goodboy
/
tractor
1
0
Fork 1
Code Issues 1 Pull Requests 4 Packages Projects Releases Wiki Activity

Initial module import from `piker.data._sharemem` 

More or less a verbatim copy-paste minus some edgy variable naming and
internal `piker` module imports. There is a bunch of OHLC related
defaults that need to be dropped and we need to adjust to an optional
dependence on `numpy` by supporting shared lists as per the mp docs.
shm_apis
Tyler Goodlet 2022-10-15 16:35:32 -04:00
parent 7507e269ec
commit 9716d86825
1 changed files with 706 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

706
tractor/_shm.py 100644
View File

@ -0,0 +1,706 @@
# tractor: structured concurrent "actors".
# Copyright 2018-eternity Tyler Goodlet.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
SC friendly shared memory management geared at real-time
processing.
Support for ``numpy`` compatible array-buffers is provided but is
considered optional within the context of this runtime-library.
"""
from __future__ import annotations
from sys import byteorder
import time
from typing import Optional
from multiprocessing.shared_memory import (
SharedMemory,
_USE_POSIX,
)
if _USE_POSIX:
from _posixshmem import shm_unlink
from msgspec import Struct
import numpy as np
from numpy.lib import recfunctions as rfn
import tractor
from .log import get_logger
log = get_logger(__name__)
# how much is probably dependent on lifestyle
_secs_in_day = int(60 * 60 * 24)
# we try for a buncha times, but only on a run-every-other-day kinda week.
_days_worth = 16
_default_size = _days_worth * _secs_in_day
# where to start the new data append index
_rt_buffer_start = int((_days_worth - 1) * _secs_in_day)
def disable_mantracker():
'''
Disable all ``multiprocessing``` "resource tracking" machinery since
it's an absolute multi-threaded mess of non-SC madness.
'''
from multiprocessing import resource_tracker as mantracker
# Tell the "resource tracker" thing to fuck off.
class ManTracker(mantracker.ResourceTracker):
def register(self, name, rtype):
pass
def unregister(self, name, rtype):
pass
def ensure_running(self):
pass
# "know your land and know your prey"
# https://www.dailymotion.com/video/x6ozzco
mantracker._resource_tracker = ManTracker()
mantracker.register = mantracker._resource_tracker.register
mantracker.ensure_running = mantracker._resource_tracker.ensure_running
# ensure_running = mantracker._resource_tracker.ensure_running
mantracker.unregister = mantracker._resource_tracker.unregister
mantracker.getfd = mantracker._resource_tracker.getfd
disable_mantracker()
class SharedInt:
"""Wrapper around a single entry shared memory array which
holds an ``int`` value used as an index counter.
"""
def __init__(
self,
shm: SharedMemory,
) -> None:
self._shm = shm
@property
def value(self) -> int:
return int.from_bytes(self._shm.buf, byteorder)
@value.setter
def value(self, value) -> None:
self._shm.buf[:] = value.to_bytes(self._shm.size, byteorder)
def destroy(self) -> None:
if _USE_POSIX:
# We manually unlink to bypass all the "resource tracker"
# nonsense meant for non-SC systems.
name = self._shm.name
try:
shm_unlink(name)
except FileNotFoundError:
# might be a teardown race here?
log.warning(f'Shm for {name} already unlinked?')
class _Token(Struct, frozen=True):
'''
Internal represenation of a shared memory "token"
which can be used to key a system wide post shm entry.
'''
shm_name: str # this servers as a "key" value
shm_first_index_name: str
shm_last_index_name: str
dtype_descr: tuple
size: int # in struct-array index / row terms
@property
def dtype(self) -> np.dtype:
return np.dtype(list(map(tuple, self.dtype_descr))).descr
def as_msg(self):
return self.to_dict()
@classmethod
def from_msg(cls, msg: dict) -> _Token:
if isinstance(msg, _Token):
return msg
# TODO: native struct decoding
# return _token_dec.decode(msg)
msg['dtype_descr'] = tuple(map(tuple, msg['dtype_descr']))
return _Token(**msg)
# _token_dec = msgspec.msgpack.Decoder(_Token)
# TODO: this api?
# _known_tokens = tractor.ActorVar('_shm_tokens', {})
# _known_tokens = tractor.ContextStack('_known_tokens', )
# _known_tokens = trio.RunVar('shms', {})
# process-local store of keys to tokens
_known_tokens = {}
def get_shm_token(key: str) -> _Token:
"""Convenience func to check if a token
for the provided key is known by this process.
"""
return _known_tokens.get(key)
def _make_token(
key: str,
size: int,
dtype: np.dtype,
) -> _Token:
'''
Create a serializable token that can be used
to access a shared array.
'''
return _Token(
shm_name=key,
shm_first_index_name=key + "_first",
shm_last_index_name=key + "_last",
dtype_descr=tuple(np.dtype(dtype).descr),
size=size,
)
class ShmArray:
'''
A shared memory ``numpy`` (compatible) array API.
An underlying shared memory buffer is allocated based on
a user specified ``numpy.ndarray``. This fixed size array
can be read and written to by pushing data both onto the "front"
or "back" of a set index range. The indexes for the "first" and
"last" index are themselves stored in shared memory (accessed via
``SharedInt`` interfaces) values such that multiple processes can
interact with the same array using a synchronized-index.
'''
def __init__(
self,
shmarr: np.ndarray,
first: SharedInt,
last: SharedInt,
shm: SharedMemory,
# readonly: bool = True,
) -> None:
self._array = shmarr
# indexes for first and last indices corresponding
# to fille data
self._first = first
self._last = last
self._len = len(shmarr)
self._shm = shm
self._post_init: bool = False
# pushing data does not write the index (aka primary key)
dtype = shmarr.dtype
if dtype.fields:
self._write_fields = list(shmarr.dtype.fields.keys())[1:]
else:
self._write_fields = None
# TODO: ringbuf api?
@property
def _token(self) -> _Token:
return _Token(
shm_name=self._shm.name,
shm_first_index_name=self._first._shm.name,
shm_last_index_name=self._last._shm.name,
dtype_descr=tuple(self._array.dtype.descr),
size=self._len,
)
@property
def token(self) -> dict:
"""Shared memory token that can be serialized and used by
another process to attach to this array.
"""
return self._token.as_msg()
@property
def index(self) -> int:
return self._last.value % self._len
@property
def array(self) -> np.ndarray:
'''
Return an up-to-date ``np.ndarray`` view of the
so-far-written data to the underlying shm buffer.
'''
a = self._array[self._first.value:self._last.value]
# first, last = self._first.value, self._last.value
# a = self._array[first:last]
# TODO: eventually comment this once we've not seen it in the
# wild in a long time..
# XXX: race where first/last indexes cause a reader
# to load an empty array..
if len(a) == 0 and self._post_init:
raise RuntimeError('Empty array race condition hit!?')
# breakpoint()
return a
def ustruct(
self,
fields: Optional[list[str]] = None,
# type that all field values will be cast to
# in the returned view.
common_dtype: np.dtype = np.float,
) -> np.ndarray:
array = self._array
if fields:
selection = array[fields]
# fcount = len(fields)
else:
selection = array
# fcount = len(array.dtype.fields)
# XXX: manual ``.view()`` attempt that also doesn't work.
# uview = selection.view(
# dtype='<f16',
# ).reshape(-1, 4, order='A')
# assert len(selection) == len(uview)
u = rfn.structured_to_unstructured(
selection,
# dtype=float,
copy=True,
)
# unstruct = np.ndarray(u.shape, dtype=a.dtype, buffer=shm.buf)
# array[:] = a[:]
return u
# return ShmArray(
# shmarr=u,
# first=self._first,
# last=self._last,
# shm=self._shm
# )
def last(
self,
length: int = 1,
) -> np.ndarray:
'''
Return the last ``length``'s worth of ("row") entries from the
array.
'''
return self.array[-length:]
def push(
self,
data: np.ndarray,
field_map: Optional[dict[str, str]] = None,
prepend: bool = False,
update_first: bool = True,
start: Optional[int] = None,
) -> int:
'''
Ring buffer like "push" to append data
into the buffer and return updated "last" index.
NB: no actual ring logic yet to give a "loop around" on overflow
condition, lel.
'''
length = len(data)
if prepend:
index = (start or self._first.value) - length
if index < 0:
raise ValueError(
f'Array size of {self._len} was overrun during prepend.\n'
f'You have passed {abs(index)} too many datums.'
)
else:
index = start if start is not None else self._last.value
end = index + length
if field_map:
src_names, dst_names = zip(*field_map.items())
else:
dst_names = src_names = self._write_fields
try:
self._array[
list(dst_names)
][index:end] = data[list(src_names)][:]
# NOTE: there was a race here between updating
# the first and last indices and when the next reader
# tries to access ``.array`` (which due to the index
# overlap will be empty). Pretty sure we've fixed it now
# but leaving this here as a reminder.
if prepend and update_first and length:
assert index < self._first.value
if (
index < self._first.value
and update_first
):
assert prepend, 'prepend=True not passed but index decreased?'
self._first.value = index
elif not prepend:
self._last.value = end
self._post_init = True
return end
except ValueError as err:
if field_map:
raise
# should raise if diff detected
self.diff_err_fields(data)
raise err
def diff_err_fields(
self,
data: np.ndarray,
) -> None:
# reraise with any field discrepancy
our_fields, their_fields = (
set(self._array.dtype.fields),
set(data.dtype.fields),
)
only_in_ours = our_fields - their_fields
only_in_theirs = their_fields - our_fields
if only_in_ours:
raise TypeError(
f"Input array is missing field(s): {only_in_ours}"
)
elif only_in_theirs:
raise TypeError(
f"Input array has unknown field(s): {only_in_theirs}"
)
# TODO: support "silent" prepends that don't update ._first.value?
def prepend(
self,
data: np.ndarray,
) -> int:
end = self.push(data, prepend=True)
assert end
def close(self) -> None:
self._first._shm.close()
self._last._shm.close()
self._shm.close()
def destroy(self) -> None:
if _USE_POSIX:
# We manually unlink to bypass all the "resource tracker"
# nonsense meant for non-SC systems.
shm_unlink(self._shm.name)
self._first.destroy()
self._last.destroy()
def flush(self) -> None:
# TODO: flush to storage backend like markestore?
...
def open_shm_array(
key: Optional[str] = None,
size: int = _default_size, # see above
dtype: Optional[np.dtype] = None,
readonly: bool = False,
) -> ShmArray:
'''Open a memory shared ``numpy`` using the standard library.
This call unlinks (aka permanently destroys) the buffer on teardown
and thus should be used from the parent-most accessor (process).
'''
# create new shared mem segment for which we
# have write permission
a = np.zeros(size, dtype=dtype)
a['index'] = np.arange(len(a))
shm = SharedMemory(
name=key,
create=True,
size=a.nbytes
)
array = np.ndarray(
a.shape,
dtype=a.dtype,
buffer=shm.buf
)
array[:] = a[:]
array.setflags(write=int(not readonly))
token = _make_token(
key=key,
size=size,
dtype=dtype,
)
# create single entry arrays for storing an first and last indices
first = SharedInt(
shm=SharedMemory(
name=token.shm_first_index_name,
create=True,
size=4, # std int
)
)
last = SharedInt(
shm=SharedMemory(
name=token.shm_last_index_name,
create=True,
size=4, # std int
)
)
# start the "real-time" updated section after 3-days worth of 1s
# sampled OHLC. this allows appending up to a days worth from
# tick/quote feeds before having to flush to a (tsdb) storage
# backend, and looks something like,
# -------------------------
# | | i
# _________________________
# <-------------> <------->
# history real-time
#
# Once fully "prepended", the history section will leave the
# ``ShmArray._start.value: int = 0`` and the yet-to-be written
# real-time section will start at ``ShmArray.index: int``.
# this sets the index to 3/4 of the length of the buffer
# leaving a "days worth of second samples" for the real-time
# section.
last.value = first.value = _rt_buffer_start
shmarr = ShmArray(
array,
first,
last,
shm,
)
assert shmarr._token == token
_known_tokens[key] = shmarr.token
# "unlink" created shm on process teardown by
# pushing teardown calls onto actor context stack
stack = tractor.current_actor().lifetime_stack
stack.callback(shmarr.close)
stack.callback(shmarr.destroy)
return shmarr
def attach_shm_array(
token: tuple[str, str, tuple[str, str]],
readonly: bool = True,
) -> ShmArray:
'''
Attach to an existing shared memory array previously
created by another process using ``open_shared_array``.
No new shared mem is allocated but wrapper types for read/write
access are constructed.
'''
token = _Token.from_msg(token)
key = token.shm_name
if key in _known_tokens:
assert _Token.from_msg(_known_tokens[key]) == token, "WTF"
# XXX: ugh, looks like due to the ``shm_open()`` C api we can't
# actually place files in a subdir, see discussion here:
# https://stackoverflow.com/a/11103289
# attach to array buffer and view as per dtype
_err: Optional[Exception] = None
for _ in range(3):
try:
shm = SharedMemory(
name=key,
create=False,
)
break
except OSError as oserr:
_err = oserr
time.sleep(0.1)
else:
if _err:
raise _err
shmarr = np.ndarray(
(token.size,),
dtype=token.dtype,
buffer=shm.buf
)
shmarr.setflags(write=int(not readonly))
first = SharedInt(
shm=SharedMemory(
name=token.shm_first_index_name,
create=False,
size=4, # std int
),
)
last = SharedInt(
shm=SharedMemory(
name=token.shm_last_index_name,
create=False,
size=4, # std int
),
)
# make sure we can read
first.value
sha = ShmArray(
shmarr,
first,
last,
shm,
)
# read test
sha.array
# Stash key -> token knowledge for future queries
# via `maybe_opepn_shm_array()` but only after we know
# we can attach.
if key not in _known_tokens:
_known_tokens[key] = token
# "close" attached shm on actor teardown
tractor.current_actor().lifetime_stack.callback(sha.close)
return sha
def maybe_open_shm_array(
key: str,
dtype: Optional[np.dtype] = None,
**kwargs,
) -> tuple[ShmArray, bool]:
'''
Attempt to attach to a shared memory block using a "key" lookup
to registered blocks in the users overall "system" registry
(presumes you don't have the block's explicit token).
This function is meant to solve the problem of discovering whether
a shared array token has been allocated or discovered by the actor
running in **this** process. Systems where multiple actors may seek
to access a common block can use this function to attempt to acquire
a token as discovered by the actors who have previously stored
a "key" -> ``_Token`` map in an actor local (aka python global)
variable.
If you know the explicit ``_Token`` for your memory segment instead
use ``attach_shm_array``.
'''
size = kwargs.pop('size', _default_size)
try:
# see if we already know this key
token = _known_tokens[key]
return attach_shm_array(token=token, **kwargs), False
except KeyError:
log.warning(f"Could not find {key} in shms cache")
if dtype:
token = _make_token(
key,
size=size,
dtype=dtype,
)
try:
return attach_shm_array(token=token, **kwargs), False
except FileNotFoundError:
log.warning(f"Could not attach to shm with token {token}")
# This actor does not know about memory
# associated with the provided "key".
# Attempt to open a block and expect
# to fail if a block has been allocated
# on the OS by someone else.
return open_shm_array(key=key, dtype=dtype, **kwargs), True
def try_read(
array: np.ndarray
) -> Optional[np.ndarray]:
'''
Try to read the last row from a shared mem array or ``None``
if the array read returns a zero-length array result.
Can be used to check for backfilling race conditions where an array
is currently being (re-)written by a writer actor but the reader is
unaware and reads during the window where the first and last indexes
are being updated.
'''
try:
return array[-1]
except IndexError:
# XXX: race condition with backfilling shm.
#
# the underlying issue is that a backfill (aka prepend) and subsequent
# shm array first/last index update could result in an empty array
# read here since the indices may be updated in such a way that
# a read delivers an empty array (though it seems like we
# *should* be able to prevent that?). also, as and alt and
# something we need anyway, maybe there should be some kind of
# signal that a prepend is taking place and this consumer can
# respond (eg. redrawing graphics) accordingly.
# the array read was emtpy
return None