投稿時間:2023-02-24 14:37:27 RSSフィード2023-02-24 14:00 分まとめ(37件)

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IT	ITmedia 総合記事一覧	[ITmedia エンタープライズ] 景気後退がセキュリティ部門に与える影響は？　（ISC）2が調査レポートを公開	https://www.itmedia.co.jp/enterprise/articles/2302/24/news111.html	itmedia	2023-02-24 13:48:00
IT	ITmedia 総合記事一覧	[ITmedia エンタープライズ] 「IT予算を削減しつつ、自治体DXを推進せよ」　デジタル田園都市国家構想は“ジレンマ解消”の切り札となるか？	https://www.itmedia.co.jp/enterprise/articles/2302/24/news028.html	ITmediaエンタープライズ「IT予算を削減しつつ、自治体DXを推進せよ」デジタル田園都市国家構想は“ジレンマ解消の切り札となるか「DX推進のためにIT予算を拡大しよう」と考える多くの民間企業とは異なり、自治体DXには「DXを推進しつつIT予算は削減したい」という政府の思惑がある。	2023-02-24 13:15:00
IT	ITmedia 総合記事一覧	[ITmedia PC USER] Synology、10GbEポートを備えた8ベイ搭載ビジネス向けNASキット	https://www.itmedia.co.jp/pcuser/articles/2302/24/news124.html	diskstationdsxs	2023-02-24 13:14:00
TECH	Techable（テッカブル）	東急、慶應義塾大学院とVRデバイス活用の「リアル×バーチャルの謎解きイベント」を実施	https://techable.jp/archives/197047	polipro	2023-02-24 04:00:11
IT	情報システムリーダーのためのIT情報専門サイト IT Leaders	田辺三菱製薬、3年半で500超の業務をRPAで自動化、7万時間を削減 | IT Leaders	https://it.impress.co.jp/articles/-/24496	itleaders	2023-02-24 13:02:00
AWS	lambdaタグが付けられた新着投稿 - Qiita	SlackからOpenAI(GPT3)を使う	https://qiita.com/ura1020/items/50f8f2fde1714356fd11	chatgpt	2023-02-24 13:48:36
python	Pythonタグが付けられた新着投稿 - Qiita	SlackからOpenAI(GPT3)を使う	https://qiita.com/ura1020/items/50f8f2fde1714356fd11	chatgpt	2023-02-24 13:48:36
python	Pythonタグが付けられた新着投稿 - Qiita	while文	https://qiita.com/rion3376/items/64ca424ebd99c7dd48cf	while	2023-02-24 13:40:21
js	JavaScriptタグが付けられた新着投稿 - Qiita	paizaラーニング レベルアップ問題集 新・Bランクレベルアップメニュー JavaScript 【全探索 4】ストラックアウト	https://qiita.com/ZampieriIsa/items/845f0e1ff5ec4d3b9283	javascript	2023-02-24 13:07:24
Ruby	Rubyタグが付けられた新着投稿 - Qiita	AWS-SDK lambda invokeAsyncをinovokeに変更しろくださいの通知をやっと対応したので備忘録てきな	https://qiita.com/sawvistlip/items/52287263c833fe6e67bc	respon	2023-02-24 13:53:51
Ruby	Rubyタグが付けられた新着投稿 - Qiita	【Ruby on Rails】更新ボタン・削除ボタンを作成する方法	https://qiita.com/kdbrnm24/items/64d86383a3da69a1c85d	rubyonrails	2023-02-24 13:01:20
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	Ubuntuのアップデートしたら ssh hostname.local でログインできなくなった事の対策 (avahi-daemonの設定)	https://qiita.com/taiyodayo/items/3da07a4e201332e891dd	avahidaemon	2023-02-24 13:17:00
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS DR（災害対策）戦略設計	https://qiita.com/tanamoru/items/08b01ae459a4ddac2988	awsdr	2023-02-24 13:56:03
AWS	AWSタグが付けられた新着投稿 - Qiita	AWS-SDK lambda invokeAsyncをinovokeに変更しろくださいの通知をやっと対応したので備忘録てきな	https://qiita.com/sawvistlip/items/52287263c833fe6e67bc	respon	2023-02-24 13:53:51
Ruby	Railsタグが付けられた新着投稿 - Qiita	RSpecのWARN: Clearing out unresolved specsのトラブルシュート	https://qiita.com/pyon_kiti_jp/items/d60a8143743845a3e74f	iousspecsduringgemspecifi	2023-02-24 13:57:40
Ruby	Railsタグが付けられた新着投稿 - Qiita	【Ruby on Rails】更新ボタン・削除ボタンを作成する方法	https://qiita.com/kdbrnm24/items/64d86383a3da69a1c85d	rubyonrails	2023-02-24 13:01:20
海外TECH	DEV Community	[TECH]PCI SSC Security Requirements for Fintech Apps	https://dev.to/teamliapp/techpci-ssc-security-requirements-for-fintech-apps-bp5	TECH PCI SSC Security Requirements for Fintech AppsHello this is LIAPP TEAM The security issue of Fintech apps is emerging as a hot topic in various fields worldwide xpectations for Fintech apps are rising as financial services become more advanced and payments in non financial IT sectors are active Still the frequency of personal information leakage accidents is also increasing Therefore to make lesser concerns about the exposure of personal information of Fintech app users Fintech companies should do their best to strengthen security with safety as collateral away from reckless evasion of responsibility For this reason the payment card industry data security standard PCI DSS Payment Card Industry Data Security Standard is emerging as a security standard for Fintech companies Five multinational card payment brands VISA MasterCard American Express DISCOVER and JCB International have established the Payment Card Industry Security Standards Committee Payment Card Industry Security Standards Council hereafter PCI SSC Their mission is to protect personal information related to payment and provide technical requirements for protecting cardholders data and sensitive personal information data This content has been written to help you understand each item of the PCI Mobile Payment Acceptance Security Guidelines issued separately by the PCI SSC for the security of mobile card payment systems In addition we will introduce the security features of LIAPP that can be applied to each item in order to comply with PCI regulations and show you how to protect Fintech apps strongly LIAPP Auth Create server side controls and report unauthorized accessThis recommendation is for developing a comprehensive payment authorization solution that can detect report and disconnect unauthorized access attempts or abnormal behavior to mobile apps This is the LIAPP Auth function of LIAPP which blocks bypass connections directly to the app server and can be set up to prevent the app from running through an abnormal path Detect and block LIAPP Root Jailbroken and Virtual Machine Devices Prevent escalation of privilegesIt is recommended to block routing run apps on escaped devices and increase security by sending alarms or warning messages if a risk is detected However mobile hacking is primarily caused by not blocking unauthorized connections LIAPP can detect unauthorized access from routing rooted or jailbroken devices OS tampered devices and virtual machines sending out alarms and strongly blocking app execution and access LIAPP Anti Tampering Anti Debugging and Anti Repackaging Harden the applicationThis item is an application enhancement that prevents users from unintentionally accessing mobile apps or inserting malicious code and recommends anti tempering with reverse engineering LIAPP prevents analysis through decompile or reverse engineering by encrypting critical source codes dynamic analysis during app execution with an anti debugging function and blocks tampering with the app by detecting signs of app modulation Anti repacking blocking can also prevent malicious redistribution by protecting sensitive information files used by apps LIAPP Realtime Hacking Tools Registration Protect against known vulnerabilitiesIt recommends patching mobile devices and apps to ensure they are always up to date As a result LIAPP can strongly block known hacking techniques and directly register hacking tools to address the latest security vulnerabilities LIAPP s premium plans LIAPP Enterprise and LIAPP For Game provide servers and monitoring dashboards dedicated to customers enabling real time reporting of the number of app users hacking incidence and hacking types In addition users can immediately change the on off button to enable features such as anti debugging integrity modulation detection virtual machine detection hacking tool detection and administrator rights detection Compliance with PCI SSC security regulations is not just about preventing privacy leaks In addition it increases the reliability of Fintech apps improving its reputation for Fintech services As mobile payments through Fintech apps gradually play a central role in the payment industry compliance with related regulations is now becoming an essential factor Compliance with PCI SSC may initially seem complicated but mobile security services such as LIAPP make it easier and simpler to apply security features If you ve already released or are preparing for a Fintech app why don t you take this opportunity to strengthen your mobile app security policy with LIAPP About a month before the app s launch LIAPP team recommends a schedule to distribute it to the market by strengthening security in advance We hope that it will become a Fintech app service that runs fast in the global market with LIAPP in the future Source of data PCI Mobile Payment Acceptance Security Guidelines PCI Mobile Payment Acceptance Security Guidelines for Developers pdfLIAPP we only offer the best service	2023-02-24 04:40:19
海外TECH	DEV Community	SvelteKit: Easy code-based router for enterprise, instead of file-based router for home pages [February 2023]	https://dev.to/maxcore/sveltekit-easy-code-based-router-for-enterprise-instead-of-file-based-router-for-home-pages-february-2023-2fk	SvelteKit Easy code based router for enterprise instead of file based router for home pages February Dear dudes It s the third time I ve made that patch For those who build enterprises and need total control over files folders and their names structure If it s what you ve been searching ーwelcome reading In result there is some urls js file with routes declaration const layouts marketing component src marketing svelte pages about pattern about component src about svelte app component src layout svelte layouts settings component src settings svelte pages privacy pattern privacy component src privacy svelte profile pattern profile component src profile svelte pages pattern component src home svelte username pattern component src user svelte post slug pattern post component src post svelte Here we have independent layouts marketing and app Each layout has its pages collection and app layout even has nested layout ーsettings Guess you ve mentioned that components paths are already nice More of that ーorder is respected Not only between patterns but between layout and pages privacy and profile matches first and only then its username turn Along with component all other params could be passed universal src anyname js aka xxx js server src anyname js aka xxx server js endpoint src anyname js aka server js error src anyname svelte aka error svelte params name slug matcher undefined optional false rest false chained false In case you need full control If it looks nice and you are ready to forget about page app svelte here is the implementation SvelteKit s magic is happening there node modules sveltejs kit src core sync create manifest data index jsFirstly some walk function creates some routes object based on file system Then validates and enriches it You can print and see that routes object prevent conflicts routes console log routes You ll see something like id segment pattern params layout depth child pages component src routes layout svelte error null leaf null page null endpoint null id home segment home pattern home params layout null error null leaf depth component src routes home page svelte page null endpoint null So the goal is to build that routes object somehow ourselves So what exactly has to be done Create urls js somewhere guess in root will be nice with contents const layouts is just for an example const layouts marketing component src marketing svelte pages about pattern about component src about svelte app component src layout svelte layouts settings component src settings svelte pages privacy pattern privacy component src privacy svelte profile pattern profile component src profile svelte pages pattern component src home svelte username pattern component src user svelte post slug pattern post component src post svelte export function routes const result function run depth items parent for const id item of Object entries items const route id id segment id split id pattern item pattern params item params error item error depth depth component item error null endpoint item endpoint file item endpoint null page null layout null leaf null parent parent const details depth depth child pages universal item universal server item server component item component if id startsWith means ーif layout route layout details else route leaf details if route params route params length const matches id match g for const match of matches route params push name match replace replace matcher undefined optional false rest false chained false result push route Do it like this to save order for const field object of Object entries item const layouts field layouts object null const pages field pages object null if layouts amp amp Object keys layouts length run depth layouts route if pages amp amp Object keys pages length run depth pages route run layouts null return result In svelte config js import routes from urls js const config routes routes Open node modules sveltejs kit src core sync create manifest data index jsa Find prevent conflicts routes const root routes Paste between that two lines right in line routes length routes push config routes b Find routes sort routes routes Replace with just routes routesNo need for additional magic sorting everything is under control Minor notes Layouts names may be anything but unique and must not start with Pages names must match pattern and start with If you want you could install patch package so this changes will be automatically applied in future without manual hacks gt npm i patch package gt npx patch package sveltejs kitpackage json scripts postinstall patch package lt ーadd this Hope it helps	2023-02-24 04:39:01
海外TECH	DEV Community	Determining the RGB "Distance" Between Two Colors	https://dev.to/bytebodger/determining-the-rgb-distance-between-two-colors-4n91	Determining the RGB quot Distance quot Between Two Colors NOTE The live web app that encompasses this functionality can be found here All of the underlying code for that site can be found here At this point in the series I ve done things that are quite honestly pretty simple from a programmatic sense at least Loading an image in React isn t exactly rocket science Transferring that image into a lt canvas gt element has been done a million times before Pixelating the image and repainting the canvas requires a bit of code but it really ain t that hard Here s the point where I hit an unexpected hurdle I now have a complete inventory of paints with their associated RGB equivalents Or at least as close as I could determine That particular journey was outlined in the first article in this series I also have an image that s been loaded into memory and I ve parsed through the image pixel by pixel in order to create a pixelated version of the original So now that I have the pixelated blocks I need to look at the color of each block and determine which color in my inventory of paints most closely matches the block The easy approachWhen I set about creating the underlying code for Paint Map Studio I thought that the color matching aspect of the equation would be simple I was working off a few basic assumptions Now that I have the pixelated version of the image each block is represented by RGB values I ve captured all of the RGB values for every paint in my inventory RGB values are nothing more than numbers And as long as I have the numbers associated with the pixelated block and I have the numbers associated with every paint in my inventory it should be elementary to do some simple math and determine which paint is closest to the color in the original block Then I started noticing some quirks First let s look again at the pixelated image that I m trying to match against my inventory of paints We generated it in the last article but I ll display it again here Then let s look at my inventory of paints They look like this Obviously if those paint colors are represented onscreen then I must have the underlying RGB values for each color You can see the full list of paints here There are more than of them RGB matchingPreviously we weren t trying to match the pixelated blocks against any kinda reference color So if we want to do that we ll need to add some more logic to the pixelate function The new function looks like this const pixelate gt const height width canvas current const stats colorCounts colors map const blockSize loadPalettes for let y y lt height y blockSize const row for let x x lt width x blockSize const remainingX width x const remainingY height y const blockX remainingX gt blockSize blockSize remainingX const blockY remainingY gt blockSize blockSize remainingY const referenceColor calculateAverageColor context current getImageData x y blockX blockY referenceColor name const closestColor getClosestColorInThePalette referenceColor row push closestColor if Object hasOwn stats colorCounts closestColor name stats colorCounts closestColor name else stats colorCounts closestColor name stats colors push closestColor context current fillStyle rgb closestColor red closestColor green closestColor blue context current fillRect x y blockX blockY stats map push row return stats This is pretty similar to the pixelate function that I showed you in the last article with a few key additions Before we enter the for loops I ve added a call to loadPalettes This will grab the RGB data from all of the existing paints in my inventory After we calculate the average color we need to take the resulting RGB object and pass it into getClosestColorInThePalette This will compare the RGB values of the pixelated blocks to all of the RGB values for the existing paints I m not going to bother illustrating the code in loadPalettes It grabs a static object that contains all of the RGB values for my paint inventory Those values look like this red green blue name Liquitex Alizarin Crimson Hue Permanent red green blue name Liquitex Raw Umber red green blue name Liquitex Yellow Light Hansa red green blue name Liquitex Cadmium Red Medium red green blue name Liquitex Parchment red green blue name Liquitex Bright Aqua Green red green blue name Liquitex Brilliant Blue red green blue name Liquitex Brilliant Purple red green blue name Liquitex Brilliant Yellow Green red green blue name Liquitex Bronze Yellow red green blue name Liquitex Burnt Sienna red green blue name Liquitex Burnt Umber and on and on and on Much more important is what happens inside getClosestColorInThePalette That s shown here const getClosestColorInThePalette referenceColor rgbModel gt const key referenceColor red referenceColor green referenceColor blue use the existing calculation if we already determined the closest color for this given referenceColor if closestColors key return closestColors key let closestColor blue green name red let shortestDistance Number MAX SAFE INTEGER loop through every paint color this was already loaded inside loadPalettes palette forEach paletteColor gt if we already found an exact match then short circuit the comparisons if shortestDistance return calculate the distance between referenceColor and this particular paint color const distance Math abs paletteColor red referenceColor red Math abs paletteColor green referenceColor green Math abs paletteColor blue referenceColor blue if this paint color is closer than any of the others we examined save it as the paint that is currently the closest if distance lt shortestDistance shortestDistance distance closestColor paletteColor closestColors key paletteColor return closestColor Imagine that our reference block has RGB values of rgb Then imagine that we re looping through the colors in the palette and we re comparing it to Liquitex Alizarin Crimson Hue Permanent That paint has RGB values of rgb We can determine the difference between both colors by adding the difference between each color s red to the difference between each color s blue to the difference between each color s green Therefore the formula for determining the RGB difference between the reference color and Liquitex Alizarin Crimson Hue Permanent would be const distance Math abs Math abs Math abs So the distance between the reference color and Liquitex Alizarin Crimson Hue Permanent would be Moving onward through the array of paint colors we see that Liquitex Bright Aqua Green has RGB values of rgb Therefore the formula for determining the RGB difference between the reference color and Liquitex Bright Aqua Green would be const distance Math abs Math abs Math abs So the distance between the reference color and Liquitex Bright Aqua Green would be This means that according to our RGB calculations Liquitex Alizarin Crimson Hue Permanent is closer to our reference color than Liquitex Bright Aqua Green Obviously this doesn t necessarily mean that either of those two paint colors are particularly close to the reference color At least not as judged by the human eye But mathematically speaking Liquitex Alizarin Crimson Hue Permanent is a closer match to the reference color than Liquitex Bright Aqua Green The idea here is that once you ve cycled through every single paint in the inventory more than different colors the algorithm will find the closest paint color to the reference block And the hope is that the closest color will be a pretty good match to the human eye So how does it perform Well let s take a look Oof When we use the RGB color difference algorithm shown above to match all of the blocks in the target image to our inventory of known paint colors this is what we get That s not great It s still pretty obvious that we re looking at a pixelated image of a woman s face a woman with brown skin tone But the algorithm has done some wonky things Her face looks as though it s been smeared with pink and tan paint When I first started doing this results like these baffled me I reasoned that with colors available in my inventory I should be able to come up with pretty close matches for nearly any color in the image But there were a few things that I didn t understand First colors is not really a lot of colors with which to recreate something as nuanced as a human face When you re setting up to do some painting that may feel like a huge inventory but it s nothing compared to the millions of colors that we perceive in any given photo So one problem is the breadth of my palette However I m not going to dive into that issue yet because that s the subject for a future article Second although RGB values feel to us as programmers as though they re nothing more than numbers and numbers feel as though they should be easy to manage programmatically mathematically the human eye does not perceive color in the same way that our program does when we we re trying to parse through the image algorithmically It s entirely possible for two colors to be relatively close in terms of their RGB values but for our eyes to perceive those colors as being quite different For example if you look closely at her hair you ll see that there s a lot of dark green in the translated image If you look at the original image you probably won t perceive any green in her hair But when the algorithm tried to find the closest RGB match for some of those darker shades of grey in her hair the closest match it could find was a dark green So aside from the problems we encounter with our limited palette again this will be covered in a future article we also have problems with the basic algorithm that we re using to find the closest colors Some colors that are relatively close in the RGB color space look as though they are not close at all to the human eye To be fair calculating the basic RGB difference between colors is not always inherently bad For some target images it may be perfectly fine to use this basic algorithm But when trying to match against more nuanced values like those found on a human face the RGB difference algorithm often falls short What images would work best for this sort of basis RGB analysis Images that feature a limited color palette Images that feature subjects with high contrast For example this image Fares far better with only a basic RGB analysis Every pixel on those balls is perfectly matched to one of the colors in my paint inventory And there aren t too many parts of the image that look outright wrong This happens because those balls feature much higher contrast and it s easier for the basic algorithm to match them against the core set of paints And BTW I m noticing that there are a few odd black pixels in the processed image That represents a bug that I ll have to troubleshoot But when we take a low contrast image like this We get strange color matching again Notice how those odd greens have popped up in our processed image again In the next installment Thankfully there are more ways than simple RGB difference calculations to accomplish color matching In the next installment I ll show different models we can use to find better matches These include calculations based on different color spaces XYZ CMYK and L a b and a fancy sounding formula called Delta E	2023-02-24 04:17:11
海外TECH	DEV Community	Casual meet and greet with Drive LLC.	https://dev.to/dipisha03/casual-meet-and-greet-with-drive-llc-7nc	Casual meet and greet with Drive LLC Hi everyone I m Founder of Drive LLC where we help people find their Drive in tech We are hosting a casual virtual meet and greet tomorrow Friday at pm EST If you are looking to find a tech opportunity that is aligned with your skill sets highly recommend attending and building a network and learning how to do so via Drive LLC Link to sign up	2023-02-24 04:16:00
海外TECH	DEV Community	How JuiceFS Accelerates Edge Rendering Performance in Volcengine	https://dev.to/daswu/how-juicefs-accelerates-edge-rendering-performance-in-volcengine-5geo	How JuiceFS Accelerates Edge Rendering Performance in VolcengineAbout the AuthorLanzhou He Senior Development Engineer of Volcengine Edge Computing is in charge of technology selection development and SRE of edge storage Major research areas are distributed storage and distributed caching Also a fan of cloud native open source community Volcengine is a subsidiary of ByteDance providing cloud based services Edge Rendering a product of the Volcengine Edge Cloud can help users achieve easy scheduling of millions of rendering frame tasks nearby scheduling of rendering tasks and rendering multi task multi node parallel rendering greatly improving rendering efficiency Storage Challenges for Edge ComputingHere is a brief overview of the problems encountered in edge rendering The object storage and the file system metadata are not unified and thus data cannot be directly manipulated through POSIX after uploading through object storage It cannot meet the needs of high throughput scenarios especially when reading The S API and POSIX interface have not been fully implemented To solve the storage problems encountered in edge rendering the team spent almost half a year conducting storage selection tests Initially the team chose an in house storage component that met our needs relatively well in terms of sustainability and performance When it comes to edge computing cases there are two specific problems First the in house components are designed for the IDC and have high requirements for machine specifications which are difficult to meet Second the entire company s storage components are packaged together including object storage block storage distributed storage file storage etc while the edge side mainly requires file storage and object storage which needs to be tailored and modified Besides it takes effort to achieve stability Based on those problems the Volvengine team came up with a feasible solution after discussion CephFS MinIO gateway with MinIO providing the object storage interface and the final result is written to CephFS The rendering engine mounts CephFS for rendering operations During testing and validation the performance of CephFS started to degrade with occasional lags when the files reached the million level which did not meet the requirements of the internal users After more than three months of testing we landed on several core requirements for storage in edge rendering Simple Operations and maintenance O amp M storage developers can get started easily with the O amp M documentation future expansion and outage handling need to be simple enough High data Reliability as it provides storage service directly to users the data written can not be lost or tampered with The use of a single metadata engine to support object storage and file storage it reduces complexity by eliminating the need to upload and download multiple times Better performance for reads Improve read performance as read occurs much more than write Community activity An active community means faster development and is more helpful when problems occur Why JuiceFSThe Volcengine Edge Storage team learned about JuiceFS in September and had some communications with the Juicedata team Thereafter Volcengine decided to try JuiceFS They started with PoC testing in the test environment mainly focusing on feasibility evaluation the complexity of operation and deployment application adoption and whether it meets the application s needs Two sets of environments have been deployed one based on a single node Redis plus Ceph and the other on a single instance MySQL plus Ceph The overall deployment process is very smooth because Redis MySQL and Ceph deployed via Rook are relatively mature the documentation for the deployment is comprehensive and the JuiceFS client can easily work with these databases and Ceph In terms of application adoption Edge Cloud is based on cloud native development and deployment while JuiceFS supports S API is fully compatible with POSIX protocol and also supports Kubernetes CSI which fully meets the application requirements of Edge Cloud After comprehensive testing JuiceFS turned out to fully fit the needs of the application adoption side It can also be deployed and run in production to meet the online needs of the application adoption side Application Process OptimizationBefore using JuiceFS edge rendering mainly utilized ByteDance sinternal object storage service TOS where users upload data to TOS the rendering engine downloads the files to local reads the local files generates rendering results and then uploads results back to TOS and finally users download the rendering results from TOS The overall process involves many network requests and data copying so any network jitter or high latency in the process will affect user experience After using JuiceFS the data flow is greatly simplified user uploads through the JuiceFS S gateway Since JuiceFS supports both S and POSIX API it can be mounted directly to the rendering engine which reads and writes the files via POSIX so that the end user downloads the rendering results directly from the JuiceFS S gateway making the overall process more concise and efficient as well as more stable Read file acceleration large file sequential write accelerationThanks to the caching mechanism of the JuiceFS Client we can cache frequently read files to the rendering engine which greatly accelerates read speed Our comparison test shows that using caching can improve throughput by about times Similarly because the write model of JuiceFS is to commit to memory first when a chunk default M is full or when the application calls close or fsync the data is then uploaded to the object storage and the file metadata is updated after the data is successfully uploaded Therefore when writing large files they are written to memory first and then persisted to disk which can greatly improve the writing speed of large files The current scenario of Edge is mainly rendering where the file system reads much more than writes and the files written are usually large JuiceFS matches these scenarios very well How to use JuiceFS in Edge StorageJuiceFS is primarily deployed on Kubernetes with a DaemonSet responsible for mounting the JuiceFS file system and then providing hostPath to rendering engine pods If the mount point fails DaemonSet takes care of automatically restoring the mount point In terms of permission control Edge Storage authenticates the identity of the JuiceFS cluster nodes through LDAP and each JuiceFS Client authenticates with the LDAP through the LDAP client Hands on experience in JuiceFS storage production environment Metadata EnginesJuiceFS supports many databases as metadata engines e g MySQL Redis MySQL is used in the production environment for the Volcengine Edge Storage as MySQL does a better job in terms of operations data reliability and transactions MySQL currently uses both single instance and multi instance one master two slaves deployment which is flexible for different Edge scenarios In environments with low resources it is possible to use single instance deployment with a relatively stable MySQL throughput Both deployment scenarios use high performance cloud disk provided by Ceph cluster as MySQL data storage ensuring data security In a resource rich scenario you can set up a multiple instance deployment The master slave synchronization of multiple instances is achieved by the orchestrator component provided by MySQL Operator It will be considered healthy only if all the two slave instances are synchronized successfully however as the timeout is also set if the synchronization does not complete in time it will trigger the alarm When the disaster recovery solution becomes ready later we may use the local disk as the MySQL data storage to further improve read and write performance reducing latency and improving throughput MySQL single instance ConfigurationContainer Resources CPU CMemory GDisk G based on Ceph RBD metadata takes about G for millions of files Container image mysql MySQL s my cnf configuration ignore db dir lost found delete when using MySQL and above max connections innodb buffer pool size G Object StorageWe use a Ceph cluster as object storage which is deployed via Rook and currently the production environment uses the Octopus release With Rook Ceph clusters can be operated and maintained in a cloud native way and Ceph components are managed through Kubernetes which greatly reduces the complexity of Ceph cluster deployment and management Ceph server hardware configuration core CPUGB RAMSystem Disk T NVMe SSDData disk T NVMe SSDCeph server software configuration OS Debian Kernel modify proc sys kernel pid maxCeph version OctopusCeph storage backend BlueStoreCeph copies Turn off automatic adjustment of Placement GroupThe main focus of Edge rendering is low latency and high performance so in terms of hardware selection NVMe SSD disks are preferred and Debian is chosen as the operating system Triple replication is configured for Ceph Because erasure code in edge computing environments might take up too many resources JuiceFS ClientThe JuiceFS client works with Ceph RADOS better performance than Ceph RGW but this feature is not enabled by default in the official binary so you need to recompile the JuiceFS client Librados needs to be installed first It is recommended to match the version of librados to the version of Ceph Debian does not come with a librados dev package that matches the version of Ceph Octopus v so you need to download it manually After installing librados dev you can start compiling the JuiceFS client We use Go for compiling which can control the maximum memory allocation to prevent OOM in extreme cases where the JuiceFS client takes up too much memory make juicefs cephYou can create a file system and mount the JuiceFS on the computing node once the compiling is completed Please refer to the JuiceFS official documentation OutlookJuiceFS is a cloud native distributed storage system which provides CSI Driver to support cloud native deployment methods In terms of O amp M JuiceFS provides a lot of flexibility and users can choose either cloud or on premise deployment JuiceFS is fully compatible with POSIX thus file manipulations are very convenient Although JuiceFS can come with high latency and low IOPS when reading and writing small random files because of object storage as back end storage it has a great advantage in read only or read intensive scenarios which fits the application needs of edge rendering scenarios very well The future collaboration between Volcengine Edge Cloud and JuiceFS will focus on the following aspects more cloud native Volvengine currently uses JuiceFS via hostPath and later may switch to JuiceFS CSI Driver for elastic scaling scenarios Metadata engine upgrade Extract metadata service into a gRPC service providing multi level caching capabilities for better performance in read intensive scenarios The underlying metadata engine might migrate to TiKV for better scaling compared to MySQL New features and bug fixes for the current scenario some features will be added and some bugs will be fixed we expect to contribute PR to the community and give back to the community From JuiceFS Juicedata	2023-02-24 04:11:06
海外科学	BBC News - Science & Environment	England's archaeological history gathers dust as museums fill up	https://www.bbc.co.uk/news/science-environment-64707488?at_medium=RSS&at_campaign=KARANGA	crisis	2023-02-24 04:21:54
医療系	医療介護 CBnews	病床使用率が低下傾向、全ての地域で3割下回る-厚労省がコロナアドバイザリーボードの評価公表	https://www.cbnews.jp/news/entry/20230224132426	厚生労働省	2023-02-24 13:40:00
医療系	医療介護 CBnews	資格確認書の診療報酬「保険証と同様に」-加藤厚労相が認識「問診などに手間」	https://www.cbnews.jp/news/entry/20230224130309	健康保険証	2023-02-24 13:15:00
金融	ニッセイ基礎研究所	IAIGsの指定の公表に関する最近の状況（７）－５２のIAIGsを指定－	https://www.nli-research.co.jp/topics_detail1/id=74005?site=nli	目次ーはじめにーIAIGsとはIAIGsの選定基準今回のIAIGsの指定に関する情報の公表IAIGsに対する監督・規制ーIAISによるIAIGsの指定に関する登録簿の最新情報今回の情報更新に基づいて、IAIGsに指定された保険グループの状況新たにIAIGsに指定された保険グループの概要ーまとめ各国・地域の保険監督当局等によるIAIGs国際的に活動する保険グループの指定を巡る状況については、年に、つの保険年金フォーカス「IAIGsの指定の公表に関する最近の状況ーグループのうちのグループが明らかにー」、「IAIGsの指定の公表に関する最近の状況ーグループのうちのグループが明らかにー」、「IAIGsの指定の公表に関する最近の状況ーグループのうちのグループが明らかにー」及び「IAIGsの指定の公表に関する最近の状況ー情報が更新され、グループのうちのグループが明らかにー」で報告した。	2023-02-24 13:16:26
ニュース	BBC News - Home	UK phone repair apprenticeship needed, says firm	https://www.bbc.co.uk/news/technology-64704104?at_medium=RSS&at_campaign=KARANGA	standard	2023-02-24 04:15:28
ニュース	BBC News - Home	Los Angeles blizzard warning is first since 1989	https://www.bbc.co.uk/news/world-us-canada-64753583?at_medium=RSS&at_campaign=KARANGA	california	2023-02-24 04:20:18
ニュース	BBC News - Home	New Zealand vs England: Harry Brook hits sublime 184 to put tourists in control	https://www.bbc.co.uk/sport/cricket/64753742?at_medium=RSS&at_campaign=KARANGA	New Zealand vs England Harry Brook hits sublime to put tourists in controlA magnificent not out from Harry Brook puts England in the ascendancy on day one of the second Test against New Zealand	2023-02-24 04:50:40
ビジネス	ダイヤモンド・オンライン - 新着記事	アリババ、次の難題は競争激化 - WSJ発	https://diamond.jp/articles/-/318406	競争激化	2023-02-24 13:04:00
ビジネス	電通報 | 広告業界動向とマーケティングのコラム・ニュース	「2022年 日本の広告費」解説――過去最高を15年ぶりに更新する7兆円超え。インターネット広告は３兆円を突破	https://dentsu-ho.com/articles/8492	電通メディアイノベーションラボ	2023-02-24 13:30:00
ビジネス	東洋経済オンライン	部下の仕事｢60点｣で納得せぬ上司が実はダメな訳 ｢自分1人で何でもできる｣というのは勘違い | リーダーシップ・教養・資格・スキル | 東洋経済オンライン	https://toyokeizai.net/articles/-/654100?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2023-02-24 13:30:00
ニュース	Newsweek	留学中の中国人学生も怯える、中国共産党の監視の目...国外にいても党の支配からは逃れられない	https://www.newsweekjapan.jp/stories/world/2023/02/post-100949.php	留学中の中国人学生も怯える、中国共産党の監視の目国外にいても党の支配からは逃れられないオーストラリアの大学で学ぶ中国人留学生のローラ仮名には、同胞の留学生が中国共産党に忠誠を誓っているかどうかを簡単に判別できる方法があるという。	2023-02-24 13:40:00
マーケティング	MarkeZine	Repro✕エアークローゼット✕コニカミノルタに訊く　インサイトを発見できる人材の育成論【視聴無料】	http://markezine.jp/article/detail/41471	repro	2023-02-24 13:30:00
IT	週刊アスキー	『ウマ娘』新ストーリーイベントが開催！報酬はSSR「カツラギエース」	https://weekly.ascii.jp/elem/000/004/126/4126219/	cygames	2023-02-24 13:45:00
IT	週刊アスキー	相鉄・東急新横浜線の開業記念に豪華賞品がもらえるスタンプラリーを開催！　「相鉄・東急新横浜線スタンプラリー」	https://weekly.ascii.jp/elem/000/004/126/4126183/	新横浜線	2023-02-24 13:30:00
マーケティング	AdverTimes	大広社長に泉恭雄氏　若返りで経営体制一新	https://www.advertimes.com/20230224/article412183/	執行役員	2023-02-24 04:35:25
マーケティング	AdverTimes	京王線仙川駅にサラダスタンド設置　データ活用の実証実験を開始	https://www.advertimes.com/20230224/article412195/	京王線仙川駅にサラダスタンド設置データ活用の実証実験を開始京王電鉄とReGACYInnovationGroup以下、ReGACYは、オープンイノベーションの実現を目指す「KEIOOPENINNOVATIONPROGRAM」の採択企業であるKOMPEITO社と共同で実施する実証実験の第二弾として、京王線仙川駅に自動販売機「SALADSTANDサラダスタンドbyOFFICEDEYASAI」以下、「SALADSTAND」を設置する。	2023-02-24 04:20:26