Partition method of Python strings

Adarsh D — Thu, 25 Jan 2024 08:19:34 +0000

The partition method of str covers a niche use case - It is useful when a string needs to be split into exactly two parts based on a separator. It returns a three-membered tuple containing the part before, the separator itself, and the part after the separator.

The split method is commonly used for splitting strings, which returns a list whose length can change based on the number of times the separator is present.

host = "localhost:8000"
print(host.split(":"))  # Output: ['localhost', '8000']

host = "localhost"
print(host.split(":"))  # Output:['localhost']

host = ""
print(host.split(":"))  # Output: ['']

partition is helpful when we need to get precisely two parts from a string using a separator.

server_name = "localhost:5000"
print(server_name.partition(":"))  # Output: ('localhost', ':', '5000')

server_name = "localhost"
print(server_name.partition(":"))  # Output: ('localhost', '', '')

server_name = ""
print(server_name.partition(":"))  # Output: ('', '', '')

Using partition instead of split saves us from additional checks on the result's length when exactly two parts are required.

Flask uses the partition method to determine the host and port to use when running the Flask app. You can see the source code here.

A simplified version of the code is added as below:

def run(
    self,
    host: str | None = None,
    port: int | None = None,
    debug: bool | None = None,
    load_dotenv: bool = True,
    **options: t.Any,
) -> None:
    ...

    server_name = self.config.get("SERVER_NAME")
    sn_host = sn_port = None

    if server_name:
        sn_host, _, sn_port = server_name.partition(":")
        # The `partition` method will always return a three-membered tuple

    if not host:
        if sn_host:
            host = sn_host
        else:
            host = "127.0.0.1"

    if port or port == 0:
        port = int(port)
    elif sn_port:
        port = int(sn_port)
    else:
        port = 5000

In the example above, the server_name string is partitioned into host and port values.

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Thread Safety in Python

Adarsh D — Wed, 11 Oct 2023 05:11:29 +0000

Before getting started with Thread safety, let's look into race conditions.

Race Conditions

From Wikipedia:

A race condition or race hazard is the condition of an electronics, software, or other system where the system's substantive behavior is dependent on the sequence or timing of other uncontrollable events. It becomes a bug when one or more of the possible behaviors is undesirable.

Race conditions can occur especially in logic circuits, multithreaded, or distributed software programs.

For an example of a race condition on a real-world web app, see this disclosed security bug: https://hackerone.com/reports/759247

Here the hacker could redeem a gift card (intended for single use) multiple times.

A developer may think of implementing a gift card redemption flow like below:

Get a gift card code from the user
Validate the code: Check if it exists in DB, is not expired, and is not used.
If validation succeeds, add the gift card value to the user's wallet and mark the gift card as used in the DB.

Think of the case where a user sends multiple requests at the same time. All parallel requests arriving before the execution of step 3, could succeed and can result in adding the gift card amount to the user's wallet once per request. This might have happened in the case of the above bug.

Threading in Python

Threading is a method to implement concurrency in Python programs. Due to the presence of CPython's GIL, threading is helpful in the case of I/O Bound programs.

threading module and concurrent.future's ThreadPoolExecutor helper is commonly used to implement multithreading in Python.

Let's dive into some Python examples after exploring the levels of thread safety.

Levels of thread safety

From Wikipedia, levels of thread safety:

Thread safe: Implementation is guaranteed to be free of race conditions when accessed by multiple threads simultaneously.

Conditionally safe: Different threads can access different objects simultaneously, and access to shared data is protected from race conditions.

Not thread-safe: Data structures should not be accessed simultaneously by different threads.

Race conditions in Python Threaded Programs

Example 1: Incrementing a counter

import concurrent.futures

count = 0


def increase_counter():
    global count
    for i in range(1000):
    count += 1

with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
    for _ in range(10_000):
        executor.submit(increase_counter)

print(F"{count=}")

The expected value of the count is 10,000 * 1000.

Running with Python 3.9, the program output varies per execution and gives outputs like below:

count=2163149
count=5642376

From this, we can conclude that the program is not thread-safe, since it gives unexpected results when run using multiple threads.

In the above examples, there are at most 10 parallel threads working since we have given max_workers=10

On adjusting max_workers value,

When max_workers=3, the output varies like below:

count=10000000
count=9999000

On reducing max_workers, which is the number of threads running in parallel, there is a visible reduction in errors, and at times, the expected output is also returned.

Consider the above scenario where each thread reads a variable and increments its value with 1. Between reads and writes, context switches to different threads can happen.
With multiple threads, the following scenario may be possible:

Thread_1 reads the value of count, gets 0
At the same time thread 2 reads count, and gets 0
Thread_1 sets count=count+1, as 1.
Thread_3 reads count, gets 1.
Thread_3 sets count=count+1, as 2
Thread_2 sets count=count+1, as 1 (since thread_2 holds a stale value of count, i.e., 0)

This explains the counter value being far less than expected.

Example 2: Singleton class in Python

Let's take an example of a Singleton class created using Python.
Singleton pattern is used to make sure that only one instance is created for a class. This can be used in cases where the class initialization performs some steps which should be performed exactly once. Singletons are useful in cases where there are restrictions on certain resource usage such as concurrent connection limits.

Below is a commonly used example for a Singleton class in Python:

class SingletonClass:
    def __new__(cls):
        if not hasattr(cls, 'instance'):
            cls.instance = super(SingletonClass, cls).__new__(cls)
        return cls.instance


obj1 = SingletonClass()
obj2 = SingletonClass()

print(obj1 is obj2) # True
print(id(obj1)) # 2402721138768
print(id(obj2)) # 2402721138768

In the above example, it is possible because two threads can evaluate the if check for instance (if not hasattr(cls, 'instance')) at near similar times and proceed to create two new objects.

Example 3: The print function

from concurrent import futures

def printer():
    print('testing thread safety of print')


with futures.ThreadPoolExecutor(max_workers=10) as executor:
    for _ in range(10):
        executor.submit(printer)

Output can vary from expected:

testing thread safety of print
testing thread safety of print
testing thread safety of printtesting thread safety of print
testing thread safety of print

testing thread safety of printtesting thread safety of print
testing thread safety of print
testing thread safety of print

testing thread safety of print

Before printing ending newlines, context switches happen sometimes printing output from another thread.

Making programs thread-safe

Synchronization primitives

The below synchronization primitives can be used to make a program thread safe.

Lock

A lock can be used for exclusive access to a resource. Once a thread acquires a lock, no other threads can acquire it (and proceed further) unless the lock is released.

RLock

RLock is a re-entrant lock. It allows when a holding thread requests the lock again.
The simple lock is not aware of the current locking thread and this can lead to deadlocks.

import threading

num = 0
lock = Threading.Lock()

lock.acquire()
num += 1
lock.acquire() # This will block. The program goes into a deadlock after this.
num += 2
lock.release()


# With RLock, that problem doesn't happen.
lock = Threading.RLock()

lock.acquire()
num += 3
lock.acquire() # This won't block.
num += 4
lock.release()
lock.release() # We need to call release once for each call to acquire

For brevity, let's skip the other primitives (Semaphore & Barrier, Events & Conditions).

Lock acquisition and release can be managed using context managers.

lock = Threading.Lock()

lock.acquire()
num += 1
lock.release()

is equivalent to:

lock = Threading.Lock()

with lock:
    num += 1

Thread safe counter implementation

We can use a lock at places of code where race conditions(simultaneous modifications in this case) should be avoided.

import concurrent.futures
import dis
import time
from threading import Lock

count = 0
lock = Lock()


def increase_counter():
    global count
    for i in range(1000):

        lock.acquire()
        count += 1
        lock.release()

        """
        or:
        with lock:
        count += 1
        """


with concurrent.futures.ThreadPoolExecutor(max_workers=3) as executor:
    for _ in range(10_000):
        executor.submit(increase_counter)

print(F"{count=}") # count=10000000, for all executions

Thread-safe singleton implementation

class Singleton:
  _instance = None
  _lock = threading.Lock()

  def __new__(cls):
      if cls._instance is None:
        with cls._lock:

          if cls._instance is None:
              cls._instance = super().__new__(cls)

    return cls._instance

After acquiring the lock (using with), we are rechecking the presence of an instance to prevent the below-race condition.

Consider two threads thread1 and thread2.

Both execute new dunder of singleton class at the same time.
cls_.instance is currently None, so both thread tries to acquire the lock
thread1 gets the lock while thread2 is blocked waiting.
thread1 creates the singleton instance and completes execution inside the context manager. Once it exits the with block, the lock (cls._lock) is released.
Now, thread2 is unblocked and enters the context manager body by acquiring the lock. Since the None check for cls._instance fails, thread2 leaves execution and the already existing instance is returned.

Thread safe print implementation

Similar to the previous examples, we can use a lock here.

from concurrent import futures
from threading import Lock

lock = Lock()

def printer():
    with lock:
        print('testing thread safety of print')


with futures.ThreadPoolExecutor(max_workers=10) as executor:
    for _ in range(100):
        executor.submit(printer)

Context switching & Thread switch interval

The switch interval in Python specifies how long the Python interpreter will allow a Python thread to run, after which it is forced for a context switch.

Since only one thread runs at a time due to GIL, the interpreter tries to switch threads after this interval.

The default interval is 5 milliseconds.

>>> import sys
>>> sys.getswitchinterval()
0.005

We can set custom thread switch interval using sys.setswitchinterval

If the threads are waiting (e.g.: for IO operations) this switching allows for a parallel execution-like effect. If threads are running any CPU-bound operations, this context switch causes a performance penalty.

Eg: Network IO blocked tasks:

import time

import requests

start = time.perf_counter()
req1 = requests.get("https://reqres.in/api/users?delay=3")
req2 = requests.get("https://reqres.in/api/users?delay=3")
end = time.perf_counter()

print(end-start, " seconds") # 6.9710999 seconds

Using threads:

import threading
import time
import requests


def make_request():
    return requests.get("https://reqres.in/api/users?delay=3")


start = time.perf_counter()

t1 = threading.Thread(target=make_request)
t2 = threading.Thread(target=make_request)

t1.start()
t2.start()

t1.join()
t2.join()

end = time.perf_counter()

print(end-start, " seconds") # 3.494618 seconds

In case of CPU Blocked tasks:

import time

start = time.perf_counter()

s1 = sum(range(100000000))
s2 = sum(range(100000000))

end = time.perf_counter()

print(end-start, " seconds") # 3.8891145 seconds

Using threads:

import threading
import time
import requests


def make_request(): 
    return sum(range(100000000))


start = time.perf_counter()

t1 = threading.Thread(target=make_request)
t2 = threading.Thread(target=make_request)

t1.start()
t2.start()

t1.join()
t2.join()

end = time.perf_counter()

print(end-start, " seconds") # 3.9622078999999997 seconds

As expected, the use of threading slowed down this CPU-bound operation.

Revisiting the increment_counter example

On running the non-thread-safe increment_counter() used above using Python 3.11 always gives the expected result and race conditions are not seen.

We can use dis module to inspect the Python bytecode for the increase_counter function

import dis

count = 0


def increase_counter():
    global count
    for i in range(1000):
        count += 1

dis.dis(increase_counter)

Python 3.9 output:

9 0 LOAD_GLOBAL 0 (range)
2 LOAD_CONST 1 (1000)
4 CALL_FUNCTION 1
6 GET_ITER
\>> 8 FOR_ITER 12 (to 22)
10 STORE_FAST 0 (i)

10 12 LOAD_GLOBAL 1 (count)
14 LOAD_CONST 2 (1)
16 INPLACE_ADD
18 STORE_GLOBAL 1 (count)
20 JUMP_ABSOLUTE 8
\>\> 22 LOAD_CONST 0 (None)
24 RETURN_VALUE

Python 3.11 output:

7 0 RESUME 0

9 2 LOAD_GLOBAL 1 (NULL + range)
14 LOAD_CONST 1 (1000)
16 PRECALL 1
20 CALL 1
30 GET_ITER
>> 32 FOR_ITER 12 (to 58)
34 STORE_FAST 0 (i)

10 36 LOAD_GLOBAL 2 (count)
48 LOAD_CONST 2 (1)
50 BINARY_OP 13 (+=)
54 STORE_GLOBAL 1 (count)
56 JUMP_BACKWARD 13 (to 32)

9 >> 58 LOAD_CONST 0 (None)
60 RETURN_VALUE

We can see that INPLACE_ADD opcode has been replaced with BINARY_OP in Python 3.11. I guess that the BINARY_OP is now a thread-safe operation.