Metadata-Version: 2.1
Name: fastrlock
Version: 0.7
Summary: Fast, re-entrant optimistic lock implemented in Cython
Home-page: https://github.com/scoder/fastrlock
Author: Stefan Behnel
Author-email: stefan_ml@behnel.de
License: MIT style
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: Topic :: Software Development
License-File: LICENSE

FastRLock
---------

This is a C-level implementation of a fast, re-entrant, optimistic lock for CPython.
It is a drop-in replacement for
`threading.RLock <https://docs.python.org/3/library/threading.html#threading.RLock>`_.
FastRLock is implemented in `Cython <http://cython.org>`_ and also provides a C-API
for direct use from Cython code via ``from fastrlock cimport rlock``.

Under normal conditions, it is about 10x faster than threading.RLock in Python 2.7
because it avoids all locking unless two or more threads try to acquire it at the
same time.  Under congestion, it is still about 10% faster than RLock due to being
implemented in Cython.

This is mostly equivalent to the revised RLock implementation in Python 3.2,
but still faster due to being implemented in Cython.  However, in Python 3.4 and
later, the ``threading.RLock`` implementation in the stdlib tends to be as fast
or even faster than the lock provided by this package, when called through the
Python API.  ``FastRLock`` is still faster also on these systems when called
through its Cython API from other Cython modules.

It was initially published as a code recipe here:
https://code.activestate.com/recipes/577336-fast-re-entrant-optimistic-lock-implemented-in-cyt/

FastRLock has been used and tested in `Lupa <https://github.com/scoder/lupa>`_ for several years.


How does it work?
-----------------

The FastRLock implementation optimises for the non-congested case.  It works by
exploiting the availability of the GIL.  Since it knows that it holds the GIL when
the acquire()/release() methods are called, it can safely check the lock for being
held by other threads and just count any re-entries as long as it is always the
same thread that acquires it.  This is a lot faster than actually acquiring the
underlying lock.

When a second thread wants to acquire the lock as well, it first checks the lock
count and finds out that the lock is already owned.  If the underlying lock is also
held by another thread already, it then just frees the GIL and asks for acquiring
the lock, just like RLock does.  If the underlying lock is not held, however, it
acquires it immediately and basically hands over the ownership by telling the
current owner to free it when it's done.  Then, it falls back to the normal
non-owner behaviour that asks for the lock and will eventually acquire it when it
gets released.  This makes sure that the real lock is only acquired when at least
two threads want it.

All of these operations are basically atomic because any thread that modifies the
lock state always holds the GIL.  Note that the implementation must not call any
Python code while handling the lock, as calling into Python may lead to a context
switch which hands over the GIL to another thread and thus breaks atomicity.
Therefore, the code misuses Cython's 'nogil' annotation to make sure that no Python
code slips in accidentally.


How fast is it?
---------------

Here are some timings for Python 2.7 for the following scenarios:

1) five acquire-release cycles ('lock_unlock')
2) five acquire calls followed by five release calls (nested locking, 'reentrant_lock_unlock')
3) a mixed and partly nested sequence of acquire and release calls ('mixed_lock_unlock')
4) five acquire-release cycles that do not block ('lock_unlock_nonblocking')

All four are benchmarked for the single threaded case and the multi threaded case
with 10 threads.  I also tested it with 20 threads only to see that it then takes
about twice the time for both versions.  Note also that the congested case is
substantially slower for both locks, so I only looped 1000x here to get useful
timings instead of 100000x for the single threaded case.

::

    Testing threading.RLock

    sequential (x100000):
    lock_unlock              : 1.408 sec
    reentrant_lock_unlock    : 1.089 sec
    mixed_lock_unlock        : 1.212 sec
    lock_unlock_nonblocking  : 1.415 sec

    threaded 10T (x1000):
    lock_unlock              : 1.188 sec
    reentrant_lock_unlock    : 1.039 sec
    mixed_lock_unlock        : 1.068 sec
    lock_unlock_nonblocking  : 1.199 sec

    Testing FastRLock

    sequential (x100000):
    lock_unlock              : 0.122 sec
    reentrant_lock_unlock    : 0.124 sec
    mixed_lock_unlock        : 0.137 sec
    lock_unlock_nonblocking  : 0.156 sec

    threaded 10T (x1000):
    lock_unlock              : 0.911 sec
    reentrant_lock_unlock    : 0.938 sec
    mixed_lock_unlock        : 0.953 sec
    lock_unlock_nonblocking  : 0.916 sec


How does it compare to Python 3.2 and later?
--------------------------------------------

Here is the same benchmark run with Py3.2::

    Testing threading.RLock

    sequential (x100000):
    lock_unlock              : 0.134 sec
    reentrant_lock_unlock    : 0.120 sec
    mixed_lock_unlock        : 0.151 sec
    lock_unlock_nonblocking  : 0.177 sec

    threaded 10T (x1000):
    lock_unlock              : 0.885 sec
    reentrant_lock_unlock    : 0.972 sec
    mixed_lock_unlock        : 0.883 sec
    lock_unlock_nonblocking  : 0.911 sec

    Testing FastRLock

    sequential (x100000):
    lock_unlock              : 0.093 sec
    reentrant_lock_unlock    : 0.093 sec
    mixed_lock_unlock        : 0.104 sec
    lock_unlock_nonblocking  : 0.112 sec

    threaded 10T (x1000):
    lock_unlock              : 0.943 sec
    reentrant_lock_unlock    : 0.871 sec
    mixed_lock_unlock        : 0.920 sec
    lock_unlock_nonblocking  : 0.908 sec

So, in the single-threaded case, the C implementation in Py3.2 is only
about 20-50% slower than the Cython implementation here, whereas it is
more or less as fast in the congested case.


===================
fastrlock changelog
===================

0.7 (2021-10-21)
================

* Adapted for unsigned thread IDs, as used by Py3.7+.
  (original patch by Guilherme Dantas)

* Build with Cython 0.29.24 to support Py3.10 and later.


0.6 (2021-03-21)
================

* Rebuild with Cython 0.29.22 to support Py3.9 and later.


0.5 (2020-06-05)
================

* Rebuild with Cython 0.29.20 to support Py3.8 and later.


0.4 (2018-08-24)
================

* Rebuild with Cython 0.28.5.

* Linux wheels are faster through profile guided optimisation.

* Add missing file to sdist.
  (patch by Mark Harfouche, Github issue #5)


0.3 (2017-08-10)
================

* improve cimport support of C-API
  (patch by Naotoshi Seo, Github issue #3)

* provide ``fastrlock.__version__``


0.2 (2017-08-09)
================

* add missing readme file to sdist


0.1 (2017-06-04)
================

* initial release


