投稿時間:2020-12-24 07:19:29 RSSフィード2020-12-24 07:00 分まとめ(26件)

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IT	ITmedia 総合記事一覧	[ITmedia News] 都会にいては分からない、地方銀行のオンラインバンキングに嘆息した話	https://www.itmedia.co.jp/news/articles/2012/24/news065.html	itmedia	2020-12-24 06:35:00
Google	カグア！Google Analytics 活用塾:事例や使い方	ショート動画SNSでのアナリティクス比較	https://www.kagua.biz/social/tredns/shortmovie-compare-insights.html	instagram	2020-12-23 21:00:46
python	Pythonタグが付けられた新着投稿 - Qiita	実行結果の出力先をモードレスウィンドウとして起動したVimにする	https://qiita.com/wordijp/items/3e6533fea5b90b76d8d4	実行結果の出力先をモードレスウィンドウとして起動したVimにするはじめにVim上で起動した実行結果は、基本的にはウィンドウ分割された別のバッファに出力されるのですが、ソースコード用の画面が狭くなるサブモニタに結果を出力したいという個人的な不満がありました。	2020-12-24 06:03:55
python	Pythonタグが付けられた新着投稿 - Qiita	Python Conference 2020 アジア制覇した男がおすすめするPyCon発表動画12選	https://qiita.com/tetsuya0617/items/8507100d961f7876547d	本当は、私の発表内容について書くべきなのですが、自分で発表しすぎて飽きている部分があるので、当記事では、これ見たほうがいいぞおすすめPyCon発表動画選をお届けします。	2020-12-24 06:02:29
js	JavaScriptタグが付けられた新着投稿 - Qiita	Dockerコンテナ上でReactを動かす	https://qiita.com/junkin_au/items/5ec46fa380899dcf9fa8	コンテナ起動dockercomposeupdReactアプリケーション作成dockercomposeexecreactnpxcreatereactappサーバ起動dockercomposeexecreactnpmstart動作確認httplocalhostにアクセスして画面が表示されたら成功です。	2020-12-24 06:56:07
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	作成するファイルのパスを固定したい	https://teratail.com/questions/312095?rss=all	作成するファイルのパスを固定したい以下のプログラムを実行するとカレントディレクトリにconfigjsonが作成されるのですが、どのディレクトリから実行されてもこのファイルの作成場所が実行ファイルと同じディレクトリになるようにしたいです。	2020-12-24 06:30:35
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Ruby学習#99 自分用プログラミングノート	https://qiita.com/KogosRuby/items/6ec8cc8ab2c3cd3b704d	print	2020-12-24 06:47:15
Ruby	Rubyタグが付けられた新着投稿 - Qiita	Ruby学習#2 Drawing a Shape	https://qiita.com/KogosRuby/items/037dbe6f3715edde2c3a	Ruby学習DrawingaShape本日の学習内容print文とput文の違いprint改行しないputs改行する実装したコードYourcodehereputsMileputsiscoolprintGirafeAcademyprintMileputsiscoolputsputsputsputs環境デザイン・学習を阻害する要因を消し去ったスマホのyoutube動画明日やること・【継続】既に基礎学習を終了しているため、プログラミングノートの作成に注力し、あまり時間をかけないこと。	2020-12-24 06:43:29
Docker	dockerタグが付けられた新着投稿 - Qiita	Dockerコンテナ上でReactを動かす	https://qiita.com/junkin_au/items/5ec46fa380899dcf9fa8	コンテナ起動dockercomposeupdReactアプリケーション作成dockercomposeexecreactnpxcreatereactappサーバ起動dockercomposeexecreactnpmstart動作確認httplocalhostにアクセスして画面が表示されたら成功です。	2020-12-24 06:56:07
海外TECH	Ars Technica	Trump vetoes $740B defense bill, citing “failure to terminate” Section 230	https://arstechnica.com/?p=1731780	christmas	2020-12-23 21:36:05
海外TECH	Ars Technica	Microsoft Flight Simulator in VR: A turbulent start for wide-open skies	https://arstechnica.com/?p=1731540	impressions	2020-12-23 21:28:11
Apple	AppleInsider - Frontpage News	Apple anti-tracking privacy feature starting to show up in iOS 14.4 beta	https://appleinsider.com/articles/20/12/23/apple-anti-tracking-privacy-feature-starting-to-show-up-in-ios-144-beta	Apple anti tracking privacy feature starting to show up in iOS betaApple s anti tracking privacy feature is starting to show up for users running the first beta version of the company s upcoming iOS update Credit AppleInsiderThe privacy feature makes a specific type of user tracking meant for advertisers opt in by default That means apps will need to explicitly ask users for permission to track them across other sites and apps Read more	2020-12-23 22:00:21
海外TECH	Engadget	California clears Nuro's driverless cars to start making commercial deliveries	https://www.engadget.com/nuro-california-dmv-deployment-permit-214528906.html	California clears Nuro x s driverless cars to start making commercial deliveriesIt was only earlier this year that delivery service Nuro became the second company to get permission to test fully driverless vehicles in California and now it can claim another milestone California s DMV has granted the state s first Autonomous Ve	2020-12-23 21:45:28
ニュース	@日本経済新聞 電子版	マイナンバーカードが2021年3月から健康保険証代わりに本格的に使えるようになります。マイナカードをスマホに搭載できるようにする法改正も検討、6%にとどまる申請の拡大を目指します。 https://t.co/NZ25XwUaXK	https://twitter.com/nikkei/statuses/1341861558901743618	マイナンバーカードが年月から健康保険証代わりに本格的に使えるようになります。	2020-12-23 21:41:40
ニュース	@日本経済新聞 電子版	NYダウ反発、114ドル高　ワクチン普及期待で https://t.co/n1wJSH00X5	https://twitter.com/nikkei/statuses/1341859115245522944	普及	2020-12-23 21:31:58
ニュース	@日本経済新聞 電子版	「ピンチの時の半沢」「まずは人の話を聞き、問題解決に向けた最短経路を示してくれる」。三菱UFJ銀行の頭取に昇格する半沢淳一氏（55）の銀行員人生と人物像を探ります。 https://t.co/1MBdoPlF6U	https://twitter.com/nikkei/statuses/1341858873410502662		2020-12-23 21:31:00
ニュース	@日本経済新聞 電子版	民主はトランプ氏に同調　93兆円対策、増額修正の動き https://t.co/uOsgLQpuCF	https://twitter.com/nikkei/statuses/1341851309612285952	民主	2020-12-23 21:00:57
ニュース	BBC News - Home	Stoke City 1-3 Tottenham Hotspur: Jose Mourinho's side one win from final	https://www.bbc.co.uk/sport/football/55327848	Stoke City Tottenham Hotspur Jose Mourinho x s side one win from finalTottenham move to within two wins of a first major trophy since after seeing off Championship Stoke City in the Carabao Cup	2020-12-23 21:52:55
ビジネス	ダイヤモンド・オンライン - 新着記事	トランプ大統領、国防権限法案に拒否権発動 - WSJ発	https://diamond.jp/articles/-/258368	発動	2020-12-24 06:11:00
LifeHuck	ライフハッカー［日本版］	ストロング系の常飲はハイリスク。アルコールと依存症に関する5つの誤解	https://www.lifehacker.jp/2020/12/226612book_to_read-671.html	青春出版社	2020-12-24 06:30:00
サブカルネタ	ラーブロ	男気らーめん アカギ@桐生/小俣 〜お初〜	http://feedproxy.google.com/~r/rablo/~3/OefRNMUprEE/single_feed.php	配信	2020-12-23 21:21:00
ビジネス	東洋経済オンライン	アメリカ財務省の本音は｢まだドルは高すぎる｣ ｢為替政策報告書｣が各通貨に発した警告を読む | 市場観測 | 東洋経済オンライン	https://toyokeizai.net/articles/-/398809?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2020-12-24 06:40:00
ビジネス	東洋経済オンライン	｢SDGsで危機は脱せない｣"緑の資本主義"の欠陥 失敗したらやり直せない地点に近づいている | 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/-/395704?utm_source=rss&utm_medium=http&utm_campaign=link_back	二酸化炭素	2020-12-24 06:30:00
ビジネス	東洋経済オンライン	新型コロナの致死率低下による株高は限界だ ｢ワクチン相場｣はやって来ないかもしれない？ | 市場観測 | 東洋経済オンライン	https://toyokeizai.net/articles/-/398343?utm_source=rss&utm_medium=http&utm_campaign=link_back	東洋経済オンライン	2020-12-24 06:10:00
GCP	Cloud Blog	Introducing Monitoring Query Language, now GA in Cloud Monitoring	https://cloud.google.com/blog/products/management-tools/introducing-monitoring-query-language-or-mql/	Introducing Monitoring Query Language now GA in Cloud MonitoringDevelopers and operators on IT and development teams want powerful metric querying analysis charting and alerting capabilities to troubleshoot outages perform root cause analysis create custom SLI SLOs reports and analytics set up complex alert logic and more So today we re excited to announce the General Availability of Monitoring Query Language MQL in Cloud Monitoring MQL represents a decade of learnings and improvements on Google s internal metric query language The same language that powers advanced querying for internal Google production users is now available to Google Cloud users as well For instance you can use MQL to Create ratio based charts and alertsPerform time shift analysis compare metric data week over week month over month year over year etc Apply mathematical logical table operations and other functions to metricsFetch join and aggregate over multiple metricsSelect by arbitrary rather than predefined percentile valuesCreate new labels to aggregate data by using arbitrary string manipulations including regular expressionsLet s take a look at how to access and use MQL from within Cloud Monitoring Getting started with MQLIt s easy to get started with MQL To access the MQL Query Editor just click on the button in Cloud Monitoring Metrics Explorer Then create a query in the Metrics Explorer UI and click the Query Editor button This converts the existing query into an MQL query MQL is built using operations and functions Operations are linked together using the common pipe idiom where the output of one operation becomes the input to the next Linking operations makes it possible to build up complex queries incrementally In the same way you would compose and chain commands and data via pipes on the Linux command line you can fetch metrics and apply operations using MQL  For a more advanced example suppose you ve built a distributed web service that runs on Compute Engine VM instances and uses Cloud Load Balancing and you want to analyze error rateーone of the SRE “golden signals You want to see a chart that displays the ratio of requests that return HTTP responses internal errors to the total number of requests that is the request failure ratio The loadbalancing googleapis com https request count metric type has a response code class label which captures the class of response codes In this example because the numerator and denominator for the ratio are derived from the same time series you can also compute the ratio by grouping The following query shows this approach This query uses an aggregation expression built on the ratio of two sums The first sum uses the if function to count valued HTTP responses and a count of for other HTTP response codes The sum function computes the count of the requests that returned The second sum adds up the counts for all requests as represented by val The two sums are then divided resulting in the ratio of responses to all responses Now let s say that we want to create an alert policy from this query You can go to Alerting click “Create Policy click “Add Condition and you ll see the same “Query Editor button you saw in Metrics Explorer You can use the same query as above but with a condition operator that provides the threshold for the alert The condition tests each data point in the aligned input table to determine whether the ratio value exceeds the threshold value of The string specifies that the value should be used as a percentage In addition to ratios another common use case for MQL is time shifting For brevity we won t cover this in our blog post but the example documentation walks you through performing week over week or month over month comparisons This is particularly powerful when coupled with long term retention of months of custom and Prometheus metrics Take monitoring to the next levelThe sky s the limit for the use cases that MQL makes possible Whether you need to perform joins display arbitrary percentages or make advanced calculations we re excited to make this available to all customers and we are interested to see how you will use MQL to solve your monitoring alerting and operations needs To learn more about MQL check out the documentation quickstarts examples queries alerts a language and function reference and more Related ArticleExtended retention for custom and Prometheus metrics in Cloud MonitoringRetention times for custom and Prometheus metrics in Cloud Monitoring has gone from six weeks to months Read Article	2020-12-23 22:00:00
GCP	Cloud Blog	Tips for writing and deploying Node.js apps on Cloud Functions	https://cloud.google.com/blog/products/serverless/running-effective-nodejs-apps-on-cloud-functions/	Tips for writing and deploying Node js apps on Cloud FunctionsThe DPE Client Library team at Google handles the release maintenance and support of Google Cloud client libraries Essentially we act as the open source maintainers of Google s repositories on GitHub It s a big job For this work to scale it s been critical to automate various common tasks such as validating licenses managing releases and merging pull requests PRs once tests pass To build our various automations we decided to use the Node js based framework Probot which simplifies the process of writing web applications that listen for Webhooks from the GitHub API Editor s note The team has deep expertise in Node js The co author Benjamin Coe was the third engineer at npm Inc and is currently a core collaborator on Node js Along with the Probot framework we decided to use Cloud Functions to deploy those automations with the goal of reducing our operational overhead We found that Cloud Functions are a great option for quickly and easily turning Node js applications into hosted services Cloud Functions can scale automatically as your user base grows without the need to provision and manage additional hardware If you re familiar with creating an npm module it only takes a few additional steps to deploy it as a Cloud function either with the gcloud CLI or from the Google Cloud Console see “Your First Function Node js Cloud Functions integrate automatically with Google Cloud services such as Cloud Logging and Cloud Monitoring Cloud Functions can be triggered by events from services such as Firestore PubSub Cloud Storage and Cloud Tasks Jump forward two years we now manage automations that handle over million requests from GitHub each day And we continue to use Cloud Functions to deploy our automations Contributors can concentrate on writing their automations and it s easy for us to deploy them as functions in our production environment  Designing for serverless comes with its own set of challenges around how you structure deploy and debug your applications but we ve found the trade offs work for us Throughout the rest of this article drawing on these first hand experiences we outline best practices for deploying Node js applications on Cloud Functions with an emphasis on the following goals Performance Writing functions that serve requests quickly and minimize cold start times Observability Writing functions that are easy to debug when exceptions do occur Leveraging the platform Understanding the constraints that Cloud Functions and Google Cloud introduce to application development e g understanding regions and zones With these concepts under your belt you too can reap the operational benefits of running Node js based applications in a serverless environment while avoiding potential pitfalls   Best practices for structuring your applicationIn this section we discuss attributes of the Node js runtime that are important to keep in mind when writing code intended to deploy Cloud Functions Of most concern The average package on npm has a tree of transitive dependencies see How much do we really know about how packages behave on the npm registry It s important to consider the total size of your application s dependency tree Node js APIs are generally non blocking by default and these asynchronous operations can interact surprisingly with your function s request lifecycle Avoid unintentionally creating asynchronous work in the background of your application  With that as the backdrop here s our best advice for writing Node js code that will run in Cloud Functions   Choose your dependencies wiselyDisk operations in the gVisor sandbox which Cloud Functions run within will likely be slower than on your laptop s typical operating system that s because gVisor provides an extra layer of security on top of the operating system at the cost of some additional latency As such minimizing your npm dependency tree reduces the reads necessary to bootstrap your application improving cold start performance You can run the command npm ls production to get an idea of how many dependencies your application has Then you can use the online tool bundlephobia com to analyze individual dependencies including their total byte size You should remove any unused dependencies from your application and favor smaller dependencies Equally important is being selective about the files you import from your dependencies Take the library googleapis on npm running require googleapis pulls in the entire index of Google APIs resulting in hundreds of disk read operations Instead you can pull in just the Google APIs you re interacting with like so It s common for libraries to allow you to pull in the methods you use selectivelyーbe sure to check if your dependencies have similar functionality before pulling in the whole index Use require so slow to analyze require time performanceA great tool for analyzing the require time performance of your application is require so slow This tool allows you to output a timeline of your application s require statements which can be loaded in DevTools Timeline Viewer As an example let s comparet loading the entire catalog of googleapis versus a single required API in this case the SQL API Timeline of require googleapis The graphic above demonstrates the total time to load the googleapis dependency Cold start times will include the entire s span of the chart Timeline of require googleapis build src apis sql The graphic above demonstrates the total time to load just the sql submodule The cold start time is a more respectable ms In short requiring the SQL API directly is over times faster than loading the full googleapis index Understand the request lifecycle and avoid its pitfallsThe Cloud Functions documentation issues the following warning about execution timelines A function has access to the resources requested CPU and memory only for the duration of function execution Code run outside of the execution period is not guaranteed to execute and it can be stopped at any time  This problem is easy to bump into with Node js as many of its APIs are asynchronous by default It s important when structuring your application that res send is called only after all asynchronous work has completed  Here s an example of a function that would have its resources revoked unexpectedly In the example above the promise created by set will still be running when res send is called It should be rewritten like this This code will no longer run outside the execution period because we ve awaited set before calling res send  A good way to debug this category of bug is with well placed logging Add debug lines following critical asynchronous steps in your application Include timing information in these logs relative to when your function begins a request Using Logs Explorer you can then examine a single request and ensure that the output matches your expectation missing log entries or entries coming significantly later leaking into subsequent requests are indicative of an unhandled promise During cold starts code in the global scope at the top of your source file outside of the handler function will be executed outside of the context of normal function execution You should avoid asynchronous work entirely in the global scope e g fs read as it will always run outside of the execution period Understand and use the global scope effectivelyIt s okay to have expensive synchronous operations such as require statements in the global scope When benchmarking cold start times we found that moving require statements to the global scope rather than lazy loading within your function lead to a ms to s improvement in cold start times This can be attributed to the fact that Cloud Functions are allocated compute resources while bootstrapping Also consider moving other expensive one time synchronous operations e g fs readFileSync into the global scope The important thing to avoid asynchronous operations as they will be performed outside of the execution period Cloud functions recycle the execution environment this means that you can use the global scope to cache expensive one time operations that remain constant during function invocations It s critical that we await asynchronous operations before sending a response but it s okay to cache their response in the global scope Move expensive background operations into Cloud TasksA good way to improve the throughput of your Cloud function i e reduce overall latency during cold starts and minimize the necessary instances during traffic spikes is to move work outside of the request handler Take the following application that performs several expensive database operations The response sent to the user does not require any information returned by our database updates Rather than waiting for these operations to complete we could instead use Cloud Tasks to schedule this operation in another Cloud function and respond to the user immediately This has the added benefit that Cloud Task queues support retry attempts shielding your application from intermittent errors e g a one off failure writing to the database Here s our prior example split into a user facing function and a background function User facing function Background function Deploying your applicationThe next section of this article discusses settings such as memory and location that you should take into account when deploying your application Consider memory s relationship to performanceAllocating more memory to your functions will also result in the allocation of more CPU see Compute Time For CPU bound applications e g applications that require a significant number of dependencies at start up or that are performing computationally expensive operations see “ImageMagick Tutorial you should experiment with various instance sizes as a first step towards improving request and cold start performance You should also be mindful of whether your function has a reasonable amount of available memory when running applications that run too close to their memory limit will occasionally crash with out of memory errors and may have unpredictable performance in general  You can use the Cloud Monitoring Metrics Explorer to view the memory usage of your Cloud functions In practice my team found that Mb functions did not provide enough memory for our Node js applications which average Mb Consequently we moved to the Mb setting for our functions and stopped seeing memory issues Location location locationThe speed of light dictates that the best case for TCP IP traffic will be ms latency per miles This means that a request between New York City and London has a minimum of ms of latency You should take these constraints into account when designing your application If your Cloud functions are interacting with other Google Cloud services deploy your functions in the same region as these other services This will ensure a high bandwidth low latency network connection between your Cloud function and these services see “Regions and Zones Make sure you deploy your Cloud functions close to your users If people using your application are in California deploy in us west rather than us east this alone can save ms of latency Debugging and analyzing your applicationThe next section of this article provides some recommendations for effectively debugging your application once it s deployed Add debug logging to your application In a Cloud Functions environment avoid using client libraries such as google cloud logging and google cloud monitoring for telemetry These libraries buffer writes to the backend API which can lead to work remaining in the background after calling res send outside of your application s execution period Cloud functions are instrumented with monitoring and logging by default which you can access with Metrics Explorer and Logs Explorer For structured logging you can simply use JSON stringify which Cloud Logging interprets as structured logs The entry payload follows the structure described here Note the timingDelta as discussed in “Understand the request lifecycle ーthis information can help you debug whether you have any unhandled promises hanging around after res send There are CPU and network costs associated with logging so be mindful about the size of entries that you log For example avoid logging huge JSON payloads when you could instead log a couple of actionable fields Consider using an environment variable to vary logging levels default to relatively terse actional logs with the ability to turn on verbose logging for portions of your application using util debuglog Our takeaways from using Cloud FunctionsCloud Functions work wonderfully for many types of applications Cloud Scheduler tasks We have a Cloud function that checks for releases stuck in a failed state every minutes Pub Sub consumers One of our functions parses XML unit test results from a queue and opens issues on GitHub for flaky tests HTTP APIs We use Cloud Functions to accept Webhooks from the GitHub API for us it s okay if requests occasionally take a few extra seconds due to cold starts As it stands today though it s not possible to completely eliminate cold starts with Cloud Functions instances are occasionally restarted bursts of traffic lead to new instances being started As such Cloud Functions still isn t a great fit for applications that can t shoulder the additional seconds that cold starts occasionally add As an example blocking a user facing UI update on the response from a Cloud Function is not a good idea We want Cloud Functions to work for these types of time sensitive applications and have features in the works to make this a reality Allowing a minimum number of instances to be specified this will allow you to avoid cold starts for typical traffic patterns with new instances only being allocated when requests are made above the threshold of minimum instances Performance improvements to disk operations in gVisor the sandbox that Cloud Functions run within A percentage of cold start time is spent loading resources into memory from disk which these changes will speed up Publishing individual APIs from googleapis on npm This will make it possible for people to write Cloud functions that interact with popular Google APIs without having to pull in the entire googleapis dependency With all that said it s been a blast developing our automation framework on Cloud Functions which if you accept the constraints and follow the practices outlined in this article is a great option for deploying small Node js applications Have feedback on this article Have an idea as to how we can continue to improve Cloud Functions for your use case Don t hesitate to open an issue on our public issue tracker  High Performance Browser NetworkingRelated ArticleNew in Cloud Functions languages availability portability and moreCloud Functions includes a wealth of new capabilities that make it a robust platform on which to build your applicationsRead Article	2020-12-23 21:30:00