##// END OF EJS Templates
wireproto: add streams to frame-based protocol...
wireproto: add streams to frame-based protocol Previously, the frame-based protocol was just a series of frames, with each frame associated with a request ID. In order to scale the protocol, we'll want to enable the use of compression. While it is possible to enable compression at the socket/pipe level, this has its disadvantages. The big one is it undermines the point of frames being standalone, atomic units that can be read and written: if you add compression above the framing protocol, you are back to having a stream-based protocol as opposed to something frame-based. So in order to preserve frames, compression needs to occur at the frame payload level. Compressing each frame's payload individually will limit compression ratios because the window size of the compressor will be limited by the max frame size, which is 32-64kb as currently defined. It will also add CPU overhead, as it is more efficient for compressors to operate on fewer, larger blocks of data than more, smaller blocks. So compressing each frame independently is out. This means we need to compress each frame's payload as if it is part of a larger stream. The simplest approach is to have 1 stream per connection. This could certainly work. However, it has disadvantages (documented below). We could also have 1 stream per RPC/command invocation. (This is the model HTTP/2 goes with.) This also has disadvantages. The main disadvantage to one global stream is that it has the very real potential to create CPU bottlenecks doing compression. Networks are only getting faster and the performance of single CPU cores has been relatively flat. Newer compression formats like zstandard offer better CPU cycle efficiency than predecessors like zlib. But it still all too common to saturate your CPU with compression overhead long before you saturate the network pipe. The main disadvantage with streams per request is that you can't reap the benefits of the compression context for multiple requests. For example, if you send 1000 RPC requests (or HTTP/2 requests for that matter), the response to each would have its own compression context. The overall size of the raw responses would be larger because compression contexts wouldn't be able to reference data from another request or response. The approach for streams as implemented in this commit is to support N streams per connection and for streams to potentially span requests and responses. As explained by the added internals docs, this facilitates servers and clients delegating independent streams and compression to independent threads / CPU cores. This helps alleviate the CPU bottleneck of compression. This design also allows compression contexts to be reused across requests/responses. This can result in improved compression ratios and less overhead for compressors and decompressors having to build new contexts. Another feature that was defined was the ability for individual frames within a stream to declare whether that individual frame's payload uses the content encoding (read: compression) defined by the stream. The idea here is that some servers may serve data from a combination of caches and dynamic resolution. Data coming from caches may be pre-compressed. We want to facilitate servers being able to essentially stream bytes from caches to the wire with minimal overhead. Being able to mix and match with frames are compressed within a stream enables these types of advanced server functionality. This commit defines the new streams mechanism. Basic code for supporting streams in frames has been added. But that code is seriously lacking and doesn't fully conform to the defined protocol. For example, we don't close any streams. And support for content encoding within streams is not yet implemented. The change was rather invasive and I didn't think it would be reasonable to implement the entire feature in a single commit. For the record, I would have loved to reuse an existing multiplexing protocol to build the new wire protocol on top of. However, I couldn't find a protocol that offers the performance and scaling characteristics that I desired. Namely, it should support multiple compression contexts to facilitate scaling out to multiple CPU cores and compression contexts should be able to live longer than single RPC requests. HTTP/2 *almost* fits the bill. But the semantics of HTTP message exchange state that streams can only live for a single request-response. We /could/ tunnel on top of HTTP/2 streams and frames with HEADER and DATA frames. But there's no guarantee that HTTP/2 libraries and proxies would allow us to use HTTP/2 streams and frames without the HTTP message exchange semantics defined in RFC 7540 Section 8. Other RPC protocols like gRPC tunnel are built on top of HTTP/2 and thus preserve its semantics of stream per RPC invocation. Even QUIC does this. We could attempt to invent a higher-level stream that spans HTTP/2 streams. But this would be violating HTTP/2 because there is no guarantee that HTTP/2 streams are routed to the same server. The best we can do - which is what this protocol does - is shoehorn all request and response data into a single HTTP message and create streams within. At that point, we've defined a Content-Type in HTTP parlance. It just so happens our media type can also work as a standalone, stream-based protocol, without leaning on HTTP or similar protocol. Differential Revision: https://phab.mercurial-scm.org/D2907

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worker.py
327 lines | 11.4 KiB | text/x-python | PythonLexer
# worker.py - master-slave parallelism support
#
# Copyright 2013 Facebook, Inc.
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
from __future__ import absolute_import
import errno
import os
import signal
import sys
import threading
import time
from .i18n import _
from . import (
encoding,
error,
pycompat,
scmutil,
util,
)
def countcpus():
'''try to count the number of CPUs on the system'''
# posix
try:
n = int(os.sysconf(r'SC_NPROCESSORS_ONLN'))
if n > 0:
return n
except (AttributeError, ValueError):
pass
# windows
try:
n = int(encoding.environ['NUMBER_OF_PROCESSORS'])
if n > 0:
return n
except (KeyError, ValueError):
pass
return 1
def _numworkers(ui):
s = ui.config('worker', 'numcpus')
if s:
try:
n = int(s)
if n >= 1:
return n
except ValueError:
raise error.Abort(_('number of cpus must be an integer'))
return min(max(countcpus(), 4), 32)
if pycompat.isposix or pycompat.iswindows:
_startupcost = 0.01
else:
_startupcost = 1e30
def worthwhile(ui, costperop, nops):
'''try to determine whether the benefit of multiple processes can
outweigh the cost of starting them'''
linear = costperop * nops
workers = _numworkers(ui)
benefit = linear - (_startupcost * workers + linear / workers)
return benefit >= 0.15
def worker(ui, costperarg, func, staticargs, args):
'''run a function, possibly in parallel in multiple worker
processes.
returns a progress iterator
costperarg - cost of a single task
func - function to run
staticargs - arguments to pass to every invocation of the function
args - arguments to split into chunks, to pass to individual
workers
'''
enabled = ui.configbool('worker', 'enabled')
if enabled and worthwhile(ui, costperarg, len(args)):
return _platformworker(ui, func, staticargs, args)
return func(*staticargs + (args,))
def _posixworker(ui, func, staticargs, args):
rfd, wfd = os.pipe()
workers = _numworkers(ui)
oldhandler = signal.getsignal(signal.SIGINT)
signal.signal(signal.SIGINT, signal.SIG_IGN)
pids, problem = set(), [0]
def killworkers():
# unregister SIGCHLD handler as all children will be killed. This
# function shouldn't be interrupted by another SIGCHLD; otherwise pids
# could be updated while iterating, which would cause inconsistency.
signal.signal(signal.SIGCHLD, oldchldhandler)
# if one worker bails, there's no good reason to wait for the rest
for p in pids:
try:
os.kill(p, signal.SIGTERM)
except OSError as err:
if err.errno != errno.ESRCH:
raise
def waitforworkers(blocking=True):
for pid in pids.copy():
p = st = 0
while True:
try:
p, st = os.waitpid(pid, (0 if blocking else os.WNOHANG))
break
except OSError as e:
if e.errno == errno.EINTR:
continue
elif e.errno == errno.ECHILD:
# child would already be reaped, but pids yet been
# updated (maybe interrupted just after waitpid)
pids.discard(pid)
break
else:
raise
if not p:
# skip subsequent steps, because child process should
# be still running in this case
continue
pids.discard(p)
st = _exitstatus(st)
if st and not problem[0]:
problem[0] = st
def sigchldhandler(signum, frame):
waitforworkers(blocking=False)
if problem[0]:
killworkers()
oldchldhandler = signal.signal(signal.SIGCHLD, sigchldhandler)
ui.flush()
parentpid = os.getpid()
for pargs in partition(args, workers):
# make sure we use os._exit in all worker code paths. otherwise the
# worker may do some clean-ups which could cause surprises like
# deadlock. see sshpeer.cleanup for example.
# override error handling *before* fork. this is necessary because
# exception (signal) may arrive after fork, before "pid =" assignment
# completes, and other exception handler (dispatch.py) can lead to
# unexpected code path without os._exit.
ret = -1
try:
pid = os.fork()
if pid == 0:
signal.signal(signal.SIGINT, oldhandler)
signal.signal(signal.SIGCHLD, oldchldhandler)
def workerfunc():
os.close(rfd)
for i, item in func(*(staticargs + (pargs,))):
os.write(wfd, '%d %s\n' % (i, item))
return 0
ret = scmutil.callcatch(ui, workerfunc)
except: # parent re-raises, child never returns
if os.getpid() == parentpid:
raise
exctype = sys.exc_info()[0]
force = not issubclass(exctype, KeyboardInterrupt)
ui.traceback(force=force)
finally:
if os.getpid() != parentpid:
try:
ui.flush()
except: # never returns, no re-raises
pass
finally:
os._exit(ret & 255)
pids.add(pid)
os.close(wfd)
fp = os.fdopen(rfd, r'rb', 0)
def cleanup():
signal.signal(signal.SIGINT, oldhandler)
waitforworkers()
signal.signal(signal.SIGCHLD, oldchldhandler)
status = problem[0]
if status:
if status < 0:
os.kill(os.getpid(), -status)
sys.exit(status)
try:
for line in util.iterfile(fp):
l = line.split(' ', 1)
yield int(l[0]), l[1][:-1]
except: # re-raises
killworkers()
cleanup()
raise
cleanup()
def _posixexitstatus(code):
'''convert a posix exit status into the same form returned by
os.spawnv
returns None if the process was stopped instead of exiting'''
if os.WIFEXITED(code):
return os.WEXITSTATUS(code)
elif os.WIFSIGNALED(code):
return -os.WTERMSIG(code)
def _windowsworker(ui, func, staticargs, args):
class Worker(threading.Thread):
def __init__(self, taskqueue, resultqueue, func, staticargs,
group=None, target=None, name=None, verbose=None):
threading.Thread.__init__(self, group=group, target=target,
name=name, verbose=verbose)
self._taskqueue = taskqueue
self._resultqueue = resultqueue
self._func = func
self._staticargs = staticargs
self._interrupted = False
self.daemon = True
self.exception = None
def interrupt(self):
self._interrupted = True
def run(self):
try:
while not self._taskqueue.empty():
try:
args = self._taskqueue.get_nowait()
for res in self._func(*self._staticargs + (args,)):
self._resultqueue.put(res)
# threading doesn't provide a native way to
# interrupt execution. handle it manually at every
# iteration.
if self._interrupted:
return
except util.empty:
break
except Exception as e:
# store the exception such that the main thread can resurface
# it as if the func was running without workers.
self.exception = e
raise
threads = []
def trykillworkers():
# Allow up to 1 second to clean worker threads nicely
cleanupend = time.time() + 1
for t in threads:
t.interrupt()
for t in threads:
remainingtime = cleanupend - time.time()
t.join(remainingtime)
if t.is_alive():
# pass over the workers joining failure. it is more
# important to surface the inital exception than the
# fact that one of workers may be processing a large
# task and does not get to handle the interruption.
ui.warn(_("failed to kill worker threads while "
"handling an exception\n"))
return
workers = _numworkers(ui)
resultqueue = util.queue()
taskqueue = util.queue()
# partition work to more pieces than workers to minimize the chance
# of uneven distribution of large tasks between the workers
for pargs in partition(args, workers * 20):
taskqueue.put(pargs)
for _i in range(workers):
t = Worker(taskqueue, resultqueue, func, staticargs)
threads.append(t)
t.start()
try:
while len(threads) > 0:
while not resultqueue.empty():
yield resultqueue.get()
threads[0].join(0.05)
finishedthreads = [_t for _t in threads if not _t.is_alive()]
for t in finishedthreads:
if t.exception is not None:
raise t.exception
threads.remove(t)
except (Exception, KeyboardInterrupt): # re-raises
trykillworkers()
raise
while not resultqueue.empty():
yield resultqueue.get()
if pycompat.iswindows:
_platformworker = _windowsworker
else:
_platformworker = _posixworker
_exitstatus = _posixexitstatus
def partition(lst, nslices):
'''partition a list into N slices of roughly equal size
The current strategy takes every Nth element from the input. If
we ever write workers that need to preserve grouping in input
we should consider allowing callers to specify a partition strategy.
mpm is not a fan of this partitioning strategy when files are involved.
In his words:
Single-threaded Mercurial makes a point of creating and visiting
files in a fixed order (alphabetical). When creating files in order,
a typical filesystem is likely to allocate them on nearby regions on
disk. Thus, when revisiting in the same order, locality is maximized
and various forms of OS and disk-level caching and read-ahead get a
chance to work.
This effect can be quite significant on spinning disks. I discovered it
circa Mercurial v0.4 when revlogs were named by hashes of filenames.
Tarring a repo and copying it to another disk effectively randomized
the revlog ordering on disk by sorting the revlogs by hash and suddenly
performance of my kernel checkout benchmark dropped by ~10x because the
"working set" of sectors visited no longer fit in the drive's cache and
the workload switched from streaming to random I/O.
What we should really be doing is have workers read filenames from a
ordered queue. This preserves locality and also keeps any worker from
getting more than one file out of balance.
'''
for i in range(nslices):
yield lst[i::nslices]