{"id":8407,"date":"2020-04-13T00:33:48","date_gmt":"2020-04-13T07:33:48","guid":{"rendered":"https:\/\/www.couchbase.com\/blog\/?p=8407"},"modified":"2025-06-13T17:21:46","modified_gmt":"2025-06-14T00:21:46","slug":"flexible-query-indexing-for-flexible-json-model","status":"publish","type":"post","link":"https:\/\/www.couchbase.com\/blog\/flexible-query-indexing-for-flexible-json-model\/","title":{"rendered":"Flexible Query & Indexing for Flexible JSON Model."},"content":{"rendered":"
\n

Use N1QL when you’re in a JSON pickle. — Confucius<\/h4>\n<\/blockquote>\n

For the JSON data model, the advice is to think of collections as tables, JSON document as denormalized rows and field names as columns – roughly. All this holds in databases like Couchbase and MongoDB when the recommendations are strictly followed. There are many reasons why users don\u2019t simply follow this key-value pair model <\/span>all the time. <\/span><\/i>Here are the main reasons.\u00a0<\/span><\/p>\n

    \n
  1. \n
      \n
    1. JSON is too verbose<\/a>.<\/span><\/li>\n
    2. You want to convert a map\/hashmap data structure where the keys are dynamic.<\/span><\/li>\n
    3. Timeseries data when the field names are usually encoded timestamps.<\/span><\/li>\n
    4. Dictionary based encoding<\/span><\/a><\/li>\n
    5. Existing document formats and standards disallow redesign<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      If your database and query language the query language doesn’t deal with the situation, you’ve to go through an elaborate redesign.\u00a0 In addition to simply accessing the information, how do you make the queries on JSON efficient when you <\/span>don\u2019t even know the name of the field you’ve to index on? <\/span> Fortunately,\u00a0Couchbase N1QL has a variety of query and index features to deal with flexible metadata as well. <\/span><\/p>\n

      Let’s consider these use cases.<\/span><\/p>\n

      Use Case 1: Value transformation.<\/h4>\n

      Here’s a sample JSON document.<\/p>\n

      {\r\n    \"cname\": \"Jane Smith\",\r\n    \"dob\" : \"1990-01-30\",\r\n    \"phones\" : [\r\n    \"+1 510-523-3529\", \"+1 650-392-4923\"\r\n    ],\r\n    \"billing\": [\r\n      {\r\n        \"type\": \"visa\",\r\n        \"cardnum\": \"5827-2842-2847-3909\",\r\n        \"expiry\": \"2019-03\"\r\n      }, \r\n      {\r\n        \"type\": \"master\",\r\n        \"cardnum\": \"6274-2542-5847-3949\",\r\n        \"expiry\": \"2018-12\"\r\n      }\r\n    ]\r\n}\r\n<\/pre>\n
      JSON data model is described simply as a set of key-value pairs.\u00a0 Each key is a string, unique at that its level of the hierarchy and value can be scalars, objects or arrays.\u00a0 A rigorous definition can be read <\/span>here<\/span><\/a>. JSON is also self-describing and that makes it flexible for a <\/span>database document model<\/span><\/a>. Not every customer has to have a fixed number of telephone numbers or cars or any other type of attributes.\u00a0<\/span><\/p>\n
      The same information above can be reorganized as the JSON below without loss of information, but some implicit sche<\/span><\/p>\n
      {\r\n    \"Jane Smith\": \"1990-01-30\",\r\n    \"home\": \"+1 510-523-3529\",\r\n    \"office\": \"+1 650-392-4923\",\r\n    \"Billing\": [\r\n      {\r\n        \"visa\": \"5827-2842-2847-3909\",\r\n        \"expiry\": \"2019-03\"\r\n      }, \r\n      {\r\n        \"master\": \"6274-2542-5847-3949\",\r\n        \"expiry\": \"2018-12\"\r\n      }\r\n    ]\r\n}\r\n<\/pre>\n
      This is all well and good if you\u2019re simply putting and setting the document.\u00a0 It doesn\u2019t matter what the structure of JSON. Simply schlep is back and forth.<\/span><\/p>\n
      Now, let\u2019s see how this affects querying.<\/span><\/p>\n
      Q1: SELECT * FROM customers WHERE cxname = \u201cJane Smith\u201d; <\/span><\/p>\n
      With the new JSON model, there isn\u2019t a field name called cxname<\/strong>\u00a0here.<\/span><\/p>\n
      Q2: SELECT p FROM people p \r\nWHERE ANY o IN object_names(p) SATISFIES o = \"Jane Smith\" END<\/pre>\n
      What the magic of <\/span>object_pairs()<\/span><\/a> function?\u00a0 This transforms the JSON {\u201ckey\u201d:\u201dvalue\u201d} pairs into an array of name-value pairs.\u00a0 Here\u2019s an example.<\/span><\/p>\n
      SELECT OBJECT_NAMES({\"Jane Smith\": \"1990-01-30\", \"home\": \"+1 510-523-3529\"})\r\n\r\n    \"$1\": [\r\n      \"Jane Smith\",\r\n      \"home\"\r\n    ]\r\n  }\r\n<\/pre>\n
      The OBJECT_NAMES() function extracts the key (here \u201cJane Smith\u201d) and returns as a value, which then can be indexed. Since the function returns not just one value, but an array of \u201ckey names\u201d as values, you need to create an array index.\u00a0 Queries Q1 and Q2 do the same job for the respective data model.\u00a0 But, we need each of those queries to execute in milliseconds.\u00a0<\/span><\/p>\n
      For Q1, we simply create the index on cxname.<\/span><\/p>\n
      CREATE INDEX ix_cxname ON customers(cxname)<\/span><\/p>\n
      For Q2,\u00a0\u00a0<\/span><\/p>\n
      CREATE INDEX ix_people ON people(DISTINCT OBJECT_NAMES(self))<\/span><\/p>\n
      With this index, for Q2, you’ll get a plan that uses the index.<\/p>\n
              {\r\n            \"#operator\": \"DistinctScan\",\r\n            \"scan\": {\r\n                \"#operator\": \"IndexScan3\",\r\n                \"as\": \"p\",\r\n                \"cardinality\": 1,\r\n                \"cost\": 0.273,\r\n                \"index\": \"ix_people\",\r\n                \"index_id\": \"4a2df8dd85543aa4\",\r\n                \"index_projection\": {\r\n                    \"primary_key\": true\r\n                },\r\n                \"keyspace\": \"people\",\r\n                \"namespace\": \"default\",\r\n                \"spans\": [\r\n                    {\r\n                        \"exact\": true,\r\n                        \"range\": [\r\n                            {\r\n                                \"high\": \"\\\"Jane Smith\\\"\",\r\n                                \"inclusion\": 3,\r\n                                \"low\": \"\\\"Jane Smith\\\"\"\r\n                            }\r\n                        ]\r\n                    }\r\n                ],<\/pre>\n
       <\/p>\n
      Use Case 2: Dynamic Key names.<\/h4>\n
      This is use case is taken from the Couchbase <\/span>forum post.<\/span><\/a><\/p>\n
      {\r\n    \"id\": \"05a9b954-bdee-4d7f-9715-8e9e08f8cb75\",\r\n    \"type\": \"article\",\r\n    \"translations\": {\r\n        \"en\": \"Hello\",\r\n        \"de\": \"Hallo\", \r\n        \"fr\": \"Bonjour\",\r\n        \"es\": \"Hola\"\r\n    }\r\n}<\/pre>\n
      Question:<\/strong> <\/span>What would be the best way to index the values within <\/span>translations<\/strong> dynamically<\/strong>? I.e. a generic index that indexes all keys within the <\/span>translations<\/span> object. <\/span><\/p>\n
      If the need is to simply query for English documents all the time,\u00a0 to <\/span>query all documents that have <\/span>translations.en = “Hello”<\/span>.<\/span><\/p>\n
      If you\u2019re always looking for translations to English, you can simply create the index on transactions.en.<\/span><\/p>\n
      CREATE INDEX ix_tren ON info(translations.en);\r\nSELECT * FROM info WHERE translation.en = \u201cHello\u201d;<\/pre>\n
      If the keys are dynamic, you don’t know what specific language is going to be in the data and which ones can be queries upon, you’ve to make them both dynamic.<\/p>\n
      \/* Query *\/\r\nSELECT *\r\nFROM info\r\n    WHERE  ANY v IN OBJECT_PAIRS(translations) \r\n            SATISFIES [v.name,v.val] = [\"en\", \"Hello\"] \r\n        END\r\n\r\n\/* Index *\/\r\nCREATE INDEX ix_infoname ON info (\r\n    DISTINCT ARRAY [v.name, v.val ]\r\n    FOR v IN OBJECT_PAIRS(translations) END\r\n)<\/pre>\n
      Here’s the explain to verify that the index is indeed picked up and the predicates are pushed down to the index scan.<\/p>\n
              {\r\n            \"#operator\": \"DistinctScan\",\r\n            \"scan\": {\r\n                \"#operator\": \"IndexScan3\",\r\n                \"cardinality\": 0.5,\r\n                \"cost\": 0.1665,\r\n                \"index\": \"ix_infoname\",\r\n                \"index_id\": \"bebbfd22a022fb75\",\r\n                \"index_projection\": {\r\n                    \"primary_key\": true\r\n                },\r\n                \"keyspace\": \"info\",\r\n                \"namespace\": \"default\",\r\n                \"spans\": [\r\n                    {\r\n                        \"exact\": true,\r\n                        \"range\": [\r\n                            {\r\n                                \"high\": \"[\\\"en\\\", \\\"Hello\\\"]\",\r\n                                \"inclusion\": 3,\r\n                                \"low\": \"[\\\"en\\\", \\\"Hello\\\"]\"\r\n                            }\r\n                        ]\r\n                    }\r\n                ],\r\n                \"using\": \"gsi\"\r\n            }\r\n        },<\/pre>\n
      Not to worry, if the index definition looks a bit more complicated than normal.\u00a0 The Index Advisor has your covered.<\/p>\n
      ADVISE SELECT * \r\n       FROM info \r\n       WHERE ANY v IN OBJECT_PAIRS(translations) \r\n                 SATISFIES [v.name,v.val] = [\"en\", \"Hello\"] \r\n       END\r\n\r\n\r\n{\r\n    \"index_statement\": \"CREATE INDEX adv_DISTINCT_object_pairs_translations_name_val ON `info`(DISTINCT ARRAY [`v`.`name`, `v`.`val`] FOR v in object_pairs((`translations`)) END)\",\r\n    \"keyspace_alias\": \"info\",\r\n    \"recommending_rule\": \"Index keys follow order of predicate types: 2. equality\/null\/missing.\"\r\n}<\/pre>\n
      You can even add expressions on top of each expression you’re evaluating.<\/p>\n
      ADVISE SELECT * \r\n       FROM info \r\n       WHERE ANY v IN OBJECT_PAIRS(translations) \r\n                 SATISFIES [LOWER(v.name),LOWER(v.val)] = [\"en\", \"Hello\"] \r\n       END\r\n\r\n\r\n{\r\n      \"index_statement\": \"CREATE INDEX adv_DISTINCT_object_pairs_translations_lower_name_lower_val ON `info`(DISTINCT ARRAY [lower((`v`.`name`)), lower((`v`.`val`))] FOR v in object_pairs((`translations`)) END)\",\r\n      \"keyspace_alias\": \"info\",\r\n      \"recommending_rule\": \"Index keys follow order of predicate types: 2. equality\/null\/missing.\"\r\n}<\/pre>\n
       <\/p>\n
      More Object Functions<\/strong><\/h4>\n
      N1QL has additional object<\/a> and nested data functions to help with complex data models.\u00a0 Check out the full set of object functions and the token functions<\/a>.<\/p>\n
      References:<\/b><\/p>\n
        \n
      1. Couchbae N1QL Object Functions Documentation<\/a><\/li>\n
      2. Couchbase Array indexing\u00a0<\/a><\/li>\n
      3. Couchbase index blog<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"
        Use N1QL when you’re in a JSON pickle. — Confucius For the JSON data model, the advice is to think of collections as tables, JSON document as denormalized rows and field names as columns – roughly. All this holds in […]<\/p>\n","protected":false},"author":55,"featured_media":8408,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1814,1815,2453,1812],"tags":[1447,1505,1261],"ppma_author":[8929],"class_list":["post-8407","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-application-design","category-best-practices-and-tutorials","category-global-secondary-index","category-n1ql-query","tag-data-modeling","tag-index","tag-json"],"acf":[],"yoast_head":"\nFlexible Query & Indexing for Flexible JSON Model. - 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\/flexible-query-indexing-for-flexible-json-model\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Flexible Query & Indexing for Flexible JSON Model.\" \/>\n<meta property=\"og:description\" content=\"Use N1QL when you’re in a JSON pickle. — Confucius For the JSON data model, the advice is to think of collections as tables, JSON document as denormalized rows and field names as columns – roughly. 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