投稿時間:2022-01-05 22:22:27 RSSフィード2022-01-05 22:00 分まとめ(27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
python	Pythonタグが付けられた新着投稿 - Qiita	市区町村コードの一覧をjson化するpythonスクリプトを書いた	https://qiita.com/inchoXD/items/fd64402114958b20d9c1	都道府県コードuriの都道府県コードの部分にindex番号した数字を入れるとその都道府県に属する地区町村の名前と市区町村コードを入手することができるようになります。	2022-01-05 21:58:52
python	Pythonタグが付けられた新着投稿 - Qiita	【Django】備忘録-01	https://qiita.com/tatsuya-w/items/823bc248edb3c7170ddd	django	2022-01-05 21:58:49
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	PHP連想配列から、「キーが１つでも存在する場合」を boolean で得る方法	https://teratail.com/questions/376784?rss=all	PHP連想配列から、「キーがつでも存在する場合」をbooleanで得る方法前提・実現したいこと連装配列を複数キーと比較し、「キーがつでも存在する場合」をnbspbooleannbspで得たいです。	2022-01-05 21:38:22
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	ファイル内のある行を10倍して，そこだけ差し替えて特殊文字が入らないように出力したい	https://teratail.com/questions/376783?rss=all		2022-01-05 21:25:06
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	ROS2をインストールする際に起きたエラー解決に対して	https://teratail.com/questions/376782?rss=all	ROSをインストールする際に起きたエラー解決に対して前提・実現したいこと下記サイトを参考にubuntuにROSをインストールしたかったのですが、インストールする際に、「gpgnbsp有効なOpenPGPデータが見つかりません。	2022-01-05 21:19:38
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	VScodeで一部のファイルが新規作成（Untracked）扱いにならない	https://teratail.com/questions/376781?rss=all	VScodeで一部のファイルが新規作成Untracked扱いにならない前提・実現したいことVScodeで作成したファイルを新規作成Untracked扱いにしたい新規作成したファイルをコミットしたい。	2022-01-05 21:16:36
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	form forにurlパラメータクエリ文字を入れたい	https://teratail.com/questions/376780?rss=all	formforにurlパラメータクエリ文字を入れたい更新、登録、戻る、削除ボタン押下したとき、検索ページを保持したい実装を行っています。	2022-01-05 21:14:24
Program	[全てのタグ]の新着質問一覧｜teratail（テラテイル）	TS if文ネストの書き方	https://teratail.com/questions/376779?rss=all	TSif文ネストの書き方開発環境TypeScriptReactを使っています。	2022-01-05 21:10:36
Docker	dockerタグが付けられた新着投稿 - Qiita	[docker] `docker compose` （docker コマンドのサブコマンドである compose）を全ユーザ向けにインストールする	https://qiita.com/JunkiHiroi/items/3bf722af3e77c73a1625	つまづいた例全ユーザ向けのインストールに指定したパスがusrlocallibdockerclipluginsdockercomposeではなくusrlocalbindockercomposeでした。	2022-01-05 21:06:36
技術ブログ	Developers.IO	根本原因分析について考える〜テンプレスカウトの乱れ打ちが導く閑古鳥の未来	https://dev.classmethod.jp/articles/causal-analysis/	閑古鳥	2022-01-05 12:09:51
海外TECH	DEV Community	What's your new year resolution (2022 edition)? 🎉 🥳	https://dev.to/meatboy/whats-your-new-year-resolution-41hb	What x s your new year resolution edition New year and new us What are your new year resolution In private life and in career What do you plan to learn to do or to experience this year Share it with others and seal the stick to the plan contract with community To improve is to change to be perfect is to change often Winston Churchill	2022-01-05 12:44:54
海外TECH	DEV Community	Introduction to Streaming Ingestion and Stream Processing	https://dev.to/aws-builders/introduction-to-streaming-ingestion-and-stream-processing-3m4a	Introduction to Streaming Ingestion and Stream ProcessingProcessing real time streaming data requires throughput scalability reliability high availability and low latency to support a variety of applications and workloads Some examples include streaming ETL real time analytics fraud detection API microservices integration fraud detection activity tracking real time inventory and recommendations and click stream log file and IoT device analysis Streaming data architectures are built on five core constructs data sources stream ingestion stream storage stream processing and destinations Each of these components can be created and launched using AWS Managed Services and deployed and managed as a purpose built solution on Amazon EC Amazon Elastic Container Service Amazon ECS or Amazon Elastic Kubernetes Service Amazon EKS Architecture Options for Building an Analytics Application on AWS is a Series containing different articles that cover the key scenarios that are common in many analytics applications and how they influence the design and architecture of your analytics environment in AWS These series present the assumptions made for each of these scenarios the common drivers for the design and a reference architecture for how these scenarios should be implemented Stream processing data processing on its head is all about processing a flow of events A typical stream application consists of a number of producers that generate new events and a set of consumers that process these events Events in the system can be any number of things such as financial transactions user activity on a website or application metrics Consumers can aggregate incoming data send automatic alerts in real time or produce new streams of data that can be processed by other consumers Examples of each of these components include Data sources Application and click stream logs mobile apps existing transactional relational and NoSQL databases IoT sensors and metering devices Stream ingestion and producers Both open source and proprietary toolkits libraries and SDKs for Kinesis Data Streams and Apache Kafka to create custom stream producers AWS service integrations such as AWS IoT Core CloudWatch Logs and Events Amazon Kinesis Data Firehose AWS Data Migration Service DMS and third party integrations Stream storage Kinesis Data Streams Amazon Managed Streaming for Apache Kafka Amazon MSK and Apache Kafka Stream processing and consumers Amazon EMR Spark Structured Streaming Apache Flink AWS Glue ETL Streaming Kinesis Data Analytics for Apache Flink third party integrations and build your own custom applications using AWS and open source community SDKs and libraries Downstream destinations Databases data warehouses purpose built systems such as OpenSearch services data lakes and various third party integrations With these five components in mind next let s consider the characteristics as you design your stream processing pipeline for real time ingestion and nearly continuous stream processing CharacteristicsScalable throughput For real time analytics you should plan a resilient stream storage infrastructure that can adapt to changes in the rate of data flowing through the stream Scaling is typically performed by an administrative application that monitors shard and partition data handling metrics Dynamic stream processor consumption and collaboration Stream processors and consumers should automatically discover newly added Kinesis shards or Kafka partitions and distribute them equitably across all available resources to process independently or collaboratively as a consumption group Kinesis Application Name Kafka Consumer Group Durable Real time streaming systems should provide high availability and data durability For example Amazon Kinesis Data Streams and Amazon Managed Streaming for Apache Kafka replicate data across Availability Zones providing the high durability that streaming applications need Replay ability Stream storage systems should provide the ordering of records within shards and partitions as well as the ability to independently read or replay records in the same order to stream processors and consumers Fault tolerance checkpoint and replay Checkpointing refers to recording the farthest point in the stream that data records have been consumed and processed If the consuming application crashes it can resume reading the stream from that point instead of having to start at the beginning Loosely coupled integration Loosely coupled integration A key benefit of streaming applications is the construct of loose coupling The value of loose coupling is the ability of stream ingestion stream producers stream processors and stream consumers to act and behave independently of one another Examples include the ability to scale consumers outside of the producer configuration and adding additional stream processors and consumers to receive from the same stream or topic as existing stream processors and consumers but perform different actions Allow multiple processing applications in parallel The ability for multiple applications to consume the same stream concurrently is an essential characteristic of a stream processing system For example you might have one application that updates a real time dashboard and another that archives data to Amazon Redshift You want both applications to consume data from the same stream concurrently and independently Messaging semantics In a distributed messaging system components might fail independently Different messaging systems implement different semantic guarantees between a producer and a consumer in the case of such a failure The most common message delivery guarantees implemented are At most once Messages that could not be delivered or are lost are never redeliveredAt least once Message might be delivered more than once to the consumerExactly once Message is delivered exactly onceDepending on your application needs you need to choose a message delivery system that supports one or more of these required semantics Security Streaming ingest and processing systems need to be secure by default You need to grant access by using the principal of least privilege to the streaming APIs and infrastructure and encrypt data at rest and in transit Both Kinesis Data Streams and Amazon MSK can be configured to use AWS IAM policies to grant least privilege access For stream storage in particular allow encryption in transit for producers and consumers and encryption at rest Reference architectureStreaming data analytics reference architectureThe preceding streaming reference architecture diagram is segmented into the previously described components of streaming scenarios Data sourcesStream ingestion and producersStream storageStream processing and consumersDownstream destinationsAll or portions of this reference architecture can be used for workloads such as application modernization with microservices streaming ETL ingest real time inventory recommendations or fraud detection In this section we will identify each layer of components shown in the preceding diagram with specific examples The examples are not intended to be an exhaustive list but rather an attempt to describe some of the more popular options The subsequent Configuration notes section provides recommendations and considerations when implementing streaming data scenarios Note There are two bidirectional event flow lanes noted with an asterisk in the diagram We will review the five core components of streaming architecture first and then discuss these specialized flows Data sources The number of potential data sources is in the millions Examples include application logs mobile apps and applications with REST APIs IoT sensors existing application databases RDBMS NoSQL and metering records Stream ingestion and producers Multiple data sources generate data continually that might amount to terabytes of data per day Toolkits libraries and SDKs can be used to develop custom stream producers to streaming storage In contrast to custom developed producers examples of pre built producers include Kinesis Agent Change Data Capture CDC solutions and Kafka Connect Source connectors Streaming storage Kinesis Data Streams Managed Streaming for Apache Kafka and self managed Apache Kafka are all examples of stream storage options for ingesting processing and storing large streams of data records and events Streaming storage implementations are modeled on the idea of a distributed immutable commit log Events are stored for a configurable duration hours to days to months or even permanently in some cases While stored events are available to any client Stream processing and consumers Real time data streams can be processed sequentially and incrementally on a record by record basis over sliding time windows using a variety of services Or put another way this can be where particular domain specific logic resides and is computed With Kinesis Data Analytics for Apache Flink or Kinesis Data Analytics Studio you can process and analyze streaming data using standard SQL in a serverless way The service allows you to quickly author and run SQL queries against streaming sources to perform time series analytics feed real time dashboards and create real time metrics If you work in an Amazon EMR environment you can process streaming data using multiple optionsーApache Flink or Spark Structured Streaming Finally there are options for AWS Lambda third party integrations and build your own custom applications using AWS SDKs libraries and open source libraries and connectors for consuming from Kinesis Data Streams Managed Streaming for Apache Kafka and Apache Kafka Downstream destinations Data can be persisted to durable storage to serve a variety of use cases including ad hoc analytics and search machine learning alerts data science experiments and additional custom actions A special note on the data flow lanes noted with asterisk There are two examples which both involve bidirectional flow of data to and from layer streaming storage The first example is the bidirectional flow of in stream ETL between stream processor which uses one or more raw event sources from stream storage and performs filtering aggregations joins etc and writes results back to streaming storage to a refined that is curated hydrated result stream or topic where it can be used by a different stream processor or downstream consumer The second bidirectional flow example is the ubiquitous application modernization microservice design which often use a streaming storage layer for decoupled microservice interaction The key takeaway here is for us to not presume that the streaming event flows exclusively from left to right over time in the reference architecture diagram Configuration notesAs explored so far we know streaming data architects have options for implementing particular components in their stack for example different options for streaming storage streaming ingest and streaming producers While it s impractical to provide in depth recommendations for each layer s options in this document there are some high level concepts to consider as guide posts which we will present next For more in depth analysis of a particular layer in your design consider exploring the provided links within the following guidelines Streaming application guidelinesDetermine business requirements first It s always a best practice and practical to focus on your workload s particular needs first rather than starting with a feature by feature comparison between the technical options For example we often see organizations prioritizing Technical Feature A vs Technical Feature B before determining their workload s requirements This is the wrong order Determine your workload s requirements first because AWS has a wide variety of purpose built options at each streaming architecture layer to best match your requirements Technical comparisons second After business requirements have been clearly established the next step is to match your business requirements with the technical options that offer the best chance for success For example if your team has few technical operators serverless might be a good option Other technical questions about your workload might be whether you require a large number of independent stream processors and consuming applications that is one vs many stream processors and consumers What kind of manual or automatic scaling options are available to match business requirement throughput latency SLA RPO and RTO objectives Is there a desire to use open source based solutions What are the security options and how well do they integrate into existing security postures Is one path easier or more straightforward to migrate to versus another for example self managed Apache Kafka to Amazon MSK To learn more about your options for various layers in the reference architecture stack refer to the following Streaming ingest and producers ーCan be workload dependent and use AWS service integrations such as AWS IoT Core CloudWatch Logs and Events AWS Data Migration Service AWS DMS and third party integrations Refer to Writing Data to Amazon Kinesis Data Streams in Amazon Kinesis Data Streams Developer Guide Streaming storage ーKinesis Data Streams Kinesis Data Firehose Amazon Managed Streaming for Apache Kafka Amazon MSK and Apache Kafka Refer to Best Practices in Amazon Managed Streaming for Apache Kafka Developer Guide Stream processing and consumers ーKinesis Data Analytics for Apache Flink Kinesis Data Firehose AWS Lambda open source and proprietary SDKs Refer to Advanced Topics for Amazon Kinesis Data Streams Consumers in Amazon Kinesis Data Streams Developer Guide and Best Practices for Kinesis Data Analytics for Apache Flink in Amazon Kinesis Data Analytics Developer Guide Remember separation of concerns Separation of concerns is the application design principle that promotes segmenting an application into distinct particular area of concerns For instance your application might require that stream processors and consumers are performing an aggregation computation in addition to recording the computation results to a downstream destination While it can be tempting to clump both of these concerns into one stream processors or consumers it is recommended to consider separation instead It s often better in the segment isolate into multiple stream processors or consumers for operation monitoring performance tuning isolation and reducing the downtime blast radius DevelopmentExisting or desired skills match Realizing value from streaming architectures can be difficult and often a new endeavor for various roles within organizations Increase your chances of success by aligning your team s existing skillsets or desired skillsets wherever possible For example is your team familiar with Java or do they prefer a different language such as Python or Go Does your team prefer a graphical user interface for writing and deploying code Work backwards from your existing skill resources and preferences to appropriate options for each component Build vs buy Write your own or use off the shelf Consider whether an integration between components already exists or if you need to write your own Or perhaps both options are available Many teams new to streaming incorrectly assume that everything needs to be written from scratch Instead consider services such as Kafka Connect Connectors for inbound and outbound traffic AWS Lambda and Kinesis Data Firehose PerformanceAggregate records before sending to stream storage for increased throughput When using Kinesis Amazon MSK or Kafka ensure that the messages are accumulated on the producer side before sending to stream dtorage This is also referred to as batching records to increase throughput but at the cost of increased latency When working with Kinesis Data Streams use Kinesis Client Library KCL to de aggregate records KCL takes care of many of the complex tasks associated with distributed computing such as load balancing across multiple instances responding to instance failures checkpointing processed records and reacting to re sharding Initial planning and adjustment of shards and partitions The most common mechanism to scale stream storage for stream processors and consumers is through the number of configured shards Kinesis Data Streams or partitions Apache Kafka Amazon MSK for a particular stream This is a common element across Kinesis Data Streams Amazon MSK and Apache Kafka but options for scaling out and in the number of shards or partitions vary Amazon Kinesis Data Streams Developer Guide Resharding a StreamApache Kafka Documentation Operations Expanding your cluster also applicable to Amazon MSK Amazon Managed Streaming for Apache Kafka Developer Guide Using LinkedIn s Cruise Control for Apache Kafka with Amazon MSK for partition rebalancing Use Spot Instances and automatic scaling to process streaming data cost effectively You can also process the data using AWS Lambda with Kinesis Amazon MSK or both and Kinesis record aggregation and deaggregation modules for AWS Lambda Various AWS services offer automatic scaling options to keep costs lower than provisioning for peak volumes OperationsMonitor Kinesis Data Streams and Amazon MSK metrics using Amazon CloudWatch You can get basic stream and topic level metrics in addition to shard and partition level metrics Amazon MSK also provides an Open Monitoring with Prometheus option Amazon Kinesis Data Streams Developer Guide Monitoring Amazon Kinesis Data StreamsAmazon Managed Streaming for Apache Kafka Developer Guide Monitoring an Amazon MSK ClusterPlan for the unexpected No single point of failure Some components in your streaming architecture will offer different options for durability in case of a failure Kinesis Data Streams replicates to three different Availability Zones With Apache Kafka and Amazon Managed Streaming for Apache Kafka Amazon MSK for example producers can be configured to require acknowledgement for partition leader as well as a configurable amount in sync replica followers before considering the write successful In this example you are able to plan for possible disruptions in your AWS environment for example if an Availability Zone goes offline without possible downtime of your producing and consuming layers Security Authentication and authorization Amazon Managed Streaming for Apache Kafka Developer Guide Authentication and Authorization for Apache Kafka APIsAmazon Kinesis Data Streams Developer Guide Controlling Access to Amazon Kinesis Data Streams Resources Using IAM Encryption in transit encryption at rest Streaming data actively moves from one layer to another such as from a streaming data producer to stream storage over the internet or through a private network Protecting data in transit enterprises can and often choose to use encrypted connections HTTPS SSL TLS to protect the contents of data in transit Many AWS streaming services offer protection of data at rest through encryption AWS Well Architected Framework Security Pillar Identity and Access ManagementAWS Lake Formation Developer Guide Security in AWS Lake FormationAmazon Managed Streaming for Apache Kafka Developer Guide Authentication and Authorization for Apache Kafka APIsAmazon Kinesis Data Streams Developer Guide Controlling Access to Amazon Kinesis Data Streams Resources Using IAMHope this guide gives you an Introduction to Streaming Ingestion and Stream Processing covering the Characteristics and Reference Architecture for Streaming Data Let me know your thoughts in the comment section And if you haven t yet make sure to follow me on below handles connect with me on LinkedInconnect with me on Twitter‍follow me on github️Do Checkout my blogs Like share and follow me for more content ltag user id follow action button background color important color fac important border color important Adit ModiFollow Cloud Engineer AWS Community Builder x AWS Certified x Azure Certified Author of Cloud Tech DailyDevOps amp BigDataJournal DEV moderator Reference Guide	2022-01-05 12:30:55
Apple	AppleInsider - Frontpage News	Nanoleaf products will soon act as Thread border routers for HomeKit devices	https://appleinsider.com/articles/22/01/05/nanoleaf-products-will-soon-act-as-thread-border-routers-for-homekit-devices?utm_medium=rss	Nanoleaf products will soon act as Thread border routers for HomeKit devicesDuring CES Nanoleaf announced a forthcoming firmware update that will allow its products to act as Thread border routers to all HomeKit over Thread devices Nanoleaf products can now act as Thread border routers for HomeKitExpected to roll out in Q of the new firmware update broadens support for Thread beyond what Nanoleaf s products are currently capable of Read more	2022-01-05 12:12:29
海外TECH	Engadget	The Morning After: Sony reveals PlayStation VR2 specs	https://www.engadget.com/the-morning-after-sony-reveals-specs-and-more-play-station-vr-2-details-121559066.html?src=rss	The Morning After Sony reveals PlayStation VR specsOf course the year when many media outlets and companies decide to skip on attending CES in person Sony decides this is the year to make some news at its press conference While we got more news on its EV plans and next gen TVs Tom Holland was also drafted into the showcase to promote the forthcoming Uncharted feature film Then it hit us with a barrage of specs for the highly anticipated next gen PlayStation VR headset It will of course be compatible with the PS and the VR Sense controllers we ve already seen It will have a display resolution of x pixel per eye a degree field of view and be capable of to Hz frame rates all while supporting K HDR The world of consumer VR has rocketed forward since the original PSVR so Sony is playing catchup with features like inside out tracking which uses multiple embedded cameras to track the movements of your head and controllers There will also be “headset feedback which sounds like the headset will shake and vibrate like a DualShock DualSense controller We re still waiting to see what it ll look like how much it ll cost and when we ll get to buy one but the company did announce one of the first games arriving on the platform Horizon Call of the Mountain which will be a VR experience set in the Horizon universe Mat SmithSamsung s portable Freestyle projector focuses and levels automaticallyThe floodlight style device weighs just lbs SamsungSamsung s new portable projector uses autofocus and auto leveling features that ll help align whatever you re watching meaning you ll have a lot of options for where to place it You ll be able to project content at a display size ranging from to inches with a p resolution It has a degree cradle stand so you can point it toward a ceiling and watch things while you re lying down Alternatively using a base accessory the Freestyle can even connect to a standard E light bulb socket Continue reading TP Link s new WiFi E router has motorized antennas that follow your devicesLess excuses for poor connections TP LinkHow do you make a router cute Look at it wiggle ASUS ROG Flow Z is a gaming tablet with NVIDIA s RTX TiThink of it like a Super Surface The ROG Flow Z packs in Intel s new th gen processors and up to NVIDIA s RTX Ti all in a sleek slate Weighing in at pounds it s clearly not meant to replace smaller tablets like the iPad Instead ASUS sees it as a way to bring your PC games everywhere ーsay a cramped airplane tray table ーwithout the bulk of a laptop It can even connect to external GPUs Continue reading ​​The RTX Ti is NVIDIA s new new flagship GPUThere s also an “RTX at the low end NVIDIA just teased a new flagship GPU the RTX Ti More details will arrive soon but the company did reveal a few specs to keep high end GPU fans intrigued The RTX Ti will become NVIDIA s ultra high end GPU outside of its creator line supplanting the RTX Like the the Ti will have GB of GDDRX memory but running at Gbit s as opposed to the Gbit s of the s memory NVIDIA also says the GPU is capable of calculating shader teraflops RT teraflops and tensor AI teraflops More vital statistics but no price after the fold Continue reading Dell s XPS Plus is a beautiful ultraportable with no headphone jackWhat good is a sleek design if we can t plug in our headphones The company s flagship ultraportable that sparked the slim bezel trend and has remained one of our favorite notebooks for years is evolving The XPS Plus a more powerful and ambitiously designed notebook with a lattice less keyboard read no space between the keys and a glass haptic touchpad It also gasp lacks a headphone jack Continue reading Sony reveals its follow up Vision S EV prototypeIt s an SUV SonySony has unveiled a follow up to the electric car it revealed at the same event two years ago The new prototype is an SUV called the Vision S which features a large interior that can seat seven The electric SUV has sensors all over its body including CMOS image and LiDAR sensors for its driver assistance system Sony says it s already conducting tests in Europe as part of its efforts to release Level driver assistance technology on public roads Inside there are ime of flight sensors for driver authentication as well as support for intuitive gesture and voice commands Sony now has ambitions to become a player in the electric vehicle industry and sell its cars to the public It will establish an operating company named quot Sony Mobility Inc quot this spring and will explore entry into the EV market Continue reading nbsp The biggest news stories you might have missed​​Sony s Quantum Dot OLED TVs can tweak quality settings using a cameraHyperX boasts hours of battery life for its latest gaming headsetNASA s James Webb Space Telescope has successfully deployed its foot sunshieldWatch LG s CES event in under five minutesEargo s latest smart hearing aid adapts to your environmentLet s watch two minutes of the upcoming Uncharted movieJohn Deere says its self driving tractor is ready for production	2022-01-05 12:55:59
金融	RSS FILE - 日本証券業協会	ＳＤＧｓ特設サイトのトピックスと新着情報（リダイレクト）	https://www.jsda.or.jp/about/torikumi/sdgs/sdgstopics.html	特設サイト	2022-01-05 13:23:00
海外ニュース	Japan Times latest articles	Bad news for London and New York: Finance hubs are becoming obsolete	https://www.japantimes.co.jp/opinion/2022/01/05/commentary/world-commentary/finance-hubs/	Bad news for London and New York Finance hubs are becoming obsoleteCOVID has shown just how little location now matters for many jobs and businesses in finance and gave executives confidence that more operations could be	2022-01-05 21:10:16
ニュース	BBC News - Home	Covid: PCR not needed after positive lateral flow under new plans	https://www.bbc.co.uk/news/uk-59878823?at_medium=RSS&at_campaign=KARANGA	agency	2022-01-05 12:56:28
ニュース	BBC News - Home	Novak Djokovic: Australian Open vaccine exemption ignites backlash	https://www.bbc.co.uk/news/world-australia-59876203?at_medium=RSS&at_campaign=KARANGA	locals	2022-01-05 12:48:50
ニュース	BBC News - Home	Covid: Greater Manchester hospitals cancel surgery as NHS pressures mount	https://www.bbc.co.uk/news/uk-england-manchester-59878179?at_medium=RSS&at_campaign=KARANGA	manchester	2022-01-05 12:31:03
ニュース	BBC News - Home	Covid: French uproar as Macron vows to 'piss off' unvaccinated	https://www.bbc.co.uk/news/world-europe-59873833?at_medium=RSS&at_campaign=KARANGA	condemn	2022-01-05 12:05:50
LifeHuck	ライフハッカー［日本版］	Xiaomi Mi スマートバンド、HUAWEI Watchがお得に！中華スマートウォッチ買うなら今	https://www.lifehacker.jp/2022/01/248912amazon-hatsuuri-smart-watch.html	amazon	2022-01-05 21:30:00
LifeHuck	ライフハッカー［日本版］	【Amazon初売り】美容グッズがお買い得。ホワイトニングパウダーが57％オフ、ヘアワックスが30％オフなど	https://www.lifehacker.jp/2022/01/amazon-timesale-hatsuuri-2022-0105-3.html	amazon	2022-01-05 21:15:00
北海道	北海道新聞	香港、８カ国からの旅客機禁止　米英仏豪など、オミクロン対策	https://www.hokkaido-np.co.jp/article/630366/	新型コロナウイルス	2022-01-05 21:13:00
北海道	北海道新聞	新リンク「滑りやすい」　豊頃町造成　６日から一般開放	https://www.hokkaido-np.co.jp/article/630363/	開放	2022-01-05 21:06:00
北海道	北海道新聞	中国・西安で４万２千人集中隔離　コロナ感染拡大、都市封鎖続く	https://www.hokkaido-np.co.jp/article/630364/	感染拡大	2022-01-05 21:09:00
北海道	北海道新聞	活況願い初競り　釧路・根室の両市場　「養殖へ　新しい漁業構築を」	https://www.hokkaido-np.co.jp/article/630361/	養殖	2022-01-05 21:03:09
北海道	北海道新聞	十勝管内の三が日、人出回復　帯広神社の初詣１万７千人増　初売りに長い列　ばんえい入場者、発売額４割アップ	https://www.hokkaido-np.co.jp/article/630362/	十勝管内	2022-01-05 21:05:00