Core Python Functionalities

June 15, 2025

Summary of core Python libraries and functionalities for software design.

AspectThreadingMultiprocessingSubprocessAsyncio
Execution ModelPreemptive multitaskingTrue parallelismExternal process executionCooperative multitasking
Memory SharingShared memory spaceSeparate memory spacesCompletely isolatedShared memory space
GIL ImpactLimited by GILNot affected by GILNot affected by GILNot affected by GIL
CPU UtilizationSingle core (due to GIL)Multiple coresDepends on external programSingle core
Startup OverheadLowHighModerate to HighVery Low
Memory OverheadLowHighVariableVery Low
CommunicationDirect variable sharingIPC (pipes, queues, shared memory)stdin/stdout/stderrDirect variable sharing
SynchronizationLocks, semaphores, conditionsLocks, queues, shared memoryProcess control signalsNo locks needed
Error IsolationShared - one thread crash can affect othersIsolated - process crashes don't affect othersCompletely isolatedShared - exceptions can propagate
ScalabilityLimited (typically 10s-100s threads)Limited by CPU coresLimited by system resourcesHigh (1000s-10000s tasks)
Best ForI/O-bound tasksCPU-intensive tasksSystem integration, external toolsHigh-concurrency I/O
Debugging ComplexityModerate (race conditions)High (inter-process debugging)Low to ModerateLow to Moderate
Use CasesFile I/O, network requests, UI responsivenessMathematical computations, image processingSystem commands, shell scripts, external APIsWeb servers, real-time systems, concurrent I/O

Performance Considerations

Threading

  • Pros: Low overhead, good for I/O-bound tasks, easy data sharing
  • Cons: GIL limits CPU-bound performance, potential race conditions

Multiprocessing

  • Pros: True parallelism, fault isolation, bypasses GIL
  • Cons: High memory usage, expensive process creation, complex IPC

Subprocess

  • Pros: Complete isolation, language flexibility, system integration
  • Cons: High overhead, complex data exchange, platform dependencies

Asyncio

  • Pros: Excellent for high-concurrency I/O, low overhead, no race conditions
  • Cons: Single-threaded, requires async-compatible libraries, learning curve

Choosing the Right Approach

  1. For I/O-bound tasks with moderate concurrency: Use Threading
  2. For CPU-intensive parallel computation: Use Multiprocessing
  3. For system integration and external tools: Use Subprocess
  4. For high-concurrency I/O operations: Use Asyncio
  5. For mixed workloads: Consider combining approaches (e.g., asyncio with thread pools)