投稿時間:2021-09-30 18:46:55 RSSフィード2021-09-30 18:00 分まとめ(58件)

カテゴリー等	サイト名等	記事タイトル・トレンドワード等	リンクURL	頻出ワード・要約等/検索ボリューム	登録日
TECH	Engadget Japanese	ディズニープラスが国内ドラマを世界配信。TBS『TOKYO MER〜走る緊急救命室〜』から	https://japanese.engadget.com/disney-plus-tbs-083006466.html	tokyomer	2021-09-30 08:30:06
TECH	Engadget Japanese	Apple、ついにApp StoreでiOS純正アプリの評価に対応	https://japanese.engadget.com/apple-spp-store-first-party-app-review-080039688.html	apple	2021-09-30 08:00:39
ROBOT	ロボスタ	【世界初】ヒューマノイド型「音楽DJ」ロボット「Lynx」のお披露目イベントを六本木のディスコで開催へ　高専OBらが開発	https://robotstart.info/2021/09/30/dj-robot-lynx-unveiling-event.html	djrobotics	2021-09-30 08:35:49
IT	ITmedia 総合記事一覧	[ITmedia News] ドコモの「dTVチャンネル」終了へ　専門チャンネルの見放題サービス	https://www.itmedia.co.jp/news/articles/2109/30/news145.html	itmedia	2021-09-30 17:06:00
IT	ITmedia 総合記事一覧	[ITmedia News] Apple Watch Series 7の販売価格とモデル構成が明らかに　前シリーズから値上げ	https://www.itmedia.co.jp/news/articles/2109/30/news144.html	itmedia	2021-09-30 17:03:00
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	PaaS型のローコード開発・実行基盤「Accel-Mart Quick」、20ユーザーで月額1万5400円 | IT Leaders	https://it.impress.co.jp/articles/-/22122	PaaS型のローコード開発・実行基盤「AccelMartQuick」、ユーザーで月額万円ITLeadersNTTデータイントラマートは年月日、Webアプリケーション開発実行環境「AccelMartQuick」を発表した。	2021-09-30 17:42:00
js	JavaScriptタグが付けられた新着投稿 - Qiita	Vue.jsでローディング中にテンプレートが見えるのを防ぎたい	https://qiita.com/HolaSoyNaoki/items/c1708610857b284fb17c	Vuejsでローディング中にテンプレートが見えるのを防ぎたいこの記事では、Vuejsでローディング中にテンプレートが見えるのを防ぐ方法について紹介します。	2021-09-30 17:52:45
js	JavaScriptタグが付けられた新着投稿 - Qiita	【JavaScript】var と let, const の挙動の違い3点	https://qiita.com/ydammatsu/items/3a98a6a91b808b8b0676	代入した後に宣言をするということができてしまう。	2021-09-30 17:28:49
js	JavaScriptタグが付けられた新着投稿 - Qiita	Stripe.js ElementsがCordovaでロードされない時	https://qiita.com/natsuho__memo/items/abf3a52b5f21d50f0010	StripejsElementsがCordovaでロードされない時はじめにWebで動いてるStripejsとStripeElementsがCordovaのビルド後に動かなかった時のメモ。	2021-09-30 17:13:53
js	JavaScriptタグが付けられた新着投稿 - Qiita	WebMidi.jsを使ってみて感動した話	https://qiita.com/CreateLGC/items/400b9c040ce8fff718db	WebMidijsを使ってみて感動した話WebMIDIAPIのライブラリWebMidijsを使ってみました。	2021-09-30 17:13:29
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	RayCast2D、常にどのRayに当たっているかを判定したい	https://teratail.com/questions/362099?rss=all	RayCastD、常にどのRayに当たっているかを判定したい前提・実現したいことUnityを使ってDゲームを制作しています。	2021-09-30 17:55:38
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	respond_to のあとにフロントで追加の処理をしたい	https://teratail.com/questions/362098?rss=all	respondtoのあとにフロントで追加の処理をしたい【分からないこと】formwithのsubmitボタンを押した後、バックエンドのアクションが実行されますが、完了したらフロント側で感知したいです。	2021-09-30 17:49:31
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	railsでRouting Errorが出た場合の解決策が知りたい	https://teratail.com/questions/362097?rss=all	railsでRoutingErrorが出た場合の解決策が知りたい・エラーコードのどこの部分に解決の糸口になる問題点が書かれているか知りたい・エラーの意味も詳しく知りたいCloudを使ってサンプルでRailsで閲覧機能を作ろうとしています。	2021-09-30 17:44:02
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	AnsibleでvCenterに接続したい	https://teratail.com/questions/362096?rss=all	AnsibleでvCenterに接続したいAnsibleでvCenterサーバドメイン参加済みに接続したく設定をしております。	2021-09-30 17:22:19
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Notice: Undefined index の原因が知りたい。name="file" を宣言しているはずなのに未定義となる理由とは？	https://teratail.com/questions/362095?rss=all	NoticeUndefinedindexの原因が知りたい。	2021-09-30 17:20:15
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	djangoのtemplateでforがうまく実行できない	https://teratail.com/questions/362094?rss=all	djangoのtemplateでforがうまく実行できないdjangoでforを使って商品を全て表示させたいのですが、表示できません。	2021-09-30 17:08:31
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	rails 投稿したコメントを削除したい。	https://teratail.com/questions/362093?rss=all	rails投稿したコメントを削除したい。	2021-09-30 17:06:35
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	日時情報（年月日時分秒）をできるだけ少ない文字数で表現したい	https://teratail.com/questions/362092?rss=all	日時情報年月日時分秒をできるだけ少ない文字数で表現したい前提・実現したいことあるデータに対して、ユニークなキーを付与する目的として、「時刻情報」をその一部として利用したいと考えています。	2021-09-30 17:04:14
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	MVVM構造のUserControlに依存関係プロパティを生やして親画面のVMにバインドしたい	https://teratail.com/questions/362091?rss=all	MVVM構造のUserControlに依存関係プロパティを生やして親画面のVMにバインドしたい質問VMを持たないUserControlの場合、コードビハインドに依存関係プロパティを実装してDataContextを設定しなければ、親画面のViewで配置したときに親画面のDataContext親画面のViewModelが引き継がれますよね。	2021-09-30 17:01:13
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Railsで遷移元のURLを取得する	https://qiita.com/wihan23/items/dd3976632ed8c6d540c6	Railsで遷移元のURLを取得するはじめに開発ですぐに忘れそうなことを、簡単に再度調べられるようにするための備忘録です。	2021-09-30 17:59:37
Linux	Ubuntuタグが付けられた新着投稿 - Qiita	JetsonNano Jetpack4.6をubuntu20.04にする	https://qiita.com/rikupo/items/cc807c45e46166b422b6		2021-09-30 17:04:02
AWS	AWSタグが付けられた新着投稿 - Qiita	Windows10/WSL2からAWS/S3にファイルをAWS/CLIアップロード	https://qiita.com/yono2844/items/0edce54def0e9a1a11a3	awssrbsyonoforce	2021-09-30 17:07:59
Git	Gitタグが付けられた新着投稿 - Qiita	VSCodeでGitのコミットメッセージを書きやすくする拡張機能	https://qiita.com/habu1010/items/7155377b90998e72b033	入力欄が狭いこのVSCodeのソース管理機能が表示される左側のペインは、通常それほどの幅は取っていないと思います。	2021-09-30 17:14:58
Ruby	Railsタグが付けられた新着投稿 - Qiita	Railsで遷移元のURLを取得する	https://qiita.com/wihan23/items/dd3976632ed8c6d540c6	Railsで遷移元のURLを取得するはじめに開発ですぐに忘れそうなことを、簡単に再度調べられるようにするための備忘録です。	2021-09-30 17:59:37
技術ブログ	Developers.IO	くらめその情シス：【小ネタ】Google共有ドライブで複雑な権限のフォルダへファイル移動するTips	https://dev.classmethod.jp/articles/%e3%81%8f%e3%82%89%e3%82%81%e3%81%9d%e3%81%ae%e6%83%85%e3%82%b7%e3%82%b9%ef%bc%9a%e3%80%90%e5%b0%8f%e3%83%8d%e3%82%bf%e3%80%91google%e5%85%b1%e6%9c%89%e3%83%89%e3%83%a9%e3%82%a4%e3%83%96%e3%81%a7/	google	2021-09-30 08:53:16
技術ブログ	Developers.IO	How To Static Website EP4: สอนวิธีการเคลียร์ CloudFront Cache File	https://dev.classmethod.jp/articles/how-to-static-website-ep4-clear-cloudfront-cache/	How To Static Website EP สอนวิธีการเคลียร์CloudFront Cache Fileสวัสดีค่ะทุกคนกลับมาพบกับพิชชาอีกแล้วนะคะวันนี้พิชชาก็มาพร้อมกับบทความต่อจากHow To Static Website EP เปลี	2021-09-30 08:53:08
海外TECH	DEV Community	Nebula Operator Kind, oneliner installer for Nebula K8s Operator Playground	https://dev.to/lisahui/nebula-operator-kind-oneliner-installer-for-nebula-k8s-operator-playground-27k8	Nebula Operator Kind oneliner installer for Nebula Ks Operator PlaygroundNebula Kind an one liner command to try Ks Operator based Nebula Graph Cluster on your machine with the help of KIND Ks in Docker Nebula Operator KindAs a Cloud Native Distributed Database Nebula Graph comes with an open source Ks Operator to enable boostrap and maintain Nebula Graph Cluster from a Ks CRD Normally it takes you some time to setup all the dependencies and control plane resources of the Nebula Operator If you are as lazy as I am this Nebula Operator Kind is made for you to quick start and play with Nebula Graph in KIND Nebula Operator Kind is the one liner for setup everything for you including DockerKs KIND PV ProviderNebula OperatorNebula ConsolenodePort for accessing the ClusterKubectl for playing with KIND and Nebula Operator How To UseInstall Nebula Operator Kind curl sL nebula kind siwei io install sh bashYou will see this after it s doneYou can connect to the cluster via nebula kind bin console as below nebula kind bin console u user p password address port MoreIt s in GitHub with more information you may be intrested in please try and feedback there Install on KubeSphere all in on cluster curl sL nebula kind siwei io install ks sh bashInstall on existing Ks cluster curl sL nebula kind siwei io install on ks sh bash	2021-09-30 08:54:38
海外TECH	DEV Community	18 amazing 🤩 GitHub repositories that will help you 🪄 make a Beautiful Project	https://dev.to/tu77y/18-amazing-github-repositories-that-will-help-you-make-a-beautiful-project-44e8	amazing GitHub repositories that will help you 🪄make a Beautiful ProjectIntroductionToday I would like to give examples of good GitHub repositories that will help you implement complex animations as well as useful functions in your projects So let s get started Read More amazing GitHub repositories that will help you 🪄make a Beautiful Project	2021-09-30 08:50:18
海外TECH	DEV Community	Web Animation with CSS - Animation Property	https://dev.to/basecampxd/web-animation-with-css-animation-property-bdb	Web Animation with CSS Animation Property IntroductionContinuing with the series of Web Animation with CSS Today we are going to learn more about animation using CSS The last part of the series paved the way to start animating in web pages for beginners You can read here So let s get started with today s topic Animation DirectionAs the name suggests this property is used to alter the direction of animation It has four values normal reverse alternate and alternate reverse Code animation direction reverse Codepen ExampleIn the above example I have used the animation direction with the value alternate The animation first started as normal and for nd time it reversed This happens alternately once normal and other times reverse It becomes a sequence of animation looping again and again Animation Timing FunctionThe animation timing function helps you to control the animation speed curve This curve defines the speed of animation transition at the different time frames This curve helps you to make the transition smoothly Code animation timing function linear CodePen ExampleIn the above Codepen the ball is moving from top to bottom The animation is not linear as the speed of transition changes We have used animation timing function with the value ease Using ease value the animation has a slow start and end but fast in between slow and end There is various value to animation timing function you can learn more about it here One such value of the animation timing function is cubic bezier It let you create your timing function to control your animation It takes numeric values between and You can visit Cubic Bezier to create your animation timing function Animation ShorthandTill now we have used lot of animation property to define animation such as animation direction animation delay animation timing function etc You can use shorthand technique to define all animation related property into single property that is animation Code animation bounce s infinite ease CodePen Example Weekly Newsletter of SurajOnDev What you will get Read of the Week best articles hand picked by myself from different platforms This article will be developer self growth and productivity oriented Tool of the Week A resource or tool link that will help in easing your work Our latest blog post Latest blog post from SurajOnDev that is me Free eBook and Resources Occasionally you will get free eBook that are by developers and for developers Frequency WeeklySubscribe Here Weekly Newsletter of SurajOnDev Last NoteNow with these properties of CSS you can have more control over your animation I highly recommend you to read our previous blog post of this series Web Animation with CSS Learn the Basics Thank You for reading the blog post	2021-09-30 08:45:29
海外TECH	DEV Community	Droplet Loader NFT	https://dev.to/nftmake/droplet-loader-nft-4llg	Droplet Loader NFTSomething pretty because I love pretty things You can buy the iNFT on BeyondNFT here	2021-09-30 08:40:45
海外TECH	DEV Community	Modernizing Amazon database infrastructure - migrating from Oracle to AWS | AWS White Paper Summary	https://dev.to/awsmenacommunity/modernizing-amazon-database-infrastructure-migrating-from-oracle-to-aws-aws-white-paper-summary-ej7	Modernizing Amazon database infrastructure migrating from Oracle to AWS AWS White Paper Summary Challenges with using Oracle databasesAmazon started facing a number of challenges with using Oracle databases to scale its services Complex database engineering required to scale•Hundreds of hours spent each year trying to scale the Oracle databases horizontally •Database shards was used to handle the additional service throughputs and manage the growing data volumes but in doing so increased the database administration workloads Complex expensive and error prone database administrationHundreds of hours spent each month monitoring database performance upgrading database backups and patching the operating system for each instance and shard Inefficient and complex hardware provisioning•Database and the infrastructure teams expended substantial time forecasting demand and planning hardware capacity to meet it •After forecasting hundreds of hours spent in purchasing installing and testing the hardware •Additionally teams had to maintain a sufficiently large pool of spare infrastructure to fix any hardware issues and perform preventive maintenance •The high licensing costs were just some of the compelling reasons for the Amazon consumer and digital business to migrate the persistence layer of all its services to AWS AWS ServicesOverview about the key AWS database Services Purpose built databases•Amazon expects all its services be globally available operate with microsecond to millisecond latency handle millions of requests per second operate with near zero downtime cost only what is needed and be managed efficiently by offering a range of purpose built databases The three key database services to host the persistence layer of their services Amazon DynamoDB Amazon Aurora Amazon Relational Database Service Amazon RDS for MySQL or PostgreSQL Other AWS Services used in implementation•Amazon Simple Storage Service Amazon S •AWS Database Migration Service•Amazon Elastic Compute Cloud Amazon EC •Amazon EMR•AWS Glue Picking the right database•Pick the most appropriate database based on scale complexity and features of its service •Business units running services that use relatively static schemas perform complex table lookups and experience high service throughputs picked Amazon Aurora •Business units using operational data stores that had moderate read and write traffic and relied on the features of relational databases selected Amazon RDS for PostgreSQL or MySQL Challenges during migrationThe key challenges faced by Amazon during the transformation journey Diverse application architectures inherited•Amazon has been defined by a culture of decentralized ownership that offered engineers the freedom to make design decisions that would deliver value to their customers This freedom proliferated a wide range of design patterns and frameworks across teams Another source of diversity was infrastructure management and its impact on service architectures Distributed and geographically dispersed teams•Amazon operates in a range of customer business segments in multiple geographies which operate independently •Managing the migration program across this distributed workforce posed challenges including effectively communicating the program vision and mission driving goal alignment with business and technical leaders across these businesses defining and setting acceptable yet ambitious goals for each business units and dealing with conflicts Interconnected and highly interdependent servicesAmazon operates a vast set of microservices that are interconnected and use common databases Migrating interdependent and interconnected services and their underlying databases required finely coordinated movement between teams Gap in skillsAs Amazon engineers used Oracle databases they developed expertise over the years in operating maintaining and optimizing them Most service teams shared databases that were managed by a shared pool of database engineers and the migration to AWS was a paradigm shift for them Competing initiativesLastly each business unit was grappling with competing initiatives In certain situations competing priorities created resource conflicts that required intervention from the senior leadership People processes and toolsThe following three sections discuss how three levers were engaged to drive the project forward PeopleOne of the pillars of success was founding the Center of Excellence CoE The CoE was staffed with experienced enterprise program managers The leadership team ensured that these program managers had a combination of technical knowledge and program management capabilities Processes and mechanismsThis section elaborates on the processes and mechanisms established by the CoE and their impact on the outcome of the project Goal setting and leadership review It was realized early in the project that the migration would require attention from senior leaders They used the review meeting to highlight systemic risks recurrent issues and progress Establishing a hub and spoke model It would be arduous to individually track the status of each migration Therefore they established a hub and spoke model where service teams nominated a team member typically a technical program manager who acted as the spoke and the CoE program managers were the hub Training and guidance A key objective for the CoE was to ensure that Amazon engineers were comfortable moving their services to AWS To achieve this it was essential to train these teams on open source and AWS native databases and cloud based design patterns Establishing product feedback cycles with AWS This feedback mechanism was instrumental in helping AWS rapidly test and release features to support internet scale workloads This feedback mechanism also enabled AWS to launch product features essential for its other customers operating similar sized workloads Establishing positive reinforcement To ensure that teams make regular progress towards goals it is important to promote and reinforce positive behaviors recognize teams and celebrate their progress The CoE established multiple mechanisms to achieve this Risk management and issue tracking Enterprise scale projects involving large numbers of teams across geographies are bound to face issues and setbacks ToolsDue to the complexity of the project management process the CoE decided to invest in tools that would automate the project management and tracking Common migration patterns and strategiesThe following section describes the migration of four systems used in Amazon from Oracle to AWS Migrating to Amazon DynamoDB FLASHOverview of FLASH •Set of critical services called the Financial Ledger and Accounting Systems Hub FLASH •Enable various business entities to post financial transactions to Amazon s sub ledger •It supports four categories of transactions compliant with Generally Accepted Accounting Principles GAAP ーaccount receivables account payables remittances and payments •FLASH aggregates these sub ledger transactions and populates them to Amazon s general ledger for financial reporting auditing and analytics gt gt gt gt gt gt gt gt gt gt gt gt gt gt Data flow diagram of FLASH lt lt lt lt lt lt lt lt lt lt lt lt lt lt Challenges with operating FLASH services on Oracle FLASH is a high throughput complex and critical system at Amazon It experienced many challenges while operating on Oracle databases a Poor latency The poor service latency despite having performed extensive database optimization b Escalating database costs Each year the database hosting costs were growing by at least and the FLASH team was unable to circumvent the excessive database administration overhead associated with this growth c Difficult to achieve scale As FLASH used a monolithic Oracle database service the interdependencies between the various components of the FLASH system were preventing efficient scaling of the system Reasons to choose Amazon DynamoDB as the persistence layer a Easier to scale b Easier change management c Speed of transactions d Easier database management Challenges and design considerations during refactoring The FLASH team faced the following challenges during the re design of its services on DynamoDB a Time stamping transactions and indexed ordering After a timestamp was assigned these transactions were logged in a S bucket for durable backup DynamoDB Streams along with Amazon Kinesis Client Libraries were used to ensure exactly once ordered indexing of records When enabled DynamoDB Streams captures a time ordered sequence of item level modifications in a DynamoDB table and durably stores the information for up to hours Applications can access a series of stream records which contain an item change from a DynamoDB stream in near real time After a transaction appears on the DynamoDB stream it is routed to a Kinesis stream and indexed b Providing data to downstream services •Enable financial analytics •FLASH switched the model to an event sourcing model where an S backup of commit logs was created continuously •The use of unstructured and disparate tables was eliminated for analytics and data processing •The team created a single source of truth and converged all the data models to the core event log journal to ensure deterministic data processing •Amazon S was used as an audit trail of all changes to the DynamoDB journal table •Amazon SNS was used to publish these commit logs in batches for downstream consumption •The artifact creation was coordinated using Amazon SQS The entire system is SOX compliant •These data batches were delivered to the general ledger for financial reporting and analysis c Archiving historical data FLASH used a common data model and columnar format for ease of access and migrated historical data to Amazon S buckets that are accessible by Amazon Athena Amazon Athena was ideal as it allows for a query as you go model which works well as this data is queried on average once every two years Also because Amazon Athena is serverless Performing data backfill AWS DMS was used to ensure reliable and secure data transfer It is also SOX compliant from source to target provided the team granular insights during the process gt gt gt gt gt gt gt gt gt gt Lift and shift using AWS DMS and RDS lt lt lt lt lt lt lt lt lt lt Benefits •Rearchitecting the FLASH system to work on AWS database services improved its performance •Although FLASH provisioned more compute and larger storage the database operating costs have remained flat or reduced despite processing higher throughputs •The migration reduced administrative overhead enabling focus on optimizing the application •Automatic scaling has also allowed the FLASH team to reduce costs Migration to Amazon DynamoDB Items and OffersOverview of Items and Offers •A system manages three components associated with an item item information offer information and relationship information •A key service within the Items and Offers system is the Item Service which updates the item information Challenges faced when operating Item Service on Oracle databases The Item Service team was facing many challenges when operating on Oracle databases Challenging to administer partitions The Item data was partitioned using hashing and partition maps were used to route requests to the correct partition These partitioned databases were becoming difficult to scale and manage Difficult to achieve high availability To optimize space utilization by the databases all tables were partitioned and stored across databases Reaching scaling limits Due to the preceding challenges of operating the Items and Offers system on Oracle databases the team was not able to support the growing service throughputs gt gt gt gt gt gt gt gt gt gt gt gt gt gt gt Scale of the Item Service lt lt lt lt lt lt lt lt lt lt lt lt lt lt lt Reasons for choosing Amazon DynamoDB Amazon DynamoDB was the best suited persistence layer for IMS It offered an ideal combination of features suited for easily operating a highly available and large scale distributed system like IMS a Automated database managementb Automatic scaling c Cost effective and secureThe following figure displays one of the index tables on Oracle that stored SKU to ASIN mappings gt gt gt gt gt gt gt gt gt gt Table structure of Item Service on Oracle lt lt lt lt lt lt lt lt lt lt The following figure shows the equivalent table represented in DynamoDB All other Item Service schemas were redesigned using similar principles gt gt gt gt gt gt gt gt gt Table structure of Item Service on DynamoDB lt lt lt lt lt lt lt lt lt Execution After building the new data model the next challenge was performing the migration The Item Service team devised a two phased approach to achieve the migration ーlive migration and backfill i Live migrationTransition the main store from Oracle to DynamoDB without any failures and actively migrate all the data being processed by the application The item Service team used three stages to achieve the goal a The copy mode Validate the correctness scale and performance of DynamoDB b The compatibility mode Allowed the Item Service team to pause the migration should issues arise c The move mode After the move mode the Item Service team began the backfill phase of migration that would make DynamoDB the single main database and deprecate Oracle ii Backfill •AWS DMS was used to backfill records that were not migrated by the application write logic •Oracle source tables were partitioned across databases and the destination store on DynamoDB was elastically scalable •The migration has scaled by running multiple AWS DMS replication instances per table and each instance had parallel loads configured •To handle AWS DMS replication errors the process automated by creating a library using the AWS DMS SDK •Finally fine tune configurations on AWS DMS and Amazon DynamoDB to maximize the throughput and minimize cost gt gt gt gt gt gt gt gt gt gt gt gt gt gt gt Backfill process of IMS lt lt lt lt lt lt lt lt lt lt lt lt lt lt lt Benefits After the migration the availability of Item Service has improved ensuring consistent performance and significantly reduced the operational workload for the team Also the team used the point in time recovery feature to simplify backup and restore operations The team received these benefits at a lower overall cost than previously due to dynamic automatic scaling capacity feature Migrating to Aurora for PostgreSQL Amazon Fulfillment Technologies AFT Overview of AFT The Amazon Fulfillment Technologies AFT business unit builds and maintains the dozens of services that facilitate all fulfillment activities A set of services called the Inventory Management Services facilitate inventory movement and are used by all other major services to perform critical functions within the FC Challenges faced operating AFT on Oracle databases The AFT team faced many challenges operating its services on Oracle databases in the past a Difficult to scale All the services were becoming difficult to scale and were facing availability issues during peak throughputs due to both hardware and software limitations b Complex hardware management Hardware management was also becoming a growing concern due to the custom hardware requirements required from these Oracle clusters gt gt gt gt gt gt gt gt gt gt gt gt gt Databases services used by AFT lt lt lt lt lt lt lt lt lt lt lt lt lt Reasons for choosing Amazon Aurora for PostgreSQL Picking Amazon Aurora for three primary reasons a Static schemas and relational lookups b Ease of scaling and feature parity c Automated administration Before the migration the team decided to re platform the services rather than rearchitect them Re platforming accelerated the migration by preserving the existing architecture while minimizing service disruptions Migration strategy and challenges The migration to Aurora was performed in three phases a Preparation phase •Separate production and non production accounts to ensure secure and reliable deployment •Aurora offers fifteen near real time read replicas while a central node manages all writes •Aurora uses SSL AES to secure connections between the database and the application Important differences to note are i How Oracle and PostgreSQL treat time zones differently ii Oracle and PostgreSQL is different partitioning strategies and their implementations b Migration phase AWS SCT was used to convert the schemas from Oracle to PostgreSQL Subsequently DMS performed a full load and ongoing Change Data Capture CDC replication to move real time transactional data gt gt gt Steps in the migration of schemas using AWS SCT and AWS DMS lt lt lt The maxFileSize parameter specifies the maximum size in KB of any CSV file used to transfer data to PostgreSQL It was observed that setting maxFileSize to GB significantly improved migration speed Since version x AWS DMS has been to increase this parameter to GB c Post migration phase Monitoring the health of the database becomes paramount in this phase One important activity that must occur in PostgreSQL is vacuuming Aurora PostgreSQL sets auto vacuum settings according to instance size by default but one size does not always fit all different workloads so it is important to ensure auto vacuum is working properly as expected Benefits •After migrating to Amazon Aurora provisioning additional capacity is achieved through a few simple mouse clicks or API calls reducing the scaling effort by as much as •High availability is another key benefit of Amazon Aurora •The business unit is no longer limited by the input output operations Migrating to Amazon Aurora buyer fraud detectionOverview Amazon retail websites operate a set of services called Transaction Risk Management Services TRMS to protect brands sellers and consumers from transaction fraud by actively detecting and preventing it The Buyer Fraud Service applies machine learning algorithms over real time and historical data to detect and prevent fraudulent activity Challenges of operating on oracle The Buyer Fraud Service team faced three challenges operating its services using on premises Oracle databases a Complex error prone database administration The Buyer Fraud Service business unit shared an Oracle cluster of more than one hundred databases with other fraud detection services at Amazon b Poor latency To maintain performance at scale Oracle databases were horizontally partitioned As application code required new database shards to handle the additional throughput each shard added incremental workload on the infrastructure in terms of backups patching and performance c Complication hardware provisioning After capacity planning the hardware business unit coordinated suppliers vendors and Amazon finance business units to purchase the hardware and prepare for installation and testing Application design and migration strategy The Buyer Fraud Service business unit decided to migrate its databases from Oracle to Amazon Aurora The team chose to re factor the service to accelerate the migration and minimize service disruption The migration was accomplished in two phases i Preparation phase •Amazon Aurora clusters were launched to replicate the existing Oracle databases •A shim layer has built to perform simultaneous r w operations to both database engines •The business unit migrated the initial data and used AWS DMS to establish active replication from Oracle to Aurora •Once the migration was complete AWS DMS was used to perform a row by row validation and a sum count to ensure that the replication was accurate gt gt gt gt Dual write mode of the Buyer Fraud Service using SHIM layer lt lt lt lt ii Execution phase Buyer Fraud Service began load testing the Amazon Aurora databases to evaluate read write latencies and simulate peak throughput events such as Prime Day Results from these load tests indicated that Amazon Aurora could handle twice the throughput of the legacy infrastructure Benefits •Performance scalability availability hardware management cloud based automation and cost •AWS manages patching maintenance backups and upgrades improved the application performance •The migration has also lowered the cost of delivering the same performance as before •The improved performance of Amazon Aurora has allowed to handle high throughput •Buyer Fraud service was able to scale its largest workloads support strict latency requirements with no impact to snapshot backups •Hardware management has gotten exponentially easier with new hardware being commissioned in minutes instead of months Organization wide benefits•Services that migrated to DynamoDB saw significant performance improvements such as a drop in th percentile latency OS patching database maintenance and software upgrades •Additionally the elastic capacity of preconfigured database hosts on AWS has eliminated administrative overhead to scale by allowing for capacity provisioning Post migration operating modelThis section discusses key changes in the operating model for service teams and its benefits Distributed ownership of databases•The migration transformed the operating model to one focused on distributed ownership •Individual teams now control every aspect of their infrastructure including capacity provisioning forecasting and cost allocation •Each team also had the option to launch Reserved or On Demand Instances to optimize costs based on the nature of demand •The CoE developed heuristics to identify the optimal ratio of On Demand to Reserved Instances based on service growth cyclicality and price discounts •Focusing on innovation on behalf of customers Career growthThe migration presented an excellent opportunity to advance the career paths of scores of database engineers These engineers who exclusively managed Oracle databases in data centers were offered new avenues of growth and development in the rapidly growing field of cloud services NoSQL databases and open source databases	2021-09-30 08:40:20
海外TECH	DEV Community	Lambda Functions in Dart	https://dev.to/baransel/lambda-functions-in-dart-4f70	Lambda Functions in DartEvery function in dart is an object Functions that we use very often without a name are also called lambda functions You can see usage example and output below Follow my blog for more baransel dev	2021-09-30 08:10:33
医療系	医療介護 CBnews	ワクチン2回接種の職員・患者にも厳重な対策必要-コロナ会議専門家意見、医療機関でコロナ感染報告	https://www.cbnews.jp/news/entry/20210930170047	医療機関	2021-09-30 18:00:00
医療系	医療介護 CBnews	臨床意思決定支援システムで経営の質向上へ-ウォルターズ・クルワー・ヘルスがセミナー	https://www.cbnews.jp/news/entry/20210929164851	uptodatereg	2021-09-30 18:00:00
金融	RSS FILE - 日本証券業協会	選択権付債券売買取引状況	https://www.jsda.or.jp/shiryoshitsu/toukei/sentaku/index.html	選択	2021-09-30 09:00:00
金融	RSS FILE - 日本証券業協会	短期社債等及び私募社債の取引状況等	https://www.jsda.or.jp/shiryoshitsu/toukei/kokunai/index.html	私募	2021-09-30 09:00:00
金融	RSS FILE - 日本証券業協会	インターネット取引に係る株式売買等データ(月次)	https://www.jsda.or.jp/shiryoshitsu/toukei/datakaiji.html	株式	2021-09-30 09:00:00
金融	JPX マーケットニュース	[東証]監理銘柄（確認中）の指定：パイプドＨＤ（株）	https://www.jpx.co.jp/news/1023/20210930-11.html	監理銘柄	2021-09-30 17:40:00
海外ニュース	Japan Times latest articles	Kishida taps ex-economy minister Akira Amari as LDP No. 2	https://www.japantimes.co.jp/news/2021/09/30/national/politics-diplomacy/ldp-executives-kishida/	Kishida taps ex economy minister Akira Amari as LDP No Kishida also picked former internal affairs minister Sanae Takaichi who lost to him in the party s presidential race Wednesday as the LDP s new policy chief	2021-09-30 17:22:58
海外ニュース	Japan Times latest articles	Soul food photo boosts Twitter profile of Japan’s next leader	https://www.japantimes.co.jp/news/2021/09/30/national/kishida-soul-food/	media	2021-09-30 17:00:47
ニュース	BBC News - Home	Furlough scheme ends with almost 1 million left in limbo	https://www.bbc.co.uk/news/business-58735299?at_medium=RSS&at_campaign=KARANGA	scheme	2021-09-30 08:18:29
ニュース	BBC News - Home	Sarah Everard's murderer to be sentenced	https://www.bbc.co.uk/news/uk-england-london-58745581?at_medium=RSS&at_campaign=KARANGA	everard	2021-09-30 08:10:36
ニュース	BBC News - Home	Britney Spears' father suspended as conservator	https://www.bbc.co.uk/news/world-us-canada-58742331?at_medium=RSS&at_campaign=KARANGA	spears	2021-09-30 08:37:54
ニュース	BBC News - Home	Face-to-face GP visits still near lockdown levels	https://www.bbc.co.uk/news/health-58670560?at_medium=RSS&at_campaign=KARANGA	consultations	2021-09-30 08:36:45
ビジネス	ダイヤモンド・オンライン - 新着記事	サムティ(3244)、5期連続となる「増配」を発表し、 配当利回り3.8％に！ 年間配当額は5年で3倍に増加、 2021年11月期は前期比8円増の｢1株あたり90円｣に！ - 配当【増配・減配】最新ニュース！	https://diamond.jp/articles/-/283600	サムティ、期連続となる「増配」を発表し、配当利回りに年間配当額は年で倍に増加、年月期は前期比円増の「株あたり円」に配当【増配・減配】最新ニュースサムティが、年月期の配当予想の修正増配を発表し、配当利回りがにサムティは、年月期の年間配当を前回予想比で「円」の増配、前期比では「円」の増配となる「株あたり円」に修正すると発表した。	2021-09-30 17:45:00
ビジネス	ダイヤモンド・オンライン - 新着記事	リコーリース、株主優待を変更！ 300株以上の配布区 分が新設されるものの、保有期間が1年未満の100株の 株主がもらえるQUOカードは3000⇒2000円分に減額 - 株主優待【新設・変更・廃止】最新ニュース	https://diamond.jp/articles/-/283601		2021-09-30 17:35:00
ビジネス	不景気.com	ヤマト・インダストリーが希望退職者募集で10名を削減へ - 不景気.com	https://www.fukeiki.com/2021/09/yamato-industry-cut-10-job.html	希望退職	2021-09-30 08:43:02
ビジネス	不景気.com	グローセルの希望退職者募集に79名が応募、想定3割多い - 不景気.com	https://www.fukeiki.com/2021/09/glosel-cut-79-job.html	希望退職	2021-09-30 08:31:34
GCP	Google Cloud Platform Japan 公式ブログ	データドリブンな価格最適化	https://cloud.google.com/blog/ja/products/data-analytics/centralize-data-sources-into-bigquery-with-dataprep/	価格を最適化するためには、市場の変化に迅速に対応する必要があります。	2021-09-30 10:00:00
北海道	北海道新聞	稲葉篤紀監督「最高の結果」　野球で東京五輪金、退任会見で涙	https://www.hokkaido-np.co.jp/article/594855/	東京五輪	2021-09-30 17:17:00
北海道	北海道新聞	高校側に３千万円の賠償命令　福岡いじめ自殺、二審で増額	https://www.hokkaido-np.co.jp/article/594846/	男子生徒	2021-09-30 17:06:00
IT	週刊アスキー	PC『SDガンダムオペレーションズ』でエクストラチャレンジ「ELSとの遭遇」を開催！	https://weekly.ascii.jp/elem/000/004/070/4070676/	開催	2021-09-30 17:25:00
IT	週刊アスキー	クラブツーリズムとKDDI、趣味を深堀できるサブスクサービス「クラブツーリズムパス」を10月1日より提供開始	https://weekly.ascii.jp/elem/000/004/070/4070675/	提供開始	2021-09-30 17:15:00
IT	週刊アスキー	【PS Plus情報】10月のフリープレイにPS4『悪魔城ドラキュラＸ・セレクション 月下の夜想曲 ＆ 血の輪廻』などが登場！	https://weekly.ascii.jp/elem/000/004/070/4070673/	playstationplus	2021-09-30 17:05:00
マーケティング	AdverTimes	『週刊新潮』の広告見納め 「中づり文化」の特別コラムも	https://www.advertimes.com/20210930/article364321/	週刊新潮	2021-09-30 08:50:12
マーケティング	AdverTimes	カードローン広告はオタク気質に 性格や認知のクセで配信 NTT系	https://www.advertimes.com/20210930/article364302/	開始	2021-09-30 08:04:23
海外TECH	reddit	Seeking help regarding divorce drama	https://www.reddit.com/r/japanlife/comments/pyfhbv/seeking_help_regarding_divorce_drama/	Seeking help regarding divorce dramaHey fellow Reddits I seek your help I have recently gotten into the world of Japanese dramas and was wondering whether you know any good ones which treat themes like divorce and separation I am a total beginner in the world of Japanese dramas so any guidance would be welcome I like dramas like Tokyo Love Story so I don t mind them being a bit on the older side submitted by u the hatori to r japanlife link comments	2021-09-30 08:24:19
GCP	Cloud Blog JA	データドリブンな価格最適化	https://cloud.google.com/blog/ja/products/data-analytics/centralize-data-sources-into-bigquery-with-dataprep/	価格を最適化するためには、市場の変化に迅速に対応する必要があります。	2021-09-30 10:00:00