Python Worker Dispatcher

A flexible task dispatcher for Python with multiple threading or processing control

Features

• Tasks Dispatching to managed workers

• Elegant Interface for setup and use

• Various modes to choose from

---

OUTLINE

• Demonstration
• Introduction
• Installation
• Usage
  • Options
    • task.function
    • task.callback.on_done
    • task.callback.on_all_done
  • Other Methods
    • get_results()
    • get_logs()
    • get_result_info()
    • get_tps()
    • print()
  • Scenarios
    • Stress Test
    • Background Execution
• Appendix
  • Mode Explanation

---

DEMONSTRATION

Just write your own callback functions using the library, then run it and collect the result details:

$ python3 main.py

Worker Dispatcher Configutation:
- Local CPU core: 10
- Tasks Count: 100
- Runtime: Unlimited
- Dispatch Mode: Fixed Workers (Default)
- Workers Info:
  └ Worker Type: Processing
  └ Number of Workers : 10
  └ Max Worker: 10

--- Start to dispatch workers at 2024-06-14T17:46:30.996685+08:00 ---

...(User-defined output)...

--- End of worker dispatch at 2024-06-14T17:46:41.420888+08:00---

Spend Time: 10.424203 sec
Completed Tasks Count: 100
Uncompleted Tasks Count: 0
Undispatched Tasks Count: 0

Use 20 theads concurrently to dispatch tasks for HTTP reqeusts

import worker_dispatcher
import requests

def each_task(id: int, config, task, metadata):
    response = requests.get(config['my_endpoint'] + task)
    return response

responses = worker_dispatcher.start({
    'task': {
        'list': ['ORD_AH001', 'ORD_KL502', '...' , 'ORD_GR393'],
        'function': each_task,
        'config': {
            'my_endpoint': 'https://your.name/order-handler/'
        },
    },
    'worker': {
        'number': 20,
    }
})

Utilizes all CPU cores on the machine to compute tasks.

import worker_dispatcher

def each_task(id: int, config, task, metadata):
    result = sum(id * i for i in range(10**9))
    return result

if __name__ == '__main__':
    results = worker_dispatcher.start({
        'task': {
            'list': 10,
            'function': each_task,
        },
        'worker': {   
            'use_processing': True
        }
    })

---

INTRODUCTION

This library helps to efficiently consume tasks by using multiple threading or processing and returns all results jointly.

---

INSTALLATION

To install the current release:

$ pip install worker-dispatcher

---

USAGE

By calling the start() method with the configuration parameters, the package will begin dispatching tasks while managing threading or processing based on the provided settings. Once the tasks are completed, the package will return all the results.

An example configuration setting with all options is as follows:

import worker_dispatcher 

results = worker_dispatcher.start({
    'debug': False,
    'task': {
        'list': [],                         # Support list and integer. Integer represent the number of tasks to be generated.
        'function': task_function_sample,   # The main function to execute per task
        'config': {},
        'callback': {
            'on_done': False,               # Called with each task's result after each task completes; the return value will overwrite and define the task result
            'on_all_done': False,           # Called with each task's result after all tasks complete; the return value will overwrite and define the task result
        }
    },
    'worker': {
        'number': 8,
        'frequency_mode': {             # Changing from assigning tasks to a fixed number of workers once, to assigning tasks and workers frequently.
            'enabled': False, 
            'interval': 1,              # The second(s) of interval
            'accumulated_workers': 0,   # Accumulate the number of workers for each interval for next dispatch.
            'max_workers': None,        # limit the maximum number of workers to prevent system exhaustion.
        },
        'use_processing': False,        # To break GIL, workers will be based on processing pool.
        'parallel_processing': {        # To break GIL and require a number of workers greater than the number of CPU cores.
            'enabled': False,           # `worker.use_processing` setting will be ignored when enabled. The actual number of workers will be adjusted to a multiple of the CPU core count.
            'use_queue': False,         # Enable a task queue to specify the number of workers without adjustment, though the maximum may be limited by your device.
        },   
    },
    'runtime': None,                    # Dispatcher max runtime in seconds
    'verbose': True
})

Options

Option	Type	Deafult	Description
debug	bool	False	Debug mode
task.list	multitype	list	The tasks for dispatching to each worker. * - List: Each value will be passed as a parameter to your callback function. - Integer: The number of tasks to be generated.
task.function	callable	(sample)	The main function to execute per task
task.config	multitype	list	The custom variable to be passed to the callback function
task.callback.on_done	callable	Null	The callback function is called with each task's result after each task completes; the return value will overwrite and define the task result
task.callback.on_all_done	callable	Null	The callback function is called with each task's result after all tasks complete; the return value will overwrite and define the task result
worker.number	int	(auto)	The number of workers to fork. (The default value is the number of local CPU cores)
worker.frequency_mode.enabled	bool	False	Changing from assigning tasks to a fixed number of workers once, to assigning tasks and workers frequently.
worker.frequency_mode.interval	float	1	The second(s) of interval.
worker.frequency_mode.accumulated_workers	int	0	Accumulate the number of workers for each interval for next dispatch.
worker.frequency_mode.max_workers	int	None	limit the maximum number of workers to prevent system exhaustion.
worker.use_processing	boolean	False	To break GIL, workers will be based on processing pool.
worker.parallel_processing.enabled	bool	False	worker.use_processing setting will be ignored when enabled. The actual number of workers will be adjusted to a multiple of the CPU core count.
worker.parallel_processing.use_queue	bool	False	Enable the use of a task queue instead of task dispatch, which allows specifying the number of workers but may be limited by your device.
runtime	float	None	Dispatcher max runtime in seconds.
verbose	bool	True	Enables or disables verbose mode for detailed output.

task.function

The main function to execute per task

task_function (id: int, config, task, metadata: dict) -> Any

Argument	Type	Deafult	Description
id	int	(auto)	The sequence number generated by each task starting from 1
config	multitype	{}	The custom variable to be passed to the callback function
task	multitype	(custom)	Each value from the task.list
metadata	dict	{}	A user-defined dictionary for custom metadata per task, saved in its log.

The return value can be False to indicate task failure in TPS logs.
Alternatively, it can be a requests.Response, indicating failure if the status code is not 200.

task.callback.on_done

The callback function is called with each task's result after each task completes; the return value will overwrite and define the task result

callback_on_done_function (id: int, config, result, log: dict) -> Any

Argument	Type	Deafult	Description
id	int	(auto)	The sequence number generated by each task starting from 1
config	multitype	{}	The custom variable to be passed to the callback function
result	multitype	(custom)	Each value returned back from task.callback
log	dict	(auto)	Reference: get_logs()

task.callback.on_all_done

The callback function is called with each task's result after all tasks complete; the return value will overwrite and define the task result

callback_on_all_done_function (id: int, config, result, log: dict) -> Any

Argument	Type	Deafult	Description
id	int	(auto)	The sequence number generated by each task starting from 1
config	multitype	{}	The custom variable to be passed to the callback function
result	multitype	(custom)	Each value returned back from task.callback
log	dict	(auto)	Reference: get_logs()

Other Methods

• get_results()

  Get all results in list type after completing start()

• get_logs()

  Get all logs in list type after completing start()

  Each log is of type dict, containing the results of every task processed by the worker:

  • task_id (Auto-increased number)
  • started_at (Unixtime)
  • ended_at (Unixtime)
  • duration (Seconds)
  • result (Boolean or user-defined)
  • metadata (can be set within each task function)

• get_result_info()

  Get a dict with the whole spending time and started/ended timestamps after completing start()

• get_tps()

  Get TPS report in dict type after completing start() or by passing a list data.

  def get_tps(logs: dict=None, display_intervals: bool=False, interval: float=0, reverse_interval: bool=False, use_processing: bool=False, verbose: bool=False, debug: bool=False,) -> dict:

  The log dict matches the format of the get_logs() and refers to it by default. Each task within a log will be validated for success according to the callback_function() result rule.

  Enabling use_processing can speed up the peak-finding process, particularly for large tasks with long durations.

  Example output with debug mode and use_processing enabled:

  --- Start calculating the TPS data ---
    - Average TPS: 0.83, Total Duration: 1202.3867809772491s, Success Count: 999
  --- Start to compile intervals with an interval of 13 seconds ---
    - Interval - Start Time: 1734937209.851285, End Time: 1734937222.851285, TPS: 51.23
      * Peak detected above the current TPS threshold - Interval TPS: 51.23, Main TPS: 0.83
    - Interval - Start Time: 1734937222.851285, End Time: 1734937235.851285, TPS: 18.0
    - Interval - Start Time: 1734937235.851285, End Time: 1734937248.851285, TPS: 0.0
    ...
    - Interval - Start Time: 1734938405.851285, End Time: 1734938412.238066, TPS: 0.0
  --- Start to find the peak TPS ---
    - Detecting from Start Time: 1734937210, Count: 67, Current TPS Threshold: 51.23, Worker: 104
      * Peak detected above the current TPS threshold - TPS: 53.5, Started at: 1734937210, Ended at: 1734937220
      * Peak detected above the current TPS threshold - TPS: 53.857142857142854, Started at: 1734937210, Ended at: 1734937224
      * Peak detected above the current TPS threshold - TPS: 55.13333333333333, Started at: 1734937210, Ended at: 1734937225
      * Peak detected above the current TPS threshold - TPS: 55.166666666666664, Started at: 1734937210, Ended at: 1734937228
    - Detecting from Start Time: 1734937224, Count: 73, Current TPS Threshold: 55.166666666666664, Worker: 105
    ...
    - Detecting from Start Time: 1734937212, Count: 82, Current TPS Threshold: 55.166666666666664, Worker: 102
      * Peak detected above the current TPS threshold - TPS: 55.53846153846154, Started at: 1734937212, Ended at: 1734937225

• print(*objects, sep=' ', end='\n', file=None, flush=True)

  A custom print function that sets flush=True by default to ensure immediate output to stdout, useful when redirecting output to a file.

Scenarios

Stress Test

Perform a stress test scenario with 10 requests per second.

import worker_dispatcher

def each_task(id, config, task, metadata):
    response = None
    try:
        response = requests.get(config['my_endpoint'], timeout=(5, 10))
    except requests.exceptions.RequestException as e:
        print("An error occurred:", e)
    return response

responses = worker_dispatcher.start({
    'task': {
        'list': 600,
        'function': each_task,
        'config': {
            'my_endpoint': 'https://your.name/api'
        },
    },
    # Light Load with 10 RPS
    'worker': {
        'number': 10,
        'frequency_mode': {
            'enabled': True, 
            'interval': 1,
        },
    },
})

print(worker_dispatcher.get_logs())
print(worker_dispatcher.get_tps())

The stress tool, based on this dispatcher, along with statistical TPS reports, is as follows: yidas / python-stress-tool

Background Execution

You can run the script as a background process by adding & at the end of the command, and redirect the output to a file:

$ python3 main.py > log.txt 2>&1 &

2>&1 means redirecting stderr (2) to the same location as stdout (1), so both standard output and error messages go to the same file.

For immediate stdout output (e.g., when logging to a file), use worker_dispatcher.print(), which enables flushing by default. Alternatively, use the built-in print(..., flush=True).

import worker_dispatcher

def each_task(id, config, task, metadata):
    # Print immediately to file (stdout is flushed)
    if id % 10 == 0:
        worker_dispatcher.print(f"TaskId: {id}")
    return True

responses = worker_dispatcher.start({
    'task': {
        'list': 600,
        'function': each_task,
        ...
})

---

Appendix

Mode Explanation

Here are the differences between the various modes, such as enabling use_processing or parallel_processing

The suitable application scenarios are as follows:

• default:
  Suitable for asynchronous I/O tasks. Using too many workers (threads) may lead to significant context switching on a CPU core, which can degrade performance.
• use_processing:
  Intended for CPU-intensive tasks. Using too many workers (processes) may slow down initialization and increase memory usage accordingly.
• parallel_processing:
  Optimized for tasks that fully utilize the CPU with many workers in frequency_mode, maintaining both performance and resources.