{"id":2022,"date":"2015-11-20T14:55:00","date_gmt":"2015-11-20T14:55:00","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=2022"},"modified":"2023-06-27T07:14:32","modified_gmt":"2023-06-27T14:14:32","slug":"efficiently-querying-for-deleted-documents-with-n1ql","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/efficiently-querying-for-deleted-documents-with-n1ql\/","title":{"rendered":"Efficiently querying for deleted documents with N1QL&#8217;s MISSING"},"content":{"rendered":"<p style=\"color: #333333;line-height: 20.8px;text-align: left\">When you&#8217;re building an application, a situation often comes up where your users want to delete some content but\u00a0you still need to know that the data once existed.<\/p>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">Let&#8217;s say you&#8217;re building a messaging app. You might want to give your users the chance to restore\u00a0deleted conversations.<\/p>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">So, when someone comes to delete a conversation you need to do something other than\u00a0simply deleting\u00a0the document from Couchbase. Instead, you could use a flag in the JSON body that identifies the document as somehow non-live. For example:<\/p>\n<pre style=\"font-size: 13px;margin-left: 40px;color: #333333;line-height: 20.8px;text-align: left\" class=\"\">{\r\n    \"deleted\": true\r\n}<\/pre>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">Underneath that you&#8217;d create a GSI index to give you efficient access to all of those\u00a0<em>deleted<\/em>\u00a0conversations:<\/p>\n<pre style=\"font-size: 13px;margin-left: 40px;color: #333333;line-height: 20.8px;text-align: left\">CREATE INDEX deletedmsgs ON `conversations`(deleted);<\/pre>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">Then, to populate the view of deleted conversations in your app&#8217;s UI you&#8217;d run a <a href=\"https:\/\/www.couchbase.com\/products\/n1ql\/\">N1QL query<\/a> such as:<\/p>\n<pre style=\"font-size: 13px;margin-left: 40px;color: #333333;line-height: 20.8px;text-align: left\">SELECT * FROM `conversations` WHERE deleted = true;<\/pre>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">So far, everything seems pretty obvious.<\/p>\n<h2 style=\"color: #333333;line-height: 20.8px;text-align: left\">Adding this to an existing data set<\/h2>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">Now, what happens if you&#8217;re only just introducing this feature to your application? The likelihood is that you&#8217;d already have many thousands of conversations whose underlying documents don&#8217;t have the\u00a0<em>deleted<\/em>\u00a0flag.<\/p>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">You might think of this as a great opportunity to use N1QL&#8217;s\u00a0<em>IS MISSING<\/em>. You&#8217;d be right but you might be thinking along slightly different lines to what I&#8217;m going to suggest.<\/p>\n<p style=\"color: #333333;line-height: 20.8px;text-align: left\">You\u00a0<em>coulc<\/em>\u00a0account for documents that don&#8217;t have the\u00a0<em>deleted<\/em>\u00a0flag by changing your N1QL query to something like this:<\/p>\n<pre style=\"font-size: 13px;margin-left: 40px;color: #333333;line-height: 20.8px;text-align: left\">SELECT * FROM `conversations` WHERE deleted = true OR deleted IS MISSING;<\/pre>\n<p>To make it a bit more realistic, we might\u00a0also filter our query based on the person involved:<\/p>\n<pre style=\"font-size: 13px;margin-left: 40px;color: #333333;line-height: 20.8px;text-align: left\">SELECT * FROM `conversations` WHERE sender = user123 AND deleted = true OR deleted IS MISSING;<\/pre>\n<p>This would do the job but it wouldn&#8217;t be the most efficient way to do it.<\/p>\n<p>Once we introduce N1QL&#8217;s MISSING keyword in an OR situation, then we set N1QL off on a full document scan: our query is asking N1QL to check every document for the presence\u00a0of the deleted JSON key. Our index helps only with those documents where <em>deleted<\/em>\u00a0exists, because it doesn&#8217;t index documents where the key isn&#8217;t present.<\/p>\n<p>Instead, in this case, we&#8217;d be better running a one-off query that would return all of the documents that are missing\u00a0<em>deleted<\/em>. We could then work through each of them and update them to include\u00a0<em>deleted = false<\/em> and so all those documents would now be in our efficient index.<\/p>\n<p>If the query takes a little longer, that&#8217;s okay: we&#8217;d be running it as part of a background data janitoring task, so none of our users would even be aware of it. Once we&#8217;d run this task over all the documents, we&#8217;d no longer need <em>MISSING<\/em> in our user-facing query\u00a0because we&#8217;d know that all our conversation documents would be either\u00a0<em>deleted: true\u00a0<\/em>or\u00a0<em>deleted: false<\/em>.<\/p>\n<h3>Using schema versions instead<\/h3>\n<p>We could avoid using MISSING altogether by maintaing\u00a0schema versioning:<\/p>\n<pre style=\"margin-left: 40px\" class=\"\">{\r\n   \"schema\": \"1.2.3\"\r\n}<\/pre>\n<p>If we imagine that our new schema version, featuring the\u00a0<em>deleted<\/em>\u00a0flag, was 2.0.0 then our\u00a0background janitor task could look for all the documents that have a schema version older than 2.0.0 and then run them through an upgrade process, part of which would be adding the\u00a0<em>deleted<\/em>\u00a0flag.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>When you&#8217;re building an application, a situation often comes up where your users want to delete some content but\u00a0you still need to know that the data once existed. Let&#8217;s say you&#8217;re building a messaging app. You might want to give [&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":[1],"tags":[],"ppma_author":[8982],"class_list":["post-2022","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"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>Efficiently querying deleted documents with N1QL\u2019s MISSING<\/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\/efficiently-querying-for-deleted-documents-with-n1ql\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Efficiently querying for deleted documents with N1QL&#039;s MISSING\" \/>\n<meta property=\"og:description\" content=\"When you&#8217;re building an application, a situation often comes up where your users want to delete some content but\u00a0you still need to know that the data once existed. Let&#8217;s say you&#8217;re building a messaging app. You might want to give [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.couchbase.com\/blog\/efficiently-querying-for-deleted-documents-with-n1ql\/\" \/>\n<meta property=\"og:site_name\" content=\"The Couchbase Blog\" \/>\n<meta property=\"article:published_time\" content=\"2015-11-20T14:55:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-27T14:14:32+00:00\" \/>\n<meta name=\"author\" content=\"Matthew Revell, Lead Developer Advocate, EMEA, Couchbase\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Matthew Revell, Lead Developer Advocate, EMEA, Couchbase\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"3 minutes\" \/>\n<script type=\"application\/ld+json\" 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