{"id":4295,"date":"2017-12-04T18:26:28","date_gmt":"2017-12-05T02:26:28","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=4295"},"modified":"2025-11-07T04:29:38","modified_gmt":"2025-11-07T12:29:38","slug":"comparing-couchbase-views-couchbase-n1ql-indexing","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/pt\/comparing-couchbase-views-couchbase-n1ql-indexing\/","title":{"rendered":"Compara\u00e7\u00e3o das visualiza\u00e7\u00f5es do Couchbase com o Couchbase N1QL e a indexa\u00e7\u00e3o."},"content":{"rendered":"

\u00c0 medida que a plataforma de dados do Couchbase evoluiu, servi\u00e7os como N1QL e GSI Indexing lidaram com os casos de uso que o Couchbase VIEWS costumava lidar e muito mais. \u00c9 l\u00f3gico fazer a pergunta comparativa entre eles. Aqui est\u00e1 uma tabela que compara ambos. Este artigo \u00e9 destinado a desenvolvedores e arquitetos familiarizados com ambos, e n\u00e3o como um artigo introdut\u00f3rio. Use os links aqui para saber mais e brincar com os respectivos recursos.<\/p>\n

\"\"<\/p>\n

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
T\u00f3pico<\/strong><\/th>\nExibi\u00e7\u00f5es do Couchbase Map-Reduce<\/strong><\/th>\nCouchbase N1QL+GSI<\/strong><\/th>\n<\/tr>\n
Abordagem<\/td>\nCom base nas fun\u00e7\u00f5es map() e reduce() definidas pelo usu\u00e1rio que operam nos dados em segundo plano. Como map() e reduce() s\u00e3o escritas em Javascript, voc\u00ea pode codificar l\u00f3gicas complexas nessas fun\u00e7\u00f5es.<\/td>\nBaseado na consulta declarativa N1QL (SQL para JSON). Usa \u00edndices apropriados para otimizar a execu\u00e7\u00e3o e \u00e9 executada dinamicamente por meio da orquestra\u00e7\u00e3o de servi\u00e7os de dados de consulta-\u00edndice. O N1QL permite consultas facilmente grav\u00e1veis e leg\u00edveis para JSON. Por ser inspirado no SQL, \u00e9 flex\u00edvel e compost\u00e1vel. Por ter sido estendido para JSON, funciona com dados JSON avan\u00e7ados. Usa l\u00f3gica booleana de quatro valores (true, false, NULL, MISSING)<\/td>\n<\/tr>\n
Mais informa\u00e7\u00f5es<\/td>\nDocumentos do Couchbase:\u00a0https:\/\/bit.ly\/2jQrY11<\/a><\/td>\n\n1. https:\/\/query.couchbase.com<\/a>
\n2. https:\/\/www.couchbase.com\/blog\/n1ql-practical-guide-second-edition\/<\/a><\/td>\n<\/tr>\n
Consulta<\/td>\nConsulta baseada em<\/strong><\/p>\n

\n1. Chave \u00fanica
\n2. Conjunto de chaves
\n3. Tecla Iniciar-Fim
\n4. Teclas de in\u00edcio e fim do documento
\n5. Group BY, Agrega\u00e7\u00e3o
\n6. Pagina\u00e7\u00e3o<\/p>\n<\/td>\n

Declara\u00e7\u00f5es de consulta<\/strong><\/p>\n

1. SELECIONAR
\n2. INSERIR
\n3. ATUALIZA\u00c7\u00c3O
\n4. EXCLUIR
\n5. MERGE
\n6. INFERIR
\n7. EXPLICAR
\nOpera\u00e7\u00f5es de consulta:<\/strong>
\n1. Chave \u00fanica
\n2. Conjunto de chaves
\n3. Teclas de alcance
\n4. Intervalo de chaves de documento
\n5. Predicados arbitrariamente complexos
\n6. JUN\u00c7\u00c3O INTERNA, JUN\u00c7\u00c3O EXTERNA ESQUERDA
\n7. NEST, UNNEST
\n8. GRUPO POR
\n9. Agrega\u00e7\u00e3o
\n10. Pagina\u00e7\u00e3o (OFFSET, LIMIT)
\n11. Otimiza\u00e7\u00e3o
\n12. ORDENAR POR
\n13. TER
\n14. Subconsultas (correlacionadas, n\u00e3o correlacionadas)
\n15. Tabelas derivadas
\n16. Opera\u00e7\u00f5es SET: UNION, UNION ALL, EXCEPT, EXCEPT ALL, INTERSECT
\n17. Consultas altamente compostas, o que significa que essas opera\u00e7\u00f5es podem ser simplesmente combinadas umas com as outras para expressar facilmente perguntas e opera\u00e7\u00f5es comerciais complexas.<\/p>\n<\/td>\n<\/tr>\n

Indexa\u00e7\u00e3o<\/td>\n\u00cdndice simples para visualiza\u00e7\u00f5es.<\/td>\n\n \t1. \u00cdndice prim\u00e1rio
\n \t2. \u00cdndice prim\u00e1rio nomeado
\n \t3. \u00cdndice secund\u00e1rio
\n \t4. \u00cdndice secund\u00e1rio composto
\n 5. \u00cdndice funcional
\n \t6. \u00cdndice da matriz
\n \t7. Matriz ALL
\n \t8. Matriz ALL DISTINCT
\n \t9. \u00cdndice parcial
\n \t10. \u00cdndice adaptativo
\n \t11. \u00cdndices duplicados
\n \t12. \u00cdndice de cobertura<\/p>\n<\/td>\n<\/tr>\n
Particionamento<\/td>\nAlinhado ao particionamento de dados.<\/td>\nServi\u00e7os independentes.<\/p>\n

O N1QL e o GSI s\u00e3o dimensionados independentemente do servi\u00e7o de dados e entre si.<\/span><\/td>\n<\/tr>\n

Escala<\/td>\nEscalas com servi\u00e7o de dados<\/td>\nEscalonamento independente por meio de escalonamento multidimensional (MDS)<\/td>\n<\/tr>\n
Buscar com a chave do documento<\/td>\nComo os dados s\u00e3o particionados na chave do documento, o documento \u00e9 obtido diretamente do n\u00f3<\/td>\nEspecifique a consulta por meio da cl\u00e1usula USE KEYS.<\/p>\n

Como os dados s\u00e3o particionados na chave do documento, o documento \u00e9 obtido diretamente do n\u00f3<\/span><\/td>\n<\/tr>\n

Buscar com chave de \u00edndice<\/td>\nDispers\u00e3o-coleta<\/td>\nCada varredura de \u00edndice em um \u00fanico n\u00f3; dados em v\u00e1rios n\u00f3s.<\/p>\n

P\u00f3s-processamento no n\u00f3 de consulta<\/span><\/td>\n<\/tr>\n

Varredura de alcance<\/td>\nDispers\u00e3o-coleta<\/td>\nVarredura de \u00edndice em um \u00fanico n\u00f3.<\/p>\n

P\u00f3s-processamento no n\u00f3 de consulta<\/span><\/td>\n<\/tr>\n

Agrupamento, agrega\u00e7\u00e3o<\/td>\nIntegrado com a API de visualiza\u00e7\u00f5es<\/td>\nIncorporado ao N1QL<\/td>\n<\/tr>\n
Armazenamento em cache<\/td>\nSistema de arquivos<\/td>\nPool de buffer de \u00edndice<\/p>\n

Cache de dados<\/span><\/td>\n<\/tr>\n

Armazenamento<\/td>\nLoja de sof\u00e1s<\/td>\nMecanismo de armazenamento de plasma (5.0 e superior)<\/p>\n

\u00cdndice otimizado para mem\u00f3ria (4.5 e superior)<\/span><\/p>\n

ForestDB (comunidade)<\/span><\/td>\n<\/tr>\n

Disponibilidade<\/td>\nBaseado em r\u00e9plicas<\/td>\n5.0: R\u00e9plicas<\/p>\n

4.x: \u00cdndices equivalentes<\/span><\/td>\n<\/tr>\n

Lat\u00eancia da consulta<\/p>\n

(Consultas simples)<\/span><\/td>\n

10 milissegundos a 100 milissegundos<\/td>\n5 milissegundos+<\/td>\n<\/tr>\n
Taxa de transfer\u00eancia de consulta<\/p>\n

(Consultas simples)<\/span><\/td>\n

3K a 4K consultas por segundo<\/td>\n40 mil consultas por segundo<\/td>\n<\/tr>\n
Escalabilidade<\/td>\nModerado (dimensionamento vinculado ao servi\u00e7o de dados)<\/td>\nAlta (dimensionamento independente de servi\u00e7os de \u00edndice e consulta: MDS)<\/td>\n<\/tr>\n
Aplicabilidade<\/td>\nAgrega\u00e7\u00f5es, o melhor das agrega\u00e7\u00f5es em grande escala para requisitos de lat\u00eancia baixa e moderada. As opera\u00e7\u00f5es de redu\u00e7\u00e3o de mapas nos dados s\u00e3o feitas em segundo plano \u00e0 medida que os dados s\u00e3o modificados.<\/td>\nMelhor para pesquisa baseada em atributos, varreduras de intervalo, select-join-project-array complexo<\/p>\n

Opera\u00e7\u00f5es. Oferece suporte a agrupamento, agrega\u00e7\u00e3o e ordena\u00e7\u00e3o - essas opera\u00e7\u00f5es s\u00e3o feitas dinamicamente como parte da execu\u00e7\u00e3o da consulta.<\/span><\/td>\n<\/tr>\n

Requisitos de aplica\u00e7\u00e3o<\/td>\nRelat\u00f3rio sobre m\u00e9tricas bem definidas<\/p>\n

Agrega\u00e7\u00f5es em grande escala<\/span><\/p>\n

Sens\u00edvel \u00e0 lat\u00eancia<\/span><\/td>\n

Pesquisas de chaves secund\u00e1rias<\/p>\n

Varreduras de alcance<\/span><\/p>\n

Agrega\u00e7\u00f5es operacionais<\/span><\/p>\n

Consultas filtradas<\/span><\/p>\n

Consultas ad-hoc com predicados complexos, jun\u00e7\u00f5es, agrega\u00e7\u00f5es, pesquisa de aplicativos, pagina\u00e7\u00e3o, atualiza\u00e7\u00f5es baseadas em chaves secund\u00e1rias.<\/span><\/td>\n<\/tr>\n

Espacial<\/td>\nSuportado por visualiza\u00e7\u00f5es espaciais<\/td>\nN\u00e3o diretamente.<\/p>\n

https:\/\/dzone.com\/articles\/speed-up-spatial-search-in-couchbase-n1ql<\/a><\/span><\/td>\n<\/tr>\n

Consist\u00eancia<\/td>\nVencido = UPDATE_AFTER<\/p>\n

Vencido = OK<\/p>\n

Stale = FALSE<\/td>\n

N\u00e3o limitado (obsoleto = OK)<\/p>\n

AT_PLUS (leia suas pr\u00f3prias grava\u00e7\u00f5es)<\/span><\/p>\n

REQUEST_PLUS (lido ap\u00f3s as atualiza\u00e7\u00f5es do \u00edndice at\u00e9 now(). Stale = False).<\/td>\n<\/tr>\n

Ferramentas<\/td>\nConsole da Web<\/td>\nConsole da Web, Workbench do desenvolvedor, Monitoramento de consultas, Perfil de consultas, Explica\u00e7\u00e3o visual, INFER.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>","protected":false},"excerpt":{"rendered":"

\u00c0 medida que a plataforma de dados do Couchbase evoluiu, servi\u00e7os como N1QL e GSI Indexing lidaram com os casos de uso que o Couchbase VIEWS costumava lidar e muito mais. \u00c9 l\u00f3gico fazer a pergunta comparativa entre eles. Aqui est\u00e1 uma tabela comparando ambos. Esta \u00e9 [...]<\/p>","protected":false},"author":55,"featured_media":4301,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,1815,1821,1819,9417,1812],"tags":[1696,1248,1641],"ppma_author":[8929],"class_list":["post-4295","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-best-practices-and-tutorials","category-couchbase-architecture","category-data-modeling","category-performance","category-n1ql-query","tag-indexing","tag-mapreduce","tag-secondary-indexing"],"acf":[],"yoast_head":"\nComparing Couchbase Views with Couchbase N1QL & Indexing<\/title>\n<meta name=\"description\" content=\"The blog focuses on the table comparison between Couchbase Map-Reduce Views and Couchbase N1QL+GSI indexing. This is intended for developers and architects.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.couchbase.com\/blog\/pt\/comparing-couchbase-views-couchbase-n1ql-indexing\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparing Couchbase Views with Couchbase N1QL & Indexing.\" \/>\n<meta property=\"og:description\" content=\"The blog focuses on the table comparison between Couchbase Map-Reduce Views and Couchbase N1QL+GSI indexing. This is intended for developers and architects.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/pt\/comparing-couchbase-views-couchbase-n1ql-indexing\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2017-12-05T02:26:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-07T12:29:38+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2017\/12\/questions.png\" \/>\n\t<meta property=\"og:image:width\" content=\"512\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Keshav Murthy\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@rkeshavmurthy\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Keshav Murthy\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/\"},\"author\":{\"name\":\"Keshav Murthy\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/#\/schema\/person\/c261644262bf98e146372fe647682636\"},\"headline\":\"Comparing Couchbase Views with Couchbase N1QL & Indexing.\",\"datePublished\":\"2017-12-05T02:26:28+00:00\",\"dateModified\":\"2025-11-07T12:29:38+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/\"},\"wordCount\":652,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/#organization\"},\"image\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2017\/12\/questions.png\",\"keywords\":[\"Indexing\",\"MapReduce\",\"Secondary Indexing\"],\"articleSection\":[\"Application Design\",\"Best Practices and Tutorials\",\"Couchbase Architecture\",\"Data Modeling\",\"High Performance\",\"SQL++ \/ N1QL Query\"],\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/\",\"url\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/\",\"name\":\"Comparing Couchbase Views with Couchbase N1QL & Indexing\",\"isPartOf\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.couchbase.com\/blog\/comparing-couchbase-views-couchbase-n1ql-indexing\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.couchbase.com\/blog\/wp-content\/uploads\/sites\/1\/2017\/12\/questions.png\",\"datePublished\":\"2017-12-05T02:26:28+00:00\",\"dateModified\":\"2025-11-07T12:29:38+00:00\",\"description\":\"The blog focuses on the table comparison between Couchbase Map-Reduce Views and Couchbase N1QL+GSI indexing. 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