投稿時間:2023-06-17 06:16:49 RSSフィード2023-06-17 06:00 分まとめ(21件)

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IT	気になる、記になる…	UNiCASE、計294アイテムの値下げセールを開催中 ｰ iPhone用ケースやApple Watch用バンドなど	https://taisy0.com/2023/06/17/173096.html	applewatch	2023-06-16 20:40:36
IT	気になる、記になる…	Ankerが直営店の5周年を記念したスタンプラリーを本日より開催 ｰ 全店舗制覇で1年間何度でも25%オフになるスペシャルパス贈呈	https://taisy0.com/2023/06/17/173092.html	anker	2023-06-16 20:30:14
IT	気になる、記になる…	｢MacBook Air 15インチ｣の分解動画 ｰ 基本的には13インチと同じ内部設計	https://taisy0.com/2023/06/17/173087.html	apple	2023-06-16 20:13:07
IT	InfoQ	Presentation: Amazon DynamoDB: Evolution of a Hyperscale Cloud Database Service	https://www.infoq.com/presentations/dynamodb-scale-aws/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=global	Presentation Amazon DynamoDB Evolution of a Hyperscale Cloud Database ServiceAkshat Vig presents Amazon s experience operating DynamoDB at scale and how the architecture continues to evolve to meet the ever increasing demands of customer workloads By Akshat Vig	2023-06-16 20:34:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 「ひんやりグッズ」需要高まる、ドンキが注力　風力1.5倍のハンディファン、電動ネッククーラー	https://www.itmedia.co.jp/business/articles/2306/17/news002.html	itmedia	2023-06-17 05:40:00
AWS	AWS Database Blog	Generate a distinct set of partition keys for an Amazon DynamoDB table efficiently	https://aws.amazon.com/blogs/database/generate-a-distinct-set-of-partition-keys-for-an-amazon-dynamodb-table-efficiently/	Generate a distinct set of partition keys for an Amazon DynamoDB table efficientlyAmazon DynamoDB is a fully managed serverless NoSQL database service that provides fast and predictable performance with seamless scalability Every table in DynamoDB has a schema which specifies if it has a simple partition key for pure key value lookups or a partition key and sort key both for more complex query patterns You use these …	2023-06-16 20:30:48
js	JavaScriptタグが付けられた新着投稿 - Qiita	JavaScript でデバイスのカメラ使って撮影画像を取得	https://qiita.com/sueasen/items/5ee8c1f3351089275c20	javascript	2023-06-17 05:37:35
海外TECH	MakeUseOf	How to Back Up Saved Data for Games Installed With Epic Games Launcher	https://www.makeuseof.com/how-to-back-up-saved-data-for-games-with-epic-games-launcher/	launcher	2023-06-16 20:15:19
海外TECH	DEV Community	Medalhas do GitHub como conseguir ?	https://dev.to/doccaio/medalhas-do-github-como-conseguir--f7p	Medalhas do GitHub como conseguir Como vocêjádeve ter visto alguns usuários do GitHub possuem algumas Medalhas ACHIEVEMENTS neste artigo vamos saber o significado de cada uma e como ganhar DeslumbradoEssa medalha indica que o usuário possui um repositório que possui muitas estrelas Isso acontece quando outros usuários favoritaram deram estrelas no repositório Além disso como podemos ver na imagem acima ela possui níveis de acordo com o número de estrelas Par extraordinário Pair extraordinaire Essa medalha indica que o usuário realizou um Pull Request e desse jeito colaborou em conjunto no mesmo commit Ela possui níveis de acordo com as Pull Request em co autoria feitas Cérebro da galáxia Galaxy brain Essa medalha indica que o usuário teve duas respostas aceitas em um Pull Request Ela possui níveis de acordo com as respostas aceitas Tubarão do Pull Request Pull Shark Essa medalha indica que o usuário teve dois pull requests mergeados Ou seja ésóabrir uma branch e juntou os trabalhos ésócriar um projeto e da merge duas vezes Rápido no gatilho Quickdraw Tem que encerrar um problema ou um pull request em até minutos após a abertura video basicamente éda um commit push e depois cancelar ou fechar o pull request vai ficar vermelho ao invés de verde YOLO YOLO Essa medalha indica que o usuário finalizou um pull request sem revisão ou seja vocêprecisa de duas pessoa registrada no mesmo projeto então coloca essa pessoa para revisar o codigo e pede para essa pessoa aceitar aprovar a PR no entanto tu aprova a PR sem ninguém ter revisado ou ter aceitado a PR Apoiador público Public Sponsor Patrocínio de trabalho de código aberto via GitHub Sponsors ésódoar para algum projeto de sua preferência Heart On Your SleeveNão se sabe ao certo como conquistar essa Open SourcererNão se sabe ao certo como conquistar essa Medalhas especiaisContribuidor do projeto Marte Mars Contributor Conquista rara contribuição de código para repositórios usados na Missão de Helicóptero Mars Contribuidor do projeto Arctic Code Vault Arctic Code Vault Contributor Conquista rara contribuiu com código para repositórios no GitHub Archive Program Infelizmente não émais possível receber essas medalhas especiais Fonte ab channel tecnologiaemvideo	2023-06-16 20:22:43
海外TECH	DEV Community	Visualize Real-Time Data With Python, Dash, and RisingWave	https://dev.to/bobur/visualize-real-time-data-with-python-dash-and-risingwave-33g0	Visualize Real Time Data With Python Dash and RisingWaveReal time data is important for businesses to make quick decisions Seeing this data visually can help make decisions even faster We can create visual representations of data using various data apps or dashboards Dash is a Python library that provides a wide range of built in components for creating interactive charts graphs tables and other UI elements RisingWave is a SQL based streaming database for real time data processing This article will explain how to use Python open source project Dash and RisingWave to make visualizations of real time data How to visualize data in real timeWe know that real time data is data that is generated and processed immediately as it is collected from different data sources Sources can be typical databases such as Postgres or MySQL and message brokers like Kafka A real time data visualization consists of a few steps first we ingest then process and finally show this data in a dashboard In the case of order delivery data visualizing this data in real time can provide valuable insights into the performance of a restaurant or delivery service For example we can use real time data to monitor how long it takes for orders to be delivered identify bottlenecks in the delivery process and track changes in order volume over time When dealing with data that is constantly changing it can be difficult to keep track of everything that is happening and identify patterns or trends Using free tools such as Dash and RisingWave we can create interactive visualizations that allow us to explore and analyze this continually changing data When it comes to working with data the first programming language you might think of is Python because has a range of libraries Dash is one of them that allows us to create a data app with rich and customizable user interface using only Python code Dash is built on top of Flask Plotly js and React js which are popular web development tools so you do not need to know HTML CSS or other JavaScript frameworks With RisingWave we can consume data streams from various sources create materialized views that are optimized for complex queries and query real time data using SQL As RisingWave is wire compatible with PostgreSQL we can use the  psycopg PostgreSQL client library in Python  driver to connect to RisingWave make query operations See in the next section Visualize order delivery data demoIn the demo tutorial we ll leverage the following GitHub repository with RisingWave demos where we assume that all necessary things are set up using Docker compose You can check other ways to run RisingWave on the official website We have a Kafka topic named delivery orders that contains events for every order placed on a food delivery website Each event includes information about the order such as the order ID  restaurant ID and delivery status The workload generator Python script called Datagen simulates generating of random mock data continuously and streams them into Kafka topics In reality this mock data can be replaced with data coming from your web app or backend service Before You BeginTo complete this tutorial you need the following Ensure you have Docker and Docker Compose installed in your environment Ensure that the PostgreSQL interactive terminal psql is installed in your environment For detailed instructions see Download PostgreSQL Download and install Python for your OS pip command will be automatically installed The demo I tested on Windows OS Docker desktop and Python version installed Step Setting Up RisingWave s demo clusterFirst clone the RisingWave sample repository to your local environment git clone Then the integration tests delivery directory and start the demo cluster from the docker compose file cd risingwave integration tests deliverydocker compose up dMake sure that all containers are up and running Step Install Dash and Psycopg librariesTo install Dash you can also refer to Dash installation guide on the website Basically we need to install two libraries Dash itself and Pandas by running the following pip install command This also brings along the Plotly graphing library Plotly is known for its interactive charts Plotly Express requires Pandas to be installed too pip install dash pandasWe should also install psycopg to interact with the RisingWave streaming database pip install psycopg binary Step Create a data sourceTo ingest real time data with RisingWave you first need to set up a data source In the demo project Kafka should be defined as the data source We are going to create a new file called create a source py in the same integration tests delivery directory with Python script where we connect to the RisingWave and create a table to consume and persist delivery orders Kafka topics You can simply copy and paste the below code into the new file import psycopgconn psycopg connect database dev user root password host localhost port Connect to RisingWave conn autocommit True Set queries to be automatically committed with conn cursor as cur cur execute CREATE TABLE delivery orders source order id BIGINT restaurant id BIGINT order state VARCHAR order timestamp TIMESTAMP WITH connector kafka topic delivery orders properties bootstrap server message queue scan startup mode earliest ROW FORMAT JSON Execute the query conn close Close the connection After you create the file you run python create a source py and it will create the source table in the RisingWave Step Create a materialized viewNext we create a new materialized view similar to how we created the table we create a new file called create a materialized view py and run SQL query using the psycopg library It is also possible to merge above last two steps into one Python script file import psycopgconn psycopg connect database dev user root password host localhost port conn autocommit Truewith conn cursor as cur cur execute CREATE MATERIALIZED VIEW restaurant orders view ASSELECT window start restaurant id COUNT AS total orderFROM HOP delivery orders source order timestamp INTERVAL MINUTE INTERVAL MINUTE WHERE order state CREATED GROUP BY restaurant id window start conn close Above the SQL query calculates the number of total orders created from a specific restaurant within the last mins in real time and caches the result in the materialized view If there any data change happens or new Kafka topics arrives RisingWave automatically increments and updates the result of materialized view Once you have set up the data source materialized view you can start ingesting data and visualize this data using Dash Step Building a Dash appNow we build our a Dash app to query and visualize the materialized view content we have in the RisingWave You can follow the tutorial Dash in mins to understand the basic building blocks of Dash Our example application code displays restaurant orders data in both table and graph formats See the below Python code in dash example py import psycopgimport pandas as pdimport dashfrom dash import dash tablefrom dash import dccimport dash html components as htmlimport plotly express as px Connect to the PostgreSQL databaseconn psycopg connect database dev user root password host localhost port Retrieve data from the materialized view using pandasdf pd read sql query SELECT window start restaurant id total order FROM restaurant orders view conn Create a Dash applicationapp dash Dash name Define layoutapp layout html Div children html H Restaurant Orders Table dash table DataTable id restaurant orders table columns name i id i for i in df columns data df to dict records page size html H Restaurant Orders Graph dcc Graph id restaurant orders graph figure px bar df x window start y total order color restaurant id barmode group Run the applicationif name main app run server debug True This code snippet retrieves the data from the restaurant orders view materialized view using pandas and displays it in a Dash table using dash table DataTable and a bar chart using dcc Graph The table and bar chart has columns that correspond to the columns in the materialized view window start total order and restaurant id and rows that correspond to the data in the materialized view Step View the ResultsYou can run the application by running the above dash example py script and navigating to http localhost in your web browser You will get a message in the terminal telling you to go to this link SummaryOverall Dash is a powerful tool for creating data analytic views that require complex UIs and data visualization capabilities all using the simplicity and elegance of the Python programming language When we use it together with RisingWave streaming database we gain insights into real time data and can help us make more informed decisions and take action to optimize performance Related resourcesReal time data analytics with Apache Superset Redpanda and RisingWave How to monitor live stream metrics Recommended contentIs RisingWave the Right Streaming Database Rethinking Stream Processing and Streaming Databases Community Join the Risingwave Community About the authorVisit my blog  www iambobur comFollow me on Twitter   BoburUmurzokov	2023-06-16 20:09:30
ニュース	BBC News - Home	Ukraine war: Putin confirms first nuclear weapons moved to Belarus	https://www.bbc.co.uk/news/world-europe-65932700?at_medium=RSS&at_campaign=KARANGA	defeat	2023-06-16 20:18:02
ニュース	BBC News - Home	Daniel Ellsberg: Pentagon Papers whistleblower dies aged 92	https://www.bbc.co.uk/news/world-us-canada-65932944?at_medium=RSS&at_campaign=KARANGA	america	2023-06-16 20:51:59
ニュース	BBC News - Home	Malta 0-4 England: Three Lions cruise to Euro 2024 qualifying win as Trent Alexander-Arnold impresses	https://www.bbc.co.uk/sport/football/65852136?at_medium=RSS&at_campaign=KARANGA	Malta England Three Lions cruise to Euro qualifying win as Trent Alexander Arnold impressesEngland continue their perfect record in Euro qualifying with a comfortable win at Malta with Trent Alexander Arnold Harry Kane and Callum Wilson on the scoresheet	2023-06-16 20:42:14
ビジネス	ダイヤモンド・オンライン - 新着記事	【自動車保険ランキング2023】「年齢」「車種」「免責金額」別に保険料を徹底比較！ - ［激変］生保・損保・代理店 保険大国の限界	https://diamond.jp/articles/-/324200	【自動車保険ランキング】「年齢」「車種」「免責金額」別に保険料を徹底比較激変生保・損保・代理店保険大国の限界自動車保険は、代理店を介した大手損害保険会社の商品から、代理店を介さずにインターネットや電話で直接契約するダイレクト系損害保険会社の商品までさまざまだ。	2023-06-17 05:25:00
ビジネス	ダイヤモンド・オンライン - 新着記事	【無料公開】電子部品業界で「5年後に大化け」しそうな2社とは？需要は5Gから自動車へシフト鮮明に - Diamond Premiumセレクション	https://diamond.jp/articles/-/324670	diamond	2023-06-17 05:20:00
ビジネス	ダイヤモンド・オンライン - 新着記事	ChatGPTに聖徳太子やナポレオンを憑依させる！ビジネスにも使える「AI個性化」マル秘テク - ChatGPT完全攻略 最新・仕事術革命の決定版	https://diamond.jp/articles/-/323976	chatgpt	2023-06-17 05:15:00
ビジネス	ダイヤモンド・オンライン - 新着記事	最強の保険見直し術！損しない・無駄のない保険を選ぶための「超基礎知識」 - 有料記事限定公開	https://diamond.jp/articles/-/324199	生命保険	2023-06-17 05:10:00
ビジネス	ダイヤモンド・オンライン - 新着記事	おじさんは「週刊少年ジャンプ」を卒業すべき？ガーシーの差し入れ論争に悩む42歳 - 井の中の宴 武藤弘樹	https://diamond.jp/articles/-/324625	取り沙汰	2023-06-17 05:05:00
ビジネス	ダイヤモンド・オンライン - 新着記事	中国人研究員「先端技術情報」漏えい事件で露呈、“スパイ天国”日本のあきれた実態【元公安捜査官が解説】 - 元公安捜査官が教える「見抜く力」	https://diamond.jp/articles/-/324668	不正競争防止法違反	2023-06-17 05:02:00
ビジネス	東洋経済オンライン	有名音楽家にみる｢子供が自発的に練習する瞬間｣ チェリスト佐藤晴真氏が語る幼少時代 | 教育 | 東洋経済オンライン	https://toyokeizai.net/articles/-/678766?utm_source=rss&utm_medium=http&utm_campaign=link_back	幼少時代	2023-06-17 05:40:00
ビジネス	東洋経済オンライン	星乃珈琲店｢モーニングセット｣の抜群の安定感 ミニパンケーキorトースト･ゆで卵がついてくる | チェーン店最強のモーニングを探して | 東洋経済オンライン	https://toyokeizai.net/articles/-/678527?utm_source=rss&utm_medium=http&utm_campaign=link_back	星乃珈琲店	2023-06-17 05:20:00