I use recon_alloc:memory(allocated_types) and get info like below.
34> recon_alloc:memory(allocated_types).
[{binary_alloc,1546650440},
{driver_alloc,21504840},
{eheap_alloc,28704768840},
{ets_alloc,526938952},
{fix_alloc,145359688},
{ll_alloc,403701800},
{sl_alloc,688968},
{std_alloc,67633992},
{temp_alloc,21504840}]
The eheap_alloc is using 28G. But sum up with heap_size of all process
>lists:sum([begin {_, X}=process_info(P, heap_size), X end || P <- processes()]).
683197586
Only 683M !Any idea where is the 28G ?
You are not comparing the right values. From erlang:process_info
{heap_size, Size}
Size is the size in words of youngest heap generation of the
process. This generation currently include the stack of the process.
This information is highly implementation dependent, and may change if
the implementation change.
recon_alloc:memory(allocated_types) is in bytes by default. You can change it using set_unit. It is not the memory that is currently used but it is the memory reserved by the VM grouped into different allocators. You can use recon_alloc:memory(used) instead. More details in allocator() - Recon Library
Searching through the Erlang source code for the eheap_alloc keyword I didn't come up with much. The most relevant piece of code was this XML code from erts_alloc.xml (https://github.com/erlang/otp/blob/172e812c491680fbb175f56f7604d4098cdc9de4/erts/doc/src/erts_alloc.xml#L46):
<tag><c>eheap_alloc</c></tag>
<item>Allocator used for Erlang heap data, such as Erlang process heaps.</item>
This says that process heaps are stored in eheap_alloc but it doesn't say what else is stored in eheap_alloc. The eheap_alloc stores everything your application needs to run along with some extra memory along with some additional space, so the VM doesn't have to request more memory from the OS every time something needs to be added. There are things the VM must keep in memory that aren't associated with a specific process. For example, large binaries, even though they may used within a process, are not stored inside that processes heap. They are stored in a shared process binary heap called binary_alloc. The binary heap, along with the process heaps and some extra memory, are what make up eheap_alloc.
In your case it looks like you have a lot of memory in your binary_alloc. binary_alloc is probably using a significant portion of your eheap_alloc.
For more details on binary handling checkout these pages:
http://blog.bugsense.com/post/74179424069/erlang-binary-garbage-collection-a-love-hate
http://www.erlang.org/doc/efficiency_guide/binaryhandling.html#id65224
Related
I'm using Prometheus 2.9.2 for monitoring a large environment of nodes.
As part of testing the maximum scale of Prometheus in our environment, I simulated a large amount of metrics on our test environment.
My management server has 16GB ram and 100GB disk space.
During the scale testing, I've noticed that the Prometheus process consumes more and more memory until the process crashes.
I've noticed that the WAL directory is getting filled fast with a lot of data files while the memory usage of Prometheus rises.
The management server scrapes its nodes every 15 seconds and the storage parameters are all set to default.
I would like to know why this happens, and how/if it is possible to prevent the process from crashing.
Thank you!
The out of memory crash is usually a result of a excessively heavy query. This may be set in one of your rules. (this rule may even be running on a grafana page instead of prometheus itself)
If you have a very large number of metrics it is possible the rule is querying all of them. A quick fix is by exactly specifying which metrics to query on with specific labels instead of regex one.
This article explains why Prometheus may use big amounts of memory during data ingestion. If you need reducing memory usage for Prometheus, then the following actions can help:
Increasing scrape_interval in Prometheus configs.
Reducing the number of scrape targets and/or scraped metrics per target.
P.S. Take a look also at the project I work on - VictoriaMetrics. It can use lower amounts of memory compared to Prometheus. See this benchmark for details.
Because the combination of labels lies on your business, the combination and the blocks may be unlimited, there's no way to solve the memory problem for the current design of prometheus!!!! But i suggest you compact small blocks into big ones, that will reduce the quantity of blocks.
Huge memory consumption for TWO reasons:
prometheus tsdb has a memory block which is named: "head", because head stores all the series in latest hours, it will eat a lot of memory.
each block on disk also eats memory, because each block on disk has a index reader in memory, dismayingly, all labels, postings and symbols of a block are cached in index reader struct, the more blocks on disk, the more memory will be cupied.
in index/index.go, you will see:
type Reader struct {
b ByteSlice
// Close that releases the underlying resources of the byte slice.
c io.Closer
// Cached hashmaps of section offsets.
labels map[string]uint64
// LabelName to LabelValue to offset map.
postings map[string]map[string]uint64
// Cache of read symbols. Strings that are returned when reading from the
// block are always backed by true strings held in here rather than
// strings that are backed by byte slices from the mmap'd index file. This
// prevents memory faults when applications work with read symbols after
// the block has been unmapped. The older format has sparse indexes so a map
// must be used, but the new format is not so we can use a slice.
symbolsV1 map[uint32]string
symbolsV2 []string
symbolsTableSize uint64
dec *Decoder
version int
}
We used the prometheus version 2.19 and we had a significantly better memory performance. This Blog highlights how this release tackles memory problems. i will strongly recommend using it to improve your instance resource consumption.
I need to be extremely concerned with speed/latency in my current multi-threaded project.
Cache access is something I'm trying to understand better. And I'm not clear on how lock-free queues (such as the boost::lockfree::spsc_queue) access/use memory on a cache level.
I've seen queues used where the pointer of a large object that needs to be operated on by the consumer core is pushed into the queue.
If the consumer core pops an element from the queue, I presume that means the element (a pointer in this case) is already loaded into the consumer core's L2 and L1 cache. But to access the element, does it not need to access the pointer itself by finding and loading the element either from either the L3 cache or across the interconnect (if the other thread is on a different cpu socket)? If so, would it maybe be better to simply send a copy of the object that could be disposed of by the consumer?
Thank you.
C++ principally a pay-for-what-you-need eco-system.
Any regular queue will let you choose the storage semantics (by value or by reference).
However, this time you ordered something special: you ordered a lock free queue.
In order to be lock free, it must be able to perform all the observable modifying operations as atomic operations. This naturally restricts the types that can be used in these operations directly.
You might doubt whether it's even possible to have a value-type that exceeds the system's native register size (say, int64_t).
Good question.
Enter Ringbuffers
Indeed, any node based container would just require pointer swaps for all modifying operations, which is trivially made atomic on all modern architectures.
But does anything that involves copying multiple distinct memory areas, in non-atomic sequence, really pose an unsolvable problem?
No. Imagine a flat array of POD data items. Now, if you treat the array as a circular buffer, one would just have to maintain the index of the buffer front and end positions atomically. The container could, at leisure update in internal 'dirty front index' while it copies ahead of the external front. (The copy can use relaxed memory ordering). Only as soon as the whole copy is known to have completed, the external front index is updated. This update needs to be in acq_rel/cst memory order[1].
As long as the container is able to guard the invariant that the front never fully wraps around and reaches back, this is a sweet deal. I think this idea was popularized in the Disruptor Library (of LMAX fame). You get mechanical resonance from
linear memory access patterns while reading/writing
even better if you can make the record size aligned with (a multiple) physical cache lines
all the data is local unless the POD contains raw references outside that record
How Does Boost's spsc_queue Actually Do This?
Yes, spqc_queue stores the raw element values in a contiguous aligned block of memory: (e.g. from compile_time_sized_ringbuffer which underlies spsc_queue with statically supplied maximum capacity:)
typedef typename boost::aligned_storage<max_size * sizeof(T),
boost::alignment_of<T>::value
>::type storage_type;
storage_type storage_;
T * data()
{
return static_cast<T*>(storage_.address());
}
(The element type T need not even be POD, but it needs to be both default-constructible and copyable).
Yes, the read and write pointers are atomic integral values. Note that the boost devs have taken care to apply enough padding to avoid False Sharing on the cache line for the reading/writing indices: (from ringbuffer_base):
static const int padding_size = BOOST_LOCKFREE_CACHELINE_BYTES - sizeof(size_t);
atomic<size_t> write_index_;
char padding1[padding_size]; /* force read_index and write_index to different cache lines */
atomic<size_t> read_index_;
In fact, as you can see, there are only the "internal" index on either read or write side. This is possible because there's only one writing thread and also only one reading thread, which means that there could only be more space at the end of write operation than anticipated.
Several other optimizations are present:
branch prediction hints for platforms that support it (unlikely())
it's possible to push/pop a range of elements at once. This should improve throughput in case you need to siphon from one buffer/ringbuffer into another, especially if the raw element size is not equal to (a whole multiple of) a cacheline
use of std::unitialized_copy where possible
The calling of trivial constructors/destructors will be optimized out at instantiation time
the unitialized_copy will be optimized into memcpy on all major standard library implementations (meaning that e.g. SSE instructions will be employed if your architecture supports it)
All in all, we see a best-in-class possible idea for a ringbuffer
What To Use
Boost has given you all the options. You can elect to make your element type a pointer to your message type. However, as you already raised in your question, this level of indirection reduces locality of reference and might not be optimal.
On the other hand, storing the complete message type in the element type could become expensive if copying is expensive. At the very least try to make the element type fit nicely into a cache line (typically 64 bytes on Intel).
So in practice you might consider storing frequently used data right there in the value, and referencing the less-of-used data using a pointer (the cost of the pointer will be low unless it's traversed).
If you need that "attachment" model, consider using a custom allocator for the referred-to data so you can achieve memory access patterns there too.
Let your profiler guide you.
[1] I suppose say for spsc acq_rel should work, but I'm a bit rusty on the details. As a rule, I make it a point not to write lock-free code myself. I recommend anyone else to follow my example :)
i was testing the application by inserting some 1000 users and each user having 1000 contacts in a database table under mnesia and during insertion at some part the error i got is as follows:
Crash dump was written to: erl_crash.dump
binary_alloc: Cannot allocate 422879872 bytes of memory (of type "binary").
Aborted
i started the erl emulator with erl +MBas af (B-binary allocator af- a fit) and tried again but the error was same,
note:: i am using erlang r12b version and the system ram is 8gb on ubuntu 10.04
so may i know how to solve it?
the records definitions are:
%% database
-record(database,{dbid,guid,data}).
%% changelog
-record(changelog,{dbid,timestamp,changelist,type}).
here data is a vcard(contact info) , dbid and type is "contacts", guid is an integer automatically generated by the server
the database record contains all the vcard data of all users.if there are 1000 users and each user having 1000 contacts then we will have 10^6 records.
the changelog record will contain what are the changes done on the database table at that timestamp
the code for creation of tables are::
mnesia:create_table(database, [{type,bag}, {attributes,Record_of_database},
{record_name,database},
{index,guid},
{disc_copies,[node()]}])
mnesia:create_table(changelog, [{type,set}, {attributes,Record_of_changelog},
{record_name,changelog},
{index,timestamp},
{disc_copies,[node()]}])
the insertion of records on table is:
commit_data(DataList = [#database{dbid=DbID}|_]) ->
io:format("commit data called~n"),
[mnesia:dirty_write(database,{database,DbId,Guid,Key})|| {database,DbId,Guid,X}<-DataList].
write_changelist(Username,Dbname,Timestamp,ChangeList) ->
Type="contacts",
mnesia:dirty_write(changelog,{changelog,DbID,Timestamp,ChangeList,Type}).
I suppose that the list DataList is huge and should not be sent at once from a remote node. It should be sent in small pieces. The client can send one by one item from the DataList generated at the client. Also, because this problem occurs during insertion, i think that we should parallelise the list comprehension. We could have a parallel map where for each item in the list, the insertion is done in a separate process. Then, i also think that something is still wrong with the list comprehension. Variable Key is unbound and variable X is unused. Otherwise, probably the entire methodology needs a change. Lets see what others think. Thanks
This error normally occurs when there is no memory to allocate for binary heap by ERTS memory allocator called binary_alloc. Check the current binary heap size using erlang:system_info() or erlang:memory() or erlang:memory(binary) commands. If the binary heap size is huge then run erlang:garbage_collect() to free all non-referenced binary objects in binary heap. This will free the memory ..
In case you use long strings (it is just list in erlang) for vcard or somewehre else, they consumes much memory.
If this is the case, you change them to binary to suppress memory usage (use list_to_binary before insert to mnesia).
This may be not helpfull, because I don't know about your data structure (type, length and so on)...
Ok, I asked the difference between Stackoverflow and bufferoverflow yesterday and almost getting voted down to oblivion and no new information.
So it got me thinking and I decided to rephrase my question in the hopes that I get reply which actually solves my issue.
So here goes nothing.
I am aware of four memory segments(correct me if I am wrong). The code, data, stack and heap. Now AFAIK the the code segment stores the code, while the data segment stores the data related to the program. What seriously confuses me is the purpose of the stack and the heap!
From what I have understood, when you run a function, all the related data to the function gets stored in the stack and when you recursively call a function inside a function, inside of a function... While the function is waiting on the output of the previous function, the function and its necessary data don't pop out of the stack. So you end up with a stack overflow. (Again please correct me if I am wrong)
Also I know what the heap is for. As I have read someplace, its for dynamically allocating data when a program is executing. But this raises more questions that solves my problems. What happens when I initially initialize my variables in the code.. Are they in the code segment or in the data segment or in the heap? Where do arrays get stored? Is it that after my code executes all that was in my heap gets erased? All in all, please tell me about heap in a more simplified manner than just, its for malloc and alloc because I am not sure I completely understand what those terms are!
I hope people when answering don't get lost in the technicalities and can keep the terms simple for a layman to understand (even if the concept to be described is't laymanish) and keep educating us with the technical terms as we go along. I also hope this is not too big a question, because I seriously think they could not be asked separately!
What is the stack for?
Every program is made up of functions / subroutines / whatever your language of choice calls them. Almost always, those functions have some local state. Even in a simple for loop, you need somewhere to keep track of the loop counter, right? That has to be stored in memory somewhere.
The thing about functions is that the other thing they almost always do is call other functions. Those other functions have their own local state - their local variables. You don't want your local variables to interfere with the locals in your caller. The other thing that has to happen is, when FunctionA calls FunctionB and then has to do something else, you want the local variables in FunctionA to still be there, and have their same values, when FunctionB is done.
Keeping track of these local variables is what the stack is for. Each function call is done by setting up what's called a stack frame. The stack frame typically includes the return address of the caller (for when the function is finished), the values for any method parameters, and storage for any local variables.
When a second function is called, then a new stack frame is created, pushed onto the top of the stack, and the call happens. The new function can happily work away on its stack frame. When that second function returns, its stack frame is popped (removed from the stack) and the caller's frame is back in place just like it was before.
So that's the stack. So what's the heap? It's got a similar use - a place to store data. However, there's often a need for data that lives longer than a single stack frame. It can't go on the stack, because when the function call returns, it's stack frame is cleaned up and boom - there goes your data. So you put it on the heap instead. The heap is a basically unstructured chunk of memory. You ask for x number of bytes, and you get it, and can then party on it. In C / C++, heap memory stays allocated until you explicitly deallocate. In garbage collected languages (Java/C#/Python/etc.) heap memory will be freed when the objects on it aren't used anymore.
To tackle your specific questions from above:
What's the different between a stack overflow and a buffer overflow?
They're both cases of running over a memory limit. A stack overflow is specific to the stack; you've written your code (recursion is a common, but not the only, cause) so that it has too many nested function calls, or you're storing a lot of large stuff on the stack, and it runs out of room. Most OS's put a limit on the maximum size the stack can reach, and when you hit that limit you get the stack overflow. Modern hardware can detect stack overflows and it's usually doom for your process.
A buffer overflow is a little different. So first question - what's a buffer? Well, it's a bounded chunk of memory. That memory could be on the heap, or it could be on the stack. But the important thing is you have X bytes that you know you have access to. You then write some code that writes X + more bytes into that space. The compiler has probably already used the space beyond your buffer for other things, and by writing too much, you've overwritten those other things. Buffer overruns are often not seen immediately, as you don't notice them until you try to do something with the other memory that's been trashed.
Also, remember how I mentioned that return addresses are stored on the stack too? This is the source of many security issues due to buffer overruns. You have code that uses a buffer on the stack and has an overflow vulnerability. A clever hacker can structure the data that overflows the buffer to overwrite that return address, to point to code in the buffer itself, and that's how they get code to execute. It's nasty.
What happens when I initially initialize my variables in the code.. Are they in the code segment or in the data segment or in the heap?
I'm going to talk from a C / C++ perspective here. Assuming you've got a variable declaration:
int i;
That reserves (typically) four bytes on the stack. If instead you have:
char *buffer = malloc(100);
That actually reserves two chunks of memory. The call to malloc allocates 100 bytes on the heap. But you also need storage for the pointer, buffer. That storage is, again, on the stack, and on a 32-bit machine will be 4 bytes (64-bit machine will use 8 bytes).
Where do arrays get stored...???
It depends on how you declare them. If you do a simple array:
char str[128];
for example, that'll reserve 128 bytes on the stack. C never hits the heap unless you explicitly ask it to by calling an allocation method like malloc.
If instead you declare a pointer (like buffer above) the storage for the pointer is on the stack, the actual data for the array is on the heap.
Is it that after my code executes all that was in my heap gets erased...???
Basically, yes. The OS will clean up the memory used by a process after it exits. The heap is a chunk of memory in your process, so the OS will clean it up. Although it depends on what you mean by "clean it up." The OS marks those chunks of RAM as now free, and will reuse it later. If you had explicit cleanup code (like C++ destructors) you'll need to make sure those get called, the OS won't call them for you.
All in all, please tell me about heap in a more simplified manner than just, its for malloc and alloc?
The heap is, much like it's name, a bunch of free bytes that you can grab a piece at a time, do whatever you want with, then throw back to use for something else. You grab a chunk of bytes by calling malloc, and you throw it back by calling free.
Why would you do this? Well, there's a couple of common reasons:
You don't know how many of a thing
you need until run time (based on
user input, for example). So you
dynamically allocate on the heap as
you need them.
You need large data structures. On
Windows, for example, a thread's
stack is limited by default to 1
meg. If you're working with large
bitmaps, for example, that'll be a
fast way to blow your stack and get
a stack overflow. So you grab that
space of the heap, which is usually
much, much larger than the stack.
The code, data, stack and heap?
Not really a question, but I wanted to clarify. The "code" segment contains the executable bytes for your application. Typically code segments are read only in memory to help prevent tampering. The data segment contains constants that are compiled into the code - things like strings in your code or array initializers need to be stored somewhere, the data segment is where they go. Again, the data segment is typically read only.
The stack is a writable section of memory, and usually has a limited size. The OS will initialize the stack and the C startup code calls your main() function for you. The heap is also a writable section of memory. It's reserved by the OS, and functions like malloc and free manage getting chunks out of it and putting them back.
So, that's the overview. I hope this helps.
With respect to stack... This is precicely where the parameters and local variables of the functions / procedures are stored. To be more precise, the params and local variables of the currently executing function is only accessible from the stack... Other variables that belong to chain of functions that were executed before it will be in stack but will not be accessible until the current function completed its operations.
With respect global variables, I believe these are stored in data segment and is always accessible from any function within the created program.
With respect to Heap... These are additional memories that can be made allotted to your program whenever you need them (malloc or new)... You need to know where the allocated memory is in heap (address / pointer) so that you can access it when you need. Incase you loose the address, the memory becomes in-accessible, but the data still remains there. Depending on the platform and language this has to be either manually freed by your program (or a memory leak occurs) or needs to be garbage collected. Heap is comparitively huge to stack and hence can be used to store large volumes of data (like files, streams etc)... Thats why Objects / Files are created in Heap and a pointer to the object / file is stored in stack.
In terms of C/C++ programs, the data segment stores static (global) variables, the stack stores local variables, and the heap stores dynamically allocated variables (anything you malloc or new to get a pointer to). The code segment only stores the machine code (the part of your program that gets executed by the CPU).
How is a program (e.g. C or C++) arranged in computer memory? I kind of know a little about segments, variables etc, but basically I have no solid understanding of the entire structure.
Since the in-memory structure may differ, let's assume a C++ console application on Windows.
Some pointers to what I'm after specifically:
Outline of a function, and how is it called?
Each function has a stack frame, what does that contain and how is it arranged in memory?
Function arguments and return values
Global and local variables?
const static variables?
Thread local storage..
Links to tutorial-like material and such is welcome, but please no reference-style material assuming knowledge of assembler etc.
Might this be what you are looking for:
http://en.wikipedia.org/wiki/Portable_Executable
The PE file format is the binary file structure of windows binaries (.exe, .dll etc). Basically, they are mapped into memory like that. More details are described here with an explanation how you yourself can take a look at the binary representation of loaded dlls in memory:
http://msdn.microsoft.com/en-us/magazine/cc301805.aspx
Edit:
Now I understand that you want to learn how source code relates to the binary code in the PE file. That's a huge field.
First, you have to understand the basics about computer architecture which will involve learning the general basics of assembly code. Any "Introduction to Computer Architecture" college course will do. Literature includes e.g. "John L. Hennessy and David A. Patterson. Computer Architecture: A Quantitative Approach" or "Andrew Tanenbaum, Structured Computer Organization".
After reading this, you should understand what a stack is and its difference to the heap. What the stack-pointer and the base pointer are and what the return address is, how many registers there are etc.
Once you've understood this, it is relatively easy to put the pieces together:
A C++ object contains code and data, i.e., member variables. A class
class SimpleClass {
int m_nInteger;
double m_fDouble;
double SomeFunction() { return m_nInteger + m_fDouble; }
}
will be 4 + 8 consecutives bytes in memory. What happens when you do:
SimpleClass c1;
c1.m_nInteger = 1;
c1.m_fDouble = 5.0;
c1.SomeFunction();
First, object c1 is created on the stack, i.e., the stack pointer esp is decreased by 12 bytes to make room. Then constant "1" is written to memory address esp-12 and constant "5.0" is written to esp-8.
Then we call a function that means two things.
The computer has to load the part of the binary PE file into memory that contains function SomeFunction(). SomeFunction will only be in memory once, no matter how many instances of SimpleClass you create.
The computer has to execute function SomeFunction(). That means several things:
Calling the function also implies passing all parameters, often this is done on the stack. SomeFunction has one (!) parameter, the this pointer, i.e., the pointer to the memory address on the stack where we have just written the values "1" and "5.0"
Save the current program state, i.e., the current instruction address which is the code address that will be executed if SomeFunction returns. Calling a function means pushing the return address on the stack and setting the instruction pointer (register eip) to the address of the function SomeFunction.
Inside function SomeFunction, the old stack is saved by storing the old base pointer (ebp) on the stack (push ebp) and making the stack pointer the new base pointer (mov ebp, esp).
The actual binary code of SomeFunction is executed which will call the machine instruction that converts m_nInteger to a double and adds it to m_fDouble. m_nInteger and m_fDouble are found on the stack, at ebp - x bytes.
The result of the addition is stored in a register and the function returns. That means the stack is discarded which means the stack pointer is set back to the base pointer. The base pointer is set back (next value on the stack) and then the instruction pointer is set to the return address (again next value on the stack). Now we're back in the original state but in some register lurks the result of the SomeFunction().
I suggest, you build yourself such a simple example and step through the disassembly. In debug build the code will be easy to understand and Visual Studio displays variable names in the disassembly view. See what the registers esp, ebp and eip do, where in memory your object is allocated, where the code is etc.
What a huge question!
First you want to learn about virtual memory. Without that, nothing else will make sense. In short, C/C++ pointers are not physical memory addresses. Pointers are virtual addresses. There's a special CPU feature (the MMU, memory management unit) that transparently maps them to physical memory. Only the operating system is allowed to configure the MMU.
This provides safety (there is no C/C++ pointer value you can possibly make that points into another process's virtual address space, unless that process is intentionally sharing memory with you) and lets the OS do some really magical things that we now take for granted (like transparently swap some of a process's memory to disk, then transparently load it back when the process tries to use it).
A process's address space (a.k.a. virtual address space, a.k.a. addressable memory) contains:
a huge region of memory that's reserved for the Windows kernel, which the process isn't allowed to touch;
regions of virtual memory that are "unmapped", i.e. nothing is loaded there, there's no physical memory assigned to those addresses, and the process will crash if it tries to access them;
parts the various modules (EXE and DLL files) that have been loaded (each of these contains machine code, string constants, and other data); and
whatever other memory the process has allocated from the system.
Now typically a process lets the C Runtime Library or the Win32 libraries do most of the super-low-level memory management, which includes setting up:
a stack (for each thread), where local variables and function arguments and return values are stored; and
a heap, where memory is allocated if the process calls malloc or does new X.
For more about the stack is structured, read about calling conventions. For more about how the heap is structured, read about malloc implementations. In general the stack really is a stack, a last-in-first-out data structure, containing arguments, local variables, and the occasional temporary result, and not much more. Since it is easy for a program to write straight past the end of the stack (the common C/C++ bug after which this site is named), the system libraries typically make sure that there is an unmapped page adjacent to the stack. This makes the process crash instantly when such a bug happens, so it's much easier to debug (and the process is killed before it can do any more damage).
The heap is not really a heap in the data structure sense. It's a data structure maintained by the CRT or Win32 library that takes pages of memory from the operating system and parcels them out whenever the process requests small pieces of memory via malloc and friends. (Note that the OS does not micromanage this; a process can to a large extent manage its address space however it wants, if it doesn't like the way the CRT does it.)
A process can also request pages directly from the operating system, using an API like VirtualAlloc or MapViewOfFile.
There's more, but I'd better stop!
For understanding stack frame structure you can refer to
http://en.wikipedia.org/wiki/Call_stack
It gives you information about structure of call stack, how locals , globals , return address is stored on call stack
Another good illustration
http://www.cs.uleth.ca/~holzmann/C/system/memorylayout.pdf
It might not be the most accurate information, but MS Press provides some sample chapters of of the book Inside Microsoft® Windows® 2000, Third Edition, containing information about processes and their creation along with images of some important data structures.
I also stumbled upon this PDF that summarizes some of the above information in an nice chart.
But all the provided information is more from the OS point of view and not to much detailed about the application aspects.
Actually - you won't get far in this matter with at least a little bit of knowledge in Assembler. I'd recoomend a reversing (tutorial) site, e.g. OpenRCE.org.