{"id":8342,"date":"2020-03-23T12:07:56","date_gmt":"2020-03-23T19:07:56","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=8342"},"modified":"2024-03-26T09:39:54","modified_gmt":"2024-03-26T16:39:54","slug":"json-case-sensitive-insensitive-search-index-data","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/json-case-sensitive-insensitive-search-index-data\/","title":{"rendered":"JSON faz distin\u00e7\u00e3o entre mai\u00fasculas e min\u00fasculas. Voc\u00ea n\u00e3o precisa fazer isso."},"content":{"rendered":"

Um RoSe em qualquer outro caso teria o mesmo cheiro. William Shakespeare<\/a><\/p><\/blockquote>\n

Voc\u00ea deve ter aprendido <\/span>regras de capitaliza\u00e7\u00e3o<\/span><\/a> em sua escola de gram\u00e1tica, mas a pesquisa no mundo real n\u00e3o \u00e9 t\u00e3o sens\u00edvel \u00e0 capitaliza\u00e7\u00e3o. Charles de Gaulle usa letra min\u00fascula para o \"de\" do meio, Tony La Russa usa letra mai\u00fascula para \"La\" - pode haver raz\u00f5es etimol\u00f3gicas para isso, mas \u00e9 improv\u00e1vel que seu agente de atendimento ao cliente se lembre.\u00a0\u00a0 <\/span>Os bancos de dados t\u00eam uma variedade de sensibilidades. O SQL, por padr\u00e3o, n\u00e3o diferencia mai\u00fasculas de min\u00fasculas para identificadores e palavras-chave, mas diferencia mai\u00fasculas de min\u00fasculas para dados. <\/span>O JSON diferencia mai\u00fasculas de min\u00fasculas para nomes de campos e dados. O mesmo acontece com o N1QL. O JSON pode ter o seguinte. O N1QL selecionar\u00e1 cada campo e valor como um campo e valor distintos.<\/p>\n

SELECT {\"City\": \"San Francisco\", \"city\": \"san francisco\", \"citY\": \"saN fanciscO\"}\r\n\r\n[\r\n  {\r\n    \"$1\": {\r\n      \"City\" (Cidade): \"San Francisco\",\r\n      \"citY\": \"saN fanciscO\",\r\n      \"city\": \"san francisco\"\r\n    }\r\n  }\r\n]<\/pre>\n
Neste artigo, discutiremos como lidar com sensibilidade dos dados a mai\u00fasculas e min\u00fasculas<\/strong>. Suas refer\u00eancias de campo ainda s\u00e3o diferencia mai\u00fasculas de min\u00fasculas<\/strong>. Se voc\u00ea usar a caixa incorreta para o nome do campo, o N1QL assumir\u00e1 que se trata de um campo ausente e atribuir\u00e1 o valor MISSING a esse campo.<\/p>\n
Vamos considerar um predicado simples no N1QL para pesquisar todas as permuta\u00e7\u00f5es de casos.<\/p>\n
WHERE name in [\"joe\", \"joE\", \"jOe\", \"Joe\", \"JoE\", \"JOe\", \"JOE\"]\r\n<\/pre>\n
Isso requer sete pesquisas diferentes no \u00edndice.  \"John\" requer mais pesquisas no \u00edndice e \"Fitzerald\" requer ainda mais. <\/span>H\u00e1 uma maneira padr\u00e3o de fazer isso. Basta criar um \u00edndice diminuindo o case do campo e o literal.<\/span><\/p>\n
WHERE LOWER(name) = \"joe\"<\/pre>\n
Essa pesquisa pode ser agilizada com a cria\u00e7\u00e3o do \u00edndice com a express\u00e3o correta.<\/span><\/p>\n
CREATE INDEX i1 ON customer(LOWER(name));<\/pre>\n
Certifique-se de que sua consulta esteja pegando o \u00edndice correto e empurre o predicado para a varredura do \u00edndice. E essa \u00e9 a ideia. As consultas que t\u00eam predicados enviados para a varredura de \u00edndice s\u00e3o executadas muito mais rapidamente do que as consultas que n\u00e3o t\u00eam.  Isso \u00e9 verdadeiro para predicados e verdadeiro para pushdown agregado<\/a> tamb\u00e9m.<\/span><\/p>\n
EXPLAIN SELECT * FROM `customer` WHERE LOWER(name) = \"joe\";\r\n\r\n      {\r\n        \"#operator\": \"IndexScan3\",\r\n        \"index\": \"i1\",\r\n        \"index_id\": \"c117bdf583c2e276\",\r\n        \"index_projection\": {\r\n          \"primary_key\": true\r\n        },\r\n        \"keyspace\": \"customer\",\r\n        \"namespace\": \"default\",\r\n        \"spans\": [\r\n          {\r\n            \"exact\": true,\r\n            \"range\" (intervalo): [\r\n              {\r\n                \"high\": \"\\\"joe\\\"\",\r\n                \"inclusion\" (inclus\u00e3o): 3,\r\n                \"baixa\": \"\\\"joe\\\"\"\r\n              }\r\n            ]\r\n          }\r\n        ],<\/pre>\n
Insensibilidade de casos em um cen\u00e1rio de \u00edndice composto.<\/strong><\/h5>\n
WHERE LOWER(name) = \"joe\"\r\nAND zip = 94821\r\nE sal\u00e1rio > 500\r\nAND join_date <= "2017-01-01"\r\nAND LOWER(county) LIKE "san%"<\/pre>\n
CREATE INDEX i2 ON customer(LOWER(name),\r\n                            zip,\r\n                            LOWER(county),\r\n                            join_date,\r\n                            salary)<\/pre>\n
Insensibilidade a mai\u00fasculas e min\u00fasculas em fun\u00e7\u00f5es de matriz.<\/strong><\/h5>\n
Fun\u00e7\u00f5es de cadeia de caracteres<\/span><\/a> como SPLIT(), SUFFIXES(), muitos dos <\/span>fun\u00e7\u00f5es de matriz<\/span><\/a> e <\/span>fun\u00e7\u00f5es de objeto<\/span><\/a> retornam matrizes. Ent\u00e3o, como us\u00e1-los de maneira que n\u00e3o diferencie mai\u00fasculas de min\u00fasculas?<\/span><\/p>\n
Seguimos o mesmo princ\u00edpio de antes.  Crie uma express\u00e3o para reduzir os valores a min\u00fasculas antes de process\u00e1-los por meio dessas fun\u00e7\u00f5es.\u00a0<\/span><\/p>\n
SELECT SPLIT(\"Good Morning, Joe\") as splitresult;\r\n    \"splitresult\": [\r\n      \"Good\",\r\n      \"Bom dia,\",\r\n      \"Joe\"\r\n    ]\r\n\r\nSELECT SPLIT(LOWER(\"Good Morning, Joe\"));\r\n    \"splitresult\": [\r\n      \"good\",\r\n      \"morning,\",\r\n      \"joe\"\r\n    ]\r\n<\/pre>\n
Agora, o que voc\u00ea realmente deseja \u00e9 filtrar com base em um valor dentro da cadeia de caracteres.<\/span><\/p>\n
WHERE LOWER(xyz) LIKE \"%good%\";<\/pre>\n
Esse \u00e9 provavelmente o pior predicado do SQL, em termos de desempenho.<\/span><\/p>\n
SELECT * FROM customer\r\nWHERE x IN SPLIT(LOWER(xyz)) SATISFA\u00c7\u00d5ES x = \"bom\" END<\/pre>\n
Agora, que \u00edndice voc\u00ea criaria para isso?\u00a0 CONSELHO<\/a> \u00e9 \u00fatil.<\/span><\/p>\n
CREATE INDEX adv_DISTINCT_split_lower_xyz ON `customer`\r\n           (DISTINCT ARRAY `x` FOR x in split(lower((`xyz`))) END)<\/pre>\n
Como sempre, verifique sua explica\u00e7\u00e3o.<\/p>\n
        {\r\n            \"#operator\": \"DistinctScan\",\r\n            \"scan\": {\r\n                \"#operator\": \"IndexScan3\",\r\n                \"index\" (\u00edndice): \"adv_DISTINCT_split_lower_xyz\",\r\n                \"index_id\": \"552ab6c643616fbc\",\r\n                \"index_projection\": {\r\n                    \"primary_key\": true\r\n                },\r\n                \"keyspace\": \"customer\",\r\n                \"namespace\": \"default\",\r\n                \"spans\": [\r\n                    {\r\n                        \"exact\": true,\r\n                        \"range\" (intervalo): [\r\n                            {\r\n                                \"high\": \"\\\"good\\\"\",\r\n                                \"inclusion\" (inclus\u00e3o): 3,\r\n                                \"low\" (baixo): \"\\\"good\\\"\"\r\n                            }\r\n                        ]\r\n                    }\r\n                ],\r\n<\/pre>\n
Se voc\u00ea quiser fazer UNNEST e tiver uma cl\u00e1usula WHERE simples, use esta consulta. Sempre verifique sua explica\u00e7\u00e3o para garantir que os predicados sejam enviados para a varredura do \u00edndice.<\/span><\/p>\n
SELECT * FROM customer\r\n              UNNEST SPLIT(LOWER(xyz)) AS x\r\nWHERE x = \"good\" (bom)<\/pre>\n
Uso de tokens\u00a0<\/strong><\/h5>\n
A fun\u00e7\u00e3o TOKENS() simplifica a obten\u00e7\u00e3o de letras min\u00fasculas ao usar essa op\u00e7\u00e3o como argumento.  Consulte o artigo <\/span>Mais do que LIKE: Pesquisa eficiente de JSON com N1QL<\/span><\/a> para obter detalhes e exemplos<\/span><\/p>\n
Express\u00f5es complexas.<\/strong><\/h5>\n
SELECT *\r\n\r\nFROM customer\r\nWHERE lower(fname) || lower(mname) || lower(lname) = \"JoeMSmith\"<\/pre>\n
Como poder\u00edamos otimizar isso?  O Index Advisor veio em socorro. Novamente.<\/span><\/p>\n
CREATE INDEX adv_lower_fname_concat_lower_mname_concat_lower_lname\r\n  ON `customer`(lower((`fname`))||lower((`mname`))||lower((`lname`)))<\/pre>\n
Explique para confirmar o plano:<\/span><\/p>\n
       {\r\n            \"#operator\": \"IndexScan3\",\r\n            \"index\": \"adv_lower_fname_concat_lower_mname_concat_lower_lname\",\r\n            \"index_id\": \"aaa14cbdf14e9cd8\",\r\n            \"index_projection\": {\r\n                \"primary_key\": true\r\n            },\r\n            \"keyspace\": \"customer\",\r\n            \"namespace\": \"default\",\r\n            \"spans\": [\r\n                {\r\n                    \"exact\": true,\r\n                    \"range\" (intervalo): [\r\n                        {\r\n                            \"high\": \"\\\"JoeMSmith\\\"\",\r\n                            \"inclusion\" (inclus\u00e3o): 3,\r\n                            \"baixa\": \"\\\"JoeMSmith\\\"\"\r\n                        }\r\n                    ]\r\n                }\r\n            ],\r\n            \"using\": \"gsi\"\r\n        },\r\n<\/pre>\n
Trazendo as grandes armas: pesquisa de texto completo<\/strong><\/h4>\n
Como voc\u00ea percebeu, esse \u00e9 um problema de processamento e consulta de texto. O FTS pode digitalizar, armazenar e pesquisar textos de v\u00e1rias maneiras.  A pesquisa sem distin\u00e7\u00e3o entre mai\u00fasculas e min\u00fasculas \u00e9 uma delas. Vamos ver o plano de uma consulta de pesquisa simples.<\/p>\n
select * from customer\r\nwhere search (name, \"joe\")\r\n\r\n  \"~children\": [\r\n        {\r\n            \"#operator\": \"PrimaryScan3\",\r\n            \"index\" (\u00edndice): \"#primary\",\r\n            \"index_projection\": {\r\n                \"primary_key\": true\r\n            },\r\n            \"keyspace\": \"customer\",\r\n            \"namespace\": \"default\",\r\n            \"using\": \"gsi\"\r\n        },\r\n        {\r\n            \"#operator\": \"Fetch\",\r\n            \"keyspace\": \"customer\",\r\n            \"namespace\": \"default\"\r\n        },\r\n        {\r\n            \"#operator\": \"Paralelo\",\r\n            \"~child\": {\r\n                \"#operator\": \"Sequence\" (Sequ\u00eancia),\r\n                \"~children\": [\r\n                    {\r\n                        \"#operator\": \"Filter\" (Filtro),\r\n                        \"condition\": \"search((`customer`.`name`), \\\"joe\\\")\"\r\n                    },\r\n                    {\r\n                        \"#operator\": \"InitialProject\",\r\n                        \"result_terms\": [\r\n                            {\r\n                                \"expr\": \"self\",\r\n                                \"star\": true\r\n                         }\r\n                        ]\r\n                    }\r\n                ]\r\n            }\r\n        }\r\n    ]\r\n}\r\n<\/pre>\n
Esse N\u00c3O \u00e9 o plano que voc\u00ea deseja... Isso \u00e9 usar um exame prim\u00e1rio!<\/span><\/p>\n
Depois de criar o \u00edndice de texto no cliente do balde, as coisas ficaram muito melhores:<\/span><\/p>\n
select * from customer\r\nwhere search (name, \"joe\")\r\n\r\n{\r\n    \"#operator\": \"Sequence\" (Sequ\u00eancia),\r\n    \"~children\": [\r\n        {\r\n            \"#operator\": \"IndexFtsSearch\",\r\n            \"index\": \"trname\",\r\n            \"index_id\": \"3bdb61e5010e8838\",\r\n            \"espa\u00e7o-chave\": \"customer\",\r\n            \"namespace\": \"default\",\r\n            \"search_info\": {\r\n                \"field\": \"\\\"`name`\\\"\",\r\n                \"outname\": \"out\",\r\n                \"query\" (consulta): \"\\\"joe\\\"\"\r\n            },\r\n            \"using\": \"fts\"\r\n        },\r\n<\/pre>\n
O analisador padr\u00e3o padr\u00e3o reduz todos os tokens e, portanto, voc\u00ea encontrar\u00e1 todos os \"joe \"s : JOE, joe, Joe, JOe, etc.   Voc\u00ea pode definir um analisador personalizado e fornecer instru\u00e7\u00f5es espec\u00edficas para reduzir os tokens.  Aqui est\u00e1 um exemplo.<\/span><\/p>\n
 \"mapping\": {\r\n   \"analysis\" (an\u00e1lise): {\r\n    \"analyzers\": {\r\n     \"mylower\": {\r\n      \"token_filters\": [\r\n       \"to_lower\"\r\n      ],\r\n      \"tokenizer\": \"unicode\",\r\n      \"type\" (tipo): \"custom\"\r\n     }\r\n    }\r\n   },\r\n<\/pre>\n
Veja como adicion\u00e1-lo na interface do usu\u00e1rio.  Ver blog fino 8 maneiras de personalizar os \u00edndices do Couchbase Full-Text Search<\/a> para obter detalhes sobre as v\u00e1rias maneiras de personalizar o \u00edndice FTS.\"\"<\/a><\/p>\n
 <\/p>\n
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 <\/p>","protected":false},"excerpt":{"rendered":"
A RoSe by any other case would smell as sweet. William Shakespeare You must have learned capitalization rules in your grammar school, but the real-world search is not so sensitive to capitalization. Charles de Gaulle uses lower case for the […]<\/p>","protected":false},"author":55,"featured_media":13873,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"ppma_author":[8929],"class_list":["post-8342","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"yoast_head":"\nIs JSON Case Sensitive or Not? | Upper + Lowercase Sensitivity<\/title>\n<meta name=\"description\" content=\"In this post, Couchbase demonstrates why JSON is case sensitive in certain fields and how to work with this sensitivity. 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