RevPDB

A "reverse debugger" is a debugger where you can go forward and backward in time. It is an uncommon feature, at least in the open source world, but I have no idea why. I have used undodb-gdb and rr, which are reverse debuggers for C code, and I can only say that they saved me many, many days of poking around blindly in gdb.

The PyPy team is pleased to give you "RevPDB", a reverse-debugger similar to rr but for Python.

An example is worth a thousand words. Let's say your big Python program has a bug that shows up inconsistently. You have nailed it down to something like:

• start x.py, which does stuff (maybe involving processing files, answering some web requests that you simulate from another terminal, etc.);
• sometimes, after a few minutes, your program's state becomes inconsistent and you get a failing assert or another exception.

This is the case where RevPDB is useful.

RevPDB is available only on 64-bit Linux and OS/X right now, but should not be too hard to port to other OSes. It is very much alpha-level! (It is a debugger full of bugs. Sorry about that.) I believe it is still useful---it helped me in one real use case already.

How to get RevPDB

The following demo was done with an alpha version for 64-bit Linux, compiled for Arch Linux. I won't provide the binary; it should be easy enough to retranslate (much faster than a regular PyPy because it contains neither a JIT nor a custom GC). Grab the PyPy sources from Mercurial, and then:

hg update reverse-debugger
# or "hg update ff376ccacb36" for exactly this demo
cd pypy/goal
../../rpython/bin/rpython -O2 --revdb targetpypystandalone.py  \
                  --withoutmod-cpyext --withoutmod-micronumpy

and possibly rename the final pypy-c to pypy-revdb to avoid confusion.

Other platforms than 64-bit Linux and OS/X need some fixes before they work.

Demo

For this demo, we're going to use this x.py as the "big program":

import os

class Foo(object):
    value = 5

lst1 = [Foo() for i in range(100)]
lst1[50].value += 1
for x in lst1:
    x.value += 1

for x in lst1:
    if x.value != 6:
        print 'oops!'
        os._exit(1)

Of course, it is clear what occurs in this small example: the check fails on item 50. For this demo, the check has been written with os._exit(1), because this exits immediately the program. If it was written with an assert, then its failure would execute things in the traceback module afterwards, to print the traceback; it would be a minor mess just to find the exact point of the failing assert. (This and other issues are supposed to be fixed in the future, but for now it is alpha-level.)

Anyway, with a regular assert and a regular post-mortem pdb, we could observe that x.value is indeed 7 instead of 6 when the assert fails. Imagine that the program is much bigger: how would we find the exact chain of events that caused this value 7 to show up on this particular Foo object? This is what RevPDB is for.

First, we need for now to disable Address Space Layout Randomization (ASLR), otherwise replaying will not work. This is done once with the following command line, which changes the state until the next reboot:

echo 0 | sudo tee /proc/sys/kernel/randomize_va_space

UPDATE: the above is no longer necessary from revision ff376ccacb36.

Run x.py with RevPDB's version of PyPy instead of the regular interpreter (CPython or PyPy):

PYPYRDB=log.rdb ./pypy-revdb x.py

This pypy-revdb executable is like a slow PyPy executable, running (for now) without a JIT. This produces a file log.rdb which contains a complete log of this execution. (If the bug we are tracking occurs rarely, we need to re-run it several times until we get the failure. But once we got the failure, then we're done with this step.)

Start:

rpython/translator/revdb/revdb.py log.rdb

We get a pdb-style debugger. This revdb.py is a normal Python program, which you run with an unmodified Python; internally, it looks inside the log for the path to pypy-revdb and run it as needed (as one forking subprocess, in a special mode).

Initially, we are at the start of the program---not at the end, like we'd get in a regular debugger:

File "<builtin>/app_main.py", line 787 in setup_bootstrap_path:
(1)$

The list of commands is available with help.

Go to the end with continue (or c):

(1)$ continue
File "/tmp/x.py", line 14 in <module>:
...
  lst1 = [Foo() for i in range(100)]
  lst1[50].value += 1
  for x in lst1:
      x.value += 1

  for x in lst1:
      if x.value != 6:
          print 'oops!'
>         os._exit(1)
(19727)$

We are now at the beginning of the last executed line. The number 19727 is the "time", measured in number of lines executed. We can go backward with the bstep command (backward step, or bs), line by line, and forward again with the step command. There are also commands bnext, bcontinue and bfinish and their forward equivalents. There is also "go TIME" to jump directly to the specified time. (Right now the debugger only stops at "line start" events, not at function entry or exit, which makes some cases a bit surprising: for example, a step from the return statement of function foo() will jump directly to the caller's caller, if the caller's current line was return foo() + 2, because no "line start" event occurs in the caller after foo() returns to it.)

We can print Python expressions and statements using the p command:

(19727)$ p x
$0 = <__main__.Foo object at 0xfffffffffffeab3e>
(19727)$ p x.value
$1 = 7
(19727)$ p x.value + 1
8

The "$NUM =" prefix is only shown when we print an object that really exists in the debugged program; that's why the last line does not contain it. Once a $NUM has been printed, then we can use it in further expressions---even at a different point time. It becomes an anchor that always refers to the same object:

(19727)$ bstep

File "/tmp/x.py", line 13 in <module>:
...

  lst1 = [Foo() for i in range(100)]
  lst1[50].value += 1
  for x in lst1:
      x.value += 1

  for x in lst1:
      if x.value != 6:
>         print 'oops!'
          os._exit(1)
(19726)$ p $0.value
$1 = 7

In this case, we want to know when this value 7 was put in this attribute. This is the job of a watchpoint:

(19726)$ watch $0.value
Watchpoint 1 added
updating watchpoint value: $0.value => 7

This watchpoint means that $0.value will be evaluated at each line. When the repr() of this expression changes, the watchpoint activates and execution stops:

(19726)$ bcontinue
[searching 19629..19726]
[searching 19338..19629]

updating watchpoint value: $0.value => 6
Reverse-hit watchpoint 1: $0.value
File "/tmp/x.py", line 9 in <module>:
  import os

  class Foo(object):
      value = 5

  lst1 = [Foo() for i in range(100)]
  lst1[50].value += 1
  for x in lst1:
>     x.value += 1

  for x in lst1:
      if x.value != 6:
          print 'oops!'
          os._exit(1)
(19524)$

Note that using the $NUM syntax is essential in watchpoints. You can't say "watch x.value", because the variable x will go out of scope very soon when we move forward or backward in time. In fact the watchpoint expression is always evaluated inside an environment that contains the builtins but not the current locals and globals. But it also contains all the $NUM, which can be used to refer to known objects. It is thus common to watch $0.attribute if $0 is an object, or to watch len($1) if $1 is some list. The watch expression can also be a simple boolean: for example, "watch $2 in $3" where $3 is some dict and $2 is some object that you find now in the dict; you would use this to find out the time when $2 was put inside $3, or removed from it.

Use "info watchpoints" and "delete <watchpointnum>" to manage watchpoints.

There are also regular breakpoints, which you set with "b FUNCNAME". It breaks whenever there is a call to a function that happens to have the given name. (It might be annoying to use for a function like __init__() which has many homonyms. There is no support for breaking on a fully-qualified name or at a given line number for now.)

In our demo, we stop at the line x.value += 1, which is where the value was changed from 6 to 7. Use bcontinue again to stop at the line lst1[50].value += 1, which is where the value was changed from 5 to 6. Now we know how this value attribute ends up being 7.

(19524)$ bcontinue
[searching 19427..19524]
[searching 19136..19427]

updating watchpoint value: $0.value => 5
Reverse-hit watchpoint 1: $0.value
File "/tmp/x.py", line 7 in <module>:
  import os

  class Foo(object):
      value = 5

  lst1 = [Foo() for i in range(100)]
> lst1[50].value += 1
  for x in lst1:
      x.value += 1

  for x in lst1:
      if x.value != 6:
...
(19422)$

Try to use bcontinue yet another time. It will stop now just before $0 is created. At that point in time, $0 refers to an object that does not exist yet, so the watchpoint now evaluates to an error message (but it continues to work as before, with that error message as the string it currently evaluates to).

(19422)$ bcontinue
[searching 19325..19422]

updating watchpoint value: $0.value => RuntimeError:
               '$0' refers to an object created later in time
Reverse-hit watchpoint 1: $0.value
File "/tmp/x.py", line 6 in <module>:
  import os

  class Foo(object):
      value = 5

> lst1 = [Foo() for i in range(100)]
  lst1[50].value += 1
  for x in lst1:
      x.value += 1

  for x in lst1:
...
(19371)$

In big programs, the workflow is similar, just more complex. Usually it works this way: we find interesting points in time with some combination of watchpoints and some direct commands to move around. We write down on a piece of (real or virtual) paper these points in history, including most importantly their time, so that we can construct an ordered understanding of what is going on.

The current revdb can be annoying and sometimes even crash; but the history you reconstruct can be kept. All the times and expressions printed are still valid when you restart revdb. The only thing "lost" is the $NUM objects, which you need to print again. (Maybe instead of $0, $1, ... we should use $<big number>, where the big number identifies uniquely the object by its creation time. These numbers would continue to be valid even after revdb is restarted. They are more annoying to use than just $0 though.)

Screencast: Here's a (slightly typo-y) screencast of cfbolz using the reverse debugger:

Current issues

General issues:

• If you are using revdb on a log that took more than a few minutes to record, then it can be painfully slow. This is because revdb needs to replay again big parts of the log for some operations.
• The pypy-revdb is currently missing the following modules:
  • thread (implementing multithreading is possible, but not done yet);
  • cpyext (the CPython C API compatibility layer);
  • micronumpy (minor issue only);
  • _continuation (for greenlets).
• Does not contain a JIT, and does not use our fast garbage collectors. You can expect pypy-revdb to be maybe 3 times slower than CPython.
• Only works on Linux and OS/X. There is no fundamental reason for this restriction, but it is some work to fix.
• Replaying a program uses a lot more memory; maybe 15x as much than during the recording. This is because it creates many forks. If you have a program that consumes 10% of your RAM or more, you will need to reduce MAX_SUBPROCESSES in process.py.

Replaying also comes with a bunch of user interface issues:

• Attempted to do I/O or access raw memory: we get this whenever trying to print some expression that cannot be evaluated with only the GC memory---or which can, but then the __repr__() method of the result cannot. We need to reset the state with bstep + step before we can print anything else. However, if only the __repr__() crashes, you still see the $NUM = prefix, and you can use that $NUM afterwards.
• id() is globally unique, returning a reproducible 64-bit number, so sometimes using id(x) is a workaround for when using x doesn't work because of Attempted to do I/O issues (e.g. p [id(x) for x in somelist]).
• as explained in the demo, next/bnext/finish/bfinish might jump around a bit non-predictably.
• similarly, breaks on watchpoints can stop at apparently unexpected places (when going backward, try to do "step" once). The issue is that it can only stop at the beginning of every line. In the extreme example, if a line is foo(somelist.pop(getindex())), then somelist is modified in the middle. Immediately before this modification occurs, we are in getindex(), and immediately afterwards we are in foo(). The watchpoint will stop the program at the end of getindex() if running backward, and at the start of foo() if running forward, but never actually on the line doing the change.
• watchpoint expressions must not have any side-effect at all. If they do, the replaying will get out of sync and revdb.py will complain about that. Regular p expressions and statements can have side-effects; these effects are discarded as soon as you move in time again.
• sometimes even "p import foo" will fail with Attempted to do I/O. Use instead "p import sys; foo = sys.modules['foo']".
• use help to see all commands. backtrace can be useful. There is no up command; you have to move in time instead, e.g. using bfinish to go back to the point where the current function was called.

How RevPDB is done

If I had to pick the main advantage of PyPy over CPython, it is that we have got with the RPython translation toolchain a real place for experimentation. Every now and then, we build inside RPython some feature that gives us an optionally tweaked version of the PyPy interpreter---tweaked in a way that would be hard to do with CPython, because it would require systematic changes everywhere. The most obvious and successful examples are the GC and the JIT. But there have been many other experiments along the same lines, from the so-called stackless transformation in the early days, to the STM version of PyPy.

RevPDB works in a similar way. It is a version of PyPy in which some operations are systematically replaced with other operations.

To keep the log file at a reasonable size, we duplicate the content of all GC objects during replaying---by repeating the same actions on them, without writing anything in the log file. So that means that in the pypy-revdb binary, the operations that do arithmetic or read/write GC-managed memory are not modified. Most operations are like that. However, the other operations, the ones that involve either non-GC memory or calls to external C functions, are tweaked. Each of these operations is replaced with code that works in two modes, based on a global flag:

• in "recording" mode, we log the result of the operation (but not the arguments);
• in "replaying" mode, we don't really do the operation at all, but instead just fetch the result from the log.

Hopefully, all remaining unmodified operations (arithmetic and GC load/store) are completely deterministic. So during replaying, every integer or non-GC pointer variable will have exactly the same value as it had during recording. Interestingly, it means that if the recording process had a big array in non-GC memory, then in the replaying process, the array is not allocated at all; it is just represented by the same address, but there is nothing there. When we record "read item 123 from the array", we record the result of the read (but not the "123"). When we replay, we're seeing again the same "read item 123 from the array" operation. At that point, we don't read anything; we just return the result from the log. Similarly, when recording a "write" to the array, we record nothing (this write operation has no result); so that when replaying, we redo nothing.

Note how that differs from anything managed by GC memory: GC objects (including GC arrays) are really allocated, writes really occur, and reads are redone. We don't touch the log in this case.

Other reverse debuggers for Python

There are already some Python experiments about reverse debugging. This is also known as "omniscient debugging". However, I claim that the result they get to is not very useful (for the purpose presented here). How they work is typically by recording changes to some objects, like lists and dictionaries, in addition to recording the history of where your program passed through. However, the problem of Python is that lists and dictionaries are not the end of the story. There are many, many, many types of objects written in C which are mutable---in fact, the immutable ones are the exception. You can try to systematically record all changes, but it is a huge task and easy to forget a detail.

In other words it is a typical use case for tweaking the RPython translation toolchain, rather than tweaking the CPython (or PyPy) interpreter directly. The result that we get here with RevPDB is more similar to rr anyway, in that only a relatively small number of external events are recorded---not every single change to every single list and dictionary.

Some links:

• epdb: https://github.com/native-human/epdb
• pode: https://github.com/rodsenra/pode

For C:

• rr: https://rr-project.org/
• undodb-gdb: https://undo.io/

Future work

As mentioned above, it is alpha-level, and only works on Linux and OS/X. So the plans for the immediate future are to fix the various issues described above, and port to more operating systems. The core of the system is in the C file and headers in rpython/translator/revdb/src-revdb.

For interested people, there is also the Duhton interpreter and its reverse-debugger branch, which is where I prototyped the RPython concept before moving to PyPy. The basics should work for any interpreter written in RPython, but they require some specific code to interface with the language; in the case of PyPy, it is in pypy/interpreter/reverse_debugging.py.

In parallel, there are various user interface improvements that people could be interested in, like a more "pdb++" experience. (And the script at rpython/translator/revdb/revdb.py should be moved out into some more "official" place, and the reverse-debugger branch should be merged back to default.)

I would certainly welcome any help!

-+- Armin

Highlights

• Python 3.3.5 support!
  • Being an early alpha release, there are some missing features such as a
    PEP 393-like space efficient string representation and known issues
    including performance issues (e.g. issue #2305). The focus for this
    release has been updating to 3.3 compatibility. Windows is also not yet
    supported.
• ensurepip is also included (it's only included in CPython 3 >= 3.4).

What is PyPy?

PyPy is a very compliant Python interpreter, almost a drop-in replacement for
CPython 2.7.10 and one day 3.3.5. It's fast due to its integrated tracing JIT
compiler.

We also welcome developers of other dynamic languages to see what RPython
can do for them.

This release supports:

• x86 machines on most common operating systems except Windows
  (Linux 32/64, Mac OS X 64, OpenBSD, FreeBSD),
• newer ARM hardware (ARMv6 or ARMv7, with VFPv3) running Linux,
• big- and little-endian variants of PPC64 running Linux,
• s390x running Linux

Please try it out and let us know what you think. We welcome feedback, we know
you are using PyPy, please tell us about it!

We'd especially like to thank these people for their contributions to this
release:

Manuel Jacob, Ronan Lamy, Mark Young, Amaury Forgeot d'Arc, Philip Jenvey,
Martin Matusiak, Vasily Kuznetsov, Matti Picus, Armin Rigo and many others.

Cheers

The PyPy Team