zhongxiang, 2,liuxiangqian, 3,baweining) scala> val infoRDD = sc.parallelize(infoList) infoRDD: org.apache.spark.rdd.RDD[String] = ParallelCollectionRDD[31] at parallelize at <console>:29 scala> val infoPairRDD
* Spark笔记之使用UDF(User Define Function) * 2.1 在SQL语句中使用UDF * 2.2 直接对列应用UDF(脱离sql) * 2.3 scala-处理Spark UDF中的所有列/整行 * * https://dzone.com/articles/how-to-use-udf-in-spark-without-register-them * How to Use UDF in Spark Without Register Them * This article...
org.apache.spark.rdd.RDD#treeAggregate with a parameter to do the final aggregation on the executor def treeAggregate[U](zeroValue: U)(seqOp: (U, T) ⇒ U, combOp: (U, U) ⇒ U, depth: Int = 2)(implicit arg0: ClassTag[U]): U Aggregates the elements of this RDD in a mu...
Spark SQL是Apache Spark中的一个模块,它提供了一种用于处理结构化数据的高级数据处理接口。UNION ALL操作是Spark SQL中的一个关系操作,用于将两个或多个具有相同结构的数据集合并为一个结果集,同时保留重复的行。 UNION ALL操作的语法如下: 代码语言:txt 复制 SELECT column1, column2, ... FROM table1 UNION...
(CheckAnalysis.scala:293)atorg.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$12$$anonfun$apply$13.apply(CheckAnalysis.scala:290)atscala.collection.immutable.List.foreach(List.scala:392)atorg.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$check...
scala>val mappedRDD = rdd.map(2*_) mappedRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[1] at map at <console>:23 scala>mappedRDD.collect 得到 res0: Array[Int] = Array(2, 4, 6, 8, 10) scala> scala>val filteredRDD = mappedRDD.filter(_ > 4) ...
我们使用union all即可。那么这样我们就可以输出重复的值了： SELECT name1 FROM table1 UNION ...
通过SparkSQL,对两个存在map类型字段的Hive表进行union操作,报如下错误: org.apache.spark.sql.AnalysisException: Cannot have map type columns in DataFrame which calls set operations(intersect, except, etc.), but the type of column map is map<string,string>; 1. 场景模拟 1)通过函数str_to_map/ma...
In this Spark article, you will learn how to union two or more tables of the same schema which are from different Hive databases with Scala examples. Advertisements First, let’s create two tables with the same schema in different Hive databases. To create tables we need hive in this proces...
scala>val mappedRDD = rdd.map(2*_) mappedRDD: org.apache.spark.rdd.RDD[Int] = MapPartitionsRDD[1] at map at <console>:23 scala>mappedRDD.collect 得到 res0: Array[Int] = Array(2, 4, 6, 8, 10) scala> scala>val filteredRDD = mappedRDD.filter(_ > 4) ...