# processing Published articles for processing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How Request processing has changed over the years DevFeed: [How Request processing has changed over the years]() Original publisher: [Read original article]() Published: 2025-02-03T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [processing](), [Computer science](), [Concurrency](), [scheduling](), [Kernel](), [Processes](), [Ruby](), [Python]() Tags: [concurrency](), [cpu](), [kernel](), [process](), [processing](), [python](), [request](), [ruby](), [scheduling]() ### AI overview This article explains how web request processing works, distinguishing blocking and non-blocking tasks and introducing concepts such as CPU control, kernel behavior, and cooperative versus preemptive scheduling. It begins comparing request-processing architectures in the context of Ruby, Python, NodeJS, and Rails services. ### Source excerpt . [How Request Processing Has Changed Over the Years](how-request-processing-has-changed-over-the-years-cover... ## Temporal use case roundup: Generative AI DevFeed: [Temporal use case roundup: Generative AI]() Original publisher: [Read original article]() Author: Clair Byrd Published: 2024-11-19T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Generative AI](), [AI Development](), [agentic workflows](), [Orchestration](), [Programming](), [Conversational AI](), [Synthetic Data Generation](), [data-processing]() Tags: [agentic](), [agentic-workflows](), [ai](), [applications](), [conversational-ai](), [datasets](), [generative-ai](), [processing](), [temporal-voices](), [video](), [workflows]() ### AI overview This use-case roundup describes how Temporal orchestrates reliable, long-running workflows for generative AI applications. It covers video processing and translation, conversational AI and call transcription, synthetic data generation, predictive analytics, and cryptocurrency data enrichment. ### Source excerpt See how Temporal boosts generative AI applications across industries, from video processing to conversational AI, by orchestrating complex, reliable workflows. ## MVCC and serializability in HyPer DevFeed: [MVCC and serializability in HyPer]() Original publisher: [Read original article]() Published: 2024-11-06T00:00:00Z Content type: article Language: en Sources: [Gaurav Sarma's Blog]() Topics: [Database](), [Transactions](), [Concurrency](), [locking](), [Deadlock](), [processing](), [systems]() Tags: [concurrency](), [concurrency-control](), [database](), [deadlock](), [locking](), [mvcc](), [processing](), [transactions]() ### AI overview This article explains Multi-Version Concurrency Control (MVCC), contrasts it with Two-Phase Locking, and introduces MVCC patterns and techniques used in HyPer. It discusses locking phases, deadlocks, cascading rollbacks, and concurrency limitations. ### Source excerpt . [MVCC and Serializability in HyPer](mvcc-and-serializability-in-hyper-cover... ## Temporal supercharges video processing at The Washington Post DevFeed: [Temporal supercharges video processing at The Washington Post]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2024-11-04T07:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Orchestration](), [Content Management System](), [airflow](), [Web]() Tags: [cms](), [community](), [distributed-systems](), [efficiency](), [media](), [open-source](), [orchestration](), [processing](), [programming](), [video](), [workflows]() ### AI overview The Washington Post's Arc XP division used Temporal to replace a difficult-to-maintain internal orchestration system for video and audio workflows. The article reports a 600% improvement in runtime efficiency and describes support for complex, long-running processes across multiple programming languages. ### Source excerpt See how The Washington Post used Temporal to supercharge video processing, achieving a 600% improvement in runtime efficiency and streamlining media workflows. ## Part 3: Processing Payments - Ethereum Payment DevFeed: [Part 3: Processing Payments - Ethereum Payment]() Original publisher: [Read original article]() Author: Emmanuel Goossaert Published: 2022-11-29T19:18:12Z Content type: tutorial Language: en Sources: [Code Capsule]() Topics: [Ethereum](), [Blockchain](), [web applications](), [API]() Tags: [algorithms-and-programming](), [algorithms-and-programming-blockchain-crypto-ethereum-mongodb-nodejs-payment-system-typescri](), [api](), [blockchain](), [crypto](), [ethereum](), [mongodb](), [nodejs](), [payment](), [processing](), [system](), [typescript](), [web](), [web3]() ### AI overview A tutorial walkthrough of processing and validating payments from a web application on the Ethereum blockchain. It covers server-side prevalidation, submitting purchase transactions, tracking transaction status through polling, and handling successful or failed payments. ### Source excerpt A walkthrough of the steps needed to process and validate payments from a web application on the Ethereum blockchain. The post Part 3: Processing Payments - Ethereum Payment first appeared on Code Capsule. ## Media processing Workflows DevFeed: [Media processing Workflows]() Original publisher: [Read original article]() Author: Nir Padmanabhan Published: 2021-07-19T07:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [distributed-systems](), [Orchestration](), [Internet of things](), [data](), [API](), [Microservice](), [Remote Procedure Call (RPC)]() Tags: [api](), [distributed](), [fault-tolerance](), [iot](), [processing](), [reliability](), [rpc](), [temporal-concepts](), [workflows]() ### AI overview This tutorial presents a reliable, fault-tolerant workflow for processing video footage from IoT devices. It describes triggers, vendor API requests, data downloads and transformation, downstream processing, and failure conditions such as connectivity loss, API downtime, worker crashes, and hardware failures, using Temporal as the workflow technology. ### Source excerpt Dive into a media processing problem that requires high reliability and fault tolerance, and see how using Temporal can help build a robust solution. ## Why You Cannot Simply Print a Safe mRNA Vaccine DevFeed: [Why You Cannot Simply Print a Safe mRNA Vaccine]() Original publisher: [Read original article]() Published: 2021-03-30T13:46:36Z Content type: opinion Language: en Sources: [Bert Hubert's writings]() Topics: [processing]() Tags: [automated](), [biology](), [code](), [covid-19](), [dna](), [future](), [processing](), [quality-control](), [technology]() ### AI overview This post explains why knowing an mRNA vaccine's sequence does not make it possible to print a safe vaccine. Production requires complex biochemical processing, lipid nanoparticle packaging, extensive quality control, and sterile manufacturing. ### Source excerpt Brief post, in response to the many many many technical people that propose to print their own mRNA vaccines "now that the source is out there". I've spent a lot of time individually explaining why this is unlikely to happen any time soon, and I thought it a good idea to explain it here as well. Mountains of details on how the vaccines are actually produced can be found on this awesome page by Jonas Neubert and Cornelia Scheitz. ## PyConFr 2018 DevFeed: [PyConFr 2018]() Original publisher: [Read original article]() Published: 2018-10-06T22:00:00Z Content type: article Language: en Sources: [Dimitri Fontaine]() Topics: [SQL](), [PostgreSQL](), [Back end](), [Python](), [integrity](), [data-processing]() Tags: [backend](), [integrity](), [postgresql](), [processing](), [python](), [sql]() ### AI overview A PyConFr 2018 talk about using advanced SQL and PostgreSQL for application backends, including deciding which backend logic belongs in the database and which is easier to write as a SQL query. ### Source excerpt Python is often used to maintain application backends. When the backend should implement user oriented workflows, it may rely on a RDBMS component to take care of the system's integrity. PostgreSQL is the world's most advanced open source relational database, and is very good at taking care of your system's integrity. PostgreSQL also comes with a ton of data processing power, and in many cases a simple enough SQL statement may replace hundreds of lines of code written in Python. In this talk, we learn advanced SQL techniques and how to reason about which part of the backend code should be done in the database, and which parf of the backend code is so easier to write as a SQL query. ## Candles, Programming and Archetypes DevFeed: [Candles, Programming and Archetypes]() Original publisher: [Read original article]() Published: 2018-05-22T00:00:00Z Content type: opinion Language: en Sources: [Robin Ward]() Topics: [Programming](), [TypeScript](), [JavaScript](), [C++](), [browser]() Tags: [browser](), [c-plus-plus](), [graphics](), [javascript](), [memory](), [processing](), [programming](), [typescript]() ### AI overview The author reflects on how specialized modern computing knowledge can obscure foundational concepts. They trace TypeScript backward through JavaScript, web browsers, C++, machine language, operating systems, streams, and CPUs, while acknowledging gaps and inaccuracies, and connect this reflection to the simplicity of older computers. ### Source excerpt Once in a while, I daydream about being thrown back in time. I'd have no Wikipedia, no books, or any access to information except what's already in my head. If I were thrown into Victorian London, what could I do? What could I teach them? Well, the first thing I would do is tell them to wash their hands. With soap. Frequently. That would probably be the most significant contribution I could make. ## PostgreSQL Data Types: Text Processing DevFeed: [PostgreSQL Data Types: Text Processing]() Original publisher: [Read original article]() Author: Dimitri Fontaine PostgreSQL Major Contributor; Author Published: 2018-04-11T21:15:42Z Content type: tutorial Language: en Sources: [Dimitri Fontaine]() Topics: [PostgreSQL](), [Regular expression](), [pattern matching](), [CSV]() Tags: [csv](), [dataset](), [functions](), [pattern-matching](), [postgresql](), [processing](), [regexp](), [regular](), [text]() ### AI overview This tutorial introduces PostgreSQL text-processing functions and operators, including string functions, aggregates, regular expressions, and regexp_split_to_table(). It also discusses text and varchar data types and demonstrates processing comma-separated and hierarchical values in a CSV dataset. ### Source excerpt Continuing our series of PostgreSQL Data Types today we're going to introduce some of the PostgreSQL text processing functions. There's a very rich set of PostgreSQL functions to process text -- you can find them all in the string functions and operators documentation chapter -- with functions such as overlay(), substring(), position() or trim(). Or aggregates such as string_agg(). There are also regular expression functions, including the very powerful regexp_split_to_table(). In this article we see practical example putting them in practice. ## Twig Processing Order & Scope DevFeed: [Twig Processing Order & Scope]() Original publisher: [Read original article]() Author: andrew@nystudio107.com (Andrew Welch) Published: 2016-11-28T15:31:00Z Content type: tutorial Language: en Sources: [nystudio107 | Articles on modern web development.]() Topics: [Template](), [Front end](), [Code](), [Content Management System]() Tags: [code](), [frontend](), [important](), [insights](), [language](), [little-known](), [order](), [processing](), [scope](), [templates](), [templating](), [twig](), [understand]() ### AI overview This tutorial explains Twig's processing order and variable scope. It describes how extended, included, and block templates are processed, and why variables defined in one template context are not available in others. ### Source excerpt The Twig templating language has a little-known processing order & scope that is important to understand ## Moving past averages in SQL (Postgres) - Percentiles DevFeed: [Moving past averages in SQL (Postgres) - Percentiles]() Original publisher: [Read original article]() Author: Map Published: 2015-06-07T20:55:56Z Content type: tutorial Language: en Sources: [Craig Kerstiens]() Topics: [SQL](), [data]() Tags: [postgres](), [processing](), [rank](), [sql](), [window](), [window-functions]() ### AI overview This tutorial explains why medians and percentiles can provide more useful insight than averages when analyzing metrics in Postgres. It demonstrates using window functions, ntile, and ordered set functions to calculate percentile rankings directly in SQL. ### Source excerpt Often when you're tracking a metric for the first time you take a look at your average. For example what is your ARPU - Average Revenue Per User. In theory this tells you if you can acquire new user how much you'll make off that user. Or maybe what's your average life time value of a customer. Yet, many that are more familiar looking and extracting meaning from data median or a few different looks at percentiles can be much more meaningful. And while you can very easily get the AVG in Postgres, with a small amount more effort you can report on percentiles as well. Window functions have been around for some time in Postgres. They allow you to order your result set over a certain group. The most basic example is if you want to order by date, but know which one falls at place 10 in order you can use a window function and project out the rank(). Beyond outputting the rank yourself and doing extra manipulation Postgres has some great utilities to make the most common uses even easier. Being able to compute things such as the perc 95 directly on the data, or lay out for every record in the result where it falls within a percentile is hugely useful. Let's take a look: Assuming you have a table called purchases, which has a total in it we could try: SELECT id, total, ntile(100) OVER (ORDER BY total) AS perc_rank FROM purchases This would give us something like: id | total | perc_rank ----------|---------|----------- 264 | 12034 | 100 643 | 11830 | 100 ... ... 304 | 751 | 95 What this would tell us is we have less than 5% of our purchases that have a total over 751. From here you can start to dig in and extract all sorts of different meanings, and by doing directly in SQL you're closer to the data and have one less processing step. Percentiles get even more fun with the ordered set functions that came out in Postgres 9.4. They even allow you to project out hypothetical values in certain cases. For now I'd encourage adding ntile to your toolbox anytime you're analyzing averag ## Seam Carving for Content-Aware Image Scaling DevFeed: [Seam Carving for Content-Aware Image Scaling]() Original publisher: [Read original article]() Published: 2013-03-04T22:19:19Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [scaling](), [Mathematics](), [pixel](), [implementation](), [Web Development]() Tags: [calculus](), [graphics](), [image](), [implementation](), [javascript](), [mathematics](), [photoshop](), [processing](), [programming](), [scaling](), [seam-carving](), [web-development]() ### AI overview This tutorial explains seam carving, a content-aware image-rescaling method that removes or adds pixels the viewer is least likely to notice. It introduces the motivation, mathematical background, and a working implementation for reducing and enlarging images. ### Source excerpt The Problem with Cropping Every programmer or graphic designer with some web development experience can attest to the fact that finding good images that have an exactly specified size is a pain. Since the dimensions of the sought picture are usually inflexible, an uncomfortable compromise can come in the form of cropping a large image down to size or scaling the image to have appropriate dimensions. Both of these solutions are undesirable. ## Quick intro to Google Guava DevFeed: [Quick intro to Google Guava]() Original publisher: [Read original article]() Author: Carlos Alexandro Becker Published: 2012-08-08T00:00:00Z Content type: tutorial Language: en Sources: [Carlos Becker]() Topics: [Java](), [Collections](), [Caching](), [Concurrency](), [IO]() Tags: [caching](), [collections](), [concurrency](), [google](), [java](), [libraries](), [processing]() ### AI overview An introduction to Google Guava, covering its Java libraries for collections, caching, concurrency, string processing, I/O, null handling, immutable collections, EventBus, and related utilities. ### Source excerpt According to the project site on GitHub, The Guava project contains several of Google's core libraries that we rely on in our Java-based projects: collections, caching, primitives support, concurrency libraries, common annotations, string processing, I/O, and so forth. ## The Fast Fourier Transform DevFeed: [The Fast Fourier Transform]() Original publisher: [Read original article]() Published: 2012-07-18T08:00:54Z Content type: tutorial Language: en Sources: [Jeremy Kun]() Topics: [Algorithm](), [processing]() Tags: [algorithm](), [analysis](), [audio](), [divide-and-conquer](), [fourier-analysis](), [fourier-transform](), [graphics](), [mathematics](), [processing](), [programming](), [python](), [signal-processing](), [sound]() ### AI overview A tutorial on the Fast Fourier Transform explains its historical development, the improvement from O(n^2) to O(n log n) computation for the discrete Fourier transform, and a derivation and implementation approach. It also explores audio denoising by filtering a noisy signal's frequency spectrum. ### Source excerpt It's often said that the Age of Information began on August 17, 1964 with the publication of Cooley and Tukey's paper, "An Algorithm for the Machine Calculation of Complex Fourier Series." They published a landmark algorithm which has since been called the Fast Fourier Transform algorithm, and has spawned countless variations. Specifically, it improved the best known computational bound on the discrete Fourier transform from $ O(n^2)$ to $ O(n \log n)$, which is the difference between uselessness and panacea.