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393a1f3149
drgn has pretty thorough in-program documentation, but it doesn't have a nice overview or introduction to the basic concepts. This commit adds that using Sphinx. In order to avoid documenting everything in two places, the libdrgn bindings have their docstrings generated from the API documentation. The alternative would be to use Sphinx's autodoc extension, but that's not as flexible and would also require building the extension to build the docs. The documentation for the helpers is generated using autodoc and a small custom extension.
285 lines
9.3 KiB
ReStructuredText
285 lines
9.3 KiB
ReStructuredText
User Guide
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==========
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Quick Start
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-----------
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.. include:: ../README.rst
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:start-after: start-quick-start
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:end-before: end-quick-start
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Core Concepts
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-------------
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.. highlight:: pycon
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Programs
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^^^^^^^^
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A program being debugged is represented by an instance of the
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:class:`drgn.Program` class. The drgn CLI is initialized with a ``Program``
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named ``prog``; unless you are using the drgn library directly, this is usually
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the only ``Program`` you will need.
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A ``Program`` is used to look up type definitions, access variables, and read
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arbitrary memory::
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>>> prog.type('unsigned long')
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int_type(name='unsigned long', size=8, is_signed=False)
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>>> prog['jiffies']
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Object(prog, 'volatile unsigned long', address=0xffffffffbe405000)
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>>> prog.read(0xffffffffbe411e10, 16)
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b'swapper/0\x00\x00\x00\x00\x00\x00\x00'
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The :meth:`drgn.Program.type()`, :meth:`drgn.Program.variable()`,
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:meth:`drgn.Program.constant()`, and :meth:`drgn.Program.function()` methods
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look up those various things in a program. :meth:`drgn.Program.read()` reads
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memory from the program's address space. The :meth:`[]
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<drgn.Program.__getitem__>` operator looks up a variable, constant, or
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function::
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>>> prog['jiffies'] == prog.variable('jiffies')
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True
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It is usually more convenient to use the ``[]`` operator rather than the
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``variable()``, ``constant()``, or ``function()`` methods unless the program
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has multiple objects with the same name, in which case the methods provide more
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control.
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Objects
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^^^^^^^
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Variables, constants, functions, and computed values are all called *objects*
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in drgn. Objects are represented by the :class:`drgn.Object` class. An object
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may exist in the memory of the program (a *reference*)::
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>>> Object(prog, 'int', address=0xffffffffc09031a0)
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Or, an object may be a temporary computed value (a *value*)::
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>>> Object(prog, 'int', value=4)
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What makes drgn scripts expressive is that objects can be used almost exactly
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like they would be in the program's own source code. For example, structure
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members can be accessed with the dot (``.``) operator, arrays can be
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subscripted with ``[]``, arithmetic can be performed, and objects can be
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compared::
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>>> print(prog['init_task'].comm[0])
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(char)115
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>>> print(repr(prog['init_task'].nsproxy.mnt_ns.mounts + 1))
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Object(prog, 'unsigned int', value=34)
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>>> prog['init_task'].nsproxy.mnt_ns.pending_mounts > 0
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False
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A common use case is converting a ``drgn.Object`` to a Python value so it can
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be used by a standard Python library. There are a few ways to do this:
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* The :meth:`drgn.Object.value_()` method gets the value of the object with the
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directly corresponding Python type (i.e., integers and pointers become
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``int``, floating-point types become ``float``, booleans become ``bool``,
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arrays become ``list``, structures and unions become ``dict``).
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* The :meth:`drgn.Object.string_()` method gets a null-terminated string as
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``bytes`` from an array or pointer.
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* The :class:`int() <int>`, :class:`float() <float>`, and :class:`bool()
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<bool>` functions do an explicit conversion to that Python type.
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Objects have several attributes; the most important are
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:attr:`drgn.Object.prog_` and :attr:`drgn.Object.type_`. The former is the
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:class:`drgn.Program` that the object is from, and the latter is the
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:class:`drgn.Type` of the object.
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Note that all attributes and methods of the ``Object`` class end with an
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underscore (``_``) in order to avoid conflicting with structure or union
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members. The ``Object`` attributes and methods always take precedence; use
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:meth:`drgn.Object.member_()` if there is a conflict.
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References vs. Values
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"""""""""""""""""""""
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The main difference between reference objects and value objects is how they are
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evaluated. References are read from the program's memory every time they are
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evaluated; values simply return the stored value (:meth:`drgn.Object.read_()`
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reads a reference object and returns it as a value object)::
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>>> import time
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>>> jiffies = prog['jiffies']
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>>> jiffies.value_()
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4391639989
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>>> time.sleep(1)
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>>> jiffies.value_()
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4391640290
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>>> jiffies2 = jiffies.read_()
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>>> jiffies2.value_()
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4391640291
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>>> time.sleep(1)
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>>> jiffies2.value_()
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4391640291
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>>> jiffies.value_()
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4391640593
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References have a :attr:`drgn.Object.address_` attribute, which is the object's
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address as a Python ``int``. This is slightly different from the
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:meth:`drgn.Object.address_of_()` method, which returns the address as a
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``drgn.Object``. Of course, both references and values can have a pointer type;
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``address_`` refers to the address of the pointer object itself, and
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:meth:`drgn.Object.value_()` refers to the value of the pointer (i.e., the
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address it points to)::
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>>> address = prog['jiffies'].address_
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>>> type(address)
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<class 'int'>
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>>> print(hex(address))
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0xffffffffbe405000
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>>> jiffiesp = prog['jiffies'].address_of_()
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>>> jiffiesp
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Object(prog, 'volatile unsigned long *', value=0xffffffffbe405000)
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>>> print(hex(jiffiesp.value_()))
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0xffffffffbe405000
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Types
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^^^^^
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drgn automatically obtains type definitions from the program. Types are
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represented by the :class:`drgn.Type` class and created by various factory
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functions like :func:`int_type()`::
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>>> prog.type('int')
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int_type(name='int', size=4, is_signed=True)
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You won't usually need to work with types directly, but see
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:ref:`api-reference-types` if you do.
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Helpers
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^^^^^^^
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Some programs have common data structures that you may want to examine. For
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example, consider linked lists in the Linux kernel:
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.. code-block:: c
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struct list_head {
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struct list_head *next, *prev;
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};
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#define list_for_each(pos, head) \
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for (pos = (head)->next; pos != (head); pos = pos->next)
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When working with these lists, you'd probably want to define a function:
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.. code-block:: python3
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def list_for_each(head):
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pos = head.next
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while pos != head:
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yield pos
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pos = pos.next
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Then, you could use it like so for any list you need to look at::
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>>> for pos in list_for_each(head):
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... do_something_with(pos)
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Of course, it would be a waste of time and effort for everyone to have to
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define these helpers for themselves, so drgn includes a collection of helpers
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for many use cases. See :doc:`helpers`.
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Command Line Interface
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----------------------
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The drgn CLI is basically a wrapper around the drgn library which automatically
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creates a :class:`drgn.Program`. The CLI can be run in interactive mode or
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script mode.
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Script Mode
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^^^^^^^^^^^
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Script mode is useful for reusable scripts. Simply pass the path to the script
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along with any arguments:
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.. code-block:: console
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$ cat script.py
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import sys
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from drgn.helpers.linux import find_task
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pid = int(sys.argv[1])
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uid = find_task(prog, pid).cred.uid.val.value_()
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print(f'PID {pid} is being run by UID {uid}')
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$ sudo drgn -k script.py 601
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PID 601 is being run by UID 1000
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It's even possible to run drgn scripts directly with the proper `shebang
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<https://en.wikipedia.org/wiki/Shebang_(Unix)>`_::
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$ cat script2.py
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#!/usr/bin/drgn -k
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mounts = prog['init_task'].nsproxy.mnt_ns.mounts.value_()
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print(f'You have {mounts} filesystems mounted')
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$ sudo ./script2.py
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You have 36 filesystems mounted
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You usually shouldn't depend on an executable being installed at a specific
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absolute path. With newer versions of GNU coreutils (since v8.30), you can
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use |env -S|_:
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.. |env -S| replace:: ``env --split-string``
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.. _env -S: https://www.gnu.org/software/coreutils/manual/html_node/env-invocation.html#g_t_002dS_002f_002d_002dsplit_002dstring-usage-in-scripts
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.. code-block:: sh
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#!/usr/bin/env -S drgn -k
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Interactive Mode
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^^^^^^^^^^^^^^^^
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Interactive mode uses the Python interpreter's `interactive mode
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<https://docs.python.org/3/tutorial/interpreter.html#interactive-mode>`_ and
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adds a few nice features, including:
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* History
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* Tab completion
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* Automatic import of relevant helpers
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* Pretty printing of objects and types
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The default behavior of the Python `REPL
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<https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop>`_ is to
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print the output of :func:`repr()`. For :class:`drgn.Object` and
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:class:`drgn.Type`, this is a raw representation::
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>>> print(repr(prog['jiffies']))
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Object(prog, 'volatile unsigned long', address=0xffffffffbe405000)
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>>> print(repr(prog.type('atomic_t')))
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typedef_type(name='atomic_t', type=struct_type(tag=None, size=4, members=((int_type(name='int', size=4, is_signed=True), 'counter', 0, 0),)))
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The standard :func:`print()` function uses the output of :func:`str()`. For
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drgn objects and types, this is a representation in programming language
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syntax::
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>>> print(prog['jiffies'])
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(volatile unsigned long)4395387628
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>>> print(prog.type('atomic_t'))
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typedef struct {
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int counter;
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} atomic_t
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In interactive mode, the drgn CLI automatically uses ``str()`` instead of
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``repr()`` for objects and types, so you don't need to call ``print()``
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explicitly::
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$ sudo drgn -k
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>>> prog['jiffies']
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(volatile unsigned long)4395387628
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>>> prog.type('atomic_t')
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typedef struct {
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int counter;
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} atomic_t
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Next Steps
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----------
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Refer to the :doc:`api_reference`. Look through the :doc:`helpers`. Browse
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through the official `examples
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<https://github.com/osandov/drgn/tree/master/examples>`_.
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