I wanna, I wanna,
I wanna, I wanna,
I wanna be trash.

The Whip, Trash

We say Lox is a high-level language because it frees programmers worrying about details irrelevant to the problem theyre solving. The user becomes an executive giving the machine abstract goals and letting the lowly computer figure out how to get there.

Dynamic memory allocation is a perfect candidate for automation. Its necessary for a working program, tedious to do by hand, and yet still error-prone. The inevitable mistakes can be catastrophic, leading to crashes, memory corruption or security violations. Its the kind of risky-yet-boring work that machines excel at over humans.

This is why Lox is a managed language, which means that the language implementation manages memory allocation and freeing on the users behalf. When a user performs an operation that requires some dynamic memory, the VM automatically allocates it. The programmer never worries about deallocating anything. As long as they use a piece of memory, the machine ensures that memory is still there.

Lox provides the illusion that the computer has an infinite amount of memory. Users can allocate and allocate and allocate and never once think about where all these bytes are coming from. Of course, computers do not yet have infinite memory. So the way managed languages maintain this illusion is by going behind the programmers back and reclaiming memory that the program no longer needs. The component that does this is called a garbage collector.

This raises a surprisingly difficult question: how does a VM tell what memory is not needed? Memory is only needed if it is read in the future, but short of having a time machine, how can an implementation tell what code the program will execute and which data it will use? Spoiler alert: VMs cannot travel into the future. Instead, the language makes a conservative approximation: it considers a piece of memory to still be in use if it could possibly be read in the future.

That sounds too conservative. Couldnt any bit of memory potentially be read? Actually, no, at least not in a memory-safe language like Lox. Heres an example:

var a = "first value";
a = "updated";
// GC here.
print a;

Say we run the GC after the assignment has completed on the second line. The string first value is still sitting in memory, but there is no way for the users program to ever get to it. Once a got reassigned, the program lost any reference to that string. We can safely free it. A value is reachable if there is some way for a user program to reference it.

Many values can be directly accessed by the VM. Take a look at:

var global = "string";
{
  var local = "another";
  print global + local;
}

Pause the program right after the two strings have been concatenated but before the print statement has executed. The VM can reach "string" by looking through the global variable table and finding the entry for global. It can find "another" by walking the value stack and hitting the slot for the local variable local. It can even find the concatenated string "stringanother" since that temporary value is also sitting on the VMs stack at the point when we paused our program.

All of these values are called roots. A root is any object that the VM can reach directly without going through a reference in some other object. Most roots are global variables or on the stack, but as well see, there are a couple of other places the VM stores references to objects that it can find.

Other values can be found by going through a reference inside another value. Fields on instances of classes are the most obvious case, but we dont have those yet. Even without those, our VM still has indirect references. Consider:

fun makeClosure() {
  var a = "data";
  fun f() { print a; }
  return f;
}
var closure = makeClosure();
// GC here.
closure();

Say we pause the program on the marked line and run the garbage collector. When the collector is done and the program resumes, it will call the closure, which will in turn print "data". So the collector needs to not free that string. But heres what the stack looks like when we pause the program:

The "data" string is nowhere on it. It has already been hoisted off the stack and moved into the closed upvalue that the closure uses. The closure itself is on the stack. But to get to the string, we need to trace through the closure and its upvalue array. Since it is possible for the users program to do that, all of these indirectly accessible objects are also considered reachable.

This gives us an inductive definition of reachability:

• All roots are reachable.

• Any object referred to from a reachable object is itself reachable.

These are the values that are still live and need to stay in memory. Any value that doesnt meet this definition is fair game for the collector to reap. That recursive pair of rules hints at a recursive algorithm we can use to free up unneeded memory:

1. Starting with the roots, traverse through object references to find the full set of reachable objects.

2. Free all objects not in that set.

Many different garbage collection algorithms are in use today, but they all roughly follow that same structure. Some may interleave the steps or mix them, but the three fundamental operations are there. They mostly differ in how they perform each step.

The first managed language was Lisp, the second high level language to be invented, right after Fortran. John McCarthy considered using manual memory management or reference counting, but eventually settled on (and coined) garbage collectiononce the program was out of memory it would go back and find unused storage it could reclaim.

He designed the very first, simplest garbage collection algorithm, called mark-and-sweep or just mark-sweep. Its description fits in three short paragraphs in the initial paper on Lisp. Despite its age and simplicity, the same fundamental algorithm underlies many modern memory managers. Some corners of CS seem to be timeless.

As the name implies, mark-sweep works in two phases:

• Marking. We start with the roots and traverse or trace through all of the objects those roots refer to. This is a classic graph traversal of all of the reachable objects. Each time we visit an object, we mark it in some way. (Implementations differ in how they record the mark.)

• Sweeping. Once the mark phase completes, every reachable object in the heap is marked. That means any unmarked object is unreachable and ripe for reclamation. We go through all the unmarked objects and free each one.

It looks something like this:

Thats what were gonna implement. Whenever we decide its time to reclaim some bytes, well trace everything and mark all the reachable objects, free what didnt get marked, and then resume the users program.

This entire chapter is about implementing one function:

void* reallocate(void* previous, size_t oldSize, size_t newSize);

void collectGarbage();

void freeObjects();

Well work our way up to a full implementation starting with this empty shell:

void collectGarbage() {
}

The first question you might ask is, When does this function get called? It turns out thats a subtle question that well spend some time on later in the chapter. For now well sidestep the issue and build ourselves a handy diagnostic tool in the process:

#define DEBUG_TRACE_EXECUTION      

#define DEBUG_STRESS_GC            

#define UINT8_COUNT (UINT8_MAX + 1)

Well add an optional stress test mode for the garbage collector. When this flag is defined, the GC runs as often as it possibly can. This is, obviously, horrendous for performance. But its great for flushing out memory management bugs that only occur when a GC is triggered at just the right moment. If every moment triggers a GC, youre likely to hit it.

void* reallocate(void* previous, size_t oldSize, size_t newSize) {

  if (newSize > oldSize) {
#ifdef DEBUG_STRESS_GC                                            
    collectGarbage();
#endif                                                            
  }

  if (newSize == 0) {

Whenever we call reallocate() to acquire more memory, we force a collection to run. The if check is because reallocate() is also called to free or shrink an allocation. We dont want to trigger a GC for thatin particular because the GC itself will call reallocate() to free memory.

Collecting right before allocation is the classic way to wire a GC into a VM. Youre already calling into the memory manager, so its an easy place to hook in the code. Also, allocation is the only time when you really need some freed up memory, so that you can reuse it. If you dont use allocation to trigger a GC, you have to make sure every possible place in code where you can loop and allocate memory also has a way to trigger the collector. Otherwise, the VM can get into a starved state where it needs more memory but never collects any.

While were on the subject of diagnostics, lets put some more in. A real challenge Ive found with garbage collectors is that they are opaque. Weve been running lots of Lox programs just fine without any GC at all so far. Once we add one, how do we tell if its doing anything useful? Can we only tell if we write programs that plow through acres of memory? How do we debug that?

An easy way to shine a light into the GCs inner workings is with some logging:

#define DEBUG_STRESS_GC            

#define DEBUG_LOG_GC               

#define UINT8_COUNT (UINT8_MAX + 1)

When this is enabled, clox prints information to the console when it does something with dynamic memory. We need a couple of includes:

#include "vm.h"                                                   

#ifdef DEBUG_LOG_GC                                               
#include                                                 
#include "debug.h"                                                
#endif                                                            

void* reallocate(void* previous, size_t oldSize, size_t newSize) {

We dont have a collector yet, but we can start putting in some of the logging now. Well want to know when a collection run starts:

void collectGarbage() {

#ifdef DEBUG_LOG_GC       
  printf("-- gc begin\n");
#endif                    

}
void freeObjects() {

Eventually we will log some other operations during the collection, so well also want to know when the shows over:

  printf("-- gc begin\n");
#endif                    

#ifdef DEBUG_LOG_GC       
  printf("-- gc end\n");
#endif                    

}

We dont have any code for the collector yet, but we do have functions for allocating and freeing, so we can instrument those now:

  vm.objects = object;

#ifdef DEBUG_LOG_GC                                             
  printf("%p allocate %ld for %d\n", (void*)object, size, type);
#endif                                                          

  return object;

And at the end of an objects lifespan:

static void freeObject(Obj* object) {

#ifdef DEBUG_LOG_GC                                        
  printf("%p free type %d\n", (void*)object, object->type);
#endif                                                     

  switch (object->type) {

With these two flags, we should be able to see that were making progress as we work through the rest of the chapter.

Objects are scattered across the heap like stars in the inky night sky. A reference from one object to another forms a connection and these constellations are the graph that the mark phase traverses. Marking begins at the roots:

#ifdef DEBUG_LOG_GC       
  printf("-- gc begin\n");
#endif                    

  markRoots();

#ifdef DEBUG_LOG_GC       

Most roots are local variables or temporaries sitting right in the VMs stack, so we start by walking that:

static void markRoots() {
  for (Value* slot = vm.stack; slot < vm.stackTop; slot++) {
    markValue(*slot);
  }
}

To mark a Lox value, we use this new function:

void* reallocate(void* previous, size_t oldSize, size_t newSize);

void markValue(Value value);

void collectGarbage();

Its implementation is:

void markValue(Value value) {
  if (!IS_OBJ(value)) return;
  markObject(AS_OBJ(value));
}

Some Lox valuesnumbers, Booleans, and nilare stored directly inline in Value and require no heap allocation. The garbage collector doesnt need to worry them at all, so the first thing we do is ensure that the value is an actual heap object. If so, the real work happens in this function:

void* reallocate(void* previous, size_t oldSize, size_t newSize);

void markObject(Obj* object);

void markValue(Value value);

Which is defined here:

void markObject(Obj* object) {
  if (object == NULL) return;
  object->isMarked = true;
}

The NULL check is unnecessary when called from markValue(). A Lox Value that is some kind of Obj type will always have a valid pointer. But later we will call this function directly from other code and in some of those places, the object being pointed to is optional.

Assuming we do have a valid object, we mark it by setting a flag. That new field lives in the Obj header struct all objects share:

  ObjType type;

  bool isMarked;

  struct sObj* next;

Every new object begins life unmarked because we havent determined if it is reachable or not yet:

  object->type = type;

  object->isMarked = false;

  object->next = vm.objects;

Before we go any farther, lets add some logging to markObject():

void markObject(Obj* object) {
  if (object == NULL) return;

#ifdef DEBUG_LOG_GC                 
  printf("%p mark ", (void*)object);
  printValue(OBJ_VAL(object));
  printf("\n");
#endif                              

  object->isMarked = true;

Marking the stack covers local variables and temporaries. The other main source of roots are the global variables:

    markValue(*slot);
  }

  markTable(&vm.globals);

}

Those live in a hash table owned by the VM, so well declare another helper function for marking all of the objects in a table:

ObjString* tableFindString(Table* table, const char* chars, int length,
                           uint32_t hash);

void markTable(Table* table);

#endif                                                                 

We implement that in the table module:

void markTable(Table* table) {
  for (int i = 0; i < table->capacity; i++) {
    Entry* entry = &table->entries[i];
    markObject((Obj*)entry->key);
    markValue(entry->value);
  }
}

Pretty straightforward. We walk the entry array and mark every key string and value, uh, value.

Those cover the roots that we typically think ofthe values that are obviously reachable because theyre stored in variables the users program can see. But the VM has a few of its own hidey holes where it squirrels away references to values that it directly accesses.

Most function call state lives in the value stack, but the VM maintains a separate stack of CallFrames. Each CallFrame contains a pointer to the closure being called. The VM uses those pointers to access constants and upvalues, so those closures need to be kept around too:

  }

  for (int i = 0; i < vm.frameCount; i++) {
    markObject((Obj*)vm.frames[i].closure);
  }

  markTable(&vm.globals);

Speaking of upvalues, the open upvalue list is another set of values that the VM can directly reach:

  for (int i = 0; i < vm.frameCount; i++) {
    markObject((Obj*)vm.frames[i].closure);
  }

  for (ObjUpvalue* upvalue = vm.openUpvalues;
       upvalue != NULL;
       upvalue = upvalue->next) {
    markObject((Obj*)upvalue);
  }

  markTable(&vm.globals);

Remember also that a collection can begin during any allocation. Those allocations dont just happen while the users program is running. The compiler itself periodically grabs memory from the heap for literals and the constant table. If the GC runs while were in the middle of compiling, then any values the compiler directly accesses need to be treated as roots too.

To keep the compiler module cleanly separated from the rest of the VM, well do that in a separate function:

  markTable(&vm.globals);

  markCompilerRoots();

}

Its declared here:

ObjFunction* compile(const char* source);

void markCompilerRoots();

#endif                                   

Which means the memory module needs an include:

#include "common.h"  

#include "compiler.h"

#include "memory.h"  

And the definition is over in the compiler module:

void markCompilerRoots() {
  Compiler* compiler = current;
  while (compiler != NULL) {
    markObject((Obj*)compiler->function);
    compiler = compiler->enclosing;
  }
}

Fortunately, the compiler doesnt have too many values that it hangs onto. The only object it uses is the ObjFunction it is compiling into. Since function declarations can nest, the compiler has a linked list of those and we walk the whole list.

Since the compiler module is calling markObject(), it also needs an include:

#include "compiler.h"

#include "memory.h"  

#include "scanner.h" 

Those are all the roots. After running this, every object that the VMruntime and compilercan get to without going through some other object has its mark bit set.

The next step in the marking process is tracing through the graph of references between objects to find the indirectly reachable values. We dont have instances with fields yet, so there arent many objects that contain references, but we do have some. In particular, ObjClosure has the list of ObjUpvalues it closes over as well as a reference to the raw ObjFunction that it wraps. ObjFunction in turn has a constant table containing references to all of the literals created in the functions body. This is enough to build a fairly complex web of objects for our collector to crawl through.

Now its time to implement that traversal. We can go breadth-first, depth-first, or some other order. Since we just need to find the set of all reachable objects, the order we visit them mostly doesnt matter.

As the collector wanders through the graph of objects, we need to make sure it doesnt lose track of where it is or get stuck going in circles. This is paricularly a concern for advanced implementations like incremental GCs that interleave marking with running pieces of the users program. The collector needs to be able to pause and then pick up where it left off later.

To help us soft-brained humans reason about this complex process, VM hackers came up with a metaphor called the tri-color abstraction. Each object has a conceptual color that tracks what state the object is in and what work is left to do.

• White At the beginning of a garbage collection, every object is white. This color means we have not reached or processed the object at all.

• Gray During marking, when we first reach an object, we darken it gray. This color means we know it is reachable and should not be collected. But we have not yet traced through it to see what other objects it references. In graph algorithm terms, this is the worklistthe set of objects we know about but havent processed yet.

• Black When we take a gray object and mark all of the objects it references, we then turn it black. This color means the mark phase is done with that object.

In terms of that abstraction, the marking process now looks like this:

1. Start off with all objects white.

2. Find all the roots and mark them gray.

3. As long as there are still gray objects:

   1. Pick a gray object. Turn any white objects that the object mentions to gray.

   2. Mark the original gray object black.

I find it helps to visualize this. You have a web of objects with references between them. Initially, they are all little white dots. Off to the side are some incoming edges from the VM that point to the roots. Those roots turn gray. Then each gray objects siblings turn gray while the object itself turns black. The full effect is a gray wavefront that passes through the graph, leaving a field of reachable black objects behind it. Unreachable objects are not touched by the wavefront and stay white.

At the end, youre left with a sea of reached black objects sprinkled with islands of white objects that can be swept up and freed. Once the unreachable objects are freed, the remaining objectsall blackare reset to white for the next garbage collection cycle.

In our implementation we have already marked the roots. Theyre all gray. The next step is to start picking them and traversing their references. But we dont have any easy way to find them. We set a field on the object, but thats it. We dont want to have to traverse the entire object list looking for objects with that field set.

Instead, well create a separate worklist to keep track of all of the gray objects. When an object turns grayin addition to setting the mark fieldwell also add it to the worklist:

  object->isMarked = true;

  if (vm.grayCapacity < vm.grayCount + 1) {
    vm.grayCapacity = GROW_CAPACITY(vm.grayCapacity);
    vm.grayStack = realloc(vm.grayStack,
                           sizeof(Obj*) * vm.grayCapacity);
  }
  vm.grayStack[vm.grayCount++] = object;

}

We could use any kind of data structure that lets us put items in and take them out easily. I picked a stack because thats the simplest to implement with a dynamic array in C. It works mostly like other dynamic arrays weve built in Lox, except note that it calls the system realloc() function and not our own reallocate() wrapper. The memory for the gray stack itself is not managed by the garbage collector. We dont want growing the gray stack during a GC to cause the GC to recursively start a new GC. That could tear a hole in the space-time continuum.

Well manage its memory ourselves, explicitly. The VM owns the gray stack:

  Obj* objects;

  int grayCount;
  int grayCapacity;
  Obj** grayStack;

} VM;

It starts out empty:

  vm.objects = NULL;

  vm.grayCount = 0;
  vm.grayCapacity = 0;
  vm.grayStack = NULL;

  initTable(&vm.globals);

And, because we manage it ourselves, we need to free it when the VM shuts down:

    object = next;
  }

  free(vm.grayStack);

}

OK, now when were done marking the roots we have both set a bunch of fields and filled our work list with objects to chew through. Its time for the next phase:

  markRoots();

  traceReferences();

Heres the implementation:

static void traceReferences() {
  while (vm.grayCount > 0) {
    Obj* object = vm.grayStack[--vm.grayCount];
    blackenObject(object);
  }
}

Its as close to that textual algorithm as you can get. Until the stack empties, we keep pulling out gray objects, traversing their references, and then marking them black. Traversing an objects references may turn up new white objects that get marked gray and added to the stack. So this function swings back and forth between turning white objects gray and gray objects black, gradually advancing the entire wavefront forward.

Heres where we traverse a single objects references:

static void blackenObject(Obj* object) {
  switch (object->type) {
    case OBJ_NATIVE:
    case OBJ_STRING:
      break;
  }
}

Each object kind has different fields that might reference other objects, so we need a specific blob of code for each type. We start with the easy onesstrings and native function objects contain no outgoing references so there is nothing to traverse.

Note that we dont set any state in the traversed object itself. There is no direct encoding of black in the objects state. A black object is any object whose isMarked field is set and that is no longer in the gray stack.

Now lets start adding in the other object types. The simplest is upvalues:

static void blackenObject(Obj* object) {
  switch (object->type) {

    case OBJ_UPVALUE:
      markValue(((ObjUpvalue*)object)->closed);
      break;

    case OBJ_NATIVE:

When an upvalue is closed, it contains a reference to the closed-over value. Since the value is no longer on the stack, we need to make sure we trace the reference to it from the upvalue.

Next are functions:

  switch (object->type) {

    case OBJ_FUNCTION: {
      ObjFunction* function = (ObjFunction*)object;
      markObject((Obj*)function->name);
      markArray(&function->chunk.constants);
      break;
    }

    case OBJ_UPVALUE:

Each function has a reference to an ObjString containing the functions name. More importantly, the function has a constant table packed full of references to other objects. We trace all of those using this helper:

static void markArray(ValueArray* array) {
  for (int i = 0; i < array->count; i++) {
    markValue(array->values[i]);
  }
}

The last object type we have nowwell add more in later chaptersis closures:

  switch (object->type) {

    case OBJ_CLOSURE: {
      ObjClosure* closure = (ObjClosure*)object;
      markObject((Obj*)closure->function);
      for (int i = 0; i < closure->upvalueCount; i++) {
        markObject((Obj*)closure->upvalues[i]);
      }
      break;
    }

    case OBJ_FUNCTION: {

Each closure has a reference to the bare function it wraps, as well as an array of pointers to the upvalues it captures.

Thats the basic mechanism for processing a gray object, but there are two loose ends to tie off. First, some logging:

static void blackenObject(Obj* object) {

#ifdef DEBUG_LOG_GC                     
  printf("%p blacken ", (void*)object);
  printValue(OBJ_VAL(object));
  printf("\n");
#endif                                  

  switch (object->type) {

This way, we can watch the tracing percolate through the object graph. Speaking of which, note that I said graph. References between objects are directed, but that doesnt mean theyre acyclic! Its entirely possible to have cycles of objects. When that happens, we need to ensure our collector doesnt get stuck in an infinite loop as it continually re-adds the same series of objects to the gray stack.

The fix is easy:

  if (object == NULL) return;

  if (object->isMarked) return;

#ifdef DEBUG_LOG_GC            

If the object is already marked, we dont mark it again and thus dont add it to the gray stack. This ensures that an already-gray object is not redundantly added and that a black object is not inadvertently turned back to gray. In other words, it keeps the wavefront moving forward through the white objects.

When the loop in traceReferences() exits, we have processed all the objects we could get our hands on. The gray stack is empty and every object in the heap is either black or white. The black objects are reachable and we want to hang on to them. Anything still white never got touched by the trace and is thus garbage. All thats left is to reclaim them:

  traceReferences();

  sweep();

All of the logic lives in one function:

static void sweep() {
  Obj* previous = NULL;
  Obj* object = vm.objects;
  while (object != NULL) {
    if (object->isMarked) {
      previous = object;
      object = object->next;
    } else {
      Obj* unreached = object;
      object = object->next;
      if (previous != NULL) {
        previous->next = object;
      } else {
        vm.objects = object;
      }
      freeObject(unreached);
    }
  }
}

I know thats kind of a lot of code and pointer shenanigans but there isnt much to it once you work through it. The outer while loop walks the linked list of every object in the heap, checking their mark bits. If an object is unmarked (white), we unlink it from the list and free it using the freeObject() function we already wrote.

Most of the other code in here deals with the fact that removing a node from a singly-linked list is cumbersome. We have to continously remember the previous node so we can unlink its next pointer, and we have to handle the edge case where we are freeing the first node. But, otherwise, its pretty simpledelete every node in a linked list that doesnt have a bit set in it.

Theres one little addition:

    if (object->isMarked) {

      object->isMarked = false;

      previous = object;

After sweep() completes, the only remaining objects are the live black ones with their mark bits set. Thats correct, but when the next collection cycle starts, we need every object to be white. So whenever we reach a black object, we go ahead and clear the bit now in anticipation of the next run.

We are almost done collecting. There is one remaining corner of the VM that has some unusual requirements around memory. Recall that when we added strings to clox we made the VM intern them all. That means the VM has a hash table containing a pointer to every single string in the heap. The VM uses this to de-duplicate strings.

During the mark phase, we deliberately did not treat the VMs string table as a source of roots. If we had, no string would ever be collected. The string table would grow and grow and never yield a single byte of memory back to the operating system. That would be bad.

At the same time, if we do let the GC free strings, then the VMs string table will be left with dangling pointers to freed memory. That would be even worse.

The string table is special and we need special support for it. In particular, it needs a special kind of reference. The table should be able to refer to a string, but that link should not be considered a root when determining reachability. That implies that the referenced object can be freed. When that happens, the dangling reference must be fixed too, sort of like a magic self-clearing pointer. This particular set of semantics comes up frequently enough that it has a name: a weak reference.

We have already implicitly implemented half of the string tables unique behavior by virtue of the fact that we dont traverse it during marking. That means it doesnt force strings to be reachable. The remaining piece is clearing out any dangling pointers for strings that are freed.

To remove references to unreachable strings, we need to know which strings are unreachable. We dont know that until after the mark phase has completed. But we cant wait until after the sweep phase is done because by then the objectsand their mark bitsare no longer around to check. So the right time is exactly between the marking and sweeping phases:

  traceReferences();

  tableRemoveWhite(&vm.strings);

  sweep();

The logic for removing the about-to-be-deleted strings exists in a new function in the table module:

ObjString* tableFindString(Table* table, const char* chars, int length,
                           uint32_t hash);

void tableRemoveWhite(Table* table);

void markTable(Table* table);

The implementation is here:

void tableRemoveWhite(Table* table) {
  for (int i = 0; i < table->capacity; i++) {
    Entry* entry = &table->entries[i];
    if (entry->key != NULL && !entry->key->obj.isMarked) {
      tableDelete(table, entry->key);
    }
  }
}

We walk every entry in the table. The string intern table only uses the key of each entryits basically a hash set not a hash map. If the key string objects mark bit is not set, then it is a white object that is moments from being swept away. We delete it from the hash table first and thus ensure we wont see any dangling pointers.

We have a fully functioning mark-sweep garbage collector now. When the stress testing flag is enabled, it gets called all the time, and with the logging enabled too we can watch it do its thing and see that it is indeed reclaiming memory. But, when the stress testing flag is off, it never runs at all. Its time to decide when the collector should be invoked during normal program execution.

As far as I can tell, this question is poorly answered by the literature. When garbage collectors were first invented, computers had a tiny, fixed amount of memory. Many of the early GC papers assume that you set aside a few thousand words of memoryin other words, most of itand invoke the collector whenever you run out. Simple.

Modern machines have gigs of physical RAM, hidden behind the operating systems even larger virtual memory abstraction, which is shared among a slew of other programs all fighting for their chunk of memory. The operating system will let your program request as much as it wants and then page in and out from the disc when physical memory gets full. You never really run out of memory, you just get slower and slower.

It no longer makes sense to wait until you have to, to run the GC, so we need a more subtle timing strategy. To reason about this more precisely, its time to introduce two fundamental numbers used when measuring a memory managers performance: throughput and latency.

Evvery managed language has pays a performance price compared to explicit, user-authored deallocation. The time spent actually freeing memory is the same, but the GC spends cycles figuring out which memory to free. That is time not spent running the users code and doing useful work. In our implementation, thats the entirety the mark phase falls under this. The goal of a sophisticated garbage collector is to minimize that overhead.

There are two key metrics we can use to understand that cost better:

• Throughput is the total fraction of time spent running user code versus doing garbage collection work. Say you run a clox program for ten seconds and it spends a second of that inside collectGarbage(). That means the throughput is 90%it spent 90% of the time running the program and 10% on GC overhead.

  Throughput is the most fundamental measure because it tracks the total cost of collection overhead. All else being equal, you want to maximize throughput. Up until this chapter, clox had no GC at all and thus 100% throughput. Thats pretty hard to beat. Of course, it came at the slight expense of potentially running out of memory and crashing if the users program ran long enough. You can look at the goal of a GC as fixing that glitch while sacrificing as little throughput as possible.

• Latency is the longest continuous chunk of time where the users program is completely paused while garbage collection happens. Its a measure of how chunky the collector is. Latency is an entirely different metric than throughput.

  Consider two runs of a clox program that both take ten seconds. In the first run, the GC kicks in once and spends a solid second in collectGarbage() in one massive collection. In the second run, the GC gets invoked five times, each for a fifth of a second. The total amount of time spent collecting is still a second, so the throughput is 90% in both cases. But in the second run, the latency is only 1/5th of a second, five times less than in the first.

If you like analogies, imagine your program is a bakery selling fresh-baked bread to customers. Throughput is the total number of warm, crusty baguettes you can serve to customers in a single day. Latency is how long the unluckiest customer has to wait in line before they get served.

Running the garbage collector is like shutting down the bakery temporarily to go through all of the dishes, sort out the dirty from the clean, and then wash the used ones. In our analogy, we dont have dedicated dishwashers, so while this is going on, no baking is happening. The baker is washing up.

Selling fewer loaves of bread a day is bad, and making any particular customer sit and wait while you clean all the dishes is too. The goal is to maximize throughput and minimize latency, but there is no free lunch, even inside a bakery. Garbage collectors make different trade-offs between how much throughput they sacrifice and latency they tolerate.

Being able to make these trade-offs is useful because different user programs have different needs. An overnight batch job that is generating a report from a terabyte of data just needs to get as much work done as fast as possible. Throughput is queen. Meanwhile, an app running on a users smartphone needs to always respond immediately to user input so that dragging on the screen feels buttery smooth. The app cant freeze for a few seconds while the GC mucks around in the heap.

As a garbage collector author, you control some of the trade-off between throughput and latency by your choice of collection algorithm. But even within a single algorithm, we have a lot of control over how frequently the collector runs.

Our collector is a stop-the-world GC which means the users program is paused until the entire garbage collection process has completed. If we wait a long time before we run the collector, then a large number of dead objects will accumulate. That leads to a very long pause while the collector runs and thus high latency. So, clearly, we want to run the collector really frequently.

But every time the collector runs, it spends some time visiting live objects. That doesnt really do anything useful (aside from ensuring that they dont incorrectly get deleted). Time visiting live objects is time not freeing memory and also time not running user code. If you run the GC really frequently, then the users program doesnt have enough time to even generate new garbage for the VM to collect. The VM will spend all of its time obsessively revisiting the same set of live objects over and over, and throughput suffers. So, clearly, we want to run the collector really in-frequently.

In fact, we want something in the middle, and the frequency of when the collector runs is one of our main knobs for tuning the trade-off between latency and throughput.

We want our GC to run frequently enough to minimize latency but infrequently enough to maintain decent throughput. But how do we find the balance between these when we have no idea how much memory the users program needs and how often it allocates? We could pawn the problem onto the user and force them to pick by exposing GC tuning parameters. Many VMs do this. But if we, the GC authors, dont know how to tune it well, odds are good most users wont either. They deserve a reasonable default behavior.

Ill be honest with you, this is not my area of expertise. Ive talked to a number of professional GC hackersthis is something you can build an entire career onand read a lot of the literature, and all of the answers I got were vague. The strategy I ended up picking is common, pretty simple, and (I hope!) good enough for most uses.

The idea is that the collector frequency automatically adjusts based on the live size of the heap. We track the total number of bytes of managed memory that the VM has allocated. When it goes above some threshold, we trigger a GC. After that, we note how many bytes of memory remainhow many were not freed. Then we adjust the threshold to some value larger than that.

The result is that as the amount of live memory increases, we collect less frequently in order to avoid sacrificing throughput by re-traversing the growing pile of live objects. As the amount of live memory goes down, we collect more frequently so that we dont lose too much latency by waiting too long.

The implementation requies two new bookkeeping fields in the VM:

  ObjUpvalue* openUpvalues;

  size_t bytesAllocated;
  size_t nextGC;

  Obj* objects;

The first is a running total of the number of bytes of managed memory the VM has allocated. The second is the threshold that triggers the next collection. We initialize them when the VM starts up:

  vm.objects = NULL;

  vm.bytesAllocated = 0;
  vm.nextGC =  * ;

  vm.grayCount = 0;

The starting threshold here is arbitrary. Its similar to the initial capacity we picked for our various dynamic arrays. The goal is to not trigger the first few GCs too quickly but also to not wait too long. If we had some real-world Lox programs, we could profile those to tune this. But since all we have is toy programs, I just picked a number.

Every time we allocate or free some memory, we adjust the counter by that delta:

void* reallocate(void* previous, size_t oldSize, size_t newSize) {

  vm.bytesAllocated += newSize - oldSize;

  if (newSize > oldSize) {

When the total crosses the limit, we run the collector:

  if (newSize > oldSize) {
#ifdef DEBUG_STRESS_GC                  
    collectGarbage();
#endif                                  

    if (vm.bytesAllocated > vm.nextGC) {
      collectGarbage();
    }

  }

Now, finally, our garbage collector actually does something when the user runs a program without our hidden diagnostic flag enabled. The sweep phase frees objects by calling reallocate(), which lowers the value of bytesAllocated, so after the collection completes we know how many live bytes remain. We adjust the threshold of the next GC based on that:

  sweep();

  vm.nextGC = vm.bytesAllocated * GC_HEAP_GROW_FACTOR;

#ifdef DEBUG_LOG_GC                                   

The threshold is a multiple of the heap size. This way, as the amount of memory the program uses grows, the threshold moves farther out to limit the total time spent re-traversing the larger live set. Like other numbers in this chapter, the scaling factor is basically arbitrary:

#endif                                                            

#define GC_HEAP_GROW_FACTOR 2                                     

void* reallocate(void* previous, size_t oldSize, size_t newSize) {

Youd want to tune this in your implementation once you had some real programs to benchmark it on. Right now, we can at least log some of the statistics that we have. We capture the heap size before the collection:

  printf("-- gc begin\n");

  size_t before = vm.bytesAllocated;

#endif                              

And then print the results at the end:

  printf("-- gc end\n");

  printf("   collected %ld bytes (from %ld to %ld) next at %ld\n",
         before - vm.bytesAllocated, before, vm.bytesAllocated,
         vm.nextGC);

#endif                                                            

This way we can see how much the garbage collector accomplished while it ran.

In theory, we are all done now. We have a GC. It kicks in periodically, collects what it can, and leaves the rest. If this were a typical textbook, we would wipe the dust from our hands and bask in the soft glow of the flawless marble edifice we have created.

But I aim to teach you not just the theory of programming languages but the sometimes painful reality. I am going to roll over a rotten log and show you the nasty bugs that live under it, and garbage collector bugs really are some of the grossest invertebrates out there.

The collectors job is to free dead objects and preserve live ones. Mistakes are easy to make in both directions. If the VM fails to free objects that arent needed, it can slowly leak memory over time. If it frees an object that is in use, the users program can access invalid memory. These failures often dont immediately cause a crash, which makes it harder for us to trace backwards in time to find the bug.

This is made harder by the fact that we dont know when the collector will run. Any call that eventually allocates some memory is a place in the VM where a collection could happen. Its like musical chairs. At any point, the GC might stop the music. Every single heap-allocated object that we want to keep needs to find a chair quicklyget marked as a root or stored as a reference in some other objectbefore the sweep phase comes to kick it out of the game.

How is it possible for the VM to use an object later that the GC itself doesnt see? How can the VM find it? The most common answer is through a pointer stored in some local variable on the C stack. The GC walks the VMs value and CallFrame stacks, but the C stack is hidden to it.

In previous chapters, we wrote seemingly pointless code that pushed an object onto the VMs value stack, did a little work, and then popped it right back off. Most times, I said this was for the GCs benefit. Now you see why. The code between pushing and popping potentially allocates memory and thus can trigger a GC. We had to make sure the object was on the value stack so that the collectors mark phase would find it and keep it alive.

I wrote the entire clox implementation before splitting it into chapters and writing the prose, so I had plenty of time to find all of these corners and flush out most of these bugs. The stress testing code we put in at the beginning of this chapter and a pretty good test suite were very helpful.

But I only fixed most of them. I left a couple in because I want to give you a hint of what its like to encounter these bugs in the wild. If you enable the stress test flag and run some toy Lox programs, you can probably stumble onto a few. Give it a try and see if you can fix any yourself.

You are very likely to hit the first bug. The constant table each chunk owns is a dynamic array. When the compiler adds a new constant to the current functions table, that array may need to grow. The constant itself may also be some heap-allocated object like a string or a nested function.

The new object being added to the constant table is passed to addConstant(). At that moment, the object can only be found in the parameter to that function on the C stack. That function appends the object to the constant table. If the table doesnt have enough capacity and needs to grow, it calls reallocate(). That in turn triggers a GC, which fails to mark the new constant object and thus sweeps it right before we have a chance to add it to the table. Crash.

The fix, as youve seen in other places, is to push it onto the stack temporarily:

int addConstant(Chunk* chunk, Value value) {

  push(value);

  writeValueArray(&chunk->constants, value);

Once the constant table contains the object, we pop it off the stack:

  writeValueArray(&chunk->constants, value);

  pop();

  return chunk->constants.count - 1;

When the GC is marking roots, it walks the chain of compilers and marks each of their functions, so the new constant is reachable now. We do need an include to call into the VM from the chunk module:

#include "memory.h"           

#include "vm.h"               

void initChunk(Chunk* chunk) {

Heres another similar one. All strings are interned in clox so whenever we create a new string we also add it to the intern table. You can see where this is going. Since the string is brand new, it isnt reachable anywhere. And resizing the string pool can trigger a collection. Again, we go ahead and stash the string on the stack first:

  string->hash = hash;

  push(OBJ_VAL(string));

  tableSet(&vm.strings, string, NIL_VAL);

And then pop it back off once its safely nestled in the table:

  tableSet(&vm.strings, string, NIL_VAL);

  pop();

  return string;

This ensures the string is safe while the table is being resized. Once it survives that, allocateString() will return it to some caller which can then take responsibility for ensuring the string is still reachable before the next heap allocation occurs.

One last example: Over in the interpreter, the OP_ADD instruction can be used to concatenate two strings. As it does with numbers, it pops the two operands from the stack, computes the result, and pushes that new value back onto the stack. For numbers thats perfectly safe.

But concatenating two strings requires allocating a new character array on the heap, which can in turn trigger a GC. Since weve already popped the operand strings by that point, they can potentially be missed by the mark phase and get swept away. Instead of popping them off the stack eagerly, we peek them:

static void concatenate() {

  ObjString* b = AS_STRING(peek(0));
  ObjString* a = AS_STRING(peek(1));

  int length = a->length + b->length;

That way, they are still hanging out on the stack when we create the result string. Once thats done, we can safely pop them off and replace them with the result:

  ObjString* result = takeString(chars, length);

  pop();
  pop();

  push(OBJ_VAL(result));

Those were all pretty easy, especially because I showed you were the fix was. In practice, finding them is the hard part. All you see is an object that should be there but isnt. Its not like other bugs where youre looking for the code that causes some problem. Youre looking for the absence of code which fails to prevent a problem, and thats a much harder search.

But, for now at least, you can rest easy. As far as I know, weve found all of the collection bugs in clox and now we have a working, robust, self-tuning mark-sweep garbage collector.