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JSONã«ã‚±ãƒ„カンマを求ã‚ã‚‹ã®ã¯é–“é•ã£ã¦ã„ã‚‹ã ã‚ã†ã‹ - An Epicurean
é–“é•ã£ã¦ã„ã¾ã™ã€‚JSONã¯ä»•æ§˜ãŒç°¡æ½”ã§èžé€šãŒåˆ©ã‹ãªã„ã¨ã“ã‚ãŒãƒ¡ãƒªãƒƒãƒˆã§ã‚ã‚Šã€ã‚±ãƒ„カンマやコメントを許容ã—ãªã„ã“ã¨ã‚‚ãã‚Œã«ä¸€å½¹è²·ã£ã¦ã„ã¾ã™ã€‚ケツカンマやコメントãŒæ¬²ã—ã‘ã‚Œã°YAMLを使ã„ã¾ã—ょã†ã€‚YAMLã¯JSONã®ã‚¹ãƒ¼ãƒ‘ーセットãªã®ã§ã€JSONã«ã‚±ãƒ„カンマやコメントをã¤ã‘ã‚Œã°YAMLã¨ã—ã¦èªã‚€ã“ã¨ã¯å‡ºæ¥ã¾ã™ã€‚ JSONã¨YAMLã«ã¯ãã‚Œãžã‚Œã«è‰¯ã•ãŒã‚ã‚Šã€ãã‚Œãžã‚Œã®è‰¯ã•ã‚’ä»–æ–¹ã«æ±‚ã‚ã‚‹ã¨ã€ãã®ä¾¡å€¤ã‚’毀æã—ã¾ã™ã€‚言ã£ã¦ã—ã¾ãˆã°ã€JSONã¯ã‚·ãƒ³ãƒ—ルã§ã‚ã‚ŠYAMLã¯ã‚¤ãƒ¼ã‚¸ãƒ¼ãªã®ã§ã™ã€‚ å‚考: Simple Made Easy JSONã¯ã‚·ãƒ³ãƒ—ル ã”å˜çŸ¥ã®é€šã‚Šã€JSONã¯ãƒ«ãƒ¼ãƒ«ã‚„覚ãˆã‚‹ã“ã¨ãŒå°‘ãªã„åé¢ã€è¡¨ç¾åŠ›ã«ä¹ã—ã„ã§ã™ã€‚çµæžœã¨ã—ã¦äººé–“ã«ã‚‚機械ã«ã‚‚比較的優ã—ã„シリアライゼーションフォーマットã¨ã—ã¦å„ªã‚Œã¦ã„ã¾ã™ã€‚「ã²ã¨ã¤ã®ã“ã¨ã‚’ã†ã¾ãã‚„ã‚‹ã€ã§ã™ã€‚å¿…ãš1è¡Œã«åœ§ç¸®ã§ãã‚‹å¼·ã¿ã‚‚ã‚ã‚Šã€ãƒã‚°ãƒ•ã‚©ãƒ¼ãƒžãƒƒãƒˆã¨ã—ã¦ã‚‚使
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TL;DR AWSインフラエンジニアãŒã€æ—¥å¸¸æ¥å‹™ã§ã®ã€ŒJSONã‚„YAMLã®æ§‹é€ 把æ¡ã€ã®èª²é¡Œã‚’解決ã™ã‚‹ãŸã‚ã«é–‹ç™ºã—ãŸCLIツールhawkã®é–‹ç™ºèƒŒæ™¯ã‚’ãŠè©±ã—ã—ã¾ã™ã€‚ 💡 課題: AWS CLI出力やK8sマニフェストã®æ§‹é€ 把æ¡ã«æ™‚é–“ãŒã‹ã‹ã‚‹ 🎯 解決: info機能ã«ã‚ˆã‚‹ä¸€ç™ºæ§‹é€ æŠŠæ¡ + pandas風ã®çµ±ä¸€ã‚¯ã‚¨ãƒªè¨€èªž 🚀 çµæžœ: å¦ç¿’コストを下ã’ãªãŒã‚‰ã€æ—¥å¸¸æ¥å‹™ã‚’効率化 背景：インフラエンジニアã®æ—¥å¸¸çš„ãªå›°ã‚Šã”㨠AWS CLIレスãƒãƒ³ã‚¹ã¨ã®æ ¼é—˜ aws ec2 describe-instances --instance-ids i-1234567890abcdef0 > instance.json cat instance.json # 100行超ã®JSONãŒæµã‚Œã‚‹...
DuckDB 㧠JSON Lines å½¢å¼ã®ãƒã‚°ã‚’精査ã™ã‚‹
ã¾ã¨ã‚ 圧縮ã•ã‚ŒãŸè¤‡æ•°ã® JSON Lines ã‚’èªã¿è¾¼ã‚ã‚‹ S3 ã‹ã‚‰ç›´æŽ¥èªã¿è¾¼ã‚ã‚‹ 好ããªã‚¿ã‚¤ãƒŸãƒ³ã‚°ã§ Parquet ã§å‡ºåŠ› DuckDB ã¨ã¯ ã“ã¡ã‚‰ã‚’ã©ã†ãžã€‚ DuckDB雑紹介(1.1対応版)@DuckDB座談会 - Speaker Deck 圧縮済ã¿ã®ãƒã‚°ã‚’èªã¿è¾¼ã‚€ 解æžå¯¾è±¡ã®ãƒã‚°ãƒ•ã‚¡ã‚¤ãƒ«ã£ã¦ spam-20240501.jsonl.gz ã‚„ egg-20240601.jsonl.zst ã¿ãŸã„ã«åœ§ç¸®ã•ã‚Œã¦ã„ãŸã‚Šã—ã¾ã™ã€‚ 特ã«æ—¥ä»˜å˜ä½ã§åˆ†ã‹ã‚Œã¦ãŸã‚Šã™ã‚‹ã“ã¨ã¯å¤šã„ã§ã™ã€‚ãã“ã‹ã‚‰ç›®çš„ã®ãƒã‚°ã‚’探ã—出ã™ã®ã¯ã€åŸºæœ¬ã¯ã‚¹ã‚¯ãƒªãƒ—トを書ãã“ã¨ãŒå¤šã„ã¨æ€ã„ã¾ã™ã€‚ DuckDB を使ãˆã°è‰²ã€…解決ã—ã¾ã™ã€‚
ドã‚ュメントをMarkdownã‚„JSONã«å¤‰æ›ã—ã¦ãれる「Doclingã€ã‚’試ã™
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How to use JSON Path · Bump.sh
API doc platform Publish API doc portals from OpenAPI and AsyncAPI documents. Automated changelog, versioning and governance. Documentation that scales with your API ecosystem. Managed MCP platform Turn your API ecosystem into deterministic, production-ready MCP servers. Define how agents consume your APIs, with built-in authentication and observability.
JSON をプレビューã—ãªãŒã‚‰ jq ã®ãƒ•ã‚£ãƒ«ã‚¿ã‚’書ãã“ã¨ãŒã§ãる「jnvã€ã‚’試ã—ã¦ã¿ã‚‹
ã¡ãªã¿ã« jq ãŒã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ã•ã‚Œã¦ã„ã‚‹å¿…è¦ã¯ã‚ã‚Šã¾ã›ã‚“。 jnv does not require users to install jq on their system, because it utilizes j9 Rust bindings. https://github.com/ynqa/jnv#installation JSON navigator and interactive filter leveraging jq Usage: jnv [OPTIONS] [INPUT] Examples: - Read from a file: jnv data.json - Read from standard input: cat data.json | jnv Arguments: [INPUT] Optional path to a JSON file. If not prov
JSON Canvas
An open file format for infinite canvas data. Infinite canvas tools are a way to view and organize information spatially, like a digital whiteboard. Infinite canvases encourage freedom and exploration, and have become a popular interface pattern across many apps. The JSON Canvas format was created to provide longevity, readability, interoperability, and extensibility to data created with infinite
JSONã®å·®åˆ†ã‚’å–ã£ã¦JSON Patchã‚’å¾—ã‚‹ã«ã¯diffsonãŒãŠã™ã™ã‚ - Lambdaカクテル
ã“ã†ã„ã†ãƒ„イートを見ãŸã€‚ Scala (or Java) ã§ã€jsonã®diffã‚’patchファイルã¿ãŸã„ãªæ„Ÿã˜ã§ã‚ã‹ã‚Šã‚„ã™ã„テã‚ストã§å‡ºåŠ›ã—ã¦ãれるライブラリãªã„ã‹ãªã‚。ãã—ã¦jacksonã«ä¾å˜ã—ã¦ãªã„ã¨ã„ã„ãªâ€” Arthur (@Arthur1__) 2024å¹´1月13æ—¥ ç¾ä»£ã®ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã§ã¯JSONã®å·®åˆ†ã‚’å–ã£ãŸã‚Šã€é€†ã«ãƒ‘ッãƒã‚’当ã¦ã‚‹ã¨ã„ã†ã“ã¨ãŒã‚ˆãã‚る。å¯èƒ½ãªã‚‰ãã‚ŒãŒPretty Printã§ãã‚‹ã¨è‰¯ã„。 JSONã®å·®åˆ†ã‚’Scalaã§å–る方法ã«ã¤ã„ã¦ã„ãã¤ã‹èª¿ã¹ã¦ã¿ãŸã®ã§ãƒ¡ãƒ¢ã€‚ JSONã®å·®åˆ†ã‚’ã©ã†è¡¨ç¾ã™ã‚‹ï¼Ÿ JSON Patch diffson diffsonã§JSON Patchを生æˆã™ã‚‹ diffsonã§JSON Patchã‚’é©ç”¨ã™ã‚‹ diffsonã§JSON Merge Patchを生æˆã™ã‚‹ diffsonã§JSON Merge Patchã‚’é©ç”¨ã™ã‚‹ JSON Pat
プãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã®åŽŸå‰‡ï¼šæ§‹é€ 化テã‚ストを文å—列çµåˆã§ä½œã‚‰ãªã„ã€ç½®æ›ã§ã„ã˜ã‚‰ãªã„ - Uzabase for Engineers
ã“ã‚“ã«ã¡ã¯ã€ã‚½ãƒ¼ã‚·ãƒ£ãƒ«çµŒæ¸ˆãƒ¡ãƒ‡ã‚£ã‚¢ã€ŒNewsPicksã€ã®ã‚€ã¨ã†ã§ã™ã€‚ 先日ã‹ã‚‰ã€ŽGhost of Tsushimaã€ã®é–‹ç™ºè€…ãŒæ›¸ã„ãŸã€Žãƒ«ãƒ¼ãƒ«ã‚ºãƒ»ã‚ªãƒ–・プãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ã€ã¨ã„ã†æœ¬ã‚’ã¡ã‚‡ã£ã¨ãšã¤èªã¿é€²ã‚ã¦ã„ã¦ã€ãƒ—ãƒã‚°ãƒ©ãƒŸãƒ³ã‚°ç†±ãŒé«˜ã¾ã£ã¦ã„ã¾ã™ã€‚ã“ã®æœ¬ã¯å¤§ããªæŒ‡é‡ã‚’示ã™ã ã‘ã§å…·ä½“ã®è©±ã‚’ã™ã‚‹ã‚‚ã®ã§ã¯ãªã„ã®ã§ã™ãŒã€èªã¿ç‰©ã¨ã—ã¦é¢ç™½ã„ã®ã§ç§ã‚‚ä¼¼ãŸã‚ˆã†ãªã“ã¨ã‚’ã‚„ã£ã¦ã¿ãŸããªã‚Šã¾ã—ãŸã€‚ 何年もã“ã†ã„ã†ä»•äº‹ã‚’ã—ã¦ã„ã‚‹ã¨ãƒã‚°ãŒå…¥ã‚‹ãƒ‘ターンã¨ã„ã†ã®ãŒè¦‹ãˆã¦ãã¾ã™ã€‚ãã—ã¦ã ã„ãŸã„ã©ã“ã«è¡Œã£ã¦ã‚‚何ã®ä»•äº‹ã§ã‚‚ä¼¼ãŸã‚ˆã†ãªã“ã¨ã‚’ã™ã‚‹ã“ã¨ã«ãªã‚‹ã®ã§ã™ãŒã€ä»Šå›žã®è©±ã‚‚ãã®ä¸€ã¤ã§ã™ã€‚ æ§‹é€ åŒ–ãƒ†ã‚ストを文å—列çµåˆã§ä½œã‚‰ãªã„ã€ç½®æ›ã§ã„ã˜ã‚‰ãªã„ã¨ã„ã†ã®ã¯ã“ã‚Œã ã‘ã¿ã‚‹ã¨ä½•ã®ã“ã¨ã‹åˆ†ã‹ã‚Šã¥ã‚‰ã„ã‹ã‚‚知れã¾ã›ã‚“ãŒSaaS Product Team ã‚»ã‚ュアコーディングã®å•“è’™ 第2回 （SQL インジェクション編）ã®å†…容ã¨ã‚ã‚‹é¢ã§ã¯åŒã˜è©±ã§ã™ã€‚
Building a high performance JSON parser
This talk is a case study of designing an efficient Go package. I’m going to use the example of building a high performance JSON parser. Supports streaming operations It’s unrealistic to expect to have the entire input in memory. Buffering in memory is a availability risk, input sizes are usually unknown and potentially unbounded. Buffering before processing introduces latency. Streaming reads let
JSONã¨BigInt
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JSONã¨JSON Schemaを改ã‚ã¦ç†è§£ã™ã‚‹ / tokyo_study
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JSON ã‚’è¡Œå˜ä½ã«ã‚ã‹ã‚Šã‚„ã™ã展開ã—ã¦ãれるコマンド gron ãŒãƒ”ンãƒã‚¤ãƒ³ãƒˆã§ã¨ã¦ã‚‚便利
JSON ã‚’è¡Œå˜ä½ã«ã‚ã‹ã‚Šã‚„ã™ã展開ã—ã¦ãれるコマンド gron ãŒãƒ”ンãƒã‚¤ãƒ³ãƒˆã§ã¨ã¦ã‚‚便利 2022-06-13-1 [Programming] コマンドラインã§ã® JSON ã®æ“作ã«ã¯ jq (軽é‡JSONパーサ)[2017-10-24-1] を使ã£ã¦ã„ã¾ã™ã€‚万能ã§ä¾¿åˆ©ãªã‚“ã§ã™ãŒã€å¥¥ã¾ã£ãŸã¨ã“ã‚ã«ã‚る値ã ã‘ã‚’å–ã‚ŠãŸã„ã¨ãã¨ã‹ã¡ã‚‡ã£ã¨é¢å€’ãªã‚“ã§ã™ã‚ˆã。ã¡ã‚‰ã£ã¨å€¤ã‚’見ãŸã„ã ã‘ãªã®ã«ãŠãŠã’ã•ã«ãªã£ã¡ã‚ƒã„ãŒã¡ã€‚ ã¨ã„ã†ã“ã¨ã§ã€ gron ã¨ã„ã†ã®ã‚’使ã£ã¦ã¿ã¾ã—ãŸã€‚ GitHub - tomnomnom/gron: Make JSON greppable!JSONã‚’grepã—ã‚„ã™ãã™ã‚‹ã‚³ãƒžãƒ³ãƒ‰ãƒ©ã‚¤ãƒ³ãƒ„ールgronã®ç´¹ä»‹ - Qiitagronã§jsonã®æ‰±ã„ãŒæ¥½ã«ãªã£ãŸè©± – Tower of Engineers ã‚ã€ã“ã‚Œã§ã„ã„や。 ã„ã‚ゆる「顧客（ï¼ç§ï¼‰ãŒæœ¬å½“ã«å¿…è¦ã ã£ãŸã‚‚ã®ã€ã ã‚。 例ãˆã°ã€
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Google Cloud Next 2021ã§ç™ºè¡¨ã•ã‚Œã€Private Previewã ã£ãŸBigQueryã®ãƒã‚¤ãƒ†ã‚£ãƒ–JSONåž‹ãŒå…ˆæ—¥Public Previewã«ãªã£ãŸã¨ã®ã“ã¨ã§ã™ã€‚ BigQuery now natively supports semi-structured data | Google Cloud Blog 発表ã•ã‚ŒãŸå½“åˆã¯ã€Œã‚ã‚Œã€ã™ã§ã«å‡ºã¦ã„ã‚‹JSON_EXTRACT()ã¨ã‹ã®JSON関数ã¨ä½•ãŒé•ã†ã®ã ã‚ã†ã€ã¨æ€ã£ã¦ã„ãŸã®ã§ã™ãŒã€å®Ÿéš›ã«è§¦ã£ã¦ã¿ã‚‹ã¨ãƒã‚¤ãƒ†ã‚£ãƒ–JSONåž‹ã®æŸ”軟性ã«æ„Ÿå‹•ã—ãŸã®ã§ç´¹ä»‹ã—ãŸã„ã¨æ€ã„ã¾ã™ã€‚ サンプルJSON 以下ã®ã‚ˆã†ãªJSONデータをパースã—ãŸã„ã¨ã—ã¾ã™ã€‚ { "id": 12345, "name": "ohsawa0515", "person_info": [ { "key": "height", "value": "178" }, { "
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GitHub - ynqa/jnv: Interactive JSON filter using jq
jqコマンドã®ã‚¹ãƒˆãƒªãƒ¼ãƒŸãƒ³ã‚°å‡¦ç† (--stream) をパイプã§awkã«ã¤ãªã方法ã®ã¾ã¨ã‚ - Qiita
JSON Crack | Online JSON Viewer - Transform your data into interactive graphs
GitHub - kellyjonbrazil/jc: CLI tool and python library that converts the output of popular command-line tools, file-types, and common strings to JSON, YAML, or Dictionaries. This allows piping of output to tools like jq and simplifying automation scripts
GitHub - multiprocessio/dsq: Commandline tool for running SQL queries against JSON, CSV, Excel, Parquet, and more.
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