A basic review of hashing by Dzejla Medjedovic, a co-author of Algorithms and Data Structures for Massive Datasets. This video is an excerpt from a live coding session by Dzejla Medjedovic. Watch the full video at: http://mng.bz/v1Am 📚📚📚
Algorithms and Data Structures for Massive Datasets | http://mng.bz/82qZ
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📚📚📚 "Algorithms and Data Structures for Massive Datasets" teaches you to take advantage of data processing and analytics techniques specifically designed for large distributed datasets. And you'll be amazed how easy it is to learn such a challenging topic from this friendly guide! Complex concepts are illustrated with interesting, entertaining graphics and fascinating industry stories that show how these techniques have succeeded in the real world. By the time you're done, you'll be able to identify the perfect algorithm to deliver faster and more reliable results for any data intensive system. https://www.youtube.com/watch?v=O9N8Hy3unfw

Node.js contributors are always working towards adding useful and awesome features to improve the runtime. In this talk, you will learn what to expect in future Node.js releases, the most exciting features, and tips on how to use those features. PUBLICATION PERMISSIONS:
NearForm Organizers provided Coding Tech with the permission to republish NearForm tech talks. CREDITS:
NearForm YouTube channel: https://www.youtube.com/@NearForm https://www.youtube.com/watch?v=qdjn7Ryaq5Y

You have a performance problem, and you don't know what to do. All you know is that one of your endpoints or applications is too slow; and perhaps it only affects a certain user or customer. How do you figure out why it's slow, and what can you do to catch performance problems before they hurt users in production? We'll go through a wide range of strategies for detecting and diagnosing performance problems in typical production workloads. We'll cover both web-based domains as well as backend domains and other analytical applications involving number-crunching and big-data applications. We'll step through the following high-level strategies: – Tracing: through instrumentation of your code, you will get detailed traces of where the time is spent in generating your web server responses.
– Profiling: we'll look at profiling strategies using both the Python built-in cProfile tool, as well as awesome 3rd party libraries like pyspy, including how to use these with pytest
– Isolation: how to figure out if performance is affected by CPU, or memory, disk, or network IO limitations.
– Reasoning: we'll look at common scenarios that result in performance regressions such as the needless execution of sub-queries in rendering web views, or algorithmic analysis and ""big-O"" notation, or concurrency problems resulting from exhaustion of threads in a pool and asyncio concurrency limitations resulting from overloaded subscription.
– Prophylaxis: we'll look at how to include benchmarks within your CI pipeline, including with pytest and other technologies to catch performance regressions ahead of time. PUBLICATION PERMISSIONS:
Original video was published with the Creative Commons Attribution license (reuse allowed). Link: https://www.youtube.com/watch?v=3BnGyoyhSmM https://www.youtube.com/watch?v=RRCGywYTsxI