投稿時間:2022-09-07 19:26:31 RSSフィード2022-09-07 19:00 分まとめ(30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
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「ひろゆきに適当なことを喋らせよう！」　本人公認の音声ジェネレーターに反響　運営元「アクセス数が数十倍に」	https://www.itmedia.co.jp/business/articles/2209/07/news176.html	itmedia	2022-09-07 18:22:00
AWS	lambdaタグが付けられた新着投稿 - Qiita	「terraformを利用してVPC内のlambda起動→VPC外（パブリック）に戻す」の注意点	https://qiita.com/docdocdoc/items/d6729a1a4b37d15539f1	errordelet	2022-09-07 18:30:40
js	JavaScriptタグが付けられた新着投稿 - Qiita	初投稿	https://qiita.com/Apedy/items/52be3ed4d5ecde6132de	javascript	2022-09-07 18:39:05
Ruby	Rubyタグが付けられた新着投稿 - Qiita	typeprof で自分の書いたコードの間違いが分かるようにしたい	https://qiita.com/kagesumi3m/items/46f8bd1b233a05932164	typeprof	2022-09-07 18:56:19
AWS	AWSタグが付けられた新着投稿 - Qiita	「terraformを利用してVPC内のlambda起動→VPC外（パブリック）に戻す」の注意点	https://qiita.com/docdocdoc/items/d6729a1a4b37d15539f1	errordelet	2022-09-07 18:30:40
Ruby	Railsタグが付けられた新着投稿 - Qiita	Ruby on Rails備忘録	https://qiita.com/taxinumber1729/items/0d96c4b683e8d87a18a5	rubyonrails	2022-09-07 18:43:08
技術ブログ	Developers.IO	Lookerの円グラフ使い方をとことん紹介するよ	https://dev.classmethod.jp/articles/looker-pie-howtouse/	looker	2022-09-07 09:40:47
技術ブログ	Developers.IO	アライアンス統括部にジョインした昴です！	https://dev.classmethod.jp/articles/subarujoinblog/	学生時代	2022-09-07 09:38:26
海外TECH	DEV Community	How to Choose the Right Programming Language	https://dev.to/blst-security/how-to-choose-the-right-programming-language-33ap	How to Choose the Right Programming Language How to Choose the Right Programming LanguageIf you re just starting out in the world of programming it can be tough to know which language to choose There are so many options out there and it can be difficult to know which one is right for you Here are a few things to consider when making your decision What do you want to use the programming language for Is the language easy to learn Are there plenty of resources available for learning the language Once you ve learned the language how easy is it to keep practicing Are you willing to experiment with different languages until you find one that suits your needs Answering these questions can help you narrow down your options and choose the right programming language for your needs How to Determine Which Language is Right for YouThe best way to learn a language is to use it There are plenty of resources available to help you get started such as tutorials books and online courses Start by doing some simple tasks in the language to get a feel for how it works Then as you become more comfortable you can start tackling more complex projects The more you use the language the more fluent you ll become With so many programming languages available it can be tough to choose the right one for your needs However by taking the time to understand your needs and research different languages you can find the perfect language for your project Factors to ConsiderFeatures each language has its own unique features Some languages are better suited for certain tasks than others Consider what you want to use the language for before making your decision Target platform Different languages are designed to run on different platforms so it s important to choose a language that is compatible with the platform you re targeting For example if you want to develop a program for Windows you would need to choose a language that can run on the Windows platform Development environmentPerformance There are types of performances that you should consider The first is the language s runtime performance This is the speed at which your code will execute A language with a high runtime performance will be able to take advantage of your hardware s resources and run faster The second factor is the language s compile time performance This is the speed at which your code will compile A language with a high compile time performance will be able to compile your code faster making it easier to iterate on your code and make changes Popularity Some languages are more popular than others and this popularity can have a number of benefits For one a more popular language is likely to have more resources available such as tutorials libraries and support forums This can make it easier to learn and use the language Popular languages also tend to be more stable and have more features than less popular languages This can make them more suitable for large projects Finally popular languages are often more compatible with other languages and tools making it easier to integrate them into your workflow Ease of use The factor of ease of use is important when choosing the right programming language because it can determine how quickly you are able to learn and use the language If a language is too difficult to use it can take a long time to learn and you may never be able to use it to its full potential However if a language is too easy to use it may be lacking in features and power The right balance of ease of use and features is what you should look for when choosing a programming language Before choosing a programming language you need to understand your needs Different languages are better suited for different purposes Some languages are better for more complex applications while others are more suited for more simplistic applications Do your research and choose the language that will work best for you and your project Star our Github repo and join the discussion in our Discord channel Test your API for free now at BLST	2022-09-07 09:20:19
海外TECH	DEV Community	Why we don’t use Spark	https://dev.to/otainsight/why-we-dont-use-spark-4ihh	Why we don t use Spark Big Data amp SparkMost people working in big data know Spark if you don t check out their website as the standard tool to Extract Transform amp Load ETL their heaps of data Spark the successor of Hadoop amp MapReduce works a lot like Pandas a data science package where you run operators over collections of data These operators then return new data collections which allows the chaining of operators in a functional way while keeping scalability in mind For most data engineers Spark is the go to when requiring massive scale due to the multi language nature the ease of distributed computing or the possibility to stream and batch The many integrations with different persistent storages infrastructure definitions and analytics tools make it a great solution for most companies Even though it has all these benefits it is still not the holy grail Especially if your business is built upon crunching data At OTA Insight our critical view on infrastructure made us choose to go a different route focused on our needs as a company both from a technical a people perspective and a long term vision angle Humble beginningsEarly on you have only focus building a product that solves a need and that people want to pay for as quickly as possible This means that spending money on things that accelerate you getting to this goal is a good thing In the context of this article this means you don t want to spend time managing your own servers or fine tuning your data pipeline s efficiency You want to focus on making it work Specifically we heavily rely on managed services from our cloud provider Google Cloud Platform GCP for hosting our data in managed databases like BigTable and Spanner For data transformations we initially heavily relied on DataProc a managed service from Google to manage a Spark cluster Managing managed servicesOur first implementation was a self hosted Spark setup paired with a Kafka service containing our job queue This had clear downsides and in hindsight we don t consider it managed A lot of side developments had to be done to cover all edge cases of the deployment amp its scaling needs Things like networking node failures and concurrency should be investigated mitigated and modelled This would have put a heavy strain on our development efficiency Secondly pricing of running a full Spark cluster with a uptime was quite high and creating auto scaling strategies for it was quite hard Our second implementation was migrated to use the same Kafka event stream that streamed workload messages into the Spark DataProc instances instead of the initially self hosted Spark instance The Kafka Dataproc combination served us well for some time until GCP released its own message queue implementation Google Cloud Pub Sub At the time we investigated the value of switching There is always an inherent switching cost but what we had underestimated with Kafka is that there is a substantial overhead in maintaining the system This is especially true if the ingested data volume increases rapidly As an example the Kafka service requires you to manually shard the data streams while a managed service like Pub Sub does the re sharding behind the scenes Pub Sub on the other hand also had some downsides e g it didn t allow for longer term data retention which can easily be worked around by storing the data on Cloud Storage after processing Persisting the data and keeping logs on the interesting messages made Kafka obsolete for our use case Now as we had no Kafka service anymore we found that using DataProc was also less effective when paired with Pub Sub relative to the alternatives After researching our options regarding our types of workloads we chose to go a different route It is not that DataProc was bad for our use cases but there were some clear downsides to DataProc and some analysis taught us that there were better options First DataProc at the time had scaling issues as it was mainly focussed on batch jobs while our main pipelines were all running on streaming data With the introduction of Spark Streaming this issue was alleviated a bit though not fully for our case Spark Streaming still works in a micro batched way under the hood which is required to conform to the exactly once delivery pattern This gives issues for workloads that do not have uniform running times Our processors require fully real time streaming without exactly once delivery due to the idempotency of our services Secondly the product was not very stable at the time meaning we had to monitor quite closely what was happening and spent quite some time on alerts Lastly most of our orchestration amp scheduling was done by custom written components making it hard to maintain and hard to update to newer versions Building for the futureIt was clear we needed something that was built specifically for our big data SaaS requirements Dataflow was our first idea as the service is fully managed highly scalable fairly reliable and has a unified model for streaming amp batch workloads Sadly the cost of this service was quite large Secondly at that moment in time the service only accepted Java implementations of which we had little knowledge within the team This would have been a major bottleneck in developing new types of jobs as we would either need to hire the right people or apply the effort to dive deeper in Java Finally the data point processing happens mainly in our API making much of the benefits not weigh up against the disadvantages Small spoiler we didn t choose DataFlow as our main processor We still use DataFlow within the company currently but for fairly specific and limited jobs that require very high scalability None of the services we researched were an exact match each service lacked certain properties Each service lacks something that is a hard requirement to scale the engineering effort with the pace the company is and keeps growing with At this point we reached product market fit and were ready to invest in building the pipelines of the future Our requirements were mainly keeping the development efficiency high keeping the structure open enough for new flows to be added while also keeping the running costs low As our core business is software keeping an eye on how much resources this software burns through is definitely a necessity Taking into account the cost of running your software on servers can make the difference between a profit and a loss and this balance can change very quickly We have processes in place to keep our bare metal waste as low as possible without hindering new developments which in turn gives us ways to optimise our bottomline Being good custodians of resources helps us keep our profit margins high on the software we provide After investigating pricing of different services and machine types we had a fairly good idea of how we could combine different services such that we had the perfect balance between maintainability and running costs At this point we made the decision to for the majority of our pipelines combine Cloud Pub Sub amp Kubernetes containers Sometimes the best solution is the simplest The reasoning behind using Kubernetes was quite simple Kubernetes had been around a couple of years and had been used to host most of our backend microservices as well as frontend apps As such we had extensive knowledge on how to automate most of the manual management away from the engineers and into Kubernetes and our CI CD Secondly as we already had other services using Kubernetes this knowledge was quickly transferable to the pipelines which made for a unified landscape between our different workloads The ease of scaling of Kubernetes is its main selling point Pair this with the managed autoscaling the Kubernetes Engine gives and you have a match made in heaven It might come as a surprise but bare metal Kubernetes containers are quite cheap on most cloud platforms especially if your nodes can be pre emptible As all our data was stored in persistent storages or in message queues in between pipeline steps our workloads could be exited at any time and we would still keep our consistent state Combine the cost of Kubernetes with the very low costs of Pub Sub as a message bus and we have our winner Building around simplicityBoth Kubernetes and Pub Sub are quite barebones without a lot of bells amp whistles empowering developers As such we needed a simple framework to build new pipelines fast We dedicated some engineering effort into building this pipeline framework to the right level of abstraction where a pipeline had an input a processor and an output With this simple framework we ve been able to build the entire OTA Insight platform at a rapid scale while not constricting ourselves to the boundaries of certain services or frameworks Secondly as most of our product level aggregations are done in our Go APIs which are optimised for speed and concurrency we can replace Spark with our own business logic which is calculated on the fly This helps us move fast within this business logic and helps keep our ingestion simple The combination of both the framework and the aggregations in our APIs create an environment where Spark becomes unnecessary and complexity of business logic is spread evenly across teams SummaryDuring our growth path from our initial Spark environment DataProc to our own custom pipelines we ve learned a lot about costs engineering effort engineering experience and growth amp scale limitations Spark is a great tool for many big data applications that deserves to be the most common name in data engineering but we found it limiting in our day to day development as well as financials Secondly it did not fit entirely in the architecture we envisioned for our business Currently we know and own our pipelines like no framework could ever provide This has led to rapid growth in new pipelines new integrations and more data ingestion than ever before without having to lay awake at night pondering if this new integration would be one too many All in all we are glad we took the time to investigate the entire domain of services and we encourage others to be critical in choosing their infrastructure and aligning it with their business requirements as it can make or break your software solutions either now or when scaling Want to know more Come talk to us	2022-09-07 09:03:59
海外TECH	Engadget	Signal makes Google strike organizer Meredith Walker its first president	https://www.engadget.com/signal-hires-meredith-walker-as-president-092649184.html?src=rss	Signal makes Google strike organizer Meredith Walker its first presidentPrivacy focused messaging app Signal has hired former Google manager and tech critic Meredith Whittaker as its first president it announced in a blog post She s already on the board of directors along with WhatsApp founder interim CEO and Facebook critic Brian Acton and former CEO encryption evangelist Moxie Marlinspike Her focus she said will be on strategy communication and the foundation s long term financial health quot I will be working with Signal s CEO and leadership with a particular focus on guiding Signal s strategy ensuring our financial sustainability sharpening and broadening Signal s public communications and whatever else is needed to strengthen the app and the org quot she said Whittaker will also aid in the search for a permanent CEO to replace Acton Whittaker rose to prominence as the founder of Google s Open Research Group and organizer of a walkout after it emerged that Google had paid off executives accused of sexual harassment including Andy Rubin She left the search giant in and went on to form the AI Now Institute at NYU aiming to quot ensure that AI systems are accountable to the communities and contexts in which they re applied quot according to its mission statement She s also a senior adviser on AI to the Federal Trade Commission and joined the Signal Foundation s board in Signal currently counts over million users after receiving a significant boost following a backlash against WhatsApp s privacy policy changes last year Along with Firefox Signal is one of the few apps committed to privacy rather than revenue via data collection To that end Whittaker plans to focus on maintaining the company s health via small donations from millions of users who quot kick in a little bit quot she told The Washington Post quot We do have growth goals but they are driven by our mission not by a desire for profit quot she added	2022-09-07 09:26:49
Java	Java Code Geeks	Building a Distributed Task Queue in Python	https://www.javacodegeeks.com/2022/09/building-a-distributed-task-queue-in-python.html	Building a Distributed Task Queue in PythonWhy not just use Celery RQ Huey TaskTiger Unfortunately WakaTime has been using Celery for almost years now During that time I ve experienced many critical bugs some still open years after being introduced Celery used to be pretty good but feature bloat made the project difficult to maintain Also in my opinion splitting the code into three	2022-09-07 09:13:48
医療系	医療介護 CBnews	支払基金と逆、医療費伸びが件数より高め-国保連7月審査分、入院と歯科が高い	https://www.cbnews.jp/news/entry/20220907180609	国民健康保険	2022-09-07 18:25:00
医療系	医療介護 CBnews	使用者による障害者虐待、通報・届出件数が微減-厚労省が2021年度の結果を公表	https://www.cbnews.jp/news/entry/20220907175402	厚生労働省	2022-09-07 18:15:00
ニュース	BBC News - Home	Olivia Pratt-Korbel: Man held on suspicion of murder bailed	https://www.bbc.co.uk/news/uk-england-merseyside-62818410?at_medium=RSS&at_campaign=KARANGA	death	2022-09-07 09:25:22
ニュース	BBC News - Home	Ryan Giggs faces retrial after previous jury discharged	https://www.bbc.co.uk/news/uk-wales-62806864?at_medium=RSS&at_campaign=KARANGA	girlfriend	2022-09-07 09:44:45
ニュース	BBC News - Home	Thomas Tuchel: Chelsea sack manager following Champions League defeat	https://www.bbc.co.uk/sport/football/62817993?at_medium=RSS&at_campaign=KARANGA	dinamo	2022-09-07 09:30:13
ニュース	BBC News - Home	Ukraine war: Putin says West's sanctions fever wrecks European lives	https://www.bbc.co.uk/news/world-europe-62817560?at_medium=RSS&at_campaign=KARANGA	european	2022-09-07 09:45:22
ニュース	BBC News - Home	School trips could be axed in bid to keep staff	https://www.bbc.co.uk/news/education-62670576?at_medium=RSS&at_campaign=KARANGA	teachers	2022-09-07 09:08:18
ニュース	BBC News - Home	Pakistan floods: Biggest lake subsides amid race to help victims	https://www.bbc.co.uk/news/world-asia-62811704?at_medium=RSS&at_campaign=KARANGA	subsides	2022-09-07 09:08:14
ニュース	BBC News - Home	Harry Styles and Chris Pine deny 'ridiculous' spitting story	https://www.bbc.co.uk/news/entertainment-arts-62818523?at_medium=RSS&at_campaign=KARANGA	darling	2022-09-07 09:25:27
ニュース	BBC News - Home	T20 World Cup: Alex Hales gets first England call-up since 2019 as Jonny Bairstow replacement	https://www.bbc.co.uk/sport/cricket/62818043?at_medium=RSS&at_campaign=KARANGA	T World Cup Alex Hales gets first England call up since as Jonny Bairstow replacementNottinghamshire batsman Alex Hales gets first England call up in three years as he replaces the injured Jonny Bairstow in the T World Cup squad	2022-09-07 09:21:34
ニュース	BBC News - Home	Copa Libertadores: Athletico Paranaense stage comeback to reach final	https://www.bbc.co.uk/sport/av/football/62819345?at_medium=RSS&at_campaign=KARANGA	Copa Libertadores Athletico Paranaense stage comeback to reach finalWatch highlights as Athletico Paranaense edge past Palmeiras in dramatic fashion to book their place in the Copa Libertadores final	2022-09-07 09:45:42
北海道	北海道新聞	道内の農業、水産高校の食材でフランス料理　京王プラザホテル札幌	https://www.hokkaido-np.co.jp/article/727632/	京王プラザホテル札幌	2022-09-07 18:24:00
北海道	北海道新聞	レスリング藤波、世界選手権欠場　女子５３キロ級、左足甲負傷	https://www.hokkaido-np.co.jp/article/727630/	世界選手権	2022-09-07 18:15:00
北海道	北海道新聞	駐車場サービス会社を家宅捜索　五輪スポンサーの「パーク２４」	https://www.hokkaido-np.co.jp/article/727629/	東京都品川区	2022-09-07 18:09:00
北海道	北海道新聞	８日の予告先発　日本ハムはメネズ	https://www.hokkaido-np.co.jp/article/727627/	予告先発	2022-09-07 18:07:59
北海道	北海道新聞	尾身会長「最悪想定し準備を」　災害時の感染症対策で講演	https://www.hokkaido-np.co.jp/article/727628/	新型コロナウイルス	2022-09-07 18:06:00
北海道	北海道新聞	大樹で新ロケット発射場着工　人工衛星向け	https://www.hokkaido-np.co.jp/article/727626/	人工衛星	2022-09-07 18:03:00
IT	週刊アスキー	Switch／PS4『アーケードアーカイブス チャンピオンレスラー』が配信決定！	https://weekly.ascii.jp/elem/000/004/104/4104625/	nintendo	2022-09-07 18:15:00