timer file descriptor HOWTO

timer file descriptor HOWTO¶
- Release:
1.13
This HOWTO discusses Python’s support for the linux timer file descriptor.
Examples¶
The following example shows how to use a timer file descriptor to execute a function twice a second:
# Practical scripts should use really use a non-blocking timer,
# we use a blocking timer here for simplicity.
import os, time
# Create the timer file descriptor
fd = os.timerfd_create(time.CLOCK_REALTIME)
# Start the timer in 1 second, with an interval of half a second
os.timerfd_settime(fd, initial=1, interval=0.5)
try:
# Process timer events four times.
for _ in range(4):
# read() will block until the timer expires
_ = os.read(fd, 8)
print("Timer expired")
finally:
# Remember to close the timer file descriptor!
os.close(fd)
To avoid the precision loss caused by the float
type,
timer file descriptors allow specifying initial expiration and interval
in integer nanoseconds with _ns
variants of the functions.
This example shows how epoll()
can be used with timer file
descriptors to wait until the file descriptor is ready for reading:
import os, time, select, socket, sys
# Create an epoll object
ep = select.epoll()
# In this example, use loopback address to send "stop" command to the server.
#
# $ telnet 127.0.0.1 1234
# Trying 127.0.0.1...
# Connected to 127.0.0.1.
# Escape character is '^]'.
# stop
# Connection closed by foreign host.
#
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("127.0.0.1", 1234))
sock.setblocking(False)
sock.listen(1)
ep.register(sock, select.EPOLLIN)
# Create timer file descriptors in non-blocking mode.
num = 3
fds = []
for _ in range(num):
fd = os.timerfd_create(time.CLOCK_REALTIME, flags=os.TFD_NONBLOCK)
fds.append(fd)
# Register the timer file descriptor for read events
ep.register(fd, select.EPOLLIN)
# Start the timer with os.timerfd_settime_ns() in nanoseconds.
# Timer 1 fires every 0.25 seconds; timer 2 every 0.5 seconds; etc
for i, fd in enumerate(fds, start=1):
one_sec_in_nsec = 10**9
i = i * one_sec_in_nsec
os.timerfd_settime_ns(fd, initial=i//4, interval=i//4)
timeout = 3
try:
conn = None
is_active = True
while is_active:
# Wait for the timer to expire for 3 seconds.
# epoll.poll() returns a list of (fd, event) pairs.
# fd is a file descriptor.
# sock and conn[=returned value of socket.accept()] are socket objects, not file descriptors.
# So use sock.fileno() and conn.fileno() to get the file descriptors.
events = ep.poll(timeout)
# If more than one timer file descriptors are ready for reading at once,
# epoll.poll() returns a list of (fd, event) pairs.
#
# In this example settings,
# 1st timer fires every 0.25 seconds in 0.25 seconds. (0.25, 0.5, 0.75, 1.0, ...)
# 2nd timer every 0.5 seconds in 0.5 seconds. (0.5, 1.0, 1.5, 2.0, ...)
# 3rd timer every 0.75 seconds in 0.75 seconds. (0.75, 1.5, 2.25, 3.0, ...)
#
# In 0.25 seconds, only 1st timer fires.
# In 0.5 seconds, 1st timer and 2nd timer fires at once.
# In 0.75 seconds, 1st timer and 3rd timer fires at once.
# In 1.5 seconds, 1st timer, 2nd timer and 3rd timer fires at once.
#
# If a timer file descriptor is signaled more than once since
# the last os.read() call, os.read() returns the number of signaled
# as host order of class bytes.
print(f"Signaled events={events}")
for fd, event in events:
if event & select.EPOLLIN:
if fd == sock.fileno():
# Check if there is a connection request.
print(f"Accepting connection {fd}")
conn, addr = sock.accept()
conn.setblocking(False)
print(f"Accepted connection {conn} from {addr}")
ep.register(conn, select.EPOLLIN)
elif conn and fd == conn.fileno():
# Check if there is data to read.
print(f"Reading data {fd}")
data = conn.recv(1024)
if data:
# You should catch UnicodeDecodeError exception for safety.
cmd = data.decode()
if cmd.startswith("stop"):
print(f"Stopping server")
is_active = False
else:
print(f"Unknown command: {cmd}")
else:
# No more data, close connection
print(f"Closing connection {fd}")
ep.unregister(conn)
conn.close()
conn = None
elif fd in fds:
print(f"Reading timer {fd}")
count = int.from_bytes(os.read(fd, 8), byteorder=sys.byteorder)
print(f"Timer {fds.index(fd) + 1} expired {count} times")
else:
print(f"Unknown file descriptor {fd}")
finally:
for fd in fds:
ep.unregister(fd)
os.close(fd)
ep.close()
This example shows how select()
can be used with timer file
descriptors to wait until the file descriptor is ready for reading:
import os, time, select, socket, sys
# In this example, use loopback address to send "stop" command to the server.
#
# $ telnet 127.0.0.1 1234
# Trying 127.0.0.1...
# Connected to 127.0.0.1.
# Escape character is '^]'.
# stop
# Connection closed by foreign host.
#
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("127.0.0.1", 1234))
sock.setblocking(False)
sock.listen(1)
# Create timer file descriptors in non-blocking mode.
num = 3
fds = [os.timerfd_create(time.CLOCK_REALTIME, flags=os.TFD_NONBLOCK)
for _ in range(num)]
select_fds = fds + [sock]
# Start the timers with os.timerfd_settime() in seconds.
# Timer 1 fires every 0.25 seconds; timer 2 every 0.5 seconds; etc
for i, fd in enumerate(fds, start=1):
os.timerfd_settime(fd, initial=i/4, interval=i/4)
timeout = 3
try:
conn = None
is_active = True
while is_active:
# Wait for the timer to expire for 3 seconds.
# select.select() returns a list of file descriptors or objects.
rfd, wfd, xfd = select.select(select_fds, select_fds, select_fds, timeout)
for fd in rfd:
if fd == sock:
# Check if there is a connection request.
print(f"Accepting connection {fd}")
conn, addr = sock.accept()
conn.setblocking(False)
print(f"Accepted connection {conn} from {addr}")
select_fds.append(conn)
elif conn and fd == conn:
# Check if there is data to read.
print(f"Reading data {fd}")
data = conn.recv(1024)
if data:
# You should catch UnicodeDecodeError exception for safety.
cmd = data.decode()
if cmd.startswith("stop"):
print(f"Stopping server")
is_active = False
else:
print(f"Unknown command: {cmd}")
else:
# No more data, close connection
print(f"Closing connection {fd}")
select_fds.remove(conn)
conn.close()
conn = None
elif fd in fds:
print(f"Reading timer {fd}")
count = int.from_bytes(os.read(fd, 8), byteorder=sys.byteorder)
print(f"Timer {fds.index(fd) + 1} expired {count} times")
else:
print(f"Unknown file descriptor {fd}")
finally:
for fd in fds:
os.close(fd)
sock.close()
sock = None


Socket Programming HOWTO

Socket Programming HOWTO¶
- Author:
Gordon McMillan
Sockets¶
I’m only going to talk about INET (i.e. IPv4) sockets, but they account for at least 99% of the sockets in use. And I’ll only talk about STREAM (i.e. TCP) sockets - unless you really know what you’re doing (in which case this HOWTO isn’t for you!), you’ll get better behavior and performance from a STREAM socket than anything else. I will try to clear up the mystery of what a socket is, as well as some hints on how to work with blocking and non-blocking sockets. But I’ll start by talking about blocking sockets. You’ll need to know how they work before dealing with non-blocking sockets.
Part of the trouble with understanding these things is that “socket” can mean a number of subtly different things, depending on context. So first, let’s make a distinction between a “client” socket - an endpoint of a conversation, and a “server” socket, which is more like a switchboard operator. The client application (your browser, for example) uses “client” sockets exclusively; the web server it’s talking to uses both “server” sockets and “client” sockets.
History¶
Of the various forms of IPC, sockets are by far the most popular. On any given platform, there are likely to be other forms of IPC that are faster, but for cross-platform communication, sockets are about the only game in town.
They were invented in Berkeley as part of the BSD flavor of Unix. They spread like wildfire with the internet. With good reason — the combination of sockets with INET makes talking to arbitrary machines around the world unbelievably easy (at least compared to other schemes).
Creating a Socket¶
Roughly speaking, when you clicked on the link that brought you to this page, your browser did something like the following:
# create an INET, STREAMing socket
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# now connect to the web server on port 80 - the normal http port
s.connect(("www.python.org", 80))
When the connect
completes, the socket s
can be used to send
in a request for the text of the page. The same socket will read the
reply, and then be destroyed. That’s right, destroyed. Client sockets
are normally only used for one exchange (or a small set of sequential
exchanges).
What happens in the web server is a bit more complex. First, the web server creates a “server socket”:
# create an INET, STREAMing socket
serversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
# bind the socket to a public host, and a well-known port
serversocket.bind((socket.gethostname(), 80))
# become a server socket
serversocket.listen(5)
A couple things to notice: we used socket.gethostname()
so that the socket
would be visible to the outside world. If we had used s.bind(('localhost',
80))
or s.bind(('127.0.0.1', 80))
we would still have a “server” socket,
but one that was only visible within the same machine. s.bind(('', 80))
specifies that the socket is reachable by any address the machine happens to
have.
A second thing to note: low number ports are usually reserved for “well known” services (HTTP, SNMP etc). If you’re playing around, use a nice high number (4 digits).
Finally, the argument to listen
tells the socket library that we want it to
queue up as many as 5 connect requests (the normal max) before refusing outside
connections. If the rest of the code is written properly, that should be plenty.
Now that we have a “server” socket, listening on port 80, we can enter the mainloop of the web server:
while True:
# accept connections from outside
(clientsocket, address) = serversocket.accept()
# now do something with the clientsocket
# in this case, we'll pretend this is a threaded server
ct = make_client_thread(clientsocket)
ct.start()
There’s actually 3 general ways in which this loop could work - dispatching a
thread to handle clientsocket
, create a new process to handle
clientsocket
, or restructure this app to use non-blocking sockets, and
multiplex between our “server” socket and any active clientsocket
s using
select
. More about that later. The important thing to understand now is
this: this is all a “server” socket does. It doesn’t send any data. It doesn’t
receive any data. It just produces “client” sockets. Each clientsocket
is
created in response to some other “client” socket doing a connect()
to the
host and port we’re bound to. As soon as we’ve created that clientsocket
, we
go back to listening for more connections. The two “clients” are free to chat it
up - they are using some dynamically allocated port which will be recycled when
the conversation ends.
IPC¶
If you need fast IPC between two processes on one machine, you should look into
pipes or shared memory. If you do decide to use AF_INET sockets, bind the
“server” socket to 'localhost'
. On most platforms, this will take a
shortcut around a couple of layers of network code and be quite a bit faster.
See also
The multiprocessing
integrates cross-platform IPC into a higher-level
API.
Using a Socket¶
The first thing to note, is that the web browser’s “client” socket and the web
server’s “client” socket are identical beasts. That is, this is a “peer to peer”
conversation. Or to put it another way, as the designer, you will have to
decide what the rules of etiquette are for a conversation. Normally, the
connect
ing socket starts the conversation, by sending in a request, or
perhaps a signon. But that’s a design decision - it’s not a rule of sockets.
Now there are two sets of verbs to use for communication. You can use send
and recv
, or you can transform your client socket into a file-like beast and
use read
and write
. The latter is the way Java presents its sockets.
I’m not going to talk about it here, except to warn you that you need to use
flush
on sockets. These are buffered “files”, and a common mistake is to
write
something, and then read
for a reply. Without a flush
in
there, you may wait forever for the reply, because the request may still be in
your output buffer.
Now we come to the major stumbling block of sockets - send
and recv
operate
on the network buffers. They do not necessarily handle all the bytes you hand
them (or expect from them), because their major focus is handling the network
buffers. In general, they return when the associated network buffers have been
filled (send
) or emptied (recv
). They then tell you how many bytes they
handled. It is your responsibility to call them again until your message has
been completely dealt with.
When a recv
returns 0 bytes, it means the other side has closed (or is in
the process of closing) the connection. You will not receive any more data on
this connection. Ever. You may be able to send data successfully; I’ll talk
more about this later.
A protocol like HTTP uses a socket for only one transfer. The client sends a request, then reads a reply. That’s it. The socket is discarded. This means that a client can detect the end of the reply by receiving 0 bytes.
But if you plan to reuse your socket for further transfers, you need to realize
that there is no EOT on a socket. I repeat: if a socket
send
or recv
returns after handling 0 bytes, the connection has been
broken. If the connection has not been broken, you may wait on a recv
forever, because the socket will not tell you that there’s nothing more to
read (for now). Now if you think about that a bit, you’ll come to realize a
fundamental truth of sockets: messages must either be fixed length (yuck), or
be delimited (shrug), or indicate how long they are (much better), or end by
shutting down the connection. The choice is entirely yours, (but some ways are
righter than others).
Assuming you don’t want to end the connection, the simplest solution is a fixed length message:
class MySocket:
"""demonstration class only
- coded for clarity, not efficiency
"""
def __init__(self, sock=None):
if sock is None:
self.sock = socket.socket(
socket.AF_INET, socket.SOCK_STREAM)
else:
self.sock = sock
def connect(self, host, port):
self.sock.connect((host, port))
def mysend(self, msg):
totalsent = 0
while totalsent < MSGLEN:
sent = self.sock.send(msg[totalsent:])
if sent == 0:
raise RuntimeError("socket connection broken")
totalsent = totalsent + sent
def myreceive(self):
chunks = []
bytes_recd = 0
while bytes_recd < MSGLEN:
chunk = self.sock.recv(min(MSGLEN - bytes_recd, 2048))
if chunk == b'':
raise RuntimeError("socket connection broken")
chunks.append(chunk)
bytes_recd = bytes_recd + len(chunk)
return b''.join(chunks)
The sending code here is usable for almost any messaging scheme - in Python you
send strings, and you can use len()
to determine its length (even if it has
embedded \0
characters). It’s mostly the receiving code that gets more
complex. (And in C, it’s not much worse, except you can’t use strlen
if the
message has embedded \0
s.)
The easiest enhancement is to make the first character of the message an
indicator of message type, and have the type determine the length. Now you have
two recv
s - the first to get (at least) that first character so you can
look up the length, and the second in a loop to get the rest. If you decide to
go the delimited route, you’ll be receiving in some arbitrary chunk size, (4096
or 8192 is frequently a good match for network buffer sizes), and scanning what
you’ve received for a delimiter.
One complication to be aware of: if your conversational protocol allows multiple
messages to be sent back to back (without some kind of reply), and you pass
recv
an arbitrary chunk size, you may end up reading the start of a
following message. You’ll need to put that aside and hold onto it, until it’s
needed.
Prefixing the message with its length (say, as 5 numeric characters) gets more
complex, because (believe it or not), you may not get all 5 characters in one
recv
. In playing around, you’ll get away with it; but in high network loads,
your code will very quickly break unless you use two recv
loops - the first
to determine the length, the second to get the data part of the message. Nasty.
This is also when you’ll discover that send
does not always manage to get
rid of everything in one pass. And despite having read this, you will eventually
get bit by it!
In the interests of space, building your character, (and preserving my competitive position), these enhancements are left as an exercise for the reader. Lets move on to cleaning up.
Binary Data¶
It is perfectly possible to send binary data over a socket. The major problem is
that not all machines use the same formats for binary data. For example,
network byte order
is big-endian, with the most significant byte first,
so a 16 bit integer with the value 1
would be the two hex bytes 00 01
.
However, most common processors (x86/AMD64, ARM, RISC-V), are little-endian,
with the least significant byte first - that same 1
would be 01 00
.
Socket libraries have calls for converting 16 and 32 bit integers - ntohl,
htonl, ntohs, htons
where “n” means network and “h” means host, “s” means
short and “l” means long. Where network order is host order, these do
nothing, but where the machine is byte-reversed, these swap the bytes around
appropriately.
In these days of 64-bit machines, the ASCII representation of binary data is
frequently smaller than the binary representation. That’s because a surprising
amount of the time, most integers have the value 0, or maybe 1.
The string "0"
would be two bytes, while a full 64-bit integer would be 8.
Of course, this doesn’t fit well with fixed-length messages.
Decisions, decisions.
Disconnecting¶
Strictly speaking, you’re supposed to use shutdown
on a socket before you
close
it. The shutdown
is an advisory to the socket at the other end.
Depending on the argument you pass it, it can mean “I’m not going to send
anymore, but I’ll still listen”, or “I’m not listening, good riddance!”. Most
socket libraries, however, are so used to programmers neglecting to use this
piece of etiquette that normally a close
is the same as shutdown();
close()
. So in most situations, an explicit shutdown
is not needed.
One way to use shutdown
effectively is in an HTTP-like exchange. The client
sends a request and then does a shutdown(1)
. This tells the server “This
client is done sending, but can still receive.” The server can detect “EOF” by
a receive of 0 bytes. It can assume it has the complete request. The server
sends a reply. If the send
completes successfully then, indeed, the client
was still receiving.
Python takes the automatic shutdown a step further, and says that when a socket
is garbage collected, it will automatically do a close
if it’s needed. But
relying on this is a very bad habit. If your socket just disappears without
doing a close
, the socket at the other end may hang indefinitely, thinking
you’re just being slow. Please close
your sockets when you’re done.
When Sockets Die¶
Probably the worst thing about using blocking sockets is what happens when the
other side comes down hard (without doing a close
). Your socket is likely to
hang. TCP is a reliable protocol, and it will wait a long, long time
before giving up on a connection. If you’re using threads, the entire thread is
essentially dead. There’s not much you can do about it. As long as you aren’t
doing something dumb, like holding a lock while doing a blocking read, the
thread isn’t really consuming much in the way of resources. Do not try to kill
the thread - part of the reason that threads are more efficient than processes
is that they avoid the overhead associated with the automatic recycling of
resources. In other words, if you do manage to kill the thread, your whole
process is likely to be screwed up.
Non-blocking Sockets¶
If you’ve understood the preceding, you already know most of what you need to know about the mechanics of using sockets. You’ll still use the same calls, in much the same ways. It’s just that, if you do it right, your app will be almost inside-out.
In Python, you use socket.setblocking(False)
to make it non-blocking. In C, it’s
more complex, (for one thing, you’ll need to choose between the BSD flavor
O_NONBLOCK
and the almost indistinguishable POSIX flavor O_NDELAY
, which
is completely different from TCP_NODELAY
), but it’s the exact same idea. You
do this after creating the socket, but before using it. (Actually, if you’re
nuts, you can switch back and forth.)
The major mechanical difference is that send
, recv
, connect
and
accept
can return without having done anything. You have (of course) a
number of choices. You can check return code and error codes and generally drive
yourself crazy. If you don’t believe me, try it sometime. Your app will grow
large, buggy and suck CPU. So let’s skip the brain-dead solutions and do it
right.
Use select
.
In C, coding select
is fairly complex. In Python, it’s a piece of cake, but
it’s close enough to the C version that if you understand select
in Python,
you’ll have little trouble with it in C:
ready_to_read, ready_to_write, in_error = \
select.select(
potential_readers,
potential_writers,
potential_errs,
timeout)
You pass select
three lists: the first contains all sockets that you might
want to try reading; the second all the sockets you might want to try writing
to, and the last (normally left empty) those that you want to check for errors.
You should note that a socket can go into more than one list. The select
call is blocking, but you can give it a timeout. This is generally a sensible
thing to do - give it a nice long timeout (say a minute) unless you have good
reason to do otherwise.
In return, you will get three lists. They contain the sockets that are actually readable, writable and in error. Each of these lists is a subset (possibly empty) of the corresponding list you passed in.
If a socket is in the output readable list, you can be
as-close-to-certain-as-we-ever-get-in-this-business that a recv
on that
socket will return something. Same idea for the writable list. You’ll be able
to send something. Maybe not all you want to, but something is better than
nothing. (Actually, any reasonably healthy socket will return as writable - it
just means outbound network buffer space is available.)
If you have a “server” socket, put it in the potential_readers list. If it comes
out in the readable list, your accept
will (almost certainly) work. If you
have created a new socket to connect
to someone else, put it in the
potential_writers list. If it shows up in the writable list, you have a decent
chance that it has connected.
Actually, select
can be handy even with blocking sockets. It’s one way of
determining whether you will block - the socket returns as readable when there’s
something in the buffers. However, this still doesn’t help with the problem of
determining whether the other end is done, or just busy with something else.
Portability alert: On Unix, select
works both with the sockets and
files. Don’t try this on Windows. On Windows, select
works with sockets
only. Also note that in C, many of the more advanced socket options are done
differently on Windows. In fact, on Windows I usually use threads (which work
very, very well) with my sockets.


Isolating Extension Modules

Isolating Extension Modules¶
Who should read this¶
This guide is written for maintainers of C-API extensions who would like to make that extension safer to use in applications where Python itself is used as a library.
Background¶
An interpreter is the context in which Python code runs. It contains configuration (e.g. the import path) and runtime state (e.g. the set of imported modules).
Python supports running multiple interpreters in one process. There are two cases to think about—users may run interpreters:
in sequence, with several
Py_InitializeEx()
/Py_FinalizeEx()
cycles, andin parallel, managing “sub-interpreters” using
Py_NewInterpreter()
/Py_EndInterpreter()
.
Both cases (and combinations of them) would be most useful when embedding Python within a library. Libraries generally shouldn’t make assumptions about the application that uses them, which include assuming a process-wide “main Python interpreter”.
Historically, Python extension modules don’t handle this use case well.
Many extension modules (and even some stdlib modules) use per-process
global state, because C static
variables are extremely easy to use.
Thus, data that should be specific to an interpreter ends up being shared
between interpreters. Unless the extension developer is careful, it is very
easy to introduce edge cases that lead to crashes when a module is loaded in
more than one interpreter in the same process.
Unfortunately, per-interpreter state is not easy to achieve. Extension authors tend to not keep multiple interpreters in mind when developing, and it is currently cumbersome to test the behavior.
Enter Per-Module State¶
Instead of focusing on per-interpreter state, Python’s C API is evolving to better support the more granular per-module state. This means that C-level data should be attached to a module object. Each interpreter creates its own module object, keeping the data separate. For testing the isolation, multiple module objects corresponding to a single extension can even be loaded in a single interpreter.
Per-module state provides an easy way to think about lifetime and resource ownership: the extension module will initialize when a module object is created, and clean up when it’s freed. In this regard, a module is just like any other PyObject*; there are no “on interpreter shutdown” hooks to think—or forget—about.
Note that there are use cases for different kinds of “globals”: per-process, per-interpreter, per-thread or per-task state. With per-module state as the default, these are still possible, but you should treat them as exceptional cases: if you need them, you should give them additional care and testing. (Note that this guide does not cover them.)
Isolated Module Objects¶
The key point to keep in mind when developing an extension module is that several module objects can be created from a single shared library. For example:
>>> import sys
>>> import binascii
>>> old_binascii = binascii
>>> del sys.modules['binascii']
>>> import binascii # create a new module object
>>> old_binascii == binascii
False
As a rule of thumb, the two modules should be completely independent. All objects and state specific to the module should be encapsulated within the module object, not shared with other module objects, and cleaned up when the module object is deallocated. Since this just is a rule of thumb, exceptions are possible (see Managing Global State), but they will need more thought and attention to edge cases.
While some modules could do with less stringent restrictions, isolated modules make it easier to set clear expectations and guidelines that work across a variety of use cases.
Surprising Edge Cases¶
Note that isolated modules do create some surprising edge cases. Most
notably, each module object will typically not share its classes and
exceptions with other similar modules. Continuing from the
example above,
note that old_binascii.Error
and binascii.Error
are
separate objects. In the following code, the exception is not caught:
>>> old_binascii.Error == binascii.Error
False
>>> try:
... old_binascii.unhexlify(b'qwertyuiop')
... except binascii.Error:
... print('boo')
...
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
binascii.Error: Non-hexadecimal digit found
This is expected. Notice that pure-Python modules behave the same way: it is a part of how Python works.
The goal is to make extension modules safe at the C level, not to make
hacks behave intuitively. Mutating sys.modules
“manually” counts
as a hack.
Making Modules Safe with Multiple Interpreters¶
Managing Global State¶
Sometimes, the state associated with a Python module is not specific to that module, but to the entire process (or something else “more global” than a module). For example:
The
readline
module manages the terminal.A module running on a circuit board wants to control the on-board LED.
In these cases, the Python module should provide access to the global state, rather than own it. If possible, write the module so that multiple copies of it can access the state independently (along with other libraries, whether for Python or other languages). If that is not possible, consider explicit locking.
If it is necessary to use process-global state, the simplest way to avoid issues with multiple interpreters is to explicitly prevent a module from being loaded more than once per process—see Opt-Out: Limiting to One Module Object per Process.
Managing Per-Module State¶
To use per-module state, use multi-phase extension module initialization. This signals that your module supports multiple interpreters correctly.
Set PyModuleDef.m_size
to a positive number to request that many
bytes of storage local to the module. Usually, this will be set to the
size of some module-specific struct
, which can store all of the
module’s C-level state. In particular, it is where you should put
pointers to classes (including exceptions, but excluding static types)
and settings (e.g. csv
’s field_size_limit
)
which the C code needs to function.
Note
Another option is to store state in the module’s __dict__
,
but you must avoid crashing when users modify __dict__
from
Python code. This usually means error- and type-checking at the C level,
which is easy to get wrong and hard to test sufficiently.
However, if module state is not needed in C code, storing it in
__dict__
only is a good idea.
If the module state includes PyObject
pointers, the module object
must hold references to those objects and implement the module-level hooks
m_traverse
, m_clear
and m_free
. These work like
tp_traverse
, tp_clear
and tp_free
of a class. Adding them will
require some work and make the code longer; this is the price for
modules which can be unloaded cleanly.
An example of a module with per-module state is currently available as xxlimited; example module initialization shown at the bottom of the file.
Opt-Out: Limiting to One Module Object per Process¶
A non-negative PyModuleDef.m_size
signals that a module supports
multiple interpreters correctly. If this is not yet the case for your
module, you can explicitly make your module loadable only once per
process. For example:
// A process-wide flag
static int loaded = 0;
// Mutex to provide thread safety (only needed for free-threaded Python)
static PyMutex modinit_mutex = {0};
static int
exec_module(PyObject* module)
{
PyMutex_Lock(&modinit_mutex);
if (loaded) {
PyMutex_Unlock(&modinit_mutex);
PyErr_SetString(PyExc_ImportError,
"cannot load module more than once per process");
return -1;
}
loaded = 1;
PyMutex_Unlock(&modinit_mutex);
// ... rest of initialization
}
If your module’s PyModuleDef.m_clear
function is able to prepare
for future re-initialization, it should clear the loaded
flag.
In this case, your module won’t support multiple instances existing
concurrently, but it will, for example, support being loaded after
Python runtime shutdown (Py_FinalizeEx()
) and re-initialization
(Py_Initialize()
).
Module State Access from Functions¶
Accessing the state from module-level functions is straightforward.
Functions get the module object as their first argument; for extracting
the state, you can use PyModule_GetState
:
static PyObject *
func(PyObject *module, PyObject *args)
{
my_struct *state = (my_struct*)PyModule_GetState(module);
if (state == NULL) {
return NULL;
}
// ... rest of logic
}
Note
PyModule_GetState
may return NULL
without setting an
exception if there is no module state, i.e. PyModuleDef.m_size
was
zero. In your own module, you’re in control of m_size
, so this is
easy to prevent.
Heap Types¶
Traditionally, types defined in C code are static; that is,
static PyTypeObject
structures defined directly in code and
initialized using PyType_Ready()
.
Such types are necessarily shared across the process. Sharing them
between module objects requires paying attention to any state they own
or access. To limit the possible issues, static types are immutable at
the Python level: for example, you can’t set str.myattribute = 123
.
CPython implementation detail: Sharing truly immutable objects between interpreters is fine, as long as they don’t provide access to mutable objects. However, in CPython, every Python object has a mutable implementation detail: the reference count. Changes to the refcount are guarded by the GIL. Thus, code that shares any Python objects across interpreters implicitly depends on CPython’s current, process-wide GIL.
Because they are immutable and process-global, static types cannot access
“their” module state.
If any method of such a type requires access to module state,
the type must be converted to a heap-allocated type, or heap type
for short. These correspond more closely to classes created by Python’s
class
statement.
For new modules, using heap types by default is a good rule of thumb.
Changing Static Types to Heap Types¶
Static types can be converted to heap types, but note that the heap type API was not designed for “lossless” conversion from static types—that is, creating a type that works exactly like a given static type. So, when rewriting the class definition in a new API, you are likely to unintentionally change a few details (e.g. pickleability or inherited slots). Always test the details that are important to you.
Watch out for the following two points in particular (but note that this is not a comprehensive list):
Unlike static types, heap type objects are mutable by default. Use the
Py_TPFLAGS_IMMUTABLETYPE
flag to prevent mutability.Heap types inherit
tp_new
by default, so it may become possible to instantiate them from Python code. You can prevent this with thePy_TPFLAGS_DISALLOW_INSTANTIATION
flag.
Defining Heap Types¶
Heap types can be created by filling a PyType_Spec
structure, a
description or “blueprint” of a class, and calling
PyType_FromModuleAndSpec()
to construct a new class object.
Note
Other functions, like PyType_FromSpec()
, can also create
heap types, but PyType_FromModuleAndSpec()
associates the module
with the class, allowing access to the module state from methods.
The class should generally be stored in both the module state (for
safe access from C) and the module’s __dict__
(for access from
Python code).
Garbage-Collection Protocol¶
Instances of heap types hold a reference to their type. This ensures that the type isn’t destroyed before all its instances are, but may result in reference cycles that need to be broken by the garbage collector.
To avoid memory leaks, instances of heap types must implement the garbage collection protocol. That is, heap types should:
Have the
Py_TPFLAGS_HAVE_GC
flag.Define a traverse function using
Py_tp_traverse
, which visits the type (e.g. usingPy_VISIT(Py_TYPE(self))
).
Please refer to the documentation of
Py_TPFLAGS_HAVE_GC
and tp_traverse
for additional considerations.
The API for defining heap types grew organically, leaving it somewhat awkward to use in its current state. The following sections will guide you through common issues.
tp_traverse
in Python 3.8 and lower¶
The requirement to visit the type from tp_traverse
was added in Python 3.9.
If you support Python 3.8 and lower, the traverse function must not
visit the type, so it must be more complicated:
static int my_traverse(PyObject *self, visitproc visit, void *arg)
{
if (Py_Version >= 0x03090000) {
Py_VISIT(Py_TYPE(self));
}
return 0;
}
Unfortunately, Py_Version
was only added in Python 3.11.
As a replacement, use:
PY_VERSION_HEX
, if not using the stable ABI, orsys.version_info
(viaPySys_GetObject()
andPyArg_ParseTuple()
).
Delegating tp_traverse
¶
If your traverse function delegates to the tp_traverse
of its base class (or another type), ensure that Py_TYPE(self)
is visited
only once.
Note that only heap type are expected to visit the type in tp_traverse
.
For example, if your traverse function includes:
base->tp_traverse(self, visit, arg)
…and base
may be a static type, then it should also include:
if (base->tp_flags & Py_TPFLAGS_HEAPTYPE) {
// a heap type's tp_traverse already visited Py_TYPE(self)
} else {
if (Py_Version >= 0x03090000) {
Py_VISIT(Py_TYPE(self));
}
}
It is not necessary to handle the type’s reference count in
tp_new
and tp_clear
.
Defining tp_dealloc
¶
If your type has a custom tp_dealloc
function,
it needs to:
call
PyObject_GC_UnTrack()
before any fields are invalidated, anddecrement the reference count of the type.
To keep the type valid while tp_free
is called, the type’s refcount needs
to be decremented after the instance is deallocated. For example:
static void my_dealloc(PyObject *self)
{
PyObject_GC_UnTrack(self);
...
PyTypeObject *type = Py_TYPE(self);
type->tp_free(self);
Py_DECREF(type);
}
The default tp_dealloc
function does this, so
if your type does not override
tp_dealloc
you don’t need to add it.
Not overriding tp_free
¶
The tp_free
slot of a heap type must be set to
PyObject_GC_Del()
.
This is the default; do not override it.
Avoiding PyObject_New
¶
GC-tracked objects need to be allocated using GC-aware functions.
If you use PyObject_New()
or PyObject_NewVar()
:
Get and call type’s
tp_alloc
slot, if possible. That is, replaceTYPE *o = PyObject_New(TYPE, typeobj)
with:TYPE *o = typeobj->tp_alloc(typeobj, 0);
Replace
o = PyObject_NewVar(TYPE, typeobj, size)
with the same, but use size instead of the 0.If the above is not possible (e.g. inside a custom
tp_alloc
), callPyObject_GC_New()
orPyObject_GC_NewVar()
:TYPE *o = PyObject_GC_New(TYPE, typeobj); TYPE *o = PyObject_GC_NewVar(TYPE, typeobj, size);
Module State Access from Classes¶
If you have a type object defined with PyType_FromModuleAndSpec()
,
you can call PyType_GetModule()
to get the associated module, and then
PyModule_GetState()
to get the module’s state.
To save a some tedious error-handling boilerplate code, you can combine
these two steps with PyType_GetModuleState()
, resulting in:
my_struct *state = (my_struct*)PyType_GetModuleState(type);
if (state == NULL) {
return NULL;
}
Module State Access from Regular Methods¶
Accessing the module-level state from methods of a class is somewhat more complicated, but is possible thanks to API introduced in Python 3.9. To get the state, you need to first get the defining class, and then get the module state from it.
The largest roadblock is getting the class a method was defined in, or that method’s “defining class” for short. The defining class can have a reference to the module it is part of.
Do not confuse the defining class with Py_TYPE(self)
. If the method
is called on a subclass of your type, Py_TYPE(self)
will refer to
that subclass, which may be defined in different module than yours.
Note
The following Python code can illustrate the concept.
Base.get_defining_class
returns Base
even
if type(self) == Sub
:
class Base:
def get_type_of_self(self):
return type(self)
def get_defining_class(self):
return __class__
class Sub(Base):
pass
For a method to get its “defining class”, it must use the
METH_METHOD | METH_FASTCALL | METH_KEYWORDS
calling convention
and the corresponding PyCMethod
signature:
PyObject *PyCMethod(
PyObject *self, // object the method was called on
PyTypeObject *defining_class, // defining class
PyObject *const *args, // C array of arguments
Py_ssize_t nargs, // length of "args"
PyObject *kwnames) // NULL, or dict of keyword arguments
Once you have the defining class, call PyType_GetModuleState()
to get
the state of its associated module.
For example:
static PyObject *
example_method(PyObject *self,
PyTypeObject *defining_class,
PyObject *const *args,
Py_ssize_t nargs,
PyObject *kwnames)
{
my_struct *state = (my_struct*)PyType_GetModuleState(defining_class);
if (state == NULL) {
return NULL;
}
... // rest of logic
}
PyDoc_STRVAR(example_method_doc, "...");
static PyMethodDef my_methods[] = {
{"example_method",
(PyCFunction)(void(*)(void))example_method,
METH_METHOD|METH_FASTCALL|METH_KEYWORDS,
example_method_doc}
{NULL},
}
Module State Access from Slot Methods, Getters and Setters¶
Note
This is new in Python 3.11.
Slot methods—the fast C equivalents for special methods, such as
nb_add
for __add__
or
tp_new
for initialization—have a very simple API that
doesn’t allow passing in the defining class, unlike with PyCMethod
.
The same goes for getters and setters defined with
PyGetSetDef
.
To access the module state in these cases, use the
PyType_GetModuleByDef()
function, and pass in the module definition.
Once you have the module, call PyModule_GetState()
to get the state:
PyObject *module = PyType_GetModuleByDef(Py_TYPE(self), &module_def);
my_struct *state = (my_struct*)PyModule_GetState(module);
if (state == NULL) {
return NULL;
}
PyType_GetModuleByDef()
works by searching the
method resolution order (i.e. all superclasses) for the first
superclass that has a corresponding module.
Note
In very exotic cases (inheritance chains spanning multiple modules
created from the same definition), PyType_GetModuleByDef()
might not
return the module of the true defining class. However, it will always
return a module with the same definition, ensuring a compatible
C memory layout.
Lifetime of the Module State¶
When a module object is garbage-collected, its module state is freed. For each pointer to (a part of) the module state, you must hold a reference to the module object.
Usually this is not an issue, because types created with
PyType_FromModuleAndSpec()
, and their instances, hold a reference
to the module.
However, you must be careful in reference counting when you reference
module state from other places, such as callbacks for external
libraries.
Open Issues¶
Several issues around per-module state and heap types are still open.
Discussions about improving the situation are best held on the discuss forum under c-api tag.
Per-Class Scope¶
It is currently (as of Python 3.11) not possible to attach state to individual types without relying on CPython implementation details (which may change in the future—perhaps, ironically, to allow a proper solution for per-class scope).
Lossless Conversion to Heap Types¶
The heap type API was not designed for “lossless” conversion from static types; that is, creating a type that works exactly like a given static type.