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Execution Trace Format

This document describes the execution trace format that serves as the interface between the frontend and backend of Online Python Tutor (thereafter abbreviated as OPT).

It is a starting point for anyone who wants to create a different backend (e.g., for another programming language) or a different frontend (e.g., for visually-impaired students). View it online at:

https://github.com/pgbovine/OnlinePythonTutor/blob/master/v3/docs/opt-trace-format.md

Look at the Git history to see when this document was last updated; the more time elapsed since that date, the more likely things are out-of-date.

I'm assuming that you're competent in Python, JavaScript, command-line-fu, and Google-fu, and command-line BS. You may still be confused about details after reading this doc, so feel free to email philip@pgbovine.net if you have questions.

Trace Overview

Before reading this file, I suggest for you to first read the Overview for Developers doc: https://github.com/pgbovine/OnlinePythonTutor/blob/master/v3/docs/developer-overview.md

Pay particular attention to what generate_json_trace.py is and how to run it: https://github.com/pgbovine/OnlinePythonTutor/blob/master/v3/docs/developer-overview.md#two-quick-tips-for-starters

Let's start with a simple example. Create an example.py file with the following contents:

x = 5
y = 10
z = x + y

Now run:

python generate_json_trace.py example.py

and you should see the following output:

{
  "code": "x = 5\ny = 10\nz = x + y\n\n", 
  "trace": [
    {
      "ordered_globals": [], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 1, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [
        "x"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "x": 5
      }, 
      "heap": {}, 
      "line": 2, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [
        "x", 
        "y"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "y": 10, 
        "x": 5
      }, 
      "heap": {}, 
      "line": 3, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [
        "x", 
        "y", 
        "z"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "y": 10, 
        "x": 5, 
        "z": 15
      }, 
      "heap": {}, 
      "line": 3, 
      "event": "return"
    }
  ]
}

Recall that when OPT is deployed on a webserver, the backend generates this trace and sends it to the frontend, where it will be turned into a visualization.

Click here to see the visualization of this trace (open it in a new window if possible). Note that the trace object contains all of the information required to create this visualization.

The trace is a JSON object with two fields: code is the string contents of the code to be visualized, and trace is the actual execution trace, which consists of a list of execution points.

In the above example, trace is a list of four elements since there are four execution points. If you step through the visualization, you'll notice that there are exactly four steps, one for each element of the trace list. (Sometimes the frontend will filter out some redundant entries in trace, but a simplifying assumption is that trace.length is the number of execution steps that the frontend renders.)

Ok, still with me? Let's now dig into what an individual element in trace looks like.

Interlude: Viewing a trace in the OPT frontend

To view any trace locally on your computer without setting up a Webserver, load up v3/demo.html in your browser. Now create a trace file called v3/test-trace.js containing a single trace variable set to the value of your desired trace object:

var trace = { ... your trace object ... };

Here is the example test-trace.js in the repo.

Now reload v3/demo.html, and if everything works out, it will create a new visualization using the trace in the trace variable from v3/test-trace.js. I use this technique as a quick way of testing out what different traces look like without setting up a server.

If you open up your browser console, you will see the current trace entry being printed out as you step through the visualization, which can help you debug.

P.S. to create a trace file in the appropriate format using the default OPT Python backend, run generate_json_trace.py using the --create_jsvar option:

python generate_json_trace.py --create_jsvar=trace example.py > test-trace.js

Execution Point Objects

The central type of object in a trace is an "execution point", which represents the state of the computer's (abstract) memory at a certain point in execution. Recall that a trace is an ordered list of execution points.

The key concept to understand is that the frontend renders an execution point by simply looking at the contents of the corresponding execution point object, without consulting any of its neighbors.

Ok, let's now look at the four execution points in our above example in order. The first point is what the frontend visualizes when it says "Step 1 of 3":

    {
      "ordered_globals": [], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 1, 
      "event": "step_line"
    }

This is pretty much what an "empty" execution point object looks like. line shows the line number of the line that is about to execute, which is line 1 in this case. And event is step_line, which indicates that an ordinary single-line step event is about to occur. func_name is the function that's currently executing: In this case, <module> is the faux name for top-level code that's not in any function. All of the other fields are empty, and if you look at the visualization, nothing is rendered in the "Frames" or "Objects" panes.

Ok now let's look at the second point, which corresponds to the frontend visualization when it says "Step 2 of 3":

    {
      "ordered_globals": [
        "x"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "x": 5
      }, 
      "heap": {}, 
      "line": 2, 
      "event": "step_line"
    }

Ok note that line is now 2, which means that line 2 is about to execute (yes, this convention is a bit confusing, but it's what the bdb debugger gives us). globals is now populated with one key-value pair: the global variable x has a value of 5. That makes sense since we just executed line 1 (from the previous execution point), which was the code x = 5. If you look at the visualization at this step, you'll see that x has been assigned to 5.

Ok let's keep marching to the next execution point, which is the one that corresponds to "Step 3 of 3" in the frontend:

    {
      "ordered_globals": [
        "x", 
        "y"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "y": 10, 
        "x": 5
      }, 
      "heap": {}, 
      "line": 3, 
      "event": "step_line"
    }

Now line is 3, because we're about to execute line 3 (we just executed lines 1 and 2). Notice that there is a new key-value pair inglobals showing that y has been assigned to 10. No surprises here, since we just executed the line y = 10.

Ok now this is where I want to talk about ordered_globals, which is a list of global variables (i.e., keys in globals) in the order that the frontend should visualize them. The backend appends variable names in their order of appearance throughout execution. Why is this list necessary? Because globals is an object whose keys are unsorted, so if you don't also keep an ordered_globals sorted list, then the visualization might end up being jarring. For instance, at one execution point, it might render x and then y, and at the next execution point, it might render y and then x, thereby causing the visualization to "jitter" unnecessarily. And I've found that it looks aesthetically pleasing when variables are sorted in their order of appearance as you step forwards through execution.

Still with me? Ok, let's get to the final execution point, which corresponds to the frontend displaying "Program terminated" (click here to jump directly there).

    {
      "ordered_globals": [
        "x", 
        "y", 
        "z"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "y": 10, 
        "x": 5, 
        "z": 15
      }, 
      "heap": {}, 
      "line": 3, 
      "event": "return"
    }

This time, the event is a return, which signifies "returning" from the top-level module code (meaning the program has terminated). Note that now there is another new variable z, which is bound to 15 since z = x + y just executed. Note that, again, ordered_globals shows all three variables in their order of appearance.

Ok, that's it for the basic tour. Next let's talk about what happens when the heap field isn't empty.

Heap Objects

The previous example contained only primitive values that JSON could encode directly within the globals object. JSON natively supports numbers, strings, and boolean values (which map well to Python's "primitive" data types). But what happens when the user's program contains compound Python data types such as lists, tuples, dicts, sets, etc.?

Create an example.py file with the following contents:

x = [1, 2, 3]
y = ('Alice', 'Bob', 'Cindy')
z = {'carrot': 'vegetable', 'mouse': 'animal', 'rock': 'mineral'}

You should know how to generate a trace by now. The trace again contains four elements since there are four execution steps (one for the very beginning of execution plus three executed lines).

"Step 1 of 3" is boring since nothing is displayed. Let's jump to "Step 2 of 3" (click here)

The visualization now shows x pointing to a list containing [1, 2, 3]. This is the corresponding execution point object:

    {
      "ordered_globals": [
        "x"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "x": [
          "REF", 
          1
        ]
      }, 
      "heap": {
        "1": [
          "LIST", 
          1, 
          2, 
          3
        ]
      }, 
      "line": 2, 
      "event": "step_line"
    }

Note that in globals, x now refers to a ["REF", 1] object, which means a reference (pointer) to a heap object with an ID of 1.

Let's now look at heap, which is a mapping of heap object IDs to their contents. The current heap has one object with an ID of 1. That object is a list of [1, 2, 3], which is encoded in JSON as:

["LIST", 1, 2, 3]

Let's skip forward to the end of execution ("Program terminated"): click here

Now there are three variables -- x points to a list, y points to a tuple, and z points to a dict. This execution point object is getting kinda big:

    {
      "ordered_globals": [
        "x", 
        "y", 
        "z"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "y": [
          "REF", 
          2
        ], 
        "x": [
          "REF", 
          1
        ], 
        "z": [
          "REF", 
          3
        ]
      }, 
      "heap": {
        "1": [
          "LIST", 
          1, 
          2, 
          3
        ], 
        "2": [
          "TUPLE", 
          "Alice", 
          "Bob", 
          "Cindy"
        ], 
        "3": [
          "DICT", 
          [
            "carrot", 
            "vegetable"
          ], 
          [
            "mouse", 
            "animal"
          ], 
          [
            "rock", 
            "mineral"
          ]
        ]
      }, 
      "line": 3, 
      "event": "return"
    }

Note that in globals, x refers to heap object 1, y to heap object 2, and z to 3. If you then look at heap, you'll see that objects 1, 2, and 3 map to the corresponding list, tuple, and dict, respectively.

Look at the comments at the top of pg_encoder.py to learn the JSON encoding formats for various Python data types:

https://github.com/pgbovine/OnlinePythonTutor/blob/master/v3/pg_encoder.py

The basic idea behind the encoding format is that each compound object is encoded as a JSON list where the first element is a string "tag" identifying its type (e.g., "LIST", "TUPLE", "DICT").

Heap-to-Heap References

In the above example, the heap objects contained only primitives (numbers and strings), which can be directly encoded within those objects' representations in heap.

But heap objects can themselves point to other heap objects. Let's look at the following example:

c = (1, (2, None))
d = (1, c)

Jump to the end of execution and notice that:

  • c points to a tuple
  • the second element of that tuple points to another tuple
  • d points to a tuple whose second element points to what c points to

Let's look at the execution point object that corresponds to this visualization:

    {
      "ordered_globals": [
        "c", 
        "d"
      ], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {
        "c": [
          "REF", 
          1
        ], 
        "d": [
          "REF", 
          3
        ]
      }, 
      "heap": {
        "1": [
          "TUPLE", 
          1, 
          [
            "REF", 
            2
          ]
        ], 
        "2": [
          "TUPLE", 
          2, 
          null
        ], 
        "3": [
          "TUPLE", 
          1, 
          [
            "REF", 
            1
          ]
        ]
      }, 
      "line": 2, 
      "event": "return"
    }

What's going on here? Let's start with globals again. c points to heap object 1 (["REF", 1]), and d points to heap object 3 (["REF", 3]).

Let's now look at heap. Heap object 1 is:

["TUPLE", 1, ["REF", 2]]

What does this mean? It means that it represents a tuple whose first element is the number 1 and whose second element is a reference (pointer) to heap object 2.

Ok let's look at heap object 2:

["TUPLE", 2, null]

which corresponds to the Python tuple object (2, None).

Finally, heap object 3 (which d points to) is:

["TUPLE", 1, ["REF", 1]]

which corresponds to the Python object (1, c).

The ability to put "REF" objects inside of heap objects enables an arbitrary object graph to be represented in the execution trace.

Capturing stdout Output

The stdout field in an execution point object represents the sum total of all output sent to stdout so far during execution. For example, given this program:

print 1
print "two"
print (3, 4, 5)

The complete trace object is:

{
  "code": "print 1\nprint \"two\"\nprint (3, 4, 5)\n\n", 
  "trace": [
    {
      "ordered_globals": [], 
      "stdout": "", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 1, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [], 
      "stdout": "1\n", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 2, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [], 
      "stdout": "1\ntwo\n", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 3, 
      "event": "step_line"
    }, 
    {
      "ordered_globals": [], 
      "stdout": "1\ntwo\n(3, 4, 5)\n", 
      "func_name": "<module>", 
      "stack_to_render": [], 
      "globals": {}, 
      "heap": {}, 
      "line": 3, 
      "event": "return"
    }
  ]
}

By now you should be getting pretty good at reading these objects :)

Let's just focus on the stdout field at each execution point. Note that its contents start as an empty string at the beginning of execution and then grow incrementally as more stuff is printed to stdout at each subsequent execution point. If we grep for stdout in the trace, we see the following progression:

      "stdout": "", 
      "stdout": "1\n", 
      "stdout": "1\ntwo\n", 
      "stdout": "1\ntwo\n(3, 4, 5)\n", 

This isn't rocket science; but just be aware that stdout contains the cumulative contents of the stdout buffer up to that execution point, not only what's been printed by the most recently executed line.

Potential stdout optimizations

The default trace format is completely unoptimized. It's designed so as to be "memoryless" -- i.e., a frontend can render an execution state by simply reading that entry and not any of its neighbors.

However, the stdout field can often take up A LOT OF SPACE since it's "cumulative" -- i.e., at each step, stdout represents the entire contents of the stdout buffer, not simply what's been printed out in the current step. Thus, it suffers from N^2 space explosions. Even a simple program like this one prints out an ENORMOUS amount to stdout:

choices = ['pizza', 'pasta', 'salad', 'nachos']
for i in enumerate(choices):
    print choices # if you comment this out, the trace is much smaller
    choices.append(i)

Thus, this is one target for optimization if deemed necessary down the line. However, I'm hesitant to optimize the trace since it makes trace-handling code in the frontend and backend more complicated :/

One elegant way to optimize is to simply store the completed stdout buffer ONCE in the trace. And then within each execution point, store a byte index into the buffer. Thus, to render the stdout contents for each point, all we do is slice the buffer from the start to that index. This is similar to the Unix-y idea of file pointers.

Function Stack Frames

So far our example programs contained no function calls. Let's now kick it up a notch and see an example with function calls:

def foo(x, y, z):
  return bar(x, y)

def bar(a, b):
  return baz(a)

def baz(c):
  return c

result = foo(1, 2, 3)

Let's jump straight to Step 11 of 13, when the program is about to return from the call to baz.

Study the visualization for a bit. Note that there are four frames currently on the stack: globals, foo, bar, and baz. Each frame consists of a name and a mapping between constituent variable names and values. There is a special variable called Return value (stored in the trace as __return__), which represents the value that baz is about to return to its caller.

Let's now look at the execution point object corresponding to this visualization:

    {
      "ordered_globals": [
        "foo", 
        "bar", 
        "baz"
      ], 
      "stdout": "", 
      "func_name": "baz", 
      "stack_to_render": [
        {
          "frame_id": 1, 
          "encoded_locals": {
            "y": 2, 
            "x": 1, 
            "z": 3
          }, 
          "is_highlighted": false, 
          "is_parent": false, 
          "func_name": "foo", 
          "is_zombie": false, 
          "parent_frame_id_list": [], 
          "unique_hash": "foo_f1", 
          "ordered_varnames": [
            "x", 
            "y", 
            "z"
          ]
        }, 
        {
          "frame_id": 2, 
          "encoded_locals": {
            "a": 1, 
            "b": 2
          }, 
          "is_highlighted": false, 
          "is_parent": false, 
          "func_name": "bar", 
          "is_zombie": false, 
          "parent_frame_id_list": [], 
          "unique_hash": "bar_f2", 
          "ordered_varnames": [
            "a", 
            "b"
          ]
        }, 
        {
          "frame_id": 3, 
          "encoded_locals": {
            "__return__": 1, 
            "c": 1
          }, 
          "is_highlighted": true, 
          "is_parent": false, 
          "func_name": "baz", 
          "is_zombie": false, 
          "parent_frame_id_list": [], 
          "unique_hash": "baz_f3", 
          "ordered_varnames": [
            "c", 
            "__return__"
          ]
        }
      ], 
      "globals": {
        "bar": [
          "REF", 
          2
        ], 
        "foo": [
          "REF", 
          1
        ], 
        "baz": [
          "REF", 
          3
        ]
      }, 
      "heap": {
        "1": [
          "FUNCTION", 
          "foo(x, y, z)", 
          null
        ], 
        "2": [
          "FUNCTION", 
          "bar(a, b)", 
          null
        ], 
        "3": [
          "FUNCTION", 
          "baz(c)", 
          null
        ]
      }, 
      "line": 8, 
      "event": "return"
    }, 

First things first: This is a return event occurring on line 8 (the return c line in baz). The currently-active function is baz. There are three global variables: foo, bar, and baz, which all point to function objects on the heap.

The only new kind of field is stack_to_render, which is (unsurprisingly) a list of stack frames to render. In this case, stack_to_render contains three elements -- the frames for foo, bar, and baz, in that exact order. The frontend simply walks down stack_to_render and renders each frame in a similar way that it renders global variables.

Let's now zoom in on one particular frame in stack_to_render. Here is the frame for bar:

        {
          "frame_id": 2, 
          "encoded_locals": {
            "a": 1, 
            "b": 2
          }, 
          "is_highlighted": false, 
          "is_parent": false, 
          "func_name": "bar", 
          "is_zombie": false, 
          "parent_frame_id_list": [], 
          "unique_hash": "bar_f2", 
          "ordered_varnames": [
            "a", 
            "b"
          ]
        }, 

For starters, func_name is the name of the function, and is_highlighted is true only if the current frame is the "top-most" one (telling the frontend to highlight it in a brighter color).

encoded_locals is a mapping from local variable names to their values, similar to how globals provides a mapping from global variable names to their values.

ordered_varnames is an ordered list of keys from encoded_locals, usually sorted by order of appearance during execution. The global analogue for this field is ordered_globals. (I suppose this field should be named ordered_locals, but I haven't gotten around to renaming yet.)

frame_id is an integer that uniquely identifies this frame; the first function call (of any function) gets a frame ID of 1, and then subsequent calls get successively increasing frame IDs.

unique_hash is a unique string that identifies this frame. For now, a simple way to construct unique_hash is by concatenating the frame's function name with frame_id. Note that unique_hash seems redundant with frame_id, since the latter is already unique. However, you'll see in the "Closures and Zombie Frames" section why unique_hash is required.

Finally, ignore is_parent, is_zombie, and parent_frame_id_list for now. We'll cover those in the more advanced "Closures and Zombie Frames" section below.

Event types

The event field can either be:

  • step_line - ordinary single step. most trace entries will be of this type.
  • call - first line during an executed function call; very important to match with corresponding return for visualization to work properly ... every frame should have matching call and return entries.
  • return - about to return from a function, usually the __return__ value is set at this point in the proper stack frame entry.
  • exception and uncaught_exception - when an exception happens - the exception_msg field should be set to a reasonable error message. use uncaught_exception at the top-level of the tracer to indicate what happens when the user's program itself didn't catch the exception, and a regular exception when the user's program throws an exception (which might be caught in another part of their program). If the trace has exactly 1 entry and it's an uncaught_exception, then the OPT frontend doesn't switch to the visualization at all ... instead it displays a "syntax error"-like thingy in the code editor. It will highlight the faulting line, indicated by the line field and display exception_msg there. So if you want to indicate a syntax error, then create a trace with exactly ONE entry that's an uncaught_exception. (If there's more than one trace entry and the last one happens to be uncaught_exception, then right now OPT still won't switch to the visualization and instead issue an error message. TODO: figure out what's a more desirable behavior here.)
  • instruction_limit_reached - if you want to cap execution at a certain limit (e.g., 300 executed steps), issue this special event at the end. fill in the exception_msg field to say something like "(stopped after N steps to prevent possible infinite loop)"

Closures and Zombie Frames (advanced)

As soon as a function becomes the parent of another function (i.e., a nested function is defined inside of it), set the is_parent field of its stack entry to true. Also change its unique_hash by appending _p to it. The reason why we want to change unique_hash is that we want the frontend to refresh its display, and d3 might not refresh if it sees that the hash is identical to the prior frame. Conceptually, we want unique_hash to be different for a function before and after it turns into the parent of another function since we want to display it differently.

For example, when function foo is executing, its frame will have is_parent=false and unique_hash = foo_f1 (or something similar) when its first two lines are running to define x and y, respectively. Then when its third line executes, that defines bar as a nested function within itself, so at that step, foo should have is_parent=true and unique_hash = foo_f1_p (or something similar).

def foo(y):
    x = 10
    y = 20
    def bar(x):
        return x + y

foo(1)

[TODO: talk about parent_frame_id_list]

[TODO: talk about appending _z onto unique_hash when the frame becomes a zombie.]

[TODO: IMPROVE ME!]