投稿時間:2023-07-24 20:33:48 RSSフィード2023-07-24 20:00 分まとめ(41件)

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IT	ITmedia 総合記事一覧	[ITmedia News] 「Twitter Blue」も名前変わる？　公式アカウントに変化	https://www.itmedia.co.jp/news/articles/2307/24/news164.html	bluesubscription	2023-07-24 19:12:00
AWS	AWS Podcast	#606: July 2023 Update Show 2	https://aws.amazon.com/podcasts/aws-podcast/#606	July Update Show It is sharp informative and fun Simon Hawn and Jillian take you through all the latest AWS updates Chapters AWS Marketplace Analytics Application Integration Business Applications Compute Customer Engagement Databases Developer Tools End User Computing Front End Web Mobile GameTech Internet of Things IoT Machine Learning Management Governance Media Services Migration Transfer Networking Content Delivery Security Identity and Compliance StorageShownotes AWS Podcast Audio Feedback	2023-07-24 10:02:36
python	Pythonタグが付けられた新着投稿 - Qiita	tan((x+y)/2)の値「2023山口大学後期理学部物理情報科,化,生物学科【1】」をChatGPTとMathematicaとWolframAlphaとsympyでやってみたい。	https://qiita.com/mrrclb48z/items/b5701756b81b1d204eca	chatgpt	2023-07-24 19:40:52
python	Pythonタグが付けられた新着投稿 - Qiita	Amazonエンジニアの年収上限が4000万円にアップした背景まとめてみた	https://qiita.com/eri_issue/items/d51407e9e721edcc777b	amazon	2023-07-24 19:31:21
js	JavaScriptタグが付けられた新着投稿 - Qiita	Oracle Database 23cで追加されたMLE JavaScriptにおけるJSONデータ型を扱う機能を試してみた	https://qiita.com/ssfujita/items/685d635c3d1ba4e98115	oracl	2023-07-24 19:28:38
js	JavaScriptタグが付けられた新着投稿 - Qiita	useContextについての学習メモ	https://qiita.com/makoto-ogata@github/items/818e4b22dd320afe7f5f	githubitemsfa	2023-07-24 19:23:06
Ruby	Rubyタグが付けられた新着投稿 - Qiita	管理者側　注文内容一覧ページ	https://qiita.com/mirimu/items/a8ebad58163c1ce25fc0	bootstrap	2023-07-24 19:51:33
Ruby	Rubyタグが付けられた新着投稿 - Qiita	letter_opener_webって？	https://qiita.com/runchan/items/4d4e70a615e1625e6dde	letteropenerweb	2023-07-24 19:38:08
AWS	AWSタグが付けられた新着投稿 - Qiita	IMDS Packet Analyzerについて調べてみた	https://qiita.com/utsunomiya_ff/items/0b7f89624a8a72d1db09	imdspacketanalyzer	2023-07-24 19:58:37
AWS	AWSタグが付けられた新着投稿 - Qiita	Amazonエンジニアの年収上限が4000万円にアップした背景まとめてみた	https://qiita.com/eri_issue/items/d51407e9e721edcc777b	amazon	2023-07-24 19:31:21
Ruby	Railsタグが付けられた新着投稿 - Qiita	Restful APIとGraphQLの違い	https://qiita.com/ryuuuuuuuuuu/items/70b3de79b1b63758ad01	graphql	2023-07-24 19:57:35
Ruby	Railsタグが付けられた新着投稿 - Qiita	管理者側　注文内容一覧ページ	https://qiita.com/mirimu/items/a8ebad58163c1ce25fc0	bootstrap	2023-07-24 19:51:33
Ruby	Railsタグが付けられた新着投稿 - Qiita	letter_opener_webって？	https://qiita.com/runchan/items/4d4e70a615e1625e6dde	letteropenerweb	2023-07-24 19:38:08
技術ブログ	Developers.IO	AWS Organizations 管理下の全アカウントの Trusted Advisor による指摘事項を確認できる「組織ビュー」のレポートで確認・可視化できる内容を調べてみた	https://dev.classmethod.jp/articles/what-can-be-visualized-in-the-organizational-view/	trusteda	2023-07-24 10:11:31
海外TECH	DEV Community	What is Truthy and Falsy in Javascript ?	https://dev.to/diwakarkashyap/what-is-truthy-and-falsy-in-javascript--p3e	What is Truthy and Falsy in Javascript In JavaScript truthy and falsy are terms used to describe values that are treated as true or false in certain situations A truthy value is something that JavaScript sees as true when it needs to make a decision If JavaScript was a person and you asked it Is this thing true and the thing was a truthy value JavaScript would say Yes A falsy value is the opposite It s something that JavaScript sees as false when it needs to make a decision If you asked JavaScript Is this thing true and the thing was a falsy value JavaScript would say No Here are some examples Falsy values these are seen as false false itself the number zero an empty string null a special value meaning nothing undefined a special value meaning this thing doesn t exist NaN a special value meaning Not a Number Everything else in JavaScript is truthy That means if it s not in the list above JavaScript thinks of it as true Some examples are Truthy values these are seen as true Any number that isn t zero like etc Any non empty string like hello false etc Arrays like or Objects like or name John age Remember these values are only treated as true or false when JavaScript needs to make a decision like in an if statement In other situations these values are just themselves Thank you for reading please follow me on Twitter i regularly share content about Javascript and React and contribute to Opensource ProjectsTwitter Github	2023-07-24 10:53:44
海外TECH	DEV Community	Create an advanced search engine with PostgreSQL	https://dev.to/xata/create-an-advanced-search-engine-with-postgresql-23e5	Create an advanced search engine with PostgreSQLThis is part of a blog mini series in which we explore the full text search functionality in PostgreSQL and investigate how much of the typical search engine functionality we can replicate In part we ll do a comparison between PostgreSQL s full text search and Elasticsearch If you want to follow along and try out the sample queries which we recommend it s more fun that way the code samples are executed against the Wikipedia Movie Plots data set from Kaggle To import it download the CSV file then create this table CREATE TABLE movies ReleaseYear int Title text Origin text Director text Casting text Genre text WikiPage text Plot text And import the CSV file like this COPY movies ReleaseYear Title Origin Director Casting Genre WikiPage Plot FROM wiki movie plots deduped csv DELIMITER CSV HEADER The dataset contains movie titles and is about MB in CSV format PostgreSQL full text search primitivesThe Postgres approach to full text search offers building blocks that you can combine to create your own search engine This is quite flexible but it also means it generally feels lower level compared to search engines like Elasticsearch Typesense or Mellisearch for which full text search is the primary use case The main building blocks which we ll cover via examples are The tsvector and tsquery data typesThe match operator to check if a tsquery matches a tsvectorFunctions to rank each match ts rank ts rank cd The GIN index type an inverted index to efficiently query tsvectorWe ll start by looking at these building blocks and then we ll get into more advanced topics covering relevancy boosters typo tolerance and faceted search tsvectorThe tsvector data type stores a sorted list of lexemes A lexeme is a string just like a token but it has been normalized so that different forms of the same word are made For example normalization almost always includes folding upper case letters to lower case and often involves removal of suffixes such as s or ing in English Here is an example using the to tsvector function to parse an English phrase into a tsvector SELECT FROM unnest to tsvector english I m going to make him an offer he can t refuse Refusing is not an option lexeme positions weights go D m D make D offer D option D refus D D rows As you can see stop words like “I “to or “an are removed because they are too common to be useful for search The words are normalized and reduced to their root e g “refuse and “Refusing are both transformed into “refus The punctuation signs are ignored For each word the positions in the original phrase are recorded e g “refus is the th and the th word in the text and the weights which are useful for ranking and we ll discuss later In the example above the transformation rules from words to lexemes are based on the english search configuration Running the same query with the simple search configuration results in a tsvector that includes all the words as they were found in the text SELECT FROM unnest to tsvector simple I m going to make him an offer he can t refuse Refusing is not an option lexeme positions weights an D D can D going D he D him D i D is D m D make D not D offer D option D refuse D refusing D t D to D rows As you can see “refuse and “refusing now result in different lexemes The simple configuration is particularly useful when you have columns that contain labels or tags PostgreSQL has built in configurations for a pretty good set of languages You can see the list by running SELECT cfgname FROM pg ts config Notably however there is no configuration for CJK Chinese Japanese Korean which is worth keeping in mind if you need to create a search query in those languages While the simple configuration should work in practice quite well for unsupported languages I m not sure if that is enough for CJK tsqueryThe tsquery data type is used to represent a normalized query A tsquery contains search terms which must be already normalized lexemes and may combine multiple terms using AND OR NOT and FOLLOWED BY operators There are functions like to tsquery plainto tsquery and websearch to tsquery that are helpful in converting user written text into a proper tsquery primarily by normalizing words appearing in the text To get a feeling of tsquery let s see a few examples using websearch to tsquery SELECT websearch to tsquery english the darth vader websearch to tsquery darth amp vader That is a logical AND meaning that the document needs to contain both darth and vader in order to match You can do logical OR as well SELECT websearch to tsquery english darth OR vader websearch to tsquery darth vader And you can exclude words SELECT websearch to tsquery english darth vader wars websearch to tsquery darth amp vader amp war Also you can represent phrase searches SELECT websearch to tsquery english the darth vader son websearch to tsquery darth lt gt vader lt gt son This means “darth followed by “vader followed by “son Note however that the “the word is ignored because it s a stop word as per the english search configuration This can be an issue on phrases like this SELECT websearch to tsquery english do or do not there is no try websearch to tsquery tri row Oops almost the entire phrase was lost Using the simple config gives the expected result SELECT websearch to tsquery simple do or do not there is no try websearch to tsquery do lt gt or lt gt do lt gt not lt gt there lt gt is lt gt no lt gt try You can check whether a tsquery matches a tsvector by using the match operator SELECT websearch to tsquery english darth vader to tsvector english Darth Vader is my father column tWhile the following example doesn t match SELECT websearch to tsquery english darth vader father to tsvector english Darth Vader is my father column f GINNow that we ve seen tsvector and tsquery at work let s look at another key building block the GIN index type is what makes it fast GIN stands for Generalized Inverted Index GIN is designed for handling cases where the items to be indexed are composite values and the queries to be handled by the index need to search for element values that appear within the composite items This means that GIN can be used for more than just text search notably for JSON querying You can create a GIN index on a set of columns or you can first create a column of type tsvector to include all the searchable columns Something like this ALTER TABLE movies ADD search tsvector GENERATED ALWAYS AS to tsvector english Title to tsvector english Plot to tsvector simple Director to tsvector simple Genre to tsvector simple Origin to tsvector simple Casting STORED And then create the actual index CREATE INDEX idx search ON movies USING GIN search You can now perform a simple test search like this SELECT title FROM movies WHERE search websearch to tsquery english darth vader title Star Wars Episode IV A New Hope aka Star Wars Return of the Jedi Star Wars Episode III Revenge of the Sith rows To see the effects of the index you can compare the timings of the above query with and without the index The GIN index takes it from over ms to about ms on my computer ts rankSo far we ve seen how ts vector and ts query can match search queries However for a good search experience it is important to show the best results first meaning that the results need to be sorted by relevancy Taking it directly from the docs PostgreSQL provides two predefined ranking functions which take into account lexical proximity and structural information that is they consider how often the query terms appear in the document how close together the terms are in the document and how important is the part of the document where they occur However the concept of relevancy is vague and very application specific Different applications might require additional information for ranking e g document modification time The built in ranking functions are only examples You can write your own ranking functions and or combine their results with additional factors to fit your specific needs The two ranking functions mentioned are ts rank and ts rank cd The difference between them is that while they both take into account the frequency of the term ts rank cd also takes into account the proximity of matching lexemes to each other To use them in a query you can do something like this SELECT title ts rank search websearch to tsquery english darth vader rank FROM movies WHERE search websearch to tsquery english darth vader ORDER BY rank DESC LIMIT title rank The Empire Strikes Back Star Wars Episode IV A New Hope aka Star Wars Star Wars Episode III Revenge of the Sith Rogue One A Star Wars Story film Return of the Jedi American Honey One thing to note about ts rank is that it needs to access the search column for each result This means that if the WHERE condition matches a lot of rows PostgreSQL needs to visit them all in order to do the ranking and that can be slow To exemplify the above query returns in ms on my computer If I modify the query to do search for darth OR vader it returns in about ms because there are now over matching result that need ranking and sorting Relevancy tuningWhile relevancy based on word frequency is a good default for the search sorting quite often the data contains important indicators that are more relevant than simply the frequency Here are some examples for a movies dataset Matches in the title should be given higher importance than matches in the description or plot More popular movies can be promoted based on ratings and or the number of votes they receive Certain categories can be boosted more considering user preferences For instance if a particular user enjoys comedies those movies can be given a higher priority When ranking search results newer titles can be considered more relevant than very old titles This is why dedicated search engines typically offer ways to use different columns or fields to influence the ranking Here are example tuning guides from Elastic Typesense and Meilisearch If you want a visual demo of the impact of relevancy tuning here is a quick minutes video about it Numeric date and exact value boostersWhile Postgres doesn t have direct support for boosting based on other columns the rank is ultimately just a sort expression so you can add your own signals to it For example if you want to add a boost for the number of votes you can do something like this SELECT title ts rank search websearch to tsquery english jedi numeric booster example log NumberOfVotes FROM movies WHERE search websearch to tsquery english jedi ORDER BY rank DESC LIMIT The logarithm is there to smoothen the impact and the factor brings the booster to a comparable scale with the ranking score You can also design more complex boosters for example boost by the rating but only if the ranking has a certain number of votes To do this you can create a function like this create function numericBooster rating numeric votes numeric voteThreshold numeric returns numeric as select case when votes lt voteThreshold then else rating end language sql And use it like this SELECT title ts rank search websearch to tsquery english jedi numeric booster example numericBooster Rating NumberOfVotes FROM movies WHERE search websearch to tsquery english jedi ORDER BY rank DESC LIMIT Let s take another example Say we want to boost the ranking of comedies You can create a valueBooster function that looks like this create function valueBooster col text val text factor integer returns integer as select case when col val then factor else end language sql The function returns a factor if the column matches a particular value and instead Use it in a query like this SELECT title genre ts rank search websearch to tsquery english jedi value booster example valueBooster Genre comedy rankFROM movies WHERE search websearch to tsquery english jedi ORDER BY rank DESC LIMIT title genre rank The Men Who Stare at Goats comedy Clerks comedy Star Wars The Clone Wars animation Star Wars Episode I The Phantom Menace D sci fi Star Wars Episode I The Phantom Menace space opera Star Wars Episode II Attack of the Clones science fiction Star Wars Episode III Revenge of the Sith science fiction action Star Wars The Last Jedi action adventure fantasy sci fi Return of the Jedi science fiction Star Wars Episode IV A New Hope aka Star Wars science fiction rows Column weightsRemember when we talked about the tsvector lexemes and that they can have weights attached Postgres supports weights named A B C and D A is the biggest weight while D is the lowest and the default You can control the weights via the setweight function which you would typically call when building the tsvector column ALTER TABLE movies ADD search tsvector GENERATED ALWAYS AS setweight to tsvector english Title A to tsvector english Plot to tsvector simple Director to tsvector simple Genre to tsvector simple Origin to tsvector simple Casting STORED Let s see the effects of this Without setweight a search for jedi returns SELECT title ts rank search websearch to tsquery english jedi rank FROM movies WHERE search websearch to tsquery english jedi ORDER BY rank DESC title rank Star Wars The Clone Wars Star Wars Episode I The Phantom Menace Star Wars Episode I The Phantom Menace D Star Wars Episode III Revenge of the Sith Star Wars Episode II Attack of the Clones Star Wars The Last Jedi Return of the Jedi Star Wars Episode IV A New Hope aka Star Wars Clerks The Empire Strikes Back The Men Who Stare at Goats How to Deal rows And with the setweight on the title column SELECT title ts rank search websearch to tsquery english jedi rank FROM movies WHERE search websearch to tsquery english jedi ORDER BY rank DESC title rank Star Wars The Last Jedi Return of the Jedi Star Wars The Clone Wars Star Wars Episode I The Phantom Menace Star Wars Episode I The Phantom Menace D Star Wars Episode III Revenge of the Sith Star Wars Episode II Attack of the Clones Star Wars Episode IV A New Hope aka Star Wars The Empire Strikes Back Clerks The Men Who Stare at Goats How to Deal rows Note how the movie titles with “jedi in their name have jumped to the top of the list and their rank has increased It s worth pointing out that having only four weight “classes is somewhat limiting and that they need to be applied when computing the tsvector Typo tolerance fuzzy searchPostgreSQL doesn t support fuzzy search or typo tolerance directly when using tsvector and tsquery However working on the assumptions that the typo is in the query part we can implement the following idea index all lexemes from the content in a separate tablefor each word in the query use similarity or Levenshtein distance to search in this tablemodify the query to include any words that are foundperform the searchHere is how it works First use ts stats to get all words in a materialized view CREATE MATERIALIZED VIEW unique lexeme AS SELECT word FROM ts stat SELECT search FROM movies Now for each word in the query check if it is in the unique lexeme view If it s not do a fuzzy search in that view to find possible misspellings of it SELECT FROM unique lexeme WHERE levenshtein less equal word pregant lt word premant pregrant pregnant paegantIn the above we use the Levenshtein distance because that s what search engines like Elasticsearch use for fuzzy search Once you have the candidate list of words you need to adjust the query include them all Faceted searchFaceted search is popular especially on e commerce sites because it helps customers to iteratively narrow their search Here is an example from amazon com The above can implemented by manually defining categories and then adding them as WHERE conditions to the search Another approach is to create the categories algorithmically based on the existing data For example you can use the following to create a “Decade facet SELECT ReleaseYear decade count Title cnt FROM movies WHERE search websearch to tsquery english star wars GROUP BY decade ORDER BY cnt DESC decade cnt rows This also provides counts of matches for each decade which you can display in brackets If you want to get multiple facets in a single query you can combine them for example by using CTEs WITH releaseYearFacets AS SELECT Decade facet ReleaseYear text val count Title cnt FROM movies WHERE search websearch to tsquery english star wars GROUP BY val ORDER BY cnt DESC genreFacets AS SELECT Genre facet Genre val count Title cnt FROM movies WHERE search websearch to tsquery english star wars GROUP BY val ORDER BY cnt DESC LIMIT SELECT FROM releaseYearFacets UNION SELECT FROM genreFacets facet val cnt Decade Decade Decade Decade Decade Decade Decade Decade Decade Decade Decade Genre comedy Genre drama Genre musical Genre unknown Genre war rows The above should work quite well on small to medium data sets however it can become slow on very large data sets ConclusionWe ve seen the PostgreSQL full text search primitives and how we can combine them to create a pretty advanced full text search engine which also happens to support things like joins and ACID transactions In other words it has functionality that the other search engines typically don t have There are more advanced search topics that would be worth covering in detail suggesters auto completeexact phrase matchinghybrid search semantic keyword by combining with pg vectorEach of these would be worth their own blog post coming but by now you should have an intuitive feeling about them they are quite possible using PostgreSQL but they require you to do the work of combining the primitives and in some cases the performance might suffer on very large datasets In part we ll make a detailed comparison with Elasticsearch looking to answer the question on when is it worth it to implement search into PostgreSQL versus adding Elasticsearch to your infrastructure and syncing the data If you want to be notified when this gets published you can follow us on Twitter or join our Discord	2023-07-24 10:45:24
海外TECH	DEV Community	LGTM or TL;DR? The Problem of Cop-out Code Reviews	https://dev.to/codesandboxio/lgtm-or-tldr-the-problem-of-cop-out-code-reviews-5917	LGTM or TL DR The Problem of Cop out Code ReviewsAt CodeSandbox we run your code in our cloud infrastructure configure the environment for you and keep your code always ready behind a shareable URL Give it a try with this Next js example or import your GitHub repo You are completely focused on writing some code in a perfect state of flow when that Slack message comes in Hey Can you have a look at this PR If this sounds familiar chances are you have shared the pain of countless other developersーhaving to switch context to handle a code review especially when it s urgent But that s just the tip of the iceberg The concept of “code review itself seems to be very polarizing in the development community There s a reason “LGTM became a meme for cop out code reviews And as a developer myself I can understand the frustration with some parts of this process This got me wondering if this may be a symptom of something fundamentally broken in how we do code reviews So earlier this month we ran a survey focusing on code reviews And today we published the full report “The State of Code Reviews which you can download for free I m very excited to dive into the data to answer this main questionーare most developers eyeballing code diffs while reviewing PRs or actually running the code to visually inspect changes Code Diffs and Running PreviewsI ll start with a hot takeーin of PR reviews you should be running the branch to visually inspect changes But I ll take a wild guess that is probably not something most developers enjoy You have to stash the changes of your current work checkout into the PR branch make sure your setup is up to date and possibly deal with troubleshooting it from time to time wait for the dev server to start and finally open the preview to visually inspect the changes So what did we hear from developers when we asked about this Within our sample of developers the majority say they most often only review a PR by looking at the code diff When we asked a similar question on “I locally run the branch under review to see the running code said they never or very rarely do it I find this to be very curious In a way it matches this hypothesis that perhaps many developers are relying a lot on code diffs and or just trying to get reviews out of the way as fast as possible After reading some of the additional feedback we received in our survey I m pretty sure this is the case for many teams “Unfortunately many companies pretend they do serious code reviews while in fact devs are approving everything after minutes spent reading it and leaving no feedback in the vast majority of PRs At first glance it seems that the lack of time and guidelines might be two key reasons behind hasty PR reviews Plus a lot of the feedback we received seems to indicate that this could lead to a decline in code quality So let s dig deeper Impact on Code QualityWhile the original scope of our survey was to simply get a picture of how the different stakeholders in code reviews feel about and address this process the results got us wondering if there s any sort of correlation between code quality and running the branch locally While there s no clear cut correlation the data suggests a pattern where developers that more frequently run the branch locally to see the running code are also more satisfied with the quality of the code being released This is all very fascinating and might indicate that implementing robust guidelines and proper tooling for the code review process could have a significant impact on code quality as mentioned by a few respondents “I think currently tooling for code review is bad you just have a diff and then it depends on your own process you established based on your experience and your attentiveness and time you currently have And here s where it gets better because we also sent out our survey to Engineering Managers we got their unique insight on this matter But let s leave that for a follow up post If you don t want to wait download the full report Code Reviews in CodeSandboxAt CodeSandbox we have been thinking long and hard about improving the PR review workflow As our report shows there are three fundamental problems with this process Context switching breaks the coding flow and siphons energy from developers Setting up and running a branch locally takes too much time and is prone to errors and inconsistencies The lack of guidelines and tooling for code reviews results in ambiguity cop out reviews and lower code quality I have good reason to believe we have fixed all of these problems with CodeSandbox The “new version of CodeSandbox we started building in provides instant cloud development environments powered by our unique microVM architecture And I say “unique because we made the opinionated choice of giving a dedicated microVM to every branch and every PR while making sure that it resumes in second In practice this means that if you re using GitHub as your git provider CodeSandbox gives you a live running environment for every PR that is one click away Inside you will find the PR s diff a pre configured running preview all the review tooling you need comments suggestions commits approvals etc plus a built in AI assistant This makes reviewing PRs much faster as it removes context switching the PR opens on a browser tab and you can actually open as many as you want in parallel and only takes second for the dev server to resume We are thrilled to hear how teams that have been using CodeSandbox for reviews have reported massive time savings and increased contributions from team members So if you or your team have been looking for a better way to do code reviews give CodeSandbox a try by importing a repo We have a day free trial you can use to test all our features We truly believe we re solving some of the main pains development teams have been facing in code reviews And if nothing else we will help everyone “LGTM with confidence again	2023-07-24 10:31:02
海外TECH	DEV Community	Scaling Databases: A Developer's Journey	https://dev.to/alejandro_du/scaling-databases-a-developers-journey-1np8	Scaling Databases A Developer x s JourneyIn the vast world of software development navigating the world of databases is quite an adventure There are so many options tailored to general and specific use cases that trying to explore the database ecosystem is a challenge in itself That s one of the reasons I find MariaDB so compelling to software developersーit s an advanced battle tested database that supports many kinds of workloads Today MariaDB has differentiated itself from its predecessor MySQL With more features better in my opinion SQL syntax and more storage engines Join me on a developer s journey as we explore how MariaDB supports application evolution from a modest beginning to handling massive workloads supporting developers along the way Note Decorative AI generated images of sea lions are included in this article see the official MariaDB logo here Chapter A Small Application s DiscoveryOur journey begins with a small scale application And for it we need a database that just works Reliably And fast During our search we stumble upon MariaDB Community Server an open source database that has become the default database in most Linux distributions there must be a good reason for this This database management system built upon the ISO SQL PSM standard offers high performance and reliability With MariaDB Community Server we confidently embark on our journey knowing that our modest requirements will be met without the burden of database limitations And we are not alone A huge online community gathers around MariaDB on social media and more recently on Slack Chapter Scaling Up for Growing DemandsAs our application gains traction the demands placed on our system grow exponentially It s no longer an intranet or internal application that a few of our colleagues use No Now our application is mission critical It has much more than a handful of users Some of them are external to our organization which increases the pressure on handling security risks To address this challenge we turn to MariaDB Enterprise Server Built on the foundation of the Community Server the Enterprise variant provides battle tested features tailored to production environments With improved defaults and configurations and features such as enterprise audit enterprise backup and enterprise cluster MariaDB Enterprise Server equips us to handle larger workloads and ensures secure and optimal performance instilling confidence in our ability to manage sensitive data and scale up in the future Additional we can rest assured that team of experts is available for technical support when needed Chapter Navigating Traffic and High AvailabilityThe popularity of our application skyrockets bringing not only good money to the business but also high availability and scaling challenges to tackle MariaDB MaxScale emerges as a solutionーa reliable database proxy that handles read write splitting for scaling reads automatic failover and transaction reply and many other functionalities MaxScale makes our database cluster look as if it was a single logical application and frees application developers from implementing things such as managing multiple data sources load balancing transparent data masking and even offers the possibility to translate NoSQL MongoDB queries to SQL and store the data in one single ACID compliant relational database Chapter Reports and Analytical Ad Hoc QueriesOur journey takes a turn into real time data analytics We now have tons of data available Stakeholders know this and demand the implementation of reporting features to allow them to extract valuable insights from the data Enter MariaDB ColumnStore a specialized storage engine designed for lightning fast analytical queries In our application we implemented a dynamic report generation module that lets users pick and join data from multiple tables in the database We don t really know which columns and tables are going to be used so we don t know exactly which columns should have indexes By organizing data in a columnar fashion ColumnStore frees us from dealing with database indexes as each column is persisted in the hard drive in a columnar fashion as opposed to row based storage A great solution for ad hoc queries like the ones generated by our report module or data engineers and data scientists Chapter Scaling Horizontally for Unprecedented WorkloadsAs our application continues to flourish once again we encounter unprecedented levels of database transactions Scaling reads was possible with read write splitting automatically handled by MaxScale However write operations start to suffer with the increase in load Scaling writes and preserving the ACID properties may seem daunting for example when using manual data sharding but MariaDB Xpand comes to our rescue This distributed SQL database allows for horizontal scalability of reads and writes With automated data slicing replication and rebalancing MariaDB Xpand ensures high availability and empowers us to handle billions of transactions per day without compromising the responsiveness and robustness of our application We are ready to scale linearly just by adding more nodes both reads and writes Chapter Embracing the Cloud for Infinite PossibilitiesOur journey leads us to the boundless realm of the cloud where MariaDB SkySQL awaits SkySQL a second generation cloud database service revolutionizes the way we deploy MariaDB products It simplifies cloud native deployments of MariaDB Enterprise Server ColumnStore and Xpand unlocking serverless analytical capabilities geospatial services remote monitoring and cloud backup As a fully managed service we no longer have to worry about software updates or manual scaling clusters With a few clicks we can have a database service that scales with no limits and is maintained by the experts developing MariaDB And not only that we get access to DBAs with tons of experience with MariaDB through the SkyDBA offer Our applications are now unstoppable ConclusionAs software developers our journey is a quest for scalable and reliable database solutions MariaDB stands out offering a diverse suite of tools tailored to every need From the simplicity of MariaDB Community Server to the scalability of MariaDB Xpand and the cloud native capabilities of MariaDB SkySQL MariaDB empowers us to conquer the complexities of database management I hope I have inspired you to learn more about databases and MariaDB I m happy to answer any questions or present this topic internally at your company conference developer group meetup…anything Just let me know	2023-07-24 10:11:07
Apple	AppleInsider - Frontpage News	Drop Paragon Series Deep Dive Keyboard review: An expensive wired keyboard	https://appleinsider.com/articles/23/07/24/drop-paragon-series-deep-dive-keyboard-review-an-expensive-wired-keyboard?utm_medium=rss	Drop Paragon Series Deep Dive Keyboard review An expensive wired keyboardThe Drop Paragon Series Deep Dive Keyboard offers a great typing experience with soft keys and a beautiful design However this may not be enough to support its price tag Drop Paragon Series Deep Dive KeyboardMechanical keyboards allow you to customize nearly every aspect from their feel sound and look They add another level of customization to your desk space and make the keyboard unique Read more	2023-07-24 10:36:51
Apple	AppleInsider - Frontpage News	Twitter has now rebranded as 'X'	https://appleinsider.com/articles/23/07/24/twitter-has-now-rebranded-as-x?utm_medium=rss	Twitter has now rebranded as x X x As recently teased by CEO Elon Musk Twitter is now called X as the rebranding goes live Launched on March the revolutionary social media platform was called Twitter for years months and days but now it is X Following the announcement that it was coming soon the company completed the rebrand early on Monday July Now when users log in on the web there is a brief X splash screen before the normal service loads The service appears to still be called Twitter on the official app but it s possible that the new design has yet to finish rolling out worldwide Read more	2023-07-24 10:19:29
海外TECH	Engadget	Twitter begins its transition to 'X'	https://www.engadget.com/twitter-begins-its-transition-to-x-100901444.html?src=rss	Twitter begins its transition to x X x Unlike when Dogecoin s Shiba Inu briefly replaced it it seems Twitter s longstanding bird logo is genuinely having its last curtain call Elon Musk and Twitter or should we say X CEO Linda Yaccarino announced that the company was rebranding as quot X quot and projected the new emblem onto the company s San Francisco headquarters So far the simple white logo with a black background has replaced the bird in the top left spot of the website and the pair have it next to their respective names and blue checks Twitter s official account has also been renamed X with the new logo and a stark black background As of publication the blue bird still exists in the browser icon but that will likely change soon Lights Camera X pic twitter com KOuQbRーLinda Yaccarino lindayacc July Musk has long had an affinity for the letter X naming his banking startup x com aerospace company SpaceX and recent AI venture xAI Speaking of x com type that into your search bar and it will automatically reroute you to Twitter s homepage ー nbsp Musk bought x com back from PayPal in Musk and co have made hefty claims about Twitter s future since he first took ownership and its rebrand is no exception quot X is the future state of unlimited interactivity centered in audio video messaging payments banking creating a global marketplace for ideas goods services and opportunities quot Yaccarino said in a Twitter thread repeating much of what Musk has said in the past quot Powered by AI X will connect us all in ways we re just beginning to imagine quot We ll have to wait and see if the rebrand does anything to bring back all the advertising dollars the company has lost or help it compete against Meta s Threads This article originally appeared on Engadget at	2023-07-24 10:09:01
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ニュース	Newsweek	ミサイル攻撃で燃え上がるオデーサの大聖堂　ゼレンスキー大統領「報復は確実」	https://www.newsweekjapan.jp/stories/world/2023/07/post-102253.php	ミサイル攻撃で燃え上がるオデーサの大聖堂ゼレンスキー大統領「報復は確実」【動画】砲撃で破壊され、燃え上がる大聖堂の内部日未明、ウクライナ南部の港湾都市オデーサがロシア軍による大規模なミサイル攻撃を受け、ウクライナ正教会の救世主顕栄大聖堂が甚大な被害を受けた。	2023-07-24 19:40:00
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