投稿時間:2023-02-03 09:39:00 RSSフィード2023-02-03 09:00 分まとめ(41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	気になる、記になる…	Apple、M2/M2 Proチップを搭載した新型｢Mac mini｣を本日発売	https://taisy0.com/2023/02/03/168023.html	apple	2023-02-02 23:02:09
IT	気になる、記になる…	Apple、M2 Pro/M2 Maxチップ搭載｢MacBook Pro 14/16インチ｣を本日発売	https://taisy0.com/2023/02/03/168018.html	macbookp	2023-02-02 23:02:07
IT	気になる、記になる…	Apple、｢HomePod (第2世代)｣を本日発売	https://taisy0.com/2023/02/03/168015.html	apple	2023-02-02 23:02:04
IT	気になる、記になる…	Appleのサービスの有料会員数は9億3,500万人以上に	https://taisy0.com/2023/02/03/168038.html	apple	2023-02-02 23:01:09
IT	ITmedia 総合記事一覧	[ITmedia News] Amazon決算は微増収98％の減益　AWSは増収も増加率は鈍化	https://www.itmedia.co.jp/news/articles/2302/03/news074.html	amazon	2023-02-03 08:26:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 最善のエコカーは「PHEV」か「HEV」か　現時点での最適解とは	https://www.itmedia.co.jp/business/articles/2302/03/news021.html	itmedia	2023-02-03 08:04:00
AWS	AWS Japan Blog	更新版の AWS Certified DevOps Engineer – Professional 試験の登録が開始されました	https://aws.amazon.com/jp/blogs/news/updated-aws-certified-devops-engineer-professional-registration-now-open/	この更新は、トレンド、業界状況、およびクラウドプロフェッショナルの業務慣行の変化を反映しています。	2023-02-02 23:27:10
AWS	AWS Japan Blog	更新版の AWS Certified Developer – Associate 試験の登録が開始されました	https://aws.amazon.com/jp/blogs/news/updated-aws-certified-developer-associate-registration-now-open/	更新版のAWSCertifiedDeveloperAssociate試験の登録が開始されました更新版のAWSCertifiedDeveloperAssociate試験の登録が開始されました。	2023-02-02 23:26:41
AWS	AWSタグが付けられた新着投稿 - Qiita	【資格合格】AWS ソリューションアーキテクト（SAA-C03）の振り返り	https://qiita.com/nishikor/items/73c4fa0d29d85031bac1	資格	2023-02-03 08:49:15
海外TECH	DEV Community	The Rise of Vector Search w/ Pinecone Founder & CEO, Edo Liberty	https://dev.to/linearb/the-rise-of-vector-search-w-pinecone-founder-ceo-edo-liberty-gb7	The Rise of Vector Search w Pinecone Founder amp CEO Edo LibertyWe re still in January and AI might already be the buzzword of and for good reason AI powered startups have been layoff resistant and machine learning engineers are one of the fastest growing jobs not in tech but in the entire economy This week we sit down with Pinecone Founder and CEO Edo Liberty An AI and machine learning expert Edo graduated from Yale with a PhD in Applied Mathematics and worked as Head of Amazon s AI Labs before founding Pinecone Listen as he explains why the biggest AI and machine learning companies in the world Google Amazon Meta and TikTok use vector search to power their products Episode Highlights Edo s start in AI What is vector data Face detection for cows Engineering applications of vector search What s so hard about vector search Where is the industry stuck today Open source projects What s the future of vector search Pinecone s recently announced hybrid search While you re here check out this video from our YouTube channel and be sure to like and subscribe when you do On Feb th learn how the smartest engineers increased productivity by up to without working longer hours or burning out their teamsEvery engineering team could use some easy wins right now including yours We re talking quick ways to improve productivity without wasting more hours at work spending a lot of money or burning out already stretched teams That s why LinearB teamed up with dozens of engineering orgs to crowdsource the real ways smart companies have improved their productivity quickly and most importantly easily On February th LinearB will be presenting this playbook at our free virtual Scaling Developer Efficiency workshop Designed to help you gain up to increased productivity this free online workshop will empower your team with strategies and solutions that let them focus on what they do best Code Reserve Your Virtual Seat	2023-02-02 23:34:52
海外TECH	DEV Community	Getting Started with Fast-Api 🏎️ and Docker🐳	https://dev.to/ken_mwaura1/getting-started-with-fast-api-and-docker-515	Getting Started with Fast Api ️and DockerNew month New blog post After the overwhelming response to my last post on Getting Started wtih Flask and Docker I decided to write another one This time I m going to show you how to get started with Fast Api and Docker Our demo project will be a simple API that allows the user to create and save notes to a Postgres database Fast Api is a modern fast high performance web framework for building APIs with Python based on standard Python type hints It is a relatively new framework that is gaining popularity in the Python community over k on GithHub It is built on top of Starlette a lightweight ASGI framework toolkit which is ideal for building high performance asyncio services and Pydantic It is fast easy to use and easy to learn making it a perfect choice for building APIs It also has built in support for OpenAPI and Swagger As the name implies Fast Api is fast It is one of the fastest Python frameworks available Using Uvicorn as the ASGI server it is capable of handling over requests per second Audience and Objectives ️This tutorial is for anyone who wants to get started with Fast Api and Docker From beginners to intermediate developers By the end of this tutorial you will be able to Create a Fast Api project Run the project locally Connect to a Postgres database to perform CRUD operations Dockerize the project Commit the code and Push to GitHub Use GitHub Actions as our CI CD pipeline to test and build Docker image and container Interact with Api via the browser or rd Party tools like Postman Insomnia etc Optionally create and connect a Vue frontend to the API Prerequisites‍The main prerequisite for this tutorial is a basic understanding of Python and Docker Have the following installed on your machine Python Git installed on your system Docker running on your system PostgreSQL installed on your system I recommend using Docker to run the database Terminal or Command Line Interface CLI of your choice Node js and NPM installed on your system if you want to create a Vue frontend Nvm is recommended Project Structure The project structure will be as follows Fast Api example├ー github│└ーworkflows│└ーdocker image yml └ーpython app yml ├ーsrc│├ーapp││├ー init py││├ーapi│││├ー init py│││├ーcrud py│││├ーmodels py│││├ーnotes py│││└ーping py ├ーmodels py│├ーdb py│├ーmain py ├ーDockerfile ├ー env├ー gitignore├ーREADME md├ーdocker compose yml├ーrequirements txtSimple architecture diagram Getting StartedThese instructions will work on most unix Linux systems If you are using Windows you can use WSL or Git Bash This tutorial will be broken down into the following sections Create a Fast Api Project Connect to a Postgres Database Dockerize the Project Commit the Code and Push to GitHub Create a GitHub Action Conclusion References Create a Fast Api Project Create a new folder and open it in your terminal mkdir Fast Api examplecd Fast Api exampleCreate a virtual environment and activate it python m venv venvsource venv bin activateInstall the required dependencies Save the following in a file called requirements txt pip install fastapi uvicorn python dotenv psycopg binarypip freeze gt requirements txtCreate a new folder src inside it file called main py and add the following code from fastapi import FastAPIapp FastAPI app get def read root return Hello World The code is a simple hello world example It imports the Fast Api class and creates an instance of it It then creates a route that returns a simple JSON object Run the project locally uvicorn src main app reload workers host port We are using Uvicorn as the ASGI server It is a lightning fast ASGI server implementation using uvloop and httptools We are also using the reload flag to enable hot reloading This means that whenever we make a change to our code the server will automatically restart The workers flag is used to specify the number of worker processes The host and port flags are used to specify the host and port to run the server on Open your browser and navigate to http localhost You should see the following Connect to a Postgres DatabaseInside the src folder create a new file called db py and add the following code src db pyimport osfrom sqlalchemy import Column Integer String Table create engine MetaData from dotenv import load dotenvfrom databases import Databasefrom datetime import datetime as dtfrom pytz import timezone as tzload dotenv Database url if none is passed the default one is usedDATABASE URL os getenv DATABASE URL postgresql hello fastapi hello fastapi localhost hello fastapi dev SQLAlchemyengine create engine DATABASE URL metadata MetaData notes Table notes metadata Column id Integer primary key True Column title String Column description String Column completed String default False Column created date String default dt now tz Africa Nairobi strftime Y m d H M Databases query builderdatabase Database DATABASE URL In the code we are using SQLAlchemy as our ORM Object Relational Mapper and Databases as our query builder Lets go through what the code does We are importing the required libraries from sqlalchemy we are importing the Column Integer String Table create engine and MetaData classes These are important classes that we will be using to create our database schema and perform CRUD operations We are also using the dotenv library to load environment variables from a env file This is good security practice escpecially in a production environment When we run the project in a test env we are providing a default database url This is the url is important for testing also if the env file is not found We are creating a table called notes with the following columns id title description completed and created date The id column is the primary key and the created date column is set to the current date and time We are also creating a database instance using the Database class from the databases library This instance will be used to perform CRUD operations Create a CRUD APIInside the src folder create a new folder called api and inside it create a new file called models py and add the following code src api models pyfrom pydantic import BaseModel Field NonNegativeIntfrom datetime import datetime as dtfrom pytz import timezone as tzclass NoteSchema BaseModel title str Field min length max length additional validation for the inputs description str Field min length max length completed str False created date str dt now tz Africa Nairobi strftime Y m d H M class NoteDB NoteSchema id int In the code above we are creating two classes The NoteSchema class is used to validate the data that is sent to the API The NoteDB class is used to validate the data that is returned from the database The id field is added to the NoteDB class because it is not sent to the API It is generated by the database The created date field is also added to the NoteDB class because it is not sent to the API It is generated by the database Here is a list of the fields that we are using title The title of the note It is a required field and it must be between and characters description The description of the note It is a required field and it must be between and characters completed The status of the note It is a required field and it must be either True or False created date The date and time when the note was created It is a required field and it must be in the format YYYY MM DD HH MM Using Pydantic we can add additional validation to the fields For example we can add a regex to the title field to ensure that it only contains letters and numbers We can also add a regex to the description field to ensure that it only contains letters and numbers We can also add a regex to the completed field to ensure that it only contains letters and numbers We can also add a regex to the created date field to ensure that it only contains letters and numbers Inside the src api folder create a new file called crud py and add the following code src api crud pyfrom app api models import NoteSchemafrom app db import notes databasefrom datetime import datetime as dtasync def post payload NoteSchema created date dt now strftime Y m d H M query notes insert values title payload title description payload description completed payload completed created date created date return await database execute query query async def get id int query notes select where id notes c id return await database fetch one query query async def get all query notes select return await database fetch all query query async def put id int payload NoteSchema created date dt now strftime Y m d H M query notes update where id notes c id values title payload title description payload description completed payload completed created date created date returning notes c id return await database execute query query async def delete id int query notes delete where id notes c id return await database execute query query In the code above we are creating five functions that will be used to perform CRUD operations The post function is used to create a new note The get function is used to get a note by its id The get all function is used to get all the notes The put function is used to update a note The delete function is used to delete a note Unlike the normal way of defining python functions using the def keyword we are using the async keyword to define the functions This is because we are using a core feature of FastApi which is that its asynchronous This means that the functions will be executed asynchronously This is good because it will allow the application to handle multiple requests at the same time Routing and API EndpointsInside the src api folder create a new file called notes py and add the following code src api notes pyfrom app api import crudfrom app api models import NoteDB NoteSchemafrom fastapi import APIRouter HTTPException Pathfrom typing import List from datetime import datetime as dtrouter APIRouter router post response model NoteDB status code async def create note payload NoteSchema note id await crud post payload created date dt now strftime Y m d H M response object id note id title payload title description payload description completed payload completed created date created date return response object router get id response model NoteDB async def read note id int Path gt note await crud get id if not note raise HTTPException status code detail Note not found return note router get response model List NoteDB async def read all notes return await crud get all router put id response model NoteDB async def update note payload NoteSchema id int Path gt Ensures the input is greater than note await crud get id if not note raise HTTPException status code detail Note not found note id await crud put id payload response object id note id title payload title description payload description completed payload completed return response object DELETE route router delete id response model NoteDB async def delete note id int Path gt note await crud get id if not note raise HTTPException status code detail Note not found await crud delete id return noteIn the code above we are creating four functions that will be used to handle the requests The create note function is used to handle the POST request The read note function is used to handle the GET request The read all notes function is used to handle the GET request The update note function is used to handle the PUT request The delete note function is used to handle the DELETE request The create note function takes in a payload of type NoteSchema and returns a response of type NoteDB The read note function takes in an id of type int and returns a response of type NoteDB The read all notes function returns a response of type List NoteDB The update note function takes in a payload of type NoteSchema and an id of type int and returns a response of type NoteDB The delete note function takes in an id of type int and returns a response of type NoteDB The router post decorator is used to define the route for the create note function The router get decorator is used to define the route for the read note function The router get decorator is used to define the route for the read all notes function The router put decorator is used to define the route for the update note function The router delete decorator is used to define the route for the delete note function Main FileInside the src folder create a new file called main py and add the following code src main pyfrom fastapi import FastAPIfrom starlette middleware cors import CORSMiddlewarefrom app api import notes pingfrom app db import engine metadata databasemetadata create all engine app FastAPI origins http localhost http localhost http localhost app add middleware CORSMiddleware allow origins allow credentials True allow methods DELETE GET POST PUT allow headers app on event startup async def startup await database connect app on event shutdown async def shutdown await database disconnect app include router ping router app include router notes router prefix notes tags notes In the code above we are creating a new FastApi application We are also adding a middleware to allow cross origin resource sharing This is to allow the frontend to make requests to the backend We are also adding the notes and ping routers to the application On app start up we are connecting to the database On app shutdown we are disconnecting from the database We are also adding the notes and ping endpoints to the application Ths notes endpoint is added to the application with the prefix notes This means that all the routes in the notes endpoint will be prefixed with notes The ping endpoint is added to the application without a prefix This means that all the routes in the ping endpoint will not be prefixed with anything One Database to Rule Them All In this section we will be creating a database to store the notes We will be using PostgreSQL as our database We will be using Docker to run the database We will be using Docker Compose to run the database and the application Docker FileInside the src folder create a new file called Dockerfile and add the following code FROM python alpineWORKDIR appCOPY requirements txt RUN pip install r requirements txtCOPY CMD uvicorn src main app reload workers host port In the code above we are creating a new Docker image We are setting the working directory to app We are copying the requirements txt file to the working directory We are installing the dependencies in the requirements txt file We are copying all the files in the current directory to the working directory We are setting the command to run when the container is started Feel free to change the port number in the command to any port number you want Docker Compose File Inside the root folder create a new file called docker compose yml and add the following code version services db image postgres container name postgres restart always environment POSTGRES USER hello fastapi POSTGRES PASSWORD hello fastapi POSTGRES DB hello fastapi dev ports volumes data var lib postgresql data app build container name fastapi restart always ports depends on db volumes app command uvicorn src main app reload workers hostnetworks default name hello fastapiIn the code above we are creating two services The db service is used to run the database The app service is used to run the application The db service is dependent on the app service This means that the db service will not start until the app service is running Running the Application To run the application locally run the following command uvicorn src main app reload workers host port The application should be running on port You can test the application by making requests to the endpoints You can use Postman Insomnia to make requests to the endpoints You can also use the frontend to make requests to the endpoints Screenshot of the application running locally Testing the Api with Thunder Client Personally am using Visual Studio Code as my editor I have installed the Thunder Client extension This allows me to make requests to the endpoints from within the editor You can install the extension and make requests to the endpoints from within the editor Examples as shown below CI CD using GitHub Actions In this section we will be setting up CI CD using GitHub Actions We will be using GitHub Actions to test the application This is important as it alllows to be sure that any proposed changes dont break the api We will also implement a workflow to test and build the docker Images for the application and the database This is important as it allows us to be sure that the application and the database are working as expected Test WorkflowInside the github workflows folder create a new file called pythonapp yml and add the following code name Python Application Teston push branches main pull request branches main jobs build runs on ubuntu latest strategy matrix python version steps uses actions checkout v name Set up Python matrix python version uses actions setup python v with python version matrix python version name Install dependencies run python m pip install upgrade pip pip install r requirements txt name Lint with flake run pip install flake stop the build if there are Python syntax errors or undefined names flake count select E F F F show source statistics exit zero treats all errors as warnings The GitHub editor is chars wide flake count exit zero max complexity max line length statistics name Setup PostgreSQL uses Harmon postgresql action v with Version of PostgreSQL to use postgresql version alpine POSTGRES DB name for the default database that is created postgresql db hello fastapi dev POSTGRES USER create the specified user with superuser power postgresql user hello fastapi POSTGRES PASSWORD superuser password postgresql password hello fastapi name Test with pytest run pip install pytest pytest In the code above we are creating a new workflow We are setting the name of the workflow to Python Application Test We are setting the workflow to run when a push is made to the main branch or when a pull request is made to the main branch We are creating a new job called build We are setting the job to run on the latest version of Ubuntu We are setting the job to run on multiple versions of Python We are checking out the code We are setting up Python We are installing the dependencies We are linting the code We are setting up PostgreSQL We are running the tests Build WorkflowInside the github workflows folder create a new file called docker image yml and add the following code github workflows docker image yml This is a basic workflow to help you get started with Actionsname Docker Compose Actions Workflowon push branches main pull request branches main jobs build runs on ubuntu latest steps uses actions checkout v name Build the Docker image run docker compose build no cache force rm test runs on ubuntu latest steps uses actions checkout v name Build the stack run docker compose up dIn the code above we are creating a new workflow We are setting the name of the workflow to Docker Compose Actions Workflow We are setting the workflow to run when a push is made to the master branch or when a pull request is made to the master branch We are creating a new job called build We are setting the job to run on the latest version of Ubuntu We are checking out the code We are building the docker images for the application and the database Running the WorkflowsTo run the workflows push the code to the main branch The workflows will run automatically You can check the status of the workflows by going to the Actions tab on GitHub ConclusionIn this article we have built a simple CRUD API using FastAPI We have also set up CI CD using GitHub Actions We have also set up a PostgreSQL database We have also set up a docker compose file to run the application locally Find the code for this article on KenMwaura Fast Api example Simple asynchronous API implemented with Fast Api framework utilizing Postgres as a Database and SqlAlchemy as ORM GiHub Actions as CI CD Pipeline FastAPI example app This repository contains code for asynchronous example api using the Fast Api framework Uvicorn server and Postgres Database to perform crud operations on notes Installation method Run application locally Clone this Repogit clone Cd into the Fast Api foldercd Fast Api exampleCreate a virtual environmentpython m venv venvActivate virtualenvsource venv bin activateFor zsh userssource venv bin activate zshFor bash userssource venv bin activate bashFor fish userssource venv bin activate fishCd into the src foldercd srcInstall the required packagespython m pip install r requirements txtStart the app using Uvicornuvicorn app main app reload workers host port Ensure you have a Postgres Database running locallyAdditionally create a fast api dev database with user fast api having required privilegesORChange the DATABASE URL variable in the env file inside then app folder to reflect database settings user password db Check the app on notesOpen your browser and… View on GitHubTill next time happy coding ReferencesFastAPIGitHub ActionsPostgreSQLDockerDocker ComposeVisual Studio CodeThunder ClientSqlAlchemy	2023-02-02 23:07:01
Apple	AppleInsider - Frontpage News	Apple stock on wild ride after earnings	https://appleinsider.com/articles/23/02/02/aapl-hammered-during-earnings-but-recovered-weirdly-after?utm_medium=rss	Apple stock on wild ride after earningsApple s stock saw an immediate and sharp decline as the company reported earnings that didn t live up to Wall Street s expectations but as Tim Cook talked about it a strange recovery happened Apple s stock rebounds after hours despite missThe stock market tends to rise and fall with positive and negative news about a company Investors generally remain bullish about large entities like Apple but the quarterly results reflected a rare revenue decline and nearly every metric was below what analysts expected Read more	2023-02-02 23:59:20
海外科学	NYT > Science	These Extinct Elephants Were Neanderthals’ ‘Biggest Calorie Bombs’	https://www.nytimes.com/2023/02/02/science/archaeology-neanderthals-elephants.html	These Extinct Elephants Were Neanderthals Biggest Calorie Bombs A study of butchered bones from years ago offers what researchers call “the first clear cut evidence of elephant hunting in human evolution	2023-02-02 23:07:47
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金融	金融総合：経済レポート一覧	マーケットデータシート：政策金利（日本・米国・ユーロエリア・英国の政策金利の推移）	http://www3.keizaireport.com/report.php/RID/525385/?rss	国際金融情報センター	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	ESG四半期レポート 2022年第4四半期～2023年のサステナビリティの展望、新興国における責任ある消費を促進するために投資家は何ができるのか	http://www3.keizaireport.com/report.php/RID/525396/?rss	責任	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	江戸時代に学ぶ お金と暮らし（第３回） 情報を制する者が相場を制する！江戸時代のトレーダーたち	http://www3.keizaireport.com/report.php/RID/525399/?rss	江戸時代	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	FOMC（1月31日・2月1日）の注目点～利上げの終着点に近づくも、焦点は引き続き雇用指標：マーケット・レポート	http://www3.keizaireport.com/report.php/RID/525403/?rss	雇用	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	2023年1月FOMCレビュー～おおむね想定内で市場は安堵：市川レポート	http://www3.keizaireport.com/report.php/RID/525404/?rss	三井住友	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	FOMC 0.25％に利上げ幅を縮小～FRB議長会見を好感、株式市場は続伸：マーケットレポート	http://www3.keizaireport.com/report.php/RID/525405/?rss	三井住友トラスト	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】電力調達	http://search.keizaireport.com/search.php/-/keyword=電力調達/?rss	検索キーワード	2023-02-03 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】1300万件のクチコミでわかった超優良企業	https://www.amazon.co.jp/exec/obidos/ASIN/4492534628/keizaireport-22/	転職	2023-02-03 00:00:00
金融	日本銀行：RSS	日銀当座預金増減要因（2月見込み）	http://www.boj.or.jp/statistics/boj/fm/juqp/juqp2302.xlsx	当座預金	2023-02-03 08:50:00
ニュース	BBC News - Home	Apple sales in biggest fall since 2019	https://www.bbc.co.uk/news/business-64506940?at_medium=RSS&at_campaign=KARANGA	apple	2023-02-02 23:17:34
ニュース	BBC News - Home	US tracking suspected Chinese surveillance balloon	https://www.bbc.co.uk/news/world-us-canada-64507225?at_medium=RSS&at_campaign=KARANGA	craft	2023-02-02 23:30:16
ビジネス	ダイヤモンド・オンライン - 新着記事	トランプ氏の24年大統領選、資金調達は低調 - WSJ発	https://diamond.jp/articles/-/317155	大統領選	2023-02-03 08:25:00
ニュース	Newsweek	なぜアメリカの「雇用」に注目が集まるのか、投資をする人が見るべきポイントは？	https://www.newsweekjapan.jp/stories/carrier/2023/02/post-100768.php	このADPが集計した非農業部門の雇用者数は、労働省労働統計局の雇用統計が発表される日前の水曜日に発表されることから、先行指標として参照されています。	2023-02-03 08:40:00
マーケティング	MarkeZine	InstagramとMimiTVの担当者が解説！美容業界のSNSマーケティング活用事例【参加無料】	http://markezine.jp/article/detail/41209	参加無料	2023-02-03 08:15:00