解决docker使用pandarallel报错OSError: [Errno 28] No space left on device

参考：https://github.com/nalepae/pandarallel/issues/127

在使用pandarallel报错OSError: [Errno 28] No space left on device，根据上述issue发现确实默认使用的MEMORY_FS_ROOT为 /dev/shm，而在docker环境下这个目录大小只有64M，完全不够处理数据

因此，需要添加如下代码：

os.environ['MEMORY_FS_ROOT'] = "/data" 
os.environ['JOBLIB_TEMP_FOLDER']  = "/data"

一开始已经加了参数而而在pandarallel/core.py中依旧没有被覆盖改写，后来经过打印os.environ发现传入的参数没有覆盖成功，需要将上述代码

import os
os.environ['MEMORY_FS_ROOT'] = "/data" 
os.environ['JOBLIB_TEMP_FOLDER']  = "/data"

放在执行文件的最上面即可成功覆盖参数。

然后收到如下报错：

File "/root/miniconda3/envs/kb_search/lib/python3.10/site-packages/pandarallel/core.py", line 422, in closure
    pool = CONTEXT.Pool(nb_workers)
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/context.py", line 119, in Pool
    return Pool(processes, initializer, initargs, maxtasksperchild,
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/pool.py", line 191, in __init__
    self._setup_queues()
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/pool.py", line 346, in _setup_queues
    self._inqueue = self._ctx.SimpleQueue()
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/context.py", line 113, in SimpleQueue
    return SimpleQueue(ctx=self.get_context())
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/queues.py", line 341, in __init__
    self._rlock = ctx.Lock()
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/context.py", line 68, in Lock
    return Lock(ctx=self.get_context())
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/synchronize.py", line 163, in __init__
    SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)
  File "/root/miniconda3/envs/kb_search/lib/python3.10/multiprocessing/synchronize.py", line 58, in __init__
    sl = self._semlock = _multiprocessing.SemLock(
OSError: [Errno 28] No space left on device

这个时候在parallel_apply处报错，仍然报错磁盘没有空间，但是指定的磁盘还有1.2T的空间。

查看代码报错：
https://fossies.org/dox/openslides-2.3-portable/classmultiprocessing_1_1synchronize_1_1SemLock.html

定义：

class SemLock(object):
  
     _rand = tempfile._RandomNameSequence()
  
     def __init__(self, kind, value, maxvalue, *, ctx):
         if ctx is None:
             ctx = context._default_context.get_context()
         name = ctx.get_start_method()
         unlink_now = sys.platform == 'win32' or name == 'fork'
         for i in range(100):
             try:
                 sl = self._semlock = _multiprocessing.SemLock(
                     kind, value, maxvalue, self._make_name(),
                     unlink_now)
             except FileExistsError:
                 pass
             else:
                 break
         else:
             raise FileExistsError('cannot find name for semaphore')
  
         util.debug('created semlock with handle %s' % sl.handle)
         self._make_methods()
  
         if sys.platform != 'win32':
             def _after_fork(obj):
                 obj._semlock._after_fork()
             util.register_after_fork(self, _after_fork)
  
         if self._semlock.name is not None:
             # We only get here if we are on Unix with forking
             # disabled.  When the object is garbage collected or the
             # process shuts down we unlink the semaphore name
             from .semaphore_tracker import register
             register(self._semlock.name)
             util.Finalize(self, SemLock._cleanup, (self._semlock.name,),
                           exitpriority=0)
  
     @staticmethod
     def _cleanup(name):
         from .semaphore_tracker import unregister
         sem_unlink(name)
         unregister(name)
  
     def _make_methods(self):
         self.acquire = self._semlock.acquire
         self.release = self._semlock.release
  
     def __enter__(self):
         return self._semlock.__enter__()
  
     def __exit__(self, *args):
         return self._semlock.__exit__(*args)
  
     def __getstate__(self):
         context.assert_spawning(self)
         sl = self._semlock
         if sys.platform == 'win32':
             h = context.get_spawning_popen().duplicate_for_child(sl.handle)
         else:
             h = sl.handle
         return (h, sl.kind, sl.maxvalue, sl.name)
  
     def __setstate__(self, state):
         self._semlock = _multiprocessing.SemLock._rebuild(*state)
         util.debug('recreated blocker with handle %r' % state[0])
         self._make_methods()
  
     @staticmethod
     def _make_name():
         return '%s-%s' % (process.current_process()._config['semprefix'],
                           next(SemLock._rand))

报错语句：

sl = self._semlock = _multiprocessing.SemLock(
                     kind, value, maxvalue, self._make_name(),
                     unlink_now)

_multiprocessing.SemLock(kind, value, maxvalue, self._make_name(),unlink_now)
实际参数为_multiprocessing.SemLock(1 ,1 ,1 ,‘/mp-ef2p1_pn’, True)

使用test.py

import os
os.environ['MEMORY_FS_ROOT'] = "/data" 
os.environ['JOBLIB_TEMP_FOLDER']  = "/data"

import _multiprocessing
_multiprocessing.SemLock(1 ,1 ,1 ,'/mp-ef2p1_pn', True)
# _multiprocessing.SemLock(1 ,1 ,1 ,'/mp-ef2p1_pn', False)

依旧收到报错
https://github.com/nalepae/pandarallel/issues/143
解决不了，放弃