投稿時間:2021-09-07 15:37:37 RSSフィード2021-09-07 15:00 分まとめ(43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	TP-Linkの無線LANルーターやWiFi中継器などがお買い得！ Amazonでセール開催中	https://japanese.engadget.com/sale-tp-link-054043058.html	amazon	2021-09-07 05:40:43
TECH	Engadget Japanese	暗号化メールサービスProtonMail、当局へのユーザー情報開示に対し説明	https://japanese.engadget.com/protonmail-climate-activist-ip-053049050.html	protonmail	2021-09-07 05:30:49
TECH	Engadget Japanese	iPhone 14(仮)はかなり割高に？TSMCがチップ製造さらに値上げのウワサ	https://japanese.engadget.com/apple-products-coud-price-rise-050009785.html	iphone	2021-09-07 05:00:09
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] アジアン屋台「熱烈観光夜市」が京都四条烏丸にオープン　9月13日から	https://www.itmedia.co.jp/business/articles/2109/07/news061.html	itmedia	2021-09-07 14:45:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] “AIオーブン”が提供時間に合わせて焼き上げる　DNPなどがフードロス削減に向けた実証実験	https://www.itmedia.co.jp/business/articles/2109/07/news096.html	itmedia	2021-09-07 14:35:00
IT	ITmedia 総合記事一覧	[ITmedia News] 喉頭摘出者を音声合成と「電気喉頭」で再び会話可能に　発話用アプリも開発、名古屋大などが臨床研究	https://www.itmedia.co.jp/news/articles/2109/07/news115.html	itmedia	2021-09-07 14:26:00
TECH	Techable（テッカブル）	IPC公式パラゲーム内で、三代目 J SOUL BROTHERSのバーチャルライブ開催	https://techable.jp/archives/161585	jpgames	2021-09-07 05:00:16
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	SlimTime、1人月額1500円の営業支援ツール「Sugar Spot」、訪問時に日報をスマホで確認 | IT Leaders	https://it.impress.co.jp/articles/-/22020	SlimTime、人月額円の営業支援ツール「SugarSpot」、訪問時に日報をスマホで確認ITLeadersSlimTimeは年月日、型営業支援ツール「SugarSpot」を発表した。	2021-09-07 14:29:00
js	JavaScriptタグが付けられた新着投稿 - Qiita	コンポーネントとprops	https://qiita.com/tac21/items/8f0cf5b321acf8433933	コンポーネントとpropsReact入門最近学習を始めたのですが、propsとコンポーネントという概念がReactを触るという点において重要だと感じたのでメモ代わりにまとめました。	2021-09-07 14:28:07
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	MAMPのスタートページが表示されない	https://teratail.com/questions/358169?rss=all	macosnbsp	2021-09-07 14:55:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	document.execCommand('copy');を使用し、preを使った範囲内の文字列を改行されたままスマホでコピーしたい	https://teratail.com/questions/358168?rss=all	documentexecCommandxcopyxを使用し、preを使った範囲内の文字列を改行されたままスマホでコピーしたい前提・実現したいことjsの「documentexecCommandaposcopyapos」を使用して、ltspangtで囲まれた部分の文字列をスマホ端末でコピーしたいです。	2021-09-07 14:54:29
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	copy コマンドでPostgreSQLにCSVデータを読み込ませたい	https://teratail.com/questions/358167?rss=all	copyコマンドでPostgreSQLにCSVデータを読み込ませたい前提・実現したいことPostgreSQLにCSVファイルを読み込ませたいのですが、エラーが発生してうまくインポートできません。	2021-09-07 14:47:26
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	エラーを消す方法が分からない	https://teratail.com/questions/358166?rss=all	エラーを消す方法が分からない前提・実現したいこと要素数の配列にのランダムな数字を入れ、そのまま表示と昇順は出来たが降順の時に下記のエラーメッセージが出てきます。	2021-09-07 14:45:49
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	onclickが効かない	https://teratail.com/questions/358165?rss=all	onclickが効かない前提・実現したいことonclickを使用して要素の表示、非表示を切り替えたいのですが、クリックしても何も起こりません。	2021-09-07 14:42:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	Word Press - MW WP FORMでエラーメッセージが出ない	https://teratail.com/questions/358164?rss=all	WordPressMWWPFORMでエラーメッセージが出ないMWnbspWPnbspFORMを利用して、問い合わせフォームを作成しています。	2021-09-07 14:38:53
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	BluePrism　ExcelからWordへデータ貼付	https://teratail.com/questions/358163?rss=all	BluePrismExcelからWordへデータ貼付【至急解答お願いいたします】【要望】BluenbspPrismでExcelからデータをコピーし、Wordへ貼付したい【状況】Excelからデータを格納すると、コレクションとして格納されます。	2021-09-07 14:35:48
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	配列の五つの要素を出力したら一つだけエラーで表示されません	https://teratail.com/questions/358162?rss=all		2021-09-07 14:12:03
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	unityのボタンクリックで困っています。	https://teratail.com/questions/358161?rss=all	そのプレハブ画像を複数のボタンクリックで種類を切り替えて出現させたい。	2021-09-07 14:07:27
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	デプロイ後の変更について	https://teratail.com/questions/358160?rss=all	rails	2021-09-07 14:07:00
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	httpsでの通信時に、IEだとアクセスできるが、モダンブラウザ（Chrome、Edge）だとアクセスできない	https://teratail.com/questions/358159?rss=all	httpsでの通信時に、IEだとアクセスできるが、モダンブラウザChrome、Edgeだとアクセスできない現在、社内システムのモダンブラウザ対応を行なっています。	2021-09-07 14:05:59
技術ブログ	Developers.IO	Pitfalls of Serverless Framework with GitHub Actions	https://dev.classmethod.jp/articles/pitfalls-of-serverless-framework-with-github-actions/	Pitfalls of Serverless Framework with GitHub ActionsSince its inception GitHub Actions is becoming the de facto platform to automate developer workflows I feel	2021-09-07 05:47:25
海外TECH	DEV Community	How to design a rate limiter	https://dev.to/salah856/how-to-design-a-rate-limiter-27d3	How to design a rate limiterIn a network system a rate limiter is used to control the rate of traffic sent by a client or a service In the HTTP world a rate limiter limits the number of client requests allowed to be sent over a specified period If the API request count exceeds the threshold defined by the rate limiter all the excess calls are blocked Here are a few examples A user can write no more than posts per second You can create a maximum of accounts per day from the same IP address You can claim rewards no more than times per week from the same device Before starting the design we first look at the benefits of using an API rate limiter Prevent resource starvation caused by Denial of Service DoS attack Almost all APIs published by large tech companies enforce some form of rate limiting For example Twitter limits the number of tweets to per hours Google docs APIs have the following default limit per user per seconds for read requests A rate limiter prevents DoS attacks either intentional or unintentional by blocking the excess calls Reduce cost Limiting excess requests means fewer servers and allocating more resources to high priority APIs Rate limiting is extremely important for companies that use paid third party APIs For example you are charged on a per call basis for the following external APIs check credit make a payment retrieve health records etc Limiting the number of calls is essential to reduce costs Prevent servers from being overloaded To reduce server load a rate limiter is used to filter out excess requests caused by bots or users misbehavior Step Understand the problem and establish design scopeRate limiting can be implemented using different algorithms each with its pros and cons The interactions between an interviewer and a candidate help to clarify the type of rate limiters we are trying to buildCandidate What kind of rate limiter are we going to design Is it a client side rate limiter or server side API rate limiter Interviewer Great question We focus on the server side API rate limiter Candidate Does the rate limiter throttle API requests based on IP the user ID or other properties Interviewer The rate limiter should be flexible enough to support different sets of throttle rules Candidate What is the scale of the system Is it built for a startup or a big company with a large user base Interviewer The system must be able to handle a large number of requests Candidate Will the system work in a distributed environment Interviewer Yes Candidate Is the rate limiter a separate service or should it be implemented in application code Interviewer It is a design decision up to you Candidate Do we need to inform users who are throttled Interviewer Yes RequirementsHere is a summary of the requirements for the system Accurately limit excessive requests Low latency The rate limiter should not slow down HTTP response time Use as little memory as possible Distributed rate limiting The rate limiter can be shared across multiple servers or processes Exception handling Show clear exceptions to users when their requests are throttled High fault tolerance If there are any problems with the rate limiter for example a cache server goes offline it does not affect the entire system Step Propose high level design and get buy inLet us keep things simple and use a basic client and server model for communication Where to put the rate limiter Intuitively you can implement a rate limiter at either the client or server side Client side implementation Generally speaking client is an unreliable place to enforce rate limiting because client requests can easily be forged by malicious actors Moreover we might not have control over the client implementation Server side implementation Figure shows a rate limiter that is placed on the server side Besides the client and server side implementations there is an alternative way Instead of putting a rate limiter at the API servers we create a rate limiter middleware which throttles requests to your APIs Assume our API allows requests per second and a client sends requests to the server within a second The first two requests are routed to API servers However the rate limiter middleware throttles the third request and returns a HTTP status code The HTTP response status code indicates a user has sent too many requests Cloud microservices have become widely popular and rate limiting is usually implemented within a component called API gateway API gateway is a fully managedservice that supports rate limiting SSL termination authentication IP whitelisting servicing static content etc For now we only need to know that the API gateway is a middleware that supports rate limiting While designing a rate limiter an important question to ask ourselves is where should the rater limiter be implemented on the server side or in a gateway There is no absolute answer It depends on your company s current technology stack engineering resources priorities goals etc Here are a few general guidelines Evaluate your current technology stack such as programming language cache service etc Make sure your current programming language is efficient to implement rate limiting on the server side Identify the rate limiting algorithm that fits your business needs When you implement everything on the server side you have full control of the algorithm However your choice might be limited if you use a third party gateway If you have already used microservice architecture and included an API gateway in the design to perform authentication IP whitelisting etc you may add a rate limiter to the API gateway Building your own rate limiting service takes time If you do not have enough engineering resources to implement a rate limiter a commercial API gateway is a better option Algorithms for rate limitingRate limiting can be implemented using different algorithms and each of them has distinct pros and cons Even though this chapter does not focus on algorithms understanding them at high level helps to choose the right algorithm or combination of algorithms to fit our use cases Here is a list of popular algorithms •Token bucket•Leaking bucket•Fixed window counter•Sliding window log•Sliding window counter Token bucket algorithmThe token bucket algorithm is widely used for rate limiting It is simple well understood andcommonly used by internet companies Both Amazon and Stripe use this algorithm to throttle their API requests The token bucket algorithm work as follows A token bucket is a container that has pre defined capacity Tokens are put in the bucket at preset rates periodically Once the bucket is full no more tokens are added The refiller puts tokens into the bucket every second Once the bucket is full extra tokens will overflow Leaking bucket algorithmThe leaking bucket algorithm is similar to the token bucket except that requests are processed at a fixed rate It is usually implemented with a first in first out FIFO queue The algorithm works as follows When a request arrives the system checks if the queue is full If it is not full the request is added to the queue Otherwise the request is dropped Requests are pulled from the queue and processed at regular intervals Leaking bucket algorithm takes the following two parameters Bucket size it is equal to the queue size The queue holds the requests to be processed at a fixed rate Outflow rate it defines how many requests can be processed at a fixed rate usually in seconds Shopify an ecommerce company uses leaky buckets for rate limitingPros Memory efficient given the limited queue size Requests are processed at a fixed rate therefore it is suitable for use cases that a stable outflow rate is needed Cons A burst of traffic fills up the queue with old requests and if they are not processed in time recent requests will be rate limited There are two parameters in the algorithm It might not be easy to tune them properly Fixed window counter algorithmFixed window counter algorithm works as follows The algorithm divides the timeline into fix sized time windows and assign a counter for each window Each request increments the counter by one Once the counter reaches the pre defined threshold new requests are dropped until a new time window starts A major problem with this algorithm is that a burst of traffic at the edges of time windows could cause more requests than allowed quota to go through Pros Memory efficient Easy to understand Resetting available quota at the end of a unit time window fits certain use cases Cons Spike in traffic at the edges of a window could cause more requests than the allowed quota to go through Sliding window log algorithmThe fixed window counter algorithm has a major issue it allows more requests to go through at the edges of a window The sliding window log algorithm fixes the issue It works as follows The algorithm keeps track of request timestamps Timestamp data is usually kept in cache such as sorted sets of Redis When a new request comes in remove all the outdated timestamps Outdated timestamps are defined as those older than the start of the current time window Add timestamp of the new request to the log If the log size is the same or lower than the allowed count a request is accepted Otherwise it is rejected In this example the rate limiter allows requests per minute Usually Linux timestamps are stored in the log However human readable representation of time is used in our example for better readability The log is empty when a new request arrives at Thus the request is allowed •A new request arrives at the timestamp is inserted into the log After the insertion the log size is not larger than the allowed count Thus the request is allowed A new request arrives at and the timestamp is inserted into the log After the insertion the log size is larger than the allowed size Therefore this request is rejected even though the timestamp remains in the log A new request arrives at Requests in the range are within the latest time frame but requests sent before are outdated Two outdated timestamps and are removed from the log After the remove operation the log size becomes therefore the request is accepted Pros Rate limiting implemented by this algorithm is very accurate In any rolling window requests will not exceed the rate limit Cons The algorithm consumes a lot of memory because even if a request is rejected its timestamp might still be stored in memory Sliding window counter algorithmThe sliding window counter algorithm is a hybrid approach that combines the fixed window counter and sliding window log The algorithm can be implemented by two different approaches We will explain one implementation in this section and provide reference for the other implementation at the end of the section Assume the rate limiter allows a maximum of requests per minute and there are requests in the previous minute and in the current minute For a new request that arrives at a position in the current minute the number of requests in the rolling window is calculated using the following formula Requests in current window requests in the previous window overlap percentage of the rolling window and previous window Using this formula we get request Depending on the use case the number can either be rounded up or down In our example it is rounded down to Since the rate limiter allows a maximum of requests per minute the current request can go through However the limit will be reached after receiving one more request ProsIt smooths out spikes in traffic because the rate is based on the average rate of the previous window Memory efficient ConsIt only works for not so strict look back window It is an approximation of the actual rate because it assumes requests in the previous window are evenly distributed However this problem may not be as bad as it seems According to experiments done by Cloudflare only of requests are wrongly allowed or rate limited among million requests High level architectureThe basic idea of rate limiting algorithms is simple At the high level we need a counter to keep track of how many requests are sent from the same user IP address etc If the counter is larger than the limit the request is disallowed Where shall we store counters Using the database is not a good idea due to slowness of disk access In memory cache is chosen because it is fast and supports time based expiration strategy For instance Redis is a popular option to implement rate limiting It is an in memory store that offers two commands INCR and EXPIRE INCR It increases the stored counter by EXPIRE It sets a timeout for the counter If the timeout expires the counter is automatically deleted The client sends a request to rate limiting middleware Rate limiting middleware fetches the counter from the corresponding bucket in Redis andchecks if the limit is reached or not If the limit is reached the request is rejected If the limit is not reached the request is sent to API servers Meanwhile the system increments the counter and saves it back to Redis Step Design deep diveThe high level design does not answer the following questions How are rate limiting rules created Where are the rules stored How to handle requests that are rate limited In this section we will first answer the questions regarding rate limiting rules and then go over the strategies to handle rate limited requests Finally we will discuss rate limiting in distributed environment a detailed design performance optimization and monitoring Rate limiting rulesLyft open sourced their rate limiting component We will peek inside of the component and look at some examples of rate limiting rules domain messagingdescriptors key message type Value marketing rate limit unit day requests per unit In the above example the system is configured to allow a maximum of marketing messages per day Here is another example domain authdescriptors key auth type Value login rate limit unit minute requests per unit Exceeding the rate limitIn case a request is rate limited APIs return a HTTP response code too many requests to the client Depending on the use cases we may enqueue the rate limited requests to be processed later For example if some orders are rate limited due to system overload we may keep those orders to be processed later How does a client know whether it is being throttled And how does a client know the number of allowed remaining requests before being throttled The answer lies in HTTP response headers The rate limiter returns the following HTTP headers to clients X Ratelimit Remaining The remaining number of allowed requests within the window X Ratelimit Limit It indicates how many calls the client can make per time window X Ratelimit Retry After The number of seconds to wait until you can make a request again without being throttled When a user has sent too many requests a too many requests error and X Ratelimit Retry After header are returned to the client Rules are stored on the disk Workers frequently pull rules from the disk and store them in the cache When a client sends a request to the server the request is sent to the rate limiter middleware first Rate limiter middleware loads rules from the cache It fetches counters and last request timestamp from Redis cache Based on the response the rate limiter decides if the request is not rate limited it is forwarded to API servers if the request is rate limited the rate limiter returns too many requests error to the client In the meantime the request is either dropped or forwarded to the queue MonitoringAfter the rate limiter is put in place it is important to gather analytics data to check whether the rate limiter is effective Primarily we want to make sure The rate limiting algorithm is effective The rate limiting rules are effective For example if rate limiting rules are too strict many valid requests are dropped In this case we want to relax the rules a little bit In another example we notice our rate limiter becomes ineffective when there is a sudden increase in traffic like flash sales In this scenario we may replace the algorithm to support burst traffic Token bucket is a good fit here ConclusionAvoid being rate limited Design your client with best practices Use client cache to avoid making frequent API calls Understand the limit and do not send too many requests in a short time frame Include code to catch exceptions or errors so your client can gracefully recover fromexceptions Add sufficient back off time to retry logic References System Design Interview Book Rate Limiting in Javascript with a Token Bucket Rate limiting strategies and techniques Twitter rate limits Google docs usage limits IBM microservices Throttle API requests for better throughput Stripe rate limiters Shopify REST Admin API rate limits Better Rate Limiting With Redis Sorted Sets System Design ーRate limiter and Data modelling saisandeepmopuri system design rate limiter and data modelling bdHow we built rate limiting capable of scaling to millions of domains Redis website Lyft rate limiting Scaling your API with rate limiters request rate limiterWhat is edge computing Rate Limit Requests with Iptables	2021-09-07 05:30:30
海外TECH	DEV Community	10 Free Online Resume Builders for professional career	https://dev.to/niteshtaliyan/10-free-online-resume-builders-for-professional-career-46o8	Free Online Resume Builders for professional careerWhen it comes to building a resume I believe less is more Content is way more important than the template So whenever I face rejection I would rather spend my time and energy building new projects researching or finding part time jobs to polish my skills than focus too much on resume aesthetics Here are some resume builders you can use to build your resume in no time TL DR MS WordCanvaResumake ioNovoresumeResume nowResume GeniusMyPerfectResumeZety free resources only VisualCVIndeed com	2021-09-07 05:08:14
金融	JPX マーケットニュース	[東証]「健康経営銘柄2022」選定に向けた調査開始のお知らせ	https://www.jpx.co.jp/news/1120/20210902-01.html	健康経営銘柄	2021-09-07 15:00:00
金融	ニッセイ基礎研究所	ふるさと納税はなぜ３割か？－課税状況データを基に最適な返礼品の割合を考える	https://www.nli-research.co.jp/topics_detail1/id=68649?site=nli	目次ー高額納税者ほどふるさと納税の利用率が高いーほとんどの所得階級においてふるさと納税利用率は増加しているー返礼品の割合はなぜ割かー課税状況データを基に最適な返礼品の割合を考える自治体に残る金額を基準に最適と判断する経済合理的な納税者を前提に、返礼品の割合とパイの大きさの関係を考える納税者による閾値の相違を考慮する場合寄付者の閾値は低く、閾値は変わらないと考える場合ー返礼品の割合引き下げると不平等も解消ーまとめ『返礼品が一種の還付となっており、所得が多い人ほど受けるメリットが大きい』ふるさと納税返礼品の経済的メリットは寄付上限が高いほど大きいため、当然、所得が多い人ほどふるさと納税制度を利用している人の割合が高いと考えられる。	2021-09-07 14:54:52
金融	ニッセイ基礎研究所	人気だったインド株式ファンドのその後～2021年８月の投信動向～	https://www.nli-research.co.jp/topics_detail1/id=68655?site=nli	また、インド・ルピーも対円で月はほど上昇したことも追い風になり、インド株式ファンドは月に総じて高パフォーマンスであった。	2021-09-07 14:19:08
金融	ニッセイ基礎研究所	公約から考えるメルケル後の独連立政権と政策	https://www.nli-research.co.jp/topics_detail1/id=68648?site=nli	CDUCSUないしSPDとFDPー緑の党の党連立rArrFDPと緑の党の歩み寄りが必要FDPと緑の党の双方が加わる党連立の場合、FDPと緑の党の財政運営へのスタンスや、気候変動対策の手法、EUの財政統合への考え方などの違いが障害となる。	2021-09-07 14:38:38
金融	日本銀行：RSS	（日銀レビュー）店頭デリバティブ取引データからみた通貨オプション市場	http://www.boj.or.jp/research/wps_rev/rev_2021/rev21j10.htm	店頭デリバティブ	2021-09-07 15:00:00
金融	日本銀行：RSS	コール市場残高（8月）	http://www.boj.or.jp/statistics/market/short/call/call.xlsx	残高	2021-09-07 15:00:00
ニュース	BBC News - Home	Nicola Sturgeon to set out plans for Holyrood year ahead	https://www.bbc.co.uk/news/uk-scotland-scotland-politics-58464674?at_medium=RSS&at_campaign=KARANGA	holyrood	2021-09-07 05:29:47
ニュース	BBC News - Home	Climate change: Shetland's power struggle between oil and wind	https://www.bbc.co.uk/news/uk-scotland-58464439?at_medium=RSS&at_campaign=KARANGA	energy	2021-09-07 05:30:12
ニュース	BBC News - Home	'Everyone has the same right to love' - Vettel on speaking out as an LGBTQ+ ally	https://www.bbc.co.uk/sport/formula1/58453220?at_medium=RSS&at_campaign=KARANGA	x Everyone has the same right to love x Vettel on speaking out as an LGBTQ allySebastian Vettel made headlines at the Hungarian Grand Prix by wearing a T shirt in support of LGBTQ rights He tells the BBC s LGBT Sport Podcast why he is taking a stand	2021-09-07 05:07:57
ニュース	BBC News - Home	World number one Djokovic battles into US Open quarter-finals	https://www.bbc.co.uk/sport/tennis/58469174?at_medium=RSS&at_campaign=KARANGA	World number one Djokovic battles into US Open quarter finalsNovak Djokovic is again made to fight hard to beat Jenson Brooksby in the US Open fourth round and keep his calendar Grand Slam hopes alive	2021-09-07 05:31:46
ビジネス	不景気.com	ミニストップが中国子会社「青島ミニストップ」を解散、赤字続き - 不景気.com	https://www.fukeiki.com/2021/09/ministop-qingdao-liqudation.html	青島	2021-09-07 05:28:45
北海道	北海道新聞	福島でダンプ３台事故、２人搬送　１台は除染土を運搬中	https://www.hokkaido-np.co.jp/article/586588/	福島県浪江町赤宇木	2021-09-07 14:02:00
北海道	北海道新聞	コロナ対策に７７１億円　道が補正予算案提出へ　ワクチン職場接種支援など	https://www.hokkaido-np.co.jp/article/586585/	新型コロナウイルス	2021-09-07 14:08:21
北海道	北海道新聞	飲酒緊急搬送、テレ朝社員を処分　東京五輪の番組スタッフ６人	https://www.hokkaido-np.co.jp/article/586589/	東京五輪	2021-09-07 14:07:00
ニュース	Newsweek	東南アジアで新型コロナ感染がピークアウトの兆し	https://www.newsweekjapan.jp/stories/world/2021/09/post-97053.php		2021-09-07 14:22:25
IT	週刊アスキー	『Halo』20周年を記念した限定モデル「Xbox Series X – Halo Infinite リミテッド エディション」が発売決定！	https://weekly.ascii.jp/elem/000/004/068/4068465/	gamescom	2021-09-07 14:45:00
IT	週刊アスキー	10歳きざみの年代別オールインワンクリームを買いに行こう！ 「E.パーフェクトクリーム」横浜高島屋にて取り扱い開始	https://weekly.ascii.jp/elem/000/004/068/4068435/	bellestudiobymul	2021-09-07 14:40:00
IT	週刊アスキー	三輪士郎作品の世界観を堪能！ 横浜ロフトにて三輪士郎個展 「Lots of Handz」9月7日～20日開催	https://weekly.ascii.jp/elem/000/004/068/4068434/	lotsofhandz	2021-09-07 14:30:00
IT	週刊アスキー	北海道の激うまグルメが大集合！ 京王百貨店新宿店にて「2021秋の大北海道展」9月7日～9月21日開催	https://weekly.ascii.jp/elem/000/004/068/4068432/	京王百貨店	2021-09-07 14:15:00
マーケティング	AdverTimes	【人事】イオンリテール、「イオンスタイルオンライン本部」新設ほか（2021年9月6日付）	https://www.advertimes.com/20210907/article362575/	衣料	2021-09-07 05:13:46