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首先,我们需要注册DataFrame为临时视图,然后使用Hive SQL语句来实现这个转换过程。 // 注册DataFrame为临时视图df.createOrReplaceTempView("my_table")// 使用Hive SQL语句提取数组并使用explode函数转为多行valresult=spark.sql("SELECT name, age, explode(hobbies) as hobby FROM my_table")result.show() 1. 2...
collect array: Array[org.apache.spark.sql.Row] = Array([zhangsan,30], [lisi,40]) 注意:此时得到的RDD存储类型为Row scala> array(0) res28: org.apache.spark.sql.Row = [zhangsan,30] scala> array(0)(0) res29: Any = zhangsan scala> array(0).getAs[String]("name") res30: String ...
Error in SQL statement: AnalysisException: [DATATYPE_MISMATCH.ARRAY_FUNCTION_DIFF_TYPES] Cannot resolve "array_append(courses, courses)" due to data type mismatch: 错误在SQL语句:分析异常: [DATATYPE_MISMATCH.ARRAY_FUNCTION_DIFF_TYPESJ由于数据类型不匹配,无法解析array_append(课程、课程) select t1.na...
创建RDDval lineRDD=sc.textFile("hdfs://node01:8020/person.txt").map(_.split(" "))//RDD[Array[String]]3.定义caseclass(相当于表的schema)caseclassPerson(id:Int,name:String,age:Int)4.将RDD和caseclass关联 val personRDD=lineRDD.map(x=>Person(x(0).toInt,x(1),x(2).toInt))//RDD...
-- Spark 3.0 中,STRING_AGG 函数被引入作为 SQL:2016 标准的一部分。你可以使用 STRING_AGG 函数将每个分组的数据拼接成一个字符串。 select name, string_agg(courses, ',') as courses from student group by name; 踩坑1 其实我先是在 Excel 中自己弄成了 ,结果没有注意,courses2是字符串类型。而...
CustomParquetRelation(path: String)(@transient val sqlContext: SQLContext)extends BaseRelation with PrunedFilteredScan with InsertableRelation {private val df = sqlContext.read.parquet(path)override def schema: StructType = df.schemaoverride def buildScan(requiredColumns: Array[String], filters: Array...
spark.sql(“selectappopen[0]fromappopentable“) struct组合map array 结构 1.hive建表语句 droptableappopendetail;createtableifnotexistsappopendetail ( username String, appname String, opencountINT)rowformat delimited fields terminatedby'|'location'/hive/table/appopendetail';createtableifnotexistsappop...
b_gen: (i:Int)Bscala>valdata = (1to10).map(b_gen) scala>valdf = spark.createDataFrame(data) df: org.apache.spark.sql.DataFrame= [c: array<struct>, d: map<string,struct> ...2more fields] scala> df.show +---+---+---+-...