rdd | 易学教程

How to find median and quantiles using Spark

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问题 How can I find median of an RDD of integers using a distributed method, IPython, and Spark? The RDD is approximately 700,000 elements and therefore too large to collect and find the median. This question is similar to this question. However, the answer to the question is using Scala, which I do not know. How can I calculate exact median with Apache Spark? Using the thinking for the Scala answer, I am trying to write a similar answer in Python. I know I first want to sort the RDD . I do not

Difference between DataFrame, Dataset, and RDD in Spark

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问题 I\'m just wondering what is the difference between an RDD and DataFrame (Spark 2.0.0 DataFrame is a mere type alias for Dataset[Row] ) in Apache Spark? Can you convert one to the other? 回答1: A DataFrame is defined well with a google search for "DataFrame definition": A data frame is a table, or two-dimensional array-like structure, in which each column contains measurements on one variable, and each row contains one case. So, a DataFrame has additional metadata due to its tabular format,

How does HashPartitioner work?

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问题 I read up on the documentation of HashPartitioner. Unfortunately nothing much was explained except for the API calls. I am under the assumption that HashPartitioner partitions the distributed set based on the hash of the keys. For example if my data is like (1,1), (1,2), (1,3), (2,1), (2,2), (2,3) So partitioner would put this into different partitions with same keys falling in the same partition. However I do not understand the significance of the constructor argument new HashPartitoner

How to convert rdd object to dataframe in spark

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问题 How can I convert an RDD ( org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] ) to a Dataframe org.apache.spark.sql.DataFrame . I converted a dataframe to rdd using .rdd . After processing it I want it back in dataframe. How can I do this ? 回答1: SqlContext has a number of createDataFrame methods that create a DataFrame given an RDD . I imagine one of these will work for your context. For example: def createDataFrame(rowRDD: RDD[Row], schema: StructType): DataFrame Creates a DataFrame from an

spark阶段测试题

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1、map和flatMap的区别理解 val words: RDD[String] = lines.flatMap(_.split(",")) 2、reduce和reduceByKey 理解 val reduced: RDD[(String, Int)] = wordAndOne.reduceByKey((x,y) => {x + y}) val reduced:RDD[(String, Int)] = wordAndOne.reduceByKey( + ) 尝试用reduce方法实现reduceByKey的功能 3、sortBy和sortByKey 理解 reduced.sortBy(_._2, false) 4、collect方法 5、sparkstream关键点在于读数据 val lines = ssc.socketTextStream(“hdp-1”, 9999) 来源： CSDN 作者： lucasmaluping 链接： https://blog.csdn.net/lucasmaluping/article/details/103222152

SparkStreaming的实战案例

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废话不多说，直接上干货！！！相关依赖： <properties> <project.build.sourceEncoding>UTF8</project.build.sourceEncoding> <maven.compiler.source>1.8</maven.compiler.source> <maven.compiler.target>1.8</maven.compiler.target> <encoding>UTF-8</encoding> <scala.version>2.11.8</scala.version> <spark.version>2.3.2</spark.version> <hadoop.version>2.7.6</hadoop.version> <scala.compat.version>2.11</scala.compat.version> </properties> <dependencies> <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> <version>${scala.version}</version> </dependency> <dependency> <groupId>org.apache.spark<

Spark RDD转换成DataFrame的两种方式

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Spark SQL支持两种方式将现有RDD转换为DataFrame。第一种方法使用反射来推断RDD的schema并创建DataSet然后将其转化为DataFrame。这种基于反射方法十分简便，但是前提是在您编写Spark应用程序时就已经知道RDD的schema类型。第二种方法是通过编程接口，使用您构建的StructType，然后将其应用于现有RDD。虽然此方法很麻烦，但它允许您在运行之前并不知道列及其类型的情况下构建DataSet 方法如下 1.将RDD转换成Rows 2.按照第一步Rows的结构定义StructType 3.基于rows和StructType使用createDataFrame创建相应的DF 测试数据为order.data 1 小王电视 12 2015-08-01 09:08:31 1 小王冰箱 24 2015-08-01 09:08:14 2 小李空调 12 2015-09-02 09:01:31 代码如下: object RDD2DF { /** * 主要有两种方式 * 第一种是在已经知道schema已经知道的情况下，我们使用反射把RDD转换成DS，进而转换成DF * 第二种是你不能提前定义好case class，例如数据的结构是以String类型存在的。我们使用接口自定义一个schema * @param args */ def main(args:

大数据分析技术与实战之 Spark Streaming

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Spark是基于内存的大数据综合处理引擎，具有优秀的作业调度机制和快速的分布式计算能力，使其能够更加高效地进行迭代计算，因此Spark能够在一定程度上实现大数据的流式处理。随着信息技术的迅猛发展，数据量呈现出爆炸式增长趋势，数据的种类与变化速度也远远超出人们的想象，因此人们对大数据处理提出了更高的要求，越来越多的领域迫切需要大数据技术来解决领域内的关键问题。在一些特定的领域中(例如金融、灾害预警等)，时间就是金钱、时间可能就是生命!然而传统的批处理框架却一直难以满足这些领域中的实时性需求。为此，涌现出了一批如S4、Storm的流式计算框架。Spark是基于内存的大数据综合处理引擎，具有优秀的作业调度机制和快速的分布式计算能力，使其能够更加高效地进行迭代计算，因此Spark能够在一定程度上实现大数据的流式处理。 Spark Streaming是Spark上的一个流式处理框架，可以面向海量数据实现高吞吐量、高容错的实时计算。Spark Streaming支持多种类型数据源，包括Kafka、Flume、trwitter、zeroMQ、Kinesis以及TCP sockets等，如图1所示。Spark Streaming实时接收数据流，并按照一定的时间间隔将连续的数据流拆分成一批批离散的数据集;然后应用诸如map、reducluce、join和window等丰富的API进行复杂的数据处理