spark sql中有一个函数叫作explode,类似于mysql中的unnest,这个函数用于将一行数据展开成多行 例如你有一张表,想要进行行列转换,可以使用下面的查询语句 select explode(array(col1,col2,col3,col4)) , col5, col6 from tb1 或者这样的查询语句 select explode(array(struct('col1',col1),struct('col2...
frompyspark.sqlimportSparkSessionfrompyspark.sql.functionsimportexplode,col# 创建 SparkSessionspark=SparkSession.builder.appName("Explode Example").getOrCreate()# 示例数据data=[(1,"Alice",["Reading","Traveling"]),(2,"Bob",["Music","Cooking"]),(3,"Charlie",["Sports"])]# 创建 DataFramedf=...
spark udtf自定义函数 spark explode函数 1.如何生成多行的序列 spark sql 提供的所有的函数的文档: https:///docs/3.1.2/api/sql/index.html 需求: 请生成一列数据, 内存为 1 , 2 , 3 , 4 ,5 -- 需求: 请生成一列数据, 内存为 1 , 2 , 3 , 4 ,5 select explode(split('1,2,3,4,5',...
sparksql类比于hive可以发现,hive在mapreduce上做了一个框架,而sparksql是在spark core里的rdd里面多出...
select id,explode(items)asitem from array_table;Error:Errorwhilecompiling statement:FAILED:SemanticException[Error10081]:UDTF's are not supported outside theSELECTclause,nor nestedinexpressions(state=42000,code=10081) 2. posexplode函数的用法与实例 ...
>SELECTelem,'Spark'FROMexplode(array(10,20))ASt(elem); 10 Spark 20 Spark >SELECTnum, val,'Spark'FROMexplode(map(1,'a',2,'b'))ASt(num, val); 1 a Spark 2 b Spark >SELECT*FROMexplode(array(1,2)),explode(array(3,4)); 1 3 1 4 2 3 2 4-- Using lateral correlation ...
適用於: Databricks SQL Databricks Runtime 12.2 LTS 和更新版本: SQL 複製 > SELECT elem, 'Spark' FROM explode_outer(array(10, 20)) AS t(elem); 10 Spark 20 Spark > SELECT num, val, 'Spark' FROM explode_outer(map(1, 'a', 2, 'b')) AS t(num, val); 1 a Spark 2 ...
from pyspark.sql import SparkSession spark = SparkSession.builder.appName("ExplodeDict").getOrCreate() Python Copy然后,我们可以读取示例数据集并显示其内容:data = [ (1, {"name": "Alice", "age": 25}), (2, {"name": "Bob", "age": 30}), (3, {"name": "Charlie", "age": 35...
(1, 'a', 2, 'b')) AS t(num, val); 1 a Spark 2 b Spark > SELECT * FROM explode(array(1, 2)), explode(array(3, 4)); 1 3 1 4 2 3 2 4 -- Using lateral correlation in Databricks 12.2 and above > SELECT * FROM explode(array(1, 2)) AS t, LATERAL explode(array(3 ...
from pyspark.sql.functions import explode, first, col, monotonically_increasing_id from pyspark.sql import Row df = spark.createDataFrame([ Row(dataCells=[Row(posx=0, posy=1, posz=.5, value=1.5, shape=[Row(_type='square', _len=1)]), ...