![]() ![]() ![]() Almost everything I have searched for end up converting rows to arrays or other similar sounding but very different requests.Elements are expanded into rows in the order that they appear in the original array/bag. Select id1, ARRAY_MAGIC_CREATOR (c1, c2, c3) from Table. For example, if my dataset looks like this - COL_01 COL_02 COL_03 1 A, B X, Y, Z 2 D, E, F V, WI have tags inside my video_tags column for an example: cats dogs birds lizards and want to individually put them in an array to be used in another code for easy …what I would like is a query that gives me the result as 2 arrays so something like. ![]() You can do this using the UNNEST operator in the following way : SELECT timestamp, volt FROM table CROSS JOIN UNNEST (voltages) AS t (volt) Using the resultant table you can pivot (convert multiple …I'm trying to explode records in multiple columns in Hive. You need to first break down each array element into it's own row. This should serve your purpose if you have arrays of fixed length. Returns true if all the elements match the predicate (a special case is when the array is empty) false if one or more elements don’t match NULL if the predicate function returns NULL for one or more elements and true for all other elements. Returns whether all elements of an array match the given predicate. ![]() Array functions# all_match (array(T), function(T, boolean)) → boolean #. Copy Download SELECT arr FROM ( SELECT generate_series(1, array_upper(arr, 1)) AS i, arr FROM (SELECT ARRAY arr) t ) t Personally, if you will need to split (or explode) an array into rows, it is better to create a quick function that would do this for you.How to explode each row that is an Array into columns in Spark (Scala)? Hot Network Questions Attaching the query output below.df.createOrReplaceTempView("df") spark.sql("SELECT x._1, x._2 FROM df LATERAL VIEW explode(_1) t AS x") Share. SELECT DISTINCT COL_NAME FROM "DB"."SCHEMA"."TABLE, LATERAL FLATTEN (INPUT=>SPLIT (COL_NAME,' ')) But the output is not as expected. I have tried using the below SQL statement. Now I would like to split them into multiple rows for each value like. ![]()
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