PacktPub - Concurrent and Parallel Programming in Python

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PacktPub - Concurrent and Parallel Programming in Python Chapter 1 Threading
  • 001. Threading, Multiprocessing, Async Introduction.mp4 (59.3 MB)
  • 002. Threading in Python.mp4 (65.6 MB)
  • 003. Creating a Threading Class.mp4 (53.7 MB)
  • 004. Creating a Wikipedia Reader.mp4 (84.1 MB)
  • 005. Creating a Yahoo Finance Reader.mp4 (86.6 MB)
  • 006. Queues and Master Scheduler.mp4 (67.6 MB)
  • 007. Creating a Postgres Worker.mp4 (94.5 MB)
  • 008. Integrating the Postgres Worker.mp4 (111.3 MB)
  • 009. Yaml File Introduction.mp4 (88.8 MB)
  • 010. Creating a Yaml Reader.mp4 (161.6 MB)
  • 011. Improving Our Wiki Worker.mp4 (153.2 MB)
  • 012. Improving All Workers and Adding Monitoring.mp4 (146.3 MB)
  • 013. Final Program Cleanup.mp4 (34.1 MB)
  • 014. Locking.mp4 (55.7 MB)
Chapter 2 Multiprocessing
  • 001. Multiprocessing Introduction.mp4 (33.6 MB)
  • 002. Multiprocessing Queues.mp4 (36.6 MB)
  • 003. Multiprocessing Pool.mp4 (44.1 MB)
  • 004. Multiprocessing Pool Map Multiple Arguments.mp4 (18.1 MB)
  • 005. Multiprocessing Multiple Varying Arguments.mp4 (16.4 MB)
  • 006. Multiprocessing Checking Elements in List in Certain Ranges.mp4 (29.4 MB)
Chapter 3 Asynchronous
  • 001. Introduction to Writing Asynchronous Programs.mp4 (44.7 MB)
  • 002. Asynchronous Tasks.mp4 (26.1 MB)
  • 003. Async Gather Method.mp4 (30.7 MB)
  • 004. Using Async Timeouts.mp4 (13.1 MB)
  • 005. Creating Asynchronous For Loops.mp4 (12.8 MB)
  • 006. Using Asynchronous Libraries.mp4 (48.1 MB)
  • 007. The Async Wait Statement.mp4 (41.2 MB)
  • 008. Combining Async and Multiprocessing.mp4 (51.4 MB)

Description

PacktPub – Concurrent and Parallel Programming in Python

English | Tutorial | Size: 1.67 GB





In a big data project, a plethora of information is retrieved, big numbers are crunched on our machine, or both. If the coding is sequential or synchronous, our application will struggle to execute. Two mechanisms to alleviate such bottlenecks are concurrency and parallelism. In Python, concurrency is represented by threading, whereas multiprocessing achieves parallelism. This course begins with an introduction about potential programming speed bottlenecks and solving them. You will delve into Python concepts and create a Wikipedia Reader, Yahoo Finance Reader, Queues, and Master Scheduler. You will build a multi-threaded program to grab data from the Internet and parse and save them into a local database. Implement multiprocessing in Python, which lets us use multiple CPUs in our code. Learn about threading, multiprocessing, asynchronous wait, locking, multiprocessing queues, Pool Map Multiple Arguments, writing asynchronous programs, and combining async and multiprocessing. Upon completion, we can spread our workload over all cores available on the used machine. We will combine both elements, multiprocessing with asynchronous programming, to maximize benefit and CPU resource usage and minimize the time spent waiting for IO responses.



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PacktPub - Concurrent and Parallel Programming in Python


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