| "use strict";(self.webpackChunk=self.webpackChunk||[]).push([[7530],{15680:(e,a,n)=>{n.d(a,{xA:()=>y,yg:()=>d});var r=n(96540);function t(e,a,n){return a in e?Object.defineProperty(e,a,{value:n,enumerable:!0,configurable:!0,writable:!0}):e[a]=n,e}function l(e,a){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);a&&(r=r.filter((function(a){return Object.getOwnPropertyDescriptor(e,a).enumerable}))),n.push.apply(n,r)}return n}function i(e){for(var a=1;a<arguments.length;a++){var n=null!=arguments[a]?arguments[a]:{};a%2?l(Object(n),!0).forEach((function(a){t(e,a,n[a])})):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):l(Object(n)).forEach((function(a){Object.defineProperty(e,a,Object.getOwnPropertyDescriptor(n,a))}))}return e}function s(e,a){if(null==e)return{};var n,r,t=function(e,a){if(null==e)return{};var n,r,t={},l=Object.keys(e);for(r=0;r<l.length;r++)n=l[r],a.indexOf(n)>=0||(t[n]=e[n]);return t}(e,a);if(Object.getOwnPropertySymbols){var l=Object.getOwnPropertySymbols(e);for(r=0;r<l.length;r++)n=l[r],a.indexOf(n)>=0||Object.prototype.propertyIsEnumerable.call(e,n)&&(t[n]=e[n])}return t}var o=r.createContext({}),g=function(e){var a=r.useContext(o),n=a;return e&&(n="function"==typeof e?e(a):i(i({},a),e)),n},y=function(e){var a=g(e.components);return r.createElement(o.Provider,{value:a},e.children)},u="mdxType",p={inlineCode:"code",wrapper:function(e){var a=e.children;return r.createElement(r.Fragment,{},a)}},m=r.forwardRef((function(e,a){var n=e.components,t=e.mdxType,l=e.originalType,o=e.parentName,y=s(e,["components","mdxType","originalType","parentName"]),u=g(n),m=t,d=u["".concat(o,".").concat(m)]||u[m]||p[m]||l;return n?r.createElement(d,i(i({ref:a},y),{},{components:n})):r.createElement(d,i({ref:a},y))}));function d(e,a){var n=arguments,t=a&&a.mdxType;if("string"==typeof e||t){var l=n.length,i=new Array(l);i[0]=m;var s={};for(var o in a)hasOwnProperty.call(a,o)&&(s[o]=a[o]);s.originalType=e,s[u]="string"==typeof e?e:t,i[1]=s;for(var g=2;g<l;g++)i[g]=n[g];return r.createElement.apply(null,i)}return r.createElement.apply(null,n)}m.displayName="MDXCreateElement"},53743:(e,a,n)=>{n.r(a),n.d(a,{assets:()=>y,contentTitle:()=>o,default:()=>d,frontMatter:()=>s,metadata:()=>g,toc:()=>u});var r=n(58168),t=n(98587),l=(n(96540),n(15680)),i=["components"],s={id:"arrays",title:"Arrays"},o=void 0,g={unversionedId:"querying/arrays",id:"querying/arrays",title:"Arrays",description:"\x3c!--",source:"@site/docs/latest/querying/arrays.md",sourceDirName:"querying",slug:"/querying/arrays",permalink:"/docs/latest/querying/arrays",draft:!1,tags:[],version:"current",frontMatter:{id:"arrays",title:"Arrays"},sidebar:"docs",previous:{title:"Multi-value dimensions",permalink:"/docs/latest/querying/multi-value-dimensions"},next:{title:"Nested columns",permalink:"/docs/latest/querying/nested-columns"}},y={},u=[{value:"Ingesting arrays",id:"ingesting-arrays",level:2},{value:"Native batch and streaming ingestion",id:"native-batch-and-streaming-ingestion",level:3},{value:"SQL-based ingestion",id:"sql-based-ingestion",level:3},{value:"SQL-based ingestion with rollup",id:"sql-based-ingestion-with-rollup",level:3},{value:"Querying arrays",id:"querying-arrays",level:2},{value:"Filtering",id:"filtering",level:3},{value:"Example: equality",id:"example-equality",level:4},{value:"Example: null",id:"example-null",level:4},{value:"Example: range",id:"example-range",level:4},{value:"Example: ARRAY_CONTAINS",id:"example-array_contains",level:4},{value:"Grouping",id:"grouping",level:3},{value:"Example: SQL grouping query with no filtering",id:"example-sql-grouping-query-with-no-filtering",level:4},{value:"Example: SQL grouping query with a filter",id:"example-sql-grouping-query-with-a-filter",level:4},{value:"Example: UNNEST",id:"example-unnest",level:4},{value:"Differences between arrays and multi-value dimensions",id:"differences-between-arrays-and-multi-value-dimensions",level:2}],p={toc:u},m="wrapper";function d(e){var a=e.components,n=(0,t.A)(e,i);return(0,l.yg)(m,(0,r.A)({},p,n,{components:a,mdxType:"MDXLayout"}),(0,l.yg)("p",null,"Apache Druid supports SQL standard ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY")," typed columns for ",(0,l.yg)("inlineCode",{parentName:"p"},"VARCHAR"),", ",(0,l.yg)("inlineCode",{parentName:"p"},"BIGINT"),", and ",(0,l.yg)("inlineCode",{parentName:"p"},"DOUBLE")," types (native types ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY<STRING>"),", ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY<LONG>"),", and ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY<DOUBLE>"),"). Other more complicated ARRAY types must be stored in ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/nested-columns"},"nested columns"),". Druid ARRAY types are distinct from ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/multi-value-dimensions"},"multi-value dimension"),", which have significantly different behavior than standard arrays."),(0,l.yg)("p",null,"This document describes inserting, filtering, and grouping behavior for ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY")," typed columns.\nRefer to the ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/sql-data-types#arrays"},"Druid SQL data type documentation")," and ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/sql-array-functions"},"SQL array function reference")," for additional details\nabout the functions available to use with ARRAY columns and types in SQL."),(0,l.yg)("p",null,"The following sections describe inserting, filtering, and grouping behavior based on the following example data, which includes 3 array typed columns:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"timestamp": "2023-01-01T00:00:00", "label": "row1", "arrayString": ["a", "b"], "arrayLong":[1, null,3], "arrayDouble":[1.1, 2.2, null]}\n{"timestamp": "2023-01-01T00:00:00", "label": "row2", "arrayString": [null, "b"], "arrayLong":null, "arrayDouble":[999, null, 5.5]}\n{"timestamp": "2023-01-01T00:00:00", "label": "row3", "arrayString": [], "arrayLong":[1, 2, 3], "arrayDouble":[null, 2.2, 1.1]} \n{"timestamp": "2023-01-01T00:00:00", "label": "row4", "arrayString": ["a", "b"], "arrayLong":[1, 2, 3], "arrayDouble":[]}\n{"timestamp": "2023-01-01T00:00:00", "label": "row5", "arrayString": null, "arrayLong":[], "arrayDouble":null}\n')),(0,l.yg)("h2",{id:"ingesting-arrays"},"Ingesting arrays"),(0,l.yg)("h3",{id:"native-batch-and-streaming-ingestion"},"Native batch and streaming ingestion"),(0,l.yg)("p",null,"When using native ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/native-batch"},"batch")," or streaming ingestion such as with ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/kafka-ingestion"},"Apache Kafka"),", arrays can be ingested using the ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/ingestion-spec#dimension-objects"},(0,l.yg)("inlineCode",{parentName:"a"},'"auto"'))," type dimension schema which is shared with ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/schema-design#type-aware-schema-discovery"},"type-aware schema discovery"),"."),(0,l.yg)("p",null,"When ingesting from TSV or CSV data, you can specify the array delimiters using the ",(0,l.yg)("inlineCode",{parentName:"p"},"listDelimiter")," field in the ",(0,l.yg)("inlineCode",{parentName:"p"},"inputFormat"),". JSON data must be formatted as a JSON array to be ingested as an array type. JSON data does not require ",(0,l.yg)("inlineCode",{parentName:"p"},"inputFormat")," configuration."),(0,l.yg)("p",null,"The following shows an example ",(0,l.yg)("inlineCode",{parentName:"p"},"dimensionsSpec")," for native ingestion of the data used in this document:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre"},'"dimensions": [\n {\n "type": "auto",\n "name": "label"\n },\n {\n "type": "auto",\n "name": "arrayString"\n },\n {\n "type": "auto",\n "name": "arrayLong"\n },\n {\n "type": "auto",\n "name": "arrayDouble"\n }\n],\n')),(0,l.yg)("h3",{id:"sql-based-ingestion"},"SQL-based ingestion"),(0,l.yg)("p",null,"Arrays can also be inserted with ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/multi-stage-query/"},"SQL-based ingestion")," when you include a query context parameter ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/multi-stage-query/reference#context-parameters"},(0,l.yg)("inlineCode",{parentName:"a"},'"arrayIngestMode":"array"')),"."),(0,l.yg)("p",null,"For example, to insert the data used in this document:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'REPLACE INTO "array_example" OVERWRITE ALL\nWITH "ext" AS (\n SELECT *\n FROM TABLE(\n EXTERN(\n \'{"type":"inline","data":"{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row1\\", \\"arrayString\\": [\\"a\\", \\"b\\"], \\"arrayLong\\":[1, null,3], \\"arrayDouble\\":[1.1, 2.2, null]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row2\\", \\"arrayString\\": [null, \\"b\\"], \\"arrayLong\\":null, \\"arrayDouble\\":[999, null, 5.5]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row3\\", \\"arrayString\\": [], \\"arrayLong\\":[1, 2, 3], \\"arrayDouble\\":[null, 2.2, 1.1]} \\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row4\\", \\"arrayString\\": [\\"a\\", \\"b\\"], \\"arrayLong\\":[1, 2, 3], \\"arrayDouble\\":[]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row5\\", \\"arrayString\\": null, \\"arrayLong\\":[], \\"arrayDouble\\":null}"}\',\n \'{"type":"json"}\',\n \'[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]\'\n )\n )\n)\nSELECT\n TIME_PARSE("timestamp") AS "__time",\n "label",\n "arrayString",\n "arrayLong",\n "arrayDouble"\nFROM "ext"\nPARTITIONED BY DAY\n')),(0,l.yg)("h3",{id:"sql-based-ingestion-with-rollup"},"SQL-based ingestion with rollup"),(0,l.yg)("p",null,"These input arrays can also be grouped for rollup:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'REPLACE INTO "array_example_rollup" OVERWRITE ALL\nWITH "ext" AS (\n SELECT *\n FROM TABLE(\n EXTERN(\n \'{"type":"inline","data":"{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row1\\", \\"arrayString\\": [\\"a\\", \\"b\\"], \\"arrayLong\\":[1, null,3], \\"arrayDouble\\":[1.1, 2.2, null]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row2\\", \\"arrayString\\": [null, \\"b\\"], \\"arrayLong\\":null, \\"arrayDouble\\":[999, null, 5.5]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row3\\", \\"arrayString\\": [], \\"arrayLong\\":[1, 2, 3], \\"arrayDouble\\":[null, 2.2, 1.1]} \\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row4\\", \\"arrayString\\": [\\"a\\", \\"b\\"], \\"arrayLong\\":[1, 2, 3], \\"arrayDouble\\":[]}\\n{\\"timestamp\\": \\"2023-01-01T00:00:00\\", \\"label\\": \\"row5\\", \\"arrayString\\": null, \\"arrayLong\\":[], \\"arrayDouble\\":null}"}\',\n \'{"type":"json"}\',\n \'[{"name":"timestamp", "type":"STRING"},{"name":"label", "type":"STRING"},{"name":"arrayString", "type":"ARRAY<STRING>"},{"name":"arrayLong", "type":"ARRAY<LONG>"},{"name":"arrayDouble", "type":"ARRAY<DOUBLE>"}]\'\n )\n )\n)\nSELECT\n TIME_PARSE("timestamp") AS "__time",\n "label",\n "arrayString",\n "arrayLong",\n "arrayDouble",\n COUNT(*) as "count"\nFROM "ext"\nGROUP BY 1,2,3,4,5\nPARTITIONED BY DAY\n')),(0,l.yg)("h2",{id:"querying-arrays"},"Querying arrays"),(0,l.yg)("h3",{id:"filtering"},"Filtering"),(0,l.yg)("p",null,"All query types, as well as ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/aggregations#filtered-aggregator"},"filtered aggregators"),", can filter on array typed columns. Filters follow these rules for array types:"),(0,l.yg)("ul",null,(0,l.yg)("li",{parentName:"ul"},"All filters match against the entire array value for the row"),(0,l.yg)("li",{parentName:"ul"},"Native value filters like ",(0,l.yg)("a",{parentName:"li",href:"/docs/latest/querying/filters#equality-filter"},"equality")," and ",(0,l.yg)("a",{parentName:"li",href:"/docs/latest/querying/filters#range-filter"},"range")," match on entire array values, as do SQL constructs that plan into these native filters"),(0,l.yg)("li",{parentName:"ul"},"The ",(0,l.yg)("a",{parentName:"li",href:"/docs/latest/querying/filters#null-filter"},(0,l.yg)("inlineCode",{parentName:"a"},"IS NULL"))," filter will match rows where the entire array value is null"),(0,l.yg)("li",{parentName:"ul"},(0,l.yg)("a",{parentName:"li",href:"/docs/latest/querying/sql-array-functions"},"Array specific functions")," like ",(0,l.yg)("inlineCode",{parentName:"li"},"ARRAY_CONTAINS")," and ",(0,l.yg)("inlineCode",{parentName:"li"},"ARRAY_OVERLAP")," follow the behavior specified by those functions"),(0,l.yg)("li",{parentName:"ul"},"All other filters do not directly support ARRAY types and will result in a query error")),(0,l.yg)("h4",{id:"example-equality"},"Example: equality"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'SELECT *\nFROM "array_example"\nWHERE arrayLong = ARRAY[1,2,3]\n')),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"__time":"2023-01-01T00:00:00.000Z","label":"row3","arrayString":"[]","arrayLong":"[1,2,3]","arrayDouble":"[null,2.2,1.1]"}\n{"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\\"a\\",\\"b\\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"}\n')),(0,l.yg)("h4",{id:"example-null"},"Example: null"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'SELECT *\nFROM "array_example"\nWHERE arrayLong IS NULL\n')),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"__time":"2023-01-01T00:00:00.000Z","label":"row2","arrayString":"[null,\\"b\\"]","arrayLong":null,"arrayDouble":"[999.0,null,5.5]"}\n')),(0,l.yg)("h4",{id:"example-range"},"Example: range"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},"SELECT *\nFROM \"array_example\"\nWHERE arrayString >= ARRAY['a','b']\n")),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"__time":"2023-01-01T00:00:00.000Z","label":"row1","arrayString":"[\\"a\\",\\"b\\"]","arrayLong":"[1,null,3]","arrayDouble":"[1.1,2.2,null]"}\n{"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\\"a\\",\\"b\\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"}\n')),(0,l.yg)("h4",{id:"example-array_contains"},"Example: ARRAY_CONTAINS"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},"SELECT *\nFROM \"array_example\"\nWHERE ARRAY_CONTAINS(arrayString, 'a')\n")),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"__time":"2023-01-01T00:00:00.000Z","label":"row1","arrayString":"[\\"a\\",\\"b\\"]","arrayLong":"[1,null,3]","arrayDouble":"[1.1,2.2,null]"}\n{"__time":"2023-01-01T00:00:00.000Z","label":"row4","arrayString":"[\\"a\\",\\"b\\"]","arrayLong":"[1,2,3]","arrayDouble":"[]"}\n')),(0,l.yg)("h3",{id:"grouping"},"Grouping"),(0,l.yg)("p",null,"When grouping on an array with SQL or a native ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/groupbyquery"},"groupBy query"),", grouping follows standard SQL behavior and groups on the entire array as a single value. The ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/sql#unnest"},(0,l.yg)("inlineCode",{parentName:"a"},"UNNEST"))," function allows grouping on the individual array elements."),(0,l.yg)("h4",{id:"example-sql-grouping-query-with-no-filtering"},"Example: SQL grouping query with no filtering"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'SELECT label, arrayString\nFROM "array_example"\nGROUP BY 1,2\n')),(0,l.yg)("p",null,"results in:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"label":"row1","arrayString":"[\\"a\\",\\"b\\"]"}\n{"label":"row2","arrayString":"[null,\\"b\\"]"}\n{"label":"row3","arrayString":"[]"}\n{"label":"row4","arrayString":"[\\"a\\",\\"b\\"]"}\n{"label":"row5","arrayString":null}\n')),(0,l.yg)("h4",{id:"example-sql-grouping-query-with-a-filter"},"Example: SQL grouping query with a filter"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'SELECT label, arrayString\nFROM "array_example"\nWHERE arrayLong = ARRAY[1,2,3]\nGROUP BY 1,2\n')),(0,l.yg)("p",null,"results:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"label":"row3","arrayString":"[]"}\n{"label":"row4","arrayString":"[\\"a\\",\\"b\\"]"}\n')),(0,l.yg)("h4",{id:"example-unnest"},"Example: UNNEST"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},'SELECT label, strings\nFROM "array_example" CROSS JOIN UNNEST(arrayString) as u(strings)\nGROUP BY 1,2\n')),(0,l.yg)("p",null,"results:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-json",metastring:"lines",lines:!0},'{"label":"row1","strings":"a"}\n{"label":"row1","strings":"b"}\n{"label":"row2","strings":null}\n{"label":"row2","strings":"b"}\n{"label":"row4","strings":"a"}\n{"label":"row4","strings":"b"}\n')),(0,l.yg)("h2",{id:"differences-between-arrays-and-multi-value-dimensions"},"Differences between arrays and multi-value dimensions"),(0,l.yg)("p",null,"Avoid confusing string arrays with ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/multi-value-dimensions"},"multi-value dimensions"),". Arrays and multi-value dimensions are stored in different column types, and query behavior is different. You can use the functions ",(0,l.yg)("inlineCode",{parentName:"p"},"MV_TO_ARRAY")," and ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY_TO_MV")," to convert between the two if needed. In general, we recommend using arrays whenever possible, since they are a newer and more powerful feature and have SQL compliant behavior."),(0,l.yg)("p",null,"Use care during ingestion to ensure you get the type you want."),(0,l.yg)("p",null,"To get arrays when performing an ingestion using JSON ingestion specs, such as ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/native-batch"},"native batch")," or streaming ingestion such as with ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/ingestion/kafka-ingestion"},"Apache Kafka"),", use dimension type ",(0,l.yg)("inlineCode",{parentName:"p"},"auto")," or enable ",(0,l.yg)("inlineCode",{parentName:"p"},"useSchemaDiscovery"),". When performing a ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/multi-stage-query/"},"SQL-based ingestion"),", write a query that generates arrays and set the context parameter ",(0,l.yg)("inlineCode",{parentName:"p"},'"arrayIngestMode": "array"'),". Arrays may contain strings or numbers."),(0,l.yg)("p",null,"To get multi-value dimensions when performing an ingestion using JSON ingestion specs, use dimension type ",(0,l.yg)("inlineCode",{parentName:"p"},"string")," and do not enable ",(0,l.yg)("inlineCode",{parentName:"p"},"useSchemaDiscovery"),". When performing a ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/multi-stage-query/"},"SQL-based ingestion"),", wrap arrays in ",(0,l.yg)("a",{parentName:"p",href:"/docs/latest/querying/multi-value-dimensions#sql-based-ingestion"},(0,l.yg)("inlineCode",{parentName:"a"},"ARRAY_TO_MV")),", which ensures you get multi-value dimensions in any ",(0,l.yg)("inlineCode",{parentName:"p"},"arrayIngestMode"),". Multi-value dimensions can only contain strings."),(0,l.yg)("p",null,"You can tell which type you have by checking the ",(0,l.yg)("inlineCode",{parentName:"p"},"INFORMATION_SCHEMA.COLUMNS")," table, using a query like:"),(0,l.yg)("pre",null,(0,l.yg)("code",{parentName:"pre",className:"language-sql"},"SELECT COLUMN_NAME, DATA_TYPE\nFROM INFORMATION_SCHEMA.COLUMNS\nWHERE TABLE_NAME = 'mytable'\n")),(0,l.yg)("p",null,"Arrays are type ",(0,l.yg)("inlineCode",{parentName:"p"},"ARRAY"),", multi-value strings are type ",(0,l.yg)("inlineCode",{parentName:"p"},"VARCHAR"),"."))}d.isMDXComponent=!0}}]); |