投稿時間:2023-06-25 18:12:33 RSSフィード2023-06-25 18:00 分まとめ(14件)

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python	Pythonタグが付けられた新着投稿 - Qiita	英語の長いドキュメントを分割加工するpart1	https://qiita.com/yukiaprogramming/items/1ec34e1d0f77502f80f2	nltktool	2023-06-25 17:48:19
Ruby	Rubyタグが付けられた新着投稿 - Qiita	ジョースター家の子孫関係を閉包テーブルで表現する	https://qiita.com/takumi555/items/362d46fbb524ca413eda	rails	2023-06-25 17:27:26
Docker	dockerタグが付けられた新着投稿 - Qiita	DockerでRailsの開発環境を構築	https://qiita.com/gensan64311466/items/bb125f1d4d1c7a28a687	docker	2023-06-25 17:09:06
Azure	Azureタグが付けられた新着投稿 - Qiita	Azure OpenAI Service の最初の一歩： Azure版 ChatGPT を Azure OpenAI Studio のチャット用プレイグラウンドで試す	https://qiita.com/youtoy/items/ff8248b2a6778038bd3c	azure	2023-06-25 17:07:46
Ruby	Railsタグが付けられた新着投稿 - Qiita	ジョースター家の子孫関係を閉包テーブルで表現する	https://qiita.com/takumi555/items/362d46fbb524ca413eda	rails	2023-06-25 17:27:26
Ruby	Railsタグが付けられた新着投稿 - Qiita	DockerでRailsの開発環境を構築	https://qiita.com/gensan64311466/items/bb125f1d4d1c7a28a687	docker	2023-06-25 17:09:06
海外TECH	DEV Community	What's New in Svelte 4: Performance Boosts and Streamlined Development	https://dev.to/this-is-learning/whats-new-in-svelte-4-performance-boosts-and-streamlined-development-42dc	What x s New in Svelte Performance Boosts and Streamlined DevelopmentOn the th of June Svelte has announced its new major release Even if it is presented as mainly about setting the ground for these future improvements it also bring its share of improvements along with the rework of some of their showcase websites If you re more a listener than a reader this release has also been recorded on Svelte Radio Live Table of ContentPerformance EnhancementsImproved Developer ExperienceUpdated WebsitesMigration GuideWhat to Expect From Svelte Performance Enhancements Drastic reduction of the Svelte packageFor this release Svelte sure has lost some weight The overall size of Svelte dropped from MB to MB almost a size decrease As its dependencies dropped from to the additional packages needed to run Svelte also have been reduced also reducing the number of dependencies of SvelteKit This reduction brings some nice side effects such as a faster REPL experience a faster experience on the interactive website and a much faster execution of npm install of whatever package manager you might be using As of this release Svelte sure is now Improved HydrationAlong with this drop in the package size Svelte also lighten the code it generates for hydration As an example the code generated for the SvelteKit website has been reduced from kB to kB almost Improved Developer Experience Transitions ScopeTransitions are now local by default This prevents them from being global by default and thus risking to interfere with others resulting in a collision of your transitions during page load Web Components AuthoringCreating a Web Component in Svelte has been pretty easy lt svelte options tag my component gt However it also has been limiting for more advanced cases such as controlling whether or not to reflect updated prop values back to the DOM disabling the shadow DOM etc Svelte makes the authoring experience of Web Component way smoother by shifting its configuration into a dedicated customElement attribute of svelte options This attribute takes a number of options that helps you configure your Web Component lt svelte options customElement tag custom element shadow none props name lt Reflects the updated value back to the DOM gt reflect true lt Reflects the value as a number gt type Number lt Name of the attribute gt attribute element index gt lt script gt export let elementIndex lt script gt ️Stricter TypesStricter types are now enforced for createEventDispatcher Action ActionReturn and onMount createEventDispatcher now looks for the correctness of the provided parameters regarding their type and number and throws the associated errors import createEventDispatcher from svelte const dispatch createEventDispatcher lt optional number null required string noArgument never gt Svelte version dispatch optional dispatch required Detail argument still omitteddispatch noArgument surprise Detail argument still provided Svelte version using TypeScript strict mode dispatch optional dispatch required Error missing argumentdispatch noArgument surprise Error cannot pass an argumentAction and ActionReturn now have a default parameter type of never if Action has no generic type provided const untyped Action node params gt Now an error as params is expected not to exist const typed Action lt HTMLElement string gt node params gt params is of type string onMount now requires its return a synchronous function as an asynchronous one might lead to bugs if the callback is expected to be called on destroy since this is the case only for synchronous functions cleanup is not called due to the function being asynconMount async gt const bar await foo return cleanup cleanup will be calledonMount gt foo then bar gt return cleanup Updated Websites Tutorial WebsiteThe tutorial has been reworked for a better user experience FormerNewerThe design still is the same a side panel with text and instructions with a code editor on the right to do the exercise However some neat things were improved such as seeing the file structure on the side reducing the number of elements in the navbar and a better navigation between sections It also feature a dark mode The tutorial now lives on while the former is reachable at Svelte WebsiteThe Svelte Website also had a rework FormerNewerThe pages have been split mobile navigation has been improved the TypeScript documentation has been enhanced and a dark mode has also been released As of now the SvelteKit website has not yet been updated but a rework to offer a similar experience is currently ongoing Migration GuideThis release is shown as a step towards Svelte and not a very impactful one In order to upgrade from Svelte to Svelte the team has updated their migration tool and it should not take much more than running the following command npx svelte migrate latest svelte Please note that the minimum version requirements have been updated and you will need NodeJS or higherSvelteKit or higherTypeScript or higherMore details about the requirements are listed in the migration guide What to Expect From Svelte Almost no information about what is upcoming in Svelte has been shared so far However as Svelte is heavily tied to ESLint so is its package size With the current rewrite of ESLint by the time Svelte is released its size might drop by over another As Svelte is focused on developer experience and efficiency the changes we will probably see a number of improvements in those areas as stated in their blog post Svelte will bring major new features and performance improvements to SvelteI hope that you learn something useful there Pierre BouillonFollow Software Engineer Privacy Advocate Coffee Lover	2023-06-25 08:26:23
海外TECH	DEV Community	Reliable Microservices Data Exchange With Streaming Database	https://dev.to/bobur/reliable-microservices-data-exchange-with-streaming-database-pdo	Reliable Microservices Data Exchange With Streaming DatabaseNowadays we usually build multiple services for a single product to work and client apps need to consume functionality from more than one service Microservices architecture has become a popular approach for building scalable and resilient applications In a microservices based system multiple loosely coupled services work together to deliver the desired functionality One of the key challenges in such systems is exchanging data between microservices in a reliable and efficient manner One pattern that can help address this challenge is the Outbox pattern In this article we will explore how to implement the outbox pattern with a streaming database which can provide a reliable solution for microservices or multiple services data exchange The need for reliable microservices data exchangeIn a microservices architecture each microservice has business logic is responsible for its own data has its own local data store and performs its own operations on that data Data per service pattern However there are scenarios where microservices need to share data with each other or notify other services about any specific data change in real time in order to maintain consistency and provide a cohesive experience to end users For example consider in a ride hailing service there may be multiple microservices responsible for different functionalities such as user management ride booking driver management and payment processing When a user requests a ride it triggers a series of events that need to be propagated to various microservices for processing and updating their data Ride hailing services are where a customer orders the ride from a ride hailing platform The best known such services are Uber Lyft and Bolt Traditional synchronous communication between microservices can lead to tight coupling and potential performance and reliability issues A sending service needs to know the location interface and contract of other microservices which can result in a complex web of dependencies This can make it challenging to evolve test and deploy microservices independently as any change in one microservice may require changes in multiple dependent microservices Sometimes these target services might temporarily not be available and can introduce performance overhead due to the need for waiting and blocking until a response is received Asynchronous and decoupled data exchangeOn the other hand the Outbox pattern promotes asynchronous and decoupled data exchange between microservices When an event or change occurs in one microservice it writes the event or changes to its outbox which acts as a buffer The outbox can be implemented as a separate database table that the service owns The microservice s outbox is then processed by an Outbox Processor by a separate component or service such as a streaming database RisingWave which is explained in the below section which reads the events or changes from the outbox and sends them to other microservices or data stores asynchronously This allows microservices to continue processing requests without waiting for the data exchange to complete resulting in improved performance and scalability Microservice B which needs to be updated with the changes from Microservice A receives the events or changes from the outbox processor and applies them to its own state or data store This ensures that Microservice B s data remains consistent with the changes made in Microservice A Now let s see how the Outbox pattern can be implemented using the streaming database with an example of RisingWave There are other streaming database options in the market this post helps you understand what is streaming database is when and why to use it and discusses some key factors that you should consider when choosing the right streaming database for your business RisingWave is a streaming database that helps in building real time event driven services It can read directly database change events from traditional databases binlogs or Kafka topics and build a materialized view by joining multiple events together RisingWave will keep the view up to date as new events come and allow you to query using SQL to have access to the latest changes made to the data Outbox pattern with a streaming databaseThe streaming database can act as a real time streaming platform outbox processor It listens to any write update operations in the specified database table using its built in Change Data Capture CDC connector captures changes and propagates those changes to other microservices in real time or near real time with the help of Kafka topics See how to sink data from RisingWave to a Kafka broker In this article the author Gunnar Morling explains another implementation of the outbox pattern based on CDC and the Debezium connector Some key advantages when using the streaming database are that you do not need to use both Debezium and Kafka connect to achieve the same also it has its own storage you never lose streaming data and you can create materialized views optimized for querying microservices which explained in my other blog post Plus it gives us the ability to analyze the data by delivering them to BI and data analytics platforms for making better business decisions based on your application usage For example a trip history view is an important feature in a ride hailing service to provide passengers and drivers with access to their trip history Instead of querying the individual trip records and calculating various statistics such as total trips earnings ratings etc a materialized view can be used to store the precomputed trip history information for each user in the streaming database With the Outbox pattern and streaming database here s how the data exchange could work for our example ride hailing service Ride Service Microservice A When a user requests a ride the Ride Service creates the ride details and writes an event such as RideRequested to its outbox table in its own database let s say MySQL By default the streaming database captures the RideRequested event from the Ride Service s outbox table using its connector for MySQL CDC It processes the event and sends it to other microservices that need to be updated such as Driver Service Microservice B Payment Service Microservice C and Notification Service Microservice D using a message broker like Apache Kafka Driver Service Microservice B The Driver Service receives the RideRequested event from the Outbox Processor and finds an available driver for the ride based on the ride details It then writes an event such as DriverAssigned to its own outbox Payment Service Microservice C The Payment Service also receives the RideRequested event from another Kafka topic and calculates the fare for the ride based on the ride details It then writes an event such as FareCalculated to its own outbox We can create multiple Kafka topics to give options to consumer services to subscribe to only specific event types ConclusionAs we understood synchronous communication between microservices can introduce tight coupling performance overhead lack of fault tolerance limited scalability versioning challenges and reduced flexibility and agility By leveraging real time streaming platforms such as RisingWave and decoupling the data exchange process from the main transactional flow by applying an outbox pattern you can achieve high performance reliability and consistency in your microservices architecture Related resourcesHow Streaming database differs from a Traditional databaseQuerying Microservices in Real Time with Materialized Views 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	2023-06-25 08:06:07
海外ニュース	Japan Times latest articles	Colorful snow algae is blooming in Japan’s alpine areas. What does this mean for climate change?	https://www.japantimes.co.jp/news/2023/06/25/national/science-health/snow-algae-blooms-japan-mountains/	Colorful snow algae is blooming in Japan s alpine areas What does this mean for climate change Scientists race to Mount Gassan to see how the algae might affect the natural balance in vulnerable ecosystems and potentially perpetuate warming	2023-06-25 17:45:24
ニュース	BBC News - Home	Rishi Sunak urges people to hold their nerve on interest rates	https://www.bbc.co.uk/news/business-66012301?at_medium=RSS&at_campaign=KARANGA	mortgage	2023-06-25 08:29:33
ニュース	BBC News - Home	999 calls: Police and fire services hit by technical glitch	https://www.bbc.co.uk/news/uk-66012340?at_medium=RSS&at_campaign=KARANGA	backup	2023-06-25 08:47:41
ニュース	BBC News - Home	NHS plans: Sunak says expansion means 'more doctors, nurses, and GPs'	https://www.bbc.co.uk/news/uk-politics-66012333?at_medium=RSS&at_campaign=KARANGA	record	2023-06-25 08:20:29
ニュース	BBC News - Home	New documents spark fresh concerns over Ferguson's ferry contract	https://www.bbc.co.uk/news/uk-scotland-65863138?at_medium=RSS&at_campaign=KARANGA	discussions	2023-06-25 08:15:50
ニュース	BBC News - Home	Plastic pollution rise 'really scary and eye opening'	https://www.bbc.co.uk/sport/sailing/66009976?at_medium=RSS&at_campaign=KARANGA	climate	2023-06-25 08:04:46