


FastAPI and Parallel Execution
Asynchronous Programming in FastAPI
By default, FastAPI employs asynchronous programming patterns, allowing it to handle multiple requests concurrently. Specifically, it uses coroutines and the event loop to execute requests in an efficient manner.
Def vs. Async Def Functions
Functions defined as def (synchronous) are processed in a separate thread within an external threadpool. Functions defined as async def (asynchronous) are directly executed in the event loop.
Running Blocking Code
Problem: When a blocking operation is run within an async def endpoint, it can block the event loop and serialize request processing.
Solution:
- Define Endpoints with def: If the endpoint does not require asynchronous operations, declare it with a normal def to avoid blocking.
- Use run_in_threadpool(): For blocking operations within async def endpoints, the FastAPI run_in_threadpool() function allows you to run the task in a separate thread within the threadpool to prevent event loop blockage.
- Use asyncio.loop.run_in_executor(): This function provides an alternative to run_in_threadpool() for executing blocking tasks asynchronously.
- Use a separate process (ProcessPoolExecutor): For CPU-intensive computations, it's recommended to run the task in a separate process to maximize parallelization.
Avoiding Thread Pool Exhaustion
- Use the httpx library along with asyncio.gather() for executing multiple asynchronous HTTP requests in parallel.
- Employ multiple FastAPI workers (uvicorn --workers) to distribute requests across multiple processes, each with its own threadpool.
- Consider using external job queuing systems like Celery for heavy background computations.
Additional Notes:
- Browser Caching: Open Incognito tabs or use a different browser session to avoid browser caching effects on API calls.
- Asynchronous I/O: Asynchronous HTTP clients like httpx and aiohttp provide better performance than synchronous clients like requests.
The above is the detailed content of How Can FastAPI Handle Parallel Execution and Avoid Blocking Operations?. For more information, please follow other related articles on the PHP Chinese website!

There are many methods to connect two lists in Python: 1. Use operators, which are simple but inefficient in large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use the = operator, which is both efficient and readable; 4. Use itertools.chain function, which is memory efficient but requires additional import; 5. Use list parsing, which is elegant but may be too complex. The selection method should be based on the code context and requirements.

There are many ways to merge Python lists: 1. Use operators, which are simple but not memory efficient for large lists; 2. Use extend method, which is efficient but will modify the original list; 3. Use itertools.chain, which is suitable for large data sets; 4. Use * operator, merge small to medium-sized lists in one line of code; 5. Use numpy.concatenate, which is suitable for large data sets and scenarios with high performance requirements; 6. Use append method, which is suitable for small lists but is inefficient. When selecting a method, you need to consider the list size and application scenarios.

Compiledlanguagesofferspeedandsecurity,whileinterpretedlanguagesprovideeaseofuseandportability.1)CompiledlanguageslikeC arefasterandsecurebuthavelongerdevelopmentcyclesandplatformdependency.2)InterpretedlanguageslikePythonareeasiertouseandmoreportab

In Python, a for loop is used to traverse iterable objects, and a while loop is used to perform operations repeatedly when the condition is satisfied. 1) For loop example: traverse the list and print the elements. 2) While loop example: guess the number game until you guess it right. Mastering cycle principles and optimization techniques can improve code efficiency and reliability.

To concatenate a list into a string, using the join() method in Python is the best choice. 1) Use the join() method to concatenate the list elements into a string, such as ''.join(my_list). 2) For a list containing numbers, convert map(str, numbers) into a string before concatenating. 3) You can use generator expressions for complex formatting, such as ','.join(f'({fruit})'forfruitinfruits). 4) When processing mixed data types, use map(str, mixed_list) to ensure that all elements can be converted into strings. 5) For large lists, use ''.join(large_li

Pythonusesahybridapproach,combiningcompilationtobytecodeandinterpretation.1)Codeiscompiledtoplatform-independentbytecode.2)BytecodeisinterpretedbythePythonVirtualMachine,enhancingefficiencyandportability.

ThekeydifferencesbetweenPython's"for"and"while"loopsare:1)"For"loopsareidealforiteratingoversequencesorknowniterations,while2)"while"loopsarebetterforcontinuinguntilaconditionismetwithoutpredefinediterations.Un

In Python, you can connect lists and manage duplicate elements through a variety of methods: 1) Use operators or extend() to retain all duplicate elements; 2) Convert to sets and then return to lists to remove all duplicate elements, but the original order will be lost; 3) Use loops or list comprehensions to combine sets to remove duplicate elements and maintain the original order.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

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

Hot Article

Hot Tools

MantisBT
Mantis is an easy-to-deploy web-based defect tracking tool designed to aid in product defect tracking. It requires PHP, MySQL and a web server. Check out our demo and hosting services.

Atom editor mac version download
The most popular open source editor

MinGW - Minimalist GNU for Windows
This project is in the process of being migrated to osdn.net/projects/mingw, you can continue to follow us there. MinGW: A native Windows port of the GNU Compiler Collection (GCC), freely distributable import libraries and header files for building native Windows applications; includes extensions to the MSVC runtime to support C99 functionality. All MinGW software can run on 64-bit Windows platforms.

Dreamweaver Mac version
Visual web development tools

Zend Studio 13.0.1
Powerful PHP integrated development environment
