 seconds is:

gcc -Wall -pedantic -o newbinary newversion.c
gcc -Wall -pedantic -o example original.c
./example
Original:       1 My PID=27272
Original:       2 My PID=27272
Original:       3 My PID=27272
Original:       4 My PID=27272
Original:       5 My PID=27272
New Binary:     6 My PID=27272
New Binary:     7 My PID=27272
New Binary:     8 My PID=27272
New Binary:     9 My PID=27272
New Binary:     10 My PID=27272
New Binary:     11 My PID=27272

February 17, 2010

Lessons I should have learned, Episode 2: hiding your data

Filed under: Lessons I should have learned — Tags: , , — Nathan @ 12:49 am

Episode 2: hiding your data

Let’s consider a contrived example where you are representing people and their relationships. We want to represent a person’s name, social security number, address, and have pointers to mother and father.

typedef struct Person {
  char *name;
  char *address;
  int ssn;
  Person *mother;
  Person *father;
} Person;

Now, let’s say that we have some library which will create a queriable tree for us.

Person *mandy = insertPerson("Mandy", \
                  123331234, "Hut #13" 0, 0);
Person *naughtius = insertPerson("Naughtius", \
                  349830123, "Centurianville #2", 0, 0);
Person *brian = insertPerson("Brian", \
                  593013297, "Hut #13", mandy, naughtius);

This is all well and good. But there’s a problem. The user of this library can do something like this:

Person *mary = insertPerson("Mary", \
                  012309821, "Bethleham", 0, 0);
brian->mother = mary;

There might possibly be a use-case where we want to allow this to happen, but in general this isn’t desirable whatsoever. We really want to keep the fields mother, father, and ssn immutable. We can do this in the following way:

/* FILE: library.h */
typedef struct Person {
  char *name;
  char *address;
} Person;

int getSSN(Person *);
Person *getMother(Person *);
Person *getFather(Person *);
Person *insertPerson(char *, int, char *, Person*, Person*);

Now we have a struct Person which exposes only information that we will allow to be mutable. How can we use this cleanly and effectively? We define the following:

/* FILE: library.c */
#include "library.h"
typedef struct privatePerson {
  Person p;
  int ssn;
  privatePerson *mother;
  privatePerson *father;
} privatePerson;
...

How does this help us? First, consider the following:

privatePerson *mary = ...;
Person *mom = (Person *) mary;

What will happen here? First, recall how struct layout works. Since Person p is the first entry in struct privatePerson then the offset in the privatePerson struct is 0. This means that the memory location of Person p is the same as the struct privatePerson which contains it. So, when we cast to the Person pointer we are, in fact pointing at a Person object. So, insertPerson(...) will use a struct privatePerson internally but will return a struct Person. As will getMother(...).

/* FILE: library.c */
...
Person *getMother(Person *p) {
  privatePerson *who = (privatePerson *) p;
  return (Person *) who->mother;
}

This example illustrates how we can also cast back to a privatePerson from a Person. Note that this REQUIRES that the pointer to the struct Person also be a pointer to a struct privatePerson, i.e. you’ve already cast it once. We can’t ensure that our user will never malloc his own struct Person. The best we can do is provide factories for construction (insertPerson above) and strongly document that doing so is unsupported and will lead to undefined behavior.

Because struct privatePerson is defined within library.c, it won’t be visible to anyone who includes library.h. Just like with the previous post on opaque pointers, this isn’t magic. The user can still opt-in to violate our model by including his own definition of a privatePerson struct and casting back to it. Though hopefully by doing so the user will be perfectly aware that he’s breaking our API and thus is writing code that could potentially break in the next minor point release of the library.

February 11, 2010

Lessons I should have learned, Episode 1: opaque pointers

Filed under: Lessons I should have learned — Tags: , , — Nathan @ 11:08 pm

I’ve spent my entire adult life in academia. For some reason academia, even in software engineering courses, doesn’t really harp on best practices or good patterns to know. It is possible that some do and that I’ve just missed it since I specialized in theory. So, this article will be the first in a larger category of articles which will describe patterns and techniques that I should have known but didn’t.

Episode 1: opaque pointers

I don’t care what anyone says. C is a great language. About a year ago I undertook the task of implementing a Fibonacci heap for use in an empirical analysis of two labeled graph algorithms. I put the code away months and months ago, but luckily I just found out that I got some CPU time on a very prestigious cluster so I’ve been reworking the implementations to collect better statistics. Also, since I’m finally going to do this analysis and it’s going to be published (at least in my dissertation) then I will most definitely release all of the code so that the experiments can be replayed and verified. So, since other people will look at this code I’ve spent several hours over the last few days cleaning it up and making the interfaces more reasonable. This lead me to actually pulling my fibheap out into its own stand alone project.

The fibheap isn’t ready for GitHub just yet (though look for it soon), but I’ve been working on the API and learning a lot about how this kind of thing should work. The user should be completely unaware of the internal structure of the data structure. He shouldn’t be able to mutate it in any way except through the accessors that the API provides. Though, sometimes he will need to hold pointers to particular elements in the heap (for DecreaseKey calls). This means that we need some way of providing access to elements without giving away any of our top-secret structure. So, the very simple concept that I should have known about: opaque pointers.

The crux of the matter is to separate declaration from definition just like we were always told (except in the case of templated classes… sigh). In the example of the fibheap, we want the user to have a handle to the fibheap but to otherwise not be able to affect the heap except through the API functions. So, we do this:

/* FILE: fibheap.h */
typedef struct FibHeap FibHeap;

FibHeapElement *fibHeapGetRoot(FibHeap*);
unsigned int fibHeapGetSize(FibHeap*);

And that’s it. There is no definition of the structure here, only a declaration of it. Then:

/* FILE: fibheap.c */
struct FibHeap {
  FibHeapElement *root;
  unsigned int size;
};

Because only the declaration is in the header, whenever the user does #include "fibheap.h" he only gets the name of the struct, not the layout. Thus the user cannot access either of the fields without the accessor functions fibHeapGetRoot(FibHeap*) and fibHeapGetSize(FibHeap*). No instantiation for you! (caveat: the user could easily define the struct himself and have access to the internals of the structure. This is the kind of hard opt-in that hopefully makes it clear to the user that he’s violating the spirit of the API and that this will not necessarily be stable with future versions.)

So, to summarize:

Opaque pointers allow you to provide handlers to structs to which your users have no idea the form or actual structure of.

The next installment will be of a couple of ways that you can provide partial information to the user instead of none. Stay tuned!

February 3, 2010

Stupid mistakes.

Filed under: Programming — Tags: , , — Nathan @ 5:55 pm

I just saw a post on /r/programming asking people to describe their dumbest coding mistakes. Instead of just posting one of mine there, I’ll do it here.

Many, many years ago, back in 2005 when I was a green grad student and an all around n00b, I got the opportunity to work on the auto-discovery daemon for OpenMosix (may it rest in peace). OpenMosix was a self-assembling cluster system. You would just install it on several machines in the same subnet, and bam you’d have a cluster that automatically distributed jobs. On with the story.

I don’t remember what part of the daemon I was working on. It was already doing some auto-discovery and self assembly. I was pretty pleased. The basic idea was that it would fork off a listener and have another process that would chatter on broadcast announcing its presence. At some point, about 5 minutes into the run, the whole thing would segfault. More precisely, at 4:38 in, it would segfault. Wait, what? The same exact amount of time to crash? That’s just weird! Well, no kidding.

This killed me for several hours. I had no idea what was up. That’s the day I got familiar with using GDB to attach to running processes. And what did I discover? I out clevered myself. I had code like this:

fprintf(stdout, "This is some stupid debugging \
message that I'll eventually remove\n");

That looks all well and good. Why is a printf breaking? Well, it’s breaking because of this line:

fclose(stdout);

See above where I said that I out-clevered myself. Basically, I wanted to turn off all that annoying debugging chatter. So, what the hell, why not just close stdout? It worked! No more chatter. Well, that’s not all. See, whenever using printf, I was writing to a closed file handler… but it was buffered. So, my program would continue to run, dumping silly messages to this buffer until the buffer ran out of space and it smashed some import part of memory causing the segfault. Oops.

So, what did I learn? Debugging messages are important, but use some kind of clever logging system, even if it’s just a series of macros. Sometimes, if your idea seems really clever, take a step back because it might lead you to more trouble than it’s worth.


  • I'm a software engineer at Google
  • I'm from Alabama
  • I live in San Francisco
  • I like to work on ridiculous things
  • I'm currently learning German, Scala, and Computer Vision
  • This book referred to JavaScript I wrote when I was 15.