{"id":1993,"date":"2016-01-04T18:31:14","date_gmt":"2016-01-04T18:31:13","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=1993"},"modified":"2025-06-13T17:24:49","modified_gmt":"2025-06-14T00:24:49","slug":"data-modelling-for-n1ql","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/data-modelling-for-n1ql\/","title":{"rendered":"Data modelling for N1QL"},"content":{"rendered":"<p>In my posts on key-value data modelling with Couchbase, the main concerns were:<\/p>\n<ul>\n<li>*\u00a0<a href=\"https:\/\/www.couchbase.com\/blog\/data-modelling-when-embed-or-refer\/\">when to embed data and when to refer<\/a><\/li>\n<li>*\u00a0<a href=\"https:\/\/www.couchbase.com\/blog\/manual-secondary-indexes\/\">building secondary indexes<\/a><\/li>\n<li>*\u00a0<a href=\"https:\/\/www.couchbase.com\/blog\/data-modelling-key-design\/\">key design<\/a>.<\/li>\n<\/ul>\n<p>In a N1QL world, we&#8217;re still thinking about similar things and in this post I&#8217;ll look at two of them:<\/p>\n<ul>\n<li>* how to represent document types<\/li>\n<li>* modelling relationships between documents.<\/li>\n<\/ul>\n<p>Fundamentally, it&#8217;s still a story of making the right trade-offs so that you can create a <a href=\"https:\/\/www.couchbase.com\/blog\/conceptual-physical-logical-data-models\/\">physical data model<\/a> that most efficiently represents your logical model. All that&#8217;s changed are some implementation details.<\/p>\n<h2>Do nothing<\/h2>\n<p>First up, it&#8217;s worth saying that you can query existing JSON data without having to make any changes. N1QL isn&#8217;t going to demand you remodel your entire existing database.<\/p>\n<p>However, you can make changes to your model that will make N1QL more efficient and your queries easier to write. And if you&#8217;re starting from scratch, you might as well consider N1QL queryability from the outset.<\/p>\n<h2>Document types<\/h2>\n<p>One of the most important parts of key-value data modelling is key design. In a key-value world, the key bears an enormous responsibility: it must tell you what you&#8217;re storing and it must be easy to find again.<\/p>\n<p>For example, the keys for customer records might take the following format:<\/p>\n<ul>\n<li><code>* cust::name<\/code> the main customer record<\/li>\n<li><code>* cust::name::payment<\/code> the customer&#8217;s payment details<\/li>\n<\/ul>\n<p>With rich query, the key name&#8217;s role changes. Rather than directly asking for the key, you&#8217;ll be asking for the data that you want. Those two responsibilities \u2014 describing what you&#8217;re storing and making the data easy to find \u2014 move into the document itself as a document type.<\/p>\n<p>This is where we hit one of the big practical differences between N1QL and SQL: FROM does less work in N1QL today. The scope of a Couchbase bucket is vastly different from the scope of a typical relational table.<\/p>\n<p>SQL FROM narrows the scope of our query to the data that interests us. N1QL FROM tells the query engine which bucket to look at. As a typical bucket contains all the data for one application, we need another way to distinguish types of document.<\/p>\n<h3>A customer<\/h3>\n<p>Let&#8217;s look further at a customer. Here&#8217;s a simple record:<\/p>\n<p><code>{ <\/code><\/p>\n<p><code>\u00a0 \"name\": \"Alan Partridge\", <\/code><\/p>\n<p><code>\u00a0 \"email\": \"alan@example.com\", <\/code><\/p>\n<p><code>\u00a0 \"location\": \"Norwich\", <\/code><\/p>\n<p><code>\u00a0 \"phone\": \"+44-1603-619-331\", <\/code><\/p>\n<p><code>\u00a0 \"docInfo\": <\/code><\/p>\n<p><code>\u00a0 { <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"type\": \"customer\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"created\": \"2015-10-22T10:17:24.731Z\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"schemaVersion\": \"1.2.3\" <\/code><\/p>\n<p><code>\u00a0 } <\/code><\/p>\n<p><code>} <\/code><\/p>\n<p>The <i>type<\/i> value steps in to narrow the scope of our N1QL query:<\/p>\n<p><code>SELECT * FROM `default` WHERE type = customer;<\/code><\/p>\n<p>Using the <code>type<\/code> we can build indexes that contain only the documents of a particular type. For example:<\/p>\n<p><code>CREATE INDEX customers ON `default` WHERE type=customer; <\/code><\/p>\n<p>The likelihood is that your document schemas already specify a type, particularly if you&#8217;ve been using Couchbase views. Either way, when you create a document you should specify its type within the document body.<\/p>\n<p>In future iterations of Couchbase and N1QL, we might see namespacing below the bucket level but types will always give us a low-cost way of differentiating documents.<\/p>\n<h2>Relationships between documents<\/h2>\n<p>The question of <a href=\"https:\/\/www.couchbase.com\/blog\/data-modelling-when-embed-or-refer\/\">when to embed and when to refer<\/a> remains an important one when modelling for N1QL. The difference is that N1QL handles these relationships for you through JOINs.<\/p>\n<p>N1QL joins can take place between:<\/p>\n<ul>\n<li>* documents in different buckets<\/li>\n<li>* documents in the same bucket<\/li>\n<\/ul>\n<p>In both cases, the JOIN matches a value in the document on the left side with the key name of a document on the right side. So, if one document contains the key name of another document, you can JOIN those two documents.<\/p>\n<p>Let&#8217;s add our fictional customer&#8217;s most recent purchase to the profile:<\/p>\n<p><code>{ lastPurchase: \"prod::drivinggloves\" } <\/code><\/p>\n<p>The value here is also the key name of a product document elsewhere in our bucket.<\/p>\n<p>So, to return the name of the customer and the price of their last purchase for every customer living in Norwuch, we&#8217;d use the following N1QL:<\/p>\n<p><code>SELECT r.name, a.price FROM `default` r JOIN `default` a ON KEYS r.lastPurchase WHERE r.location = \"Norwich\"; <\/code><\/p>\n<p>The result would look something like:<\/p>\n<p><code>{ <\/code><\/p>\n<p><code>\u00a0 \"requestID\": \"a2284985-541f-491d-b921-4c248f154293\", <\/code><\/p>\n<p><code>\u00a0 \"signature\": <\/code><\/p>\n<p><code>\u00a0 { <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"name\": \"json\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"price\": \"json\" }, <\/code><\/p>\n<p><code>\u00a0 \u00a0\"results\": <\/code><\/p>\n<p><code>\u00a0 \u00a0 [ <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 { \"name\": \"Alan Partridge\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 \"price\": \"9.99\" <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 } <\/code><\/p>\n<p><code>\u00a0 \u00a0 ], <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"status\": \"success\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \"metrics\": <\/code><\/p>\n<p><code>\u00a0 \u00a0 { <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 \"elapsedTime\": \"5.223111ms\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 \"executionTime\": \"5.124029ms\", <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 \"resultCount\": 1, <\/code><\/p>\n<p><code>\u00a0 \u00a0 \u00a0 \"resultSize\": 77 <\/code><\/p>\n<p><code>\u00a0 \u00a0 } <\/code><\/p>\n<p><code>} <\/code><\/p>\n<p>With N1QL, we can produce one to one, one to many and many to many relationships between documents. So while embedded documents were previously a large part of how we&#8217;d model our data for Couchbase, with N1QL they become effortless for us as developers.<\/p>\n<h2>In summary<\/h2>\n<p>The two main points to take from this post are:<\/p>\n<ul>\n<li>your documents should have a type that your queries can use to narrow scope<\/li>\n<li>JOINs rely on the primary key of the right-side document appearing in the body of the left-side document.<\/li>\n<\/ul>\n<p>Ultimately, not much needs to change about your JSON data in order for you to use N1QL. As you dive more into N1QL, you&#8217;ll no doubt find document shapes that work better than others. If so, share them with us on the <a href=\"https:\/\/www.couchbase.com\/forums\/\">Couchbase forums<\/a>!<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In my posts on key-value data modelling with Couchbase, the main concerns were: *\u00a0when to embed data and when to refer *\u00a0building secondary indexes *\u00a0key design. In a N1QL world, we&#8217;re still thinking about similar things and in this post [&hellip;]<\/p>\n","protected":false},"author":18,"featured_media":13873,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1819,1812],"tags":[1883],"ppma_author":[8982],"class_list":["post-1993","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-modeling","category-n1ql-query","tag-data-modelling"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.8 (Yoast SEO v25.8) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data modelling for N1QL - The Couchbase Blog<\/title>\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\/data-modelling-for-n1ql\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data modelling for N1QL\" \/>\n<meta property=\"og:description\" content=\"In my posts on key-value data modelling with Couchbase, the main concerns were: *\u00a0when to embed data and when to refer *\u00a0building secondary indexes *\u00a0key design. 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