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 = infoRDD.map(line => (line.split(",")(...
* 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...
Analysis$$anonfun$checkAnalysis$1$$anonfun$apply$12.apply(CheckAnalysis.scala:279)atscala.collection.immutable.List.foreach(List.scala:392)atorg.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1.apply(CheckAnalysis.scala:279)atorg.apache.spark.sql.catalyst.analysis.CheckAnalysis...
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) ...
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...
问使用Spark SQL执行UNION ALL操作EN如果我们要查询table1表和 table2表中的 name1的值，但是不存在...
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...
SPARK-29358 added support for `unionByName` to work when the two datasets didn't necessarily have the same schema, but it does not work with nested columns like structs. This patch adds the support to work with struct columns. The behavior before this PR: ```scala scala> val df1 = ...
import org.apache.spark.Loggingimport scala.reflect.ClassTagimport org.apache.spark.storage.StorageLevelclass RMQReceiver[T: ClassTag] extends Receiver[String](StorageLevel.MEMORY_AND_DISK_SER_2) with Logging {def fromBytes(x: Array[Byte]) = new String(x, "UTF-8")...