search
HomeBackend DevelopmentPython TutorialConcurrency Patterns: Balking Pattern

Concurrency Patterns: Balking Pattern

Introduction

The Balking Design Pattern is a behavioral design pattern used to manage state-dependent actions in a system. It ensures that operations are executed only when the system is in an appropriate state. If the required precondition is not met, the operation is aborted or the system "balks". For those like me, who don't know what Balking is, this is what google has to say about it: "hesitate or be unwilling to accept an idea or undertaking". This pattern is particularly useful in multithreaded environments or systems where invalid actions could cause conflicts or errors.

Balking pattern is also considered more of an anti-pattern than a design pattern by some people in the community. If an object cannot support its API, it should either limit the API so that the offending call is not available, or so that the call can be made without limitation. This is an old pattern which seems to have arisen when JVMs were slower and synchronization wasn't as well understood and implemented as it is today. Regardless it is worth discussing and whether to use it or not is upto the developers.

The Balking Pattern relies on three fundamental concepts

  1. Guard Condition: A condition that must be satisfied for an operation to proceed.
  2. State-Dependent Actions: Operations that depend on the current state of the system.
  3. Thread Safety: The pattern often uses locks or other synchronization mechanisms to ensure safety in concurrent environments.

Let's understand these with an example:

A printing system demonstrates the Balking Pattern:

  • Scenario: A printer can only process one print request at a time. Even though multiple processes can place the print request.
  • Guard Condition: The printing must not be actively "printing" to handle a new print request.
  • Behavior: If the printer is busy, the system balks and does not proceed with the new print requests.

Note: Yeah, we can handle this using a queue, but let's assume for now we don't know that such an elegant data structure exists.

import threading
import time

class Printer:
    def __init__(self):
        self.state = "idle"
        self.lock = threading.Lock()

    def start_printing(self, job_id):
        print(f"Attempting to start Print Job {job_id}...")

        with self.lock:  # Ensure thread safety
            if self.state == "printing":
                print(f"Balking: Print Job {job_id} cannot start. Printer is busy.")
                return
            self.state = "printing"

        # Simulate the printing process
        print(f"Print Job {job_id} started.")
        time.sleep(3)
        print(f"Print Job {job_id} completed.")

        with self.lock:
            self.printing = "idle"

# Multiple threads attempting to start print jobs
printer = Printer()

threads = [
    threading.Thread(target=printer.start_printing, args=(1,)),
    threading.Thread(target=printer.start_printing, args=(2,))
]

for t in threads:
    t.start()

for t in threads:
    t.join()

Looking at the code we can see that if we send a print request start_printing to the printer and the printer is busy it will check it's current state self.state and if the state is "printing", it will return without doing anything. Otherwise, it will take up that request and adjust its state accordingly.

When to Use the Balking Pattern

  1. Multithreaded Systems: To prevent race conditions or invalid operations.
  2. State-Dependent Workflows: When actions are permissible only in certain states.
  3. Resource Management: To guard against improper use of shared resources. Objects that use this pattern are generally only in a state that is prone to balking temporarily but for an unknown amount of time. If objects are to remain in a state which is prone to balking for a known, finite period of time, then the guarded suspension pattern may be preferred.

Advantages of the Balking Pattern

  1. Prevents Invalid Operations: Guards ensure operations occur only under valid conditions.
  2. Thread Safety: Particularly useful in multithreaded systems.
  3. Simplifies Logic: Encapsulates state-dependent actions into a clear, reusable pattern.

Disadvantages

  1. Limited Applicability: Most useful when actions are binary (allowed or not allowed).
  2. Potential Overhead: Guard checks and synchronization mechanisms can introduce performance costs.

Conclusion

The Balking Design Pattern provides an effective way to manage state-dependent actions and prevent invalid operations in software systems. By introducing clear guard conditions and ensuring thread safety, it enhances the reliability and maintainability of the system. Whether it's preventing multiple trips in a cab booking system or managing concurrent print jobs, the Balking Pattern offers a structured approach to avoid conflicts and maintain operational integrity. Ultimately, the choice to use the Balking Pattern depends on the specific requirements of your application and its concurrency needs.

References

  • Wikipedia - Balking Pattern
  • UCB

The above is the detailed content of Concurrency Patterns: Balking Pattern. For more information, please follow other related articles on the PHP Chinese website!

Statement
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn
How do you append elements to a Python array?How do you append elements to a Python array?Apr 30, 2025 am 12:19 AM

InPython,youappendelementstoalistusingtheappend()method.1)Useappend()forsingleelements:my_list.append(4).2)Useextend()or =formultipleelements:my_list.extend(another_list)ormy_list =[4,5,6].3)Useinsert()forspecificpositions:my_list.insert(1,5).Beaware

How do you debug shebang-related issues?How do you debug shebang-related issues?Apr 30, 2025 am 12:17 AM

The methods to debug the shebang problem include: 1. Check the shebang line to make sure it is the first line of the script and there are no prefixed spaces; 2. Verify whether the interpreter path is correct; 3. Call the interpreter directly to run the script to isolate the shebang problem; 4. Use strace or trusts to track the system calls; 5. Check the impact of environment variables on shebang.

How do you remove elements from a Python array?How do you remove elements from a Python array?Apr 30, 2025 am 12:16 AM

Pythonlistscanbemanipulatedusingseveralmethodstoremoveelements:1)Theremove()methodremovesthefirstoccurrenceofaspecifiedvalue.2)Thepop()methodremovesandreturnsanelementatagivenindex.3)Thedelstatementcanremoveanitemorslicebyindex.4)Listcomprehensionscr

What data types can be stored in a Python list?What data types can be stored in a Python list?Apr 30, 2025 am 12:07 AM

Pythonlistscanstoreanydatatype,includingintegers,strings,floats,booleans,otherlists,anddictionaries.Thisversatilityallowsformixed-typelists,whichcanbemanagedeffectivelyusingtypechecks,typehints,andspecializedlibrarieslikenumpyforperformance.Documenti

What are some common operations that can be performed on Python lists?What are some common operations that can be performed on Python lists?Apr 30, 2025 am 12:01 AM

Pythonlistssupportnumerousoperations:1)Addingelementswithappend(),extend(),andinsert().2)Removingitemsusingremove(),pop(),andclear().3)Accessingandmodifyingwithindexingandslicing.4)Searchingandsortingwithindex(),sort(),andreverse().5)Advancedoperatio

How do you create multi-dimensional arrays using NumPy?How do you create multi-dimensional arrays using NumPy?Apr 29, 2025 am 12:27 AM

Create multi-dimensional arrays with NumPy can be achieved through the following steps: 1) Use the numpy.array() function to create an array, such as np.array([[1,2,3],[4,5,6]]) to create a 2D array; 2) Use np.zeros(), np.ones(), np.random.random() and other functions to create an array filled with specific values; 3) Understand the shape and size properties of the array to ensure that the length of the sub-array is consistent and avoid errors; 4) Use the np.reshape() function to change the shape of the array; 5) Pay attention to memory usage to ensure that the code is clear and efficient.

Explain the concept of 'broadcasting' in NumPy arrays.Explain the concept of 'broadcasting' in NumPy arrays.Apr 29, 2025 am 12:23 AM

BroadcastinginNumPyisamethodtoperformoperationsonarraysofdifferentshapesbyautomaticallyaligningthem.Itsimplifiescode,enhancesreadability,andboostsperformance.Here'showitworks:1)Smallerarraysarepaddedwithonestomatchdimensions.2)Compatibledimensionsare

Explain how to choose between lists, array.array, and NumPy arrays for data storage.Explain how to choose between lists, array.array, and NumPy arrays for data storage.Apr 29, 2025 am 12:20 AM

ForPythondatastorage,chooselistsforflexibilitywithmixeddatatypes,array.arrayformemory-efficienthomogeneousnumericaldata,andNumPyarraysforadvancednumericalcomputing.Listsareversatilebutlessefficientforlargenumericaldatasets;array.arrayoffersamiddlegro

See all articles

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

WebStorm Mac version

WebStorm Mac version

Useful JavaScript development tools