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Illustration of a Python list being converted into a single iterator block by the iter() built-in, with a one-way arrow showing single-pass iteration.
Python

Python iter(): How the Built-in Function Works

Illustration of Python pika connecting to RabbitMQ with queues, exchanges, and routing keys, emphasizing message acknowledgement flow.
Python

Python Pika: Queues, Exchanges, Routing Keys, and Acknowledgements

Comparison of two Python Kafka client libraries with code snippets and a scale icon.
Python

Python Kafka: kafka-python vs Confluent Client

Illustration of Kafka partition offsets and manual commit flow in a Python consumer
Python

Python Kafka Offsets, Partitions, and Manual Commits

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Latest Python Posts

Illustration of Python contextvars propagating context across async tasks and threads
Python

Using Python contextvars for Async Context Management

Understand Python contextvars: how they propagate through asyncio tasks, how to copy or isolate contexts, and what to avoid with thread pools.

contextvarsasynciocontext managementconcurrency
Comparison of Python ThreadPoolExecutor and ProcessPoolExecutor for concurrent tasks
Python

Python ThreadPoolExecutor vs ProcessPoolExecutor: How to Choose

Learn when to use Python's ThreadPoolExecutor vs ProcessPoolExecutor. This guide explains how the GIL affects I/O-bound and CPU-bound tasks, and when each executor's overhead matters.

ThreadPoolExecutorProcessPoolExecutorConcurrencyParallelism
A visual metaphor of multiple futures waiting for completion in Python concurrent programming.
Python

Python Wait Futures: How to Wait for Concurrent Tasks

python wait futures: Learn how to wait for futures in Python using concurrent.futures.wait and asyncio.wait, including timeout handling and return_when options.

concurrent.futuresasynciothreadingmultiprocessing
Illustration of Python asyncio as_completed processing tasks in completion order.
Python

Python as_completed: Process Async Results as They Finish

Use asyncio.as_completed to process async results as they finish: see how to await completed tasks, handle exceptions, and control concurrency in Python.

asyncioconcurrencyasync programmingpython
A Python Future object with a result value and a checkmark, representing successful completion of a concurrent task.
Python

Python Future Result: Retrieving Values from Concurrent Tasks

Learn how to retrieve results from Python Future objects, handle exceptions, set timeouts, and use callbacks in concurrent.futures.

concurrent.futuresFuturethreadingexception handling
Illustration of a Python future object as a placeholder that resolves into a result or exception
Python

Understanding the Python Future Object

Learn how Python Future objects work: retrieve results, handle exceptions, cancel tasks, and understand the difference between concurrent.futures and asyncio.

concurrent.futuresasynciothreadingmultiprocessing
Illustration of Python Executor.map distributing tasks across a thread and process pool, with results returned in order.
Python

Python Executor.map: Parallel Mapping Explained

Learn how to use Python's concurrent.futures.Executor.map for parallel mapping, including thread vs. process pools, ordering guarantees, exception handling, and performance tradeoffs.

concurrent.futuresThreadPoolExecutorProcessPoolExecutorparallel processing
Illustration of Python executor submit dispatching tasks to thread and process pools and returning Future objects.
Python

Using Python's Executor.submit to Parallelize Tasks

Learn how to use Python's Executor.submit to run functions in parallel, retrieve results from Future objects, and handle exceptions cleanly.

PythonThreadPoolExecutorProcessPoolExecutorconcurrent.futures
Illustration of Python ProcessPoolExecutor distributing CPU-bound tasks across multiple processes.
Python

Python ProcessPoolExecutor: Parallel Execution for CPU-Bound Tasks

Learn how to use Python's ProcessPoolExecutor for CPU-bound parallelism, including map(), submit(), error handling, and performance tradeoffs.

concurrencyparallelismmultiprocessingconcurrent.futures
Illustration of Python ThreadPoolExecutor managing multiple worker threads executing tasks concurrently.
Python

Python ThreadPoolExecutor: Running Tasks Concurrently

Learn how to use Python's ThreadPoolExecutor to run I/O-bound tasks concurrently, collect results with submit() and map(), handle exceptions, and choose the right worker pool.

ThreadPoolExecutorPython concurrencyThreadingParallel execution