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"""
.. py:module:: mainthread
This module provides two ways to execute "jobs":
1. On the Binary Ninja main thread (the UI event thread when running in the GUI application):
* :py:func:`.execute_on_main_thread`
* :py:func:`.execute_on_main_thread_and_wait`
2. On a worker thread
Any manipulation of the GUI should be performed on the main thread, but any
non-GUI work is generally better to be performed using a worker. This is
especially true for any longer-running work, as the user interface will
be unable to update itself while a job is executing on the main thread.
There are three worker queues, in order of decreasing priority:
1. The Interactive Queue (:py:func:`.worker_interactive_enqueue`)
2. The Priority Queue (:py:func:`.worker_priority_enqueue`)
3. The Worker Queue (:py:func:`.worker_enqueue`)
All of these queues are serviced by the same pool of worker threads. The
difference between the queues is basically one of priority: one queue must
be empty of jobs before a worker thread will execute a job from a lower
priority queue.
The default maximum number of concurrent worker threads is controlled by the
`analysis.limits.workerThreadCount` setting but can be adjusted at runtime via
:py:func:`.set_worker_thread_count`.
The worker threads are native threads, managed by the Binary Ninja core. If
more control over the thread is required, consider using the
:py:class:`~binaryninja.plugin.BackgroundTaskThread` class.
"""
# Binary Ninja components
from . import _binaryninjacore as core
from . import scriptingprovider
from . import plugin
[docs]
def execute_on_main_thread(func):
"""
The ``execute_on_main_thread`` function takes a single parameter which is a function that will be executed
on the main Binary Ninja thread.
.. warning:: May be required for some GUI operations, but should be used sparingly as it can block the UI.
"""
action = scriptingprovider._ThreadActionContext(func)
obj = core.BNExecuteOnMainThread(0, action.callback)
if obj:
return plugin.MainThreadAction(obj)
return None
[docs]
def execute_on_main_thread_and_wait(func):
"""
The ``execute_on_main_thread`` function takes a single parameter which is a function that will be
executed on the main Binary Ninja thread and will block execution of further python until the function returns.
.. warning:: May be required for some GUI operations, but should be used sparingly as it can block the UI.
"""
action = scriptingprovider._ThreadActionContext(func)
core.BNExecuteOnMainThreadAndWait(0, action.callback)
[docs]
def worker_enqueue(func, name=""):
action = scriptingprovider._ThreadActionContext(func)
core.BNWorkerEnqueueNamed(0, action.callback, "Python " + name)
[docs]
def worker_priority_enqueue(func, name=""):
action = scriptingprovider._ThreadActionContext(func)
core.BNWorkerPriorityEnqueueNamed(0, action.callback, "Python " + name)
[docs]
def worker_interactive_enqueue(func, name=""):
action = scriptingprovider._ThreadActionContext(func)
core.BNWorkerInteractiveEnqueueNamed(0, action.callback, "Python " + name)
[docs]
def get_worker_thread_count():
"""
The ``get_worker_thread_count`` function returns the number of worker threads that are currently running.
By default, this is the number of cores on the system minus one, however this can be changed with
``set_worker_thread_count``.
"""
return core.BNGetWorkerThreadCount()
[docs]
def set_worker_thread_count(count):
"""
The ``set_worker_thread_count`` function sets the number of worker threads that are currently running.
By default, this is the number of cores on the system minus one.
"""
core.BNSetWorkerThreadCount(count)
[docs]
def is_main_thread():
return core.BNIsMainThread()