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MageComp is Leading Magento Development Company Since 2014 Offering 150+ Magento Extensions and Mage...

MageComp

magecomp

Extensions: MageComp provides various extensions to extend the capabilities of Magento stores. Purposes: They offer extensions related to frontend and backend enhancements, payment gateways, shipping methods, and more. Examples include ‘Custom Order ...

Exploring 5 Best Hyvä Compatibility Extension Providers for Magento 2

HackerNoon Reporter: Please tell us briefly about your background. I am Gaurav Jain, Co-Founder at , Adobe Certified Expert-Magento Commerce Business Practitioner, from Ahmedabad, Gujarat. Being a computer engineer and possessing extensive marketing skills, I handle all kinds of customer queries at MageComp. MageComp When I am not working, you’ll find me reading books or traveling somewhere in unexplored places. Also, I am a speaker at Magento meetups. What's your startup called? And in a sentence or two, what does it do? MageComp is a Magento company, a leading provider of Magento extensions, services, and customization. Our company is a one-stop solution for E-commerce stores from Magento development, migration, upgradation to marketing and SEO. What is the origin story? While working on internet security and ethical hacking, I got an opportunity as a marketing manager at one of the Magento-based companies. And there when my journey started in Magento. Back in 2012, I found a job in this company. It was a great opportunity, and hence I accepted the offer. That was the time when I didn’t have much knowledge about the Magento platform and its future. What do you love about your team, and why are you the ones to solve this problem? With 8+ years of experience in the Magento platform, our team has attained superpowers to solve any problem and mastered the niche from day-by-day learning and implementation. That’s why we name our team “MAvengers.” Our dedicated MAvengers team follows best practices, coding standards, agile methodology to provide flexible, cost-effective, and robust Magento solutions on a timely basis. If you weren’t building your startup, what would you be doing? I might be doing the job at the management level somewhere. At the moment, how do you measure success? What are your core metrics? We measure our metrics by the number of customers, client satisfaction, and contribution to the Magento community. To date, we have successfully completed 600+ projects and served 20,000+ customers in more than 120 countries. Also, we have developed 150+ Magento 1 and Magento 2 extensions, and our numerous services are used by more than 10,000+ store owners & developers globally. What’s most exciting about your traction to date? We escalate ourselves in the Magento industry to prioritize learning and innovation over revenue. From the beginning, we have continually tried to keep up with the requirements of our customers and thus hand over a fruitful result of the most compatible, flexible, and useful Magento development. What technologies are you currently most excited about and most worried about? And why? We are a Magento-based company, and undoubtedly I am excited about the platform. But at the same time worried about the platform too, as Adobe has acquired it. What drew you to get published on HackerNoon? What do you like most about our platform? Hackernoon is a great medium to connect with technical experts globally. Their articles are knowledgeable, informative, and well briefed. We are passionate about Magento Development and coding innovations, which attracted us to Hackernoon Community. What advice would you give to the 21-year-old version of yourself? Don't stop dreaming. Success comes to those who work hard. So go for it, chase your dreams! What is something surprising you've learned this year that your contemporaries would benefit from knowing? I keep learning every day, but I achieved my goal of becoming an Adobe Certified Expert-Adobe Commerce Business Practitioner this year. MageComp is nominated as . Go vote! Startup of the Year in Ahmedabad

"Success Comes To Those Who Work Hard" Gaurav Jain, Co-Founder of MageComp

240 Stories To Learn About Startups Of The Year

356 Stories To Learn About Ecommerce

In 2021, hosted the first-ever awards!! HackerNoon Startup of the Year The goal of this campaign was to give recognition to the hardworking startups who pulled through the pandemic -- growing and thriving (despite all of the challenges). More than 44,000 startups were nominated all across the globe! They were selected from over 4,553 cities, with a total number of * cough * “4 billion” votes. Insane, right? To see who is a winner in each respective city, visit and pick a city. The name at the top of the list is the winner. (Compromised awards are under review. Cities with tie votes will be updated to have two winners!) startups.hackernoon.com The will announce the winners per region in their respective cities. Startups Winners Announcement series Here are the winners in the : Asia region Top Startup Cities in East Asia Seoul The Startup of the Year in is with 377 votes! Seoul dable Central is the Startup of the Year! It came out ahead with 121 votes. UpNow Central Sheung Wan came out as the Startup of the Year in with 154 votes. Votee Sheung Wan Tokyo The Startup of the Year in is with 41 votes! Tokyo ACES Wan Chai The Startup of the Year in is with 54 votes! Wan Chai Zetl Osaka, Japan In , is the winner! Osaka Nanophoton Taiwan came out as the Startup of the Year in ! Tico Taiwan Dongcheng, China In , is declared the Startup of the Year! Dongcheng Waiju Taipei came out as the Startup of the Year in ! Tico Taipei Akasaka In , is the winner! Akasaka WAKUWAKU Chuo In , is declared the Startup of the Year! Chuo GO-NET Ginza came out as the Startup of the Year in ! Netyear Ginza Kanazawa In , is the winner! Kanazawa Granzella Minami-aoyama In , is declared the Startup of the Year! Minami-aoyama Tokyo6 Entertainment Shibuya came out as the Startup of the Year in ! Hupro Japan Shibuya Toranomon In , is the winner! Toranomon Small Worlds Toshima In , is declared the Startup of the Year! Toshima Altplus Guangzhou, China came out as the Startup of the Year in ! Leyaoyao Guangzhou Hong Kong, Hong Kong In , is declared the Startup of the Year! Hong Kong Episode Six Kunming In , is the winner! Kunming Green A Sapporo In , is declared the Startup of the Year! Sapporo Polar Star Space Shirokanedai came out as the Startup of the Year in ! Nupp1 Shirokanedai Wuhan In , is the winner! Wuhan CSICEE Baoan, China In , is declared the Startup of the Year! Baoan Jumper Baoshan, China In , is declared the Startup of the Year! Baoshan Renren Games Chengdu, China came out as the Startup of the Year in ! Ju You Xi Chengdu Fujisawa, Japan In , is the winner! Fujisawa Scohia Pharma Fukuoka, Japan In , is declared the Startup of the Year! Fukuoka Kyulux Haidian, China came out as the Startup of the Year in ! 51vv Haidian Hongkou, China In , is the winner! Hongkou Kidstone Jiading, China In , is declared the Startup of the Year! Jiading IMRobotic Kawasaki, Japan came out as the Startup of the Year in ! QD Laser, Inc. Kawasaki Kyoto, Japan In , is declared the Startup of the Year! Kyoto Hacarus Minato, Japan In , is the winner! Minato Visionary Fun Nagoya, Japan came out as the Startup of the Year in ! Prodrone Nagoya Ningbo, China The winner in is . Ningbo OlymTech Corporation Aomori In , is declared the Startup of the Year! Aomori Forte Japan Bandai-cho In , is the winner. Bandai-cho Delta-Fly Pharm Changchun In , the Startup of the Year is . Changchun Kaidi Animation Fuchu In , is declared the Startup of the Year! Fuchu TMS Fukuoka-shi The winner in is . Fukuoka-shi SkyDisc Fukushima In , the Startup of the Year is . Fukushima Matsuri Technologies Gotanda In , is the winner. Gotanda Basset Hachioji In , is declared the Startup of the Year! Hachioji Adriakaim Harbin The winner in is J . Harbin oining Technology Iwaki In , is the winner. Iwaki Tao Engineering Izumo In , the Startup of the Year is . Izumo PuREC Kagoshima In , is declared the Startup of the Year! Kagoshima Farmers Support Kamakura In , the Startup of the Year is . Kamakura KAYAC Kami-meguro The winner in is . Kami-meguro Buzzreach Kanagawa In , is the winner. Kanagawa LoiLo Kashiwa In , the Startup of the Year is . Kashiwa Intelligent Surfaces Kita In , is declared the Startup of the Year! Kita Ax Robotix Kobe The winner in is . Kobe Coconomi Koriyama In , the Startup of the Year is . Koriyama Tocoton Kumamoto In , is the winner. Kumamoto Bloom Technology Kunitachi In , is declared the Startup of the Year! Kunitachi Trys Liaocheng The winner in is . Liaocheng Jinzheng Animation Minami In , is declared the Startup of the Year! Minami ATLAB Miyazaki In , is the winner. Miyazaki Caster Nagano In , is declared the Startup of the Year! Nagano Precision Forestry Nagaoka The winner in is . Nagaoka Plant Form Nihombashi-hamacho In , the Startup of the Year is . Nihombashi-hamacho Shizen Energy Niigata In , is declared the Startup of the Year! Niigata UniBio Japan Nishi-shinagawa In , is the winner. Nishi-shinagawa Play Heart Okayama In , is the winner! Okayama Arch Farm Okinawa In , is declared the Startup of the Year! Okinawa BlogNinja Otemachi In , the Startup of the Year is . Otemachi beBit, Inc Qingdao Shi In , is the winner! Qingdao Shi Manyoubang Quanzhou In , is declared the Startup of the Year! Quanzhou KungFu Animation Group Roppongi The winner in is . Roppongi Holo-X Saitama In , the Startup of the Year is . Saitama GameTector Sendai In , is the winner! Sendai Sendai Smart Machines Setagaya In , is declared the Startup of the Year! Setagaya Skyestate Shenzhen In , the Startup of the Year is . Shenzhen Aiator Shinjuku In , is declared the Startup of the Year! Shinjuku Scalar Shizuoka In , is the winner! Shizuoka PiPhotonics Sumida The winner in is . Sumida Holotch Tachikawa In , the Startup of the Year is . Tachikawa PlusOne Takamatsu In , is declared the Startup of the Year! Takamatsu Ken Robotec Tokushima In , is the winner! Tokushima DennoKotsu Toyonaka In , the Startup of the Year is . Toyonaka FISH BIOTECH Tsukuba In , is declared the Startup of the Year! Tsukuba WARPSPACE Wakayama In , the Startup of the Year is . Wakayama 4D Sensor Xiangzhou In , is the winner! Xiangzhou ALLiN Edutech Group Xianyang The winner in is . Xianyang TONLY Xiaoshan In , is declared the Startup of the Year! Xiaoshan GONGHUI Yamaguchi In , the Startup of the Year is . Yamaguchi Archis Yangpu The winner in is . Yangpu Tmxmall Yau Tsim Mong In , is the winner! Yau Tsim Mong Airnugget Yeoksamdong In , the Startup of the Year is . Yeoksamdong WizSchool Yichang In , is declared the Startup of the Year! Yichang Hubei Kento Electronic Yokohama In , is the winner! Yokohama Next Innovation Yubei In , the Startup of the Year is . Yubei Ophiux Network Yueqing The winner in is . Yueqing Yueqing Easythink Media Zhuhai In , is the winner! Zhuhai Aijuba Hongo In , the Startup of the Year is . Hongo Smile Robotics, Inc Admiralty In , is declared the Startup of the Year! Admiralty BUNCH Southern In , the Startup of the Year is . Southern Retykle Yongdungpo In , is the winner! Yongdungpo Push News Taichung In , is declared the Startup of the Year! Taichung Wondercise Cyberport, Hong Kong In , the Startup of the Year is . Cyberport Animoca Brands Hefei, China In , is the winner! Hefei Tsinglink New Territories, Hong Kong The winner in is . New Territories, Hong Kong Zeek Dezhou In , the Startup of the Year is . Dezhou Dezhou Baji Kitakyushu In , is declared the Startup of the Year! Kitakyushu Reif Seongnam In , is the winner! Seongnam EOFlow Yanshi In , the Startup of the Year is . Yanshi CASI Vision Top Startup Cities in South Asia Hyderabad, India The Startup of the Year in is with 962 votes Hyderabad Quixy Lahore, Pakistan is the Startup of the Year! It came out ahead with 186 votes. SmartWindows Lahore Bangalore, India When it comes to , is the Startup of the Year with 210 votes! Bangalore vBridge Hub Pune, India couldn’t be beaten for Startup of the Year in , it won with 129 votes! InfraCloud Pune Jaipur, India The Startup of the Year in is with 166 votes Jaipur Emizen Tech Lucknow, India is the Startup of the Year! It came out ahead with 279 votes. Keeros Superfoods Lucknow Noida, India came out as the Startup of the Year in with 156 votes! Bagisto Noida Mumbai, India When it comes to , is the Startup of the Year with 96 votes! Mumbai Sequretek Sylhet couldn’t be beaten for Startup of the Year in , it won with 122 votes! DataXpie Sylhet Mylapore, India In , is the winner! Mylapore Yalimart Goa, India In , the Startup of the Year is Goa Filter Coffee Delhi, India In , is declared the Startup of the Year! Delhi EkAnek Networks Karachi, Pakistan The winner in is Karachi Bykea Ahmedabad, India In , is the winner Ahmedabad MageComp Dhaka, Bangladesh The winner in is Dhaka SOLshare Islamabad, Pakistan In Islamabad, is declared the Startup of the Year! TAG Gurgaon, India In , the Startup of the Year is Gurgaon ixigo Bengaluru, India In , is the winner! Bengaluru Prakshep Private Limited Haryana, India couldn’t be beaten for Startup of the Year in ! Footprints Childcare Haryana Kolkata, India In , is the winner! Kolkata Wow! Momo New Delhi, India In , is declared the Startup of the Year! New Delhi Rusk Media Indore, India The winner in is Indore Chattar Patar Kormangala In , is the winner Kormangala Sanctum Technologies Ghaziabad came out as the Startup of the Year in Craftfurnish.com Ghaziabad! Mohali In , the Startup of the Year is Mohali DataKund Mysore The Startup of the Year in is Mysore Excelsoft Karnal came out as the Startup of the Year in ! Dunar Karnal Kolhapur In , is the winner! Kolhapur Zanvar Group Patiya In , the Startup of the Year is Patiya Kloud Project Kathmandu In , is declared the Startup of the Year! Kathmandu Vairav Technology Bangalore City, India In is the winner! Bangalore City, PagarBook Ernakulam The winner in is Ernakulam Epixel Solutions Colombo, Sri Lanka couldn’t be beaten for Startup in the Year in MAs Foods Colombo Andheri, India When it comes to , is the Startup of the Year! Andheri DoorMint Kanpur, India came out as the Startup of the Year in ! ServiceWalaa Kanpur Faisalabad In , is the winner! Faisalabad DigiKhata Nasik The Startup of the Year in is Nasik Sahyadri Farms Patna came out as the Startup of the Year in ! Asvin Tech Patna Chennai, India In , is the winner! Chennai Agnikul Cosmos Faridabad, India In , is declared the Startup of the Year! Faridabad UClean Panjim, India The winner in is Panjim TempoGO Ranchi, India In , is the winner Ranchi Osam Dairy Surat, India In , is declared the Startup of the Year! Surat Wishbook Thane, India In , is the winner! Thane Delight Foods Trivandrum, India came out as the Startup of the Year in ! GenRobotic Trivandrum Vijayawada, India In , the Startup of the Year is Vijayawada Eruvaka Technologies Visakhapatnam, India In , Ltd is declared the Startup of the Year! Visakhapatnam Greenline Labs Pvt Ahmadabad In , is the winner! Ahmadabad KiwiQA Services Bandra came out as the Startup of the Year in ! Car Club Bandra Bombay In , is the winner! Bombay Locobuddy Mobile Technology (Sportobuddy) Cochin In , the Startup of the Year is Cochin Tuttifrutti Interactive Gandhinagar In , is declared the Startup of the Year! Gandhinagar Kroop AI Guwahati In , the Startup of the Year is Guwahati GNRC Hospitals Jamshedpur In , is declared the Startup of the Year! Jamshedpur Intelliber Kabul The winner in is Kabul Miracle Shop (Vocinno Technology) Karur In , is the winner Karur Karur Vysya Bank Kochi The winner in is Kochi BestDoc Kozhikode In , is declared the Startup of the Year! Kozhikode OfficeKit HR Ludhiana In , the Startup of the Year is Ludhiana Trendy Beat Network Madhapur In , is the winner! Madhapur Head Digital Works Mangalore When it comes to , is the Startup of the Year Mangalore Hangyo Nagpur came out as the Startup of the Year in ! UPrudence Agro Nagpur Rawalpindi The Startup of the Year in is Rawalpindi Pro Options Marketing Rishikesh came out as the Startup of the Year in ! Descatuk Rishikesh Secunderabad couldn’t be beaten for Startup of the Year in Prakriti Biosystems Engineering Secunderabad Tirupur In , is the winner! Tirupur Tubeknit Fashions Udupi The winner in is Udupi Krishitantra Top Startup Cities in Southeast Asia Singapore, Singapore The Startup of the Year in is with 6556 votes! Singapore Cake DeFi Hanoi, Vietnam is the Startup of the Year! It came out ahead with 683 votes. DeFi For You Hanoi Kuala Lumpur, Malaysia came out as the Startup of the Year in with 109 votes! Trambellir Kuala Lumpur Pasig, Philippines When it comes to , . is the Startup of the Year with 110 votes! Pasig Quickreach Inc Bangkok, Thailand couldn’t be beaten for Startup in the Year in , it won with 60 votes! Perception Codes Bangkok Johor Bahru In , the Startup of the Year is . Johor Bahru AGR1 Bandung, Indonesia In , is declared the Startup of the Year! Bandung Prosa Portugal The winner in is . Portugal SCEMAI Tangerang, Indonesia In , is the winner Tangerang Yummy Corp Jakarta, Indonesia In , is the winner! Jakarta Tokocrypto Dki Jakarta, Indonesia In , is declared the Startup of the Year! Dki Jakarta Limakilo Bangkok Metropolis In , is the winner! Bangkok Meticuly Medan The winner in is ! Medan DIKA Manila, Philippines In , is declared the Startup of the Year! Manila PawnHero Phnom Penh, Cambodia In , the Startup of the Year is Phnom Penh vKirirom Bali In , is the winner! Bali Radius App Penang When it comes to , is the Startup of the Year Penang EasyParcel Surabaya came out as the Startup of the Year in ! Super Surabaya Setiabudi, Indonesia The Startup of the Year in is Setiabudi Chilibeli Cebu City came out as the Startup of the Year in MiCab Cebu City! Jawa couldn’t be beaten for Startup in the Year in DAUR Jawa Ho Chi Minh City, Vietnam In , is the winner! Ho Chi Minh City Pharmacity Selangor, Malaysia is the Startup of the Year! BabyDash Selangor Jakarta Pusat, Indonesia In , is the winner! Jakarta Pusat StickEarn Semarang When it comes to , is the Startup of the Year! Semarang Food Bear Petaling Jaya, Malaysia The winner in is . Petaling Jaya Deemples Kebayoran Baru, Indonesia couldn’t be beaten for Startup in the Year in ! Yogrt Kebayoran Baru Kuningan The winner in is . Kuningan Prinzio Bukit Merah came out as the Startup of the Year in ! DC Alliance Bukit Merah Huai Khwang In , the Startup of the Year is . Huai Khwang AppMan Co.,Ltd Kuta In , is declared the Startup of the Year! Kuta Gringgo Mandaluyong came out ahead as the Startup of the Year! Work Inspire Mandaluyong Midview City In , is the winner! Midview City BioMers Phuket The Startup of the Year in is ! Phuket Sapahn Yangon, Myanmar The winner in is . Yangon Wave Money Denpasar When it comes to , is the Startup of the Year! Denpasar Revasi Nakhon Ratchasima, Thailand The winner in is . Nakhon Ratchasima Siam Lottery co., Ltd Taguig, Philippines In , is the winner! Taguig PayMongo Bogor came out as the Startup of the Year in ! TIRANYX Bogor Senai, Malaysia won as the Startup of the Year. Nutrition Technologies Senai Bekasi Kota couldn’t be beaten for Startup in the Year in ! 8villages Bekasi Kota Top Startup Cities in Middle East Tel Aviv, Israel The Startup of the Year in is with 1,065 votes! Tel Aviv hyro Beirut, Lebanon is the Startup of the Year! It came out ahead with 1,276 votes. Hovi Digital Lab Beirut Riyadh, Saudi Arabia With 350 votes, came out as the Startup of the Year in ! Flicron Riyadh Tel Aviv-yafo, Israel When it comes to , is the Startup of the Year with 167 votes! Tel Aviv-yafo Cymbio Haifa, Israel couldn’t be beaten for Startup in the Year in , it won with 183 votes! Pico Haifa Amman, Jordan In , with 141 votes, is the winner! Amman Edexy Dubai, United Arab Emirates In , the Startup of the Year is with 74 votes. Dubai Ziina Doha, Qatar In , is declared the Startup of the Year! Doha SkipCash Tehran, Iran The winner in is . BeyondBoard AR Tehran Jerusalem, Israel In , is the winner! Jerusalem SolidBlock Rishon Le Zion The Startup of the Year in is ! Rishon Le Zion Passparto Ness Ziona, Israel is the Startup of the Year! Soapy Ness Ziona Kuwait, Kuwait came out as the Startup of the Year in ! Brapper Shop Kuwait Manama, Bahrain couldn’t be beaten for Startup in the Year in ! Doctori Manama Lod, Israel is the winner in ! Phinergy Lod Agudat Israel, Israel When it comes to is the Startup of the Year with [vote ] votes! Agudat Israel, Aqaurius Engines Beersheba, Israel In , the Startup of the Year is . Beersheba Visual Estate Ltd Giv'atayim, Israel In , is declared the Startup of the Year! Giv'atayim WSC Sports Modi'in, Israel The winner in is . Modi'in Loginno Nazareth, Israel In , is the winner! Nazareth AR Guide In Medical Ramat Gan, Israel The Startup of the Year in is . Ramat Gan Datos Health Baghdad When it comes to , is the Startup of the Year! Baghdad Mishwar Khobar In , the Startup of the Year is . Khobar Garage Misgav couldn’t be beaten for Startup in the Year in ! Eden Shield Misgav Sharjah came out as the Startup of the Year in ! Bookends Sharjah Affula In , the Startup of the Year is . Affula eMotion Technologies Bene Beraq, Israel In , is the winner Bene Beraq Reflectiz Caesarea, Israel In , the Startup of the Year is . Caesarea CardiacSense Dubayy In , is declared the Startup of the Year! Dubayy North Ladder Muscat couldn’t be beaten for Startup in the Year in ! iLab Marine Muscat Tirat Carmel In , the Startup of the Year is . Tirat Carmel PassCall Advanced Technologies Hod Hasharon, Israel The winner in is . Hod Hasharon Idomoo Holon, Israel came out ahead as the Startup of the Year! OptimalPlus Holon Jeddah, Saudi Arabia When it comes to , is the Startup of the Year! Jeddah Arabian Combustion Technologies Ramat Hasharon, Israel is the Startup of the Year! Lumen Ramat Hasharon Al Khobar came out as the Startup of the Year in ! Swyft Al Khobar Bet Shemesh In , is the winner! Bet Shemesh Degania Medical Binyamina When it comes to , is the Startup of the Year! Binyamina OncoHost Makkah In , the Startup of the Year is Makkah Scimagine Or Akiva Last but not least, couldn’t be beaten for Startup in the Year in the city of ! WMT Or Akiva More Information: If you’re a winner, please answer ! If you’re a reader, participant, just a curious individual, consider answering one of our interviews . this interview here Read the FAQs about or . Startups here here To see all of the winners you may visit , pick any city and view the name that appears at the top. this site If you’re interested in partnering with us, we invite you to look through some of these or reach out to our business department. unique sponsorship options Perhaps creating your very own tickles your fancy or maybe you have a whole new idea for how we can partner! writing contest Thank you to all of our sponsors! We truly appreciate your contribution to the tech world and thus the future! The Startups Awards are sponsored by: , , , , , , , , , , Blockster Brex FreeCodeCamp LaunchDarkly Coil Udacity Udemy Vercel Asia Pacific Transgender Network Bybit Everscale Network The post was written by Kushal Tamrakar and Valerie Ejegi, edited by Hang Ngo. If you have any questions or concerns or would like to report a discrepancy please email ellen@hackernoon.com.

Meet the Winners of Startups in Asia

Today I'll share my notes for Magento 2.4. Probably, we can say that Consistency is also known as Magento Community! Even in the situation of COVID-19 pandemic, Magento is ready to roll out an update. Yup, you heard right Magento 2.4 is released. July 28, 2020, official release date of the latest Magento 2.4 version. Are you all anxious to inspect the features and upgrades given with the release of Magento 2.4. Magento 2.4 is geared up to smack. MageComp had discussions on this and I thought let me explore the discussion with my audience. So are you all ready to dive into Magento 2.4. As we all have assumed that the release will bring the latest features and enhancement to improve the Magento E-commerce platform. Magento 2.4 Release Highlights: Security-only patch available Store owners are able to install the time-sensitive security fixes in the absence of implementing functional fixes and enhancements. Substantial security enhancements Comprises 30+ security remote code execution (RCE) and cross-site scripting (XSS) fixes and platform security improvements. Provided 2FA by default in the Magento admin panel. Platform upgrades Includes the support of  PHP 7.4 and depreciated PHP 7.1 and 7.2. It also enables the support of Elasticsearch 7.6.x. and MySQL 8.0. Infrastructure improvements Eradicates the core integration of all the third-party payment methods and implements the seller-assisted shopping. Performance improvements Magento 2.4 has improved the caching performance of SQL queries to inventory tables and enhanced optimization of Redis performance. GraphQL Involves the support query of pickupLocations, categories & categoryList and reorderItems for in-store pickup, get a list of categories and support pagination and reorder the last order respectively. Final Say: I hope this information of the Magento 2.4 update is more than enough to from the old version to the latest Magento 2.4. Let me know if you are still waiting or started upgrading your Magento 2 store with the latest release of Magento 2.4 in the comment section. Upgrade the Magento store Happy Reading.

Magento 2.4 Release: Everything You Need to Know

Additional performance comparisons available in an updated post . Come see me speak about JSON and XML at SQL Saturday Columbus on July 22 ! Starting with the 2016 release, SQL Server offers native JSON support. Although the implementation is not perfect, . I am still a huge fan Even if a new feature like JSON support is awesome, I am only likely to use it if it is practical and performs better than the alternatives. Today I want to pit JSON against XML and see which is the better format to use in SQL Server. Enter XML, SQL’s Bad Hombre Full disclosure: I don’t love XML and I also don’t love SQL Server’s implementation of it. XML is too wordy (lots of characters wasted on closing tags), it has elements AND attributes (I don’t like having to program for two different scenarios), and depending on what language you are in, sometimes you need schema files and sometimes you don’t. programming SQL Server’s implementation of XML does have some nice features like a dedicated datatype that reduces storage space and validates syntax, but I find the querying of XML to be clumsy. All XML grievances aside, I am still willing to use XML if it outperforms JSON. So let’s run some test queries! Is JSON SQL Server’s New Sheriff in Town? Although performance is the final decider in these comparison tests, I think JSON has a head start over XML purely in terms of usability. SQL Server’s JSON function signatures are easier to remember and cleaner to write on screen. The test data I’m using is vehicle year/make/model data from . Here’s what it looks like once I loaded it into a table called : https://github.com/arthurkao/vehicle-make-model-data dbo.XmlVsJson CREATE TABLE dbo.XmlVsJson(Id INT IDENTITY PRIMARY KEY,XmlData XML,JsonData NVARCHAR(MAX)) ( if you want to play along at home) The full data query is available in this gist Data Size So XML should be larger right? It’s got all of those repetitive closing tags? SELECTDATALENGTH(XmlData)/1024.0/1024.0 AS XmlMB,DATALENGTH(JsonData)/1024.0/1024.0 AS JsonMBFROMdbo.XmlVsJson Turns out the XML is actually smaller! How can this be? This is the magic behind the . SQL doesn’t store XML as a giant string; it stores only the , leading to a reduction in space. SQL Server XML datatype XML InfoSet The JSON on the other hand is stored as regular old nvarchar(max) so its full string contents are written to disk. XML wins in this case. INSERT Performance So XML is physically storing less data when using the XML data type than JSON in the nvarchar(max) data type, does that mean it will insert faster as well? Here’s our query that tries to insert 100 duplicates of the row from our first query: SET STATISTICS TIME ON INSERT INTO dbo.XmlVsJson (XmlData)SELECT XmlData FROM dbo.XmlVsJsonCROSS APPLY(SELECT DISTINCT numberFROM master..spt_valuesWHERE number BETWEEN 1 AND 100)t WHERE Id = 1GO INSERT INTO dbo.XmlVsJson (JsonData)SELECT JsonData FROM dbo.XmlVsJsonCROSS APPLY(SELECT DISTINCT numberFROM master..spt_valuesWHERE number BETWEEN 1 AND 100)t WHERE Id = 1GO And the results? Inserting the 100 XML rows took 613ms on my machine, while inserting the 100 JSON rows took 1305ms… XML wins again! JSON ain’t looking too hot. Wait for it… I’m guessing since the XML data type physically stores less data, it makes sense that it would also write it out to the table faster as well. CRUD Operations — but how does it compare to XML on ? I’m incredibly impressed by SQL Server’s JSON performance when compared to .NET SQL Server Read Let’s select the fragment for our second car from our XML and JSON: SELECT t.XmlData.query('/cars/car[2]')FROM dbo.XmlVsJson tWHERE Id = 1 SELECT JSON_QUERY(t.JsonData, '$.cars[1]')FROM dbo.XmlVsJson tWHERE Id = 1 Result? (at 0ms vs 63ms for XML) when needing to pluck out a fragment from our larger object string. JSON wins What if we want to grab a specific value instead of a fragment? SELECT t.XmlData.value('(/cars/car[2]/model)[1]', 'varchar(100)') FROM dbo.XmlVsJson tWHERE Id = 1 SELECT JSON_VALUE(t.JsonData, '$.cars[1].model')FROM dbo.XmlVsJson tWHERE Id = 1 Once again with 0ms vs 11ms for XML. JSON wins If you look at the execution plans for these last two queries, it’s easy to see that XML has a lot more to do behind the scenes to retrieve the data: XML: JSON: Create We saw above that inserting rows of XML data is faster than inserting rows of JSON, but what if we want to insert new data into the object strings themselves? Here I want to insert the property “mileage” into the first car object: UPDATE t SET XmlData.modify('insert <mileage>100,000</mileage>into (/cars/car[1])[1]')FROM dbo.XmlVsJson tWHERE Id = 1 UPDATE t SET JsonData = JSON_MODIFY(JsonData,'$.cars[0].mileage','100,000')FROM dbo.XmlVsJson tWHERE Id = 1 In addition to the cleaner syntax ( is essentially the same as a ) the JSON insert runs in 22ms compared to the 206ms for XML. JSON_MODIFY() REPLACE() Another JSON win. Update Let’s update the mileage properties we just added to have values of 110,000: UPDATE t SET XmlData.modify('replace value of (/cars/car[1]/mileage/text())[1]with     "110,000"')FROM dbo.XmlVsJson tWHERE Id = 1 UPDATE t SET JsonData = JSON_MODIFY(JsonData, '$.cars[0].mileage','110,000')FROM dbo.XmlVsJson tWHERE Id = 1 Result? and was able to perform this update in 54ms vs XML’s 194ms. JSON has the quicker draw Delete Deleting large string data, a DBA’s dream *snicker*. Let’s delete the mileage property, undoing all of that hard work we just did: UPDATE t SET XmlData.modify('delete /cars/car[1]/mileage[1]')FROM dbo.XmlVsJson tWHERE Id = 1 UPDATE t SET JsonData = JSON_MODIFY(JsonData, '$.cars[0].mileage', null)FROM dbo.XmlVsJson tWHERE Id = 1 50ms to 159ms. JSON doesn’t take any time to reload and wins against XML again Read Part 2: Indexes So above we saw that JSON was faster than XML at reading fragments and properties from a single row of serialized data. But our SQL Server’s probably have LOTS of rows of data — how well does indexed data parsing do in our match up? First let’s expand our data — instead of storing all of our car objects in a single field, let’s build a new table that has each car on its own row: (once again, if you are playing along at home) full dataset at GitHub Now that we have our expanded data in our table, let’s add some indexes. The XML datatype in SQL Server has its own types of indexes, while . JSON simply needs a computed column with a regular index applied to it DROP INDEX IF EXISTS PXML_XmlData ON XmlVsJson2CREATE PRIMARY XML INDEX PXML_XmlDataON XmlVsJson2 (XmlData); ALTER TABLE dbo.XmlVsJson2ADD MakeComputed AS JSON_VALUE(JsonData, '$.make')CREATE NONCLUSTERED INDEX IX_JsonData ON dbo.XmlVsJson2 (MakeComputed) (Note: I also tried adding an XML secondary index for even better performance, but I couldn’t get the query engine to use that secondary index on such a basic dataset) If we try to find all rows that match a predicate: SELECT Id, XmlDataFROM dbo.XmlVsJson2 tWHERE t.XmlData.exist('/car/make[.="ACURA"]') = 1 SELECT Id, JsonDataFROM dbo.XmlVsJson2 tWHERE JSON_VALUE(t.JsonData, '$.make') = 'ACURA' XML is able to filter out 96 rows in 200ms and JSON accomplishes the same in 9ms. . A final win for JSON Conclusion If you need to store and manipulate serialized string data in SQL Server, there’s no question: JSON is the format of choice. Although JSON’s storage size is a little larger than its XML predecessor, SQL Server’s JSON functions outperform XML in speed in nearly all cases. Is there enough performance difference to rewrite all of your old XML code to JSON? Probably not, but every case is different. One thing is clear: new development should consider taking advantage of SQL Server’s new JSON functions. 💚 Enjoyed this post? Please recommend it by giving it a green heart below. Do you still plan on using XML instead of JSON for new development in SQL Server 2016+? I’d be curious to know why in the comments below.

XML vs JSON Shootout: Which Should I Use in SQL Server?

Java Testing Frameworks: Choosing the Right Tool for the Job - JUnit, AssertJ and Hamcrest

In the quest to simulate cosmic expanses and the intricate dance of celestial bodies, algorithms such as Particle-Mesh (PM) and its more nuanced counterpart, Particle-Particle-Particle-Mesh (P3M), have revolutionized our computational approach. These methods, which are deeply rooted in the seminal work of Hockney and Eastwood, provide a powerful framework for simulations, which I've explored and implemented with the help of modern GPU capabilities. Based on my peer-reviewed paper " ," this blog post provides a condensed exploration of these algorithms and their GPU implementations. Computational simulation using particles on GPU and GLSL language PM and P3M Algorithms: A Primer The PM algorithm works by assigning masses to a regular grid, solving the Poisson equation for the potential, and then differentiating the potential to obtain forces. This method simplifies the n-body problem, making it computationally feasible. The P3M algorithm extends PM by incorporating short-range corrections via direct particle-particle interactions, resulting in improved accuracy in force computations without significantly increasing computational load. GPU Implementation: A Leap in Performance Going into greater detail, my implementation of the PM algorithm makes use of the versatility and power of the GLSL language and Vulkan API, ensuring compatibility across a wide range of graphics cards, from integrated Intel GPUs to the robust Apple M1 chips. Here is how I customized the PM algorithm for the GPU: First Stage: Point Partitioning The first stage involves partitioning points within parallelepipeds, laying the groundwork for the subsequent density assignment. We build 32x32 linked lists, each of which corresponds to a region in space. The goal is for each list to contain the indices of points in the corresponding parallelepiped. We start by defining shared lists for each partition in GLSL using the compute shader: // particles3_parts.comp - List Construction
const int nl = 32; // number of linked lists along one dimension
shared int lists[nl][nl];

// Initialize lists to -1
for (uint i = from; i < to; i++) {
    list[i] = -1;
}

lists[gl_LocalInvocationID.y][gl_LocalInvocationID.x] = -1;
barrier();

float h32 = l/32.0; // grid size divided by number of lists

// Build the linked lists by x, y coordinates
for (uint i = from; i < to; i++) {
    vec2 x = vec2(Position[i]) - vec2(origin);
    ivec2 index = clamp(ivec2(floor(x/h32)), 0, 31);

    // The head of the list for the area (index.y, index.x) is 
    // in lists[index.y][index.x]
    // The previous list element is saved in list[i]
    // Each thread updates a unique element, so no synchronization is needed here
    list[i] = atomicExchange(lists[index.y][index.x], int(i));
}
... After populating the shared lists, we have a structured overview of where each point lies within our space-partitioning grid. The GPU's atomic operations ensure that this process is both fast and accurate, laying the groundwork for efficient mass distribution in the PM algorithm's subsequent steps. Take a look at this illustrative image to get a better idea of how these 2x2 linked lists are built and function within the larger context of our partitioning strategy: This visualization depicts the process on a simplified 2x2 grid. In practice, a 32x32 grid is used, but the principles remain the same, albeit on a larger scale. With our 32x32 linked lists ready, we move on to the mass distribution phase. We traverse the lists in a chessboard pattern here, which is not only systematic but also strategically designed to avoid race conditions when writing density values to our density array. This traversal is depicted in the following image, where the active lists being processed at any given time are highlighted in blue: As we cycle through the particles in each list, we distribute their mass across the grid nodes. The chessboard-like processing pattern ensures that simultaneous writes to the density array from multiple threads do not interfere with each other, avoiding the classic problem of race conditions. This method improves the algorithm's efficiency while also ensuring data integrity. The compute shader, which serves as the foundation of this initial setup, orchestrates the creation of linked lists within the GPU. For those who are interested in the actual code and its nuances, the compute shader responsible for this can be found on my GitHub repository: . particles3_parts.comp - Constructing Linked Lists on the GPU Once our lists are established, we will traverse them in a chessboard pattern to avoid data races during mass distribution. The corresponding shader, which handles this traversal and density array construction meticulously, can also be viewed in detail here: . particles3_mass.comp - Traversal and Density Distribution Shader For the mass distribution, we use linear interpolation, leveraging barycentric coordinates to weigh the mass across the grid nodes. This technique ensures a smooth and continuous mass transition, which is critical for simulating physical systems. Consider the image below, which depicts the mass distribution using the Cloud-in-Cell (CIC) method: The use of linear interpolation for mass distribution is a technique that mirrors physical reality by ensuring that the influence of a particle is felt across its immediate vicinity in a manner proportional to its position within a cell. Poisson's Equation and Fast Fourier Transform Solving Poisson's equation to calculate gravitational potential from mass distribution is a critical component of the PM algorithm. In my implementation, I use variable separation and a Fast Discrete Real Fourier Transform (FDRFT), adapting the algorithm described in A. A. Samarskii and E. S. Nikolaev's book "Numerical Methods for Grid Equations." This adaptation was specifically designed for GPU execution, leveraging its parallel processing capabilities to improve computational performance. The shader code responsible for this part of the computation is complex, reflecting both the complexities of the mathematical procedure and the optimizations required for GPU use. The shader is proof of modern computing platforms' power and ability to handle calculations that were previously the exclusive domain of supercomputers. For a more in-depth look at this process, please see the shader that handles the Poisson's equation using FDRFT on the GPU: . particles3_poisson2.comp shader The P3M Algorithm and GPU Acceleration The P3M (Particle-Particle Particle-Mesh) algorithm improves on the PM (Particle-Mesh) algorithm, particularly in how it computes local forces using Newtonian mechanics. While both algorithms share common ground, the P3M algorithm adds an extra layer of precision by partitioning points into cubes rather than parallelepipeds, improving the granularity of the mass distribution and force computation. Advancing to Cubic Partitioning The P3M implementation involves additional partitioning using lists, which is similar to the PM algorithm but takes a step further to accommodate cubic volumes. This additional step, which closely follows the classic Newtonian formulation, allows for a more precise calculation of local forces. Computing Newtonian Forces in Parallel The calculation of these local forces in parallel employs a classic parallel algorithm, which is finely detailed in the paper " " by L. Nyland, M. Harris, and J. Prins. This seminal work underpins the parallel computational approach adapted for the GPU, enabling significantly faster computations required for large-scale simulations. Fast N-Body Simulation with CUDA The shader codes that power these computations can be examined in greater depth by visiting the following links: For the sorting process that prepares the data for force calculations: . particles3_pp_sort.comp shader For the parallel force computation itself: . particles3_pp.comp shader Final Steps with Vertex Shader The calculation of velocities and positions is the culmination of the P3M algorithm on the GPU. This is accomplished deftly with the Velocity Verlet algorithm, an integration method that provides a symplectic solution—ideal for conserving energy over long simulations, which is critical for accurate astrophysical results. The shader responsible for these final calculations can be found here: . particles3.vert shader The Velocity Verlet algorithm is a pillar of computational physics, providing stable and efficient computations of particle trajectories. For those who are unfamiliar with the Velocity Verlet algorithm, a primer can be found on its . Wikipedia page Performance Benchmarks Our implementations have resulted in significant performance improvements. The following table summarizes the benchmarks: Stage M1 (CPU) M1 (GPU) Nvidia 3060 Density 9.45 11.61 2.26 Potential 8.87 5.69 0.86 Force 313.93 103.09 28.87 Positions 82.74 8.38 33.54 In the performance comparison table, we look at the execution time of each stage of the algorithm, measured in milliseconds across different processors: Apple M1 (CPU), Apple M2 (GPU), and Nvidia 3060. For the CPU version of the algorithm, OpenMP was utilized. The data from this table reveals a significant performance difference: the algorithm on the Nvidia 3060 runs 13 times faster compared to its execution on the Apple M1 CPU. These calculations were conducted with one million particles and a grid size of 64x64x64. The P3M Algorithm and GPU Acceleration: A Note on Performance When it comes to GPU acceleration, the P3M and PM algorithms present some computational challenges. Atomic operations and the management of linked lists, in particular, are not inherently optimal for GPU architectures. Despite this, the bottlenecks of these algorithms are located elsewhere. Bottlenecks in Performance The primary bottleneck in the PM algorithm is updating the density matrix, which requires significant memory bandwidth and synchronization between GPU threads. This limitation is vividly highlighted in a performance analysis using NVIDIA Nsight Graphics, as shown in this screenshot: The P3M algorithm, on the other hand, has a bottleneck in the computation of local forces, which requires intensive floating-point operations and data shuffling between GPU threads and memory. This performance barrier is also visible in a detailed Nsight Graphics analysis, as shown in this screenshot: Future Optimizations Recognizing these performance bottlenecks, the program's next iterations will concentrate on addressing these issues. The efficiency of these calculations is hoped to be significantly improved through careful optimization and possibly leveraging new GPU features or architecture improvements. While these challenges are not trivial, the solutions will not only improve the performance of the P3M and PM algorithms but may also contribute to the broader field of computational physics, where such algorithms are widely used. Further Insights For those interested in learning more about these simulations, check out these YouTube videos that show the calculation of a galaxy's evolution and the dynamics of the early universe: Galaxy Simulation Early Universe Evolution In the simulation video linked above, the Friedman universe model has been used to account for the expansion of the universe. This model describes a universe filled with homogeneously distributed fluid with pressure and energy, which governs the observed dynamics of cosmic expansion. For a more in-depth understanding of this model, see the . Friedman Universe on Wikipedia Finally, for enthusiasts and fellow developers, the source code for these algorithms, written in GLSL and using the Vulkan API to ensure compatibility across various GPUs, including Intel integrated graphics and Apple's M1, is available on GitHub: . Access the Source Code Repository Conclusion In conclusion, modern mid-range gaming graphics cards and integrated GPUs are proving to be quite capable of accelerating numerical computations. The algorithms presented are implemented efficiently, with GPU computation times comparable to cluster processing across multiple nodes. The findings are consistent with those of other researchers, confirming the accuracy of this approach. The developed program can serve as a solid foundation for particle-based computational physics problems. Subroutines like the one for solving Poisson's equation could be applied to more complex hydrodynamic problems. One key advantage of the approach described here is the ability to use a personal computer for rapid, parameter-varying numerical experiments with the luxury of real-time observation of evolving changes without the need for intermediate file storage or separate visualization software. While not all aspects of the algorithm perform optimally on GPUs (for example, mass distribution on the grid), careful optimization of GPU parameters such as computation partitioning, stream counts, and shared memory usage within thread groups has the potential for significant performance gains.

Unveiling the Power of PM and P3M Algorithms on GPUs

THE SINS OF OTHERS

PHP Classes Use Case for Database Management in a Document Builder

Storybook V2 Comic Book Builder: Part 2, The Document Generator

Welcome to other chapters of Let’s Understand Chrome V8 For performance reasons, V8 uses the compilation cache to hold the SharedFunction which is the compiler result, the cache returns the result directly once the same JavaScript code is compiled again. You may ask why the same code is compiled again. Compile first, then execute — this is the pipeline of V8. Even the exact same code, the same function, still needs to be sent to the compiler, but not always recompiled. Actually, JavaScript code may change during execution, so the first action always is that we get the JavaScript code’s hash-value from the compilation cache, return the SharedFunction if the cache is hit, and recompile the JavaScript conversely. 1. Initialization and Filling The Isolate, which is the V8 virtual machine instance, is responsible for initializing the CompilationCache, isolate::Init as shown below: bool Isolate::Init(ReadOnlyDeserializer* read_only_deserializer,
                   StartupDeserializer* startup_deserializer) {
//omit...................
#define ASSIGN_ELEMENT(CamelName, hacker_name)                  \
  isolate_addresses_[IsolateAddressId::k##CamelName##Address] = \
      reinterpret_cast<Address>(hacker_name##_address());
  FOR_EACH_ISOLATE_ADDRESS_NAME(ASSIGN_ELEMENT)
#undef ASSIGN_ELEMENT

  compilation_cache_ = new CompilationCache(this); The last line is now a CompilationCache, namely, initialization. Let’s dive into it in more depth. 1.  class V8_EXPORT_PRIVATE CompilationCache {
2.   public:
3.    MaybeHandle<SharedFunctionInfo> LookupScript(
4.        Handle<String> source, MaybeHandle<Object> name, int line_offset,
5.        int column_offset, ScriptOriginOptions resource_options,
6.        Handle<Context> native_context, LanguageMode language_mode);
7.    InfoCellPair LookupEval(Handle<String> source,
8.                            Handle<SharedFunctionInfo> outer_info,
9.                            Handle<Context> context, LanguageMode language_mode,
10.                            int position);
11.    MaybeHandle<FixedArray> LookupRegExp(Handle<String> source,
12.                                         JSRegExp::Flags flags);
13.    void PutScript(Handle<String> source, Handle<Context> native_context,
14.                   LanguageMode language_mode,
15.                   Handle<SharedFunctionInfo> function_info);
16.    void PutEval(Handle<String> source, Handle<SharedFunctionInfo> outer_info,
17.                 Handle<Context> context,
18.                 Handle<SharedFunctionInfo> function_info,
19.                 Handle<FeedbackCell> feedback_cell, int position);
20.    void PutRegExp(Handle<String> source, JSRegExp::Flags flags,
21.                   Handle<FixedArray> data);
22.    void Clear();
23.    void Remove(Handle<SharedFunctionInfo> function_info);
24.    void Iterate(RootVisitor* v);
25.    void MarkCompactPrologue();
26.    void Enable();
27.    void Disable();
28.   private:
29.    explicit CompilationCache(Isolate* isolate);
30.    ~CompilationCache() = default;
31.    base::HashMap* EagerOptimizingSet();
32.    static const int kSubCacheCount = 4;
33.    bool IsEnabled() const { return FLAG_compilation_cache && enabled_; }
34.    Isolate* isolate() const { return isolate_; }
35.    Isolate* isolate_;
36.    CompilationCacheScript script_;
37.    CompilationCacheEval eval_global_;
38.    CompilationCacheEval eval_contextual_;
39.    CompilationCacheRegExp reg_exp_;
40.    CompilationSubCache* subcaches_[kSubCacheCount];
41.    bool enabled_;
42.    friend class Isolate;
43.    DISALLOW_COPY_AND_ASSIGN(CompilationCache);
44.  }; There’re three fundamentals of the above function that are important: 1. LookupScript() at line 3. It is used to look up the cache to take out the SharedFunction that holds the bytecodes corresponding to your JavaScript code. If hit, skip compilation into the execution unit directly. 2. PutScript() at line 13. If LookupScript misses, PutScript() puts the hash corresponding to your JavaScript code and the SharedFunciton that the compiler generates into the cache before execution. 3. LookupEval and PutEval at lines 7 and 16. They are the same as LookupScript and PutScript but specialize in the JavaScript eval method because the eval needs not only SharedFunction but also context. Let’s examine LookupScript in a little more depth: 1.  MaybeHandle<SharedFunctionInfo> CompilationCache::LookupScript(
2.      Handle<String> source, MaybeHandle<Object> name, int line_offset,
3.      int column_offset, ScriptOriginOptions resource_options,
4.      Handle<Context> native_context, LanguageMode language_mode) {
5.    if (!IsEnabled()) return MaybeHandle<SharedFunctionInfo>();
6.    return script_.Lookup(source, name, line_offset, column_offset,
7.                          resource_options, native_context, language_mode);
8.  }
9.  //..................separation..........................
10.  //..................separation..........................
11.  MaybeHandle<SharedFunctionInfo> CompilationCacheScript::Lookup(
12.      Handle<String> source, MaybeHandle<Object> name, int line_offset,
13.      int column_offset, ScriptOriginOptions resource_options,
14.      Handle<Context> native_context, LanguageMode language_mode) {
15.    MaybeHandle<SharedFunctionInfo> result;
16.    {
17.      HandleScope scope(isolate());
18.      const int generation = 0;
19.      DCHECK_EQ(generations(), 1);
20.      Handle<CompilationCacheTable> table = GetTable(generation);
21.      MaybeHandle<SharedFunctionInfo> probe = CompilationCacheTable::LookupScript(
22.          table, source, native_context, language_mode);
23.      Handle<SharedFunctionInfo> function_info;
24.      if (probe.ToHandle(&function_info)) {
25.        if (HasOrigin(function_info, name, line_offset, column_offset,
26.                      resource_options)) {
27.          result = scope.CloseAndEscape(function_info);
28.        }
29.      }
30.    }
31.    Handle<SharedFunctionInfo> function_info;
32.    if (result.ToHandle(&function_info)) {
33.  #ifdef DEBUG
34.      DCHECK(HasOrigin(function_info, name, line_offset, column_offset,
35.                       resource_options));
36.  #endif
37.      isolate()->counters()->compilation_cache_hits()->Increment();
38.      LOG(isolate(), CompilationCacheEvent("hit", "script", *function_info));
39.    } else {
40.      isolate()->counters()->compilation_cache_misses()->Increment();
41.    }
42.    return result;
43.  } We can find that the CompilationCache::LookupScript is only the entrance, and the key function, as well as the important code, are all in CompilationCacheScript::Lookup(). 1. In Lookup(), the argument source is the JavaScript that you as developers are actually writing, which is going to be compiled. 2. GetTable at line 20. It is the actual table that holds data into which PutScript put. 3. CompilationCacheTable::LookupScript at line 21. Look up the table for a result in which hit means a SharedFunction or miss is null. 4. In line 25: Check if the SharedFunction is the correct one depending on Origin. Let’s take a glance at HasOrigin. 1.  // We only re-use a cached function for some script source code if the
2.  // script originates from the same place. This is to avoid issues
3.  // when reporting errors, etc.
4.  bool CompilationCacheScript::HasOrigin(Handle<SharedFunctionInfo> function_info,
5.                                         MaybeHandle<Object> maybe_name,
6.                                         int line_offset, int column_offset,
7.                                         ScriptOriginOptions resource_options) {
8.    Handle<Script> script =
9.        Handle<Script>(Script::cast(function_info->script()), isolate());
10.    Handle<Object> name;
11.    if (!maybe_name.ToHandle(&name)) {
12.      return script->name().IsUndefined(isolate());
13.    }
14.    if (line_offset != script->line_offset()) return false;
15.    if (column_offset != script->column_offset()) return false;
16.    if (!name->IsString() || !script->name().IsString()) return false;
17.    if (resource_options.Flags() != script->origin_options().Flags())
18.      return false;
19.    return String::Equals(
20.        isolate(), Handle<String>::cast(name),
21.        Handle<String>(String::cast(script->name()), isolate()));
22.  } From line 1 to line 3, the comments provide the origin check rules. Specifically, the implementation is from line 14 to line 16. Next, let’s go into PutScript. 1.  void CompilationCache::PutScript(Handle<String> source,
2.                                   Handle<Context> native_context,
3.                                   LanguageMode language_mode,
4.                                   Handle<SharedFunctionInfo> function_info) {
5.    if (!IsEnabled()) return;
6.    LOG(isolate(), CompilationCacheEvent("put", "script", *function_info));
7.    script_.Put(source, native_context, language_mode, function_info);
8.  }
9.  //.......................separation............................
10.  void CompilationCacheScript::Put(Handle<String> source,
11.                                   Handle<Context> native_context,
12.                                   LanguageMode language_mode,
13.                                   Handle<SharedFunctionInfo> function_info) {
14.    HandleScope scope(isolate());
15.    Handle<CompilationCacheTable> table = GetFirstTable();
16.    SetFirstTable(CompilationCacheTable::PutScript(table, source, native_context,
17.                                                   language_mode, function_info));
18.  } As same as LookupScript(), the CompilationCache::PutScript is entrance, all important stuff is in CompilationCacheTable::PutScript. In PutScript, I would like to inform you that the cache table will be filling. Below is the CacheTable source code: 1.  // This cache is used in two different variants. For regexp caching, it simply
2.  // maps identifying info of the regexp to the cached regexp object. Scripts and
3.  // eval code only gets cached after a second probe for the code object. To do
4.  // so, on first "put" only a hash identifying the source is entered into the
5.  // cache, mapping it to a lifetime count of the hash. On each call to Age all
6.  // such lifetimes get reduced, and removed once they reach zero. If a second put
7.  // is called while such a hash is live in the cache, the hash gets replaced by
8.  // an actual cache entry. Age also removes stale live entries from the cache.
9.  // Such entries are identified by SharedFunctionInfos pointing to either the
10.  // recompilation stub, or to "old" code. This avoids memory leaks due to
11.  // premature caching of scripts and eval strings that are never needed later.
12.  class CompilationCacheTable
13.      : public HashTable<CompilationCacheTable, CompilationCacheShape> {
14.   public:
15.    NEVER_READ_ONLY_SPACE
16.    static MaybeHandle<SharedFunctionInfo> LookupScript(
17.        Handle<CompilationCacheTable> table, Handle<String> src,
18.        Handle<Context> native_context, LanguageMode language_mode);
19.    static InfoCellPair LookupEval(Handle<CompilationCacheTable> table,
20.                                   Handle<String> src,
21.                                   Handle<SharedFunctionInfo> shared,
22.                                   Handle<Context> native_context,
23.                                   LanguageMode language_mode, int position);
24.    Handle<Object> LookupRegExp(Handle<String> source, JSRegExp::Flags flags);
25.    static Handle<CompilationCacheTable> PutScript(
26.        Handle<CompilationCacheTable> cache, Handle<String> src,
27.        Handle<Context> native_context, LanguageMode language_mode,
28.        Handle<SharedFunctionInfo> value);
29.//omit......................
30.    }; From line 1 to line 11, the comments provide more corresponding explanations enough. Let’s go back to CompilationCacheScript::Put() and ahead to line 16 which is CompilationCacheTable::PutScript(). 1.  Handle<CompilationCacheTable> CompilationCacheTable::PutScript(
2.      Handle<CompilationCacheTable> cache, Handle<String> src,
3.      Handle<Context> native_context, LanguageMode language_mode,
4.      Handle<SharedFunctionInfo> value) {
5.    Isolate* isolate = native_context->GetIsolate();
6.    Handle<SharedFunctionInfo> shared(native_context->empty_function().shared(),
7.                                      isolate);
8.    src = String::Flatten(isolate, src);
9.    StringSharedKey key(src, shared, language_mode, kNoSourcePosition);
10.    Handle<Object> k = key.AsHandle(isolate);
11.    cache = EnsureCapacity(isolate, cache, 1);
12.    int entry = cache->FindInsertionEntry(key.Hash());
13.    cache->set(EntryToIndex(entry), *k);
14.    cache->set(EntryToIndex(entry) + 1, *value);
15.    cache->ElementAdded();
16.    return cache;
17.  } I think the important thing is that PutScript binds the JavaScript source and SharedFuncation as a hash pair as well as puts it into the cache, which the implementations are from line 9 to line 15. 2. Lookup and update Let’s go into Compiler::GetSharedFunctionInfoForScript. 1.   MaybeHandle<SharedFunctionInfo> Compiler::GetSharedFunctionInfoForScript(
2.       Isolate* isolate, Handle<String> source,
3.       const Compiler::ScriptDetails& script_details,
4.       ScriptOriginOptions origin_options, v8::Extension* extension,
5.       ScriptData* cached_data, ScriptCompiler::CompileOptions compile_options,
6.       ScriptCompiler::NoCacheReason no_cache_reason, NativesFlag natives) {
7.  //omit.....................
8.       if (extension == nullptr) {
9.         bool can_consume_code_cache =
10.             compile_options == ScriptCompiler::kConsumeCodeCache;
11.         if (can_consume_code_cache) {
12.           compile_timer.set_consuming_code_cache();
13.         }
14.         maybe_result = compilation_cache->LookupScript(//lookup is here.
15.             source, script_details.name_obj, script_details.line_offset,
16.             script_details.column_offset, origin_options, isolate->native_context(),
17.             language_mode);
18.         if (!maybe_result.is_null()) {
19.           compile_timer.set_hit_isolate_cache();
20.         } else if (can_consume_code_cache) {
21.  //omit
22.           if (CodeSerializer::Deserialize(isolate, cached_data, source,
23.                                           origin_options)
24.                   .ToHandle(&inner_result) &&
25.               inner_result->is_compiled()) {
26.             is_compiled_scope = inner_result->is_compiled_scope();
27.             DCHECK(is_compiled_scope.is_compiled());
28.     		//filling is here
29.             compilation_cache->PutScript(source, isolate->native_context(),
30.                                          language_mode, inner_result);
31.             Handle<Script> script(Script::cast(inner_result->script()), isolate);
32.             maybe_result = inner_result;
33.           } else {
34.             compile_timer.set_consuming_code_cache_failed();
35.           }
36.         }
37.       }
38.       return maybe_result;
39.     } In line 14, it means that we need to compile the JavaScript code if LookupScript is missing, so we skip to line 21 to perform compilation and generate inner_result. Then at line 29, put the inner_result into the cache. We can find that the innuer_result is just the maybe_result at line 32. Figure 1 shows the call stack. Besides LookupScript and PutScript, there are also LookupEval, PutEval as well as LookupRegExp, PutRegExp in V8. The workflow of them is the same as LookupScript and PutScript, please learn by yourself. Okay, that wraps it up for this share. I’ll see you guys next time, take care! Please reach out to me if you have any issues. qq9123013 v8blink@outlook.com WeChat: Email: Also published . here

Understanding Chrome V8: Chapter 19: Compilation Cache — Make the Compiler Faster

I like to write code, especially code which presents things like web pages, and in particular code that writes web page code. I like to draw and write fiction as well. So it occurred to me that an application to share comic books or other sequential documents created from my (and other’s) content would be useful, and certainly fun to write. That is the impetus for the Storybook Comic Book Builder. The Builder is an accessible Content Creator Application that in turn creates accessible documents. We have set the following requirements for the Builder, that the comics or documents it makes use a mobile-first responsive design are very quick to load are accessible by WCAG guidelines have semantic content and logical syntax are easy to navigate using only the keyboard support GIF or WebP animations and MP3 audio content are portable to run on various media (eg thumb drives) Based upon the evidence presented by the evaluation results from running these tests On our example comic as shown in these screen shots snips above, it would be fair to say that the Builder is relatively successful in addressing the areas of accessibility, semantics, and SEO. The Web Content Accessibility Guidelines (WCAG) resources linked in the document and others were used as a basis for informing our expectations of the user provided content and how we present that content in an easy to navigate accessible fashion. Understanding WCAG 2.1 In addition to addressing those areas relative to accessibility, a good web page is going to be mobile-first responsive and fast. Why mobile-first? Simply that the large majority of web traffic today is on mobile devices. On average your likely visitor is on a mobile device. However, mobile-first should not mean that a page is unfriendly to other devices. The key is obviously to cater to your audience whatever device they may be on. Our example comic achieved these results from the Google “Mobile Friendly” evaluation. Another important consideration is the speed with which our image content is loaded and displayed. In fact, amazing as it may seem, people will abandon their visit to a site they deem to be slow or unresponsive! Usually the major contribution to the download speed is the size of the downloaded content. That can be a significant factor on a cellular network as an example. When we look at methods for the content presentation of web pages, especially image content, we are likely to encounter the concepts of adaptive and responsive design. For our purposes we will call a method that serves different content based on viewport dimensions as being an “adaptive design” while a method that modifies or scales the served content will be called a “responsive design”. In a comic much of the content is typically images, and one aspect of the quality of the comic (besides the content itself of course) is the quality of the presentation of those images. The remainder of this article will focus on a method that synergistically uses both adaptive design and responsive design to accomplish a fast responsive image download and display. All of the code for the Builder along with our example comic is available from a but we will limit our discussion to those portions relevant to our image display method. The example comic can be viewed at , and there are also some other comics in our GitHub repository Hyenas comics gallery. Image Selection as Adaptive Design The Image Selection HTML There is a relatively new paradigm for image selection in HTML5 and that is the notion of a “source set” attribute for an image, and the way that this is accomplished is basically to present the browser with a list of images that it may choose from. This idea of choosing an image from a list is of course demonstrative of “adaptive design”. In general the list may offer a number of image sizes listed by their width and it may also offer a number of image formats. A list of media breakpoints is also provided to assist the browser in making its choice. This sample shows how this HTML code appears in the comic web page as generated by our PHP builder code. As can be seen in the code sample, there are five distinct image width choices in two image formats along with five distinct media width selections, and we can see that the image width values and the media width breakpoint values correlate. The values of these dimensional choices were determined based upon the set of breakpoint values used by Bootstrap. X-Small, Less Than 576px Small, Greater Than or Equal 576px Medium, Greater Than or Equal 768px Large, Greater Than or Equal 992px X-Large, Greater Than or Equal 1200px XX-Large, Greater Than or Equal 1400px The basic premise of how this source set scheme operates is that the server provides our browser with HTML similar to our example, that is HTML that includes a list of choices for image content. Then our browser selects the appropriate image from the list and downloads just that image for display. The source list is ordered by our nominal preference among the images, and the browser stops analyzing the image list once it finds an image that satisfies its interpretation of the display environment. In our case we order the list by image format and image width in pixels. The five image size selections are presented in both WEBP and JPG format filetypes, and the filenames for a particular size image are the same in both file formats except for the filetype extension. In general there is no visual benefit to serving images that the browser has to downscale to display, and an image that is twice the size of the viewport is four times the size of an image that just fits. Images that must be upscaled for display frequently suffer in visual quality. It is usually better to serve images that approximate or that are slightly larger than the viewport dimensions. The image widths based on the Bootstrap breakpoints seem to provide an excellent range and granularity of dimension choices for the vast majority of use cases. The WEBP format is relatively new but it has been widely adopted and has some substantial benefits. On the average converting our comic JPG images to WEBP images with the default compression setting (75) has reduced the image file size of the resulting WEBP to just over 10% of the JPG file size with no obvious visual degradation. Obviously this will substantially improve download speeds, and since we have listed the WEBP format images first the browser will default to that format if it is supported. Now let’s look at how the Builder generates this image element and its contents, especially the source set list. Creating the Image Element’s Source Set contents The Builder application that creates the comics is written in PHP (our site runs version 7.3) and as was previously stated, all of the source code and an example comic along with some explanatory documentation is presented in a . The source code itself is contained in a repository subfolder named There are quite a few program files in that folder, however for our discussion we will only need to reference a few specific programs. The image display concepts we will expose in those few programs are also reused or adapted in some of the remaining code. GitHub repository Storybook. The program is the landing page for the Builder application. It largely consists of a list of forms that gather user input to obtain, for example, the name of our comic. Most of this information is used to populate a "card" for our comic which we display in our . Besides this function however, the index.php program also calls subprograms that create the folders that will be used for our comic content. So for our example comic, "Hyenas", index.php creates a folder with that name under the site Comics folder within the document root. After this is accomplished, the index.php program will then proceed by turning control over to a sequence of programs that each allow the user to select and upload the content that will be used to generate the comic. index.php comics gallery The image in our example was selected by the program. This program in turn leverages a lovely JavaScript program I found some time ago to sort and display a list of image files to be uploaded. The script also uses an AJAX call to in turn invoke the program which validates the uploaded files and constructs the image files listed in our image element content. The JS file is named and its author has a site at to share their work. However, while I am fairly certain that this is the source of the code I adapted, I cannot locate that code on the blog site. Meanwhile, the script itself can be found on the Internet and without attribution. I said lovely just now, and that aspect of the code influenced my approach to code, so the author gets “shout outs and props”, as well as attribution. Here is a link to his blog: getComic.php imgUploader.php vpb_uploader.js https://www.vasplus.info/ The getComic.php and vpb_uploader.js scripts basically serve as part of the user interface to the Builder while the actual content evaluation and creation is accomplished by the imgUploader.php code. These scripts pass each file to be uploaded to the imgUploader.php program one at a time in turn by an AJAX call for each file. The first 120 odd lines of the imgUploader code invoke the SESSION handler and set some variables, then the code checks to be sure we are here by a properly constructed POST call before it does checks for upload success and the image file size and file extension and the filename as well as to replace spaces in the filename with underscores. The Builder supports the upload of image files of filetypes JPG, PNG, and GIF. The first thing we do with an uploaded JPG image that passes our initial checks is to regenerate it using the PHP image functions, which serves to help “sanitize” the file. PNG files are converted to JPG format using the PHP image functions. However, while the Builder supports GIF images and also generates a WEBP alternate format file for the uploaded GIF file, it does not scale or generate a source set for uploaded GIF images. I anticipate adding this resizing facility by using the imagemagick utility and the imagick PHP extension. Stay tuned! Besides the present exclusion of GIF filetypes the comic has three other categories of “non-content” images that are not presented by a list of sizes in an image element source set. One such category is the image used for the gallery card, another is any logo images, and finally the OG image. Our interest in the image element source set as a list of sizes is for “content” images, for example the images that are the comic’s pages. Based upon our previously discussed breakpoint and image size choices, the optimum uploaded image size will be 1400 pixels in width. In this code segment shown below we can see that the program takes the sanitized and verified JPG upload and scales it to a 1400 pixel width if required. The JPG is then copied as a WEBP format file of the same dimensions. This gives us the two largest (dimensionally) files in the source set list. In addition to the scaling and conversion functions much of the code in this first segment serves to generate a uniquely constructed name for this pair of images. The image name syntax we use allows us to include some information about the image in its name. In this first segment we have appended a -X- to the base filename and then further we have appended a formatted string for the image height, and we append the same string to both the JPG and WEBP filenames. The scaling and conversion and copy functions along with the naming generation could well be accomplished in a function call, but in our example we have instead embraced the simple expedient of a “step and repeat” construct. So this segment shown next is virtually the same as that above save for the width dimension we seek to change (1200 instead of 1400) along with the appropriate changes to the filename suffix. The program replicates this segment slightly modified five times, once for each of the image sizes we wish to serve. Thus at the bottom of this scaling and conversion stack we will have a collection of ten images that are five sizes in two formats. You may note that some of the code in these segments generates “report entries” which we can display with a dumpsession.php utility program included in this GitHub . repository Once the images are available the imgUploader.php program continues by placing the image files in an appropriate folder and then updating a mySQL database to reflect the newly added image before finally returning to the vpb_uploader.js caller. The code to implement these two tasks is shown in these next two program segments. At this point in the Builder process we have created the Image Element’s Source Set contents for a comic image, and this process is repeated for each image or page of comic content. When all of the image and other comic content is finally gathered we can then generate a comic with this content as we discuss next. Creating the Image Element with its Source Set contents When the makeComic.php script starts it first checks to see that it was invoked by a proper POST request. If so it starts the PHP SESSION and loads some variables including font choices particular to our comic. The script then sets up a buffer to contain the comic HTML that it will next generate. The Builder can generate some fairly complex documents or comics that can include text captions, alternate images, animations, and audio. The makeComic.php program is about 800 lines of code but our focus here can concentrate on a small part of that total. You may recall that the imgUploader.php script we discussed earlier creates and saves the image files that will be listed in the Image Element Source Set, and that it also saves some information about each base image in a mySQL database. After the makeComic.php script generates the HTML Head section and some embedded CSS for our comic, it uses the database contents to obtain information in turn about each base image. The PHP class that we use as our database handler is included in the code repository for those who want to take a deep dive. This next code listing shows the procedure we use to loop through the database to process each page image. This next code segment shows how we parse the folder directory of image files we created earlier with the imgUploader.php program to extract an array of image file names that we subsequently use to generate an Image Element containing the Source Set attribute as we showed in our earlier code listing example. So we have examined the methods we use to generate the comic HTML Image Elements and their included Source Set attributes. Our generated base HTML uses this construct to present an adaptive image display for the comic contents. In and of itself the HTML5 Image Element Source Set method we have described here does a fairly decent job of serving and displaying images sized appropriately to our viewport dimensions. But now let’s consider that the Source Set image list is ordered by width, and that the browser selects an image based on this dimension. Now let’s further suppose that the content image has a portrait or vertical aspect ratio and that we are viewing the image on a landscape or horizontal aspect ratio device. We can see that this might result in an image that is taller than our viewport, an image that would be cropped. Of course a similar problem can present with landscape content on a portrait device. For our choice of content which is largely images, that is comics, it is likely better to size the page to fit within the viewport while maintaining its original aspect ratio. This next section of our discussion explains how we use some JavaScript code to appropriately scale the image that was chosen by the Source Set HTML such that it just fits within the device viewport while preserving the image aspect ratio. Image Scaling for a Responsive Design The comics that the Builder creates use a Javascript program named to modify the Source Set adaptive image choice by scaling it responsively. The Reader.js script itself is just over 400 lines of code, but that part of the script used to do our scaling functions is not very long or complex. A key part of the script’s scaling function depends upon its knowing the dimensions of the image it is expected to scale. This next figure illustrates how the browser exposes its image selection (along with some other information about the image display) as may be viewed in the debugger. Reader.js The browser exposes the name of the Current Source Image file as “currentSrc”, and this segment of Reader.js shown next illustrates how we parse that name to obtain our image dimensions. You will recall that the image name generated by the imgUploader.php program encodes these dimensions in the filename. When this code segment completes we will have the image filename along with its width and height dimensions. Note that these dimensions will be the same as those shown as the image “Intrinsic size” in the debug popup in the figure above and which for our example browser selected image file are 1400 x 1575 pixels. The viewport dimensions for our example browser meanwhile are 1150 x 1258. Why the browser chose this source image from the Source Set list I cannot say. However, and most importantly, this is the image that was downloaded to our browser. The next thing our Reader.js script does is a ratiometric scaling of our downloaded image so as to just have it fit within our viewport while preserving its aspect ratio. This is a fairly straightforward process as is shown here next. Once the new scaled dimensions are determined, the script writes these dimensions as width and height Image Element attributes, and then writes the image filename as the Image Element attribute. src The newly modified Image Element which results from our Javascript is shown here, and we can see that it differs from the originally generated HTML by the addition of the Source (src) and the Width and Height attributes. The dimensions resulting from our example source image present a width of 1118 pixels and a height of 1258 pixels. These dimensions are shown in the debug console figure above as the “Rendered size”, and as we stated previously our viewport is 1150 x 1258 pixels. Obviously the rendered image dimensions now fit wholly within the viewport at the maximum size possible while maintaining the original image aspect ratio. These newly appended attributes override the Source Set list. Another final bit of interest is the CSS code we use to be sure our scaled image is centered horizontally within the viewport: .imgblock img { position: relative; left: 50%; transform: translateX(-50%); } Conclusion Building on the foundations of design principles that embrace accessibility guidelines, we have extended our Builder application to improve the image download and display experience for our readers through a combination of adaptive and responsive design techniques. Also published at https://syntheticreality.net .

Introducing the Storybook Comic Book Builder

What Are the Top Startups in Oceania?

In this series of articles (and videos), you’ll learn the basics of Java Database Connectivity, most frequently abbreviated as JDBC. All of the most popular persistence frameworks use JDBC behind the scenes, so having a solid understanding of the key concepts in JDBC is key when using , , , , or any other for Java. JPA Hibernate MyBatis jOOQ database framework The most important concepts that you need to know are: JDBC driver Connection Connection pool In addition to these concepts, you need to understand how to make calls to the database: specifically, how to run an SQL query from a Java program, how to process the results, and how to insert, update, and delete data. This article focuses on what a JDBC driver is and how to open and safely close database connections. The next articles talk about executing SQL statements and using connection pools. The source code is available on with each part in independent Maven projects. Note: GitHub Here’s a version of this article, in case you want to see the concepts in action: video Creating a Demo Database Before we start, we need a database to play with. I assume you have on your computer or have a account with a database instance running in the Cloud (you can for free). In my case, I have it running on my computer, so I can connect to the server using the client tool using the database user that I created beforehand: installed MariaDB SkySQL create an account mariadb mariadb -u user -p You might have to specify the host, port, and database username for your own instance. For example, in the case of SkySQL, you can use something like the following: mariadb --host example.skysql.net --port 5001 \
    --user db_user --password \
    --ssl-verify-server-cert \
    --ssl-ca /path/to/skysql_chain.pem Let’s create a new database with the name and a table with the name as follows: jdbc_demo programming_language CREATE DATABASE jdbc_demo;
USE jdbc_demo;
 
CREATE TABLE programming_language(
    name VARCHAR(50) NOT NULL UNIQUE,
    Rating INT
); Use the command to exit the client tool. quit What Is a JDBC Driver? JDBC is an API : a set of interfaces that define what the technology can do. It doesn’t implement the details of how to connect to a specific database. Instead, it lets database vendors implement the logic required to “talk” to their databases. Each database has a different way to “speak” through the network (the database client/server protocol), so each database needs custom Java code compatible with JDBC. This code is packaged as a JAR file called JDBC driver. specification For example, in the case of (a multi-purpose database), the JDBC driver is . You have to download the JAR file that contains the driver and add it to your Java project. If you are using Maven, you can add the to the file. When you search for the JDBC driver of the database you want, use the latest version of the driver if possible. This way you’ll get the latest security patches, performance improvements, and features. MariaDB MariaDB Connector/J dependency pom.xml Creating a New Maven Project Let’s create a new Java application with a simple pom.xml file that includes the MariaDB JDBC driver: <?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
 
    <groupId>org.example</groupId>
    <artifactId>jdbc-demo</artifactId>
    <version>1.0-SNAPSHOT</version>
 
    <properties>
        <maven.compiler.source>17</maven.compiler.source>
        <maven.compiler.target>17</maven.compiler.target>
    </properties>
 
    <dependencies>
        <dependency>
            <groupId>org.mariadb.jdbc</groupId>
            <artifactId>mariadb-java-client</artifactId>
            <version>2.7.4</version>
        </dependency>
    </dependencies>
 
    <build>
        <plugins>
            <plugin>
                <artifactId>maven-shade-plugin</artifactId>
                <version>3.2.4</version>
                <executions>
                    <execution>
                        <phase>package</phase>
                        <goals>
                            <goal>shade</goal>
                        </goals>
                        <configuration>
                            <transformers>
                                <transformer
                                        implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
                                    <mainClass>com.example.Application</mainClass>
                                </transformer>
                            </transformers>
                        </configuration>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>
 
</project> As you can see, the JDBC driver is called . We have also added the to be able to generate an executable JAR that includes the JDBC driver (Uber JAR). mariadb-java-client Maven Shade Plugin You can place this file in an empty directory and run to download the MariaDB Connector/J dependency and check that the project builds correctly: pom.xml mvn package We are going to code a simple Java application that opens a connection and closes it: no data manipulation for now. That’s covered in the next article of this series. The application includes a Java entry point ( ) and a couple of methods. You can use any IDE to create the class or we can manually add a new subdirectory for the Java package where our code will reside. On Linux-like systems, this will look like the following: main If you are wondering what that little creature is, it’s a seal. MariaDB’s is a . Since I wasn’t able to find a sea lion emoji, instead. Close enough, I guess. logo sea lion I went for a seal Back to the tutorial... Here’s the structure of the Java app (add this into a new file in the directory we created): Application.java package com.example;
 
public class Application {
 
    public static void main(String[] args) throws SQLException {
        openDatabaseConnection();
        closeDatabaseConnection();
    }
 
    private static void openDatabaseConnection() {
    }
 
    private static void closeDatabaseConnection() {
    }
 
} Opening Database Connections From Java With JDBC, Java applications establish database connections through the interface or the class. For simplicity, we are going to use the latter in this step of the tutorial, but most serious applications should use a connection pool (we’ll learn this in the third article of this series). DataSource DriverManager To connect to a database using JDBC, we need something called a connection URL (or JDBC connection string). Depending on your specific database the connection URL will look different, but in general, a connection string looks like this: jdbc:<subprotocol>:<subname> The part identifies the kind of database. The part is database-specific and typically contains information on the location of the database instance and configuration parameters. For example, in the case of a MariaDB server running locally, the connection URL looks something like this: <subprotocol> <subname> jdbc:mariadb://localhost:3306/jdbc_demo Or, if you are using SkySQL: jdbc:mariadb://example.skysql.net:5001/jdbc_demo?useSsl=true&serverSslCert=/path/to/skysql_chain.pem You can find examples of online and in the official documentation of the JDBC drivers. connection URLs for all databases Here’s how we can connect to the database from the Java application: package com.example;
 
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
 
public class Application {
 
    private static Connection connection;
 
    public static void main(String[] args) throws SQLException {
        openDatabaseConnection();
        closeDatabaseConnection();
    }
 
    private static void openDatabaseConnection() throws SQLException{
        System.out.println("Opening database connection...");
 
        connection = DriverManager.getConnection(
                "jdbc:mariadb://localhost:3306/jdbc_demo",
                "user", "password"
        );
 
        System.out.println("Connection valid: " + connection.isValid(0));
    }
 
    private static void closeDatabaseConnection() {
    }
 
} First, we added a Java field of type to the class. This interface contains all the methods to interact with the database. Second, we used the class to get a new connection to the database using the connection URL and the database username and password. Third, we showed a message confirming that the connection is valid. Connection DriverManager Notice that we added a clause to the method signatures. Most JDBC operations throw an in case of errors. We can handle it in a catch block or in the case we want to show an error message to the user. In this demo, we won’t deal with exception handling. throws SQLException Closing Database Connections Before the application finishes, we need to close the database connection. A connection consumes resources that should be released: both operating-system-level resources and database resources such as cursors or handles. Here’s how we can close the database connection: private static void closeDatabaseConnection() throws SQLException {
        connection.close();
    } That’s it really. But, wait! There’s a catch here. Since later in this series, we will be adding functionality between the and methods, things could go wrong at any point, and we might not be able to close the database connection properly. Hopefully, this can be easily solved by enclosing the code in a block and closing the connection in a block. The block always gets executed even if a problem occurs and an exception is thrown. Here’s the final result with the problem fixed: openDatabaseConnection() closeDatabaseConnection() try finally finally package com.example;
 
import java.sql.Connection;
import java.sql.DriverManager;
import java.sql.SQLException;
 
public class Application {
 
    private static Connection connection;
 
    public static void main(String[] args) throws SQLException {
        try {
            openDatabaseConnection();
        } finally {
            closeDatabaseConnection();
        }
    }
 
    private static void openDatabaseConnection() throws SQLException{
        System.out.println("Opening database connection...");
 
        connection = DriverManager.getConnection(
                "jdbc:mariadb://localhost:3306/jdbc_demo",
                "user", "password"
        );
 
        System.out.println("Connection valid: " + connection.isValid(0));
    }
 
    private static void closeDatabaseConnection() throws SQLException {
        connection.close();
        System.out.println("Connection valid: " + connection.isValid(0));
    }
 
} Run to build the final JAR and run it from the command line as follows: mvn clean package java -jar target/jdbc-demo-1.0-SNAPSHOT.jar Here’s a screenshot of the output: Try manually throwing an exception somewhere before closing the connection to simulate an error and see how the connection is closed regardless: if (true) throw new RuntimeException("Simulated error!"); In the next article, we’ll learn how to use the object to send SQL statements to the database. connection Also Published Here

JDBC Tutorial Part 1: How to Connect to a Database

10 Best Magento 2 Mobile App Builders For Ios & Andriod 

Instagram has been growing like crazy over the past few years, and one of its key features that fueled that growth was the famous photo filters. By allowing users to apply various filters to their photos to make them better looking, Instagram has created an amazing product. Any modern photo app must support photo filters nowadays, so we are going to take a look at how to implement the Instagram photo filters in React Native. In today’s article we’re going to be talking about how to implement the Instagram photo filters. We’re going to use the package react-native-image-filter-kit . Here at Instamobile we use this library to seamlessly build this photo filters feature into our Instagram Clone . Implementing Instagram Photo Filters in React Native First of all, let’s install the package we mentioned earlier into your React Native project, by running: npm install -s react-native-image-filter-kit Here’s a simple React Native project that applies the Instagram photo filters to an image included in the codebase (the image name is “parrot.png”, so make sure you include it in your project folder, so that it can be loaded in the app). In the App.js file, simply place the following code snippet: React ; {Image, SafeAreaView} ; {Emboss} ; App = ( <SafeAreaView /> <Image
      style={styles.image}
      source={require('./parrot.png')}
      resizeMode={'contain'}
    />
    <Emboss
      image={
        <Image
          style={styles.image}
          source={require('./parrot.png')}
          resizeMode={'contain'}
        />
      }
    />
  </>
);

const styles = {
  image: {
    width: 320,
    height: 320,
    marginVertical: 10,
    alignSelf: 'center',
  },
}; import from 'react' import from 'react-native' import from 'react-native-image-filter-kit' const => () <> The Emboss component provided by the react-native-filter-kit takes an Image component as a prop and applies the filter as seen in the below screenshot. There are a ton of other filter components provided by the library react-native-image-filter-kit so we are going to find a way to implement a more comprehensive filter just like the way it is done in the Instagram app itself. First of all we need to define an array of filter components that we are going to render and select from: {
  AdenCompat,
  _1977Compat,
  BrannanCompat,
  BrooklynCompat,
  ClarendonCompat,
  EarlybirdCompat,
  GinghamCompat,
  HudsonCompat,
  InkwellCompat,
  KelvinCompat,
  LarkCompat,
  LofiCompat,
  MavenCompat,
  MayfairCompat,
  MoonCompat,
  NashvilleCompat,
  PerpetuaCompat,
  ReyesCompat,
  RiseCompat,
  SlumberCompat,
  StinsonCompat,
  ToasterCompat,
  ValenciaCompat,
  WaldenCompat,
  WillowCompat,
  Xpro2Compat,
} ; FILTERS = [
  { : , : AdenCompat,
  },
  { : , : MavenCompat,
  },
  { : , : MayfairCompat,
  },
  { : , : MoonCompat,
  },
  { : , : NashvilleCompat,
  },
  { : , : PerpetuaCompat,
  },
  { : , : ReyesCompat,
  },
  { : , : RiseCompat,
  },
  { : , : SlumberCompat,
  },
  { : , : StinsonCompat,
  },
  { : , : BrooklynCompat,
  },
  { : , : EarlybirdCompat,
  },
  { : , : ClarendonCompat,
  },
  { : , : GinghamCompat,
  },
  { : , : HudsonCompat,
  },
  { : , : InkwellCompat,
  },
  { : , : KelvinCompat,
  },
  { : , : LarkCompat,
  },
  { : , : LofiCompat,
  },
  { : , : ToasterCompat,
  },
  { : , : ValenciaCompat,
  },
  { : , : WaldenCompat,
  },
  { : , : WillowCompat,
  },
  { : , : Xpro2Compat,
  },
  { : , : AdenCompat,
  },
  { : , : _1977Compat,
  },
  { : , : BrannanCompat,
  },
]; import from 'react-native-image-filter-kit' const title 'Normal' filterComponent title 'Maven' filterComponent title 'Mayfair' filterComponent title 'Moon' filterComponent title 'Nashville' filterComponent title 'Perpetua' filterComponent title 'Reyes' filterComponent title 'Rise' filterComponent title 'Slumber' filterComponent title 'Stinson' filterComponent title 'Brooklyn' filterComponent title 'Earlybird' filterComponent title 'Clarendon' filterComponent title 'Gingham' filterComponent title 'Hudson' filterComponent title 'Inkwell' filterComponent title 'Kelvin' filterComponent title 'Lark' filterComponent title 'Lofi' filterComponent title 'Toaster' filterComponent title 'Valencia' filterComponent title 'Walden' filterComponent title 'Willow' filterComponent title 'Xpro2' filterComponent title 'Aden' filterComponent title '_1977' filterComponent title 'Brannan' filterComponent Next, we have to render a horizontal list of filters at the bottom and dynamically change the filter image as the center image when a filter is selected. Your App.js file will then become: React, {useRef, useState} ; {
  FlatList,
  Image,
  SafeAreaView,
  StyleSheet,
  Text,
  TouchableOpacity,
} ; App = { extractedUri = useRef( ); [selectedFilterIndex, setIndex] = useState( ); onExtractImage = {
    extractedUri.current = nativeEvent.uri;
  }; onSelectFilter = {
    setIndex(selectedIndex);
  }; renderFilterComponent = { FilterComponent = item.filterComponent; image = ( <TouchableOpacity onPress={() => onSelectFilter(index)}>
        <Text style={styles.filterTitle}>{item.title}</Text>
        <FilterComponent image={image} />
      </TouchableOpacity>
    );
  };

  const SelectedFilterComponent = FILTERS[selectedFilterIndex].filterComponent;

  return (
    <>
      <SafeAreaView />
      {selectedFilterIndex === 0 ? (
        <Image
          style={styles.image}
          source={require('./car.jpg')}
          resizeMode={'contain'}
        />
      ) : (
        <SelectedFilterComponent
          onExtractImage={onExtractImage}
          extractImageEnabled={true}
          image={
            <Image
              style={styles.image}
              source={require('./car.jpg')}
              resizeMode={'contain'}
            />
          }
        />
      )}

      <FlatList
        data={FILTERS}
        keyExtractor={item => item.title}
        horizontal={true}
        renderItem={renderFilterComponent}
      />
    </>
  );
};

const styles = StyleSheet.create({
  image: {
    width: 520,
    height: 520,
    marginVertical: 10,
    alignSelf: 'center',
  },
  filterSelector: {
    width: 100,
    height: 100,
    margin: 5,
  },
  filterTitle: {
    fontSize: 12,
    textAlign: 'center',
  },
}); import from 'react' import from 'react-native' const => () const 'https://www.hyundai.com/content/hyundai/ww/data/news/data/2021/0000016609/image/newsroom-0112-photo-1-2021elantranline-1120x745.jpg' const 0 const ( ) => {nativeEvent} const => selectedIndex const ( ) => {item, index} const const );

    return ( < = = ' / ')} = ' '}
      /> Image style {styles.filterSelector} source {require( . car.jpg resizeMode { contain This source code will produce a simple React Native app that supports selecting various Instagram photo filters, just like the real Instagram photo editor: After selection, the handler method onExtractImage saves the URL of the extracted image after the filter has been selected on the variable extractedUri ref which you could save on your server. Conclusion Now that we have learned how to implement Instagram filters you should check out our Instagram Clone template , which is a fully functional codebase, supporting most of the Instagram features, such as photo filters, ephemeral stories and post feeds. Previously published at https://www.instamobile.io/react-native-tutorials/instagram-photo-filters-react-native/

Instagram-like Filters In React Native

Human civilization is fast approaching the Post Human Era or Transhuman Era where machines start to become more powerful than us. I’d say 2020 was just a taste of the overwhelming power of digital technologies such as social networks, smartphones, ubiquitous connectivity, etc. This is extraordinarily overwhelming, disastrous, scary, crazy, beautiful and awe inspiring at the same time. You feel every emotion possible to know that we are living in the future. But more people need to come on board and acknowledge that human civilization is on a dramatic exponential curve currently and that exponential, futuristic and sci-fi technologies such as Artificial Intelligence, Blockchain, Cryptocurrencies, Quantum Computing, 3D Printing, Neuromorphic Chips, Neural Interfaces, Spatial Computing, Virtual Reality, Augmented Reality, Electric Vehicles, Autonomous Vehicles, VTOL Vehicles, Clean Energy, Smart Cities, Smart Homes are fast maturing. Masayoshi Son, the founder and CEO of SoftBank believes that we are approaching the technological singularity as these digital technologies start to accelerate at an unfathomable pace resulting in massive transformations to human civilization at an increasing pace. I would say we have had several inflection points with new breakthrough technologies such as the iPhone a culmination of Steve Jobs legendary work at Apple, which brought ubiquitous mobile computing, something that transformed human civilization and something we take for granted. However, when we have advanced Artificial Intelligence (Artificial General Intelligence, Artificial Super Intelligence) and Brain Computer Interfaces, which I believe we will have this decade, I believe we will truly reach the technological singularity and a transhuman, sci-fi fantasy future. I believe we are already very close to brain computer interfaces with neurotech companies like Elon Musk’s Neuralink which will connect our brains directly to the cloud and to Artificial General Intelligence, with the recent progress at the cutting-edge of AI from AI Labs such as Google DeepMind. The Fifth Wave of Computing Deep Learning, DeepMind’s MuZero and AlphaFold, OpenAI’s GPT3, Artificial General Intelligence (AGI) Dawn of a New Computing Era, The Fifth Wave of Computing, Technological Singularity, Nvidia's Arm Acquisition Thematic Technology & Innovation Investing for the 2020s Industry 4.0 As Wikipedia defines it, “the technological singularity — also, simply, the singularity — is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization.” Witnessing the dramatic acceleration of these exponential technologies in the last decade, and understanding the magnitude of exponential technologies in the post-covid world of the next decade in the 2020s, I believe we have passed the pivot point and are going through the great transformation or the singularity already. 59 Best Singularity Books of All Time Where humanity goes from here is unprecedented, but its a reckoning to realize that we as a species and civilization have come this far. What binds humanity is its exponential arc since the first cavemen to primitive civilizations to advanced civilizations to the 21st century modern society we are creating. The Civilizational Changes of The Transformation The laws of economics are changing from scarcity to abundance as everything basically becomes free and every industry goes through unprecedented digital transformation and disruption. An easier way to process this change is thinking about the exponential improvements in computing, digital technologies and the proliferation of software and data that is eating the world. Humans tend to have a linear and intuitive view of the way things progress however from a historical perspective things have been progressing exponentially. As we look back at ancient civilizations to even 50 years ago the technology we currently possess is “godlike” and highly advanced. And the technology we possess today is highly primitive compared to what it may look like 5, 10 or even 50 years from now. Technology is progressing so fast, but as humans we are hardwired to not really process that exponential change. I think we need to change our mindset however, as the institutions, corporations and governments in the world are starting to break down at this point. We need more forward thinking leadership in business, government and education. We need to acknowledge an accelerating pace of change instead of avoiding it. And we need to get as many people as possible on the same page. The Technology Long Booms that Remade the World from 2020 to 2050 We also need to be measured and have cautious optimism about the future, close monitoring and regulation of many technology developments and we need to make sure that digital technologies and mega technology corporations (megacorps) such as Google create more wealth than they destroy for more people. People also need to be open to change, engage in these discussions, embrace disruption rather than fear it and become abundance minded. Because we need more entrepreneurs and startups to challenge and compete with the magacorps before they become disproportionately powerful and impossible to compete against. More wealth will be created in this decade but it needs to become highly distributed and collectivized rather than highly concentrated to create true abundance and sustainable capitalism. I think it’s also time as humans to come together in the 2020s, work together and to build a better and brighter future where we can solve exponential challenges such as technological unemployment, climate change and mental health in times of increasing change, disruption and uncertainty. The Future History of America and the World to 2050

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