rdd | 易学教程

How to write Pyspark UDAF on multiple columns?

阅读更多关于 How to write Pyspark UDAF on multiple columns?

问题 I have the following data in a pyspark dataframe called end_stats_df : values start end cat1 cat2 10 1 2 A B 11 1 2 C B 12 1 2 D B 510 1 2 D C 550 1 2 C B 500 1 2 A B 80 1 3 A B And I want to aggregate it in the following way: I want to use the "start" and "end" columns as the aggregate keys For each group of rows, I need to do the following: Compute the unique number of values in both cat1 and cat2 for that group. e.g., for the group of start =1 and end =2, this number would be 4 because

How spark handles object

阅读更多关于 How spark handles object

问题 To test the Serialization exception in spark I wrote a task in 2 ways. First way: package examples import org.apache.spark.SparkConf import org.apache.spark.SparkContext object dd { def main(args: Array[String]):Unit = { val sparkConf = new SparkConf val sc = new SparkContext(sparkConf) val data = List(1,2,3,4,5) val rdd = sc.makeRDD(data) val result = rdd.map(elem => { funcs.func_1(elem) }) println(result.count()) } } object funcs{ def func_1(i:Int): Int = { i + 1 } } This way spark works

Is there a way to rewrite Spark RDD distinct to use mapPartitions instead of distinct?

阅读更多关于 Is there a way to rewrite Spark RDD distinct to use mapPartitions instead of distinct?

问题 I have an RDD that is too large to consistently perform a distinct statement without spurious errors (e.g. SparkException stage failed 4 times, ExecutorLostFailure, HDFS Filesystem closed, Max number of executor failures reached, Stage cancelled because SparkContext was shut down, etc.) I am trying to count distinct IDs in a particular column, for example: print(myRDD.map(a => a._2._1._2).distinct.count()) is there an easy, consistent, less-shuffle-intensive way to do the command above,

scala spark dataframe explode is slow - so, alternate method - create columns and rows from arrays in a column

阅读更多关于 scala spark dataframe explode is slow - so, alternate method - create columns and rows from arrays in a column

问题 Scala 2.11.8, spark 2.0.1 The explode function is very slow - so, looking for an alternate method. I think it is possible with RDD's with flatmap - and, help is greatly appreciated. I have an udf that returns List(String, String, String, Int) of varying lengths. For each row in the dataframe, I want to create multiple rows, and make multiple columns. def Udf = udf ( (s: String ) => { if (s=="1") Seq(("a", "b", "c", 0), ("a1", "b1", "c1", 1), ("a2", "b2", "c2", 2)).toList else Seq(("a", "b",

scala spark dataframe explode is slow - so, alternate method - create columns and rows from arrays in a column

阅读更多关于 scala spark dataframe explode is slow - so, alternate method - create columns and rows from arrays in a column

How to force Spark to evaluate DataFrame operations inline

阅读更多关于 How to force Spark to evaluate DataFrame operations inline

问题 According to the Spark RDD docs: All transformations in Spark are lazy, in that they do not compute their results right away...This design enables Spark to run more efficiently. There are times when I need to do certain operations on my dataframes right then and now . But because dataframe ops are " lazily evaluated " (per above), when I write these operations in the code, there's very little guarantee that Spark will actually execute those operations inline with the rest of the code. For

Why does RDD.foreach fail with “SparkException: This RDD lacks a SparkContext”?

阅读更多关于 Why does RDD.foreach fail with “SparkException: This RDD lacks a SparkContext”?

问题 I have a dataset (as an RDD ) that I divide into 4 RDDs by using different filter operators. val RSet = datasetRdd. flatMap(x => RSetForAttr(x, alLevel, hieDict)). map(x => (x, 1)). reduceByKey((x, y) => x + y) val Rp:RDD[(String, Int)] = RSet.filter(x => x._1.split(",")(0).equals("Rp")) val Rc:RDD[(String, Int)] = RSet.filter(x => x._1.split(",")(0).equals("Rc")) val RpSv:RDD[(String, Int)] = RSet.filter(x => x._1.split(",")(0).equals("RpSv")) val RcSv:RDD[(String, Int)] = RSet.filter(x => x

Why does RDD.foreach fail with “SparkException: This RDD lacks a SparkContext”?

阅读更多关于 Why does RDD.foreach fail with “SparkException: This RDD lacks a SparkContext”?

0905-广告点击量实时统计

阅读更多关于 0905-广告点击量实时统计

0905-广告点击量实时统计需求七：实时维护黑名单 7.1 需求概述 7.2 简要运行流程 7.3 具体运行流程 7.4 代码实现 7.4.1 加载并转换用户数据集 7.4.2 过滤掉已经上黑名单的用户 7.4.3 对实时数据进行统计,更新点击次数表 7.4.4 添加异常用户需求八：各省各城市广告点击量实时统计 8.1 需求概述 8.2 简要运行流程 8.3 具体运行流程 8.4 代码实现 8.4.1 转换key值 8.4.2 进行聚合 8.4.3 封装case class 并入库需求九：每天每个省份Top3热门广告 9.1 需求概述 9.2 简要运行流程 9.3 具体运行流程 9.4 代码实现 9.4.1 封装key 9.4.2 聚合 9.4.3 转换格式 9.4.4 创建临时表并执行查询 9.4.5 封装case class并入库需求十：最近一小时广告点击量实时统计 10.1 需求概述 10.2 简要运行流程 10.3 具体运行流程 10.4 代码实现 10.4.1 封装key 10.4.2 使用窗口操作计算 10.4.4 封装case class并入库小结需求七：实时维护黑名单 7.1 需求概述从Kafka获取实时数据，对每个用户的点击次数进行累加并写入MySQL，当一天之内一个用户对一个广告的点击次数超过100次时，将用户加入黑名单中。 7.2 简要运行流程

Spark进阶

阅读更多关于 Spark进阶

Spack进阶 1.RDD的依赖关系窄依赖(不产生shuffle) 父RDD和子RDD的patiition之间的关系是一对一,或者是多对一的关系宽依赖(会有shuffle产生(类似中间结果,会影响计算效率)) 父RDD和子RDD的patition之间的关系是多对一. 宽依赖与窄依赖示意图 2.stage 过程 spark会根据RDD之间的依赖关系,形成一个有向无环图(DAG),DAG会提交给DAGscheduler,DAGScheduler会将DAG划分为多个相互依赖的stage,划分规则就是从后往前遇到宽依赖就切割stage,每一个stage里包含task,将这些task以taskset的形式传给taskScheduler运行, 切割规则从后往前遇到宽依赖就切割成stage stage的计算模式 pipeline管道计算模式,(一条路走到黑) stage的注意点管道里面的数据说明时候可以落地呢当RDD进行持久化时当shuffle write时 stage的task的并行度由stage的最后一个RDD来决定的如何改变RDD的分区数呢 reduceByKey(XXX,3),GroupByKey(4) 3.spark的资源调度与任务调度以standalone的client为例启动集群后,worker节点向Master节点发送本机的资源情况.

订阅 rdd